Quantifying Progress Across Different CMIP Phases With the ESMValTool
Lauer, A.
Schlund, M.
Barreiro, M.
Bellouin, N.
Jones, C.
Meehl, G. A.
Predoi, V.
Roberts, M. J.
Eyring, V.
DOI: https://doi.org/10.1029/2019JD032321
Persistent URL: http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8421
Schlund, M.; 1 Deutsches Zentrum für Luft‐und Raumfahrt (DLR) Institut für Physik der Atmosphäre Oberpfaffenhofen Germany
Barreiro, M.; 2 Departamento de Ciencias de la Atmósfera, Facultad de Ciencias Universidad de la República Montevideo Uruguay
Bellouin, N.; 3 Department of Meteorology University of Reading Reading UK
Jones, C.; 4 National Centre for Atmospheric Science University of Leeds Leeds UK
Meehl, G. A.; 5 National Center for Atmospheric Research Boulder CO USA
Predoi, V.; 6 NCAS Computational Modelling Services (CMS) University of Reading Reading UK
Roberts, M. J.; 7 Met Office Hadley Centre Exeter UK
Eyring, V.; 1 Deutsches Zentrum für Luft‐und Raumfahrt (DLR) Institut für Physik der Atmosphäre Oberpfaffenhofen Germany
Abstract
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.
Key Points:
Temperature, water vapor, and zonal wind speed show improvements in CMIP6 with amplitudes of many long‐standing biases smaller than CMIP3/5. High‐resolution models show significant improvements in their historical CMIP6 simulations for temperature and precipitation mean biases. Spread in effective climate sensitivity in CMIP6 is larger than in previous phases.