ORCID Profile
0000-0002-4205-001X
Current Organisation
University of New South Wales
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Atmospheric Sciences | Climate Change Processes | Atmospheric Dynamics | Physical Oceanography | Meteorology
ANZSRC Socio-Economic Objective (SEO)
Climate Change Models | Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Climate Change Mitigation Strategies | Atmospheric Processes and Dynamics | Climate Variability (excl. Social Impacts) |
Related Links
Publications
Supplementary material to "Robust historical evapotranspiration trends across climate regimes"
Publisher: Copernicus GmbH
Date: 09-12-2020
Implementation of an optimal stomatal conductance scheme in the Australian Community Climate Earth Systems Simulator (ACCESS1.3b)
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.
Climate model dependence and the replicate Earth paradigm
Publisher: Springer Science and Business Media LLC
Date: 11-12-2012
Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle
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.
Conserving Land–Atmosphere Synthesis Suite (CLASS)
Publisher: American Meteorological Society
Date: 03-2020
Abstract: Accurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models’ ability to simulate future change. Various observational estimates are available for the in idual budget terms however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land–Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. In idual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source 2) are observationally constrained with in situ measurements 3) have uncertainty estimates that are consistent with their agreement with in situ observations and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes. CLASS variable estimates are freely available via 0.25914/5c872258dc183 .
A flux tower dataset tailored for land model evaluation
Publisher: Copernicus GmbH
Date: 03-02-2022
Abstract: Abstract. Eddy covariance flux towers measure the exchange of water, energy, and carbon fluxes between the land and atmosphere. They have become invaluable for theory development and evaluating land models. However, flux tower data as measured (even after site post-processing) are not directly suitable for land surface modelling due to data gaps in model forcing variables, inappropriate gap-filling, formatting, and varying data quality. Here we present a quality-control and data-formatting pipeline for tower data from FLUXNET2015, La Thuile, and OzFlux syntheses and the resultant 170-site globally distributed flux tower dataset specifically designed for use in land modelling. The dataset underpins the second phase of the Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER), an international model intercomparison project encompassing land surface and biosphere models. The dataset is provided in the Assistance for Land-surface Modelling Activities (ALMA) NetCDF format and is CF-NetCDF compliant. For forcing land surface models, the dataset provides fully gap-filled meteorological data that have had periods of low data quality removed. Additional constraints required for land models, such as reference measurement heights, vegetation types, and satellite-based monthly leaf area index estimates, are also included. For model evaluation, the dataset provides estimates of key water, carbon, and energy variables, with the latent and sensible heat fluxes additionally corrected for energy balance closure. The dataset provides a total of 1040 site years covering the period 1992–2018, with in idual sites spanning from 1 to 21 years. The dataset is available at 0.25914/5fdb0902607e1 (Ukkola et al., 2021).
Urban-PLUMBER: A new evaluation and benchmarking project for land surface models in urban areas
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-20987
Abstract: & & We welcome participants in the new project to evaluate land surface models (LSMs) in urban areas at multiple sites. Urban-PLUMBER will evaluate both specialised urban parameterisations and general LSMs typically used in weather/climate simulations. Assessment will be offline (uncoupled with an atmosphere model), with driving meteorology and general site characteristics provided at the neighbourhood scale.& & & & The project builds upon the PLUMBER project (PALS Land sUrface Model Benchmarking Evaluation pRoject) by assessing models using simple benchmarks as well as error metrics. The PLUMBER experience indicates benchmarking can reveal where LSMs are not utilising available information effectively, helping focus future model development.& & & & The project& #8217 s two phases are: 1) initial evaluation at one suburban site and 2) evaluation across multiple sites with varying degrees urbanised and vegetation ervious fractions. The project will establish where on the urbanised/vegetated continuum models are more skilful, and assess the progress made in modelling urban areas over the last decade since the last major offline urban model comparison project (PILPS-Urban).& & & & We expect the project to benefit both participating modelling groups and improve understanding of modelling urban areas as a whole. Contact us to get involved.& &
Towards a benchmark for land surface models
Publisher: American Geophysical Union (AGU)
Date: 11-2005
DOI: 10.1029/2005GL024419
A test of an optimal stomatal conductance scheme within the CABLE Land Surface Model
Publisher: Copernicus GmbH
Date: 15-10-2014
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 therefore constrained the key model parameter "g1" which represents a plants 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 models 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 when compared to upscaled data products was not degraded, though the new stomatal conductance scheme did not noticeably change existing model-data biases. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.
Disentangling residence time and temperature sensitivity of microbial decomposition in a global soil carbon model
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.
Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems
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.
Evaluating CMIP5 Model Agreement for Multiple Drought Metrics
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.
Towards a public, standardized, diagnostic benchmarking system for land surface models
Publisher: Copernicus GmbH
Date: 05-06-2012
Abstract: Abstract. This work examines different conceptions of land surface model benchmarking and the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and model resolutions. It additionally demonstrates how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and gives an ex le of how these expectations might be quantified. Finally, the Protocol for the Analysis of Land Surface models (PALS) is introduced – a free, online land surface model benchmarking application that is structured to meet both of these goals.
The Plumbing of Land Surface Models: Benchmarking Model Performance
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.
