Regime-based evaluation of cloudiness in CMIP5 models
- 403 Downloads
The concept of cloud regimes (CRs) is used to develop a framework for evaluating the cloudiness of 12 fifth Coupled Model Intercomparison Project (CMIP5) models. Reference CRs come from existing global International Satellite Cloud Climatology Project (ISCCP) weather states. The evaluation is made possible by the implementation in several CMIP5 models of the ISCCP simulator generating in each grid cell daily joint histograms of cloud optical thickness and cloud top pressure. Model performance is assessed with several metrics such as CR global cloud fraction (CF), CR relative frequency of occurrence (RFO), their product [long-term average total cloud amount (TCA)], cross-correlations of CR RFO maps, and a metric of resemblance between model and ISCCP CRs. In terms of CR global RFO, arguably the most fundamental metric, the models perform unsatisfactorily overall, except for CRs representing thick storm clouds. Because model CR CF is internally constrained by our method, RFO discrepancies yield also substantial TCA errors. Our results support previous findings that CMIP5 models underestimate cloudiness. The multi-model mean performs well in matching observed RFO maps for many CRs, but is still not the best for this or other metrics. When overall performance across all CRs is assessed, some models, despite shortcomings, apparently outperform Moderate Resolution Imaging Spectroradiometer cloud observations evaluated against ISCCP like another model output. Lastly, contrasting cloud simulation performance against each model’s equilibrium climate sensitivity in order to gain insight on whether good cloud simulation pairs with particular values of this parameter, yields no clear conclusions.
KeywordsCMIP5 evaluation CFMIP COSP ISCCP simulator Cloud assessment Cloud climatology Cloud regime
We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Lastly, funding by NASA’s Modeling Analysis and Prediction (MAP) program is gratefully acknowledged.
- Anderberg MR (1973) Cluster analysis for applications. Elsevier, New YorkGoogle Scholar
- Bony S, Webb M, Bretherton CS et al (2011) CFMIP: towards a better evaluation and understanding of clouds and cloud feedbacks in CMIP5 models. CLIVAR Exch 56(16):20–24Google Scholar
- Boucher O, Randall D, Artaxo P et al (2013) Clouds and aerosols. In: Stocker TF, Qin D, Plattner G-K et al (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge; New York, pp 571–658Google Scholar
- Jin D, Oreopoulos L, Lee D (2016) Simplified ISCCP cloud regimes for evaluating cloudiness in CMIP5 models. Clim Dyn (accepted) Google Scholar
- MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Oakland, CA, USA, pp 281–297Google Scholar
- Oreopoulos L, Cho N, Lee D, Kato S (2016) Radiative effects of global MODIS cloud regimes. J Geophys Res Atmos 121. doi: 10.1002/2015JD024502