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Abstract
Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.
Abstract
Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.
Abstract
Both Arctic sea ice loss and La Niña events can result in cold conditions in midlatitude Eurasia in winter. Since the two forcings sometimes occur simultaneously, determining whether they are independent of each other is undertaken first. The result suggests an overall independence. Considering possible interactions between them, their coordinated impacts on the Northern Hemisphere winter climate are then investigated based on observational data analyses, historical simulation analyses from one coupled model (MPI-ESM-LR) contributing to CMIP5, and atmospheric general circulation model sensitive experiments in ECHAM5. The results show that the impacts of the two forcings are overall linearly accumulated. In comparison with one single forcing, there is intensified cooling response in midlatitude Eurasia along with northern warmer–southern cooler dipolar temperature responses over North America. Despite the additive linearity, additive nonlinearity between the two forcings is identifiable. The nonlinearity causes midlatitude Eurasian cooling weakened by one-tenth to one-fifth as much as their individual impacts in combination. The underlying mechanisms for the weak additive nonlinearity are finally explored by transient adjustment AGCM runs with one single forcing or both the forcings switched on suddenly. The day-to-day evolution of responses suggests that the additive nonlinearity may arise initially from the forced wave dynamics and then be amplified because of the involvement of transient eddy feedbacks.
Abstract
Both Arctic sea ice loss and La Niña events can result in cold conditions in midlatitude Eurasia in winter. Since the two forcings sometimes occur simultaneously, determining whether they are independent of each other is undertaken first. The result suggests an overall independence. Considering possible interactions between them, their coordinated impacts on the Northern Hemisphere winter climate are then investigated based on observational data analyses, historical simulation analyses from one coupled model (MPI-ESM-LR) contributing to CMIP5, and atmospheric general circulation model sensitive experiments in ECHAM5. The results show that the impacts of the two forcings are overall linearly accumulated. In comparison with one single forcing, there is intensified cooling response in midlatitude Eurasia along with northern warmer–southern cooler dipolar temperature responses over North America. Despite the additive linearity, additive nonlinearity between the two forcings is identifiable. The nonlinearity causes midlatitude Eurasian cooling weakened by one-tenth to one-fifth as much as their individual impacts in combination. The underlying mechanisms for the weak additive nonlinearity are finally explored by transient adjustment AGCM runs with one single forcing or both the forcings switched on suddenly. The day-to-day evolution of responses suggests that the additive nonlinearity may arise initially from the forced wave dynamics and then be amplified because of the involvement of transient eddy feedbacks.
Abstract
To improve simulations of the Arctic climate and to quantify climate model errors, four regional climate models [the Arctic Regional Climate System Model (ARCSYM), the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS), the High-Resolution Limited-Area Model (HIRHAM), and the Rossby Center Atmospheric Model (RCA)] have simulated the annual Surface Heat Budget of the Arctic Ocean (SHEBA) under the Arctic Regional Climate Model Intercomparison Project (ARCMIP). The same lateral boundary and ocean surface boundary conditions (i.e., ice concentration and surface temperature) drive all of the models. This study evaluated modeled surface heat fluxes and cloud fields during May 1998, a month that included the onset of the surface icemelt. In general, observations agreed with simulated surface pressure and near-surface air properties. Simulation errors due to surface fluxes and cloud effects biased the net simulated surface heat flux, which in turn affected the timing of the simulated icemelt. Modeled cloud geometry and precipitation suggest that the RCA model produced the most accurate cloud scheme, followed by the HIRHAM model. Evaluation of a relationship between cloud water paths and radiation showed that a radiative transfer scheme in ARCSYM was closely matched with the observation when liquid clouds were dominant. Clouds and radiation are of course closely linked, and an additional comparison of the radiative transfer codes for ARCSYM and COAMPS was performed for clear-sky conditions, thereby excluding cloud effects. Overall, the schemes for radiative transfer in ARCSYM and for cloud microphysics in RCA potentially have some advantages for modeling the springtime Arctic.
