ORCID Profile
0000-0002-3986-1268
Current Organisation
ETH Zurich
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Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-4524
Abstract: & & Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs introduce new information into a multi-model ensemble by representing region-scale internal variability, but also introduce redundant information, by virtue of a single model being represented by 50& #8211 outcomes. To preserve the contribution of internal variability and ensure redundancy does not overwhelm uncertainty estimates, a weighting approach is used to incorporate 50-members of the Community Earth System Model (CESM1.2.2), 50-members of the Canadian Earth System Model (CanESM2), and 100-members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. The weight assigned to each multi-model ensemble member is based on the member's ability to reproduce observed climate (performance) and scaled by a measure of historical redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) diagnostics are used to determine the weights, and relationships between present and future diagnostic behavior are discussed. A new diagnostic, estimated forced trend, is proposed to replace a diagnostic with no clear emergent relationship, 50-year regional SAT trend.& & & & The influence of the weighting is assessed in estimates of Northern European winter and Mediterranean summer end-of-century warming in the CMIP5 and combined SMILE-CMIP5 multi-model ensembles. The weighting is shown to recover uncertainty obscured by SMILE redundancy, notably in Mediterranean summer. For each SMILE, the independence weight of each ensemble member as a function of the number of SMILE members included in the CMIP5 ensemble is assessed. The independence weight increases linearly with added members with a slope that depends on SMILE, region, and season. Finally, it is shown that the weighting method can be used to guide SMILE member selection if a subsetted ensemble with one member per model is sought. The weight a SMILE receives within a subsetted ensemble depends on which member is used to represent it, reinforcing the advantage of weighting and incorporating all initial condition ensemble members in multi-model ensembles.& &
Publisher: Copernicus GmbH
Date: 28-04-2020
Publisher: Copernicus GmbH
Date: 08-12-2015
Abstract: Abstract. We implement a new stomatal conductance scheme, based on the optimality approach, within the Community Atmosphere Biosphere Land Exchange (CABLEv2.0.1) land surface model. Coupled land–atmosphere simulations are then performed using CABLEv2.0.1 within the Australian Community Climate and Earth Systems Simulator (ACCESSv1.3b) with prescribed sea surface temperatures. As in most land surface models, the default stomatal conductance scheme only accounts for differences in model parameters in relation to the photosynthetic pathway but not in relation to plant functional types. The new scheme allows model parameters to vary by plant functional type, based on a global synthesis of observations of stomatal conductance under different climate regimes over a wide range of species. We show that the new scheme reduces the latent heat flux from the land surface over the boreal forests during the Northern Hemisphere summer by 0.5–1.0 mm day−1. This leads to warmer daily maximum and minimum temperatures by up to 1.0 °C and warmer extreme maximum temperatures by up to 1.5 °C. These changes generally improve the climate model's climatology of warm extremes and improve existing biases by 10–20 %. The bias in minimum temperatures is however degraded but, overall, this is outweighed by the improvement in maximum temperatures as there is a net improvement in the diurnal temperature range in this region. In other regions such as parts of South and North America where ACCESSv1.3b has known large positive biases in both maximum and minimum temperatures (~ 5 to 10 °C), the new scheme degrades this bias by up to 1 °C. We conclude that, although several large biases remain in ACCESSv1.3b for temperature extremes, the improvements in the global climate model over large parts of the boreal forests during the Northern Hemisphere summer which result from the new stomatal scheme, constrained by a global synthesis of experimental data, provide a valuable advance in the long-term development of the ACCESS modelling system.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-8398
Abstract: & & The Multi-Model Large Ensemble Archive (MMLEA) is a collection of CMIP5-generation single model initial condition large ensembles (SMILEs) and thus provides estimates of internal variability from several independently developed coupled climate models. Work is underway to determine whether these simulations provide a range of historical regional climate variability suitable for statistically increasing the observed temperature s le.& Alternative sequences of historical temperature can be constructed by combining a forced signal with estimates of internal climate noise prior studies have used the forced response from one SMILE in concert with observational noise res ling to form an & #8220 observational large ensemble& #8221 (McKinnon et al. 2018). Analogous to a SMILE, an observational large ensemble can be used to statistically contextualize monthly to half-yearly extreme events, such as the persistently mild Siberian winter of 2020, and to develop additional extended hot or cold spell storylines to explore in future projections of regional climate.& & & & In this study, an alternative approach to constructing an observational large ensemble of European surface air temperature over the historical period (1950-2014), made possible by the MMLEA, is explored. Rather than relying on forced response and internal variability, components not well-defined in the single realization of observed climate, the constructed circulation analogue method of dynamical adjustment is employed to separate temperature anomalies related to atmospheric circulation (& #8220 dynamic noise& quot ) from a more thermodynamically driven residual signal. The approach is advantageous because it can be applied in a similar manner to single realizations from both models and observations. Here, dynamic noise is computed by iding each of the seven CMIP5-generation SMILEs in half and empirically estimating the component of temperature associated with interannual sea level pressure variability in one half of the SMILE using circulation analogues from members in the other half. Because ensemble means can be computed in SMILEs, it is possible to use the relationship between unforced temperature and unforced sea level pressure anomalies to construct dynamic noise. In observations, weekly-averaged analogues are assessed as a means to increase the size of the analogue pool such that the separation between dynamic noise and thermodynamic residual signal occurs in a manner more similar to that computed in the SMILEs.