How well do CMIP5 climate models reproduce explosive cyclones in the extratropics of the Northern Hemisphere?
Extratropical explosive cyclones are rapidly intensifying low pressure systems with severe wind speeds and heavy precipitation, affecting livelihoods and infrastructure primarily in coastal and marine environments. This study evaluates how well the most recent generation of climate models reproduces extratropical explosive cyclones in the Northern Hemisphere for the period 1980–2005. An objective-feature tracking algorithm is used to identify and track cyclones from 25 climate models and three reanalysis products. Model biases are compared to biases in the sea surface temperature (SST) gradient, the polar jet stream, the Eady growth rate, and model resolution. Most models accurately reproduce the spatial distribution of explosive cyclones when compared to reanalysis data (R = 0.94), with high frequencies along the Kuroshio Current and the Gulf Stream. Three quarters of the models however significantly underpredict explosive cyclone frequencies, by a third on average and by two thirds in the worst case. This frequency bias is significantly correlated with jet stream speed in the inter-model spread (R \(\ge\) 0.51), which in the Atlantic is correlated with a negative meridional SST gradient (R = −0.56). The importance of the jet stream versus other variables considered in this study also applies to the interannual variability of explosive cyclone frequency. Furthermore, models with fewer explosive cyclones tend to underpredict the corresponding deepening rates (R \(\ge\) 0.88). A follow-up study will assess the impacts of climate change on explosive cyclones, and evaluate how model biases presented in this study affect the projections.
KeywordsExplosive cyclones CMIP5 climate models Model biases
The authors gratefully acknowledge the financial support of the Marine Environmental Observation Prediction and Response Network (MEOPAR) for this research. We thank Dr. Kevin Hodges from the University of Reading (UK) for supporting our application of his cyclone tracking algorithm. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 2 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. We thank the respective centers for providing their reanalysis data products ERA-Interim, NCEP-CFSR and NASA-MERRA. We are grateful for the constructive comments from two anonymous reviewers. Please contact Christian Seiler (email@example.com) for obtaining the output data presented in this research.
- Arora V, Scinocca J, Boer G, Christian J, Denman K, Flato G, Kharin V, Lee W, Merryfield W (2011) Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys Res Lett 38(5)Google Scholar
- Chang EK, Guo Y, Xia X (2012) CMIP5 multimodel ensemble projection of storm track change under global warming, J Geophys Res Atmos (1984–2012), 117(D23)Google Scholar
- Finnis J, Holland MM, Serreze MC, Cassano JJ (2007) Response of Northern Hemisphere extratropical cyclone activity and associated precipitation to climate change, as represented by the community climate system model. J Geophys Res Biogeosci (2005–2012) 112(G4):1–14Google Scholar
- Flato G, Marotzke J, Abiodun B, Braconnot P, Chou SC, Collins W, Cox P, et al. (2013) Evaluation of climate models. In: Climate change 2013: The Physical science basis. Working Group I contribution to the fifth assessment report of the intergovernmental panel on climate change, Tech. rep., Groupe d’experts intergouvernemental sur l’evolution du climat/Intergovernmental Panel on Climate Change-IPCC, C/O World Meteorological Organization, 7bis Avenue de la Paix, CP 2300 CH-1211 Geneva 2 (Switzerland)Google Scholar
- Harrell FE (2014) Hmisc: harrell miscellaneous, r package version 3.14-4Google Scholar
- Lambert SJ (1996), Intense extratropical Northern Hemisphere winter cyclone events: 1899–1991, J Geophys Res Atmos (1984–2012), 101(D16), 21319–21325Google Scholar
- Neu U, Caballero R, Hanley J (2012) IMILAST-a community effort to intercompare extratropical cyclone detection and tracking algorithms: assessing method-related uncertainties, Bulletin of The American Meteorological Society-(BAMS), pp. 529–547Google Scholar
- R Core Team (2013) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
- Stull RB (2000) Meteorology for scientists and engineers: a technical companion book with Ahrens’ Meteorology Today, Brooks/ColeGoogle Scholar
- Volodin E, Dianskii N, Gusev A (2010) Simulating present-day climate with the INMCM4. 0 coupled model of the atmospheric and oceanic general circulations, Izvestiya, Atmospheric and Oceanic. Physics 46(4):414–431Google Scholar
- Yukimoto S, Kenkyūjo K (2011) Meteorological Research institute earth system model version 1 (MRI-ESM1): model description. Meteorol Res Inst Tech Rep 64:83Google Scholar