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- Author or Editor: Jiande Wang x
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Abstract
Climate variability in the tropical Atlantic sector as represented in six atmospheric general circulation models is examined. On the annual mean, most simulations overestimate wind stress away from the equator although much of the variability can be accounted for by differences in drag formulations. Most models produce excessive latent heat flux as a consequence of errors in boundary layer humidity. Systematic errors are also evident in precipitation and surface wind divergence fields. The seasonal cycle of the zonal trade winds is stronger than observed in most simulations, while the meridional component is well represented.
Next interannual variability is considered, focusing on two tropical patterns (Atlantic Niño and interhemispheric modes). The directions of the surface wind anomalies in the models are found to be generally similar to observations, although the magnitude of the wind stress response varies greatly among models. However, all models fail to reproduce the wind–latent heat feedback believed to be essential to interannual variability in this basin.
Abstract
Climate variability in the tropical Atlantic sector as represented in six atmospheric general circulation models is examined. On the annual mean, most simulations overestimate wind stress away from the equator although much of the variability can be accounted for by differences in drag formulations. Most models produce excessive latent heat flux as a consequence of errors in boundary layer humidity. Systematic errors are also evident in precipitation and surface wind divergence fields. The seasonal cycle of the zonal trade winds is stronger than observed in most simulations, while the meridional component is well represented.
Next interannual variability is considered, focusing on two tropical patterns (Atlantic Niño and interhemispheric modes). The directions of the surface wind anomalies in the models are found to be generally similar to observations, although the magnitude of the wind stress response varies greatly among models. However, all models fail to reproduce the wind–latent heat feedback believed to be essential to interannual variability in this basin.
Abstract
Here, seasonal heat transport in the North Pacific and North Atlantic Oceans is compared using a 49-year-long analysis based on data assimilation. In midlatitudes surface heat flux is largely balanced by seasonal storage, while equatorward of 15°N, divergence of heat transport balances seasonal storage. The seasonal cycle of heat transport in the Pacific is in phase with the annual migration of solar radiation, transporting heat from the warm hemisphere to the cool hemisphere. Analysis shows that the cycle is large with peak-to-peak shifts of 5 PW. To examine the cause of these large shifts, a vertical and zonal decomposition of the heat budget is carried out. Important contributions are found from the annual cycle of wind drift in the mixed layer and adiabatically compensating return flow, part of the vigorous shallow tropical overturning cell. The annual cycle of heat transport in the North Atlantic is also large. Here too, wind-driven transports play a role, although not as strongly as in the Pacific, and this is an important reason for the differences in heat transport between the basins. Analysis shows the extent to which seasonally varying geostrophic currents and seasonal diabatic effects are relatively more important in the Atlantic. Thus, although the annual cycle of zonally integrated mass transport in the mixed layer is only 1/5 as large, the time-averaged heat transport is nearly as large as in the Pacific. This difference in transport mechanics gives rise to changes in the phase of seasonal heat transport with latitude in the Atlantic.
Abstract
Here, seasonal heat transport in the North Pacific and North Atlantic Oceans is compared using a 49-year-long analysis based on data assimilation. In midlatitudes surface heat flux is largely balanced by seasonal storage, while equatorward of 15°N, divergence of heat transport balances seasonal storage. The seasonal cycle of heat transport in the Pacific is in phase with the annual migration of solar radiation, transporting heat from the warm hemisphere to the cool hemisphere. Analysis shows that the cycle is large with peak-to-peak shifts of 5 PW. To examine the cause of these large shifts, a vertical and zonal decomposition of the heat budget is carried out. Important contributions are found from the annual cycle of wind drift in the mixed layer and adiabatically compensating return flow, part of the vigorous shallow tropical overturning cell. The annual cycle of heat transport in the North Atlantic is also large. Here too, wind-driven transports play a role, although not as strongly as in the Pacific, and this is an important reason for the differences in heat transport between the basins. Analysis shows the extent to which seasonally varying geostrophic currents and seasonal diabatic effects are relatively more important in the Atlantic. Thus, although the annual cycle of zonally integrated mass transport in the mixed layer is only 1/5 as large, the time-averaged heat transport is nearly as large as in the Pacific. This difference in transport mechanics gives rise to changes in the phase of seasonal heat transport with latitude in the Atlantic.
