ORCID Profile
0000-0003-0604-3274
Current Organisation
University of New South Wales
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Publisher: CSIRO Publishing
Date: 2011
DOI: 10.1071/WF10070
Abstract: Skill-selected global climate models were used to explore the effect of future climate change on regional bushfire weather in eastern Australia. Daily Forest Fire Danger Index (FFDI) was calculated in four regions of differing rainfall seasonality for the 20th century, 2050 and 2100 using the A2 scenario from the Special Report on Emissions Scenarios. Projected changes in FFDI vary along a latitudinal gradient. In summer rainfall-dominated tropical north-east Australia, mean and extreme FFDI are projected to decrease or remain close to 20th century levels. In the uniform and winter rainfall regions, which occupy south-east continental Australia, FFDI is projected to increase strongly by 2100. Projections fall between these two extremes for the summer rainfall region, which lies between the uniform and summer tropical rainfall zones. Based on these changes in fire weather, the fire season is projected to start earlier in the uniform and winter rainfall regions, potentially leading to a longer overall fire season.
Publisher: American Geophysical Union (AGU)
Date: 05-2000
DOI: 10.1029/1999GL011029
Publisher: Elsevier BV
Date: 09-2023
Publisher: Springer Science and Business Media LLC
Date: 02-2021
Publisher: Copernicus GmbH
Date: 04-04-2014
Abstract: Abstract. Climate extremes, such as heat waves and heavy precipitation events, have large impacts on ecosystems and societies. Climate models provide useful tools for studying underlying processes and lifying effects associated with extremes. The Australian Community Climate and Earth System Simulator (ACCESS) has recently been coupled to the Community Atmosphere Biosphere Land Exchange (CABLE) model. We examine how this model represents climate extremes derived by the Expert Team on Climate Change Detection and Indices (ETCCDI) and compare them to observational data sets using the AMIP framework. We find that the patterns of extreme indices are generally well represented. Indices based on percentiles are particularly well represented and capture the trends over the last 60 years shown by the observations remarkably well. The diurnal temperature range is underestimated, minimum temperatures (TMIN) during nights are generally too warm and daily maximum temperatures (TMAX) too low in the model. The number of consecutive wet days is overestimated, while consecutive dry days are underestimated. The maximum consecutive 1-day precipitation amount is underestimated on the global scale. Biases in TMIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in TMAX depend on biases in net shortwave radiation as well as evapotranspiration. The regions and season where the bias in evapotranspiration plays a role for the TMAX bias correspond to regions and seasons where soil moisture availability is limited. Our analysis provides the foundation for future experiments that will examine how land-surface processes contribute to these systematic biases in the ACCESS modelling system.
Publisher: Springer Science and Business Media LLC
Date: 09-2002
Publisher: Wiley
Date: 22-06-2021
DOI: 10.1111/GCB.15729
Abstract: Dryland vegetation productivity is strongly modulated by water availability. As precipitation patterns and variability are altered by climate change, there is a pressing need to better understand vegetation responses to precipitation variability in these ecologically fragile regions. Here we present a global analysis of dryland sensitivity to annual precipitation variations using long‐term records of normalized difference vegetation index (NDVI). We show that while precipitation explains 66% of spatial gradients in NDVI across dryland regions, precipitation only accounts for % of temporal NDVI variability over most ( %) dryland regions. We observed this weaker temporal relative to spatial relationship between NDVI and precipitation across all global drylands. We confirmed this result using three alternative water availability metrics that account for water loss to evaporation, and growing season and precipitation timing. This suggests that predicting vegetation responses to future rainfall using space‐for‐time substitution will strongly overestimate precipitation control on interannual variability in aboveground growth. We explore multiple mechanisms to explain the discrepancy between spatial and temporal responses and find contributions from multiple factors including local‐scale vegetation characteristics, climate and soil properties. Earth system models (ESMs) from the latest Coupled Model Intercomparison Project overestimate the observed vegetation sensitivity to precipitation variability up to threefold, particularly during dry years. Given projections of increasing meteorological drought, ESMs are likely to overestimate the impacts of future drought on dryland vegetation with observations suggesting that dryland vegetation is more resistant to annual precipitation variations than ESMs project.
Publisher: American Geophysical Union (AGU)
Date: 20-11-2016
DOI: 10.1029/2019JD030665
Publisher: American Geophysical Union (AGU)
Date: 04-2019
DOI: 10.1029/2018JD029945
Publisher: Wiley
Date: 15-12-2007
Publisher: Copernicus GmbH
Date: 11-12-2014
Abstract: Abstract. Recent studies have identified the first-order representation of microbial decomposition as a major source of uncertainty in simulations and projections of the terrestrial carbon balance. Here, we use a reduced complexity model representative of current state-of-the-art models of soil organic carbon decomposition. We undertake a systematic sensitivity analysis to disentangle the effect of the time-invariant baseline residence time (k) and the sensitivity of microbial decomposition to temperature (Q10) on soil carbon dynamics at regional and global scales. Our simulations produce a range in total soil carbon at equilibrium of ~ 592 to 2745 Pg C, which is similar to the ~ 561 to 2938 Pg C range in pre-industrial soil carbon in models used in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). This range depends primarily on the value of k, although the impact of Q10 is not trivial at regional scales. As climate changes through the historical period, and into the future, k is primarily responsible for the magnitude of the response in soil carbon, whereas Q10 determines whether the soil remains a sink, or becomes a source in the future mostly by its effect on mid-latitude carbon balance. If we restrict our simulations to those simulating total soil carbon stocks consistent with observations of current stocks, the projected range in total soil carbon change is reduced by 42% for the historical simulations and 45% for the future projections. However, while this observation-based selection dismisses outliers, it does not increase confidence in the future sign of the soil carbon feedback. We conclude that despite this result, future estimates of soil carbon and how soil carbon responds to climate change should be more constrained by available data sets of carbon stocks.
Publisher: Proceedings of the National Academy of Sciences
Date: 24-01-2011
Abstract: The current rate of warming due to increases in greenhouse gas (GHG) emissions is very likely unprecedented over the last 10,000 y. Although the majority of countries have adopted the view that global warming must be limited to °C, current GHG emission rates and nonagreement at Copenhagen in December 2009 increase the likelihood of this limit being exceeded by 2100. Extensive evidence has linked major changes in biological systems to 20th century warming. The “Global 200” comprises 238 ecoregions of exceptional bio ersity [Olson DM, Dinerstein E (2002) Ann Mo Bot Gard 89:199–224]. We assess the likelihood that, by 2070, these iconic ecoregions will regularly experience monthly climatic conditions that were extreme in 1961–1990. Using realizations from climate model ensembles, we show that up to 86% of terrestrial and 83% of freshwater ecoregions will be exposed to average monthly temperature patterns SDs (2σ) of the 1961–1990 baseline, including 82% of critically endangered ecoregions. The entire range of 89 ecoregions will experience extreme monthly temperatures with a local warming of °C. Tropical and subtropical ecoregions, and mangroves, face extreme conditions earliest, some with °C warming. In contrast, few ecoregions within Boreal Forests and Tundra biomes will experience such extremes this century. On average, precipitation regimes do not exceed 2σ of the baseline period, although considerable variability exists across the climate realizations. Further, the strength of the correlation between seasonal temperature and precipitation changes over numerous ecoregions. These results suggest many Global 200 ecoregions may be under substantial climatic stress by 2100.
Publisher: Springer Science and Business Media LLC
Date: 07-05-2001
Publisher: Copernicus GmbH
Date: 19-09-2014
DOI: 10.5194/HESSD-11-10431-2014
Abstract: Abstract. The strength of land–atmosphere coupling during the onset (September) through to the peak (February) of the wet season over Northern Australia is statistically diagnosed using ensembles of land surface model simulations that produce a range of different background soil moisture states. We derive coupling strength between the soil moisture and the planetary boundary layer via a statistical measure of association. The simulated evaporative fraction and the boundary layer are shown to be strongly coupled during both SON and DJF despite the differing background soil moisture states between the two seasons as among the ensemble members. The sign and magnitude of the surface layer soil moisture based coupling strength during the onset of the wet season (SON) differs from the coupling between the evaporative fraction and boundary layer from the same season, and the coupling between the surface soil moisture and boundary layer coupling during DJF. The patterns and magnitude of the surface flux-boundary layer coupling are not captured when coupling is diagnosed using the surface layer soil moisture alone. The conflicting results arise because the surface layer soil moisture lacks strong association with the atmosphere during the monsoon onset because the evapotranspiration is dominated by transpiration. Our results indicate that accurately diagnosing coupling strength in seasonally dry regions, such as Northern Australia, requires root zone soil moisture to be included.
Publisher: American Meteorological Society
Date: 05-2012
DOI: 10.1175/JCLI-D-11-00338.1
Abstract: The project Land-Use and Climate, Identification of Robust Impacts (LUCID) was conceived to address the robustness of biogeophysical impacts of historical land use–land cover change (LULCC). LUCID used seven atmosphere–land models with a common experimental design to explore those impacts of LULCC that are robust and consistent across the climate models. The biogeophysical impacts of LULCC were also compared to the impact of elevated greenhouse gases and resulting changes in sea surface temperatures and sea ice extent (CO2SST). Focusing the analysis on Eurasia and North America, this study shows that for a number of variables LULCC has an impact of similar magnitude but of an opposite sign, to increased greenhouse gases and warmer oceans. However, the variability among the in idual models’ response to LULCC is larger than that found from the increase in CO2SST. The results of the study show that although the dispersion among the models’ response to LULCC is large, there are a number of robust common features shared by all models: the amount of available energy used for turbulent fluxes is consistent between the models and the changes in response to LULCC depend almost linearly on the amount of trees removed. However, less encouraging is the conclusion that there is no consistency among the various models regarding how LULCC affects the partitioning of available energy between latent and sensible heat fluxes at a specific time. The results therefore highlight the urgent need to evaluate land surface models more thoroughly, particularly how they respond to a perturbation in addition to how they simulate an observed average state.
Publisher: American Geophysical Union (AGU)
Date: 07-2009
DOI: 10.1029/2009GL039076
Publisher: Inderscience Publishers
Date: 2007
Publisher: American Geophysical Union (AGU)
Date: 03-2002
DOI: 10.1029/2001GL013476
Publisher: American Meteorological Society
Date: 27-05-2015
Abstract: The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-s le linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.
Publisher: American Meteorological Society
Date: 05-1995
Publisher: American Geophysical Union (AGU)
Date: 05-2023
DOI: 10.1029/2022JG007144
Abstract: Ecosystem function can be affected directly by climate, including by meteorological extremes, and also by sustained lags and legacies on timescales that surpass those of the weather events themselves. However, important gaps remain in our understanding of the influence and timescale of persistence of antecedent climate, known as environmental memory, on terrestrial carbon and water fluxes. Identifying the interactions between the lagged response to climate and the legacies to climate extremes, and whether the influence of memory varies through time, has not been fully explored. Here, we used a novel k ‐means clustering plus regression approach to examine timeseries of the sensitivity of terrestrial fluxes to antecedent precipitation at 65 eddy‐covariance sites across a range of ecosystems. Quantifying the sensitivity to past precipitation and temperature reveals that the role of memory in ecosystem fluxes varies across sites and in time. When memory was accounted for in the model, relative improvement in modeled site flux r 2 compared to an instantaneous model varied between 0% and 57%, with mean of 12%. Our results show that vegetation type was a stronger predictor of memory importance than site aridity, implying a need to understand vegetation resilience conferred by physiological traits and acclimation capacity. The influence of memory varied strongly through time at many sites, with the role of different timescales exhibiting consistent non‐stationarity. Our results demonstrate the importance of accounting for time‐varying vegetation response to antecedent rainfall in land surface models to accurately predict future terrestrial fluxes.
Publisher: Wiley
Date: 12-08-2015
DOI: 10.1002/QJ.2596
Publisher: Springer Science and Business Media LLC
Date: 20-11-2011
DOI: 10.1038/NCLIMATE1294
Publisher: Copernicus GmbH
Date: 24-02-2015
Abstract: Abstract. Stomatal conductance (gs) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between gs model parameters in relation to plant functional type (PFT), but instead only in relation to photosynthetic pathway. We constrained the key model parameter "g1", which represents plant water use strategy, by PFT, based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the g1 parameter can be estimated using two long-term average (1960–1990) bioclimatic variables: (i) temperature and (ii) an indirect estimate of annual plant water availability. The new stomatal model, in conjunction with PFT parameterisations, resulted in a large reduction in annual fluxes of transpiration (~ 30% compared to the standard CABLE simulations) across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance against upscaled data products was not degraded, but did not noticeably reduce existing model–data biases. We identified assumptions relating to the coupling of the vegetation to the atmosphere and the parameterisation of the minimum stomatal conductance as areas requiring further investigation in both CABLE and potentially other LSMs. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.
Publisher: Wiley
Date: 2003
DOI: 10.1002/JOC.889
Publisher: The Sax Institute
Date: 2018
DOI: 10.17061/PHRP2841825
Abstract: By definition, extreme events are rare. Socio-economic and human systems have not experienced adverse extreme events frequently enough to develop resilience, whether this be physical, economical or structural. Humans are vulnerable to extreme events because of our physiology and because we build thresholds into our socio-economic and human health systems. When these thresholds are exceeded the consequences can be devastating. This perspective will discuss changes in heat, drought and heavy rainfall extremes in the context of climate change.
Publisher: Elsevier BV
Date: 07-1995
Publisher: IOP Publishing
Date: 12-2019
Abstract: Social, technological and climatic changes will transform the way energy is consumed over the 21st century, with important implications for energy networks and greenhouse gas emissions. Here, we develop a method to efficiently explore climate-energy interactions under various scenarios of climate, urban infrastructure and technological change. We couple the Urban Climate and Energy Model with the Conformal Cubic Atmospheric Model as a full-height single column driven with a series of global climate model simulations in an ensemble approach. The framework is evaluated against observations, then a series of century-scale simulations are undertaken to examine projected climate change impacts on electricity and gas demand in the temperate/ oceanic climate of Melbourne, Australia. With air-conditioning ownership remaining at early 21st century levels, and in the absence of other changes, climate change under radiative forcing RCP 8.5 increases peak electricity demand by 10%, and decreases peak gas demand by 22% between 2000 and 2100. However, if projected increases in air-conditioning ownership are considered, peak electricity demand increases by 84%, surpassing peak gas demand in the second half of the century. These findings highlight the complex nature of changes facing energy networks. Changes will be location and scenario dependent.
Publisher: Copernicus GmbH
Date: 13-09-2021
Abstract: Abstract. The co-occurrence of droughts and heatwaves can have significant impacts on many socioeconomic and environmental systems. Groundwater has the potential to moderate the impact of droughts and heatwaves by moistening the soil and enabling vegetation to maintain higher evaporation, thereby cooling the canopy. We use the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model, coupled to a groundwater scheme, to examine how groundwater influences ecosystems under conditions of co-occurring droughts and heatwaves. We focus specifically on south-east Australia for the period 2000–2019, when two significant droughts and multiple extreme heatwave events occurred. We found groundwater plays an important role in helping vegetation maintain transpiration, particularly in the first 1–2 years of a multi-year drought. Groundwater impedes gravity-driven drainage and moistens the root zone via capillary rise. These mechanisms reduced forest canopy temperatures by up to 5 ∘C during in idual heatwaves, particularly where the water table depth is shallow. The role of groundwater diminishes as the drought lengthens beyond 2 years and soil water reserves are depleted. Further, the lack of deep roots or stomatal closure caused by high vapour pressure deficit or high temperatures can reduce the additional transpiration induced by groundwater. The capacity of groundwater to moderate both water and heat stress on ecosystems during simultaneous droughts and heatwaves is not represented in most global climate models, suggesting that model projections may overestimate the risk of these events in the future.