& & & & The extent to which dynamic noise fields from different SMILEs are distinguishable from each other and from observational estimates is determined via spectral and spatial pattern analyses. To avoid introducing regional model bias into dynamic noise estimates, a mosaic approach will be taken noise estimates from different models are mosaiced such that observed statistical properties are maintained at each grid point of the European domain. Upon validation, SMILE-derived dynamic noise and observational thermodynamic residual signal estimates are combined into a 50-member European observational large ensemble and evaluated via a multi-month extreme temperature frequency metric against the observational large ensemble developed by McKinnon et al. (2018). Anomalously persistent hot and cold spells found in the European observational large ensemble are further compared to events in out-of-s le future projections of climate from the CMIP6 archive.& &
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-9387
Abstract: & & Too often model evaluation has no impact on how a multi-model ensemble is analysed. It has been argued that projection and prediction uncertainties can be decreased by giving more weight to those models in multi-model ensembles that are more skillful and realistic for a specific process or application. In addition, some models in multi-model ensembles are not independent and it is not always clear how to include available initial condition ensemble members which are becoming larger in number e.g. in CMIP6.& & & & A weighting approach has been proposed which takes into account both of these aspects (Climate model Weighting by Independence and Performance- ClimWIP) and is able to deal with included initial condition ensemble members. This approach has been shown to decrease uncertainties in multiple use cases such as projections of Arctic September sea ice, North American summer maximum temperatures, European temperature and precipitation, as well as projected global mean temperatures. Even though the basic equation to calculate a model's weight is straight forward, the user needs to make several decisions, such as which metric to use to measure performance or independence, which variables to include etc. and potential pitfalls were identified. For the actual implementation a range of points need to be considered: (1) data from different modelling centers need to be processed and compared in a consistent way, (2) the strength of the performance and independence contributions is determined through two parameters that must also be calibrated, (3) results should be provided in a form that allows backtracing to the original data and code to allow reproducability. To facilitate re-use for new applications, the method was recently implemented into the ESMValTool. We will discuss advantages and disadvantages of the method, show results from some of the use cases, explain how the implementation into ESMValTool was done and how the method can now be more easily used.& &
Publisher: Copernicus GmbH
Date: 21-11-2019
DOI: 10.5194/ESD-2019-69
Abstract: Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs introduce new information into a multi-model ensemble by representing region-scale internal variability, but also introduce redundant information, by virtue of a single model being represented by 50–100 outcomes. To preserve the contribution of internal variability and ensure redundancy does not overwhelm uncertainty estimates, a weighting approach is used to incorporate 50-members of the Community Earth System Model (CESM1.2.2), 50-members of the Canadian Earth System Model (CanESM2), and 100-members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. The weight assigned to each multi-model ensemble member is based on the member's ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) diagnostics are used to determine the weights, and relationships between present and future diagnostic behavior are discussed. A new diagnostic, estimated forced trend, is proposed to replace a diagnostic with no clear emergent relationship, 50-year regional SAT trend. The influence of the weighting is assessed in estimates of Northern European winter and Mediterranean summer end-of-century warming in the CMIP5 and combined SMILE-CMIP5 multi-model ensembles. The weighting is shown to recover uncertainty obscured by SMILE redundancy, notably in Mediterranean summer. For each SMILE, the independence weight of each ensemble member as a function of the number of SMILE members included in the CMIP5 ensemble is assessed. The independence weight increases linearly with added members with a slope that depends on SMILE, region, and season. Finally, it is shown that the weighting method can be used to guide SMILE member selection if a subsetted ensemble with one member per model is sought. The weight a SMILE receives within a subsetted ensemble depends on which member is used to represent it, reinforcing the advantage of weighting and incorporating all initial condition ensemble members in multi-model ensembles.
Publisher: American Geophysical Union (AGU)
Date: 19-01-2016
DOI: 10.1002/2015JD024053
Publisher: Copernicus GmbH
Date: 10-09-2020
Abstract: Abstract. The Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for evaluation and analysis of Earth system models (ESMs), is designed to facilitate a more comprehensive and rapid comparison of single or multiple models participating in the Coupled Model Intercomparison Project (CMIP). The ESM results can be compared against observations or reanalysis data as well as against other models including predecessor versions of the same model. The updated and extended version (v2.0) of the ESMValTool includes several new analysis scripts such as large-scale diagnostics for evaluation of ESMs as well as diagnostics for extreme events, regional model and impact evaluation. In this paper, the newly implemented climate metrics such as effective climate sensitivity (ECS) and transient climate response (TCR) as well as emergent constraints for various climate-relevant feedbacks and diagnostics for future projections from ESMs are described and illustrated with ex les using results from the well-established model ensemble CMIP5. The emergent constraints implemented include constraints on ECS, snow-albedo effect, climate–carbon cycle feedback, hydrologic cycle intensification, future Indian summer monsoon precipitation and year of disappearance of summer Arctic sea ice. The diagnostics included in ESMValTool v2.0 to analyze future climate projections from ESMs further include analysis scripts to reproduce selected figures of chapter 12 of the Intergovernmental Panel on Climate Change's (IPCC) Fifth Assessment Report (AR5) and various multi-model statistics.