Abstract
Different measures of wind influence the ocean in different ways. In particular, the time-averaged mixed layer turbulent energy production rate is proportional to 〈u 3 *〉, where u * is the “oceanic friction velocity” that is based on wind stress. Estimating 〈u 3 *〉 from monthly averages of wind stress or wind speed may introduce large biases due to the day-to-day variability of the direction and magnitude of the wind. The authors create monthly climatologies of 〈u 3 *〉 from daily wind stress measurements obtained from the Goddard Satellite-based Surface Turbulent Fluxes version 2 (GSSTF2; based on satellite microwave measurements), the Quick Scatterometer (QuikSCAT; based on satellite scatterometry measurements), and the National Centers for Environmental Prediction (NCEP) reanalysis wind. The differences among zonal averages of these climatologies and of a similar climatology based on the da Silva version of the Comprehensive Ocean–Atmosphere Data Set (COADS) have a complex dependence on latitude. These differences are typically 10%–30% of the climatological values. The GSSTF2 data confirm that 〈u 3 *〉 is much larger than estimates from monthly averaged wind stress or wind speed, especially outside the Tropics.
Abstract
Different measures of wind influence the ocean in different ways. In particular, the time-averaged mixed layer turbulent energy production rate is proportional to 〈u 3 *〉, where u * is the “oceanic friction velocity” that is based on wind stress. Estimating 〈u 3 *〉 from monthly averages of wind stress or wind speed may introduce large biases due to the day-to-day variability of the direction and magnitude of the wind. The authors create monthly climatologies of 〈u 3 *〉 from daily wind stress measurements obtained from the Goddard Satellite-based Surface Turbulent Fluxes version 2 (GSSTF2; based on satellite microwave measurements), the Quick Scatterometer (QuikSCAT; based on satellite scatterometry measurements), and the National Centers for Environmental Prediction (NCEP) reanalysis wind. The differences among zonal averages of these climatologies and of a similar climatology based on the da Silva version of the Comprehensive Ocean–Atmosphere Data Set (COADS) have a complex dependence on latitude. These differences are typically 10%–30% of the climatological values. The GSSTF2 data confirm that 〈u 3 *〉 is much larger than estimates from monthly averaged wind stress or wind speed, especially outside the Tropics.
Abstract
In this study, a series of ocean observing system simulation experiments (OSSEs) are conducted in support of the Tropical Pacific Observing System (TPOS) 2020 Project (TPOS 2020), which was established in 2014, with aims to develop a more sustainable and resilient observing system for the tropical Pacific. The experiments are based on an ocean data assimilation system that is under development at the Joint Center for Satellite Data Assimilation (JCSDA) and the Environmental Modeling Center (EMC)/National Centers for Environmental Prediction (NCEP). The atmospheric forcing and synthetic ocean observations are generated from a nature run, which is based on a modified CFSv2 with a vertical ocean resolution of 1 m near the ocean surface. To explore the efficacy of TAO/TRITON and Argo observations in TPOS, synthetic ocean temperature and salinity observations were constructed by sampling the nature run following their present distributions. Our experiments include a free run with no “observations” assimilated, and assimilation runs with the TAO/TRITON and Argo synthetic observations assimilated separately or jointly. These experiments were analyzed by comparing their long-term mean states and variabilities at different time scales [i.e., low-frequency (>90 days), intraseasonal (20–90 days), and high-frequency (<20 days)]. It was found that 1) both TAO/TRITON and especially Argo effectively improve the estimation of mean states and low-frequency variations; 2) on the intraseasonal time scale, Argo has more significant improvements than TAO/TRITON (except for regions close to TAO/TRITON sites); and 3) on the high-frequency time scale, both TAO/TRITON and Argo have evident deficits (although for TAO/TRITON, limited improvements were present close to TAO/TRITON sites).