Publisher: Copernicus GmbH
Date: 10-2021
DOI: 10.5194/BG-2021-254
Abstract: Abstract. The vegetation’s response to climate change is a significant source of uncertainty in future terrestrial biosphere model projections. Constraining climate-carbon cycle feedbacks requires improving our understanding of direct, as well as long-term, plant physiological responses to climate. In particular, the timescales and strength of memory effects arising from both extreme events (i.e., droughts and heatwaves) and structural lags in the systems have largely been overlooked in the development of models. This is despite the knowledge that plant responses to climatic drivers occur across multiple timescales (seconds to decades), with the impact of climate extremes resonating for many years. Using data from 13 eddy covariance sites, covering two rainfall gradients (256 to 1491 mm yr−1) in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales and magnitude of influence of antecedent drivers on daily net ecosystem exchange (NEE) and latent heat flux (λE). Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. Latent heat was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.95. When considered at a continental scale, both fluxes were more predictable when memory effects were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. The importance of environmental memory in predicting fluxes increased as site water availability declined (ρ = −0.72, p 0.01 for NEE, ρ = −0.62, p 0.05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a k-means clustering plus regression model to confirm the suitability of the Bayesian model for modelling these sites. The k-means approach performed similarly to the Bayesian model in terms of model fit, demonstrating the robustness of the Bayesian framework for exploring the role of environmental memory. Our results underline the importance of capturing memory effects in models used to project future responses to climate change, especially in water-limited ecosystems. Finally, we demonstrate a considerable variation in in idual site predictability, driven to a notable degree by environmental memory, and this should be considered when evaluating model performance across ecosystems.
Publisher: IOP Publishing
Date: 02-2018
Publisher: Springer Science and Business Media LLC
Date: 12-01-2022
DOI: 10.1038/S41612-021-00224-4
Abstract: While compound weather and climate events (CEs) can lead to significant socioeconomic consequences, their response to climate change is mostly unexplored. We report the first multi-model assessment of future changes in return periods for the co-occurrence of heatwaves and drought, and extreme winds and precipitation based on the Coupled Model Intercomparison Project (CMIP6) and three emission scenarios. Extreme winds and precipitation CEs occur more frequently in many regions, particularly under higher emissions. Heatwaves and drought occur more frequently everywhere under all emission scenarios examined. For each CMIP6 model, we derive a skill score for simulating CEs. Models with higher skill in simulating historical CEs project smaller increases in the number of heatwaves and drought in Eurasia, but larger numbers of strong winds and heavy precipitation CEs everywhere for all emission scenarios. This result is partly masked if the whole CMIP6 ensemble is used, pointing to the considerable value in further improvements in climate models.
Publisher: American Meteorological Society
Date: 02-2006
DOI: 10.1175/JHM479.1
Abstract: Data assimilation in the field of predictive land surface modeling is generally limited to using observational data to estimate optimal model states or restrict model parameter ranges. To date, very little work has attempted to systematically define and quantify error resulting from a model's inherent inability to simulate the natural system. This paper introduces a data assimilation technique that moves toward this goal by accounting for those deficiencies in the model itself that lead to systematic errors in model output. This is done using a supervised artificial neural network to “learn” and simulate systematic trends in the model output error. These simulations in turn are used to correct the model's output each time step. The technique is applied in two case studies, using fluxes of latent heat flux at one site and net ecosystem exchange (NEE) of carbon dioxide at another. Root-mean-square error (rmse) in latent heat flux per time step was reduced from 27.5 to 18.6 W m−2 (32%) and monthly from 9.91 to 3.08 W m−2 (68%). For NEE, rmse per time step was reduced from 3.71 to 2.70 μmol m−2 s−1 (27%) and annually from 2.24 to 0.11 μmol m−2 s−1 (95%). In both cases the correction provided significantly greater gains than single criteria parameter estimation on the same flux.
Publisher: American Meteorological Society
Date: 06-1997
Publisher: American Meteorological Society
Date: 10-2015
Abstract: Land–atmosphere coupling can strongly affect climate and climate extremes. Estimates of land–atmosphere coupling vary considerably between climate models, between different measures used to define coupling, and between the present and the future. The Australian Community Climate and Earth-System Simulator, version 1.3b (ACCESS1.3b), is used to derive and examine previously used measures of coupling strength. These include the GLACE-1 coupling measure derived on seasonal time scales a similar measure defined using multiyear simulations and four other measures of different complexity and data requirements, including measures that can be derived from standard model runs and observations. The ACCESS1.3b land–atmosphere coupling strength is comparable to other climate models. The coupling strength in the Southern Hemisphere summer is larger compared to the Northern Hemisphere summer and is dominated by a strong signal in the tropics and subtropics. The land–atmosphere coupling measures agree on the location of very strong land–atmosphere coupling but show differences in the spatial extent of these regions. However, the investigated measures show disagreement in weaker coupled regions, and some regions are only identified by a single measure as strongly coupled. In future projections the soil moisture trend is crucial in generating regions of strong land–atmosphere coupling, and the results suggest an expansion of coupling “hot spots.” It is concluded that great care needs to be taken in using different measures of coupling strength and shown that several measures that can be easily derived lead to inconsistent conclusions with more computationally expensive measures designed to measure coupling strength.
Publisher: Wiley
Date: 22-08-2007
Publisher: Copernicus GmbH
Date: 03-07-2015
Abstract: Abstract. We implement a new stomatal conductance model, based on the optimality approach, within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. Coupled land-atmosphere simulations are then performed using CABLE within the Australian Community Climate and Earth Systems Simulator (ACCESS) with prescribed sea surface temperatures. As in most land surface models, the default stomatal conductance scheme only accounts for differences in model parameters in relation to the photosynthetic pathway, but not in relation to plant functional types. The new scheme allows model parameters to vary by plant functional type, based on a global synthesis of observations of stomatal conductance under different climate regimes over a wide range of species. We show that the new scheme reduces the latent heat flux from the land surface over the boreal forests during the Northern Hemisphere summer by 0.5 to 1.0 mm day-1. This leads to warmer daily maximum and minimum temperatures by up to 1.0 °C and warmer extreme maximum temperatures by up to 1.5 °C. These changes generally improve the climate model's climatology and improve existing biases by 10–20 %. The change in the surface energy balance also affects net primary productivity and the terrestrial carbon balance. We conclude that the improvements in the global climate model which result from the new stomatal scheme, constrained by a global synthesis of experimental data, provide a valuable advance in the long-term development of the ACCESS modelling system.
Publisher: American Geophysical Union (AGU)
Date: 23-09-2004
DOI: 10.1029/2003JD004347
Publisher: Wiley
Date: 2003
DOI: 10.1002/JOC.893
Publisher: American Meteorological Society
Date: 10-2016
Abstract: This paper presents a methodology for examining land–atmosphere coupling in a regional climate model by examining how the resistances to moisture transfer from the land to the atmosphere control the surface turbulent energy fluxes. Perturbations were applied in idually to the aerodynamic resistance from the soil surface to the displacement height, the aerodynamic resistance from the displacement height to the reference level, the stomatal resistance, and the leaf boundary layer resistance. Only perturbations to the aerodynamic resistance from the soil surface to the displacement height systematically affected 2-m air temperature for the shrub and evergreen boreal forest plant functional types (PFTs). This was associated with this resistance systematically increasing the terrestrial and atmospheric components of the land–atmosphere coupling strength through changes in the partitioning of the surface energy balance. Perturbing the other resistances did contribute to changing the partitioning of the surface energy balance but did not lead to systematic changes in the 2-m air temperature. The results suggest that land–atmosphere coupling in the modeling system presented here acts mostly through the aerodynamic resistance from the soil surface to the displacement height, which is a function of both the friction velocity and vegetation height and cover. The results show that a resistance pathway framework can be used to examine how changes in the resistances affect the partitioning of the surface energy balance and how this subsequently influences surface climate through land–atmosphere coupling. Limitations in the present analysis include grid-scale rather than PFT-scale analysis, the exclusion of resistance dependencies, and the linearity assumption of how temperature responds to a resistance perturbation.
Publisher: Wiley
Date: 04-02-2016
DOI: 10.1002/JOC.4653
Publisher: Copernicus GmbH
Date: 08-11-2013
Abstract: Abstract. Reliable projections of future climate require land–atmosphere carbon (C) fluxes to be represented realistically in Earth system models (ESMs). There are several sources of uncertainty in how carbon is parameterised in these models. First, while interactions between the C, nitrogen (N) and phosphorus (P) cycles have been implemented in some models, these lead to erse changes in land–atmosphere fluxes. Second, while the first-order parameterisation of soil organic matter decomposition is similar between models, formulations of the control of the soil physical state on microbial activity vary widely. For the first time, we address these sources of uncertainty simultaneously by implementing three soil moisture and three soil temperature respiration functions in an ESM that can be run with three degrees of biogeochemical nutrient limitation (C-only, C and N, and C and N and P). All 27 possible combinations of response functions and biogeochemical mode are equilibrated before transient historical (1850–2005) simulations are performed. As expected, implementing N and P limitation reduces the land carbon sink, transforming some regional sinks into net sources over the historical period. Meanwhile, regardless of which nutrient mode is used, various combinations of response functions imply a two-fold difference in the net ecosystem accumulation and a four-fold difference in equilibrated total soil C. We further show that regions with initially larger pools are more likely to become carbon sources, especially when nutrient availability limits the response of primary production to increasing atmospheric CO2. Simulating changes in soil C content therefore critically depends on both nutrient limitation and the choice of respiration functions.
Publisher: Elsevier BV
Date: 12-1998
Publisher: Springer Science and Business Media LLC
Date: 21-03-2016
DOI: 10.1038/SREP23418
Abstract: Stomatal conductance links plant water use and carbon uptake and is a critical process for the land surface component of climate models. However, stomatal conductance schemes commonly assume that all vegetation with the same photosynthetic pathway use identical plant water use strategies whereas observations indicate otherwise. Here, we implement a new stomatal scheme derived from optimal stomatal theory and constrained by a recent global synthesis of stomatal conductance measurements from 314 species, across 56 field sites. Using this new stomatal scheme, within a global climate model, subtantially increases the intensity of future heatwaves across Northern Eurasia. This indicates that our climate model has previously been under-predicting heatwave intensity. Our results have widespread implications for other climate models, many of which do not account for differences in stomatal water-use across different plant functional types and hence, are also likely under projecting heatwave intensity in the future.
Publisher: MDPI AG
Date: 03-12-2015
Publisher: Wiley
Date: 22-03-2022
DOI: 10.1111/GCB.16141
Abstract: In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20 th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO 2 sink to a net CO 2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under‐represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long‐term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.
Publisher: Springer Science and Business Media LLC
Date: 09-01-2016
Publisher: Copernicus GmbH
Date: 04-02-2020
Abstract: Abstract. To resolve a series of ecological and environmental problems over the Loess Plateau, the “Grain for Green Program” (GFGP) was initiated at the end of 1990s. Following the conversion of croplands and bare land on hillslopes to forests, the Loess Plateau has displayed a significant greening trend, which has resulted in soil erosion being reduced. However, the GFGP has also affected the hydrology of the Loess Plateau, which has raised questions regarding whether the GFGP should be continued in the future. We investigated the impact of revegetation on the hydrology of the Loess Plateau using relatively high-resolution simulations and multiple realizations with the Weather Research and Forecasting (WRF) model. Results suggest that revegetation since the launch of the GFGP has reduced runoff and soil moisture due to enhanced evapotranspiration. Further revegetation associated with the GFGP policy is likely to further increase evapotranspiration, and thereby reduce runoff and soil moisture. The increase in evapotranspiration is associated with biophysical changes, including deeper roots that deplete deep soil moisture stores. However, despite the increase in evapotranspiration, our results show no impact on rainfall. Our study cautions against further revegetation over the Loess Plateau given the reduction in water available for agriculture and human settlements and the lack of any significant compensation from rainfall.
Publisher: American Library Association
Date: 07-2011
Publisher: American Meteorological Society
Date: 15-10-2020
Abstract: The relative importance of atmospheric advection and local land–atmosphere coupling to Australian precipitation is uncertain. Identifying the evaporative source regions and level of precipitation recycling can help quantify the importance of local and remote marine and terrestrial moisture to precipitation within the different hydroclimates across Australia. Using a three-dimensional Lagrangian back-trajectory approach, moisture from precipitation events across Australia during 1979–2013 was tracked to determine the source of moisture (the evaporative origin) and level of precipitation recycling. We show that source regions vary markedly for precipitation falling in different regions. Advected marine moisture was relatively more important than terrestrial contributions for precipitation in all regions and seasons. For Australia as a whole, contributions from precipitation recycling varied from ~11% in winter up to ~21% in summer. The strongest land–atmosphere coupling was in the northwest and southeast where recycled local land evapotranspiration accounted for an average of 9% of warm-season precipitation. Marine contributions to precipitation in the northwest of Australia increased in spring and, coupled with positive evaporation trends in the key source regions, suggest that the observed precipitation increase is the result of intensified evaporation in the Maritime Continent and Indian and Pacific Oceans. Less clear were the processes behind an observed shift in moisture contribution from winter to summer in southeastern Australia. Establishing the climatological source regions and the magnitude of moisture recycling enables future investigation of anomalous precipitation during extreme periods and provides further insight into the processes driving Australia’s variable precipitation.
Publisher: Springer Science and Business Media LLC
Date: 14-03-2022
DOI: 10.1038/S41612-022-00240-Y
Abstract: The sixth Intergovernmental Panel on Climate Change (IPCC) assessment report confirms that global warming drives widespread changes in the global terrestrial hydrological cycle, and that changes are regionally erse. However, reported trends and changes in the hydrological cycle suffer from significant inconsistencies. This is associated with the lack of a rigorous observationally-based assessment of simultaneous trends in the different components of the hydrological cycle. Here, we reconcile these different estimates of historical changes by simultaneously analysing trends in all the major components of the hydrological cycle, coupled with vegetation greenness for the period 1980–2012. We use observationally constrained, conserving estimates of the closure of the hydrological cycle, combined with a data assimilation approach and observationally-driven uncertainty estimates. We find robust changes in the hydrological cycle across more than 50% of the land area, with evapotranspiration (ET) changing the most and precipitation ( P ) the least. We find many instances of unambiguous trends in ET and runoff ( Q ) without robust trends in P , a result broadly consistent with a “wet gets wetter, but dry does not get drier”. These findings provide important opportunities for water resources management and climate risk assessment over a significant fraction of the land surface where hydrological trends have previously been uncertain.
Publisher: Inter-Research Science Center
Date: 20-10-2014
DOI: 10.3354/CR01258
Publisher: Copernicus GmbH
Date: 06-05-2021
DOI: 10.5194/ESD-2021-31
Abstract: Abstract. The co-occurrence of droughts and heatwaves can have significant impacts on many socioeconomic and environmental systems. Groundwater has the potential to moderate the impact of droughts and heatwaves by moistening the soil and enabling vegetation to maintain higher evaporation, thereby cooling the canopy. We use the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model, coupled to a groundwater scheme, to examine how groundwater influences ecosystems under conditions of co-occurring droughts and heatwaves. We focus specifically on South East Australia for the period 2000–2019 when two significant droughts and multiple extreme heatwave events occurred. We found groundwater plays an important role in helping vegetation maintain transpiration, particularly in the first 1–2 years of a multi-year drought. Groundwater impedes gravity-driven drainage and moistens the root zone via capillary rise. These mechanisms reduced forest canopy temperatures by up to 5 °C during in idual heatwaves, particularly where the water table depth is shallow. The role of groundwater diminishes as the drought lengthens beyond 2 years and soil water reserves are depleted. Further, the lack of deep roots or stomatal closure caused by high vapour pressure deficit or high temperatures can reduce the additional transpiration induced by groundwater. The capacity of groundwater to moderate both water and heat stress on ecosystems during simultaneous droughts and heatwaves is not represented in most global climate models, suggesting model projections may overestimate the risk of these events in the future.