Publisher: Springer Science and Business Media LLC
Date: 16-05-2016
DOI: 10.1038/NCLIMATE3029
Publisher: Zenodo
Date: 2020
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.
Publisher: Copernicus GmbH
Date: 04-04-2014
Abstract: Abstract. Climate extremes, such as heat waves and heavy precipitation events, have large impacts on ecosystems and societies. Climate models provide useful tools for studying underlying processes and lifying effects associated with extremes. The Australian Community Climate and Earth System Simulator (ACCESS) has recently been coupled to the Community Atmosphere Biosphere Land Exchange (CABLE) model. We examine how this model represents climate extremes derived by the Expert Team on Climate Change Detection and Indices (ETCCDI) and compare them to observational data sets using the AMIP framework. We find that the patterns of extreme indices are generally well represented. Indices based on percentiles are particularly well represented and capture the trends over the last 60 years shown by the observations remarkably well. The diurnal temperature range is underestimated, minimum temperatures (TMIN) during nights are generally too warm and daily maximum temperatures (TMAX) too low in the model. The number of consecutive wet days is overestimated, while consecutive dry days are underestimated. The maximum consecutive 1-day precipitation amount is underestimated on the global scale. Biases in TMIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in TMAX depend on biases in net shortwave radiation as well as evapotranspiration. The regions and season where the bias in evapotranspiration plays a role for the TMAX bias correspond to regions and seasons where soil moisture availability is limited. Our analysis provides the foundation for future experiments that will examine how land-surface processes contribute to these systematic biases in the ACCESS modelling system.
Publisher: American Geophysical Union (AGU)
Date: 28-07-2019
DOI: 10.1029/2019GL082062
Abstract: In the last two decades Europe experienced a series of high‐impact heat extremes. We here assess observed trends in temperature extremes at ECA& D stations in Europe. We demonstrate that on average across Europe the number of days with extreme heat and heat stress has more than tripled and hot extremes have warmed by 2.3 °C from 1950–2018. Over Central Europe, the warming exceeds the corresponding summer mean warming by 50%. Days with extreme cold temperatures have decreased by a factor of 2–3 and warmed by more than 3 °C, regionally substantially more than winter mean temperatures. Cold and hot extremes have warmed at about 94% of stations, a climate change signal that cannot be explained by internal variability. The clearest climate change signal can be detected in maximum heat stress. EURO‐CORDEX RCMs broadly capture observed trends but the majority underestimates the warming of hot extremes and overestimates the warming of cold extremes.
Publisher: Zenodo
Date: 2020
Publisher: Copernicus GmbH
Date: 24-02-2023
Publisher: American Meteorological Society
Date: 12-2017
Abstract: Understanding the physical drivers of heat waves is essential for improving short-term forecasts of in idual events and long-term projections of heat waves under climate change. This study provides the first analysis of the influence of the large-scale circulation on Australian heat waves, conditional on the land surface conditions. Circulation types, sourced from reanalysis, are used to characterize the different large-scale circulation patterns that drive heat wave events across Australia. The importance of horizontal temperature advection is illustrated in these circulation patterns, and the pattern occurrence frequency is shown to reorganize through different modes of climate variability. It is further shown that the relative likelihood of a particular synoptic situation being associated with a heat wave is strongly modulated by the localized partitioning of available energy between surface sensible and latent heat fluxes (as measured through evaporative fraction) in many regions in reanalysis data. In particular, a several-fold increase in the likelihood of heat wave day occurrence is found during days of reduced evaporative fraction under favorable circulation conditions. The atmospheric circulation and land surface conditions linked to heat waves in reanalysis were then examined in the context of CMIP5 climate model projections. Large uncertainty was found to exist for many regions, especially in terms of the direction of future land surface changes and in terms of the magnitude of atmospheric circulation changes. Efforts to constrain uncertainty in both atmospheric and land surface processes in climate models, while challenging, should translate to more robust regional projections of heat waves.
