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- Author or Editor: Randal D. Koster x
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Abstract
The sources of sub-Saharan precipitation are studied using diagnostic procedures integrated into the code of the GISS climate model. Water vapor evaporating from defined source region is “tagged,” allowing the determination of the relative contributions of each evaporative source to the simulated July rainfall in the Sahel. Two June–July simulations are studied to compare the moisture sources, moisture convergence patterns and the spatial variations of precipitation for rainy and drought conditions. Results for this eau study indicate that patterns of moisture convergence and divergence over northern Africa had a stronger influence on model rainfall over the sub-Sahara than did evaporation rates over the adjacent oceans or moisture advection from ocean to continent. While local continental evaporation contributed significant amounts of water to sahelian precipitation in the “rainy” simulation, moisture from the Indian Ocean did not precipitate over the Sahel in either case.
Abstract
The sources of sub-Saharan precipitation are studied using diagnostic procedures integrated into the code of the GISS climate model. Water vapor evaporating from defined source region is “tagged,” allowing the determination of the relative contributions of each evaporative source to the simulated July rainfall in the Sahel. Two June–July simulations are studied to compare the moisture sources, moisture convergence patterns and the spatial variations of precipitation for rainy and drought conditions. Results for this eau study indicate that patterns of moisture convergence and divergence over northern Africa had a stronger influence on model rainfall over the sub-Sahara than did evaporation rates over the adjacent oceans or moisture advection from ocean to continent. While local continental evaporation contributed significant amounts of water to sahelian precipitation in the “rainy” simulation, moisture from the Indian Ocean did not precipitate over the Sahel in either case.
Abstract
An equation that describes the partitioning of annual mean precipitation into annual mean evaporation and runoff, developed decades ago by Budyko, is used to derive a second equation that relates the interannual variability of evaporation to gross characteristics of the atmospheric forcing. Both Budyko’s original equation and the new variability equation perform well when tested against results from a 20-yr GCM simulation. In these tests, using knowledge of the climatological mean precipitation and net radiation alone, the authors predict the ratio of annual evaporation to annual precipitation with a standard error of 0.10 in nondesert regions, and they predict the ratio of the standard deviation of annual evaporation to that of annual precipitation there with a standard error of 0.14. In analogy with Budyko’s conclusion for the mean hydrological cycle, water and energy availability appear to be the critical factors controlling the interannual variability of surface moisture fluxes. The derived equations suggest, and the GCM results confirm, that the ratio of an evaporation anomaly to the corresponding precipitation anomaly tends to be significantly less than the ratio of mean evaporation to mean precipitation.
Abstract
An equation that describes the partitioning of annual mean precipitation into annual mean evaporation and runoff, developed decades ago by Budyko, is used to derive a second equation that relates the interannual variability of evaporation to gross characteristics of the atmospheric forcing. Both Budyko’s original equation and the new variability equation perform well when tested against results from a 20-yr GCM simulation. In these tests, using knowledge of the climatological mean precipitation and net radiation alone, the authors predict the ratio of annual evaporation to annual precipitation with a standard error of 0.10 in nondesert regions, and they predict the ratio of the standard deviation of annual evaporation to that of annual precipitation there with a standard error of 0.14. In analogy with Budyko’s conclusion for the mean hydrological cycle, water and energy availability appear to be the critical factors controlling the interannual variability of surface moisture fluxes. The derived equations suggest, and the GCM results confirm, that the ratio of an evaporation anomaly to the corresponding precipitation anomaly tends to be significantly less than the ratio of mean evaporation to mean precipitation.
Abstract
A revised framework for the analysis of soil moisture memory characteristics of climate models and observational data is derived from the approach proposed by Koster and Suarez. The resulting equation allows the expression of the month-to-month soil moisture autocorrelation as a function of 1) the initial soil moisture variability, 2) the (atmospheric) forcing variability over the considered time period, 3) the correlation between initial soil moisture and subsequent forcing, 4) the sensitivity of evaporation to soil moisture, and 5) the sensitivity of runoff to soil moisture. A specific new feature is the disentangling of the roles of initial soil moisture variability and forcing variability, which were both (for the latter indirectly) contributing to the seasonality term of the original formulation. In addition, a version of the framework entirely based on explicit equations for the underlying relationships (i.e., independent of soil moisture statistics at the following time step) is proposed. The validity of the derived equation is exemplified with atmospheric general circulation model (AGCM) simulations from the Global Land–Atmosphere Coupling Experiment (GLACE).