Abstract
To improve simulations of the Arctic climate and to quantify climate model errors, four regional climate models [the Arctic Regional Climate System Model (ARCSYM), the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS), the High-Resolution Limited-Area Model (HIRHAM), and the Rossby Center Atmospheric Model (RCA)] have simulated the annual Surface Heat Budget of the Arctic Ocean (SHEBA) under the Arctic Regional Climate Model Intercomparison Project (ARCMIP). The same lateral boundary and ocean surface boundary conditions (i.e., ice concentration and surface temperature) drive all of the models. This study evaluated modeled surface heat fluxes and cloud fields during May 1998, a month that included the onset of the surface icemelt. In general, observations agreed with simulated surface pressure and near-surface air properties. Simulation errors due to surface fluxes and cloud effects biased the net simulated surface heat flux, which in turn affected the timing of the simulated icemelt. Modeled cloud geometry and precipitation suggest that the RCA model produced the most accurate cloud scheme, followed by the HIRHAM model. Evaluation of a relationship between cloud water paths and radiation showed that a radiative transfer scheme in ARCSYM was closely matched with the observation when liquid clouds were dominant. Clouds and radiation are of course closely linked, and an additional comparison of the radiative transfer codes for ARCSYM and COAMPS was performed for clear-sky conditions, thereby excluding cloud effects. Overall, the schemes for radiative transfer in ARCSYM and for cloud microphysics in RCA potentially have some advantages for modeling the springtime Arctic.
Abstract
To improve the forecasting performance in dynamically active coastal waters forced by winds, tides, and river discharges in a coupled estuary–shelf model off Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation method has been developed and implemented. The system assimilates the conductivity–temperature–depth (CTD) profilers, time series buoy measurement, and remote sensing sea surface temperature (SST) data into a high-resolution estuary–shelf ocean model around Hong Kong. We found that the time window selection associated with the local dynamics and the number of observation samples are two key factors in improving assimilation in the unique estuary–shelf system. DA with a varied assimilation time window that is based on the intratidal variation in the local dynamics can reduce the errors in the estimation of the innovation vector caused by the model–observation mismatch at the analysis time and improve simulation greatly in both the estuary and coastal regions. Statistically, the overall root-mean-square error (RMSE) between the DA forecasts and not-yet-assimilated observations for temperature and salinity has been reduced by 33.0% and 31.9% in the experiment period, respectively. By assimilating higher-resolution remote sensing SST data instead of lower-resolution satellite SST, the RMSE of SST is improved by ~18%. Besides, by assimilating real-time buoy mooring data, the model bias can be continuously corrected both around the buoy location and beyond. The assimilation of the combined buoy, CTD, and SST data can provide an overall improvement of the simulated three-dimensional solution. A dynamics-oriented assimilation scheme is essential for the improvement of model forecasting in the estuary–shelf system under multiple forcings.
Abstract
To improve the forecasting performance in dynamically active coastal waters forced by winds, tides, and river discharges in a coupled estuary–shelf model off Hong Kong, a multivariable data assimilation (DA) system using the ensemble optimal interpolation method has been developed and implemented. The system assimilates the conductivity–temperature–depth (CTD) profilers, time series buoy measurement, and remote sensing sea surface temperature (SST) data into a high-resolution estuary–shelf ocean model around Hong Kong. We found that the time window selection associated with the local dynamics and the number of observation samples are two key factors in improving assimilation in the unique estuary–shelf system. DA with a varied assimilation time window that is based on the intratidal variation in the local dynamics can reduce the errors in the estimation of the innovation vector caused by the model–observation mismatch at the analysis time and improve simulation greatly in both the estuary and coastal regions. Statistically, the overall root-mean-square error (RMSE) between the DA forecasts and not-yet-assimilated observations for temperature and salinity has been reduced by 33.0% and 31.9% in the experiment period, respectively. By assimilating higher-resolution remote sensing SST data instead of lower-resolution satellite SST, the RMSE of SST is improved by ~18%. Besides, by assimilating real-time buoy mooring data, the model bias can be continuously corrected both around the buoy location and beyond. The assimilation of the combined buoy, CTD, and SST data can provide an overall improvement of the simulated three-dimensional solution. A dynamics-oriented assimilation scheme is essential for the improvement of model forecasting in the estuary–shelf system under multiple forcings.