Publisher: American Geophysical Union (AGU)
Date: 27-05-2016
DOI: 10.1002/2015JD024357
Publisher: Copernicus GmbH
Date: 16-02-2029
Abstract: Abstract. Multi-model ensembles can be used to estimate uncertainty in projections of regional climate, but this uncertainty often depends on the constituents of the ensemble. The dependence of uncertainty on ensemble composition is clear when single-model initial condition large ensembles (SMILEs) are included within a multi-model ensemble. SMILEs allow for the quantification of internal variability, a non-negligible component of uncertainty on regional scales, but may also serve to inappropriately narrow uncertainty by giving a single model many additional votes. In advance of the mixed multi-model, the SMILE Coupled Model Intercomparison version 6 (CMIP6) ensemble, we investigate weighting approaches to incorporate 50 members of the Community Earth System Model (CESM1.2.2-LE), 50 members of the Canadian Earth System Model (CanESM2-LE), and 100 members of the MPI Grand Ensemble (MPI-GE) into an 88-member Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble. The weights assigned are based on ability to reproduce observed climate (performance) and scaled by a measure of redundancy (dependence). Surface air temperature (SAT) and sea level pressure (SLP) predictors are used to determine the weights, and relationships between present and future predictor behavior are discussed. The estimated residual thermodynamic trend is proposed as an alternative predictor to replace 50-year regional SAT trends, which are more susceptible to internal variability. Uncertainty in estimates of northern European winter and Mediterranean summer end-of-century warming is assessed in a CMIP5 and a combined SMILE–CMIP5 multi-model ensemble. Five different weighting strategies to account for the mix of initial condition (IC) ensemble members and in idually represented models within the multi-model ensemble are considered. Allowing all multi-model ensemble members to receive either equal weight or solely a performance weight (based on the root mean square error (RMSE) between members and observations over nine predictors) is shown to lead to uncertainty estimates that are dominated by the presence of SMILEs. A more suitable approach includes a dependence assumption, scaling either by 1∕N, the number of constituents representing a “model”, or by the same RMSE distance metric used to define model performance. SMILE contributions to the weighted ensemble are smallest ( %) when a model is defined as an IC ensemble and increase slightly ( %) when the definition of a model expands to include members from the same institution and/or development stream. SMILE contributions increase further when dependence is defined by RMSE (over nine predictors) amongst members because RMSEs between SMILE members can be as large as RMSEs between SMILE members and other models. We find that an alternative RMSE distance metric, derived from global SAT and hemispheric SLP climatology, is able to better identify IC members in general and SMILE members in particular as members of the same model. Further, more subtle dependencies associated with resolution differences and component similarities are also identified by the global predictor set.
Publisher: Springer Science and Business Media LLC
Date: 07-01-2019
Publisher: American Meteorological Society
Date: 02-12-2013
DOI: 10.1175/JCLI-D-13-00040.1
Abstract: It has been hypothesized that vegetation phenology may play an important role for the midlatitude climate. This study investigates the impact of interannual and intraseasonal variations in phenology on European climate using regional climate model simulations. In addition, it assesses the relative importance of interannual variations in vegetation phenology and soil moisture on European summer climate. It is found that drastic phenological changes have a smaller effect on mean summer and spring climate than extreme changes in soil moisture (roughly ¼ of the temperature anomaly induced by soil moisture changes). However, the impact of phenological anomalies during heat waves is found to be more important. Generally, late and weak greening has lifying effects and early and strong greening has d ening effects on heat waves however, regional variations are found. The experiments suggest that in the extreme hot 2003 (western and central Europe) and 2007 (southeastern Europe) summers the decrease in leaf area index lified the heat wave peaks by about 0.5°C for daily maximum temperatures (about half of the effect induced by soil moisture deficit). In contrast to earlier hypotheses, no anomalous early greening in spring 2003 is seen in the phenological dataset employed here. Hence, the results indicate that vegetation feedbacks lified the 2003 heat wave but were not responsible for its initiation. In conclusion, the results suggest that phenology has a limited effect on European mean summer climate, but its impact can be as important as that induced by soil moisture anomalies in the context of specific extreme events.
Publisher: Copernicus GmbH
Date: 24-02-2023
DOI: 10.5194/EGUSPHERE-2022-1520
Abstract: Abstract. As the number of models in Coupled Model Intercomparison Project (CMIP) archives increase from generation to generation, there is a pressing need for guidance on how to interpret and best use the abundance of newly available climate information. CMIP6 users seeking to draw conclusions about model agreement must contend with an "ensemble of opportunity" containing similar models that appear under different names. Those who used CMIP5 as a basis for downstream applications must filter through hundreds of new CMIP6 simulations to find several best suited to their region, season, and climate horizon of interest. Here we present methods to address both issues, model dependence and model subselection, to help users previously anchored in CMIP5 to navigate CMIP6. In Part I, we refine a definition of model dependence based on climate output, initially employed in Climate model Weighting by Independence and Performance (ClimWIP), to designate discrete model families within CMIP5/6. We show that the increased presence of model families in CMIP6 bolsters the upper mode of the ensemble's bimodal effective Equilibrium Climate Sensitivity (ECS) distribution. Accounting for the mismatch in representation between model families and in idual model runs shifts the CMIP6 ECS median and 75th percentile down by 0.43 °C, achieving better alignment with CMIP5's ECS distribution. In Part II, we present a new, cost-function minimization-based approach to model subselection, Climate model Selection by Independence, Performance, and Spread (ClimSIPS), that selects sets of CMIP models based on the relative importance a user ascribes to model independence (as defined in Part I), model performance, and ensemble spread in projected climate outcome. We demonstrate ClimSIPS by selecting sets of three to five models from CMIP5/6 for European applications, evaluating the performance from the agreement with the observed mean climate, and the spread in outcome from the projected midcentury change in surface air temperature and precipitation. To accommodate different use cases, we explore two ways to represent models with multiple members in ClimSIPS, first, by ensemble mean and second, by an in idual ensemble member that maximizes midcentury change ersity within CMIP overall. Because different combinations of models are selected by the cost function for different balances of independence, performance, and spread priority, we present all selected subsets in ternary contour "subselection triangles" and guide users with recommendations based on further qualitative independence, performance, and spread standards. In CMIP6, we find that recommended subsets are populated primarily by members of several model families defined in Part I due to an inverse relationship between performance and independence. In CMIP5, recommended subsets feature model combinations used in the European branch of the Coordinated Regional Downscaling Experiment (EURO-CORDEX), suggesting the independence, performance, and spread metrics used in ClimSIPS are appropriate for European applications in CMIP6 and beyond.
