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Since 27 September 1997, the Scripps Experimental Climate Prediction Center (ECPC) has been making near real-time experimental global and regional dynamical forecasts with the National Centers for Environmental Prediction (NCEP) global spectral model (GSM) and the corresponding regional spectral model (RSM), which is based on the GSM, but which provides higher-resolution simulations and forecasts for limited regions. The global and regional forecast skill of the GSM was previously described in several papers. The purpose of this paper is to describe the RSM-based U.S. regional forecast system, various biases and errors in these regional U.S. forecasts, as well as the significant skill of the of temperature, precipitation, soil moisture, relative humidity, wind speed, and planetary boundary layer height forecasts at weekly to seasonal time scales. The skill of these RSM forecasts is comparable to the skill of the GSM forecasts.
Since 27 September 1997, the Scripps Experimental Climate Prediction Center (ECPC) has been making near real-time experimental global and regional dynamical forecasts with the National Centers for Environmental Prediction (NCEP) global spectral model (GSM) and the corresponding regional spectral model (RSM), which is based on the GSM, but which provides higher-resolution simulations and forecasts for limited regions. The global and regional forecast skill of the GSM was previously described in several papers. The purpose of this paper is to describe the RSM-based U.S. regional forecast system, various biases and errors in these regional U.S. forecasts, as well as the significant skill of the of temperature, precipitation, soil moisture, relative humidity, wind speed, and planetary boundary layer height forecasts at weekly to seasonal time scales. The skill of these RSM forecasts is comparable to the skill of the GSM forecasts.
Abstract
During the past several years, the Global Energy and Water Cycle Experiment (GEWEX) continental-scale experiments (CSEs) have started to develop regional hydroclimatological datasets and water and energy budget studies (WEBS). To provide some global background for these regional experiments, the authors describe vertically integrated global and regional water and energy budgets from the National Centers for Environmental Prediction (NCEP)–U.S. Department of Energy (DOE) Reanalysis II (NCEPRII). It is shown that maintaining the NCEPRII close to observations requires some nudging to the short-range model forecast, and this nudging is an important component of analysis budgets. Still, to first order one can discern important hydroclimatological mechanisms in the reanalysis. For example, during summer, atmospheric water vapor, precipitation, evaporation, and surface and atmospheric radiative heating all increase, while the dry static energy convergence decreases almost everywhere over the land regions. One can further distinguish differences between hydrologic cycles in midlatitudes and monsoon regions. The monsoon hydrologic cycle shows increased moisture convergence, soil moisture, and runoff, but decreased sensible heating with increasing surface temperature. The midlatitude hydrologic cycle, on the other hand, shows decreased moisture convergence and surface water, and increased sensible heating.
Abstract
During the past several years, the Global Energy and Water Cycle Experiment (GEWEX) continental-scale experiments (CSEs) have started to develop regional hydroclimatological datasets and water and energy budget studies (WEBS). To provide some global background for these regional experiments, the authors describe vertically integrated global and regional water and energy budgets from the National Centers for Environmental Prediction (NCEP)–U.S. Department of Energy (DOE) Reanalysis II (NCEPRII). It is shown that maintaining the NCEPRII close to observations requires some nudging to the short-range model forecast, and this nudging is an important component of analysis budgets. Still, to first order one can discern important hydroclimatological mechanisms in the reanalysis. For example, during summer, atmospheric water vapor, precipitation, evaporation, and surface and atmospheric radiative heating all increase, while the dry static energy convergence decreases almost everywhere over the land regions. One can further distinguish differences between hydrologic cycles in midlatitudes and monsoon regions. The monsoon hydrologic cycle shows increased moisture convergence, soil moisture, and runoff, but decreased sensible heating with increasing surface temperature. The midlatitude hydrologic cycle, on the other hand, shows decreased moisture convergence and surface water, and increased sensible heating.
