Abstract
Using a suite of lateral boundary conditions, we investigate the impact of domain size and boundary conditions on the Atlantic tropical cyclone and african easterly Wave activity simulated by a regional climate model. Irrespective of boundary conditions, simulations closest to observed climatology are obtained using a domain covering both the entire tropical Atlantic and northern African region. There is a clear degradation when the high-resolution model domain is diminished to cover only part of the African continent or only the tropical Atlantic. This is found to be the result of biases in the boundary data, which for the smaller domains, have a large impact on TC activity. In this series of simulations, the large-scale Atlantic atmospheric environment appears to be the primary control on simulated TC activity. Weaker wave activity is usually accompanied by a shift in cyclogenesis location, from the MDR to the subtropics. All ERA40-driven integrations manage to capture the observed interannual variability and to reproduce most of the upward trend in tropical cyclone activity observed during that period. When driven by low-resolution global climate model (GCM) integrations, the regional climate model captures interannual variability (albeit with lower correlation coefficients) only if tropical cyclones form in sufficient numbers in the main development region. However, all GCM-driven integrations fail to capture the upward trend in Atlantic tropical cyclone activity. In most integrations, variations in Atlantic tropical cyclone activity appear uncorrelated with variations in African easterly wave activity.
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Notes
Available online at http://www.nhc.noaa.gov/pastall.shtml.
The TC activity in the two low-resolution GCM simulations is quite poor and is not included here. The interested reader is referred to Caron et al. (2010).
Given that the map of the mean index is nearly indistinguishable from one ensemble member to the other, we show only one map for BIGLAMERA and BIGLAMGEM2d. We have also included the climatological ASO values, since it is during this three-month period that most of the TCs occur in the Atlantic, both in observations and simulations.
Although not technically exact, for simplicity we will, henceforth, refer to this region as the western Sahel.
Here, we also include two 28-year simulations performed with the smallest grid and using slightly different versions of GEM. One version uses an alternate surface flux formulation, based on Moon et al. (2007), while in the second, we also modified the threshold value required to initiate deep convection in the Kain–Fritsch convective parametrization scheme. Basic results were not significantly affected and the two simulations are used only to increase the ensemble size.
The strongest storms are those for which the lifetime-maximum intensity is located above the 90th percentile, based on the modified wind speed, for any given year.
Ideally, we would have performed additional simulations with climatological SSTs using the larger grid, but the computer resources available did not allow it.
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Acknowledgments
The authors would like to thank ECMWF for making the ERA-40 and ERA-Interim reanalyses available and the National Hurricane Center for the use of their tropical cyclone best track data (HURDAT). The authors are also grateful to Kerry Emanuel for making the MPI FORTRAN routine available and to Bernard Dugas, Katja Winger and Grigory Nikulin for their help at different stages of this projet. This research was supported by the Natural Sciences and Engineering Research Council of Canada and the Mathematics of Information Technology and Complex Systems (MITACS, grant number 61851). The first author would like to thank Katherine Barrett for her help in proofreading this document. Finally, we would also like to thank two anonymous reviewers for suggesting improvements to the original manuscript.
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Caron, LP., Jones, C.G. Understanding and simulating the link between African easterly waves and Atlantic tropical cyclones using a regional climate model: the role of domain size and lateral boundary conditions. Clim Dyn 39, 113–135 (2012). https://doi.org/10.1007/s00382-011-1160-8
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DOI: https://doi.org/10.1007/s00382-011-1160-8