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Dynamical seasonal prediction using the global environmental multiscale model with a variable resolution modeling approach

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Abstract

In this work we evaluate seasonal forecasts performed with the global environmental multiscale model (GEM) using a variable resolution approach and with a high-resolution region over different geographical locations. Therefore, using two grid positions, one over North America and the other over the tropical Pacific-eastern Indian Ocean, we compare the seasonal predictions performed with the variable resolution approach with seasonal forecast performed with the uniform grid GEM model. For each model configuration, a ten-member ensemble forecast of 4 months is performed starting from the first of December of selected ENSO winters between 1982 and 2000. The sea surface temperature anomaly of the month preceding the forecast (November) is persisted throughout the forecast period. There is not enough evidence to indicate that a Stretch-Grid configuration has a clear advantage in seasonal prediction compared to a Uniform-Grid configuration. Forecasts with highly resolved grids placed over North America have more accurate seasonal mean anomalies and more skill in representing near surface temperature over the North American continent. For 500-hPa geopotential height, however, no configuration stands out to be consistently superior in forecasting the ENSO related seasonal mean anomalies and skill score.

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Acknowledgments

We wish to thank Dr. Bernard Dugas from the Meteorological Research Division at Environment Canada, for his help in improving this work. We have benefited greatly from many discussions with Dr. Colin G. Jones from the Rossby Centre, Sweden. Many thanks to Juan Sebastian Fontecilla from the Canadian Meteorological Centre for providing the initial conditions for ensemble forecast simulations. This research was carried within the Canadian Regional Climate Modeling and Diagnostics (CRCMD) network, funded by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS) and Ouranos Consortium. Lin is partly supported by the CFCAS and the Natural Science and Engineering Research Council of Canada (NSERC) with projects related to tropical-extratropical interactions.

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Authors and Affiliations

  1. Department of Earth and Atmospheric Sciences, University of Quebec at Montreal, 201, Président Kennedy, PK-2610, Montreal, QC, H2X 3Y7, Canada

    Marko Markovic & Katja Winger

  2. Meteorological Research Division, Environment Canada, Montreal, QC, Canada

    Hai Lin

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  1. Marko Markovic
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  2. Hai Lin
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Correspondence to Marko Markovic.

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Markovic, M., Lin, H. & Winger, K. Dynamical seasonal prediction using the global environmental multiscale model with a variable resolution modeling approach. Clim Dyn 39, 1885–1904 (2012). https://doi.org/10.1007/s00382-011-1202-2

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