Search Results
summarizes and discusses the major findings of the study. 2. Data and analysis method The study of surface turbulence heat flux variability in the WBC regions and its relation with midlatitude storms requires high-resolution data both temporally and spatially. The datasets we used are 6-hourly the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR, 1979–2009; Saha et al. 2010 ), NCEP–National Center for Atmospheric Research (NCAR) reanalysis (1948–2009; Kalnay
summarizes and discusses the major findings of the study. 2. Data and analysis method The study of surface turbulence heat flux variability in the WBC regions and its relation with midlatitude storms requires high-resolution data both temporally and spatially. The datasets we used are 6-hourly the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR, 1979–2009; Saha et al. 2010 ), NCEP–National Center for Atmospheric Research (NCAR) reanalysis (1948–2009; Kalnay
. The MABL convergence in both models is determined by hydrostatic pressure adjustments. However, in section 5 we show that for strong cross-front winds, MABL convergence is primarily a function of lateral heterogeneity in turbulent mixing. 3. Regional atmospheric model experiments The atmospheric response to an idealized midlatitude SST front is explored here with the nonhydrostatic Weather Research and Forecasting model (WRF), version 3.3.1 ( Skamarock et al. 2008 ). WRF has been used to study
. The MABL convergence in both models is determined by hydrostatic pressure adjustments. However, in section 5 we show that for strong cross-front winds, MABL convergence is primarily a function of lateral heterogeneity in turbulent mixing. 3. Regional atmospheric model experiments The atmospheric response to an idealized midlatitude SST front is explored here with the nonhydrostatic Weather Research and Forecasting model (WRF), version 3.3.1 ( Skamarock et al. 2008 ). WRF has been used to study
-stationary response. Section 5 evaluates the influence of initial and lateral boundary conditions. Section 6 looks for evidence of the nonlinear circulation response from a reanalysis dataset. Section 7 is a summary and a discussion. 2. Model, data, and analysis a. Model This study uses the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), with the domain covering most of the Northern Hemisphere on a polar stereographic projection at 40-km resolution ( Fig. 2a ). There are 28 terrain
-stationary response. Section 5 evaluates the influence of initial and lateral boundary conditions. Section 6 looks for evidence of the nonlinear circulation response from a reanalysis dataset. Section 7 is a summary and a discussion. 2. Model, data, and analysis a. Model This study uses the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ), with the domain covering most of the Northern Hemisphere on a polar stereographic projection at 40-km resolution ( Fig. 2a ). There are 28 terrain
relative humidity (1000, 975, 950, 925, 900, 800, 700, 600, and 500 hPa), on a 0.1° latitude × 0.125° longitude grid. We also used hourly SAT, wind velocity, humidity, and precipitation forecast by the JMA mesoscale model, which was initialized with the 3-hourly mesoscale analysis data. Saito et al. (2006) described the mesoscale model and its evaluation in detail. The mesoscale analysis domain was 20°–50°N, 120°–150°E. Sounding and surface meteorological data from the cruise were not assimilated in
relative humidity (1000, 975, 950, 925, 900, 800, 700, 600, and 500 hPa), on a 0.1° latitude × 0.125° longitude grid. We also used hourly SAT, wind velocity, humidity, and precipitation forecast by the JMA mesoscale model, which was initialized with the 3-hourly mesoscale analysis data. Saito et al. (2006) described the mesoscale model and its evaluation in detail. The mesoscale analysis domain was 20°–50°N, 120°–150°E. Sounding and surface meteorological data from the cruise were not assimilated in
AIRS data from the winter months between December 2003 and February 2013 are analyzed. Both the TRMM and AIRS data are provided by the NASA Goddard Earth Sciences Data and Information Services Center. To assess the satellite-derived results, we used reanalysis data from the ERA-Interim (e.g., Dee et al. 2011 ), which are produced by the European Centre for Medium-Range Weather Forecasts and are available since 1979 onward at a 0.75° resolution. The atmospheric variables of the ERA-Interim data
AIRS data from the winter months between December 2003 and February 2013 are analyzed. Both the TRMM and AIRS data are provided by the NASA Goddard Earth Sciences Data and Information Services Center. To assess the satellite-derived results, we used reanalysis data from the ERA-Interim (e.g., Dee et al. 2011 ), which are produced by the European Centre for Medium-Range Weather Forecasts and are available since 1979 onward at a 0.75° resolution. The atmospheric variables of the ERA-Interim data