Abstract
Arctic sea ice intraseasonal variation (ISV) is crucial for understanding and predicting atmospheric subseasonal variations over the middle and high latitudes but unclear. Sea ice concentration (SIC) over the northern Barents Sea (NBS) features large ISV during the melting season (April–July). Based on the observed SIC, this study finds that the NBS SIC ISV in the melting season is dominated by 30–60-day periodicity. The composite analysis, using 34 significant 30–60-day sea ice melting events during 1989–2017, demonstrates that 30–60-day circumpolar clockwise-propagating atmospheric waves (CCPW) are concurrent with the NBS SIC ISV, which features zonal wavenumber 1 along 65°N and a typical quasi-barotropic structure. Further analysis finds that the 30–60-day surface air temperature (SAT) evidently leads the SIC variations by nearly 6 days over the NBS, which is primarily caused by low-level meridional thermal advection linked with the 30–60-day CCPW. The positive anomalies of the downward sensible heat and longwave radiative fluxes, caused by the increased SAT and atmospheric moisture, play the dominant roles in melting the sea ice on the 30–60-day time scale over the NBS. The increased atmospheric moisture is mainly ascribed to the increased horizontal moisture advection influence by the 30–60-day CCPW. This study strongly suggests that the atmospheric ISV is a crucial precursor for NBS sea ice intraseasonal changes in boreal summer, and more accurate subseasonal predictions of atmospheric circulation, temperature, and moisture are indispensable for improving sea ice subseasonal prediction over the Arctic region.
Significance Statement
Northern Barents Sea (NBS) sea ice intraseasonal variation (ISV) is crucial for understanding mid- to high-latitude climate variations as well as new trans-Arctic shipping predictions but lacks solid knowledge. This study found that the 30–60-day variation is the dominant ISV periodicity of NBS sea ice change during summer, which is essentially modulated by circumpolar clockwise-propagating atmospheric waves. The atmospheric wave-induced meridional thermal advection modulates the surface temperature and atmospheric moisture, causes the changes of downward sensible heat and longwave radiative fluxes, and eventually dominantly regulates the 30–60-day sea ice variations. The mechanism of sea ice ISV strongly suggests that accurately predicting the atmospheric fields is indispensable for obtaining more accurate sea ice subseasonal prediction.
Abstract
Arctic sea ice intraseasonal variation (ISV) is crucial for understanding and predicting atmospheric subseasonal variations over the middle and high latitudes but unclear. Sea ice concentration (SIC) over the northern Barents Sea (NBS) features large ISV during the melting season (April–July). Based on the observed SIC, this study finds that the NBS SIC ISV in the melting season is dominated by 30–60-day periodicity. The composite analysis, using 34 significant 30–60-day sea ice melting events during 1989–2017, demonstrates that 30–60-day circumpolar clockwise-propagating atmospheric waves (CCPW) are concurrent with the NBS SIC ISV, which features zonal wavenumber 1 along 65°N and a typical quasi-barotropic structure. Further analysis finds that the 30–60-day surface air temperature (SAT) evidently leads the SIC variations by nearly 6 days over the NBS, which is primarily caused by low-level meridional thermal advection linked with the 30–60-day CCPW. The positive anomalies of the downward sensible heat and longwave radiative fluxes, caused by the increased SAT and atmospheric moisture, play the dominant roles in melting the sea ice on the 30–60-day time scale over the NBS. The increased atmospheric moisture is mainly ascribed to the increased horizontal moisture advection influence by the 30–60-day CCPW. This study strongly suggests that the atmospheric ISV is a crucial precursor for NBS sea ice intraseasonal changes in boreal summer, and more accurate subseasonal predictions of atmospheric circulation, temperature, and moisture are indispensable for improving sea ice subseasonal prediction over the Arctic region.
