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

2020 ◽  
Vol 11 (3) ◽  
pp. 807-834 ◽  
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.

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

2019 ◽  
Author(s):  
Anna Louise 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

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.

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

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

&lt;p&gt;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&amp;#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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

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

2020 ◽  
Vol 11 (1) ◽  
pp. 139-159 ◽  
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.

scholarly journals Description and evaluation of NorESM1-F: A fast version of the Norwegian Earth System Model (NorESM)

2018 ◽  
Author(s):  
Chuncheng Guo ◽  
Mats Bentsen ◽  
Ingo Bethke ◽  
Mehmet Ilicak ◽  
Jerry Tjiputra ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Sea Ice ◽  
Large Scale ◽  
Coupled Model ◽  
Coupled System ◽  
Internal Variability ◽  
Earth System Model ◽  
Meridional Overturning Circulation ◽  
System Model ◽  
Earth System

Abstract. A new computationally efficient version of the Norwegian Earth System Model (NorESM) is presented. This new version (here termed NorESM1-F) runs about 2.5 times faster (e.g. 90 model years per day on current hardware) than the version that contributed to the fifth phase of the Coupled Model Intercomparison project (CMIP5), i.e., NorESM1-M, and is therefore particularly suitable for multi-millennial paleoclimate and carbon cycle simulations or large ensemble simulations. The speedup is primarily a result of using a prescribed atmosphere aerosol chemistry and a tripolar ocean-sea ice horizontal grid configuration that allows an increase of the ocean-sea ice component time steps. Ocean biogeochemistry can be activated for fully coupled and semi-coupled carbon cycle applications. This paper describes the model and evaluates its performance using observations and NorESM1-M as benchmarks. The evaluation emphasises model stability, important large-scale features in the ocean and sea ice components, internal variability in the coupled system, and climate sensitivity. Simulation results from NorESM1-F in general agree well with observational estimates, and show evident improvements over NorESM1-M, for example, in the strength of the meridional overturning circulation and sea ice simulation, both important metrics in simulating past and future climates. Whereas NorESM1-M showed a slight global cool bias in the upper oceans, NorESM1-F exhibits a global warm bias. In general, however, NorESM1-F has more similarities than dissimilarities compared to NorESM1-M, and some biases and deficiencies known in NorESM1-M remain.

scholarly journals Description and evaluation of NorESM1-F: a fast version of the Norwegian Earth System Model (NorESM)

2019 ◽  
Vol 12 (1) ◽  
pp. 343-362 ◽  
Author(s):  
Chuncheng Guo ◽  
Mats Bentsen ◽  
Ingo Bethke ◽  
Mehmet Ilicak ◽  
Jerry Tjiputra ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Sea Ice ◽  
Large Scale ◽  
Coupled Model ◽  
Coupled System ◽  
Internal Variability ◽  
Earth System Model ◽  
Meridional Overturning Circulation ◽  
System Model ◽  
Earth System

Abstract. A new computationally efficient version of the Norwegian Earth System Model (NorESM) is presented. This new version (here termed NorESM1-F) runs about 2.5 times faster (e.g., 90 model years per day on current hardware) than the version that contributed to the fifth phase of the Coupled Model Intercomparison project (CMIP5), i.e., NorESM1-M, and is therefore particularly suitable for multimillennial paleoclimate and carbon cycle simulations or large ensemble simulations. The speed-up is primarily a result of using a prescribed atmosphere aerosol chemistry and a tripolar ocean–sea ice horizontal grid configuration that allows an increase of the ocean–sea ice component time steps. Ocean biogeochemistry can be activated for fully coupled and semi-coupled carbon cycle applications. This paper describes the model and evaluates its performance using observations and NorESM1-M as benchmarks. The evaluation emphasizes model stability, important large-scale features in the ocean and sea ice components, internal variability in the coupled system, and climate sensitivity. Simulation results from NorESM1-F in general agree well with observational estimates and show evident improvements over NorESM1-M, for example, in the strength of the meridional overturning circulation and sea ice simulation, both important metrics in simulating past and future climates. Whereas NorESM1-M showed a slight global cool bias in the upper oceans, NorESM1-F exhibits a global warm bias. In general, however, NorESM1-F has more similarities than dissimilarities compared to NorESM1-M, and some biases and deficiencies known in NorESM1-M remain.