, recent model studies have suggested that the sea-ice variability influences the atmospheric circulation through the formation of stationary Rossby wave trains and modification of the storm track (e.g., Honda et al. 1999 ; Alexander et al. 2004 ). It is also suggested that initializing the sea-ice variability may in turn enhance the predictability of atmospheric circulations in seasonal forecasts ( Balmaseda et al. 2010 ; Doblas-Reyes et al. 2013 ). Recently, Honda et al. (2009) examined impacts
, recent model studies have suggested that the sea-ice variability influences the atmospheric circulation through the formation of stationary Rossby wave trains and modification of the storm track (e.g., Honda et al. 1999 ; Alexander et al. 2004 ). It is also suggested that initializing the sea-ice variability may in turn enhance the predictability of atmospheric circulations in seasonal forecasts ( Balmaseda et al. 2010 ; Doblas-Reyes et al. 2013 ). Recently, Honda et al. (2009) examined impacts
-level wind forecasts have been evaluated in several modeling studies ( Kindle et al. 2002 ; Haack et al. 2005 ; Pullen et al. 2006 , 2007 ; Hong et al. 2011 ), and the coupling of wind stress curl and divergence to spatial variations in SST has been documented in the U.S. West Coast region by Haack et al. (2008) . These studies have demonstrated that COAMPS is capable of predicting all-weather surface winds with good accuracy and with high spatial and temporal resolution. For this study, the model
-level wind forecasts have been evaluated in several modeling studies ( Kindle et al. 2002 ; Haack et al. 2005 ; Pullen et al. 2006 , 2007 ; Hong et al. 2011 ), and the coupling of wind stress curl and divergence to spatial variations in SST has been documented in the U.S. West Coast region by Haack et al. (2008) . These studies have demonstrated that COAMPS is capable of predicting all-weather surface winds with good accuracy and with high spatial and temporal resolution. For this study, the model
. Finally, a summary of major findings and discussion are presented in section 6 . 2. Regional climate simulations a. Model configuration Similar to Ma et al. (2015) and Willison et al. (2013) , in this study we use the Weather Research and Forecasting (WRF) Model developed by NCAR ( Skamarock et al. 2008 ). The model setup follows Ma et al. (2015) closely, and a brief description of the model is given below for completeness. The computational domain covers the entire North Pacific from 3.6° to 66
. Finally, a summary of major findings and discussion are presented in section 6 . 2. Regional climate simulations a. Model configuration Similar to Ma et al. (2015) and Willison et al. (2013) , in this study we use the Weather Research and Forecasting (WRF) Model developed by NCAR ( Skamarock et al. 2008 ). The model setup follows Ma et al. (2015) closely, and a brief description of the model is given below for completeness. The computational domain covers the entire North Pacific from 3.6° to 66
atmospheric reanalysis outputs of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis ( Kalnay et al. 1996 ; Kanamitsu et al. 2002 ) and the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40; Uppala et al. 2005 ). Daily mean SST, surface air temperature (SAT), surface specific humidity, and surface wind speed are also used, which are available from 1985. b. ERA-Interim Since some atmospheric variables (e.g., SLP
atmospheric reanalysis outputs of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis ( Kalnay et al. 1996 ; Kanamitsu et al. 2002 ) and the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40; Uppala et al. 2005 ). Daily mean SST, surface air temperature (SAT), surface specific humidity, and surface wind speed are also used, which are available from 1985. b. ERA-Interim Since some atmospheric variables (e.g., SLP
the ERA-Interim global atmospheric reanalysis ( Dee et al. 2011 ) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), in which resolution of prescribed SST has been improved twice. Our investigation targets the KOE region, or the North Pacific SAFZ, which is not merely a single frontal zone but rather characterized by a pair of SST fronts ( Yasuda 2003 ; Nonaka et al. 2006 ; Seo et al. 2014 ). In the KOE region, part of the Oyashio flows eastward to the north of the KE
the ERA-Interim global atmospheric reanalysis ( Dee et al. 2011 ) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), in which resolution of prescribed SST has been improved twice. Our investigation targets the KOE region, or the North Pacific SAFZ, which is not merely a single frontal zone but rather characterized by a pair of SST fronts ( Yasuda 2003 ; Nonaka et al. 2006 ; Seo et al. 2014 ). In the KOE region, part of the Oyashio flows eastward to the north of the KE