Non‐Stationary Lags and Legacies in Ecosystem Flux Response to Antecedent Rainfall
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.
Supplementary material to "FluxnetLSM R package (v1.0): A community tool for processing FLUXNET data for use in land surface modelling"
Publisher: Copernicus GmbH
Date: 27-03-2017
FluxnetLSM R package (v1.0): A community tool for processing FLUXNET data for use in land surface modelling
Publisher: Copernicus GmbH
Date: 27-03-2017
DOI: 10.5194/GMD-2017-58
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 latest FLUXNET2015 release into community standard NetCDF files that are directly usable by LSMs.
A test of an optimal stomatal conductance scheme within the CABLE land surface model
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.
Importance of background climate in determining impact of land-cover change on regional climate
Publisher: Springer Science and Business Media LLC
Date: 20-11-2011
DOI: 10.1038/NCLIMATE1294
Robust Future Changes in Meteorological Drought in CMIP6 Projections Despite Uncertainty in Precipitation
Publisher: American Geophysical Union (AGU)
Date: 09-06-2020
DOI: 10.1029/2020GL087820
A new constraint on global air‐sea CO2 fluxes using bottle carbon data
Publisher: American Geophysical Union (AGU)
Date: 28-04-2013
DOI: 10.1002/GRL.50342
A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products
Publisher: Copernicus GmbH
Date: 26-05-2020
DOI: 10.5194/HESS-24-2687-2020
Abstract: Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.
Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate
Publisher: Copernicus GmbH
Date: 21-02-2018
DOI: 10.5194/HESS-22-1317-2018
Abstract: Abstract. Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, in addition to being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-s le and out-of-s le tests that confirm that point-based estimates of flux towers provide information on the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in four common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.
Supplementary material to "How representative are FLUXNET measurements of surface fluxes during temperature extremes?"
Publisher: Copernicus GmbH
Date: 20-12-2018
Examining the Role of Environmental Memory in the Predictability of Carbon and Water Fluxes Across Australian Ecosystems
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.
Supplementary material to "A flux tower dataset tailored for land model evaluation"
Publisher: Copernicus GmbH
Date: 28-07-2021
Linear Optimal Runoff Aggregate (LORA): A global gridded synthesis runoff product
Publisher: Copernicus GmbH
Date: 07-08-2018
Abstract: Abstract. No synthesized global gridded runoff product, derived from multiple sources, is available despite such a product being useful to meet the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-s le tests to examine the success of the dissimilarity approach and we confirm that the weighted product performs better than its 11 constituents products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly time scales, and includes time variant uncertainty, for the period 1980–2012 on a 0.5° grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents well the seasonal runoff cycle for most of the globe. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on geonetwork.nci.org.au/.
Supplementary material to "A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture produ
Publisher: Copernicus GmbH
Date: 14-08-2019
Neural Error Regression Diagnosis (NERD): A Tool for Model Bias Identification and Prognostic Data Assimilation
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.
Selecting a climate model subset to optimise key ensemble properties
Publisher: Copernicus GmbH
Date: 21-02-2018
Abstract: Abstract. End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-s le skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for ex le, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of in idual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.
On the predictability of land surface fluxes from meteorological variables
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.
Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product
Publisher: Copernicus GmbH
Date: 13-02-2019
Abstract: Abstract. No synthesized global gridded runoff product, derived from multiple sources, is available, despite such a product being useful for meeting the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-s le tests to examine the success of the dissimilarity approach, and we confirm that the weighted product performs better than its 11 constituent products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly timescales, and includes time-variant uncertainty, for the period 1980–2012 on a 0.5∘ grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents the seasonal runoff cycle for most of the globe well. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f9617_9854_8096_5291 (last access: 31 January 2019).
Implementation of an optimal stomatal conductance model in the Australian Community Climate Earth Systems Simulator (ACCESS1.3b)
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.
Examining soil carbon uncertainty in a global model: response of microbial decomposition to temperature, moisture and nutrient limitation
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.
Response of microbial decomposition to spin-up explains CMIP5 soil carbon range until 2100
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.
Supplementary material to "Selecting a climate model subset to optimise key ensemble properties"
Publisher: Copernicus GmbH
Date: 03-04-2017
Towards a comprehensive assessment of model structural adequacy
Publisher: American Geophysical Union (AGU)
Date: 08-2012
DOI: 10.1029/2011WR011044
On the information content of surface meteorology for downward atmospheric long‐wave radiation synthesis
Publisher: American Geophysical Union (AGU)
Date: 02-2012
DOI: 10.1029/2011GL050726
Reconciling historical changes in the hydrological cycle over land
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.
Which are the most important parameters for modelling carbon assimilation in boreal Norway spruce under elevated [CO2 ] and temperature conditions?
