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.0 (COARE3.0) flux algorithm ( Fairall et al. 2003 ) using SST and surface wind from several spaceborne microwave radiometers and scatterometers, surface air specific humidity estimated from the Special Sensor Microwave Imager (SSM/I), and surface air temperature from the NCEP–Department of Energy (DOE) reanalysis 2 ( Kanamitsu et al. 2002 ). 3. Seasonal variations Figure 1 shows annual and seasonal means of precipitation for TRMM 3B43 observations, CNTL, and SMTH. The precipitation band over the Gulf
.0 (COARE3.0) flux algorithm ( Fairall et al. 2003 ) using SST and surface wind from several spaceborne microwave radiometers and scatterometers, surface air specific humidity estimated from the Special Sensor Microwave Imager (SSM/I), and surface air temperature from the NCEP–Department of Energy (DOE) reanalysis 2 ( Kanamitsu et al. 2002 ). 3. Seasonal variations Figure 1 shows annual and seasonal means of precipitation for TRMM 3B43 observations, CNTL, and SMTH. The precipitation band over the Gulf
somewhat arbitrary; however, their magnitude corresponds to the 83rd (80th) percentile of the 60-yr NDJFM reanalysis record. Using the Coupled Ocean–Atmosphere Response Experiment (COARE) 3.0 bulk algorithm ( Fairall et al. 2003 ), 100 W m −2 sensible (250 W m −2 latent) heat flux is roughly equivalent to a 6 K (4 K) air–sea temperature difference in the presence of a 10 m s −1 wind and 77% relative humidity. Results, including those presented in sections 4 and 5 , were found to be insensitive to
somewhat arbitrary; however, their magnitude corresponds to the 83rd (80th) percentile of the 60-yr NDJFM reanalysis record. Using the Coupled Ocean–Atmosphere Response Experiment (COARE) 3.0 bulk algorithm ( Fairall et al. 2003 ), 100 W m −2 sensible (250 W m −2 latent) heat flux is roughly equivalent to a 6 K (4 K) air–sea temperature difference in the presence of a 10 m s −1 wind and 77% relative humidity. Results, including those presented in sections 4 and 5 , were found to be insensitive to
latest information of these sites is released on the mooring Web sites (KEO, http://www.pmel.noaa.gov/keo/ ; JKEO, http://www.jamstec.go.jp/iorgc/ocorp/ktsfg/data/jkeo/ ). SHF and LHF are computed from the high-resolution data using the version 3.0 Coupled Ocean–Atmosphere Response Experiment (COARE) bulk algorithm ( Fairall et al. 2003 ). As the depth of the current meter at JKEO was rather deep to obtain accurate surface currents and the current record at KEO was lost before the end of September
latest information of these sites is released on the mooring Web sites (KEO, http://www.pmel.noaa.gov/keo/ ; JKEO, http://www.jamstec.go.jp/iorgc/ocorp/ktsfg/data/jkeo/ ). SHF and LHF are computed from the high-resolution data using the version 3.0 Coupled Ocean–Atmosphere Response Experiment (COARE) bulk algorithm ( Fairall et al. 2003 ). As the depth of the current meter at JKEO was rather deep to obtain accurate surface currents and the current record at KEO was lost before the end of September
perturbations in an aquaplanet GCM. J. Atmos. Sci. , 65 , 2842 – 2860 . Chelton , D. B. , M. G. Schlax , M. H. Freilich , and R. F. Milliff , 2004 : Satellite measurements reveal persistent small-scale features in ocean winds. Science , 303 , 978 – 983 . Fairall , C. , E. Bradley , J. Hare , A. Grachev , and J. Edson , 2003 : Bulk parameterization of air–sea fluxes: Updates and verification for the COARE algorithm. J. Climate , 16 , 571 – 591 . Hoskins , B. , and P
perturbations in an aquaplanet GCM. J. Atmos. Sci. , 65 , 2842 – 2860 . Chelton , D. B. , M. G. Schlax , M. H. Freilich , and R. F. Milliff , 2004 : Satellite measurements reveal persistent small-scale features in ocean winds. Science , 303 , 978 – 983 . Fairall , C. , E. Bradley , J. Hare , A. Grachev , and J. Edson , 2003 : Bulk parameterization of air–sea fluxes: Updates and verification for the COARE algorithm. J. Climate , 16 , 571 – 591 . Hoskins , B. , and P
Experiment (COARE; Fairall et al. 1996 ) bulk algorithm. (Note that oceanographic convention is used here: negative fluxes correspond to heat loss from the ocean.) The effect of heat content can be seen by the correlation between the turbulent fluxes and SSH ( Fig. 19 ). In the GS region, as the heat content (SSH) increases, the amount of heat fluxed to the atmosphere increases; flux anomalies lag heat content by approximately 3 months, which is likely related to the seasonal cycle of the ocean mixed
Experiment (COARE; Fairall et al. 1996 ) bulk algorithm. (Note that oceanographic convention is used here: negative fluxes correspond to heat loss from the ocean.) The effect of heat content can be seen by the correlation between the turbulent fluxes and SSH ( Fig. 19 ). In the GS region, as the heat content (SSH) increases, the amount of heat fluxed to the atmosphere increases; flux anomalies lag heat content by approximately 3 months, which is likely related to the seasonal cycle of the ocean mixed
-derived fields and 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses that have been optimally combined using the Coupled Ocean–Atmosphere Response Experiment (COARE) 3.0 bulk flux algorithm. The OAFlux latent and sensible heat flux estimates are unbiased, and the root-mean-square (rms) difference is less than 8 W m −2 when compared with daily flux time series
-derived fields and 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses that have been optimally combined using the Coupled Ocean–Atmosphere Response Experiment (COARE) 3.0 bulk flux algorithm. The OAFlux latent and sensible heat flux estimates are unbiased, and the root-mean-square (rms) difference is less than 8 W m −2 when compared with daily flux time series