Significance Statement
Northern Barents Sea (NBS) sea ice intraseasonal variation (ISV) is crucial for understanding mid- to high-latitude climate variations as well as new trans-Arctic shipping predictions but lacks solid knowledge. This study found that the 30–60-day variation is the dominant ISV periodicity of NBS sea ice change during summer, which is essentially modulated by circumpolar clockwise-propagating atmospheric waves. The atmospheric wave-induced meridional thermal advection modulates the surface temperature and atmospheric moisture, causes the changes of downward sensible heat and longwave radiative fluxes, and eventually dominantly regulates the 30–60-day sea ice variations. The mechanism of sea ice ISV strongly suggests that accurately predicting the atmospheric fields is indispensable for obtaining more accurate sea ice subseasonal prediction.
Abstract
The Southern Ocean, characterized by strong westerly winds and a rough sea state, exhibits the most pronounced sea spray effects. Sea spray ejected by ocean surface waves enhances heat and water exchange at the air–sea interface. However, this process has not been considered in current climate models, and the influence of sea spray on the coupled air–sea system remains largely unknown. This study incorporated a parameterization of the sea spray influence on latent and sensible heat fluxes into the First Institute of Oceanography Earth System Model version 2.0 (FIO-ESM v2.0), a climate model coupled with an ocean surface waves component. The results indicate that the spray-mediated enthalpy flux accounted for over 20%–50% of the total value. Sea spray promoted ocean evaporation and heat transport, resulting in air and ocean surface cooling and strengthened westerly winds. Furthermore, a moist and stable atmosphere favored an increase in cloud fraction over the Southern Ocean, particularly low-level clouds. Increased clouds reflected downward shortwave radiation and reduced solar radiation absorption at the surface. At present, the climate models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer notable deficiencies in reasonably reproducing the climatological features of the Southern Ocean, including warm SST and underestimated clouds biases with more absorbed shortwave radiation. Our results suggest that consideration of sea spray effects is a feasible solution to mitigate these common biases and enhance the confidence in simulations and predictions with climate models.
Abstract
The Southern Ocean, characterized by strong westerly winds and a rough sea state, exhibits the most pronounced sea spray effects. Sea spray ejected by ocean surface waves enhances heat and water exchange at the air–sea interface. However, this process has not been considered in current climate models, and the influence of sea spray on the coupled air–sea system remains largely unknown. This study incorporated a parameterization of the sea spray influence on latent and sensible heat fluxes into the First Institute of Oceanography Earth System Model version 2.0 (FIO-ESM v2.0), a climate model coupled with an ocean surface waves component. The results indicate that the spray-mediated enthalpy flux accounted for over 20%–50% of the total value. Sea spray promoted ocean evaporation and heat transport, resulting in air and ocean surface cooling and strengthened westerly winds. Furthermore, a moist and stable atmosphere favored an increase in cloud fraction over the Southern Ocean, particularly low-level clouds. Increased clouds reflected downward shortwave radiation and reduced solar radiation absorption at the surface. At present, the climate models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer notable deficiencies in reasonably reproducing the climatological features of the Southern Ocean, including warm SST and underestimated clouds biases with more absorbed shortwave radiation. Our results suggest that consideration of sea spray effects is a feasible solution to mitigate these common biases and enhance the confidence in simulations and predictions with climate models.
Abstract
Observed daily precipitation data were used to investigate the characteristics of precipitation at Antarctic Progress Station and synoptic patterns associated with extreme precipitation events during the period 2003–16. The annual precipitation, annual number of extreme precipitation events, and amount of precipitation during the extreme events have positive trends. The distribution of precipitation at Progress Station is heavily skewed with a long tail of extreme dry days and a high peak of extreme wet days. The synoptic pattern associated with extreme precipitation events is a dipole structure of negative and positive height anomalies to the west and east of Progress Station, respectively, resulting in water vapor advection to the station. For the first time, we apply self-organizing maps (SOMs) to examine thermodynamic and dynamic perspectives of trends in the frequency of occurrence of Antarctic extreme precipitation events. The changes in thermodynamic (noncirculation) processes explain 80% of the trend, followed by the changes in the interaction between thermodynamic and dynamic processes, which account for nearly 25% of the trend. The changes in dynamic processes make a negative (less than 5%) contribution to the trend. The positive trend in total column water vapor over the Southern Ocean explains the change of thermodynamic term.