Publisher: Oxford University Press (OUP)
Date: 21-03-2013
Abstract: Photosynthesis is highly responsive to environmental and physiological variables, including phenology, foliage nitrogen (N) content, atmospheric CO2 concentration ([CO2]), irradiation (Q), air temperature (T) and vapour pressure deficit (D). Each of these responses is likely to be modified by long-term changes in climatic conditions such as rising air temperature and [CO2]. When modelling photosynthesis under climatic changes, which parameters are then most important to calibrate for future conditions? To assess this, we used measurements of shoot carbon assimilation rates and microclimate conditions collected at Flakaliden, northern Sweden. Twelve 40-year-old Norway spruce trees were enclosed in whole-tree chambers and exposed to elevated [CO2] and elevated air temperature, separately and in combination. The treatments imposed were elevated temperature, +2.8 °C in July/August and +5.6 °C in December above ambient, and [CO2] (ambient CO2 ∼370 μ mol mol(-1), elevated CO2 ∼700 μ mol mol(-1)). The relative importance of parameterization of Q, T and D responses for effects on the photosynthetic rate, expressed on a projected needle area, and the annual shoot carbon uptake was quantified using an empirical shoot photosynthesis model, which was developed and fitted to the measurements. The functional form of the response curves was established using an artificial neural network. The [CO2] treatment increased annual shoot carbon (C) uptake by 50%. Most important was effects on the light response curve, with a 67% increase in light-saturated photosynthetic rate, and a 52% increase in the initial slope of the light response curve. An interactive effect of light saturated photosynthetic rate was found with foliage N status, but no interactive effect for high temperature and high CO2. The air temperature treatment increased the annual shoot C uptake by 44%. The most important parameter was the seasonality, with an elongation of the growing season by almost 4 weeks. The temperature response curve was almost flat over much of the temperature range. A shift in temperature optimum had thus an insignificant effect on modelled annual shoot C uptake. The combined temperature and [CO2] treatment resulted in a 74% increase in annual shoot C uptake compared with ambient conditions, with no clear interactive effects on parameter values.
ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing
Publisher: Copernicus GmbH
Date: 13-02-2019
Abstract: Abstract. The rationale for using multi-model ensembles in climate change projections and impacts research is often based on the expectation that different models constitute independent estimates therefore, a range of models allows a better characterisation of the uncertainties in the representation of the climate system than a single model. However, it is known that research groups share literature, ideas for representations of processes, parameterisations, evaluation data sets and even sections of model code. Thus, nominally different models might have similar biases because of similarities in the way they represent a subset of processes, or even be near-duplicates of others, weakening the assumption that they constitute independent estimates. If there are near-replicates of some models, then treating all models equally is likely to bias the inferences made using these ensembles. The challenge is to establish the degree to which this might be true for any given application. While this issue is recognised by many in the community, quantifying and accounting for model dependence in anything other than an ad-hoc way is challenging. Here we present a synthesis of the range of disparate attempts to define, quantify and address model dependence in multi-model climate ensembles in a common conceptual framework, and provide guidance on how users can test the efficacy of approaches that move beyond the equally weighted ensemble. In the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6), several new models that are closely related to existing models are anticipated, as well as large ensembles from some models. We argue that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies.
Firming 100% renewable power: Costs and opportunities in Australia's National Electricity Market
Publisher: Elsevier BV
Date: 12-2023
FluxnetLSM R package (v1.0): a community tool for processing FLUXNET data for use in land surface modelling
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.
What are the limits to statistical error correction in land surface schemes when projecting the future?
Publisher: American Geophysical Union (AGU)
Date: 28-07-2005
DOI: 10.1029/2005GL023158
Calibrating Climate Model Ensembles for Assessing Extremes in a Changing Climate
Publisher: American Geophysical Union (AGU)
Date: 12-06-2018
DOI: 10.1029/2018JD028549
Supplementary material to "Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate"
Publisher: Copernicus GmbH
Date: 03-04-2017
The CSIRO Mk3L climate system model v1.0 coupled to the CABLE land surface scheme v1.4b: evaluation of the control climatology
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.
Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model
Publisher: Copernicus GmbH
Date: 21-06-2016
DOI: 10.5194/HESS-20-2403-2016
Abstract: Abstract. Surface fluxes from land surface models (LSMs) have traditionally been evaluated against monthly, seasonal or annual mean states. The limited ability of LSMs to reproduce observed evaporative fluxes under water-stressed conditions has been previously noted, but very few studies have systematically evaluated these models during rainfall deficits. We evaluated latent heat fluxes simulated by the Community Atmosphere Biosphere Land Exchange (CABLE) LSM across 20 flux tower sites at sub-annual to inter-annual timescales, in particular focusing on model performance during seasonal-scale rainfall deficits. The importance of key model processes in capturing the latent heat flux was explored by employing alternative representations of hydrology, leaf area index, soil properties and stomatal conductance. We found that the representation of hydrological processes was critical for capturing observed declines in latent heat during rainfall deficits. By contrast, the effects of soil properties, LAI and stomatal conductance were highly site-specific. Whilst the standard model performs reasonably well at annual scales as measured by common metrics, it grossly underestimates latent heat during rainfall deficits. A new version of CABLE, with a more physically consistent representation of hydrology, captures the variation in the latent heat flux during seasonal-scale rainfall deficits better than earlier versions, but remaining biases point to future research needs. Our results highlight the importance of evaluating LSMs under water-stressed conditions and across multiple plant functional types and climate regimes.
Systematic Bias in Land Surface Models
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.