scholarly journals Description of the MIROC-ES2L Earth system model and evaluation of its climate–biogeochemical processes and feedbacks

2019 ◽  
Author(s):  
Tomohiro Hajima ◽  
Michio Watanabe ◽  
Akitomo Yamamoto ◽  
Hiroaki Tatebe ◽  
Maki A. Noguchi ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Nitrogen Cycle ◽  
Carbon Sink ◽  
Coupled Model ◽  
System Model ◽  
Climate Response ◽  
Earth System ◽  
Model Intercomparison ◽  
Transient Climate Response ◽  
Intercomparison Project

Abstract. This study developed a new Model for Interdisciplinary Research on Climate, Earth System version2 for Long-term simulations (MIROC-ES2L) Earth system model (ESM) using a state-of-the-art climate model as the physical core. This model embeds a terrestrial biogeochemical component with explicit carbon–nitrogen interaction to account for soil nutrient control on plant growth and the land carbon sink. The model’s ocean biogeochemical component is largely updated to simulate biogeochemical cycles of carbon, nitrogen, phosphorus, iron, and oxygen such that oceanic primary productivity can be controlled by multiple nutrient limitations. The ocean nitrogen cycle is coupled with the land component via river discharge processes, and external inputs of iron from pyrogenic and lithogenic sources are considered. Comparison of a historical simulation with observation studies showed the model could reproduce reasonable historical changes in climate, the carbon cycle, and other biogeochemical variables together with reasonable spatial patterns of distribution of the present-day condition. The model demonstrated historical human perturbation of the nitrogen cycle through land use and agriculture, and it simulated the resultant impact on the terrestrial carbon cycle. Sensitivity analyses in preindustrial conditions revealed modeled ocean biogeochemistry could be changed regionally (but substantially) by nutrient inputs from the atmosphere and rivers. Through an idealized experiment of a 1 %CO2 increase scenario, we found the transient climate response (TCR) in the model is 1.5 K, i.e., approximately 70 % that of our previous model. The cumulative airborne fraction (AF) is also reduced by 15 % because of the intensified land carbon sink, resulting in an AF close to the multimodel mean of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ESMs. The transient climate response to cumulative carbon emission (TCRE) is 1.3 K EgC−1, i.e., slightly smaller than the average of the CMIP5 ESMs, suggesting optimistic model performance in future climate projections. This model and the simulation results are contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). The ESM could help further understanding of climate–biogeochemical interaction mechanisms, projections of future environmental changes, and exploration of our future options regarding sustainable development by evolving the processes of climate, biogeochemistry, and human activities in a holistic and interactive manner.

scholarly journals Emulating Earth System Model temperatures: from global mean temperature trajectories to grid-point level realizations on land

2019 ◽  
Author(s):  
Lea Beusch ◽  
Lukas Gudmundsson ◽  
Sonia I. Seneviratne
Keyword(s):  
Greenhouse Gas Emission ◽  
Computational Cost ◽  
Initial Condition ◽  
Earth System ◽  
Single Model ◽  
Climate Signal ◽  
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 global spatially and temporally correlated land temperature field time series at a negligible computational cost which closely resemble ESM runs spanning from 1870 to 2099. For this purpose, we develop a modular framework that consists of (i) a global mean temperature emulator, (ii) a local mean temperature emulator, and (iii) a local residual temperature variability emulator. We first show that to successfully mimic single-model initial-condition ensembles of yearly temperature, 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 super-ensemble, i.e., a large ensemble that closely resembles a multi-model initial-condition ensemble. Furthermore, the thereby emerging ESM-specific emulator calibration parameters provide essential insights on inter-model divergences across a broad range of scales which can be viewed as a model ID of core properties of each ESM. Our results highlight that, for temperature at the spatio-temporal 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 then be extended to super-ensembles with geographically-explicit temperature emulators like the one presented here.

