Insights from Earth system model initial-condition large ensembles and future prospects

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.

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A weighting scheme to incorporate large ensembles in multi-model ensemble projections

10.5194/egusphere-egu2020-4524 ◽

2020 ◽

Author(s):

Anna Merrifield ◽

Lukas Brunner ◽

Ruth Lorenz ◽

Reto Knutti

Keyword(s):

Internal Variability ◽

Ensemble Member ◽

Earth System Model ◽

System Model ◽

Initial Condition ◽

Earth System ◽

Model Ensemble ◽

Single Model ◽

Large Ensembles ◽

Model Ensembles

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;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 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.

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Towards a conditional representation of heat wave probability in large ensemble climate model data

10.5194/egusphere-egu21-7045 ◽

2021 ◽

Author(s):

Joel Zeder ◽

Erich M. Fischer

Keyword(s):

Large Scale ◽

Climate Extremes ◽

Extreme Value Distribution ◽

Extreme Value ◽

Earth System Model ◽

Value Distribution ◽

System Model ◽

Earth System ◽

Climate Conditions ◽

Large Ensembles

Research Objective: Single-model initial condition large ensembles provide novel opportunities to study the physical drivers and risks of large-scale climate extremes in a changing climate. The probability of extremes such as weekly heatwaves, here quantified as seven-day maximum temperature (Tx7d), are usually approximated with a general extreme value distribution GEV &#160;that is stationary or accounts for non-stationarity of a warming climate. However, estimating the occurrence probability of very rare climate extremes in the presence of large internal variability further benefits from the integration of process-based covariates characterising the preceding and concurrent climate conditions both at global and local scale.Data & Methods: We here use more than 6000 years of stationary pre-industrial and 2xCO2 control simulations and an ensemble of 84 transient historical and RCP8.5 simulations performed with the Community Earth System Model CESM1.2 to develop and robustly test methods of quantifying extreme events under a broad range of climatic conditions. The generalised extreme value distribution is parametrised such that it can account for changing environmental circumstances, ranging from large-scale thermodynamic non-stationarity due to climate change, regional-scale dynamic forcing such as atmospheric blocking, or local land-surface conditions such as soil moisture deficits. Fields of covariates are integrated using approaches from statistical learning theory, accounting for the spatio-temporal correlation inherent in climate data.Preliminary results: Dynamical forcing patterns as simulated by the earth system model compare well with those obtained from reanalysis data and inform the statistical model in a physically traceable fashion. How well the latter generalises is tested with respect to further simulations of the US CLIVAR Working Group on Large Ensembles. The relevance of different covariates can inform both detection and attribution as well as risk assessment how their respective statistical models can be further refined to account for the influence of physical drivers under present and future climate conditions.

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An intercomparison of precipitation extremes from community earth system model large ensembles with historical observations

10.17760/d20236834 ◽

2016 ◽

Author(s):

Udit Bhatia

Keyword(s):

Earth System Model ◽

Precipitation Extremes ◽

System Model ◽

Earth System ◽

Large Ensembles ◽

Community Earth System Model

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An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles

Earth System Dynamics ◽

10.5194/esd-11-807-2020 ◽

2020 ◽

Vol 11 (3) ◽

pp. 807-834 ◽

Cited By ~ 2

Author(s):

Anna Louise Merrifield ◽

Lukas Brunner ◽

Ruth Lorenz ◽

Iselin Medhaug ◽

Reto Knutti

Keyword(s):

Coupled Model ◽

Internal Variability ◽

System Model ◽

Initial Condition ◽

Earth System ◽

Model Intercomparison ◽

Model Ensemble ◽

Single Model ◽

Large Ensembles ◽

Model Ensembles

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 individually 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 (<10 %) when a model is defined as an IC ensemble and increase slightly (<20 %) 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.