Abstract
Observed daily precipitation data were used to investigate the characteristics of precipitation at Antarctic Progress Station and synoptic patterns associated with extreme precipitation events during the period 2003–16. The annual precipitation, annual number of extreme precipitation events, and amount of precipitation during the extreme events have positive trends. The distribution of precipitation at Progress Station is heavily skewed with a long tail of extreme dry days and a high peak of extreme wet days. The synoptic pattern associated with extreme precipitation events is a dipole structure of negative and positive height anomalies to the west and east of Progress Station, respectively, resulting in water vapor advection to the station. For the first time, we apply self-organizing maps (SOMs) to examine thermodynamic and dynamic perspectives of trends in the frequency of occurrence of Antarctic extreme precipitation events. The changes in thermodynamic (noncirculation) processes explain 80% of the trend, followed by the changes in the interaction between thermodynamic and dynamic processes, which account for nearly 25% of the trend. The changes in dynamic processes make a negative (less than 5%) contribution to the trend. The positive trend in total column water vapor over the Southern Ocean explains the change of thermodynamic term.
Abstract
In recent decades, the Greenland ice sheet has experienced increased surface melt. However, the underlying cause of this increased surface melting and how it relates to cryospheric changes across the Arctic remain unclear. Here it is shown that an important contributing factor is the decreasing Arctic sea ice. Reduced summer sea ice favors stronger and more frequent occurrences of blocking-high pressure events over Greenland. Blocking highs enhance the transport of warm, moist air over Greenland, which increases downwelling infrared radiation, contributes to increased extreme heat events, and accounts for the majority of the observed warming trends. These findings are supported by analyses of observations and reanalysis data, as well as by independent atmospheric model simulations using a state-of-the-art atmospheric model that is forced by varying only the sea ice conditions. Reduced sea ice conditions in the model favor more extensive Greenland surface melting. The authors find a positive feedback between the variability in the extent of summer Arctic sea ice and melt area of the summer Greenland ice sheet, which affects the Greenland ice sheet mass balance. This linkage may improve the projections of changes in the global sea level and thermohaline circulation.
Abstract
In recent decades, the Greenland ice sheet has experienced increased surface melt. However, the underlying cause of this increased surface melting and how it relates to cryospheric changes across the Arctic remain unclear. Here it is shown that an important contributing factor is the decreasing Arctic sea ice. Reduced summer sea ice favors stronger and more frequent occurrences of blocking-high pressure events over Greenland. Blocking highs enhance the transport of warm, moist air over Greenland, which increases downwelling infrared radiation, contributes to increased extreme heat events, and accounts for the majority of the observed warming trends. These findings are supported by analyses of observations and reanalysis data, as well as by independent atmospheric model simulations using a state-of-the-art atmospheric model that is forced by varying only the sea ice conditions. Reduced sea ice conditions in the model favor more extensive Greenland surface melting. The authors find a positive feedback between the variability in the extent of summer Arctic sea ice and melt area of the summer Greenland ice sheet, which affects the Greenland ice sheet mass balance. This linkage may improve the projections of changes in the global sea level and thermohaline circulation.