Evaluation of the CABLEv2.3.4 Land Surface Model Coupled to NU‐WRFv3.9.1.1 in Simulating Temperature and Precipitation Means and Extremes Over CORDEX AustralAsia Within a WRF Physics Ensemble
Publisher: American Geophysical Union (AGU)
Date: 12-2019
DOI: 10.1029/2019MS001845
Evaluation of 30 urban land surface models in the Urban‐PLUMBER project: Phase 1 results
Publisher: Wiley
Date: 25-10-2023
DOI: 10.1002/QJ.4589
Robust historical evapotranspiration trends across climate regimes
Publisher: Copernicus GmbH
Date: 06-07-2021
DOI: 10.5194/HESS-25-3855-2021
Abstract: Abstract. Evapotranspiration (ET) links the hydrological, energy and carbon cycles on the land surface. Quantifying ET and its spatio-temporal changes is also key to understanding climate extremes such as droughts, heatwaves and flooding. Regional ET estimates require reliable observation-based gridded ET datasets, and while many have been developed using physically based, empirically based and hybrid techniques, their efficacy, and particularly the efficacy of their uncertainty estimates, is difficult to verify. In this work, we extend the methodology used in Hobeichi et al. (2018) to derive two new versions of the Derived Optimal Linear Combination Evapotranspiration (DOLCE) product, with observationally constrained spatio-temporally varying uncertainty estimates, higher spatial resolution, more constituent products and extended temporal coverage (1980–2018). After demonstrating the efficacy of these uncertainty estimates with out-of-s le testing, we derive novel ET climatology clusters for the land surface, based on the magnitude and variability of ET at each location on land. The new clusters include three wet and three dry regimes and provide an approximation of Köppen–Geiger climate classes. The verified uncertainty estimates and extended time period then allow us to examine the robustness of historical trends spatially and in each of these six ET climatology clusters. We find that despite robust decreasing ET trends in some regions these do not correlate with behavioural ET clusters. Each cluster, and the majority of the Earth's surface, shows clear robust increases in ET over the recent historical period. The new datasets DOLCE V2.1 and DOLCE V3 can be used for benchmarking global ET estimates and for examining ET trends respectively.
Multi-model ensemble projections of future extreme temperature change using a statistical downscaling method in south eastern Australia
Publisher: Springer Science and Business Media LLC
Date: 27-06-2016
Optimally choosing small ensemble members to produce robust climate simulations
Publisher: IOP Publishing
Date: 12-2013
Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing
Publisher: Copernicus GmbH
Date: 04-07-2018
DOI: 10.5194/ESD-2018-51
Abstract: Abstract. The rationale for using multi-model ensembles in climate change projections and impacts research is often based on the expectation that different models constitute independent estimates, so that a range of models allows a better characterisation of the uncertainties in the representation of the climate system than a single model. However, it is known that research groups share literature, ideas for representations of processes, parameterisations, evaluation data sets and even sections of model code. Thus, nominally different models might have similar biases because of similarities in the way they represent a subset of processes, or even be near duplicates of others, weakening the assumption that they constitute independent estimates. If there are near-replicates of some models, then treating all models equally is likely to bias the inferences made using these ensembles. The challenge is to establish the degree to which this might be true for any given application. While this issue is recognized by many in the community, quantifying and accounting for model dependence in anything other than an ad-hoc way is challenging. Here we present a synthesis of the range of disparate attempts to define, quantify and address model dependence in multi-model climate ensembles in a common conceptual framework, and provide guidance on how users can test the efficacy of approaches that move beyond the equally weighted ensemble. In the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6), several new models that are closely related to existing models are anticipated, as well as large ensembles from some models. We argue that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies.
Using Machine Learning to Cut the Cost of Dynamical Downscaling
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.
On the generation of climate model ensembles
Publisher: Springer Science and Business Media LLC
Date: 31-01-2014
Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation
Publisher: Wiley
Date: 08-03-2020
Taking climate model evaluation to the next level
Publisher: Springer Science and Business Media LLC
Date: 07-01-2019
The Plumbing of Land Surface Models: Is Poor Performance a Result of Methodology or Data Quality?
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.
Robust Future Changes in Meteorological Drought in CMIP6 Projections Despite Uncertainty in Precipitation
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-1889
Abstract: & & Understanding how climate change affects droughts guides adaptation planning in agriculture, water security, and ecosystem management. Earlier climate projections have highlighted high uncertainty in future drought projections, hindering effective planning. We use the latest CMIP6 projections and find more robust projections of meteorological drought compared to mean precipitation. We find coherent projected changes in seasonal drought duration and frequency (robust over & % of the global land area), despite a lack of agreement across models in projected changes in mean precipitation (24% of the land area). Future drought changes are larger and more consistent in CMIP6 compared to CMIP5. We find regionalised increases and decreases in drought duration and frequency that are driven by changes in both precipitation mean and variability. Conversely, drought intensity increases over most regions but is not simulated well historically by the climate models. These more robust projections of meteorological drought in CMIP6 provide clearer direction for water resource planning and the identification of agricultural and natural ecosystems at risk.& &
On the Predictability of Land Surface Fluxes from Meteorological Variables
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.
Does predictability of fluxes vary between FLUXNET sites?
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.