Abstract
A dynamical model incorporating observed field data is used to estimate the potential importance of linear and nonlinear vorticity advection to climate forecast models. Forecasts of 30-day averages benefit from inclusion of the linear advection term, but the nonlinear advection appears only marginally helpful. For intermediate averaging times (e.g., 10 days), both advection terms appear to be important. Analysis of the nonlinear terms suggests that they could be most adequately parameterized as a noise process that is “white” in wavenumber space and “red” in the time domain.
Abstract
A dynamical model incorporating observed field data is used to estimate the potential importance of linear and nonlinear vorticity advection to climate forecast models. Forecasts of 30-day averages benefit from inclusion of the linear advection term, but the nonlinear advection appears only marginally helpful. For intermediate averaging times (e.g., 10 days), both advection terms appear to be important. Analysis of the nonlinear terms suggests that they could be most adequately parameterized as a noise process that is “white” in wavenumber space and “red” in the time domain.
Abstract
A simplified coupled atmosphere-ocean model is used to explore the influence of evolving midlatitude sea surface temperature (SST) anomalies on the theoretical extended-range predictability of the atmospheric wintertime circulation in the Northern Hemisphere. After approximately two weeks, SST anomalies begin to significantly influence the overlying atmospheric flow, compared to flow over the climatological SST field. If the evolving sea surface temperature field is specified from model “observed” flows, then predictions of atmospheric time-averaged flow, for one month and longer averages, are significantly enhanced over predictions based on the atmospheric model with climatological SST. Predictions using the coupled model, however, are not significantly different from predictions using the atmospheric model with persistent SST anomalies, because SST anomalies are forced increasingly erroneously by atmospheric variables that rapidly lose their predictability.
Abstract
A simplified coupled atmosphere-ocean model is used to explore the influence of evolving midlatitude sea surface temperature (SST) anomalies on the theoretical extended-range predictability of the atmospheric wintertime circulation in the Northern Hemisphere. After approximately two weeks, SST anomalies begin to significantly influence the overlying atmospheric flow, compared to flow over the climatological SST field. If the evolving sea surface temperature field is specified from model “observed” flows, then predictions of atmospheric time-averaged flow, for one month and longer averages, are significantly enhanced over predictions based on the atmospheric model with climatological SST. Predictions using the coupled model, however, are not significantly different from predictions using the atmospheric model with persistent SST anomalies, because SST anomalies are forced increasingly erroneously by atmospheric variables that rapidly lose their predictability.
Abstract
This paper presents a new Experimental Climate Prediction Center (ECPC) Coupled Prediction Model (ECPM). The ECPM includes the Jet Propulsion Laboratory (JPL) version of the Massachusetts Institute of Technology (MIT) ocean model coupled to the ECPC version of the National Centers for Environmental Prediction (NCEP) Atmospheric Global Spectral Model (GSM). The adjoint and forward versions of the MIT model forced with the NCEP atmospheric analyses are routinely used at JPL for ocean state assimilation. An earlier version of the GSM was used for the NCEP–Department of Energy reanalysis-2 project and for operational seasonal forecasts at NCEP. The ECPM climatology and internal variability derived from a 56-yr-long coupled integration are compared with the observations and reanalysis data. Though the ECPM exhibits climatological biases, these biases are relatively small and comparable to the systematic errors produced by other well-known coupled models, including the recent NCEP Climate Forecast System. The internal variability of the model resembles the observations. ECPM simulates both seasonal and interannual variability in the tropical Pacific reasonably well. The model has good skill in reproducing the mechanism of ENSO evolution as well as ENSO teleconnection patterns (including the Indian monsoon–ENSO relationship). The skill of the ECPM in predicting 1994–2006 SST anomalies over the Niño-3.4 region is shown to be comparable to other coupled models. These retrospective forecasts were used for deriving a model climatology for real-time seasonal forecasts that are currently produced and displayed at ECPC.