Publisher: American Geophysical Union (AGU)
Date: 10-02-2017
DOI: 10.1002/2016GL071235
Publisher: Copernicus GmbH
Date: 18-03-2020
DOI: 10.5194/GMD-2020-60
Abstract: Abstract. The Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for evaluation and analysis of Earth system models (ESMs) is designed to facilitate a more comprehensive and rapid comparison of single or multiple models participating in the coupled model intercomparison project (CMIP). The ESM results can be compared against observations or reanalysis data as well as against other models including predecessor versions of the same model. The updated and extended version 2.0 of the ESMValTool includes several new analysis scripts such as large-scale diagnostics for evaluation of ESMs as well as diagnostics for extreme events, regional model and impact evaluation. In this paper, the newly implemented climate metrics such as effective climate sensitivity (ECS) and transient climate response (TCR) as well as emergent constraints for various climate-relevant feedbacks and diagnostics for future projections from ESMs are described and illustrated with ex les using results from the well-established model ensemble CMIP5. The emergent constraints implemented include ECS, snow-albedo effect, climate-carbon cycle feedback, hydrologic cycle intensification, future Indian summer monsoon precipitation, and year of disappearance of summer Arctic sea ice. The diagnostics included in ESMValTool v2.0 to analyze future climate projections from ESMs include analysis scripts to reproduce selected figures of chapter 12 of the Intergovernmental Panel on Climate Change’s (IPCC) Fifth Assessment report (AR5) and various multi-model statistics.
Publisher: American Meteorological Society
Date: 10-2015
Abstract: Land–atmosphere coupling can strongly affect climate and climate extremes. Estimates of land–atmosphere coupling vary considerably between climate models, between different measures used to define coupling, and between the present and the future. The Australian Community Climate and Earth-System Simulator, version 1.3b (ACCESS1.3b), is used to derive and examine previously used measures of coupling strength. These include the GLACE-1 coupling measure derived on seasonal time scales a similar measure defined using multiyear simulations and four other measures of different complexity and data requirements, including measures that can be derived from standard model runs and observations. The ACCESS1.3b land–atmosphere coupling strength is comparable to other climate models. The coupling strength in the Southern Hemisphere summer is larger compared to the Northern Hemisphere summer and is dominated by a strong signal in the tropics and subtropics. The land–atmosphere coupling measures agree on the location of very strong land–atmosphere coupling but show differences in the spatial extent of these regions. However, the investigated measures show disagreement in weaker coupled regions, and some regions are only identified by a single measure as strongly coupled. In future projections the soil moisture trend is crucial in generating regions of strong land–atmosphere coupling, and the results suggest an expansion of coupling “hot spots.” It is concluded that great care needs to be taken in using different measures of coupling strength and shown that several measures that can be easily derived lead to inconsistent conclusions with more computationally expensive measures designed to measure coupling strength.
Publisher: Copernicus GmbH
Date: 03-07-2015
Abstract: Abstract. We implement a new stomatal conductance model, based on the optimality approach, within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. Coupled land-atmosphere simulations are then performed using CABLE within the Australian Community Climate and Earth Systems Simulator (ACCESS) with prescribed sea surface temperatures. As in most land surface models, the default stomatal conductance scheme only accounts for differences in model parameters in relation to the photosynthetic pathway, but not in relation to plant functional types. The new scheme allows model parameters to vary by plant functional type, based on a global synthesis of observations of stomatal conductance under different climate regimes over a wide range of species. We show that the new scheme reduces the latent heat flux from the land surface over the boreal forests during the Northern Hemisphere summer by 0.5 to 1.0 mm day-1. This leads to warmer daily maximum and minimum temperatures by up to 1.0 °C and warmer extreme maximum temperatures by up to 1.5 °C. These changes generally improve the climate model's climatology and improve existing biases by 10–20 %. The change in the surface energy balance also affects net primary productivity and the terrestrial carbon balance. We conclude that the improvements in the global climate model which result from the new stomatal scheme, constrained by a global synthesis of experimental data, provide a valuable advance in the long-term development of the ACCESS modelling system.