Using Machine Learning to Reveal the Relationships Between Plant Functional Traits and Flux Regimes at Eddy-Covariance Towers
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-10049
Abstract: The current-generation of land surface models (LSMs) are powerful tools used in predictions of the future global climate and carbon cycle. Many of these LSMs are parametrised using plant functional types (PFTs), often of a coarse nature with only relatively few possible groups. In turn, extensive use of eddy-covariance data is utilised when calibrating these LSMs, with the model PFT matched to the classification reported by the site owners. Importantly, the PFT group is one of the few site characteristics that is consistently supplied across FLUXNET sites. However, there are issues with this method of LSM calibration. It is well-known that many PFT classification schemes cannot be predicted from climate, and that traits may vary more within species or sites than between them. Here we present our results assessing the suitability of PFTs for capturing site flux regimes using a suite of machine learning techniques. We explore natural groupings of sites based on the measurements used for LSM calibration and identify potential site characteristics and traits that might allow these natural groupings to be predicted. Our results identify driving characteristics of site flux regime differences, and can be used to direct LSM development and highlight priority locations for future eddy-covariance flux towers.
Does predictability of fluxes vary between FLUXNET sites?
Publisher: Copernicus GmbH
Date: 20-04-2018
DOI: 10.5194/BG-2018-179
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. Aside from intrinsic interest in this fundamental question, understanding site predictability would be useful for land surface model (LSM) evaluation in setting a priori expectations of model performance. It would also provide a clear rationale for selecting particular FLUXNET sites for model development, evaluation and benchmarking. Here, 155 datasets with 30 minute temporal resolution from the Tier 1 of FLUXNET2015 were analysed in a first attempt to assess in idual site predictability. Predictability was defined using the disparity between the ability to simulate fluxes at a site given specific knowledge of the site, and the ability to simulate fluxes given general land surface specifications. We then examined predictability using performance metrics including RMSE, correlation, and probability density overlap, and defined site uniqueness as the disparity between multiple empirical models trained globally and locally for each site. A number of hypotheses potentially explaining site predictability were then tested, including climatology, data quality and site characteristics. We found very few clear predictors of uniqueness across different sites including little evidence that flux behaviour is well discretised by vegetation types. While this result might relate to our definition of uniqueness, we argue that our approach is sound and provides a useful basis for site selection in LSM evaluation.
Ranking climate models by performance using actual values and anomalies: Implications for climate change impact assessments
Publisher: American Geophysical Union (AGU)
Date: 08-2010
DOI: 10.1029/2010GL043877
Supplementary material to "Does predictability of fluxes vary between FLUXNET sites?"
Publisher: Copernicus GmbH
Date: 20-04-2018
Opening Pandora's box: How to constrain regional projections of the carbon cycle
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.
Climate model simulated changes in temperature extremes due to land cover change
Publisher: American Geophysical Union (AGU)
Date: 25-02-2012
DOI: 10.1029/2011JD016382
Evaluating the performance of land surface Models
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.
Drought Influences the Accuracy of Simulated Ecosystem Fluxes: A Model-Data Meta-analysis for Mediterranean Oak Woodlands
Publisher: Springer Science and Business Media LLC
Date: 26-02-2013
How representative are FLUXNET measurements of surface fluxes during temperature extremes?
Publisher: Copernicus GmbH
Date: 20-12-2018
DOI: 10.5194/BG-2018-502
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 datasets, 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, 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) and 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 exploring responses to temperature extremes.
Toward a model space and model independence metric
Publisher: American Geophysical Union (AGU)
Date: 03-2008
DOI: 10.1029/2007GL032834
Evaluating Precipitation Datasets Using Surface Water and Energy Budget Closure
Publisher: American Meteorological Society
Date: 05-2020
Abstract: Evaluation of global gridded precipitation datasets typically entails using the in situ or satellite-based data used to derive them, so that out-of-s le testing is usually not possible. Here we detail a methodology that incorporates the physical balance constraints of the surface water and energy budgets to evaluate gridded precipitation estimates, providing the capacity for out-of-s le testing. Performance conclusions are determined by the ability of precipitation products to achieve closure of the linked budgets using adjustments that are within their prescribed uncertainty bounds. We evaluate and compare five global gridded precipitation datasets: IMERG, GPCP, GPCC, REGEN, and MERRA-2. At the spatial level, we show that precipitation is best estimated by GPCC over the high latitudes, by GPCP over the tropics, and by REGEN over North Africa and the Middle East. IMERG and REGEN appear best over Australia and South Asia. Furthermore, our results give insight into the adequacy of prescribed uncertainties of these products and shows that MERRA-2, while being less competent than the other four products in estimating precipitation, has the best representation of uncertainties in its precipitation estimates. The spatial extent of our results is not only limited to grid cells with in situ observations. Therefore, the approach enables a robust evaluation of precipitation estimates and goes some way to addressing the challenge of validation over observation scarce regions.
Selecting a climate model subset to optimise key ensemble properties
Publisher: Copernicus GmbH
Date: 03-04-2017
DOI: 10.5194/ESD-2017-28
Abstract: Abstract. End-users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally-weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-s le skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for ex le, minimise present day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of in idual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product and pre-processing steps used.
Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate
Publisher: Copernicus GmbH
Date: 03-04-2017
Abstract: Abstract. Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, as well as being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data, and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-s le and out-of-s le tests that confirm that point-based estimates of flux towers provide information at the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in three common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.