Abstract
This paper presents a new Experimental Climate Prediction Center (ECPC) Coupled Prediction Model (ECPM). The ECPM includes the Jet Propulsion Laboratory (JPL) version of the Massachusetts Institute of Technology (MIT) ocean model coupled to the ECPC version of the National Centers for Environmental Prediction (NCEP) Atmospheric Global Spectral Model (GSM). The adjoint and forward versions of the MIT model forced with the NCEP atmospheric analyses are routinely used at JPL for ocean state assimilation. An earlier version of the GSM was used for the NCEP–Department of Energy reanalysis-2 project and for operational seasonal forecasts at NCEP. The ECPM climatology and internal variability derived from a 56-yr-long coupled integration are compared with the observations and reanalysis data. Though the ECPM exhibits climatological biases, these biases are relatively small and comparable to the systematic errors produced by other well-known coupled models, including the recent NCEP Climate Forecast System. The internal variability of the model resembles the observations. ECPM simulates both seasonal and interannual variability in the tropical Pacific reasonably well. The model has good skill in reproducing the mechanism of ENSO evolution as well as ENSO teleconnection patterns (including the Indian monsoon–ENSO relationship). The skill of the ECPM in predicting 1994–2006 SST anomalies over the Niño-3.4 region is shown to be comparable to other coupled models. These retrospective forecasts were used for deriving a model climatology for real-time seasonal forecasts that are currently produced and displayed at ECPC.
Abstract
A global quasi-geostrophic barotropic model, including orography, zonal forcing and frictional dissipation, is compared to two hemispheric models, one with antisymmetric equatorial boundary conditions and one with symmetric boundary conditions. The stationary solutions in the global model and the hemispheric models are found to be different, because the hemispheric models lack either the symmetric or antisymmetric waves, and because the nonlinear feedbacks are much larger in the hemispheric models. Time-dependent calculations show that the hemispheric models can excite anomalous Rossby waves and can produce erroneous short-range forecasts in middle latitudes. We conclude that global models are preferred for making both short-range and long-range forecasts for middle latitudes.
Abstract
A global quasi-geostrophic barotropic model, including orography, zonal forcing and frictional dissipation, is compared to two hemispheric models, one with antisymmetric equatorial boundary conditions and one with symmetric boundary conditions. The stationary solutions in the global model and the hemispheric models are found to be different, because the hemispheric models lack either the symmetric or antisymmetric waves, and because the nonlinear feedbacks are much larger in the hemispheric models. Time-dependent calculations show that the hemispheric models can excite anomalous Rossby waves and can produce erroneous short-range forecasts in middle latitudes. We conclude that global models are preferred for making both short-range and long-range forecasts for middle latitudes.
Abstract
The National Centers for Environmental Prediction’s operational global data assimilation system’s (GDAS) atmospheric and surface thermodynamic energy cycles are presented for the Mississippi River basin where the Global Energy and Water Cycle Experiment Continental-Scale International Project (GCIP) is under way. At the surface, during the winter, incoming solar radiation is balanced by longwave cooling. During the summer, latent and sensible cooling are equally important. In the atmosphere, thermodynamic energy convergence is also important, especially during the winter. In most places, precipitation is largely balanced by thermodynamic energy divergence. Anomalously high surface temperatures appear to be mainly related to decreased surface evaporation. Anomalously high (low) precipitation variations may also be related to anomalously high thermodynamic energy divergence (convergence). Unfortunately, residual terms, which are slightly noticeable for the GCIP climatological balances, are especially noticeable for the anomalous atmospheric balances.