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Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land

Earth System Dynamics ◽

10.5194/esd-11-139-2020 ◽

2020 ◽

Vol 11 (1) ◽

pp. 139-159 ◽

Cited By ~ 5

Author(s):

Lea Beusch ◽

Lukas Gudmundsson ◽

Sonia I. Seneviratne

Keyword(s):

Grid Point ◽

Earth System Model ◽

System Model ◽

Initial Condition ◽

Earth System ◽

Single Model ◽

Global Mean Temperature ◽

Mean Temperature ◽

Model Ensembles ◽

Global Mean

Abstract. Earth system models (ESMs) are invaluable tools to study the climate system's response to specific greenhouse gas emission pathways. Large single-model initial-condition and multi-model ensembles are used to investigate the range of possible responses and serve as input to climate impact and integrated assessment models. Thereby, climate signal uncertainty is propagated along the uncertainty chain and its effect on interactions between humans and the Earth system can be quantified. However, generating both single-model initial-condition and multi-model ensembles is computationally expensive. In this study, we assess the feasibility of geographically explicit climate model emulation, i.e., of statistically producing large ensembles of land temperature field time series that closely resemble ESM runs at a negligible computational cost. For this purpose, we develop a modular emulation framework which consists of (i) a global mean temperature module, (ii) a local temperature response module, and (iii) a local residual temperature variability module. Based on this framework, MESMER, a Modular Earth System Model Emulator with spatially Resolved output, is built. We first show that to successfully mimic single-model initial-condition ensembles of yearly temperature from 1870 to 2100 on grid-point to regional scales with MESMER, it is sufficient to train on a single ESM run, but separate emulators need to be calibrated for individual ESMs given fundamental inter-model differences. We then emulate 40 climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to create a “superensemble”, i.e., a large ensemble which closely resembles a multi-model initial-condition ensemble. The thereby emerging ESM-specific emulator parameters provide essential insights on inter-model differences across a broad range of scales and characterize core properties of each ESM. Our results highlight that, for temperature at the spatiotemporal scales considered here, it is likely more advantageous to invest computational resources into generating multi-model ensembles rather than large single-model initial-condition ensembles. Such multi-model ensembles can be extended to superensembles with emulators like the one presented here.

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Interannual to multidecadal variability and predictability of North Atlantic circulation in a coupled earth system model with parametrized hydraulics

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v62i4.15703 ◽

2010 ◽

Author(s):

M. KÃ¶ller ◽

R. H. KÃ¤se ◽

P. Herrmann

Keyword(s):

North Atlantic ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Multidecadal Variability

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Korea Institute of Ocean Science and Technology Earth System Model and Its Simulation Characteristics

Ocean Science Journal ◽

10.1007/s12601-021-00001-7 ◽

2021 ◽

Author(s):

Gyundo Pak ◽

Yign Noh ◽

Myong-In Lee ◽

Sang-Wook Yeh ◽

Daehyun Kim ◽

...

Keyword(s):

Science And Technology ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Ocean Science

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Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs Concentration Pathways

Asia-Pacific Journal of Atmospheric Sciences ◽

10.1007/s13143-021-00225-6 ◽

2021 ◽

Author(s):

Hyun Min Sung ◽

Jisun Kim ◽

Sungbo Shim ◽

Jeong-byn Seo ◽

Sang-Hoon Kwon ◽

...

Keyword(s):

Climate Change ◽

Climate Changes ◽

Scientific Information ◽

Earth System Model ◽

First Century ◽

System Model ◽

The Arctic ◽

Earth System ◽

Future Projections ◽

Twenty First Century

AbstractThe National Institute of Meteorological Sciences-Korea Meteorological Administration (NIMS-KMA) has participated in the Coupled Model Inter-comparison Project (CMIP) and provided long-term simulations using the coupled climate model. The NIMS-KMA produces new future projections using the ensemble mean of KMA Advanced Community Earth system model (K-ACE) and UK Earth System Model version1 (UKESM1) simulations to provide scientific information of future climate changes. In this study, we analyze four experiments those conducted following the new shared socioeconomic pathway (SSP) based scenarios to examine projected climate change in the twenty-first century. Present day (PD) simulations show high performance skill in both climate mean and variability, which provide a reliability of the climate models and reduces the uncertainty in response to future forcing. In future projections, global temperature increases from 1.92 °C to 5.20 °C relative to the PD level (1995–2014). Global mean precipitation increases from 5.1% to 10.1% and sea ice extent decreases from 19% to 62% in the Arctic and from 18% to 54% in the Antarctic. In addition, climate changes are accelerating toward the late twenty-first century. Our CMIP6 simulations are released to the public through the Earth System Grid Federation (ESGF) international data sharing portal and are used to support the establishment of the national adaptation plan for climate change in South Korea.

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