A framework for benchmarking land models
Publisher: Copernicus GmbH
Date: 09-10-2012
Abstract: Abstract. Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.
How representative are FLUXNET measurements of surface fluxes during temperature extremes?
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.
Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections
Publisher: American Meteorological Society
Date: 13-03-2015
DOI: 10.1175/JCLI-D-14-00364.1
Abstract: Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This is typically done by collating model estimates from different research institutions internationally with the assumption that they constitute independent s les. Heuristically, however, several factors undermine this assumption: shared treatment of processes between models, shared observed data for evaluation, and even shared model code. Here, a “perfect model” approach is used to test whether a previously proposed ensemble dependence transformation (EDT) can improve twenty-first-century Coupled Model Intercomparison Project (CMIP) projections. In these tests, where twenty-first-century model simulations are used as out-of-s le “observations,” the mean-square difference between the transformed ensemble mean and “observations” is on average 30% less than for the untransformed ensemble mean. In addition, the variance of the transformed ensemble matches the variance of the ensemble mean about the “observations” much better than in the untransformed ensemble. Results show that the EDT has a significant effect on twenty-first-century projections of both surface air temperature and precipitation. It changes projected global average temperature increases by as much as 16% (0.2°C for B1 scenario), regional average temperatures by as much as 2.6°C (RCP8.5 scenario), and regional average annual rainfall by as much as 410 mm (RCP6.0 scenario). In some regions, however, the effect is minimal. It is also found that the EDT causes changes to temperature projections that differ in sign for different emissions scenarios. This may be as much a function of the makeup of the ensembles as the nature of the forcing conditions.
Supplementary material to "Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model"
Publisher: Copernicus GmbH
Date: 21-10-2015
A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas
Publisher: Copernicus GmbH
Date: 03-06-2016
Abstract: Abstract. The savanna ecosystem is one of the most dominant and complex terrestrial biomes, deriving from a distinct vegetative surface comprised of co-dominant tree and grass populations. While these two vegetation types co-exist functionally, demographically they are not static but are dynamically changing in response to environmental forces such as annual fire events and rainfall variability. Modelling savanna environments with the current generation of terrestrial biosphere models (TBMs) has presented many problems, particularly describing fire frequency and intensity, phenology, leaf biochemistry of C3 and C4 photosynthesis vegetation, and root-water uptake. In order to better understand why TBMs perform so poorly in savannas, we conducted a model inter-comparison of six TBMs and assessed their performance at simulating latent energy (LE) and gross primary productivity (GPP) for five savanna sites along a rainfall gradient in northern Australia. Performance in predicting LE and GPP was measured using an empirical benchmarking system, which ranks models by their ability to utilise meteorological driving information to predict the fluxes. On average, the TBMs performed as well as a multi-linear regression of the fluxes against solar radiation, temperature and vapour pressure deficit but were outperformed by a more complicated nonlinear response model that also included the leaf area index (LAI). This identified that the TBMs are not fully utilising their input information effectively in determining savanna LE and GPP and highlights that savanna dynamics cannot be calibrated into models and that there are problems in underlying model processes. We identified key weaknesses in a model's ability to simulate savanna fluxes and their seasonal variation, related to the representation of vegetation by the models and root-water uptake. We underline these weaknesses in terms of three critical areas for development. First, prescribed tree-rooting depths must be deep enough, enabling the extraction of deep soil-water stores to maintain photosynthesis and transpiration during the dry season. Second, models must treat grasses as a co-dominant interface for water and carbon exchange rather than a secondary one to trees. Third, models need a dynamic representation of LAI that encompasses the dynamic phenology of savanna vegetation and its response to rainfall interannual variability. We believe that this study is the first to assess how well TBMs simulate savanna ecosystems and that these results will be used to improve the representation of savannas ecosystems in future global climate model studies.
Global and regional coupled climate sensitivity to the parameterization of rainfall interception
Publisher: Springer Science and Business Media LLC
Date: 10-06-2010
How Well Can Land-Surface Models Represent the Diurnal Cycle of Turbulent Heat Fluxes?
Publisher: American Meteorological Society
Date: 2021
Abstract: The diurnal cycle of solar radiation represents the strongest energetic forcing and dominates the exchange of heat and mass of the land surface with the atmosphere. This diurnal heat redistribution represents a core of land–atmosphere coupling that should be accurately represented in land surface models (LSMs), which are critical parts of weather and climate models. We employ a diagnostic model evaluation approach using a signature-based metric that describes the diurnal variation of heat fluxes. The metric is obtained by decomposing the diurnal variation of surface heat fluxes into their direct response and the phase lag to incoming solar radiation. We employ the output of 13 different LSMs driven with meteorological forcing of 20 FLUXNET sites (PLUMBER dataset). All LSMs show a poor representation of the evaporative fraction and thus the diurnal magnitude of the sensible and latent heat flux under cloud-free conditions. In addition, we find that the diurnal phase of both heat fluxes is poorly represented. The best performing model only reproduces 33% of the evaluated evaporative conditions across the sites. The poor performance of the diurnal cycle of turbulent heat exchange appears to be linked to how models solve for the surface energy balance and redistribute heat into the subsurface. We conclude that a systematic evaluation of diurnal signatures is likely to help to improve the simulated diurnal cycle, better represent land–atmosphere interactions, and therefore improve simulations of the near-surface climate.
Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model
Publisher: Copernicus GmbH
Date: 21-10-2015
DOI: 10.5194/HESSD-12-10789-2015
Abstract: Abstract. Surface fluxes from land surface models (LSM) have traditionally been evaluated against monthly, seasonal or annual mean states. The limited ability of LSMs to reproduce observed evaporative fluxes under water-stressed conditions has been previously noted, but very few studies have systematically evaluated these models during rainfall deficits. We evaluated latent heat flux simulated by the Community Atmosphere Biosphere Land Exchange (CABLE) LSM across 20 flux tower sites at sub-annual to inter-annual time scales, in particular focusing on model performance during seasonal-scale rainfall deficits. The importance of key model processes in capturing the latent heat flux are explored by employing alternative representations of hydrology, leaf area index, soil properties and stomatal conductance. We found that the representation of hydrological processes was critical for capturing observed declines in latent heat during rainfall deficits. By contrast, the effects of soil properties, LAI and stomatal conductance are shown to be highly site-specific. Whilst the standard model performs reasonably well at annual scales as measured by common metrics, it grossly underestimates latent heat during rainfall deficits. A new version of CABLE, with a more physically consistent representation of hydrology, captures the variation in the latent heat flux during seasonal-scale rainfall deficits better than earlier versions but remaining biases point to future research needs. Our results highlight the importance of evaluating LSMs under water-stressed conditions and across multiple plant functional types and climate regimes.
Plant profit maximization improves predictions of European forest responses to drought
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
Weighting climate model ensembles for mean and variance estimates
Publisher: Springer Science and Business Media LLC
Date: 20-02-2015
Exploring the role of lags and legacies in the productivity of Australian ecosystems
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-3684
Abstract: & & The vegetation& #8217 s response to climate change is a major source of uncertainty in terrestrial biosphere model (TBM) projections. Constraining carbon cycle feedbacks to climate change requires improving our understanding of both the direct plant physiological responses to global change, as well as the role of legacy effects (e.g. reductions in plant growth, damage to the plant& #8217 s hydraulic transport system), that drive multi-timescale feedbacks. In particular, the role of these legacy effects - both the timescale and strength of the memory effect - have been largely overlooked in the development of model hypotheses. This is despite the knowledge that plant responses to climatic drivers occur across multiple time scales (seconds to decades), with the impact of climate extremes (e.g. drought) resonating for many years. Using data from 13 eddy covariance sites, covering two rainfall gradients in Australia, in combination with a hierarchical Bayesian model, we characterised the timescales of influence of antecedent drivers on fluxes of net carbon exchange and evapotranspiration. Using our data assimilation approach we were able to partition the influence of ecological memory into both biological and environmental components. Overall, we found that the importance of ecological memory to antecedent conditions increased as water availability declines. Our results therefore underline the importance of capturing legacy effects in TBMs used to project responses in water limited ecosystems.& &
Toward a Robust, Impact‐Based, Predictive Drought Metric
Publisher: American Geophysical Union (AGU)
Date: 02-2022
DOI: 10.1029/2021WR031829
Abstract: This work presents a new approach to defining drought by establishing an empirical relationship between historical droughts (and wet spells) documented in impact reports, and a broad range of observed climate features using Random Forest (RF) models. The new drought indicator quantifies the conditional probability of drought, considering multiple drought‐related climate features and their interactive effects, and can be used for forecasting with up to 3‐month lead time. The approach was tested out‐of‐s le across several random selections of training and testing datasets, and demonstrated better predictive capabilities than commonly used drought indicators (e.g., Standardised Precipitation Index and Evaporative Demand Drought Index) in a range of performance metrics. Furthermore, it showed comparable performance to the (expert elicitation‐based) US Drought Monitor (USDM), the current state‐of‐the‐art record of historical drought in the USA. As well as providing an alternative historical drought indicator to USDM, the RF approach offers additional advantages by being automated, by providing drought information at the grid‐scale, and by having forecasting capacity. While traditional drought metrics define drought as extreme anomalies in drought‐related variables, the approach presented here reveals the full suite of circumstances that lead to impactful droughts. We highlight several combinations of climate features—such as precipitation, potential evapotranspiration, soil moisture and change in water storage—that led to drought events not detected by commonly used drought metrics. The new RF drought indicator combines meteorological, hydrological, agricultural, and socioeconomic drought, providing drought information for all impacted sectors. As a proof‐of‐concept, the RF drought indicator was trained on Texan climate data and droughts.
Diagnosing errors in a land surface model (CABLE) in the time and frequency domains
Publisher: American Geophysical Union (AGU)
Date: 29-03-2011
DOI: 10.1029/2010JG001385
Response of microbial decomposition to spin-up explains CMIP5 soil carbon range until 2100
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.
