Projected changes in tropical cyclone activity under future warming scenarios using a high-resolution climate model
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This study examines how characteristics of tropical cyclones (TCs) that are explicitly resolved in a global atmospheric model with horizontal resolution of approximately 28 km are projected to change in a warmer climate using bias-corrected sea-surface temperatures (SSTs). The impact of mitigating from RCP8.5 to RCP4.5 is explicitly considered and is compared with uncertainties arising from SST projections. We find a reduction in overall global TC activity as climate warms. This reduction is somewhat less pronounced under RCP4.5 than under RCP8.5. By contrast, the frequency of very intense TCs is projected to increase dramatically in a warmer climate, with most of the increase concentrated in the NW Pacific basin. Extremes of storm related precipitation are also projected to become more common. Reduction in the frequency of extreme precipitation events is possible through mitigation from RCP8.5 to RCP4.5. In general more detailed basin-scale projections of future TC activity are subject to large uncertainties due to uncertainties in future SSTs. In most cases these uncertainties are larger than the effects of mitigating from RCP8.5 to RCP4.5.
KeywordsTropical cyclones Climate change High-resolution
Computing resources for this work were provided by; The Argonne Leadership Computing Facility at Argonne National Laboratory (Office of Science of the US Department of Energy) through the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program, and the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies. This work also utilized part of the “Using Petascale Computing Capabilities to Address Climate Change Uncertainties” PRAC allocation support by the National Science Foundation (NSF), and the Blue Waters sustained-petascale computing project supported by the NSF and the state of Illinois.
The authors would also like to acknowledge support from the Regional and Global Climate Modeling Program (RGCM) of the US Department of Energy, Office of Science (BER), Cooperative Agreement DE-FC02-97ER62402 and from NSF’s EaSM program.
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