Process-level improvements in CMIP5 models and their impact on tropical variability, the Southern Ocean, and monsoons

Lauer, Axel; Jones, Colin; Eyring, Veronika; Evaldsson, Martin; Hagemann, Stefan; Mäkelä, Jarmo; Martin, Gill; Roehrig, Romain; Wang, S.

doi:10.5194/esd-9-33-2018

Cited by 16 publications

(16 citation statements)

References 109 publications

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“…For CMIP6 models, Zelinka et al (2020) showed that cloud feedbacks and thus ECS in high-sensitivity models are dominated by changes in clouds over the Southern Ocean, while in CMIP3 and CMIP5 the uncertainty in cloud feedbacks is dominated by clouds in the subtropical subsidence regions. One might speculate that a possible reason for this might be an improved simulation of clouds over the Southern Ocean in some models (Bodas-Salcedo et al, 2019;Gettelman et al, 2019a) as shown for some pre-CMIP6 model versions evaluated by Lauer et al (2018). The findings of Zelinka et al (2020) could also at least partly explain the larger inter-model spread in climate sensitivity due to more and different regions / clouds types dominating the cloud feedbacks resulting in a weaker emergent constraint compared with CMIP5 models.…”

Section: Discussionmentioning

confidence: 97%

Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

Schlund¹,

Lauer²,

Gentine³

et al. 2020

Preprint

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Abstract. An important metric for temperature projections is the equilibrium climate sensitivity (ECS) which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which stem the major source of uncertainty in ECS. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here has been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a large decrease of the correlation coefficients for most emergent relationships, indicating that nearly all of the constraints are less skillful in predicting ECS than they were in CMIP5. Many do not appear sufficiently skillful to be useful in constraining ECS, and several of them do not pass a significance test. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble the emergent constraint was originally derived from. The emergent-constrained best estimate of ECS increased from CMIP5 to CMIP6, with a best estimate range of 2.97–3.88 K for CMIP5 and 3.41–4.36 K for CMIP6. This can be at least partly explained by the increased number of high-ECS models in CMIP6 with an ECS above 4.5 K without a corresponding change in the constraint predictors. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that emergent constraints focusing on specific processes contributing to ECS are more promising than emergent constraints based on statistical model analysis.

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Section: Discussionmentioning

confidence: 97%

Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

Schlund¹,

Lauer²,

Gentine³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The positive temperature bias over the Southern Ocean, however, seems to have gotten worse in time (Hyder et al, 2018) with the CMIP6 multimodel mean showing larger biases than in the two previous CMIP phases. Regional absolute biases in surface temperature of up to 6°C as seen in CMIP5 and some pre‐CMIP6 models (Lauer et al, 2018) are still present in CMIP6.…”

Section: Systematic Biasesmentioning

confidence: 92%

“…There are many different, model‐dependent causes for biases in modeled surface temperature. Common causes include biases in downward shortwave radiation at the surface because of errors in simulated cloud properties (Hyder et al, 2018; Lauer et al, 2018), errors in oceanic circulation (Kuhlbrodt et al, 2018), errors in the simulation of trade winds (Lauer et al, 2018), and errors in surface albedo and moisture propagated from the vegetation schemes (Séférian et al, 2016). Even though the multimodel mean of the surface temperature bias shows only small improvements in CMIP6, some individual models made significant progress (Danabasoglu et al, 2020).…”

Section: Systematic Biasesmentioning

confidence: 99%

Quantifying Progress Across Different CMIP Phases With the ESMValTool

et al. 2020

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More than 40 model groups worldwide are participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), providing a new and rich source of information to better understand past, present, and future climate change. Here, we use the Earth System Model Evaluation Tool (ESMValTool) to assess the performance of the CMIP6 ensemble compared to the previous generations CMIP3 and CMIP5. While CMIP5 models did not capture the observed pause in the increase in global mean surface temperature between 1998 and 2013, the historical CMIP6 simulations agree well with the observed recent temperature increase, but some models have difficulties in reproducing the observed global mean surface temperature record of the second half of the twentieth century. While systematic biases in annual mean surface temperature and precipitation remain in the CMIP6 multimodel mean, individual models and high-resolution versions of the models show significant reductions in many long-standing biases. Some improvements are also found in the vertical temperature, water vapor, and zonal wind speed distributions, and root-mean-square errors for selected fields are generally smaller with reduced intermodel spread and higher average skill in the correlation patterns relative to observations. An emerging property of the CMIP6 ensemble is a higher effective climate sensitivity with an increased range between 2.3 and 5.6 K. A possible reason for this increase in some models is improvements in cloud representation resulting in stronger shortwave cloud feedbacks than in their predecessor versions.

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“…Comparing model results to observations provides insight into the quality of model simulations and the way in which various processes are represented. Comparisons with observations can reveal shortcomings in individual models and systematic errors in a large multimodel ensemble 7,20 . An example of a systematic error is the excessive simulated band of precipitation in the tropical Pacific south of the Equator, a feature not present in observations.…”

Section: From Model Errors To Understanding Processesmentioning

confidence: 99%

Taking climate model evaluation to the next level

et al. 2019

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Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

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Process-level improvements in CMIP5 models and their impact on tropical variability, the Southern Ocean, and monsoons

Cited by 16 publications

References 109 publications

Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

Quantifying Progress Across Different CMIP Phases With the ESMValTool

Taking climate model evaluation to the next level

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