首页|Statistical Downscaling of IPCC Sea Surface Wind and Wind Energy Predictions for U.S.East Coastal Ocean,Gulf of Mexico and Caribbean Sea

Statistical Downscaling of IPCC Sea Surface Wind and Wind Energy Predictions for U.S.East Coastal Ocean,Gulf of Mexico and Caribbean Sea

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A multivariate statistical downscaling method is developed to produce regional,high-resolution,coastal surface wind fields based on the IPCC global model predictions for the U.S.east coastal ocean,the Gulf of Mexico (GOM),and the Caribbean Sea.The statistical relationship is built upon linear regressions between the empirical orthogonal function (EOF) spaces of a cross-calibrated,multi-platform,multi-instrument ocean surface wind velocity dataset (predictand) and the global NCEP wind reanalysis (predictor) over a 10 year period from 2000 to 2009.The statistical relationship is validated before applications and its effectiveness is confirmed by the good agreement between downscaled wind fields based on the NCEP reanalysis and in-situ surface wind measured at 16 National Data Buoy Center (NDBC) buoys in the U.S.east coastal ocean and the GOM during 1992-1999.The predictand-predictor relationship is applied to IPCC GFDL model output (2.0°×2.5°) of downscaled coastal wind at 0.25°×0.25° resolution.The temporal and spatial variability of future predicted wind speeds and wind energy potential over the study region are further quantified.It is shown that wind speed and power would significantly be reduced in the high CO2 climate scenario offshore of the mid-Atlantic and northeast U.S.,with the speed falling to one quarter of its original value.

climate changesstatistical downscalingsurface winds

YAO Zhigang、XUE Zuo、HE Ruoying、BAO Xianwen、SONG Jun

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Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao 266071, P.R.China

Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA

Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge 70803, USA

National Marine Data and Information Service, Tianjin 300171, P.R.China

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Research support was provided by the Fundamental Research Funds for the Central UniversitiesNational Oceanic and Atmospheric AdministrationNational National Science Foundation of ChinaNational National Science Foundation of ChinaNational National Science Foundation of ChinaNational National Science Foundation of ChinaNational National Science Foundation of ChinaNational National Science Foundation of Chinaauthors also acknowledges the support by National Aeronautics and Space Administrationpublic science and technology research funds projects of ocean

3101000-841413030NA11NOS0120033415060124137600141206013414760474143096341206004NNX13AD80G201205018

2016

10.1007/s11802-016-2869-0

中国海洋大学学报(自然科学英文版)
中国海洋大学

中国海洋大学学报(自然科学英文版)

CSTPCDCSCDSCI
影响因子：0.268
ISSN：1672-5182
年,卷(期)：2016.15(4)
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