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Abstract
Abstract. The performance of updated versions of the four earth system models (ESMs) CNRM, EC-Earth, HadGEM, and MPI-ESM is assessed in comparison to their predecessor versions used in Phase 5 of the Coupled Model Intercomparison Project. The Earth System Model Evaluation Tool (ESMValTool) is applied to evaluate selected climate phenomena in the models against observations. This is the first systematic application of the ESMValTool to assess and document the progress made during an extensive model development and improvement project. This study focuses on the South Asian monsoon (SAM) and the West African monsoon (WAM), the coupled equatorial climate, and Southern Ocean clouds and radiation, which are known to exhibit systematic biases in present-day ESMs. The analysis shows that the tropical precipitation in three out of four models is clearly improved. Two of three updated coupled models show an improved representation of tropical sea surface temperatures with one coupled model not exhibiting a double Intertropical Convergence Zone (ITCZ). Simulated cloud amounts and cloud–radiation interactions are improved over the Southern Ocean. Improvements are also seen in the simulation of the SAM and WAM, although systematic biases remain in regional details and the timing of monsoon rainfall. Analysis of simulations with EC-Earth at different horizontal resolutions from T159 up to T1279 shows that the synoptic-scale variability in precipitation over the SAM and WAM regions improves with higher model resolution. The results suggest that the reasonably good agreement of modeled and observed mean WAM and SAM rainfall in lower-resolution models may be a result of unrealistic intensity distributions.
Highlights
Despite the progress made in the past, global climate models (GCMs) and earth system models (ESMs) still show significant systematic biases in a number of key features of the simulated climate system compared with observations
Such systematic errors in the representation of observed climate features and their variability introduce considerable uncertainty in model projections of future climate. Examples of such biases include the simulation of a too-thin Arctic sea ice (Shu et al, 2015), systematic problems in simulating monsoon rainfall (Turner and Annamalai, 2012; Turner et al, 2011), a dry soil moisture bias in midlatitude continental regions, an excessively shallow equatorial ocean thermocline and double Intertropical Convergence Zone (ITCZ; e.g., Li and Xie, 2014), too-thick clouds in midlatitudes (Lauer and Hamilton, 2013), and excessive downwelling solar radiation over the Southern Ocean accompanied by a warm bias in sea surface temperatures (SSTs) in many coupled models (Trenberth and Fasullo, 2010)
EMBRACE aimed at reducing a number of these systematic model biases by targeting improvement in the representation of selected key variables and processes in ESMs. (1) The representation of the coupled tropical climate: (i) a cold bias in equatorial SSTs coupled with an incorrect location of the ITCZ (Lin, 2007), (ii) a poor representation of coastal and associated Ekman dynamics in the tropical oceans, and (iii) a poor representation of the location, intensity distribution, and seasonal and/or diurnal cycles of precipitation in monsoon regions (Kang et al, 2002)
Summary
Despite the progress made in the past, global climate models (GCMs) and earth system models (ESMs) still show significant systematic biases in a number of key features of the simulated climate system compared with observations Such systematic errors in the representation of observed climate features and their variability introduce considerable uncertainty in model projections of future climate. Typically regarded as a necessary condition for a model to be a useful tool for future climate projections to be able to reproduce the observed features of the past climate reasonably well (Flato et al, 2013) This does not answer the much more difficult question of how good is good enough for a model to be used or useful for a specific application as this strongly depends on the processes of interest, including geographical region, simulated quantity, natural variability, timescales, and time range or metric.
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