Abstract
A revised framework for the analysis of soil moisture memory characteristics of climate models and observational data is derived from the approach proposed by Koster and Suarez. The resulting equation allows the expression of the month-to-month soil moisture autocorrelation as a function of 1) the initial soil moisture variability, 2) the (atmospheric) forcing variability over the considered time period, 3) the correlation between initial soil moisture and subsequent forcing, 4) the sensitivity of evaporation to soil moisture, and 5) the sensitivity of runoff to soil moisture. A specific new feature is the disentangling of the roles of initial soil moisture variability and forcing variability, which were both (for the latter indirectly) contributing to the seasonality term of the original formulation. In addition, a version of the framework entirely based on explicit equations for the underlying relationships (i.e., independent of soil moisture statistics at the following time step) is proposed. The validity of the derived equation is exemplified with atmospheric general circulation model (AGCM) simulations from the Global Land–Atmosphere Coupling Experiment (GLACE).
Abstract
Two contrasting representations of land surface variability used in general circulation models (GCMS) are compared through an analysis of their corresponding surface energy balance equations. In one representation (the “mixture” approach), different vegetation types are assumed to be homogeneously mixed over a grid square, so that the GCM atmosphere sees near-surface conditions pertaining to the mixture only. In the second representation (the “mosaic” approach), different vegetation types are viewed as separate “tiles” of a grid-square “mosaic,” and each tile interacts with the atmosphere independently. The mosaic approach is computationally simpler and in many ways more flexible than the mixture approach.
Analytical solutions to the linearized energy balance equations and numerical solutions to the nonlinear equations both demonstrate that the mixture strategy, when applied to two coexisting vegetation types that differ only in canopy transpiration resistance, promotes both total turbulent flux and latent beat flux relative to the mosaic strategy. The effective differences between the strategies, however, are small over a wide range of conditions. In particular, the strategies are effectively equivalent when the transpiration resistances of the different vegetation types are of the saint order of magnitude.
Abstract
Two contrasting representations of land surface variability used in general circulation models (GCMS) are compared through an analysis of their corresponding surface energy balance equations. In one representation (the “mixture” approach), different vegetation types are assumed to be homogeneously mixed over a grid square, so that the GCM atmosphere sees near-surface conditions pertaining to the mixture only. In the second representation (the “mosaic” approach), different vegetation types are viewed as separate “tiles” of a grid-square “mosaic,” and each tile interacts with the atmosphere independently. The mosaic approach is computationally simpler and in many ways more flexible than the mixture approach.
Analytical solutions to the linearized energy balance equations and numerical solutions to the nonlinear equations both demonstrate that the mixture strategy, when applied to two coexisting vegetation types that differ only in canopy transpiration resistance, promotes both total turbulent flux and latent beat flux relative to the mosaic strategy. The effective differences between the strategies, however, are small over a wide range of conditions. In particular, the strategies are effectively equivalent when the transpiration resistances of the different vegetation types are of the saint order of magnitude.
Abstract
The retention of precipitation water in land surface reservoirs damps higher frequencies of evaporation variability and can thereby influence the feedback of evaporation on precipitation. The extent of this influence is examined in a series of general circulation model simulations in which the timescale of surface moisture retention is very carefully controlled. Shorter timescales lead to increased daily precipitation variance and one-day-lagged precipitation autocorrelations but to decreased autocorrelations at longer lags.
An explanation for the simulated precipitation statistics is offered in the form of a heuristic model of evaporation feedback that describes precipitation variance and autocorrelation in terms of three parameters: (i) the timescale of precipitation persistence in the absence of feedback; (ii) the surface retention timescale; and (iii) a parameter describing the atmosphere's responsiveness to variations in evaporation. The heuristic model reproduces the statistical trends seen in the GCM diagnostics, and it can be used to explain geographical variations in precipitation statistics generated by a CYCM coupled to a full biosphere model.
Abstract
The retention of precipitation water in land surface reservoirs damps higher frequencies of evaporation variability and can thereby influence the feedback of evaporation on precipitation. The extent of this influence is examined in a series of general circulation model simulations in which the timescale of surface moisture retention is very carefully controlled. Shorter timescales lead to increased daily precipitation variance and one-day-lagged precipitation autocorrelations but to decreased autocorrelations at longer lags.