Abstract
In this study, a series of ocean observing system simulation experiments (OSSEs) are conducted in support of the Tropical Pacific Observing System (TPOS) 2020 Project (TPOS 2020), which was established in 2014, with aims to develop a more sustainable and resilient observing system for the tropical Pacific. The experiments are based on an ocean data assimilation system that is under development at the Joint Center for Satellite Data Assimilation (JCSDA) and the Environmental Modeling Center (EMC)/National Centers for Environmental Prediction (NCEP). The atmospheric forcing and synthetic ocean observations are generated from a nature run, which is based on a modified CFSv2 with a vertical ocean resolution of 1 m near the ocean surface. To explore the efficacy of TAO/TRITON and Argo observations in TPOS, synthetic ocean temperature and salinity observations were constructed by sampling the nature run following their present distributions. Our experiments include a free run with no “observations” assimilated, and assimilation runs with the TAO/TRITON and Argo synthetic observations assimilated separately or jointly. These experiments were analyzed by comparing their long-term mean states and variabilities at different time scales [i.e., low-frequency (>90 days), intraseasonal (20–90 days), and high-frequency (<20 days)]. It was found that 1) both TAO/TRITON and especially Argo effectively improve the estimation of mean states and low-frequency variations; 2) on the intraseasonal time scale, Argo has more significant improvements than TAO/TRITON (except for regions close to TAO/TRITON sites); and 3) on the high-frequency time scale, both TAO/TRITON and Argo have evident deficits (although for TAO/TRITON, limited improvements were present close to TAO/TRITON sites).
Abstract
The impact of tropical Pacific sea surface temperature (SST) biases on the deterministic skill of the Unified Forecast System (UFS) coupled model Prototype 5 is evaluated during weeks 1–4 of the forecast. The evaluation is limited to the contiguous United States (CONUS) and two seasons: boreal summer (June–September) and winter (December–March). The tropical SST in the UFS model is warmer than in observations and bias patterns show seasonal dependence especially in the central and western Pacific. During boreal summer, the bias is located north of the equator whereas in winter, the bias is located in the Southern Hemisphere. A regression analysis indicates a significant relationship between these SST biases and the biases in the surface temperature and precipitation over the CONUS along with midtroposphere large-scale circulation and North Pacific storm-track activity. The SST biases affect the biases in other fields from week 1 of the forecast and the impact becomes stronger as the lead time increases to week 4. The impact of SST biases on the biases in other fields show a qualitative relationship to the patterns of forecast errors of the fields.
Abstract
The impact of tropical Pacific sea surface temperature (SST) biases on the deterministic skill of the Unified Forecast System (UFS) coupled model Prototype 5 is evaluated during weeks 1–4 of the forecast. The evaluation is limited to the contiguous United States (CONUS) and two seasons: boreal summer (June–September) and winter (December–March). The tropical SST in the UFS model is warmer than in observations and bias patterns show seasonal dependence especially in the central and western Pacific. During boreal summer, the bias is located north of the equator whereas in winter, the bias is located in the Southern Hemisphere. A regression analysis indicates a significant relationship between these SST biases and the biases in the surface temperature and precipitation over the CONUS along with midtroposphere large-scale circulation and North Pacific storm-track activity. The SST biases affect the biases in other fields from week 1 of the forecast and the impact becomes stronger as the lead time increases to week 4. The impact of SST biases on the biases in other fields show a qualitative relationship to the patterns of forecast errors of the fields.
