ESMValTool v2.0 – Extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

Eyring, Veronika; Lauer, Axel; Righi, Mattia; Schlund, Manuel; Andela, Bouwe; Arnone, Enrico; Bellprat, Omar; Brötz, Björn; Caron, Louis-Philippe; Cionni, Irene; Cortesi, Nicola; Crezee, Bas; Davin, Edouard L.; Davini, Paolo; Debeire, Kevin; Mora, L. De; Deser, Clara; Docquier, David; Earnshaw, Paul; Ehbrecht, Carsten; Gier, Bettina K.; González-Reviriego, Nube; Goodman, P. J.; Hagemann, Stefan; Hardiman, Steven C.; Haßler, Birgit; Hunter, Alasdair; Kadow, Christopher; Kindermann, Stephan; Koirala, Sujan; Koldunov, Nikolay; Lejeune, Quentin; Lembo, Valerio; Lovato, Tomas; Lucarini, Valerio; Massonnet, François; Müller, Benjamin; Pandde, Amarjiit; Pérez-Zañón, Núria; Phillips, Adam S.; Predoi, V.; Russell, J. L.; Sellar, Alistair; Serva, Federico; Stacke, Tobias; Swaminathan, Ranjini; Torralba, Verónica; Vegas-Regidor, Javier; Hardenberg, Jost von; Weigel, Katja; Zimmermann, Klaus

doi:10.5194/gmd-2019-291

Cited by 18 publications

(26 citation statements)

References 130 publications

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“…All figures of this paper are produced with the Earth System Model Evaluation Tool (ESMValTool) version 2.0 (v2.0) (Righi et al, 2020;Eyring et al, 2020;Lauer et al, 2020), a tool specifically designed to improve and facilitate the complex evaluation and analysis of CMIP models and ensembles.…”

Section: Resultsmentioning

confidence: 99%

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Tebaldi¹,

Debeire²,

Eyring³

et al. 2020

Preprint

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Abstract. The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the primary future climate projections within the Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century encompassing the Tier 1 experiments (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by 1.15 °C) reached at the upper end of the 5–95 % envelope of the highest scenario, SSP5-8.5. This is due to both the wider range of radiative forcing that the new scenarios cover and to higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensembles' spread, according to a set of initial condition ensemble simulations available under SSP3-7.0. The same experiments suggest a tendency for internal variability to decrease along the course of the century, a new result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP, but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades in mid-century, as represented in SSP5-3.4OS, does not affect the end outcome in terms of temperature and precipitation changes by 2100, which return to the same level as those reached by the gradually increasing SSP4-3.4. Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level show all scenarios reaching 1.5 °C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering 20–28 years from present. 2 °C of warming is reached as early as the late '30s by the ensemble mean under SSP5-8.5, but as late as the late '50s under SSP1-2.6. The highest warming level considered, 5 °C, is reached only by the ensemble mean under SSP5-8.5, and not until the mid-90s.

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Section: Resultsmentioning

confidence: 99%

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Tebaldi¹,

Debeire²,

Eyring³

et al. 2020

Preprint

Self Cite

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“…In this study we present diagnostics included in the ESMValTool specifically for the analysis of the hydrological cycle, extreme events, climate impacts, multi-model ensemble member sub-selection, and regional model evaluation. This article completes a series of publications documenting ESMValTool v2.0: Righi et al (2020) describes the technical aspects, (Eyring et al, 2020) the new large-scale diagnostics, and (Lauer et al, 2020) emergent constraints and diagnostics for future projections from ESMs in CMIP. This paper is organized as follows: Section 2 describes the model and observation data used.…”

Section: Introductionmentioning

confidence: 94%

“…To be able to assess the impacts of climate change on society and to develop strategies for mitigation and adaptation, a detailed knowledge of the climate system and the key processes driving climate change is necessary. This is particularly the case for changes in the hydrological cycle and climate extreme events, both having direct consequences on ecosystems and society (Eyring et al, 2020). With rising greenhouse gas concentrations the hydroclimatic regime is expected to change (Giorgi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation and analysis of Earth system models in CMIP