Publisher: Copernicus GmbH
Date: 21-12-2015
Abstract: Abstract. Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact on the carbon, water and energy fluxes. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSM and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the most mesic sites, and low drought sensitivity at the most xeric sites, was necessary to accurately model responses during drought. Our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.
Publisher: Copernicus GmbH
Date: 12-09-2017
Abstract: Abstract. Flux towers measure ecosystem-scale surface–atmosphere exchanges of energy, carbon dioxide and water vapour. The network of flux towers now encompasses ∼ 900 sites, spread across every continent. Consequently, these data have become an essential benchmarking tool for land surface models (LSMs). However, these data as released are not immediately usable for driving, evaluating and benchmarking LSMs. Flux tower data must first be transformed into a LSM-readable file format, a process which involves changing units, screening missing data and varying degrees of additional gap-filling. All of this often leads to an under-utilisation of these data in model benchmarking. To resolve some of these issues, and to help make flux tower measurements more widely used, we present a reproducible, open-source R package that transforms the FLUXNET2015 and La Thuile data releases into community standard NetCDF files that are directly usable by LSMs. We note that these data would also be useful for any other user or community seeking to independently quality control, gap-fill or use the FLUXNET data.
Publisher: Elsevier BV
Date: 06-1996
Publisher: Copernicus GmbH
Date: 26-02-2019
Abstract: Abstract. Recent experimental evidence suggests that during heat extremes, wooded ecosystems may decouple photosynthesis and transpiration, reducing photosynthesis to near zero but increasing transpiration into the boundary layer. This feedback may act to d en, rather than lify, heat extremes in wooded ecosystems. We examined eddy covariance databases (OzFlux and FLUXNET2015) to identify whether there was field-based evidence to support these experimental findings. We focused on two types of heat extremes: (i) the 3 days leading up to a temperature extreme, defined as including a daily maximum temperature ∘C (similar to the widely used TXx metric), and (ii) heatwaves, defined as 3 or more consecutive days above 35 ∘C. When focusing on (i), we found some evidence of reduced photosynthesis and sustained or increased latent heat fluxes at seven Australian evergreen wooded flux sites. However, when considering the role of vapour pressure deficit and focusing on (ii), we were unable to conclusively disentangle the decoupling between photosynthesis and latent heat flux from the effect of increasing the vapour pressure deficit. Outside of Australia, the Tier-1 FLUXNET2015 database provided limited scope to tackle this issue as it does not s le sufficient high temperature events with which to probe the physiological response of trees to extreme heat. Thus, further work is required to determine whether this photosynthetic decoupling occurs widely, ideally by matching experimental species with those found at eddy covariance tower sites. Such measurements would allow this decoupling mechanism to be probed experimentally and at the ecosystem scale. Transpiration during heatwaves remains a key issue to resolve, as no land surface model includes a decoupling mechanism, and any potential d ening of the land–atmosphere lification is thus not included in climate model projections.
Publisher: Springer Science and Business Media LLC
Date: 07-2022
DOI: 10.1038/S41558-022-01403-8
Abstract: Terrestrial ecosystems are essential for food and water security and CO 2 uptake. Ecosystem function is dependent on the availability of soil moisture, yet it is unclear how climate change will alter soil moisture limitation on vegetation. Here we use an ecosystem index that distinguishes energy and water limitations in Earth system model simulations to show a widespread regime shift from energy to water limitation between 1980 and 2100. This shift is found in both space and time. While this is mainly related to a reduction in energy-limited regions associated with increasing incoming shortwave radiation, the largest shift towards water limitation is found in regions where incoming shortwave radiation increases are accompanied by soil moisture decreases. We therefore demonstrate a widespread regime shift in ecosystem function that is stronger than implied by in idual trends in incoming shortwave radiation, soil moisture and terrestrial evaporation, with important implications for future ecosystem services.
Publisher: Elsevier BV
Date: 04-1995
Publisher: Springer Science and Business Media LLC
Date: 11-1996
Publisher: American Meteorological Society
Date: 10-2001
Publisher: Springer Science and Business Media LLC
Date: 24-11-2020
DOI: 10.1038/S41467-020-19639-3
Abstract: Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-4244
Abstract: & & Predicting ecosystem resilience to droughts and heatwaves requires a predictive capacity that is currently lacking in land-surface models (LSMs). Eco-evolutionary optimisation approaches have the potential to increase predictability, but competing approaches are yet to be probed together in LSMs. Here, we coupled schemes that optimise canopy gas-exchange vs. leaf nitrogen investment, and both approaches were extended to account for hydraulic legacies from water-stress. We assessed model predictions using observations from a South-Eastern Australian woodland exposed to repeated drought between 2013 and 2020, under both ambient and elevated [CO& sub& & /sub& ]. Our simulations were in good agreement with observations of transpiration (& em& r& sup& & /sup& & /em& & #8764 .7), leaf water potential (& #177 .1 MPa), and leaf photosynthetic capacities (& #177 % of the observations). Despite predictions of significant percentage loss of conductivity (PLC) due to water stress in 2013, 2014, 2016, and 2017 (& em& & sub& & /sub& & /em& & 45%), hydraulic legacy effects were small and recovered rapidly. Combining the optimisation schemes and hydraulic legacies led to improved model predictions and enhanced the simulated magnitude fertilisation effect on GPP at elevated [CO& sub& & /sub& ], albeit that the impact on the canopy fluxes was small overall. Our simulations suggested that leaf shedding and/or suppressed foliage growth formed an active strategy to mitigate drought risk, with leaves being grown during wet years to replenish carbon stores, whereas LAI dropped in anticipation of severe water stress to prevent high PLC. Accounting for leaf acclimation in response to drought therefore has the potential to improve predictions of ecosystem resilience to drought in water-limited regions.& &
Publisher: American Meteorological Society
Date: 15-06-2009
Abstract: The atmospheric and land components of the Geophysical Fluid Dynamics Laboratory’s (GFDL’s) Climate Model version 2.1 (CM2.1) is used with climatological sea surface temperatures (SSTs) to investigate the relative climatic impacts of historical anthropogenic land cover change (LCC) and realistic SST anomalies. The SST forcing anomalies used are analogous to signals induced by El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the background global warming trend. Coherent areas of LCC are represented throughout much of central and eastern Europe, northern India, southeastern China, and on either side of the ridge of the Appalachian Mountains in North America. Smaller areas of change are present in various tropical regions. The land cover changes in the model are almost exclusively a conversion of forests to grasslands. Model results show that, at the global scale, the physical impacts of LCC on temperature and rainfall are less important than large-scale SST anomalies, particularly those due to ENSO. However, in the regions where the land surface has been altered, the impact of LCC can be equally or more important than the SST forcing patterns in determining the seasonal cycle of the surface water and energy balance. Thus, this work provides a context for the impacts of LCC on climate: namely, strong regional-scale impacts that can significantly change globally averaged fields but that rarely propagate beyond the disturbed regions. This suggests that proper representation of land cover conditions is essential in the design of climate model experiments, particularly if results are to be used for regional-scale assessments of climate change impacts.
Publisher: American Geophysical Union (AGU)
Date: 03-2023
DOI: 10.1029/2022EF003291
Abstract: Global climate models (GCMs) are commonly downscaled to understand future local climate change. The high computational cost of regional climate models (RCMs) limits how many GCMs can be dynamically downscaled, restricting uncertainty assessment. While statistical downscaling is cheaper, its validity in a changing climate is unclear. We combine these approaches to build an emulator leveraging the merits of dynamical and statistical downscaling. A machine learning model is developed for each coarse grid cell to predict fine grid variables, using coarse‐scale climate predictors with fine grid land characteristics. Two RCM emulators, one Multilayer Perceptron (MLP) and one Multiple Linear Regression error‐reduced with Random Forest (MLR‐RF), are developed to downscale daily evapotranspiration from 12.5 km (coarse‐scale) to 1.5 km (fine‐scale). Out‐of‐s le tests for the MLP and MLR‐RF achieve Kling‐Gupta‐Efficiency of 0.86 and 0.83, correlation of 0.89 and 0.86, and coefficient of determination ( R 2 ) of 0.78 and 0.75, with a relative bias of −6% to 5% and −5% to 4%, respectively. Using histogram match for spatial efficiency, both emulators achieve a median score of ∼0.77. This is generally better than a common statistical downscaling method in a range of metrics. Additionally, through “spatial transitivity,” we can downscale GCMs for new regions at negligible cost and only minor performance loss. The framework offers a cheap and quick way to downscale large ensembles of GCMs. This could enable high‐resolution climate projections from a larger number of global models, enabling uncertainty quantification, and so better support for resilience and adaptation planning.
Publisher: American Geophysical Union (AGU)
Date: 27-05-2016
DOI: 10.1002/2015JD024357
Publisher: Wiley
Date: 08-03-2020
Publisher: American Geophysical Union (AGU)
Date: 12-03-2004
DOI: 10.1029/2003GL019233
Publisher: American Meteorological Society
Date: 25-05-2016
Abstract: The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave radiation, surface air temperature, and relative humidity. These results are explored here in greater detail and possible causes are investigated. It is examined whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation, and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. It is demonstrated that energy conservation in the observational data is not responsible for these results. It is also shown that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, evidence is presented that suggests that the nature of this partitioning problem is likely shared among all contributing LSMs. While a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER could not be found, multiple possible explanations are excluded and guidance is provided on where future research should focus.
Publisher: Elsevier BV
Date: 2022
DOI: 10.2139/SSRN.4251061
Publisher: Copernicus GmbH
Date: 12-07-2017
DOI: 10.5194/GMD-2017-153
Abstract: Abstract. Previous research has shown that Land Surface Models (LSMs) are performing poorly when compared with rela- tively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appears to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that is used by LSMs for predicting land surface fluxes, by interrogating Fluxnet data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce, and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We re-analyse previously published LSM simulations, and show that there is more ersity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.
Publisher: Wiley
Date: 2008
DOI: 10.1002/JOC.1612
Publisher: American Geophysical Union (AGU)
Date: 15-07-2017
DOI: 10.1002/2017GL073733
Publisher: American Geophysical Union (AGU)
Date: 11-2003
DOI: 10.1029/2003GL018261
Publisher: Wiley
Date: 11-2001
Publisher: American Meteorological Society
Date: 02-2018
Abstract: Global warming, in combination with the urban heat island effect, is increasing the temperature in cities. These changes increase the risk of heat stress for millions of city dwellers. Given the large populations at risk, a variety of mitigation strategies have been proposed to cool cities—including strategies that aim to reduce the ambient air temperature. This paper uses common heat stress metrics to evaluate the performance of several urban heat island mitigation strategies. The authors found that cooling via reducing net radiation or increasing irrigated vegetation in parks or on green roofs did reduce ambient air temperature. However, a lower air temperature did not necessarily lead to less heat stress because both temperature and humidity are important factors in determining human thermal comfort. Specifically, cooling the surface via evaporation through the use of irrigation increased humidity—consequently, the net impact on human comfort of any cooling was negligible. This result suggests that urban cooling strategies must aim to reduce ambient air temperatures without increasing humidity, for ex le via the deployment of solar panels over roofs or via cool roofs utilizing high albedos, in order to combat human heat stress in the urban environment.
Publisher: Copernicus GmbH
Date: 03-12-2013
Abstract: Abstract. Climate extremes, such as heat waves and heavy precipitation events, have large impacts on ecosystems and societies. Climate models provide useful tools to study underlying processes and lifying effects associated with extremes. The Australian Community Climate and Earth System Simulator (ACCESS) has recently been coupled to the Community Atmosphere Biosphere Land Exchange model. We examine how this model represents climate extremes derived by the Expert Team on Climate Change Detection and Indices and compare them to observational datasets using the AMIP framework. We find that the patterns of extreme indices are generally well represented. Indices based on percentiles are particularly well represented and capture the trends over the last 60 yr shown by the observations remarkably well. The diurnal temperature range is underestimated, minimum temperatures (TMIN) during nights are generally too warm and daily maximum temperatures (TMAX) too low in the model. The number of consecutive wet days is overestimated while consecutive dry days are underestimated. The maximum consecutive 1 day precipitation amount is underestimated on the global scale. Biases in TMIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in TMAX depend on biases in net shortwave radiation as well as evapotranspiration. The regions and season where the bias in evapotranspiration plays a role for the TMAX bias correspond to regions and seasons where soil moisture availability is limited. Our analysis provides the foundation for future experiments that will examine how land surface processes contribute to these systematic biases in the ACCESS modelling system.
Publisher: American Geophysical Union (AGU)
Date: 11-2011
DOI: 10.1029/2011GL049244
Publisher: Copernicus GmbH
Date: 25-03-2021
Abstract: Abstract. The El Niño‐-Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) central Pacific (CP) and (ii) eastern Pacific (EP). Both types of El Niño are characterised by above-average sea surface temperature anomalies at the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the litude of the concentration of interannual atmospheric carbon dioxide (CO2) variability following increased tropical near-surface air temperature and decreased precipitation. We employ the dynamic global vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) within a synthetic experimental framework to examine the sensitivity and potential long-term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the latter half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was small for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle.
Publisher: Informa UK Limited
Date: 03-2009
Publisher: American Meteorological Society
Date: 15-07-2011
Abstract: Climate change impact studies for water resource applications, such as the development of projections of reservoir yields or the assessment of likely frequency and litude of drought under a future climate, require that the year-to-year persistence in a range of hydrological variables such as catchment average rainfall be properly represented. This persistence is often attributable to low-frequency variability in the global sea surface temperature (SST) field and other large-scale climate variables through a complex sequence of teleconnections. To evaluate the capacity of general circulation models (GCMs) to accurately represent this low-frequency variability, a set of wavelet-based skill measures has been developed to compare GCM performance in representing interannual variability with the observed global SST data, as well as to assess the extent to which this variability is imparted in precipitation and surface pressure anomaly fields. A validation of the derived skill measures is performed using GCM precipitation as an input in a reservoir storage context, with the accuracy of reservoir storage estimates shown to be improved by using GCM outputs that correctly represent the observed low-frequency variability. Significant differences in the performance of different GCMs is demonstrated, suggesting that judicious selection of models is required if the climate impact assessment is sensitive to low-frequency variability. The two GCMs that were found to exhibit the most appropriate representation of global low-frequency variability for in idual variables assessed were the Istituto Nazionale di Geofisica e Vulcanologia (INGV) ECHAM4 and L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL CM4) when considering all three variables, the Max Planck Institute (MPI) ECHAM5 performed well. Importantly, models that represented interannual variability well for SST also performed well for the other two variables, while models that performed poorly for SST also had consistently low skill across the remaining variables.
Publisher: Wiley
Date: 15-05-2014
DOI: 10.1111/JVS.12190
Publisher: American Geophysical Union (AGU)
Date: 15-09-2020
DOI: 10.1029/2020GL090238
Abstract: Droughts are associated with large‐scale modes of variability, synoptic‐scale systems, and terrestrial processes. Quantifying their relative roles in influencing drought guides process understanding, helps identify weaknesses in climate models, and focuses model improvements. Using a Lagrangian back‐trajectory approach we provide the first quantification of the change in moisture supply during major droughts in southeast Australia, including the causes of the changes. Drought onset and intensification were driven by reduced moisture supply from the ocean, as moisture was circulated away from the region, combined with an absence of precipitation‐generating mechanisms over land. During termination, strengthened moist easterly flows from the Tasman and Coral Seas promoted anomalously high rainfall. Our approach reveals terrestrial moisture sources played a secondary role, lifying rainfall anomalies by less than 6%. Simulating droughts therefore requires deeper understanding of the relationship between moisture advection and synoptic‐scale circulation and how large‐scale climate variability and terrestrial processes modify these relationships.
