Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

By Stephen A. Klein, Yuying Zhang, M. Zelinka, Robert Pincus, J. Boyle, P. Gleckler. 2012

251 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f3f5f6b181185f0e87469f6f5eb1221d9f2630e4

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Citations in the corpus

The CloudSat radar‐lidar geometrical profile product (RL‐GeoProf): Updates, improvements, and selected results (2014, background): “…that while some of the most egregious biases between model predictions and observations have been reduced, the models taken as a whole continue to poorly simulate the Earth’s cloud regimes with the degree of fidelity considered necessary to accurately simulate climate [Klein et al., 2013].”
Emergent Constraints for Cloud Feedbacks (2015, unknown): “For example, feedbacks from middle-level clouds or tropical anvils associated with mesoscale convective systems may be missed entirely simply because climate models largely fail to simulate these clouds [20, 45].”
Evaluating Emergent Constraints on Equilibrium Climate Sensitivity (2018, background): “…time scale invariance found in much coarser/cruder GCM simulations analyzed by Qu et al. (2013) is important future work. f. Klein TCA and ctp-tau Klein et al. (2013) provide metrics of model skill at reproducing present-day total cloud amount (TCA) and combined cloud-top pressure and optical…”
On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models (2013, background): “…Climate Research Programme (WCRP)CoupledModel IntercomparisonProject (CMIP3), large systematic errors in precipitation, clouds, andwater vapor remain in the simulated climatemean state for majority of the climate models in the recent CMIP5 (Jiang et al. 2012; Li et al. 2012; Klein et al. 2013).”
The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. (2016, methodology): “Studies arising from CFMIP-2 include numerous singleand multi-model evaluation studies which use COSP to make quantitative and fair comparisons with a range of satellite products (e.g. Kay et al., 2012; Franklin et al., 2013; Klein et al., 2013; Lin et al., 2014; Chepfer et al., 2014).”
Challenges and Prospects for Reducing Coupled Climate Model SST Biases in the Eastern Tropical Atlantic and Pacific Oceans: The U.S. CLIVAR Eastern Tropical Oceans Synthesis Working Group (2016, background): “Phase 5 of the Coupled Model Intercomparison Project (CMIP5) models demonstrate only a slight improvement in the mean from CMIP3 [Fig.”
The DOE E3SM Coupled Model Version 1: Description and Results at High Resolution (2019, background): “Scalar metrics of model performance from Klein et al. (2013) indicate that E3SM would be in the top rank of pre-CMIP6 models (Zhang et al., 2019).”
The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations (2017, methodology): “Despite the accurate simulation of cloud being a particular strength of HadGEM2-AO (e.g., Jiang et al., 2012; Klein et al., 2013; Nam et al., 2012), Figure 9 reveals some of the issues in terms of detrainment from deep convection not being at a sufficiently high altitude, too little thin cirrus,…”
Cloud feedback mechanisms and their representation in global climate models (2017, unknown): “A fourfold difference in the simulated high (cloud top pressure ≤ 440 hPa) cloud fraction was found among an earlier generation of models,(73) though this spread has decreased in CMIP5 models.(12) Furthermore, climate models systematically underestimate the relative frequency of occurrence of tropic”