Ensemble optimisation, multiple constraints and overconfidence: a case study with future Australian precipitation change
Publisher: Springer Science and Business Media LLC
Date: 04-2019
Robust historical evapotranspiration trends across climate regimes
Publisher: Copernicus GmbH
Date: 09-12-2020
Abstract: Abstract. Evapotranspiration (ET) links the hydrological, energy, and carbon cycle on the land surface. Quantifying ET and its spatiotemporal changes is also key to understanding climate extremes such as droughts, heatwaves and flooding. Regional ET estimates require reliable observationally-based gridded ET datasets, and while many have been developed using physically-based, empirically-based and hybrid techniques, their efficacy, and particularly the efficacy of their uncertainty estimates, is difficult to verify. In this work, we extend the methodology used in Hobeichi et al. (2018) to derive a new version of the Derived Optimal Linear Combination Evapotranspiration (DOLCE) product, with observationally constrained spatiotemporally varying uncertainty estimates, higher spatial resolution, more constituent products and extended temporal reach (1980–2018). After successful evaluation of the efficacy of these uncertainty estimates out-of-s le, we derive novel ET climatology clusters for the land surface, based on the magnitude and variability of ET at each location. The verified uncertainty estimates and extended time period then allow us to examine the robustness of historical trends spatially and in each of these six ET climatology clusters. We find that despite robust decreasing ET trends in some regions, these do not correlate with behavioural ET clusters. Each cluster, and the vast majority of the Earth's surface, show clear robust increases in ET over the recent historical period.
Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts
Publisher: IOP Publishing
Date: 10-2016
A flux tower dataset tailored for land model evaluation
Publisher: Copernicus GmbH
Date: 28-07-2021
Abstract: Abstract. Eddy covariance flux towers measure the exchange of water, energy and carbon fluxes between the land and atmosphere. They have become invaluable for theory development and evaluating land models. However, flux tower data as measured (even after site post-processing) are not directly suitable for land surface modelling due to data gaps in model forcing variables, inappropriate gap-filling, formatting and varying data quality. Here we present a quality-control and data-formatting pipeline for tower data from FLUXNET2015, La Thuile and OzFlux syntheses and the resultant 170-site globally distributed flux tower dataset specifically designed for use in land modelling. The dataset underpins the second phase of the PLUMBER land surface model benchmarking evaluation project, an international model intercomparison project encompassing 20 land surface and biosphere models. The dataset is provided in the Assistance for Land-surface Modelling Activities (ALMA) NetCDF format and is CF-NetCDF compliant. For forcing land surface models, the dataset provides fully gap-filled meteorological data that has had periods of low data quality removed. Additional constraints required for land models, such as reference measurement heights, vegetation types and satellite-based monthly leaf area index estimates, are also included. For model evaluation, the dataset provides estimates of key water, carbon and energy variables, with the latent and sensible heat fluxes additionally corrected for energy balance closure. The dataset provides a total of 1040 site years covering the period 1992–2018, with in idual sites spanning from 1 to 21 years. The dataset is available at 0.25914/5fdb0902607e1 (Ukkola et al., 2021).
Sensitivity of net ecosystem exchange and heterotrophic respiration to parameterization uncertainty
Publisher: American Geophysical Union (AGU)
Date: 25-02-2013
DOI: 10.1029/2012JD018122
Challenges and opportunities in land surface modelling of savanna ecosystems
Publisher: Copernicus GmbH
Date: 24-10-2017
Abstract: Abstract. The savanna complex is a highly erse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes.Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and intercomparison frameworks to diagnose the performance of TBMs in this environment and formulate road maps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, the effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour) but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change.
Examining soil carbon uncertainty in a global model: response of microbial decomposition to temperature, moisture and nutrient limitation
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.
Are we unnecessarily constraining the agility of complex process-based models?
Publisher: American Geophysical Union (AGU)
Date: 2015
DOI: 10.1002/2014WR015820
Supplementary material to "Examining the Role of Environmental Memory in the Predictability of Carbon and Water Fluxes Across Australian Ecosystems"
Publisher: Copernicus GmbH
Date: 10-2021
Acute climate risks in the financial system: examining the utility of climate model projections
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.
Supplementary material to "On the Predictability of Land Surface Fluxes from Meteorological Variables"
Publisher: Copernicus GmbH
Date: 12-07-2017
Disentangling residence time and temperature sensitivity of microbial decomposition in a global soil carbon model
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.
A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products
Publisher: Copernicus GmbH
Date: 14-08-2019
Abstract: Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative are satellite-based rainfall estimates, yet comparisons with in-situ data suggest they're often not optimal. In this study, we developed a short-latency (i.e., 2–3 days) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM early run (IMERG-ER) with multiple satellite soil moisture-based rainfall products derived from ASCAT, SMOS and SMAP L3 satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high quality ground-based rainfall datasets (India, Conterminous United States, Australia and Europe) and over data scarce regions in Africa and South America by using Triple Collocation analysis (TC). We found the integration of satellite SM observations with in-situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long latency datasets. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.
Related Organisations
Related Funding Activities
Discovery Projects - Grant ID: DP110102618
Start Date: 06-2011
End Date: 03-2014
Amount: $300,000.00
Funder: Australian Research Council
View Funded ActivityARC Centres Of Excellence - Grant ID: CE170100023
Start Date: 08-2017
End Date: 12-2024
Amount: $30,050,000.00
Funder: Australian Research Council
View Funded Activity