Publisher: Copernicus GmbH
Date: 18-07-2022
DOI: 10.5194/EGUSPHERE-2022-623
Abstract: Abstract. Climate projections from global circulation models (GCMs) part of the Coupled Model Intercomparison Project 6 (CMIP6) are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation, which h er predictive capacity. In this study we examine different methods to constrain regional projections of the carbon cycle in Australia. We employ a dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias correct or calculate ensemble averages over the original forcing data to constrain the uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Carbon pools are insensitive to the type of bias correction method applied for both in idual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in carbon over time, highlighting the need to account for temporal properties in correction or ensemble averaging methods. Some bias correction methods reduce the ensemble uncertainty more than others. The vegetation distribution can depend on the bias correction method used. We further find that both the weighted ensemble averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies.
Publisher: Wiley
Date: 2002
DOI: 10.1002/JOC.727
Publisher: Copernicus GmbH
Date: 28-11-2006
Abstract: Abstract. First, we review the evidence that abrupt climate changes have occurred in the past and then demonstrate that climate models have developing capacity to simulate many of these changes. In particular, the processes by which changes in the ocean circulation drive abrupt changes appear to be captured by climate models to a degree that is encouraging. The evidence that past changes in the ocean have driven abrupt change in terrestrial systems is also convincing, but these processes are only just beginning to be included in climate models. Second, we explore the likelihood that climate models can capture those abrupt changes in climate that may occur in the future due to the enhanced greenhouse effect. We note that existing evidence indicates that a major collapse of the thermohaline circulation seems unlikely in the 21st century, although very recent evidence suggests that a weakening may already be underway. We have confidence that current climate models can capture a weakening, but a collapse in the 21st century of the thermohaline circulation is not projected by climate models. Worrying evidence of instability in terrestrial carbon, from observations and modelling studies, is beginning to accumulate. Current climate models used by the Intergovernmental Panel on Climate Change for the 4th Assessment Report do not include these terrestrial carbon processes. We therefore can not make statements with any confidence regarding these changes. At present, the scale of the terrestrial carbon feedback is believed to be small enough that it does not significantly affect projections of warming during the first half of the 21st century. However, the uncertainties in how biological systems will respond to warming are sufficiently large to undermine confidence in this belief and point us to areas requiring significant additional work.
Publisher: American Meteorological Society
Date: 11-2008
Abstract: This paper presents a set of analytical tools to evaluate the performance of three land surface models (LSMs) that are used in global climate models (GCMs). Predictions of the fluxes of sensible heat, latent heat, and net CO2 exchange obtained using process-based LSMs are benchmarked against two statistical models that only use incoming solar radiation, air temperature, and specific humidity as inputs to predict the fluxes. Both are then compared to measured fluxes at several flux stations located on three continents. Parameter sets used for the LSMs include default values used in GCMs for the plant functional type and soil type surrounding each flux station, locally calibrated values, and ensemble sets encompassing combinations of parameters within their respective uncertainty ranges. Performance of the LSMs is found to be generally inferior to that of the statistical models across a wide variety of performance metrics, suggesting that the LSMs underutilize the meteorological information used in their inputs and that model complexity may be hindering accurate prediction. The authors show that model evaluation is purpose specific good performance in one metric does not guarantee good performance in others. Self-organizing maps are used to ide meteorological “‘forcing space” into distinct regions as a mechanism to identify the conditions under which model bias is greatest. These new techniques will aid modelers to identify the areas of model structure responsible for poor performance.
Publisher: American Meteorological Society
Date: 2004
Publisher: IWA Publishing
Date: 07-08-2020
DOI: 10.2166/WCC.2020.230
Abstract: We examine the relative impact of population increases and climate change in affecting future water demand for Sydney, Australia. We use the Weather and Research Forecasting model, a water demand model and a stochastic weather generator to downscale four different global climate models for the present (1990–2010), near (2020–2040) and far (2060–2080) future. Projected climate change would increase median metered consumption, at 2019/2020 population levels, from around 484 GL under present climate to 484–494 GL under near future climate and 495–505 GL under far future climate. Population changes from 2014/2015 to 2024/2025 have a far larger impact, increasing median metered consumption from 457 to 508 GL under the present climate, 463 to 515 GL under near future climate and from 471 to 524 GL under far future climate. The projected changes in consumption are sensitive to the climate model used. Overall, while population growth is a far stronger driver of increasing water demand than climate change for Sydney, both act in parallel to reduce the time it would take for all storage to be exhausted. Failing to account for climate change would therefore lead to overconfidence in the reliability of Sydney's water supply.
Publisher: Elsevier BV
Date: 12-1998
Publisher: American Geophysical Union (AGU)
Date: 27-10-2002
DOI: 10.1029/2001JD000931
Publisher: Centers for Disease Control and Prevention (CDC)
Date: 11-2022
Publisher: Copernicus GmbH
Date: 30-04-2019
Abstract: Abstract. In response to a warming climate, temperature extremes are changing in many regions of the world. Therefore, understanding how the fluxes of sensible heat, latent heat and net ecosystem exchange respond and contribute to these changes is important. We examined 216 sites from the open access Tier 1 FLUXNET2015 and free fair-use La Thuile data sets, focussing only on observed (non-gap-filled) data periods. We examined the availability of sensible heat, latent heat and net ecosystem exchange observations coincident in time with measured temperature for all temperatures, and separately for the upper and lower tail of the temperature distribution, and expressed this availability as a measurement ratio. We showed that the measurement ratios for both sensible and latent heat fluxes are generally lower (0.79 and 0.73 respectively) than for temperature measurements, and the measurement ratio of net ecosystem exchange measurements are appreciably lower (0.42). However, sites do exist with a high proportion of measured sensible and latent heat fluxes, mostly over the United States, Europe and Australia. Few sites have a high proportion of measured fluxes at the lower tail of the temperature distribution over very cold regions (e.g. Alaska, Russia) or at the upper tail in many warm regions (e.g. Central America and the majority of the Mediterranean region), and many of the world's coldest and hottest regions are not represented in the freely available FLUXNET data at all (e.g. India, the Gulf States, Greenland and Antarctica). However, some sites do provide measured fluxes at extreme temperatures, suggesting an opportunity for the FLUXNET community to share strategies to increase measurement availability at the tails of the temperature distribution. We also highlight a wide discrepancy between the measurement ratios across FLUXNET sites that is not related to the actual temperature or rainfall regimes at the site, which we cannot explain. Our analysis provides guidance to help select eddy covariance sites for researchers interested in understanding and/or modelling responses to temperature extremes.
Publisher: American Geophysical Union (AGU)
Date: 20-07-2020
DOI: 10.1029/2020GL088031
Abstract: We quantify the skill of Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models to represent daily temperature extremes. We find CMIP models systematically exaggerate the magnitude of daily temperature anomalies for both cold and hot extremes. We assess the contribution to a daily temperature extreme from four terms: the long‐term mean annual cycle, the diurnal cycle, synoptic variability, and seasonal variability for both cold and hot extremes. These four terms are combined, and the overall performance of in idual climate models assessed. This identifies those models that can simulate temperature extremes well and simulate them well for the right reasons. The new error metric shows that increases in horizontal resolution usually lead to a better performance particularly for the coarser resolution models. The CMIP6 improvements relative to CMIP5 are systematic across most land regions and are only partially explained by the increase in horizontal resolution, and other differences must therefore help explain the higher CMIP6 skill.
Publisher: Copernicus GmbH
Date: 23-08-2021
DOI: 10.5194/BG-2021-218
Abstract: Abstract. Climate change is projected to increase the imbalance between the supply (precipitation) and atmospheric demand for water (i.e. increased potential evapotranspiration), stressing plants in water-limited environments. Plants may be able to offset increasing aridity because rising CO2 increases water-use-efficiency. CO2 fertilization has also been cited as one of the drivers of the widespread ‘greening’ phenomenon. However, attributing the size of this CO2 fertilization effect is complicated, due in part to a lack of long-term vegetation monitoring and interannual to decadal-scale climate variability. In this study we asked the question, how much has CO2 contributed towards greening? We focused our analysis on a broad aridity gradient spanning eastern Australia’s woody ecosystems. Next we analysed 38-years of satellite remote sensing estimates of vegetation greenness (normalized difference vegetation index, NDVI) to examine the role of CO2 in ameliorating climate change impacts. Multiple statistical techniques were applied to separate the CO2-attributable effects on greening from the changes in water supply and atmospheric aridity. Widespread vegetation greening occurred despite a warming climate, increases in vapor pressure deficit, and repeated record-breaking droughts and heatwaves. Between 1982–2019 we found that NDVI increased (median 11.3 %) across 90.5 % of the woody regions. After masking disturbance effects (e.g. fire), we statistically estimated an 11.7 % increase in NDVI attributable to CO2, broadly consistent with a hypothesized theoretical expectation of an 8.6 % increase in water-use-efficiency due to rising CO2. In contrast to reports of a weakening CO2 fertilization effect, we found no consistent temporal change in the CO2 effect. We conclude rising CO2 has mitigated the effects of increasing aridity, repeated record-breaking droughts, and record-breaking heat waves in eastern Australia. However, we were unable to determine whether trees or grasses were the primary beneficiary of the CO2 induced change in water-use-efficiency, which has implications for projecting future ecosystem resilience. A more complete understanding of how CO2 induced changes in water-use-efficiency affect trees and non-tree vegetation is needed.
Publisher: Wiley
Date: 27-01-2020
DOI: 10.1111/NPH.16376
Abstract: Knowledge of how water stress impacts the carbon and water cycles is a key uncertainty in terrestrial biosphere models. We tested a new profit maximization model, where photosynthetic uptake of CO
Publisher: Wiley
Date: 09-2009
Publisher: American Meteorological Society
Date: 04-2010
Abstract: Using a coupled atmosphere–land surface model, simulations were conducted to characterize the regional climate changes that result from the response of stomates to increases in leaf-level carbon dioxide (CO2) under differing conditions of moisture availability over Australia. Multiple realizations for multiple Januarys corresponding to dry and wet years were run, where only the leaf-level CO2 was varied at 280, 375, 500, 650, 840, and 1000 ppmv and the atmospheric CO2 was fixed at 375 ppmv. The results show the clear effect of increasing leaf-level CO2 on the transpiration via the stomatal response, particularly when sufficient moisture is available. Statistically significant reductions in transpiration generally lead to a significantly warmer land surface with decreases in rainfall. Increases in CO2 lead to increases in the magnitude and areal extent of the statistically significant mean changes in the surface climate. However, the results also show that the availability of moisture substantially affects the effect of increases in the leaf-level CO2, particularly for a moisture-limited region. The physiological feedback can indirectly lead to more rainfall via changes in the low-level moisture convergence and vertical velocity, which result in a cooling simulated over Western Australia. The significant changes in the surface climate presented in the results suggest that it is still important to incorporate these feedbacks in future climate assessments and projections for Australia. The influence of moisture availability also indicates that the capacity of the physiological feedback to affect the future climate may be affected by uncertainties in rainfall projections, particularly for water-stressed regions such as Australia.
Publisher: Springer Science and Business Media LLC
Date: 20-02-2015
Publisher: American Geophysical Union (AGU)
Date: 25-01-2017
DOI: 10.1002/2016MS000832
Abstract: The Community Atmosphere Biosphere Land Exchange (CABLE) land surface model overestimates evapotranspiration ( E ) at numerous flux tower sites during boreal spring. The overestimation of E is not eliminated when the nonlinear dependence of soil evaporation on soil moisture or a simple litter layer is introduced into the model. New resistance terms, previously developed from a pore‐scale model of soil evaporation, are incorporated into the treatment of under canopy water vapor transfer in CABLE. The new resistance terms reduce the large positive bias in spring time E at multiple flux tower sites and also improve the simulation of daily sensible heat flux. The reduction in the spring E bias allows the soil to retain water into the summer, improving the seasonality of E . The simulation of daily E is largely insensitive to the details of the implementation of the pore model resistance scheme. The more physically based treatment of soil evaporation presented here eliminates the need for empirical functions that reduce evaporation as a function of soil moisture that are included in many land surface models.
Publisher: American Meteorological Society
Date: 12-2001
Publisher: Wiley
Date: 05-1998
DOI: 10.1002/(SICI)1097-0088(199805)18:6<595::AID-JOC275>3.0.CO;2-O
Publisher: Springer Science and Business Media LLC
Date: 18-12-2020
DOI: 10.1038/S41467-020-20502-8
Abstract: A Correction to this paper has been published: 0.1038/s41467-020-20502-8.
Publisher: American Geophysical Union (AGU)
Date: 26-03-2002
DOI: 10.1029/2002EO000091
Publisher: American Geophysical Union (AGU)
Date: 05-03-2014
DOI: 10.1002/2013GL059055
Publisher: American Meteorological Society
Date: 12-2011
Publisher: American Meteorological Society
Date: 04-2003
Publisher: Elsevier BV
Date: 12-1998
Publisher: IOP Publishing
Date: 10-2016
Publisher: IOP Publishing
Date: 07-08-2023
Abstract: Dynamical downscaling (DD), and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia from grids of 12–48 km to 5 km, using the Australia Gridded Climate Data observations as the target. A separate MLP is developed for each coarse grid to predict the fine grid values within it, by combining coarse-scale time-varying meteorological variables with fine-scale static surface properties as predictors. The resulting predictions (on out-of-s le test periods) are more accurate than DD in capturing the rainfall climatology, as well as the frequency distribution and spatiotemporal variability of daily precipitation, reducing biases in daily extremes by 15%–85% with 12 km prediction fields. When prediction fields are coarsened, the skill of the MLP decreases—at 24 km relative bias increases by ∼10%, and at 48 km it increases by another ∼4%—but skill remains comparable to or, for some metrics, much better than DD. These results show that ML-based downscaling benefits from higher-resolution driving data but can still improve on DD (and at far less computational cost) when downscaling from a global climate model grid of ∼50 km.
Publisher: IOP Publishing
Date: 18-08-2022
Abstract: Efforts to assess risks to the financial system associated with climate change are growing. These commonly combine the use of integrated assessment models to obtain possible changes in global mean temperature (GMT) and then use coupled climate models to map those changes onto finer spatial scales to estimate changes in other variables. Other methods use data mined from ‘ensembles of opportunity’ such as the Coupled Model Intercomparison Project (CMIP). Several challenges with current approaches have been identified. Here, we focus on demonstrating the issues inherent in applying global ‘top-down’ climate scenarios to explore financial risks at geographical scales of relevance to financial institutions (e.g. city-scale). We use data mined from the CMIP to determine the degree to which estimates of GMT can be used to estimate changes in the annual extremes of temperature and rainfall, two compound events (heatwaves and drought, and extreme rain and strong winds), and whether the emission scenario provides insights into the change in the 20, 50 and 100 year return values for temperature and rainfall. We show that GMT provides little insight on how acute risks likely material to the financial sector (‘material extremes’) will change at a city-scale. We conclude that ‘top-down’ approaches are likely to be flawed when applied at a granular scale, and that there are risks in employing the approaches used by, for ex le, the Network of Central Banks and Supervisors for Greening the Financial System. Most fundamental, uncertainty associated with projections of future climate extremes must be propagated through to estimating risk. We strongly encourage a review of existing top-down approaches before they develop into de facto standards and note that existing approaches that use a ‘bottom-up’ strategy (e.g. catastrophe modelling and storylines) are more likely to enable a robust assessment of material risk.
