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Climate Dynamics

, Volume 50, Issue 1–2, pp 677–692 | Cite as

How does dynamical downscaling affect model biases and future projections of explosive extratropical cyclones along North America’s Atlantic coast?

  • C. Seiler
  • F. W. Zwiers
  • K. I. Hodges
  • J. F. Scinocca
Article

Abstract

Explosive extratropical cyclones (EETCs) are rapidly intensifying low pressure systems that generate severe weather along North America’s Atlantic coast. Global climate models (GCMs) tend to simulate too few EETCs, perhaps partly due to their coarse horizontal resolution and poorly resolved moist diabatic processes. This study explores whether dynamical downscaling can reduce EETC frequency biases, and whether this affects future projections of storms along North America’s Atlantic coast. A regional climate model (CanRCM4) is forced with the CanESM2 GCM for the periods 1981 to 2000 and 2081 to 2100. EETCs are tracked from relative vorticity using an objective feature tracking algorithm. CanESM2 simulates 38% fewer EETC tracks compared to reanalysis data, which is consistent with a negative Eady growth rate bias (−0.1 day\(^{-1}\)). Downscaling CanESM2 with CanRCM4 increases EETC frequency by one third, which reduces the frequency bias to −22%, and increases maximum EETC precipitation by 22%. Anthropogenic greenhouse gas forcing is projected to decrease EETC frequency (−15%, −18%) and Eady growth rate (−0.2 day\(^{-1}\), −0.2 day\(^{-1}\)), and increase maximum EETC precipitation (46%, 52%) in CanESM2 and CanRCM4, respectively. The limited effect of dynamical downscaling on EETC frequency projections is consistent with the lack of impact on the maximum Eady growth rate. The coarse spatial resolution of GCMs presents an important limitation for simulating extreme ETCs, but Eady growth rate biases are likely just as relevant. Further bias reductions could be achieved by addressing processes that lead to an underestimation of lower tropospheric meridional temperature gradients.

Keywords

Explosive extratropical cyclones Dynamical downscaling Model biases Climate change projections 

Notes

Acknowledgements

The authors gratefully acknowledge the financial support of the Marine Environmental Observation Prediction and Response Network (MEOPAR) for this research. We thank Dr. Yanjun Jiao from the Canadian Centre for Climate Modelling and Analysis (CCCma) for providing us with data from CanRCM4. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank CCCma and ECMWF 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 are grateful for the constructive comments from two anonymous reviewers.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Pacific Climate Impacts ConsortiumUniversity of VictoriaVictoriaCanada
  2. 2.University of ReadingReadingUnited Kingdom
  3. 3.Canadian Centre for Climate Modelling and AnalysisVictoriaCanada

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