Abstract
This work investigates the impact of tropical sea surface temperature (SST) biases on the Subseasonal to Seasonal Prediction project (S2S) precipitation forecast skill over the contiguous United States (CONUS) in the Unified Forecast System (UFS) coupled model Prototype 6. Boreal summer (June–September) and winter (December–March) for 2011–18 were analyzed. The impact of tropical west Pacific (WP) and tropical North Atlantic (TNA) warm SST biases is evaluated using multivariate linear regression analysis. A warm SST bias over the WP influences the CONUS precipitation remotely through a Rossby wave train in both seasons. During boreal winter, a warm SST bias over the TNA partly affects the magnitude of the North Atlantic subtropical high (NASH)’s center, which in the reforecasts is weaker than in reanalysis. The weaker NASH favors an enhanced moisture transport from the Gulf of Mexico, leading to increased precipitation over the Southeast United States. Compared to reanalysis, during boreal summer, the NASH’s center is also weaker and in addition, its position is displaced to the northeast. The displacement further affects the CONUS summer precipitation. The SST biases over the two tropical regions and their impacts become stronger as the forecast lead increases from week 1 to 4. These tropical biases explain up to 10% of the CONUS precipitation biases on the S2S time scale.
Abstract
This work investigates the impact of tropical sea surface temperature (SST) biases on the Subseasonal to Seasonal Prediction project (S2S) precipitation forecast skill over the contiguous United States (CONUS) in the Unified Forecast System (UFS) coupled model Prototype 6. Boreal summer (June–September) and winter (December–March) for 2011–18 were analyzed. The impact of tropical west Pacific (WP) and tropical North Atlantic (TNA) warm SST biases is evaluated using multivariate linear regression analysis. A warm SST bias over the WP influences the CONUS precipitation remotely through a Rossby wave train in both seasons. During boreal winter, a warm SST bias over the TNA partly affects the magnitude of the North Atlantic subtropical high (NASH)’s center, which in the reforecasts is weaker than in reanalysis. The weaker NASH favors an enhanced moisture transport from the Gulf of Mexico, leading to increased precipitation over the Southeast United States. Compared to reanalysis, during boreal summer, the NASH’s center is also weaker and in addition, its position is displaced to the northeast. The displacement further affects the CONUS summer precipitation. The SST biases over the two tropical regions and their impacts become stronger as the forecast lead increases from week 1 to 4. These tropical biases explain up to 10% of the CONUS precipitation biases on the S2S time scale.
Abstract
The predictability of the Unified Forecast System (UFS) Coupled Model Prototype 2 developed by the National Centers for Environmental Prediction is assessed for the boreal summer over the continental United States (CONUS). The retrospective forecasts of low-level horizontal wind, precipitation and 2-m temperature for 2011–17 are examined to determine the predictability at subseasonal time scale. Using a data-adaptive method, the leading modes of variability are obtained and identified to be related to El Niño–Southern Oscillation (ENSO), intraseasonal oscillation (ISO), and warming trend. In a new approach, the sources of enhanced predictability are identified by examining the forecast errors and correlations in the weekly averages of the leading modes of variability. During the boreal summer, the ISO followed by the trend in UFS are found to provide better predictability in weeks 1–4 compared to the ENSO mode and the total anomaly. The western CONUS seems to have better predictability on weekly time scale in all three modes.
Abstract
The predictability of the Unified Forecast System (UFS) Coupled Model Prototype 2 developed by the National Centers for Environmental Prediction is assessed for the boreal summer over the continental United States (CONUS). The retrospective forecasts of low-level horizontal wind, precipitation and 2-m temperature for 2011–17 are examined to determine the predictability at subseasonal time scale. Using a data-adaptive method, the leading modes of variability are obtained and identified to be related to El Niño–Southern Oscillation (ENSO), intraseasonal oscillation (ISO), and warming trend. In a new approach, the sources of enhanced predictability are identified by examining the forecast errors and correlations in the weekly averages of the leading modes of variability. During the boreal summer, the ISO followed by the trend in UFS are found to provide better predictability in weeks 1–4 compared to the ENSO mode and the total anomaly. The western CONUS seems to have better predictability on weekly time scale in all three modes.
Abstract
The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.
Abstract
The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.
The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.
CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.
Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.
The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.
CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.
Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.