Publisher: Copernicus GmbH
Date: 31-03-2014
Abstract: Abstract. Recent studies have identified the first-order parameterization of microbial decomposition as a major source of uncertainty in simulations and projections of the terrestrial carbon balance. Here, we use a reduced complexity model representative of the current state-of-the-art parameterization of soil organic carbon decomposition. We undertake a systematic sensitivity analysis to disentangle the effect of the time-invariant baseline residence time (k) and the sensitvity of microbial decomposition to temperature (Q10) on soil carbon dynamics at regional and global scales. Our simulations produce a range in total soil carbon at equilibrium of ~ 592 to 2745 Pg C which is similar to the ~ 561 to 2938 Pg C range in pre-industrial soil carbon in models used in the fifth phase of the Coupled Model Intercomparison Project. This range depends primarily on the value of k, although the impact of Q10 is not trivial at regional scales. As climate changes through the historical period, and into the future, k is primarily responsible for the magnitude of the response in soil carbon, whereas Q10 determines whether the soil remains a sink, or becomes a source in the future mostly by its effect on mid-latitudes carbon balance. If we restrict our simulations to those simulating total soil carbon stocks consistent with observations of current stocks, the projected range in total soil carbon change is reduced by 42% for the historical simulations and 45% for the future projections. However, while this observation-based selection dismisses outliers it does not increase confidence in the future sign of the soil carbon feedback. We conclude that despite this result, future estimates of soil carbon, and how soil carbon responds to climate change should be constrained by available observational data sets.
Publisher: Wiley
Date: 06-2008
DOI: 10.1111/J.1445-5994.2008.01688.X
Abstract: Climate change is unequivocal. The fourth assessment report of the Intergovermental Panel on Climate Change has recently projected that global average surface temperature will increase by 1.1 to 6.4 degrees C by 2100. Anthropogenic warming during the twenty-first century would be much greater than that observed in the twentieth century. Most of the warming observed over the last six decades is attributable to human activities. Climate change is already affecting, and will increasingly have profound effects on human health and well-being. Therefore, there is an urgent need for societies to take both preemptive and adaptive actions to protect human populations from adverse health consequences of climate change. It is time to mainstream health risks and their prevention in relation to the effects of climate change on the medical research and policy agenda.
Publisher: Wiley
Date: 04-01-2011
DOI: 10.1002/JOC.2279
Publisher: Informa UK Limited
Date: 03-2011
Publisher: Springer Science and Business Media LLC
Date: 10-11-2019
Publisher: American Meteorological Society
Date: 02-2006
DOI: 10.1175/JAM2337.1
Abstract: The Regional Atmospheric Modeling System (RAMS) was run at a 1-km grid spacing over the Sydney basin in Australia to assess the impact of land cover change on a simulated storm event. The simulated storm used NCEP–NCAR reanalysis data, first with natural (i.e., pre-European settlement in 1788) land cover and then with satellite-derived land cover representing Sydney's current land use pattern. An intense convective storm develops in the model in close proximity to Sydney's dense urban central business district under current land cover. The storm is absent under natural land cover conditions. A detailed investigation of why the change in land cover generates a storm was performed using factorial analysis, which revealed the storm to be sensitive to the presence of agricultural land in the southwest of the domain. This area interacts with the sea breeze and affects the horizontal ergence and moisture convergence—the triggering mechanisms of the storm. The existence of the storm over the dense urban area of Sydney is therefore coincidental. The results herein support efforts to develop parameterization of urban surfaces in high-resolution simulations of Sydney's meteorological environment but also highlight the need to improve the parameterization of other types of land cover change at the periphery of the urban area, given that these types dominate the explanation of the results.
Publisher: Copernicus GmbH
Date: 08-12-2015
Abstract: Abstract. We implement a new stomatal conductance scheme, based on the optimality approach, within the Community Atmosphere Biosphere Land Exchange (CABLEv2.0.1) land surface model. Coupled land–atmosphere simulations are then performed using CABLEv2.0.1 within the Australian Community Climate and Earth Systems Simulator (ACCESSv1.3b) with prescribed sea surface temperatures. As in most land surface models, the default stomatal conductance scheme only accounts for differences in model parameters in relation to the photosynthetic pathway but not in relation to plant functional types. The new scheme allows model parameters to vary by plant functional type, based on a global synthesis of observations of stomatal conductance under different climate regimes over a wide range of species. We show that the new scheme reduces the latent heat flux from the land surface over the boreal forests during the Northern Hemisphere summer by 0.5–1.0 mm day−1. This leads to warmer daily maximum and minimum temperatures by up to 1.0 °C and warmer extreme maximum temperatures by up to 1.5 °C. These changes generally improve the climate model's climatology of warm extremes and improve existing biases by 10–20 %. The bias in minimum temperatures is however degraded but, overall, this is outweighed by the improvement in maximum temperatures as there is a net improvement in the diurnal temperature range in this region. In other regions such as parts of South and North America where ACCESSv1.3b has known large positive biases in both maximum and minimum temperatures (~ 5 to 10 °C), the new scheme degrades this bias by up to 1 °C. We conclude that, although several large biases remain in ACCESSv1.3b for temperature extremes, the improvements in the global climate model over large parts of the boreal forests during the Northern Hemisphere summer which result from the new stomatal scheme, constrained by a global synthesis of experimental data, provide a valuable advance in the long-term development of the ACCESS modelling system.
Publisher: American Meteorological Society
Date: 06-1998
Publisher: Elsevier BV
Date: 09-2022
Publisher: Copernicus GmbH
Date: 05-05-2023
Abstract: Abstract. Climate projections from global circulation models (GCMs), part of the Coupled Model Intercomparison Project 6 (CMIP6), are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation. These biases have been identified as a major source of uncertainty in carbon cycle projections, h ering predictive capacity. In this study, we open the proverbial Pandora's box and peer under the lid of strategies to tackle climate model ensemble uncertainty. We employ a dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias-correct or calculate ensemble averages over the original forcing data to reduce the climate-driven uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Bias correction can improve model carbon outputs, but carbon pools are insensitive to the type of bias correction method applied for both in idual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in simulated carbon over time compared to the target dataset, highlighting the need to account for temporal properties in correction or ensemble-averaging methods. Multivariate bias correction methods tend to reduce the uncertainty more than univariate approaches, although the overall magnitude is similar. Even after correcting the bias in the meteorological forcing dataset, the simulated vegetation distribution presents different patterns when different GCMs are used to drive LPJ-GUESS. Additionally, we found that both the weighted ensemble-averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble-averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. This highlights that, where possible, an arithmetic ensemble average should be avoided. However, potential target datasets that would facilitate the application of machine learning approaches, i.e., that cover both the spatial and temporal domain required to derive a robust informed ensemble average, are sparse for ecosystem variables.
Publisher: American Meteorological Society
Date: 07-2001
Publisher: Elsevier BV
Date: 05-0002
Publisher: American Meteorological Society
Date: 14-11-2012
Publisher: Springer Science and Business Media LLC
Date: 08-1990
DOI: 10.1038/346734A0
Publisher: Copernicus GmbH
Date: 20-07-2011
Abstract: Abstract. The CSIRO Mk3L climate system model, a reduced-resolution coupled general circulation model, has previously been described in this journal. The model is configured for millennium scale or multiple century scale simulations. This paper reports the impact of replacing the relatively simple land surface scheme that is the default parameterisation in Mk3L with a sophisticated land surface model that simulates the terrestrial energy, water and carbon balance in a physically and biologically consistent way. An evaluation of the new model's near-surface climatology highlights strengths and weaknesses, but overall the atmospheric variables, including the near-surface air temperature and precipitation, are simulated well. The impact of the more sophisticated land surface model on existing variables is relatively small, but generally positive. More significantly, the new land surface scheme allows an examination of surface carbon-related quantities including net primary productivity which adds significantly to the capacity of Mk3L. Overall, results demonstrate that this reduced-resolution climate model is a good foundation for exploring long time scale phenomena. The addition of the more sophisticated land surface model enables an exploration of important Earth System questions including land cover change and abrupt changes in terrestrial carbon storage.
Publisher: Wiley
Date: 06-07-2022
DOI: 10.1111/PCE.14376
Abstract: There is a pressing need to better understand ecosystem resilience to droughts and heatwaves. Eco‐evolutionary optimization approaches have been proposed as means to build this understanding in land surface models and improve their predictive capability, but competing approaches are yet to be tested together. Here, we coupled approaches that optimize canopy gas exchange and leaf nitrogen investment, respectively, extending both approaches to account for hydraulic impairment. We assessed model predictions using observations from a native Eucalyptus woodland that experienced repeated droughts and heatwaves between 2013 and 2020, whilst exposed to an elevated [CO 2 ] treatment. Our combined approaches improved predictions of transpiration and enhanced the simulated magnitude of the CO 2 fertilization effect on gross primary productivity. The competing approaches also worked consistently along axes of change in soil moisture, leaf area, and [CO 2 ]. Despite predictions of a significant percentage loss of hydraulic conductivity due to embolism (PLC) in 2013, 2014, 2016, and 2017 (99th percentile PLC 45%), simulated hydraulic legacy effects were small and short‐lived (2 months). Our analysis suggests that leaf shedding and/or suppressed foliage growth formed a strategy to mitigate drought risk. Accounting for foliage responses to water availability has the potential to improve model predictions of ecosystem resilience.
Publisher: American Meteorological Society
Date: 04-2002
Publisher: Wiley
Date: 02-10-2008
DOI: 10.1111/J.1461-0248.2008.01231.X
Abstract: Species distribution models (SDMs) are common tools for assessing the potential impact of climate change on species ranges. Uncertainty in SDM output occurs due to differences among alternate models, species characteristics and scenarios of future climate. While considerable effort is being devoted to identifying and quantifying the first two sources of variation, a greater understanding of climate scenarios and how they affect SDM output is also needed. Climate models are complex tools: variability occurs among alternate simulations, and no single 'best' model exists. The selection of climate scenarios for impacts assessments should not be undertaken arbitrarily - strengths and weakness of different climate models should be considered. In this paper, we provide bioclimatic modellers with an overview of emissions scenarios and climate models, discuss uncertainty surrounding projections of future climate and suggest steps that can be taken to reduce and communicate climate scenario-related uncertainty in assessments of future species responses to climate change.
Publisher: Copernicus GmbH
Date: 28-01-2021
Abstract: Abstract. Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of in idual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics–highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias, using a more realistic initialisation of the groundwater aquifer state and higher vertical soil resolution informed by observed soil properties, and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site-calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ∼150 W m−2 in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.
Publisher: American Geophysical Union (AGU)
Date: 07-10-2018
DOI: 10.1029/2018GL079128
Abstract: Climate simulations of future hot extremes exhibit large uncertainties regarding the magnitude of projected warming. We identify two mechanisms that influence how strongly future heat extremes intensify in climate models. First, the magnitude of extreme temperature increases is determined by changes in preceding seasonal precipitation, connected to lified warming via soil moisture decreases. Second, there are large differences in how models respond to moisture variability those with a stronger response under current climate simulate larger future increases in hot extremes. We build on this mechanistic understanding of future uncertainty and develop a novel constraint, the observed precipitation‐hot temperature relationship, focused on the conditions on the actual hottest day, to identify climate models with realistic land‐atmosphere feedbacks on hot extremes. Applying this constraint to the Coupled Model Intercomparison Project Phase 5 ensemble reduces the probability of the largest increases in projected heat extremes, particularly over Europe and North America.
Publisher: American Meteorological Society
Date: 02-2006
DOI: 10.1175/EI154.1
Abstract: The potential role of the impacts of land-cover changes (LCCs) in the Australian climate is investigated within the context of increasing CO2 concentrations and temperature. Specifically, it is explored if possible scenarios for LCC can moderate or lify CO2-induced changes in climate over Australia. The January climate of Australia is simulated under three different land-cover-change scenarios using a high-resolution regional climate model. The land-cover-change scenarios include a steady-state land cover that is equivalent to current land cover, a low-reforestation scenario that recovers approximately 25% of the trees replaced by grasslands within the last 200 yr, and a high-reforestation scenario that recovers at least 75% of the deforested regions. The model was driven by boundary conditions taken from transitory climate simulations from a general circulation model that included two climate scenarios based on two projected scenarios of CO2 concentration increase. The results show that reforestation has the potential to reduce the projected increase in Australian temperatures in 2050 and 2100 by as much as 40% and 20%, respectively. This cooling effect, however, is highly localized and occurs only in regions of reforestation. The results therefore hint that the potential of reforestation to moderate the impact of global warming may be significantly limited by the spatial scale of reforestation. In terms of deforestation, results show that any future land clearing can exacerbate the projected warming in certain regions of Australia. Carbon-related variables are also analyzed and results show that changes in net CO2 flux may be influenced more by soil respiration than by photosynthesis. The results herein encourage studies on the inclusion of land-cover-change scenarios in future climate change projection simulations of the Australian climate.
Publisher: Copernicus GmbH
Date: 29-09-2017
Abstract: Abstract. This article extends a previous study Seneviratne et al. (2016) to provide regional analyses of changes in climate extremes as a function of projected changes in global mean temperature. We introduce the DROUGHT-HEAT Regional Climate Atlas, an interactive tool to analyse and display a range of well-established climate extremes and water-cycle indices and their changes as a function of global warming. These projections are based on simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). A selection of ex le results are presented here, but users can visualize specific indices of interest using the online tool. This implementation enables a direct assessment of regional climate changes associated with global mean temperature targets, such as the 2 and 1.5° limits agreed within the 2015 Paris Agreement.
Publisher: Public Library of Science (PLoS)
Date: 10-02-2015
Publisher: Springer Science and Business Media LLC
Date: 16-06-2023
Publisher: Wiley
Date: 14-11-2008
DOI: 10.1002/JOC.1653
Publisher: Copernicus GmbH
Date: 05-04-2022
Abstract: Abstract. The vegetation's response to climate change is a significant source of uncertainty in future terrestrial biosphere model projections. Constraining climate–carbon cycle feedbacks requires improving our understanding of both the immediate and long-term plant physiological responses to climate. In particular, the timescales and strength of memory effects arising from both extreme events (i.e. droughts and heatwaves) and structural lags in the systems (such as delays between rainfall and peak plant water content or between a precipitation deficit and down-regulation of productivity) have largely been overlooked in the development of terrestrial biosphere models. This is despite the knowledge that plant responses to climatic drivers occur across multiple timescales (seconds to decades), with the impact of climate extremes resonating for many years. Using data from 12 eddy covariance sites, covering two rainfall gradients (256 to 1491 mm yr−1) in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales and magnitude of influence of antecedent drivers on daily net ecosystem exchange (NEE) and latent heat flux (λE). By focussing our analysis on a single continent (and predominately on a single genus), we reduced the degrees of variation between each site, providing a novel chance to explore the unique characteristics that might drive the importance of memory. Model fit varied considerably across sites when modelling NEE, with R2 values of between 0.30 and 0.83. λE was considerably more predictable across sites, with R2 values ranging from 0.56 to 0.93. When considered at a continental scale, both fluxes were more predictable when memory effects (expressed as lagged climate predictors) were included in the model. These memory effects accounted for an average of 17 % of the NEE predictability and 15 % for λE. Consistent with prior studies, the importance of environmental memory in predicting fluxes increased as site water availability declined (ρ=-0.73, p .01 for NEE, ρ=-0.67, p .05 for λE). However, these relationships did not necessarily hold when sites were grouped by vegetation type. We also tested a model of k-means clustering plus regression to confirm the suitability of the Bayesian model for modelling these sites. The k-means approach performed similarly to the Bayesian model in terms of model fit, demonstrating the robustness of the Bayesian framework for exploring the role of environmental memory. Our results underline the importance of capturing memory effects in models used to project future responses to climate change, especially in water-limited ecosystems. Finally, we demonstrate a considerable variation in in idual-site predictability, driven to a notable degree by environmental memory, and this should be considered when evaluating model performance across ecosystems.