An explanation for the simulated precipitation statistics is offered in the form of a heuristic model of evaporation feedback that describes precipitation variance and autocorrelation in terms of three parameters: (i) the timescale of precipitation persistence in the absence of feedback; (ii) the surface retention timescale; and (iii) a parameter describing the atmosphere's responsiveness to variations in evaporation. The heuristic model reproduces the statistical trends seen in the GCM diagnostics, and it can be used to explain geographical variations in precipitation statistics generated by a CYCM coupled to a full biosphere model.
Abstract
A model representing a soil-atmosphere column in a GCM is developed for off-line testing of GCM soil hydrology parameterizations. Repeating three representative GCM sensitivity experiments with this one-dimensional model demonstrates that, to first order, the model reproduces a GCM's sensitivity to imposed changes in parameterization and therefore captures the essential physics of the GCM. The experiments also show that by allowing feedback between the soil and atmosphere, the model improves on off-line tests that rely on prescribed precipitation, radiation, and other surface forcing.
Abstract
A model representing a soil-atmosphere column in a GCM is developed for off-line testing of GCM soil hydrology parameterizations. Repeating three representative GCM sensitivity experiments with this one-dimensional model demonstrates that, to first order, the model reproduces a GCM's sensitivity to imposed changes in parameterization and therefore captures the essential physics of the GCM. The experiments also show that by allowing feedback between the soil and atmosphere, the model improves on off-line tests that rely on prescribed precipitation, radiation, and other surface forcing.
Abstract
The potential role of land initialization in seasonal forecasting is illustrated through ensembles of simulations with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model. For each boreal summer during 1997–2001, two 16-member ensembles of 3-month simulations were generated. The first, “AMIP style” (Atmospheric Model Intercomparison Project) ensemble establishes the degree to which a perfect prediction of SSTs would contribute to the seasonal prediction of precipitation and temperature over continents. The second ensemble is identical to the first, except that the land surface is also initialized with “realistic” soil moisture contents through the continuous prior application (within GCM simulations leading up to the start of the forecast period) of a daily observational precipitation dataset and the associated avoidance of model drift through the scaling of all surface prognostic variables. A comparison of the two ensembles shows that land initialization has a statistically significant impact on summertime precipitation over only a handful of continental regions. These regions agree, to first order, with those that satisfy three conditions: 1) a tendency toward large initial soil moisture anomalies, 2) a strong sensitivity of evaporation to soil moisture, and 3) a strong sensitivity of precipitation to evaporation. The impact on temperature prediction is more spatially extensive. The degree to which the initialization increases the skill of the forecasts is mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies.
Abstract
The potential role of land initialization in seasonal forecasting is illustrated through ensembles of simulations with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model. For each boreal summer during 1997–2001, two 16-member ensembles of 3-month simulations were generated. The first, “AMIP style” (Atmospheric Model Intercomparison Project) ensemble establishes the degree to which a perfect prediction of SSTs would contribute to the seasonal prediction of precipitation and temperature over continents. The second ensemble is identical to the first, except that the land surface is also initialized with “realistic” soil moisture contents through the continuous prior application (within GCM simulations leading up to the start of the forecast period) of a daily observational precipitation dataset and the associated avoidance of model drift through the scaling of all surface prognostic variables. A comparison of the two ensembles shows that land initialization has a statistically significant impact on summertime precipitation over only a handful of continental regions. These regions agree, to first order, with those that satisfy three conditions: 1) a tendency toward large initial soil moisture anomalies, 2) a strong sensitivity of evaporation to soil moisture, and 3) a strong sensitivity of precipitation to evaporation. The impact on temperature prediction is more spatially extensive. The degree to which the initialization increases the skill of the forecasts is mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies.
