Regional and seasonal intercomparison of CMIP3 and CMIP5 climate model ensembles for temperature and precipitation
Regional and seasonal temperature and precipitation over land are compared across two generations of global climate model ensembles, specifically, CMIP5 and CMIP3, through historical twentieth century skills and multi-model agreement, and twenty first century projections. A suite of diagnostic and performance metrics, ranging from spatial bias or model-consensus maps and aggregate time series plots, to measures of equivalence between probability density functions and Taylor diagrams, are used for the intercomparisons. Pairwise and multi-model ensemble comparisons were performed for 11 models, which were selected based on data availability and resolutions. Results suggest little change in the central tendency or variability or uncertainty of historical skills or consensus across the two generations of models. However, there are regions and seasons, at different levels of aggregation, where significant changes, performance improvements, and even degradation in skills, are suggested. The insights may provide directions for further improvements in next generations of climate models, and in the meantime, help inform adaptation and policy.
KeywordsCMIP5 models CMIP3 models Model evaluation Climate projections
The work was funded by the United States (US) National Science Foundation (NSF) Expeditions in Computing Grant # 1029711. The US Nuclear Regulatory Commission and Northeastern University provided partial funding. The climate model datasets were obtained from the PCMDI archive of the US Department of Energy at LLNL.
- Blázquez J, Nuñez MN (2013) Analysis of uncertainties in future climate projections for South America: comparison of WCRP-CMIP3 and WCRP-CMIP5 models. Clim Dyn 41:1039–1056Google Scholar
- Cattiaux J, Douville H, Peings Y (2013) European temperatures in CMIP5: origins of present-day biases and future uncertainties. Clim Dyn 41:2889–2907Google Scholar
- Knutti R, Abramowitz G, Collins M et al (2010) Good practice guidance paper on assessing and combining multi model climate projections. In: meeting report of the intergovernmental panel on climate change expert meeting on assessing and combining multi model climate projections [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, and P. M. Midgley (eds.)]. IPCC Working Group I Technical Support Unit, University of Bern, Bern, SwitzerlandGoogle Scholar
- Moss R, Babiker M, Brinkman S et al (2008) Towards new scenarios for analysis of emissions, climate change, impacts, and response Strategies. Intergovernmental Panel on Climate Change. Geneva: 132 ppGoogle Scholar
- IPCC Special Report on Emissions Scenarios (eds Nakicenovic, N. & Swart, R.) (Cambridge Univ. Press, 2007)Google Scholar
- Sperber KR, Annamalai H, Kang I-S, Kitoh A, Moise A, Turner A, Wang B, Zhou T (2013) The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Clim Dyn 41:2711–2744Google Scholar
- Taylor KE, Stouffer RJ, Meehl GA (2011a) A summary of the CMIP5 experiment design. (Program for Climate Model Diagnosis and Intercomparison (PCMDI), 2011); available online at http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf
- Taylor KE, Balaji V, Hankin S, Juckes M, Lawrence B, Pascoe S (2011b) CMIP5 Data Reference Syntax (DRS) and Controlled Vocabularies (Program for Climate Model Diagnosis and Intercomparison (PCMDI); available online at http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf
- Wilks, DS (2011) Statistical methods in the atmospheric sciences. Academic Press, 704 ppGoogle Scholar