Publisher: American Geophysical Union (AGU)
Date: 03-2009
DOI: 10.1029/2009GL037293
Publisher: American Meteorological Society
Date: 06-2018
Abstract: Global climate models play an important role in quantifying past and projecting future changes in drought. Previous studies have pointed to shortcomings in these models for simulating droughts, but systematic evaluation of their level of agreement has been limited. Here, historical simulations (1950–2004) for 20 models from the latest Coupled Model Intercomparison Project (CMIP5) were analyzed for a variety of drought metrics and thresholds using a standardized drought index. Model agreement was investigated for different types of drought (precipitation, runoff, and soil moisture) and how this varied with drought severity and duration. At the global scale, climate models were shown to agree well on most precipitation drought metrics, but systematically underestimated precipitation drought intensity compared to observations. Conversely, simulated runoff and soil moisture droughts varied significantly across models, particularly for intensity. Differences in precipitation simulations were found to explain model differences in runoff and soil moisture drought metrics over some regions, but predominantly with respect to drought intensity. This suggests it is insufficient to evaluate models for precipitation droughts to increase confidence in model performance for other types of drought. This study shows large but metric-dependent discrepancies in CMIP5 for modeling different types of droughts that relate strongly to the component models (i.e., atmospheric or land surface scheme) used in the coupled modeling systems. Our results point to a need to consider multiple models in drought impact studies to account for high model uncertainties.
Publisher: American Meteorological Society
Date: 10-2015
Abstract: The role of land–atmosphere coupling in modulating the impact of land-use change (LUC) on regional climate extremes remains uncertain. Using the Weather and Research Forecasting Model, this study combines the Global Land–Atmosphere Coupling Experiment with regional LUC to assess the combined impact of land–atmosphere coupling and LUC on simulated temperature extremes. The experiment is applied to an ensemble of planetary boundary layer (PBL) and cumulus parameterizations to determine the sensitivity of the results to model physics. Results show a consistent weakening in the soil moisture–maximum temperature coupling strength with LUC irrespective of the model physics. In contrast, temperature extremes show an asymmetric response to LUC dependent on the choice of PBL scheme, which is linked to differences in the parameterization of vertical transport. This influences convective precipitation, contributing a positive feedback on soil moisture and consequently on the partitioning of the surface turbulent fluxes. The results suggest that the impact of LUC on temperature extremes depends on the land–atmosphere coupling that in turn depends on the choice of PBL. Indeed, the sign of the temperature change in hot extremes resulting from LUC can be changed simply by altering the choice of PBL. The authors also note concerns over the metrics used to measure coupling strength that reflect changes in variance but may not respond to LUC-type perturbations.
Publisher: Informa UK Limited
Date: 07-1998
Publisher: Springer Science and Business Media LLC
Date: 17-03-2007
Publisher: American Meteorological Society
Date: 11-2002
Publisher: Springer Science and Business Media LLC
Date: 24-02-2020
Publisher: American Meteorological Society
Date: 08-2008
DOI: 10.1175/2008EI260.1
Abstract: Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining s le of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection.
Publisher: Elsevier BV
Date: 05-1996
Publisher: Wiley
Date: 02-2009
Publisher: American Geophysical Union (AGU)
Date: 09-06-2020
DOI: 10.1029/2020GL087820
Publisher: Elsevier BV
Date: 11-2006
Publisher: Springer Science and Business Media LLC
Date: 07-1995
DOI: 10.1007/BF00211680
Publisher: American Meteorological Society
Date: 02-2014
Abstract: The authors use a sophisticated coupled land–atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%–20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia.
Publisher: Australian Water Association
Date: 2019
Publisher: Wiley
Date: 06-2005
Publisher: Copernicus GmbH
Date: 28-01-2022
Abstract: Abstract. Climate change is projected to increase the imbalance between the supply (precipitation) and atmospheric demand for water (i.e., increased potential evapotranspiration), stressing plants in water-limited environments. Plants may be able to offset increasing aridity because rising CO2 increases water use efficiency. CO2 fertilization has also been cited as one of the drivers of the widespread “greening” phenomenon. However, attributing the size of this CO2 fertilization effect is complicated, due in part to a lack of long-term vegetation monitoring and interannual- to decadal-scale climate variability. In this study we asked the question of how much CO2 has contributed towards greening. We focused our analysis on a broad aridity gradient spanning eastern Australia's woody ecosystems. Next we analyzed 38 years of satellite remote sensing estimates of vegetation greenness (normalized difference vegetation index, NDVI) to examine the role of CO2 in ameliorating climate change impacts. Multiple statistical techniques were applied to separate the CO2-attributable effects on greening from the changes in water supply and atmospheric aridity. Widespread vegetation greening occurred despite a warming climate, increases in vapor pressure deficit, and repeated record-breaking droughts and heat waves. Between 1982–2019 we found that NDVI increased (median 11.3 %) across 90.5 % of the woody regions. After masking disturbance effects (e.g., fire), we statistically estimated an 11.7 % increase in NDVI attributable to CO2, broadly consistent with a hypothesized theoretical expectation of an 8.6 % increase in water use efficiency due to rising CO2. In contrast to reports of a weakening CO2 fertilization effect, we found no consistent temporal change in the CO2 effect. We conclude rising CO2 has mitigated the effects of increasing aridity, repeated record-breaking droughts, and record-breaking heat waves in eastern Australia. However, we were unable to determine whether trees or grasses were the primary beneficiary of the CO2-induced change in water use efficiency, which has implications for projecting future ecosystem resilience. A more complete understanding of how CO2-induced changes in water use efficiency affect trees and non-tree vegetation is needed.
Publisher: American Geophysical Union (AGU)
Date: 07-2003
DOI: 10.1029/2003GL017387
Publisher: American Geophysical Union (AGU)
Date: 19-08-2014
DOI: 10.1002/2014GL061179
Publisher: American Meteorological Society
Date: 04-1995
Publisher: Springer Science and Business Media LLC
Date: 05-11-2008
Publisher: Elsevier BV
Date: 06-2022
Publisher: Elsevier BV
Date: 07-2003
Publisher: Springer Science and Business Media LLC
Date: 29-01-2018
Publisher: IOP Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 06-1990
DOI: 10.1007/BF00144509
Publisher: Proceedings of the National Academy of Sciences
Date: 29-09-2009
Publisher: Copernicus GmbH
Date: 26-11-2012
Abstract: Abstract. The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, consistent changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC-induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models, the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes are lified. While we find some evidence that in idual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperatures to LULCC is relatively large, we conclude that unless this forcing is included, we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.
Publisher: American Geophysical Union (AGU)
Date: 16-01-2010
DOI: 10.1029/2009JD012767
Publisher: American Meteorological Society
Date: 02-2006
DOI: 10.1175/JHM475.1
Abstract: Six modes of complexity of the Chameleon land surface model (CHASM) are used to explore the relationship between the complexity of the surface energy balance (SEB) formulation and the capacity of the model to explain intermodel variations in results from the Rhône-Aggregation Intercomparison Project (Rhône-AGG). At an annual time scale, differences between models identified in the Rhône-AGG experiments in the partitioning of available energy and water at the spatial scale of the Rhône Basin can be reproduced by CHASM via variations in the SEB complexity. Only two changes in the SEB complexity in the model generate statistically significant differences in the mean latent heat flux. These are the addition of a constant surface resistance to the simplest mode of CHASM and the addition of tiling and temporally and spatially variable surface resistance to produce the most complex model. Further, the only statistically significant differences in runoff occur following the addition of a constant surface resistance to the simplest mode of CHASM. As the time scale is reduced from annual to monthly, specific mechanisms begin to dominate the simulations produced by each Rhône-AGG model and introduce parameterization-specific behavior that depends on the time evolution of processes operating on longer time scales. CHASM cannot capture all this behavior by varying the SEB complexity, demonstrating the contribution to intermodel differences by hydrology and snow-related processes. Despite the increasing role of hydrology and snow in simulating processes at finer time scales, provided the constant surface resistance is included, CHASM's modes perform within the range of uncertainty illustrated by other Rhône-AGG models on seasonal and annual time scales.
Publisher: Springer Science and Business Media LLC
Date: 02-2017
DOI: 10.1038/NCLIMATE3182
Publisher: Inter-Research Science Center
Date: 2001
DOI: 10.3354/CR017001
Publisher: Springer Science and Business Media LLC
Date: 21-06-2005
Publisher: Copernicus GmbH
Date: 17-01-2018
Abstract: Abstract. Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more ersity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.
Publisher: American Geophysical Union (AGU)
Date: 18-03-2019
DOI: 10.1029/2018JD029762
Publisher: Springer Science and Business Media LLC
Date: 06-2005
Publisher: Informa UK Limited
Date: 11-1997
Publisher: Copernicus GmbH
Date: 24-09-2018
DOI: 10.5194/BG-2018-399
Abstract: Abstract. Recent experimental evidence suggests that during heat extremes, wooded ecosystems may decouple photosynthesis and transpiration: reducing photosynthesis to near zero but increasing transpiration into the boundary layer. This feedback may act to d en, rather than lify, heat extremes in wooded ecosystems. We examined eddy-covariance databases (OzFlux and FLUXNET2015) to identify whether there was field-based evidence to support these experimental findings. We focused on two types of heat extremes: (i) the three days leading up to a temperature extreme, defined as including a daily maximum temperature 37 °C (similar to the widely used TXx metric) and (ii) heatwaves, defined as three or more consecutive days above 35 °C. When focussing on (i), we found some evidence of reduced photosynthesis and sustained or increased latent heat fluxes in seven Australian evergreen wooded flux sites. However, when considering the role of vapour pressure deficit and focusing on (ii), we were unable to conclusively disentangle the decoupling between photosynthesis and latent heat flux from the effect of increasing vapour pressure deficit. Outside of Australia, the Tier-1 FLUXNET2015 database provided limited scope to tackle this issue as it does not s le sufficient high temperature events with which to probe the physiological response of trees to extreme heat. Thus, further work is required to determine whether this photosynthetic decoupling occurs widely, ideally by matching experimental species with those found at eddy-covariance towers sites. Such measurements would allow this decoupling mechanism to be probed experimentally and at the ecosystem scale. Transpiration during heatwaves remains a key issue to resolve, as no land surface model includes a decoupling mechanism, and any potential d ening of the land-atmosphere lification is thus not included in climate model projections.
Publisher: Springer Science and Business Media LLC
Date: 20-06-2018
Publisher: Cambridge University Press
Date: 10-02-2011
Publisher: American Geophysical Union (AGU)
Date: 12-2001
DOI: 10.1029/2000JD000129
Publisher: Copernicus GmbH
Date: 21-05-2014
Abstract: Abstract. Soil carbon storage simulated by the Coupled Model Intercomparison Project (CMIP5) models varies 6-fold for the present day. We show that this range already exists at the beginning of the historical simulations and demonstrate that it is mostly an artifact of the representation of microbial decomposition and its response during the spin-up procedure used by the models. The 6-fold range in soil carbon, once established, is maintained through the present and to 2100 almost unchanged even under a strong business-as-usual emissions scenario. By highlighting the role of the response of decomposition to spin-up in explaining why current CMIP5 soil carbon stores vary widely, we identify the need to better constrain the outcome of this procedure as a means to reduce uncertainty in transient simulations.
Publisher: American Meteorological Society
Date: 15-07-2019
Abstract: China is several decades into large-scale afforestation programs to help address significant ecological and environmental degradation, with further afforestation planned for the future. However, the biophysical impact of afforestation on local surface temperature remains poorly understood, particularly in midlatitude regions where the importance of the radiative effect driven by albedo and the nonradiative effect driven by energy partitioning is uncertain. To examine this issue, we investigated the local impact of afforestation by comparing adjacent forest and open land pixels using satellite observations between 2001 and 2012. We attributed local surface temperature change between adjacent forest and open land to radiative and nonradiative effects over China based on the Intrinsic Biophysical Mechanism (IBM) method. Our results reveal that forest causes warming of 0.23°C (±0.21°C) through the radiative effect and cooling of −0.74°C (±0.50°C) through the nonradiative effect on local surface temperature compared with open land. The nonradiative effect explains about 79% (±16%) of local surface temperature change between adjacent forest and open land. The contribution of the nonradiative effect varies with forest and open land types. The largest cooling is achieved by replacing grasslands or rain-fed croplands with evergreen tree types. Conversely, converting irrigated croplands to deciduous broadleaf forest leads to warming. This provides new guidance on afforestation strategies, including how these should be informed by local conditions to avoid lifying climate-related warming.
Publisher: Springer Science and Business Media LLC
Date: 14-05-2018
Publisher: Springer Science and Business Media LLC
Date: 09-1996
Publisher: Wiley
Date: 19-08-2020
DOI: 10.1111/GCB.15215
Publisher: Copernicus GmbH
Date: 17-07-2012
Abstract: Abstract. The impact of historical land use induced land cover change (LULCC) on regional-scale climate extremes is examined using four climate models within the Land Use and Climate, IDentification of robust impacts project. To assess those impacts, multiple indices based on daily maximum and minimum temperatures and daily precipitation were used. We contrast the impact of LULCC on extremes with the impact of an increase in atmospheric CO2 from 280 ppmv to 375 ppmv. In general, changes in both high and low temperature extremes are similar to the simulated change in mean temperature caused by LULCC and are restricted to regions of intense modification. The impact of LULCC on both means and on most temperature extremes is statistically significant. While the magnitude of the LULCC induced change in the extremes can be of similar magnitude to the response to the change in CO2, the impacts of LULCC are much more geographically isolated. For most models the impacts of LULCC oppose the impact of the increase in CO2 except for one model where the CO2-caused changes in the extremes is lified. While we find some evidence that in idual models respond consistently to LULCC in the simulation of changes in rainfall and rainfall extremes, LULCC's role in affecting rainfall is much less clear and less commonly statistically significant, with the exception of a consistent impact over South East Asia. Since the simulated response of mean and extreme temperature to LULCC is relatively large, we conclude that unless this forcing is included we risk erroneous conclusions regarding the drivers of temperature changes over regions of intense LULCC.
Publisher: IOP Publishing
Date: 09-2016
Publisher: Wiley
Date: 28-10-2011
DOI: 10.1002/WCC.144
Abstract: This article summarizes the changes in landscape structure because of human land management over the last several centuries, and using observed and modeled data, documents how these changes have altered biogeophysical and biogeochemical surface fluxes on the local, mesoscale, and regional scales. Remaining research issues are presented including whether these landscape changes alter large‐scale atmospheric circulation patterns far from where the land use and land cover changes occur. We conclude that existing climate assessments have not yet adequately factored in this climate forcing. For those regions that have undergone intensive human landscape change, or would undergo intensive change in the future, we conclude that the failure to factor in this forcing risks a misalignment of investment in climate mitigation and adaptation. WIREs Clim Change 2011, 2:828–850. doi: 10.1002/wcc.144 This article is categorized under: Paleoclimates and Current Trends Climate Forcing
Publisher: American Meteorological Society
Date: 08-2010
Publisher: Wiley
Date: 22-04-2022
DOI: 10.1111/NPH.18129
Abstract: Predicting species‐level responses to drought at the landscape scale is critical to reducing uncertainty in future terrestrial carbon and water cycle projections. We embedded a stomatal optimisation model in the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and parameterised the model for 15 canopy dominant eucalypt tree species across South‐Eastern Australia (mean annual precipitation range: 344–1424 mm yr −1 ). We conducted three experiments: applying CABLE to the 2017–2019 drought a 20% drier drought and a 20% drier drought with a doubling of atmospheric carbon dioxide (CO 2 ). The severity of the drought was highlighted as for at least 25% of their distribution ranges, 60% of species experienced leaf water potentials beyond the water potential at which 50% of hydraulic conductivity is lost due to embolism. We identified areas of severe hydraulic stress within‐species’ ranges, but we also pinpointed resilience in species found in predominantly semiarid areas. The importance of the role of CO 2 in ameliorating drought stress was consistent across species. Our results represent an important advance in our capacity to forecast the resilience of in idual tree species, providing an evidence base for decision‐making around the resilience of restoration plantings or net‐zero emission strategies.