Publisher: American Geophysical Union (AGU)
Date: 28-08-2014
DOI: 10.1002/2014GL061017
Publisher: American Geophysical Union (AGU)
Date: 18-02-2017
DOI: 10.1002/2016GL072012
Publisher: Springer Science and Business Media LLC
Date: 21-03-2016
DOI: 10.1038/SREP23418
Abstract: Stomatal conductance links plant water use and carbon uptake and is a critical process for the land surface component of climate models. However, stomatal conductance schemes commonly assume that all vegetation with the same photosynthetic pathway use identical plant water use strategies whereas observations indicate otherwise. Here, we implement a new stomatal scheme derived from optimal stomatal theory and constrained by a recent global synthesis of stomatal conductance measurements from 314 species, across 56 field sites. Using this new stomatal scheme, within a global climate model, subtantially increases the intensity of future heatwaves across Northern Eurasia. This indicates that our climate model has previously been under-predicting heatwave intensity. Our results have widespread implications for other climate models, many of which do not account for differences in stomatal water-use across different plant functional types and hence, are also likely under projecting heatwave intensity in the future.
Publisher: American Meteorological Society
Date: 10-2015
Abstract: The role of land–atmosphere coupling in modulating the impact of land-use change (LUC) on regional climate extremes remains uncertain. Using the Weather and Research Forecasting Model, this study combines the Global Land–Atmosphere Coupling Experiment with regional LUC to assess the combined impact of land–atmosphere coupling and LUC on simulated temperature extremes. The experiment is applied to an ensemble of planetary boundary layer (PBL) and cumulus parameterizations to determine the sensitivity of the results to model physics. Results show a consistent weakening in the soil moisture–maximum temperature coupling strength with LUC irrespective of the model physics. In contrast, temperature extremes show an asymmetric response to LUC dependent on the choice of PBL scheme, which is linked to differences in the parameterization of vertical transport. This influences convective precipitation, contributing a positive feedback on soil moisture and consequently on the partitioning of the surface turbulent fluxes. The results suggest that the impact of LUC on temperature extremes depends on the land–atmosphere coupling that in turn depends on the choice of PBL. Indeed, the sign of the temperature change in hot extremes resulting from LUC can be changed simply by altering the choice of PBL. The authors also note concerns over the metrics used to measure coupling strength that reflect changes in variance but may not respond to LUC-type perturbations.
Publisher: Copernicus GmbH
Date: 03-12-2013
Abstract: Abstract. Climate extremes, such as heat waves and heavy precipitation events, have large impacts on ecosystems and societies. Climate models provide useful tools to study underlying processes and lifying effects associated with extremes. The Australian Community Climate and Earth System Simulator (ACCESS) has recently been coupled to the Community Atmosphere Biosphere Land Exchange model. We examine how this model represents climate extremes derived by the Expert Team on Climate Change Detection and Indices and compare them to observational datasets using the AMIP framework. We find that the patterns of extreme indices are generally well represented. Indices based on percentiles are particularly well represented and capture the trends over the last 60 yr shown by the observations remarkably well. The diurnal temperature range is underestimated, minimum temperatures (TMIN) during nights are generally too warm and daily maximum temperatures (TMAX) too low in the model. The number of consecutive wet days is overestimated while consecutive dry days are underestimated. The maximum consecutive 1 day precipitation amount is underestimated on the global scale. Biases in TMIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in TMAX depend on biases in net shortwave radiation as well as evapotranspiration. The regions and season where the bias in evapotranspiration plays a role for the TMAX bias correspond to regions and seasons where soil moisture availability is limited. Our analysis provides the foundation for future experiments that will examine how land surface processes contribute to these systematic biases in the ACCESS modelling system.
Publisher: Copernicus GmbH
Date: 13-11-2020
Abstract: Abstract. The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the models' historical performance based on several diagnostics as well as model interdependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17 % compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 “family tree”, which enables the application of a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (the fifth generation of the European Centre for Medium-Range Weather Forecasts Retrospective Analysis – ERA5, and the Modern-Era Retrospective analysis for Research and Applications, version 2 – MERRA-2), to constrain CMIP6 projections under weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios (SSP refers to the Shared Socioeconomic Pathways). Our results show a reduction in the projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 ∘C, compared with 4.1 ∘C without weighting the likely (66%) uncertainty range is 3.1 to 4.6 ∘C, which equates to a 13 % decrease in spread. For SSP1-2.6, the weighted end-of-century warming is 1 ∘C (0.7 to 1.4 ∘C), which results in a reduction of −0.1 ∘C in the mean and −24 % in the likely range compared with the unweighted case.