Abstract
The National Centers for Environmental Prediction’s operational global data assimilation system’s (GDAS) atmospheric and surface thermodynamic energy cycles are presented for the Mississippi River basin where the Global Energy and Water Cycle Experiment Continental-Scale International Project (GCIP) is under way. At the surface, during the winter, incoming solar radiation is balanced by longwave cooling. During the summer, latent and sensible cooling are equally important. In the atmosphere, thermodynamic energy convergence is also important, especially during the winter. In most places, precipitation is largely balanced by thermodynamic energy divergence. Anomalously high surface temperatures appear to be mainly related to decreased surface evaporation. Anomalously high (low) precipitation variations may also be related to anomalously high thermodynamic energy divergence (convergence). Unfortunately, residual terms, which are slightly noticeable for the GCIP climatological balances, are especially noticeable for the anomalous atmospheric balances.
Abstract
A regional coupled ocean–atmosphere model is introduced. It is designed to admit the air–sea feedbacks arising in the presence of an oceanic mesoscale eddy field. It consists of the Regional Ocean Modeling System (ROMS) and the Regional Spectral Model (RSM). Large-scale forcing is provided by NCEP/DOE reanalysis fields, which have physics consistent with the RSM. Coupling allows the sea surface temperature (SST) to influence the stability of the atmospheric boundary layer and, hence, the surface wind stress and heat flux fields. The system is denominated the Scripps Coupled Ocean–Atmosphere Regional (SCOAR) Model.
The model is tested in three scenarios in the eastern Pacific Ocean sector: tropical instability waves of the eastern tropical Pacific, mesoscale eddies and fronts of the California Current System, and gap winds of the Central American coast. Recent observational evidence suggests air–sea interactions involving the oceanic mesoscale in these three regions. Evolving SST fronts are shown to drive an unambiguous response of the atmospheric boundary layer in the coupled model. This results in significant model anomalies of wind stress curl, wind stress divergence, surface heat flux, and precipitation that resemble the observations and substantiate the importance of ocean–atmosphere feedbacks involving the oceanic mesoscale.
Abstract
A regional coupled ocean–atmosphere model is introduced. It is designed to admit the air–sea feedbacks arising in the presence of an oceanic mesoscale eddy field. It consists of the Regional Ocean Modeling System (ROMS) and the Regional Spectral Model (RSM). Large-scale forcing is provided by NCEP/DOE reanalysis fields, which have physics consistent with the RSM. Coupling allows the sea surface temperature (SST) to influence the stability of the atmospheric boundary layer and, hence, the surface wind stress and heat flux fields. The system is denominated the Scripps Coupled Ocean–Atmosphere Regional (SCOAR) Model.
The model is tested in three scenarios in the eastern Pacific Ocean sector: tropical instability waves of the eastern tropical Pacific, mesoscale eddies and fronts of the California Current System, and gap winds of the Central American coast. Recent observational evidence suggests air–sea interactions involving the oceanic mesoscale in these three regions. Evolving SST fronts are shown to drive an unambiguous response of the atmospheric boundary layer in the coupled model. This results in significant model anomalies of wind stress curl, wind stress divergence, surface heat flux, and precipitation that resemble the observations and substantiate the importance of ocean–atmosphere feedbacks involving the oceanic mesoscale.
Abstract
The National Meteorological Center's (NMC's) twice-daily, global 2.5° pressure analyses of temperature, relative humidity, and wind speed are compared, over the coterminous United States, to the National Climatic Data Center's twice-daily, upper-air rawinsonde observations and hourly, first-order, surface observations for the period 1 January 1988 through 31 December 1992. NMCs analyses have clearly improved during this time period. Still, there are some noticeable differences especially near the surface and at 1200 UTC. During the early morning there is a warm bias, relative humidity is too low, and the surface wind speed is too strong. Weaker systematic errors occur during the late afternoon: there is a cold bias, relative humidity is too high, and the surface wind speed is still too strong. Aloft, the bias is noticeably reduced except for the wind speed, which is somewhat too weak. The analysis wind speed also has too strong temporal variations near the surface and too weak temporal variations aloft. The analysis climatology can be corrected at each station by removing the bias. Transient variations can be corrected simply by multiplying the analysis anomalies by the ratio of the station standard deviation to the analysis standard deviation. Correcting for the biases and spatially interpolating the analysis and station collections to a 0.5° grid provides a useful guess for local conditions, especially if there is not a surface or rawinsonde station within about 200 km.