Abstract
Reanalysis projects and satellite data analysis have provided surface wind over the global ocean. To assess how well one can reconstruct the variations of surface wind in the data-sparse Southern Ocean, sea surface wind speed data from 1) the National Centers for Environmental Prediction–Department of Energy reanalysis (NCEP–DOE), 2) the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim), 3) National Climate Data Center (NCDC) blended sea winds, and 4) cross-calibrated multiplatform (CCMP) ocean surface velocity are evaluated. First, the accuracy of sea surface wind speed is validated with quality-controlled in situ measurements from research vessels. The results show that the CCMP value is closer to the ship observations than other products, whereas the NCEP–DOE value has the largest systematic positive bias. All four products show large positive biases under weak wind regimes, good agreement with the ship observations under moderate wind regimes, and large negative biases under high wind regimes. Second, the consistency and discrepancy of sea surface wind speed across different products is examined. The intercomparisons suggest that these products show encouraging agreement in the spatial distribution of the annual-mean sea surface wind speed. The largest across-data scatter is found in the central Indian sector of the Antarctic Circumpolar Current, which is comparable to its respective interannual variability. The monthly-mean correlations between pairs of products are high. However, differing from the decadal trends of NCEP–DOE, NCDC, and CCMP that show an increase of sea surface wind speed in the Antarctic Circumpolar region, ERA-Interim has an opposite sign there.
Abstract
Reanalysis projects and satellite data analysis have provided surface wind over the global ocean. To assess how well one can reconstruct the variations of surface wind in the data-sparse Southern Ocean, sea surface wind speed data from 1) the National Centers for Environmental Prediction–Department of Energy reanalysis (NCEP–DOE), 2) the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim), 3) National Climate Data Center (NCDC) blended sea winds, and 4) cross-calibrated multiplatform (CCMP) ocean surface velocity are evaluated. First, the accuracy of sea surface wind speed is validated with quality-controlled in situ measurements from research vessels. The results show that the CCMP value is closer to the ship observations than other products, whereas the NCEP–DOE value has the largest systematic positive bias. All four products show large positive biases under weak wind regimes, good agreement with the ship observations under moderate wind regimes, and large negative biases under high wind regimes. Second, the consistency and discrepancy of sea surface wind speed across different products is examined. The intercomparisons suggest that these products show encouraging agreement in the spatial distribution of the annual-mean sea surface wind speed. The largest across-data scatter is found in the central Indian sector of the Antarctic Circumpolar Current, which is comparable to its respective interannual variability. The monthly-mean correlations between pairs of products are high. However, differing from the decadal trends of NCEP–DOE, NCDC, and CCMP that show an increase of sea surface wind speed in the Antarctic Circumpolar region, ERA-Interim has an opposite sign there.
Abstract
This study presents a new high-resolution satellite-derived ocean surface flux product, XSeaFlux, which is evaluated for its potential use in hurricane studies. The XSeaFlux employs new satellite datasets using improved retrieval methods, and uses a new bulk flux algorithm formulated for high wind conditions. The XSeaFlux latent heat flux (LHF) performs much better than the existing numerical weather prediction reanalysis and satellite-derived flux products in a comparison with measurements from the Coupled Boundary Layer Air–Sea Transfer (CBLAST) field experiment. Also, the XSeaFlux shows well-organized LHF structure and large LHF values in response to hurricane conditions relative to the other flux products. The XSeaFlux dataset is used to interpret details of the ocean surface LHF for selected North Atlantic hurricanes. Analysis of the XSeaFlux dataset suggests that ocean waves, sea spray, and cold wake have substantial impacts on LHF associated with the hurricanes.
Abstract
This study presents a new high-resolution satellite-derived ocean surface flux product, XSeaFlux, which is evaluated for its potential use in hurricane studies. The XSeaFlux employs new satellite datasets using improved retrieval methods, and uses a new bulk flux algorithm formulated for high wind conditions. The XSeaFlux latent heat flux (LHF) performs much better than the existing numerical weather prediction reanalysis and satellite-derived flux products in a comparison with measurements from the Coupled Boundary Layer Air–Sea Transfer (CBLAST) field experiment. Also, the XSeaFlux shows well-organized LHF structure and large LHF values in response to hurricane conditions relative to the other flux products. The XSeaFlux dataset is used to interpret details of the ocean surface LHF for selected North Atlantic hurricanes. Analysis of the XSeaFlux dataset suggests that ocean waves, sea spray, and cold wake have substantial impacts on LHF associated with the hurricanes.