Publisher: Wiley
Date: 09-09-2014
DOI: 10.1111/AEC.12188
Publisher: Springer Science and Business Media LLC
Date: 06-03-2001
DOI: 10.1007/PL00013740
Publisher: American Geophysical Union (AGU)
Date: 28-07-2005
DOI: 10.1029/2005GL023158
Publisher: Copernicus GmbH
Date: 20-08-2020
DOI: 10.5194/BG-2020-299
Abstract: Abstract. The El Niño‐Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) Central–Pacific (CP) and (ii) Eastern–Pacific (EP). Both types of El Nino are characterised by above average sea surface temperature anomalies in the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the litude of the concentration of interannual atmospheric carbon dioxide (CO2) variability, as well as different lags in terrestrial CO2 release to the atmosphere following increased tropical near surface air temperature. We employ the dynamic global vegetation model LPJ–GUESS within a synthetic experimental framework to examine the sensitivity and potential long term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the later half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was negligible for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle.
Publisher: Copernicus GmbH
Date: 08-12-2011
Abstract: Abstract. The CSIRO Mk3L climate system model, a reduced-resolution coupled general circulation model, has previously been described in this journal. The model is configured for millennium scale or multiple century scale simulations. This paper reports the impact of replacing the relatively simple land surface scheme that is the default parameterisation in Mk3L with a sophisticated land surface model that simulates the terrestrial energy, water and carbon balance in a physically and biologically consistent way. An evaluation of the new model's near-surface climatology highlights strengths and weaknesses, but overall the atmospheric variables, including the near-surface air temperature and precipitation, are simulated well. The impact of the more sophisticated land surface model on existing variables is relatively small, but generally positive. More significantly, the new land surface scheme allows an examination of surface carbon-related quantities including net primary productivity which adds significantly to the capacity of Mk3L. Overall, results demonstrate that this reduced-resolution climate model is a good foundation for exploring long time scale phenomena. The addition of the more sophisticated land surface model enables an exploration of important Earth System questions including land cover change and abrupt changes in terrestrial carbon storage.
Publisher: Springer Science and Business Media LLC
Date: 03-09-1999
Publisher: American Meteorological Society
Date: 10-2007
DOI: 10.1175/JHM628.1
Abstract: A neural network–based flux correction technique is applied to three land surface models. It is then used to show that the nature of systematic model error in simulations of latent heat, sensible heat, and the net ecosystem exchange of CO2 is shared between different vegetation types and indeed different models. By manipulating the relationship between the dataset used to train the correction technique and that used to test it, it is shown that as much as 45% of per-time-step model root-mean-square error in these flux outputs is due to systematic problems in those model processes insensitive to changes in vegetation parameters. This is shown in the three land surface models using flux tower measurements from 13 sites spanning 2 vegetation types. These results suggest that efforts to improve the representation of fundamental processes in land surface models, rather than parameter optimization, are the key to the development of land surface model ability.
Publisher: Meteorological Society of Japan
Date: 1999
Publisher: American Geophysical Union (AGU)
Date: 13-02-2020
DOI: 10.1029/2019GL086569
Abstract: Satellite solar‐induced chlorophyll fluorescence (SIF) products from the Global Ozone Monitoring Experiment 2 (GOME‐2) and Orbiting Carbon Observatory 2 (OCO‐2) are used to investigate the responses of vegetation growth to the 2019 heat wave in Australia. Both satellite SIF data sets are more sensitive to water and heat stress than is the greenness‐based vegetation index (enhanced vegetation index). Moreover, the OCO‐2 SIF observations show a more significant reduction and earlier response to the heat stress than does GOME‐2 SIF, indicating that the two satellite SIF data sets differ in how they monitor the drought and heat wave event due to the different timing of observations. Eddy covariance measurements confirm the different responses of dryland vegetation to the 2019 heat wave at a subdaily time scale. The differences in the timing of the satellite SIF products can be used to assess different elements of the impact of heat and water stress on Australian dryland ecosystems.
Publisher: Elsevier BV
Date: 09-2010
Publisher: Elsevier BV
Date: 06-2009
DOI: 10.1016/J.JENVMAN.2009.03.013
Abstract: This study examines the bushfire (wildland fire) risk to the built environment in Australia. The most salient result is that the annual probability of building destruction has remained almost constant over the last century despite large demographic and social changes as well as improvements in fire fighting technique and resources. Most historical losses have taken place in a few extreme fires which if repeated are likely to overwhelm even the most professional of fire services. We also calculate the average annual probability of a random home on the urban-bushland interface being destroyed by a bushfire to be of the order of 1 in 6500, a factor 6.5 times lower than the ignition probability of a structural house fire. Thus on average and if this risk was perceived rationally, the incentive for in idual homeowners to mitigate and reduce the bushfire danger even further is low. This being the case and despite predictions of an increasing likelihood of conditions favouring bushfires under global climate change, we suspect that building losses due to bushfires are unlikely to alter materially in the near future.
Publisher: Springer Science and Business Media LLC
Date: 06-12-2010
Publisher: Springer Science and Business Media LLC
Date: 31-01-2014
Publisher: American Meteorological Society
Date: 09-2009
Abstract: A comparison of three global reanalyses is conducted based on probability density functions of daily maximum and minimum temperature at 2-m and 1000-hPa levels. The three reanalyses compare very favorably in both maximum and minimum temperatures at 1000 hPa, in both the mean and the 99.7th and 0.3rd percentiles of both quantities in most regions. At 2 m, there are large and widespread differences in the mean and 99.7th percentiles in maximum temperature between the three reanalyses over land commonly exceeding ±5°C and regionally exceeding ±10°C. The 2-m minimum temperatures compare unfavorably between the three reanalyses over virtually all continental surfaces with differences exceeding ±10°C over widespread areas. It is concluded that the three reanalyses are generally interchangeable in 1000-hPa temperatures. The three reanalyses of 2-m temperatures are very different owing to the methods used to diagnose these quantities. At this time, the probability distribution functions of the 2-m temperatures from the three reanalyses are sufficiently different that either the 2-m air temperatures should not be used or all three products should be used independently in any application and the differences highlighted.
Publisher: Springer Science and Business Media LLC
Date: 06-03-2017
DOI: 10.1038/SREP43938
Abstract: Cities import energy, which in combination with their typically high solar absorption and low moisture availability generates the urban heat island effect (UHI). The UHI, combined with human-induced warming, makes our densely populated cities particularly vulnerable to climate change. We examine the utility of solar photovoltaic (PV) system deployment on urban rooftops to reduce the UHI, and we price one potential value of this impact. The installation of PV systems over Sydney, Australia reduces summer maximum temperatures by up to 1 °C because the need to import energy is offset by local generation. This offset has a direct environmental benefit, cooling local maximum temperatures, but also a direct economic value in the energy generated. The indirect benefit associated with the temperature changes is between net AUD$230,000 and $3,380,000 depending on the intensity of PV systems deployment. Therefore, even very large PV installations will not offset global warming, but could generate enough energy to negate the need to import energy, and thereby reduce air temperatures. The energy produced, and the benefits of cooling beyond local PV installation sites, would reduce the vulnerability of urban populations and infrastructure to temperature extremes.
Publisher: Wiley
Date: 06-1995
Abstract: To parameterize the land surface in global climate models (GCMs) data must be provided at a variety of resolutions. In GCMs, high‐resolution data must be aggregated to the coarser resolution of the GCM. The method used for parameter aggregation can lead to the simulation of very different land surface climatologies. It is shown that the most important transition from a simulation of homogeneous tundra to one of homogeneous coniferous forest is the initial increase in forest. The differences between the simulation of tundra and the simulation of tundra with two‐ninths coniferous forest are considerable. This suggests that the land surface is sensitive to the aggregated parameters in non‐linear ways. It suggests that more care is needed in data aggregation, and that improved algorithms for data aggregation must be developed, because these data sets represent the foundations on which advanced land surface parameterizations are built. Finally, it shows that the influence of relatively small amounts of secondary vegetation should be represented in GCMs.
Publisher: Elsevier BV
Date: 06-1996
Publisher: Copernicus GmbH
Date: 25-07-2018
Abstract: Abstract. The FLUXNET dataset contains eddy covariance measurements from across the globe and represents an invaluable estimate of the fluxes of energy, water, and carbon between the land surface and the atmosphere. While there is an expectation that the broad range of site characteristics in FLUXNET result in a ersity of flux behaviour, there has been little exploration of how predictable site behaviour is across the network. Here, 155 datasets with 30 min temporal resolution from the Tier 1 of FLUXNET 2015 were analysed in a first attempt to assess in idual site predictability. We defined site uniqueness as the disparity in performance between multiple empirical models trained globally and locally for each site and used this along with the mean performance as measures of predictability. We then tested how strongly uniqueness was determined by various site characteristics, including climatology, vegetation type, and data quality. The strongest determinant of predictability appeared to be that drier sites tended to be more unique. We found very few other clear predictors of uniqueness across different sites, in particular little evidence that flux behaviour was well discretised by vegetation type. Data length and quality also appeared to have little impact on uniqueness. While this result might relate to our definition of uniqueness, we argue that our approach provides a useful basis for site selection in LSM evaluation, and we invite critique and development of the methodology.
Publisher: Elsevier BV
Date: 12-1998
Publisher: American Geophysical Union (AGU)
Date: 28-08-2014
DOI: 10.1002/2014GL061017
Publisher: Wiley
Date: 02-02-2017
DOI: 10.1002/JOC.5001
Publisher: American Meteorological Society
Date: 09-2007
DOI: 10.1175/JCLI4253.1
Abstract: The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature. The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced. Across all three variables, the coupled climate models perform better than expected. Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models. Averaged over Australia, 3 of the 14 climate models capture more than 80% of the observed probability density functions for precipitation. Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80% of the observed probability density functions. Maximum temperature is also reasonably simulated with 6 of 10 climate models capturing more than 80% of the observed probability density functions. An overall ranking of the climate models, for each of precipitation, maximum, and minimum temperatures, and averaged over these three variables, is presented. Those climate models that are skillful over Australia are identified, providing guidance on those climate models that should be used in impacts assessments where those impacts are based on precipitation or temperature. These results have no bearing on how well these models work elsewhere, but the methodology is potentially useful in assessing which of the many climate models should be used by impacts groups.
Publisher: Elsevier BV
Date: 03-2005
Publisher: CSIRO Publishing
Date: 2013
DOI: 10.1071/WF12048
Abstract: The fire weather of south-east Australia from 1985 to 2009 has been simulated using the Weather Research and Forecasting (WRF) model. The US National Oceanic and Atmospheric Administration Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis supplied the lateral boundary conditions and initial conditions. The model simulated climate and the reanalysis were evaluated against station-based observations of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual cumulative FFDI and days per year with FFDI above 50. WRF simulated the main features of the FFDI distribution and its spatial variation, with an overall positive bias. Errors in average FFDI were caused mostly by errors in the ability of WRF to simulate relative humidity. In contrast, errors in extreme FFDI values were driven mainly by WRF errors in wind speed simulation. However, in both cases the quality of the observed data is difficult to ascertain. WRF run with 50-km grid spacing did not consistently improve upon the reanalysis statistics. Decreasing the grid spacing to 10km led to fire weather that was generally closer to observations than the reanalysis across the full range of evaluation metrics used here. This suggests it is a very useful tool for modelling fire weather over the entire landscape of south-east Australia.
Publisher: Elsevier BV
Date: 2007
Publisher: American Geophysical Union (AGU)
Date: 08-2010
DOI: 10.1029/2010GL043877
Publisher: Springer Science and Business Media LLC
Date: 03-10-2016
Publisher: Springer Science and Business Media LLC
Date: 07-01-2021
DOI: 10.1038/S43247-020-00065-8
Abstract: The 2019/20 Black Summer bushfire disaster in southeast Australia was unprecedented: the extensive area of forest burnt, the radiative power of the fires, and the extraordinary number of fires that developed into extreme pyroconvective events were all unmatched in the historical record. Australia’s hottest and driest year on record, 2019, was characterised by exceptionally dry fuel loads that primed the landscape to burn when exposed to dangerous fire weather and ignition. The combination of climate variability and long-term climate trends generated the climate extremes experienced in 2019, and the compounding effects of two or more modes of climate variability in their fire-promoting phases (as occurred in 2019) has historically increased the chances of large forest fires occurring in southeast Australia. Palaeoclimate evidence also demonstrates that fire-promoting phases of tropical Pacific and Indian ocean variability are now unusually frequent compared with natural variability in pre-industrial times. Indicators of forest fire danger in southeast Australia have already emerged outside of the range of historical experience, suggesting that projections made more than a decade ago that increases in climate-driven fire risk would be detectable by 2020, have indeed eventuated. The multiple climate change contributors to fire risk in southeast Australia, as well as the observed non-linear escalation of fire extent and intensity, raise the likelihood that fire events may continue to rapidly intensify in the future. Improving local and national adaptation measures while also pursuing ambitious global climate change mitigation efforts would provide the best strategy for limiting further increases in fire risk in southeast Australia.
Publisher: Copernicus GmbH
Date: 10-07-2020
Abstract: Abstract. Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of in idual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to 4.5 m depth to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrated that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics – highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias a more realistic initialisation of the groundwater aquifer state higher vertical soil resolution informed by observed soil properties and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in simulated water fluxes remained important: using a site calibrated function reduced the median level of water stress by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration, but also affect the latent and sensible heat fluxes during droughts and heatwaves. Alternative parameterisations led to differences of ~ 150 W m−2 in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.
Publisher: American Meteorological Society
Date: 08-2006
DOI: 10.1175/JHM511.1
Abstract: The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.
Publisher: Informa UK Limited
Date: 03-2000
Publisher: Springer Science and Business Media LLC
Date: 14-09-2016
Publisher: American Geophysical Union (AGU)
Date: 25-02-2012
DOI: 10.1029/2011JD016382
Publisher: American Association for the Advancement of Science (AAAS)
Date: 20-08-2004
Abstract: Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.
Publisher: Wiley
Date: 10-04-2007
Publisher: Copernicus GmbH
Date: 06-08-2015
DOI: 10.5194/BGD-12-12349-2015
Abstract: Abstract. Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models, realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensitivities are needed to explain the observed large-scale patterns of drought impact. We implemented data-driven drought sensitivities in the Community Atmosphere Biosphere Land Exchange (CABLE) LSM and evaluated alternative sensitivities across a latitudinal gradient in Europe during the 2003 heatwave. The model predicted an overly abrupt onset of drought unless average soil water potential was calculated with dynamic weighting across soil layers. We found that high drought sensitivity at the northernmost sites, and low drought sensitivity at the southernmost sites, was necessary to accurately model responses during drought. Our results indicate that LSMs will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.
