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- Author or Editor: J. O. Roads x
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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
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
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.
Abstract
The effects of atmospheric feedbacks on tropical instability waves (TIWs) in the equatorial Atlantic Ocean are examined using a regional high-resolution coupled climate model. The analysis from a 6-yr hindcast from 1999 to 2004 reveals a negative correlation between TIW-induced wind perturbations and TIW-induced ocean currents, which implies damping of the TIWs. On the other hand, the feedback effect from the modification of Ekman pumping velocity by TIWs is small compared to the contribution to TIW growth by baroclinic instability. Overall, the atmosphere reduces the growth of TIWs by adjusting its wind response to the evolving TIWs. The analysis also shows that including ocean current (mean + TIWs) in the wind stress parameterization reduces the surface stress estimate by 15%–20% over the region of the South Equatorial Current. Moreover, TIW-induced perturbation ocean currents can significantly alter surface stress estimations from scatterometers, especially at TIW frequencies. Finally, the rectification effect from the atmospheric response to TIWs on latent heat flux is small compared to the mean latent heat flux.
Abstract
The effects of atmospheric feedbacks on tropical instability waves (TIWs) in the equatorial Atlantic Ocean are examined using a regional high-resolution coupled climate model. The analysis from a 6-yr hindcast from 1999 to 2004 reveals a negative correlation between TIW-induced wind perturbations and TIW-induced ocean currents, which implies damping of the TIWs. On the other hand, the feedback effect from the modification of Ekman pumping velocity by TIWs is small compared to the contribution to TIW growth by baroclinic instability. Overall, the atmosphere reduces the growth of TIWs by adjusting its wind response to the evolving TIWs. The analysis also shows that including ocean current (mean + TIWs) in the wind stress parameterization reduces the surface stress estimate by 15%–20% over the region of the South Equatorial Current. Moreover, TIW-induced perturbation ocean currents can significantly alter surface stress estimations from scatterometers, especially at TIW frequencies. Finally, the rectification effect from the atmospheric response to TIWs on latent heat flux is small compared to the mean latent heat flux.
Abstract
A regional coupled climate model is configured for the tropical Atlantic to explore the role of synoptic-scale African easterly waves (AEWs) on the simulation of mean precipitation in the marine intertropical convergence zone (ITCZ). Sensitivity tests with varying atmospheric resolution in the coupled model show that these easterly waves are well represented with comparable amplitudes on both fine and coarse grids of the atmospheric model. Significant differences in the model simulations are found in the precipitation fields, however, where heavy rainfall events occur in the region of strong cyclonic shear of the easterly waves only on the higher-resolution grid. This is because the low-level convergence due to the waves is much larger and more realistic in the fine-resolution simulation, which enables heavier precipitation events that skew the rainfall distributions toward longer tails. The variability in rainfall on these time scales accounts for more than 60%–70% of the total variability. As a result, the simulation of mean rainfall in the ITCZ and its seasonal migration improves in the higher-resolution case. This suggests that capturing these transient waves and the resultant strong low-level convergence is one of the key ingredients for improving the simulation of precipitation in global coupled climate models.
Abstract
A regional coupled climate model is configured for the tropical Atlantic to explore the role of synoptic-scale African easterly waves (AEWs) on the simulation of mean precipitation in the marine intertropical convergence zone (ITCZ). Sensitivity tests with varying atmospheric resolution in the coupled model show that these easterly waves are well represented with comparable amplitudes on both fine and coarse grids of the atmospheric model. Significant differences in the model simulations are found in the precipitation fields, however, where heavy rainfall events occur in the region of strong cyclonic shear of the easterly waves only on the higher-resolution grid. This is because the low-level convergence due to the waves is much larger and more realistic in the fine-resolution simulation, which enables heavier precipitation events that skew the rainfall distributions toward longer tails. The variability in rainfall on these time scales accounts for more than 60%–70% of the total variability. As a result, the simulation of mean rainfall in the ITCZ and its seasonal migration improves in the higher-resolution case. This suggests that capturing these transient waves and the resultant strong low-level convergence is one of the key ingredients for improving the simulation of precipitation in global coupled climate models.
Abstract
This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.
Abstract
This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.
Abstract
Thirteen regional climate model (RCM) simulations of June–July 1993 were compared with each other and observations. Water vapor conservation and precipitation characteristics in each RCM were examined for a 10° × 10° subregion of the upper Mississippi River basin, containing the region of maximum 60-day accumulated precipitation in all RCMs and station reports.
All RCMs produced positive precipitation minus evapotranspiration (P − E > 0), though most RCMs produced P − E below the observed range. RCM recycling ratios were within the range estimated from observations. No evidence of common errors of E was found. In contrast, common dry bias of P was found in the simulations.
Daily cycles of terms in the water vapor conservation equation were qualitatively similar in most RCMs. Nocturnal maximums of P and C (convergence) occurred in 9 of 13 RCMs, consistent with observations. Three of the four driest simulations failed to couple P and C overnight, producing afternoon maximum P. Further, dry simulations tended to produce a larger fraction of their 60-day accumulated precipitation from low 3-h totals.
In station reports, accumulation from high (low) 3-h totals had a nocturnal (early morning) maximum. This time lag occurred, in part, because many mesoscale convective systems had reached peak intensity overnight and had declined in intensity by early morning. None of the RCMs contained such a time lag. It is recommended that short-period experiments be performed to examine the ability of RCMs to simulate mesoscale convective systems prior to generating long-period simulations for hydroclimatology.
Abstract
Thirteen regional climate model (RCM) simulations of June–July 1993 were compared with each other and observations. Water vapor conservation and precipitation characteristics in each RCM were examined for a 10° × 10° subregion of the upper Mississippi River basin, containing the region of maximum 60-day accumulated precipitation in all RCMs and station reports.
All RCMs produced positive precipitation minus evapotranspiration (P − E > 0), though most RCMs produced P − E below the observed range. RCM recycling ratios were within the range estimated from observations. No evidence of common errors of E was found. In contrast, common dry bias of P was found in the simulations.
Daily cycles of terms in the water vapor conservation equation were qualitatively similar in most RCMs. Nocturnal maximums of P and C (convergence) occurred in 9 of 13 RCMs, consistent with observations. Three of the four driest simulations failed to couple P and C overnight, producing afternoon maximum P. Further, dry simulations tended to produce a larger fraction of their 60-day accumulated precipitation from low 3-h totals.
In station reports, accumulation from high (low) 3-h totals had a nocturnal (early morning) maximum. This time lag occurred, in part, because many mesoscale convective systems had reached peak intensity overnight and had declined in intensity by early morning. None of the RCMs contained such a time lag. It is recommended that short-period experiments be performed to examine the ability of RCMs to simulate mesoscale convective systems prior to generating long-period simulations for hydroclimatology.