Abstract
The National Meteorological Center's (NMC's) twice-daily, global 2.5° pressure analyses of temperature, relative humidity, and wind speed are compared, over the coterminous United States, to the National Climatic Data Center's twice-daily, upper-air rawinsonde observations and hourly, first-order, surface observations for the period 1 January 1988 through 31 December 1992. NMCs analyses have clearly improved during this time period. Still, there are some noticeable differences especially near the surface and at 1200 UTC. During the early morning there is a warm bias, relative humidity is too low, and the surface wind speed is too strong. Weaker systematic errors occur during the late afternoon: there is a cold bias, relative humidity is too high, and the surface wind speed is still too strong. Aloft, the bias is noticeably reduced except for the wind speed, which is somewhat too weak. The analysis wind speed also has too strong temporal variations near the surface and too weak temporal variations aloft. The analysis climatology can be corrected at each station by removing the bias. Transient variations can be corrected simply by multiplying the analysis anomalies by the ratio of the station standard deviation to the analysis standard deviation. Correcting for the biases and spatially interpolating the analysis and station collections to a 0.5° grid provides a useful guess for local conditions, especially if there is not a surface or rawinsonde station within about 200 km.
Abstract
Water balance calculations are becoming increasingly important for earth-system studies. Precipitation is one of the most critical input variables for such calculations because it is the immediate source of water for the land surface hydrological budget. Numerous precipitation datasets have been developed in the last two decades, but these datasets often show marked differences in their spatial and temporal distribution of this key hydrological variable. This paper compares six monthly precipitation datasets—Climate Research Unit of University of East Anglia (CRU), Willmott–Matsuura (WM), Global Precipitation Climate Center (GPCC), Global Precipitation Climatology Project (GPCP), Tropical Rainfall Measuring Mission (TRMM), and NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (NCEP-2)—to assess the uncertainties in these datasets and their impact on the terrestrial water balance. The six datasets tested in the present paper were climatologically averaged and compared by calculating various statistics of the differences. The climatologically averaged monthly precipitation estimates were applied as inputs to a water balance model to estimate runoff and the uncertainties in runoff arising directly from the precipitation estimates. The results of this study highlight the need for accurate precipitation inputs for water balance calculations. These results also demonstrate the need to improve precipitation estimates in arid and semiarid regions, where slight changes in precipitation can result in dramatic changes in the runoff response due to the nonlinearity of the runoff-generation processes.
Abstract
Water balance calculations are becoming increasingly important for earth-system studies. Precipitation is one of the most critical input variables for such calculations because it is the immediate source of water for the land surface hydrological budget. Numerous precipitation datasets have been developed in the last two decades, but these datasets often show marked differences in their spatial and temporal distribution of this key hydrological variable. This paper compares six monthly precipitation datasets—Climate Research Unit of University of East Anglia (CRU), Willmott–Matsuura (WM), Global Precipitation Climate Center (GPCC), Global Precipitation Climatology Project (GPCP), Tropical Rainfall Measuring Mission (TRMM), and NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (NCEP-2)—to assess the uncertainties in these datasets and their impact on the terrestrial water balance. The six datasets tested in the present paper were climatologically averaged and compared by calculating various statistics of the differences. The climatologically averaged monthly precipitation estimates were applied as inputs to a water balance model to estimate runoff and the uncertainties in runoff arising directly from the precipitation estimates. The results of this study highlight the need for accurate precipitation inputs for water balance calculations. These results also demonstrate the need to improve precipitation estimates in arid and semiarid regions, where slight changes in precipitation can result in dramatic changes in the runoff response due to the nonlinearity of the runoff-generation processes.