Publisher: American Geophysical Union (AGU)
Date: 23-10-2012
DOI: 10.1029/2012JD017755
Publisher: Copernicus GmbH
Date: 28-04-2020
DOI: 10.5194/ESD-2020-23
Abstract: Abstract. The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as mean and likely range. Here, we use a model weighting approach, which accounts for a model's historical performance based on several diagnostics as well as possible model inter-dependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we remove each CMIP6 model from the ensemble in turn, use it as pseudo-observation in the historical period, and evaluate the weighted CMIP6 ensemble against it in the future. This is complemented by a second perfect model test drawing on the previous-generation CMIP5 models as pseudo-observations. In addition, we show that our independence diagnostics correctly clusters models known to be similar based on a CMIP6 family tree, which enables applying a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (ERA5 and MERRA2), to constrain CMIP6 projections in weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios. Our results show a reduction in projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 °C, compared to 4.1 °C without weighting the likely (66 %) uncertainty range is 3.1 °C to 4.6 °C, a decrease of 13 %. For SSP1-2.6, weighted end-of-century warming is 1 °C (0.7 °C to 1.4 °C). Applying the weighting to estimates of Transient Climate Response (TCR) yields 1.9 °C (1.6 °C to 2.1 °C – a reduction in the likely uncertainty range of 46 %), which is consistent with estimates from previous model generations and other lines of evidence.
Publisher: IOP Publishing
Date: 09-2016
Publisher: American Geophysical Union (AGU)
Date: 05-2010
DOI: 10.1029/2010GL042764
Publisher: Copernicus GmbH
Date: 03-2022
DOI: 10.5194/EGUSPHERE-EGU22-846
Abstract: & & The Coupled Model Intercomparison Project (CMIP) is an effort to compare model simulations of the climate system and its changes. In the quarter of a century since CMIP1 models have increased considerably in complexity and improved in how well they are able to represent historical climate compared to observations. Other aspects, such as the projected changes we have to expect in a warming climate, have remained remarkably stable. Here we track the evolution of climate models based on their output and discuss it in the context of 25 years of model development.& & & & & We draw on temperature and precipitation data from CMIP1 to CMIP6 and calculate consistent metrics of model performance, inter-dependence, and consistency across multiple generations of CMIP. We find clear progress in model performance that can be related to increased resolution among other things. Our results also show that the models& #8217 development history can be tracked using their output fields with models sharing parts of their source code or common ancestors grouped together in a clustering approach.& & & & The global distribution of projected temperature and precipitation change and its robustness across different models is also investigated. Despite the considerable increase in model complexity across the CMIP generations driven, for ex le, by the inclusion of additional model components and the increase in model resolutions by several orders of magnitude, the overall structure of simulated changes remains stable, illustrating the remarkable skill of early coupled models.& &
Publisher: American Geophysical Union (AGU)
Date: 10-05-2018
DOI: 10.1029/2017JD027992
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.
Publisher: Proceedings of the National Academy of Sciences
Date: 03-09-2019
Abstract: Soil drought and atmospheric aridity can be disastrous for ecosystems and society. This study demonstrates the critical role of land–atmosphere feedbacks in driving cooccurring soil drought and atmospheric aridity. The frequency and intensity of atmospheric aridity are greatly reduced without the feedback of soil moisture to atmospheric temperature and humidity. Soil moisture can also impact precipitation to lify soil moisture deficits under dry conditions. These land–atmosphere processes lead to high probability of concurrent soil drought and atmospheric aridity. Compared to the historical period, models project future frequency and intensity of concurrent soil drought and atmospheric aridity to be further enhanced by land–atmosphere feedbacks, which may pose large risks to ecosystem services and human well-being in the future.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-7717
Abstract: & & & span& To extract reliable estimates of future warming and related uncertainties from multi model ensembles such as CMIP6, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the CMIP6 models' historical performance as well as their interdependence, to calculate constrained distributions of global mean temperature change.& /span& & & & & & span& We investigate the skill of our approach in a perfect model test framework, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17& #8201 % compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 & #8220 family tree& #8221 , which enables the application of a weighting based on the degree of inter-model dependence.& /span& & & & & & span& We then apply the weighting approach, based on two observational estimates, to constrain CMIP6 projections. Our results show a reduction in the projected mean warmingbecause some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081& #8211 & relative to& & #8211 ) for SSP5-8.5 with weighting is 3.7& #176 C, compared with 4.1& #176 C without weighting the likely (66%) uncertainty range is 3.1& to 4.6& #176 C, which equates to a 13& #8201 % decrease in spread.& /span& & & & & & br& & br& & &
Publisher: IOP Publishing
Date: 22-11-2019
Abstract: Uncertainty in model projections of future climate change arises due to internal variability, multiple possible emission scenarios, and different model responses to anthropogenic forcing. To robustly quantify uncertainty in multi-model ensembles, inter-dependencies between models as well as a models ability to reproduce observations should be considered. Here, a model weighting approach, which accounts for both independence and performance, is applied to European temperature and precipitation projections from the CMIP5 archive. Two future periods representing mid- and end-of-century conditions driven by the high-emission scenario RCP8.5 are investigated. To inform the weighting, six diagnostics based on three observational estimates are used to also account for uncertainty in the observational record. Our findings show that weighting the ensemble can reduce the interquartile spread by more than 20% in some regions, increasing the reliability of projected changes. The mean temperature change is most notably impacted by the weighting in the Mediterranean, where it is found to be 0.35 °C higher than the unweighted mean in the end-of-century period. For precipitation the largest differences are found for Northern Europe, with a relative decrease in precipitation of 2.4% and 3.4% for the two future periods compared to the unweighted case. Based on a perfect model test, it is found that weighting the ensemble leads to an increase in the investigated skill score for temperature and precipitation while minimizing the probability of overfitting.