Weigel¹,

Gier²,

Lauer³

et al. 2020

Preprint

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Abstract. This paper complements a series of now four publications that document the release of the Earth System Model Evaluation Tool (ESMValTool) v2.0. It describes new diagnostics on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth System Models (ESMs) which are participating in the Coupled Model Intercomparison Project (CMIP). The second release of this tool aims to support the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). Furthermore, data sets from other models and observations can be analysed. The diagnostics for the hydrological cycle include several precipitation and drought indices, as well as hydroclimatic intensity and indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). The latter are also used for identification of extreme events and for impact assessment, and to project and characterize the risks and impacts of climate change for natural and socio-economic systems. Further impact assessment diagnostics are included to compute daily temperature ranges and capacity factors for wind and solar energy generation. Regional scales can be analysed with new diagnostics implemented for selected regions and stochastic downscaling. ESMValTool v2.0 also includes diagnostics to analyse large multi-model ensembles including grouping and selecting ensemble members by user specified criteria. Here, we present examples for their capabilities based on the well-established CMIP Phase 5 (CMIP5) data set.

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“…An additional goal is to provide related diagnostic outputs directly to the community. Both the graphical and data outputs generated may potentially be incorporated into broader community packages such as ESMValTool (Eyring et al 2020), thus providing a unique evaluation of fully-coupled physical climate states that includes both climatological means and variability, that accounts for key uncertainties, and that benchmarks models across CMIP generations.…”

Section: Motivationsmentioning

confidence: 99%

Evaluating Simulated Climate Patterns from theCMIP Archives Using Satellite and Reanalysis Datasets

Fasullo

2020

Preprint

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Abstract. An objective approach is presented for scoring coupled climate simulations through an evaluation against satellite and reanalysis datasets during the satellite era (i.e. since 1979). Here, the approach is described and applied to available Coupled Model Intercomparison Project (CMIP) archives and the Community Earth System Model Version 1 Large Ensemble archives, with the goal of benchmarking model performance and its evolution across CMIP generations. The approach adopted is designed to minimize the sensitivity of scores to internal variability, external forcings, and model tuning. Toward this end, models are scored based on pattern correlations of their simulated mean state, seasonal contrasts, and ENSO teleconnections. A broad range of feedback-relevant fields is considered and summarized on various timescales (climatology, seasonal, interannual) and physical realms (energy budget, water cycle, dynamics). Fields are also generally chosen for which observational uncertainty is small compared to model structural differences and error. Highest mean variable scores across models are reported for well-observed fields such as sea level pressure, precipitable water, and outgoing longwave radiation while the lowest scores are reported for 500 hPa vertical velocity, net surface energy flux, and precipitation minus evaporation. The fidelity of CMIP models is found to vary widely both within and across CMIP generations. Systematic increases in model fidelity across CMIP generations are identified with the greatest improvements in dynamic and energetic fields. Examples include 500 hPa eddy geopotential height and relative humidity, and shortwave cloud forcing. Improvements for ENSO scores are substantially greater than for the annual mean or seasonal contrasts. Analysis output data generated by this approach is made freely available online for a broad range of model ensembles, including the CMIP archives and various single-model large ensembles. These multi-model archives allow for an exploration of relationships between metrics across a range of simulations while the single-model large ensemble archives enable an estimation of the influence of internal variability on reported scores. The entire output archive, updated regularly, can be accessed at: http://webext.cgd.ucar.edu/Multi-Case/CMAT/index.html chosen for which observational uncertainty is small compared to model structural error. 20 Highest mean variable scores across models are reported for well-observed fields such as sea level pressure, precipitable water, and outgoing longwave radiation while the lowest scores are reported for 500 hPa vertical velocity, net surface energy flux, and precipitation minus evaporation. The fidelity of CMIP models is found to vary widely both within and across CMIP generations. CMATv1 scores report systematic increases in model fidelity across CMIP generations with the greatest improvements in dynamic and energetic fields. Examples include 500 hPa eddy geopotential height and relative humidity, 25 and shortwave cloud forcing. Improvements for ENSO scores are substantially greater than for the annual mean or seasonal contrasts. Analysis output data is made freely available online for a broad range of model ensembles, including the CMIP archives and various single-model large ensembles. These multi-model archives allow for an exploration of relationships between metrics 30 across a range of simulations while the single-model large ensemble archives enable an estimation of the influence of internal variability on CMATV1 scores. The entire CMATv1 archive, updated regularly, can be accessed at: http://webext.cgd.ucar.edu/Multi-Case/CMAT/index.html.

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ESMValTool v2.0 – Extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

Cited by 18 publications

References 130 publications

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation and analysis of Earth system models in CMIP

Evaluating Simulated Climate Patterns from theCMIP Archives Using Satellite and Reanalysis Datasets

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