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

2020 ◽  
Author(s):  
John T. Fasullo
Keyword(s):  
Longwave Radiation ◽  
Precipitable Water ◽  
Internal Variability ◽  
Single Model ◽  
Large Ensemble ◽  
Cloud Forcing ◽  
Surface Energy Flux ◽  
Observational Uncertainty ◽  
Large Ensembles ◽  
Model Ensembles

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.

What can we learn from single model initial-condition large ensembles (SMILEs)? A Comparison of Multiple SMILEs for Precipitation

2020 ◽  
Author(s):  
Raul R. Wood ◽  
Flavio Lehner ◽  
Angeline Pendergrass ◽  
Sarah Schlunegger ◽  
Keith Rodgers
Keyword(s):  
Climate Variability ◽  
Internal Variability ◽  
Forced Response ◽  
Initial Condition ◽  
Single Model ◽  
Climate Conditions ◽  
Internal Climate Variability ◽  
Model Response ◽  
Large Ensembles ◽  
Global Precipitation

&lt;p&gt;Identifying anthropogenic influences on climate amidst the &amp;#8220;noise&amp;#8221; of internal climate variability is a central challenge for the climate research community. In recent years, several modeling groups have produced single-model initial-condition large ensembles (SMILE) to analyze the interplay of the forced climate change and internal climate variability under current and future climate conditions. These simulations help to improve our understanding of climate variability, including extreme events, and can be employed as test-beds for statistical approaches to separate forced and internal components of climate variability.&lt;/p&gt;&lt;p&gt;So far, most studies have focused on either an individual or a &amp;#160;limited number of SMILEs. In this work we compare seven large ensembles to disentangle the influence of internal variability and model response uncertainty for multiple precipitation indices (e.g. wettest day of the year, precipitation with a return period of 20 years). What can we learn from intercomparison of SMILEs, how similar are they in terms of spatial patterns and forced response, and what if they aren&amp;#8217;t? How does the forced response of an ensemble of SMILEs compare to the CMIP5 multi-model ensemble? By assessing multiple SMILEs we can identify robust signals for regional and global precipitation properties and revealing anthropogenic responses that are inherent to our current representations of the Earth system.&lt;/p&gt;

scholarly journals The Norwegian Earth System Model, NorESM2 – Evaluation of theCMIP6 DECK and historical simulations

2020 ◽  
Author(s):  
Øyvind Seland ◽  
Mats Bentsen ◽  
Lise Seland Graff ◽  
Dirk Olivié ◽  
Thomas Toniazzo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Deep Convection ◽  
Coupled Model ◽  
Momentum Conservation ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Model Intercomparison ◽  
Intercomparison Project ◽  
The Stability

Abstract. The second version of the fully coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. NorESM2 is based on the second version of the Community Earth System Model (CESM2), but has entirely different ocean and ocean biogeochemistry models; a new module for aerosols in the atmosphere model along with aerosol-radiation-cloud interactions and changes related to the moist energy formulation, deep convection scheme and angular momentum conservation; modified albedo and air-sea turbulent flux calculations; and minor changes to land and sea ice models. We show results from low (∼2°) and medium (∼1°) atmosphere-land resolution versions of NorESM2 that have both been used to carry out simulations for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The stability of the pre-industrial climate and the sensitivity of the model to abrupt and gradual quadrupling of CO2 is assessed, along with the ability of the model to simulate the historical climate under the CMIP6 forcings. As compared to observations and reanalyses, NorESM2 represents an improvement over previous versions of NorESM in most aspects. NorESM2 is less sensitive to greenhouse gas forcing than its predecessors, with an equilibrium climate sensitivity of 2.5 K in both resolutions on a 150 year frame. We also consider the model response to future scenarios as defined by selected shared socioeconomic pathways (SSP) from the Scenario Model Intercomparison Project defined under CMIP6. Under the four scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, the warming in the period 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.0, and 3.9 K in NorESM2-LM, and 1.3, 2.1, 3.1, and 3.9 K in NorESM–MM, robustly similar in both resolutions. NorESM2-LM shows a rather satisfactorily evolution of recent sea ice area. In NorESM2-LM an ice free Arctic Ocean is only avoided in the SSP1-2.6 scenario.

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