Publisher: Copernicus GmbH
Date: 23-08-2023
Abstract: Abstract. As the number of models in Coupled Model Intercomparison Project (CMIP) archives increase from generation to generation, there is a pressing need for guidance on how to interpret and best use the abundance of newly available climate information. Users of the latest CMIP6 seeking to draw conclusions about model agreement must contend with an “ensemble of opportunity” containing similar models that appear under different names. Those who used the previous CMIP5 as a basis for downstream applications must filter through hundreds of new CMIP6 simulations to find several best suited to their region, season, and climate horizon of interest. Here we present methods to address both issues, model dependence and model subselection, to help users previously anchored in CMIP5 to navigate CMIP6 and multi-model ensembles in general. In Part I, we refine a definition of model dependence based on climate output, initially employed in Climate model Weighting by Independence and Performance (ClimWIP), to designate discrete model families within CMIP5 and CMIP6. We show that the increased presence of model families in CMIP6 bolsters the upper mode of the ensemble's bimodal effective equilibrium climate sensitivity (ECS) distribution. Accounting for the mismatch in representation between model families and in idual model runs shifts the CMIP6 ECS median and 75th percentile down by 0.43 ∘C, achieving better alignment with CMIP5's ECS distribution. In Part II, we present a new approach to model subselection based on cost function minimization, Climate model Selection by Independence, Performance, and Spread (ClimSIPS). ClimSIPS selects sets of CMIP models based on the relative importance a user ascribes to model independence (as defined in Part I), model performance, and ensemble spread in projected climate outcome. We demonstrate ClimSIPS by selecting sets of three to five models from CMIP6 for European applications, evaluating the performance from the agreement with the observed mean climate and the spread in outcome from the projected mid-century change in surface air temperature and precipitation. To accommodate different use cases, we explore two ways to represent models with multiple members in ClimSIPS, first, by ensemble mean and, second, by an in idual ensemble member that maximizes mid-century change ersity within the CMIP overall. Because different combinations of models are selected by the cost function for different balances of independence, performance, and spread priority, we present all selected subsets in ternary contour “subselection triangles” and guide users with recommendations based on further qualitative selection standards. ClimSIPS represents a novel framework to select models in an informed, efficient, and transparent manner and addresses the growing need for guidance and simple tools, so those seeking climate services can navigate the increasingly complex CMIP landscape.
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-3476
Abstract: & & The Earth System Model Evaluation Tool (ESMValTool) is a free and open-source community diagnostic and performance metrics tool for the evaluation of Earth system models such as those participating in the Coupled Model Intercomparison Project (CMIP). Version 2 of the tool (Righi et al. 2020, www.esmvaltool.org) features a brand new design composed of a core that finds and processes data according to a & #8216 recipe& #8217 and an extensive collection of ready-to-use recipes and associated diagnostic codes for reproducing results from published papers. Development and discussion of the tool (mostly) takes place in public on smvalgroup and anyone with an interest in climate model evaluation is welcome to join there.& & & & & & & & & Since the initial release of version 2 in the summer of 2020, many improvements have been made to the tool. It is now more user friendly with extensive documentation available on docs.esmvaltool.org and a step by step online tutorial. Regular releases, currently planned three times a year, ensure that recent contributions become available quickly while still ensuring a high level of quality control. The tool can be installed from conda, but portable docker and singularity containers are also available.& & & & & & & & & Recent new features include a more user-friendly command-line interface, citation information per figure including CMIP6 data citation using ES-DOC, more and faster preprocessor functions that require less memory, automatic corrections for a larger number of CMIP6 datasets, support for more observational and reanalysis datasets, and more recipes and diagnostics.& & & & & & & & & The tool is now also more reliable, with improved automated testing through more unit tests for the core, as well as a recipe testing service running at DKRZ for testing the scientific recipes and diagnostics that are bundled into the tool. The community maintaining and developing the tool is growing, making the project less dependent on in idual contributors. There are now technical and scientific review teams that review new contributions for technical quality and scientific correctness and relevance respectively, two new principal investigators for generating a larger support base in the community, and a newly created user engagement team that is taking care of improving the overall user experience.& &
Publisher: American Geophysical Union (AGU)
Date: 15-03-2016
DOI: 10.1002/2016GL068062
No related grants have been discovered for Ruth Lorenz.