Publisher: Wiley
Date: 27-04-2013
DOI: 10.1002/JOC.3500
Publisher: Copernicus GmbH
Date: 15-03-2021
DOI: 10.5194/BG-2021-66
Abstract: Abstract. Australia plays an important role in the global terrestrial carbon cycle on inter-annual timescales. While the Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations of net biome production (NBP) and the carbon stored in vegetation between 1901 to 2018 from 13 DGVMs (TRENDY v8 ensemble). We focused our analysis on both Australia's short-term (inter-annual) and long-term (decadal to centennial) terrestrial carbon dynamics. The TRENDY models simulated differing magnitudes of NBP on inter-annual timescales, and these differences contributed to carbon accumulation in the vegetation on decadal to centennial timescales (−4.7–9.5 PgC). We compared the TRENDY ensemble to several satellite-derived datasets and showed that the spread in the models' simulated carbon storage resulted from varying changes in carbon residence time rather than differences in net carbon uptake. Differences in simulated long-term accumulated NBP between models were mostly due to model responses to land-use change. The DGVMs also simulated different sensitivities to atmospheric carbon dioxide (CO2) concentration, although notably, the models with nutrient cycles did not simulate the smallest NBP response to CO2. Our results suggest that a change in the climate forcing did not have a large impact on the carbon cycle on long timescales. However, the inter-annual variability in precipitation drives the year-to-year variability in NBP. We analysed the impact of key modes of climate variability, including the El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) on NBP. While the DGVMs agreed on sign of the response of NBP to El Nino and La Nina, and to positive and negative IOD events, the magnitude of inter-annual variability in NBP differed strongly between models. In addition, we identified differences in the timing of simulated phenology and fire dynamics associated with differences in simulated rescribed vegetation composition and process representation. Model disagreement in simulated vegetation carbon, phenology and apparent carbon residence time, indicates the models have different types of vegetation cover across Australia (whether prescribed or emergent). Our study highlights the need to evaluate parameter assumptions and the key processes that drive vegetation dynamics, such as phenology, mortality and fire, in an Australian context to reduce uncertainty across models.
Publisher: Wiley
Date: 07-2018
DOI: 10.1002/QJ.3317
Publisher: Elsevier BV
Date: 03-2022
Publisher: American Geophysical Union (AGU)
Date: 28-05-2017
DOI: 10.1002/2017GL073231
Publisher: Elsevier BV
Date: 05-2005
Publisher: American Geophysical Union (AGU)
Date: 28-01-2014
DOI: 10.1002/2013GL058352
Publisher: American Geophysical Union (AGU)
Date: 19-01-2016
DOI: 10.1002/2015JD024053
Publisher: Copernicus GmbH
Date: 16-09-2013
Abstract: Abstract. We examine the impact of land use and land cover change (LULCC) over the period from 1850 to 2005 using an Earth system model that incorporates nitrogen and phosphorous limitation on the terrestrial carbon cycle. We compare the estimated CO2 emissions and warming from land use change in a carbon-only version of the model with those from simulations, including nitrogen and phosphorous limitation. If we omit nutrients, our results suggest LULCC cools on the global average by about 0.1 °C. Including nutrients reduces this cooling to ~ 0.05 °C. Our results also suggest LULCC has a major impact on total land carbon over the period 1850–2005. In carbon-only simulations, the inclusion of LULCC decreases the total additional land carbon stored in 2005 from around 210 Pg C to 85 Pg C. Including nitrogen and phosphorous limitation also decreases the scale of the terrestrial carbon sink to 80 Pg C. Shown as corresponding fluxes, adding LULCC on top of the nutrient-limited simulations changes the sign of the terrestrial carbon flux from a sink to a source (12 Pg C). The CO2 emission from LULCC from 1850 to 2005 is estimated to be 130 Pg C for carbon only simulation, or 97 Pg C if nutrient limitation is accounted for in our model. The difference between these two estimates of CO2 emissions from LULCC largely results from the weaker response of photosynthesis to increased CO2 and smaller carbon pool sizes, and therefore lower carbon loss from plant and wood product carbon pools under nutrient limitation. We suggest that nutrient limitation should be accounted for in simulating the effects of LULCC on the past climate and on the past and future carbon budget.
Publisher: Springer Science and Business Media LLC
Date: 16-05-2016
DOI: 10.1038/NCLIMATE3029
Publisher: American Geophysical Union (AGU)
Date: 04-2022
DOI: 10.1029/2021MS002761
Abstract: Stomatal conductance schemes that optimize with respect to photosynthetic and hydraulic functions have been proposed to address biases in land‐surface model (LSM) simulations during drought. However, systematic evaluations of both optimality‐based and alternative empirical formulations for coupling carbon and water fluxes are lacking. Here, we embed 12 empirical and optimization approaches within a LSM framework. We use theoretical model experiments to explore parameter identifiability and understand how model behaviors differ in response to abiotic changes. We also evaluate the models against leaf‐level observations of gas‐exchange and hydraulic variables, from xeric to wet forest/woody species spanning a mean annual precipitation range of 361–3,286 mm yr −1 . We find that models differ in how easily parameterized they are, due to: (a) poorly constrained optimality criteria (i.e., resulting in multiple solutions), (b) low influence parameters, (c) sensitivities to environmental drivers. In both the idealized experiments and compared to observations, sensitivities to variability in environmental drivers do not agree among models. Marked differences arise in sensitivities to soil moisture (soil water potential) and vapor pressure deficit. For ex le, stomatal closure rates at high vapor pressure deficit range between −45% and +70% of those observed. Although over half the new generation of stomatal schemes perform to a similar standard compared to observations of leaf‐gas exchange, two models do so through large biases in simulated leaf water potential (up to 11 MPa). Our results provide guidance for LSM development, by highlighting key areas in need for additional experimentation and theory, and by constraining currently viable stomatal hypotheses.
Publisher: American Geophysical Union (AGU)
Date: 27-06-2012
DOI: 10.1029/2011JD017106
Publisher: American Geophysical Union (AGU)
Date: 20-08-1995
DOI: 10.1029/95JD01076
Publisher: Inter-Research Science Center
Date: 2004
DOI: 10.3354/CR025191
Publisher: Copernicus GmbH
Date: 13-03-2014
Abstract: Abstract. Land surface albedo, the fraction of incoming solar radiation reflected by the land surface, is a key component of the earth system. This study evaluates snow-free surface albedo simulations by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model with the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo. We compare results from two offline simulations over the Australian continent, one with prescribed background snow-free and vegetation-free soil albedo derived from MODIS (the control), and the other with a simple parameterisation based on soil moisture and colour. The control simulation shows that CABLE simulates albedo over Australia reasonably well, with differences with MODIS within an acceptable range. Inclusion of the parameterisation for soil albedo however introduced large errors for the near infra red albedo, especially for desert regions of central Australia. These large errors were not fully explained by errors in soil moisture or parameter uncertainties, but are similar to errors in albedo in other land surface models which use the same soil albedo scheme. Although this new parameterisation has introduced larger errors as compared to prescribing soil albedo, dynamic soil moisture-albedo feedbacks are now enabled in CABLE. Future directions for albedo parameterisations development in CABLE are discussed.
Publisher: American Meteorological Society
Date: 12-2017
Abstract: Understanding the physical drivers of heat waves is essential for improving short-term forecasts of in idual events and long-term projections of heat waves under climate change. This study provides the first analysis of the influence of the large-scale circulation on Australian heat waves, conditional on the land surface conditions. Circulation types, sourced from reanalysis, are used to characterize the different large-scale circulation patterns that drive heat wave events across Australia. The importance of horizontal temperature advection is illustrated in these circulation patterns, and the pattern occurrence frequency is shown to reorganize through different modes of climate variability. It is further shown that the relative likelihood of a particular synoptic situation being associated with a heat wave is strongly modulated by the localized partitioning of available energy between surface sensible and latent heat fluxes (as measured through evaporative fraction) in many regions in reanalysis data. In particular, a several-fold increase in the likelihood of heat wave day occurrence is found during days of reduced evaporative fraction under favorable circulation conditions. The atmospheric circulation and land surface conditions linked to heat waves in reanalysis were then examined in the context of CMIP5 climate model projections. Large uncertainty was found to exist for many regions, especially in terms of the direction of future land surface changes and in terms of the magnitude of atmospheric circulation changes. Efforts to constrain uncertainty in both atmospheric and land surface processes in climate models, while challenging, should translate to more robust regional projections of heat waves.
Publisher: Springer Science and Business Media LLC
Date: 10-06-2010
Publisher: Springer Science and Business Media LLC
Date: 20-01-2016
DOI: 10.1038/NATURE16542
Abstract: Global temperature targets, such as the widely accepted limit of an increase above pre-industrial temperatures of two degrees Celsius, may fail to communicate the urgency of reducing carbon dioxide (CO2) emissions. The translation of CO2 emissions into regional- and impact-related climate targets could be more powerful because such targets are more directly aligned with in idual national interests. We illustrate this approach using regional changes in extreme temperatures and precipitation. These scale robustly with global temperature across scenarios, and thus with cumulative CO2 emissions. This is particularly relevant for changes in regional extreme temperatures on land, which are much greater than changes in the associated global mean.
Publisher: Elsevier
Date: 2012
Publisher: American Geophysical Union (AGU)
Date: 05-09-2018
DOI: 10.1029/2018GL079102
Publisher: American Geophysical Union (AGU)
Date: 21-01-2021
DOI: 10.1029/2020GL091152
Abstract: Compound events have the potential to cause high socioeconomic and environmental losses. We examine the ability of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models to capture two bivariate compound events: the co‐occurrence of heavy rain and strong wind, and heat waves and meteorological drought. We evaluate the models over North America, Europe, Eurasia, and Australia using observations and reanalysis data set spanning 1980–2014. Some of the CMIP6 models capture the return periods of both bivariate compound events over North America, Europe, and Eurasia surprisingly well but perform less well over Australia. For heavy rain and strong wind, this poor performance was particularly clear in northern Australia which suggests limits in simulating tropical and extratropical cyclones, local convection, and mesoscale convective systems. We did not find higher model resolution improved performance in any region. Overall, our results show some CMIP6 models can be used to examine compound events, particularly over North America, Europe, and Eurasia.
Publisher: Wiley
Date: 20-07-2010
DOI: 10.1002/JOC.2206
Publisher: Proceedings of the National Academy of Sciences
Date: 19-07-2005
Abstract: We used correlated ergence analysis to determine which factors have been most closely associated with changes in seed mass during seed plant evolution. We found that ergences in seed mass have been more consistently associated with ergences in growth form than with ergences in any other variable. This finding is consistent with the strong relationship between seed mass and growth form across present-day species and with the available data from the paleobotanical literature. Divergences in seed mass have also been associated with ergences in latitude, net primary productivity, temperature, precipitation, and leaf area index. However, these environmental variables had much less explanatory power than did plant traits such as seed dispersal syndrome and plant growth form.
Publisher: Springer Netherlands
Date: 2005
Publisher: Copernicus GmbH
Date: 13-11-2014
Abstract: Abstract. Soil carbon storage simulated by the Coupled Model Intercomparison Project (CMIP5) models varies 6-fold for the present day. Here, we confirm earlier work showing that this range already exists at the beginning of the CMIP5 historical simulations. We additionally show that this range is largely determined by the response of microbial decomposition during each model's spin-up procedure from initialization to equilibration. The 6-fold range in soil carbon, once established prior to the beginning of the historical period (and prior to the beginning of a CMIP5 simulation), is then maintained through the present and to 2100 almost unchanged even under a strong business-as-usual emissions scenario. We therefore highlight that a commonly ignored part of CMIP5 analyses – the land surface state achieved through the spin-up procedure – can be important for determining future carbon storage and land surface fluxes. We identify the need to better constrain the outcome of the spin-up procedure as an important step in reducing uncertainty in both projected soil carbon and land surface fluxes in CMIP5 transient simulations.
Publisher: American Meteorological Society
Date: 08-2006
DOI: 10.1175/JHM510.1
Abstract: The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.
Publisher: Copernicus GmbH
Date: 21-02-2017
DOI: 10.5194/GMD-2017-33
Abstract: Abstract. This article extends a previous study (Seneviratne et al., 2016) to provide regional analyses of changes in climate extremes as a function of projected changes in global mean temperature. We introduce the DROUGHT-HEAT Regional Climate Atlas, an interactive tool to analyse and display a range of well-established climate extremes and water-cycle indices and their changes as a function of global warming. These projections are based on simulations from the 5th phase of the Coupled Model Intercomparison Project (CMIP5). A selection of ex le results are presented here, but users can visualize specific indices of interest using the online tool. This implementation enables a direct assessment of regional climate changes associated with global temperature targets, such as the 2 degree and 1.5 degree limits agreed within the 2015 Paris Agreement.
Publisher: American Geophysical Union (AGU)
Date: 25-02-2013
DOI: 10.1029/2012JD018122
Publisher: Springer Science and Business Media LLC
Date: 12-01-2012
Publisher: Copernicus GmbH
Date: 24-06-2013
DOI: 10.5194/BGD-10-10229-2013
Abstract: Abstract. Reliable projections of future climate require land–atmosphere carbon (C) fluxes to be represented realistically in Earth System Models. There are several sources of uncertainty in how carbon is parameterized in these models. First, while interactions between the C, nitrogen (N) and phosphorus (P) cycles have been implemented in some models, these lead to erse changes in land–atmosphere fluxes. Second, while the parameterization of soil organic matter decomposition is similar between models, formulations of the control of the soil physical state on microbial activity vary widely. We address these sources uncertainty by implementing three soil moisture (SMRF) and three soil temperature (STRF) respiration functions in an Earth System Model that can be run with three degrees of biogeochemical nutrient limitation (C-only, C and N, and C and N and P). All 27 possible combinations of a SMRF with a STRF and a biogeochemical mode are equilibrated before transient historical (1850–2005) simulations are performed. As expected, implementing N and P limitation reduces the land carbon sink, transforming some regions from net sinks to net sources over the historical period (1850–2005). Differences in the soil C balance implied by the various SMRFs and STRFs also change the sign of some regional sinks. Further, although the absolute uncertainty in global carbon uptake is reduced, the uncertainty due to the SMRFs and STRFs grows relative to the inter-annual variability in net uptake when N and P limitations are added. We also demonstrate that the equilibrated soil C also depend on the shape of the SMRF and STRF. Equilibration using different STRFs and SMRFs and nutrient limitation generates a six-fold range of global soil C that largely mirrors the range in available (17) CMIP5 models. Simulating the historical change in soil carbon therefore critically depends on the choice of STRF, SMRF and nutrient limitation, as it controls the equilibrated state to which transient conditions are applied. This direct effect of the representation of microbial decomposition in Earth System Models adds to recent concerns on the adequacy of these simple representations of very complex soil carbon processes.
Publisher: Copernicus GmbH
Date: 06-08-2015
DOI: 10.5194/HESS-19-3433-2015
Abstract: Abstract. The similarity of the temporal variations of land and atmospheric states during the onset (September) through to the peak (February) of the wet season over northern Australia is statistically diagnosed using ensembles of offline land surface model simulations that produce a range of different background soil moisture states. We derive the temporal correspondence between variations in the soil moisture and the planetary boundary layer via a statistical measure of rank correlation. The simulated evaporative fraction and the boundary layer are shown to be strongly correlated during both SON (September–October–November) and DJF (December–January–February) despite the differing background soil moisture states between the two seasons and among the ensemble members. The sign and magnitude of the boundary layer–surface layer soil moisture association during the onset of the wet season (SON) differs from the correlation between the evaporative fraction and boundary layer from the same season, and from the correlation between the surface soil moisture and boundary layer association during DJF. The patterns and magnitude of the surface flux–boundary layer correspondence are not captured when the relationship is diagnosed using the surface layer soil moisture alone. The conflicting results arise because the surface layer soil moisture lacks strong correlation with the atmosphere during the monsoon onset because the evapotranspiration is dominated by transpiration. Our results indicate that accurately diagnosing the correspondence and therefore coupling strength in seasonally dry regions, such as northern Australia, requires root zone soil moisture to be included.
Publisher: American Geophysical Union (AGU)
Date: 15-03-2016
DOI: 10.1002/2016GL068062
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2011
End Date: 2017
Funder: Australian Research Council
View Funded ActivityStart Date: 2010
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Funder: Australian Research Council
View Funded ActivityStart Date: 2017
End Date: 2023
Funder: Australian Research Council
View Funded ActivityStart Date: 2007
End Date: 2009
Funder: Australian Research Council
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End Date: 2012
Funder: Australian Research Council
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Funder: Australian Research Council
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End Date: 2014
Funder: Australian Research Council
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Funder: Australian Research Council
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Funder: Australian Research Council
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