Abstract
The importance of horizontal error correlations in background (i.e., model forecast) fields for large-scale soil moisture estimation is assessed by comparing the performance of one- and three-dimensional ensemble Kalman filters (EnKF) in a twin experiment. Over a domain centered on the U. S. Great Plains, gauge-based precipitation data is used to force the “true” model solution, and reanalysis data for the prior (or background) fields. The difference between the two precipitation datasets is thought to be representative of errors that might be encountered in a global land assimilation system. To ensure realistic conditions the synthetic observations of surface soil moisture match the spatiotemporal pattern and expected errors of retrievals from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite. After filter calibration, average actual estimation errors in the (volumetric) root zone moisture content are 0.015 m3 m−3 for the 3D-EnKF, 0.019 m3 m−3 for the 1D-EnKF, and 0.036 m3 m−3 without assimilation. Clearly, taking horizontal error correlations into account improves estimation accuracy. Soil moisture estimation errors in the 3D-EnKF are smallest for a correlation scale of 2° in model parameter and forcing errors, which coincides with the horizontal scale of difference fields between gauge-based and reanalysis precipitation. In this case the 3D-EnKF requires 1.6 times the computational effort of the 1D-EnKF, but this factor depends on the experiment setup.
Abstract
The importance of horizontal error correlations in background (i.e., model forecast) fields for large-scale soil moisture estimation is assessed by comparing the performance of one- and three-dimensional ensemble Kalman filters (EnKF) in a twin experiment. Over a domain centered on the U. S. Great Plains, gauge-based precipitation data is used to force the “true” model solution, and reanalysis data for the prior (or background) fields. The difference between the two precipitation datasets is thought to be representative of errors that might be encountered in a global land assimilation system. To ensure realistic conditions the synthetic observations of surface soil moisture match the spatiotemporal pattern and expected errors of retrievals from the Scanning Multichannel Microwave Radiometer (SMMR) on the Nimbus-7 satellite. After filter calibration, average actual estimation errors in the (volumetric) root zone moisture content are 0.015 m3 m−3 for the 3D-EnKF, 0.019 m3 m−3 for the 1D-EnKF, and 0.036 m3 m−3 without assimilation. Clearly, taking horizontal error correlations into account improves estimation accuracy. Soil moisture estimation errors in the 3D-EnKF are smallest for a correlation scale of 2° in model parameter and forcing errors, which coincides with the horizontal scale of difference fields between gauge-based and reanalysis precipitation. In this case the 3D-EnKF requires 1.6 times the computational effort of the 1D-EnKF, but this factor depends on the experiment setup.
Abstract
Water balance considerations at the soil surface lead to an equation that relates the autocorrelation of soil moisture in climate models to 1) seasonality in the statistics of the atmospheric forcing, 2) the variation of evaporation with soil moisture, 3) the variation of runoff with soil moisture, and 4) correlation between the atmospheric forcing and antecedent soil moisture, as perhaps induced by land–atmosphere feedback. Geographical variations in the relative strengths of these factors, which can be established through analysis of model diagnostics, lead to geographical variations in simulated soil moisture memory. The use of the equation to characterize controls on soil moisture memory is demonstrated with data from the modeling system of the National Aeronautics and Space Administration Seasonal-to-Interannual Prediction Project.
Abstract
Water balance considerations at the soil surface lead to an equation that relates the autocorrelation of soil moisture in climate models to 1) seasonality in the statistics of the atmospheric forcing, 2) the variation of evaporation with soil moisture, 3) the variation of runoff with soil moisture, and 4) correlation between the atmospheric forcing and antecedent soil moisture, as perhaps induced by land–atmosphere feedback. Geographical variations in the relative strengths of these factors, which can be established through analysis of model diagnostics, lead to geographical variations in simulated soil moisture memory. The use of the equation to characterize controls on soil moisture memory is demonstrated with data from the modeling system of the National Aeronautics and Space Administration Seasonal-to-Interannual Prediction Project.
Abstract
Observed monthly precipitation anomalies are standardized across midlatitude land, and ergodicity is invoked to combine the spatially distributed data into probability density functions (pdfs) of precipitation conditioned on the strength of earlier anomalies. The conditional pdfs, though broad and overlapping, are indeed distinct at a high (99.9%) level of confidence. This implies a nonzero degree of predictability for midlatitude precipitation, even at 3-month leads. This behavior is reproduced by an AGCM only when land–atmosphere feedback in the model is enabled.
Abstract
Observed monthly precipitation anomalies are standardized across midlatitude land, and ergodicity is invoked to combine the spatially distributed data into probability density functions (pdfs) of precipitation conditioned on the strength of earlier anomalies. The conditional pdfs, though broad and overlapping, are indeed distinct at a high (99.9%) level of confidence. This implies a nonzero degree of predictability for midlatitude precipitation, even at 3-month leads. This behavior is reproduced by an AGCM only when land–atmosphere feedback in the model is enabled.
