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Randal D. Koster and P. C. D. Milly

Abstract

The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) has shown that different land surface models (LSMs) driven by the same meteorological forcing can produce markedly different surface energy and water budgets, even when certain critical aspects of the LSMs (vegetation cover, albedo, turbulent drag coefficient, and snowcover) are carefully controlled. To help explain these differences, the authors devised a monthly water balance model that successfully reproduces the annual and seasonal water balances of the different PILPS schemes. Analysis of this model leads to the identification of two quantities that characterize an LSM’s formulation of soil water balance dynamics: 1) the efficiency of the soil’s evaporation sink integrated over the active soil moisture range, and 2) the fraction of this range over which runoff is generated. Regardless of the LSM’s complexity, the combination of these two derived parameters with rates of interception loss, potential evaporation, and precipitation provides a reasonable estimate for the LSM’s simulated annual water balance. The two derived parameters shed light on how evaporation and runoff formulations interact in an LSM, and the analysis as a whole underscores the need for compatibility in these formulations.

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Randal D. Koster and Sarith P. P. Mahanama

Abstract

Hydroclimatic means and variability are determined in large part by the control of soil moisture on surface moisture fluxes, particularly evapotranspiration and runoff. This control is examined here using a simple water balance model and multidecadal observations covering the conterminous United States. Under the assumption that the relevant soil moisture–evapotranspiration and soil moisture–runoff relationships are, to first order, universal, the simple model illustrates the degree to which they interact to determine spatial distributions of hydroclimatic means and variability. In the process, the simple model provides estimates for the underlying relationships that operate in nature. The hydroclimatic sensitivities established with the simple water balance model can be used to evaluate more complex land surface models and to guide their further development, as demonstrated herein with an example.

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Randal D. Koster and Max J. Suarez

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.

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Randal D. Koster and Peter S. Eagleson

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.

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Sarith P. P. Mahanama and Randal D. Koster

Abstract

A heavy rain or a dry period can produce an anomaly in soil moisture, and the dissipation of this anomaly may take weeks to months. It is important to understand how land surface models (LSMs) used with atmospheric general circulation models simulate this soil moisture “memory,” because this memory may have profound implications for long-term weather prediction through land–atmosphere feedback.

In order to understand better the effect of precipitation and net radiation on soil moisture memory, the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Catchment LSM and the Mosaic LSM were both forced with a wide variety of idealized climates. The imposed climates had average monthly precipitation ranging from 15 to 500 mm and monthly net radiations (in terms of water equivalent) ranging from 20 to 400 mm, with consequent changes in near-surface temperature and humidity. For an equivalent water holding capacity, the two models maximize memory in distinctly different climate regimes. Memory in the NSIPP Catchment LSM exceeds that in the Mosaic LSM when precipitation and net radiation are of the same order; otherwise, memory in the Mosaic LSM is larger.

The NSIPP Catchment and the Mosaic LSMs were also driven offline, globally, for a period of 15 yr (1979–93) with realistic atmospheric forcing. Global distributions of 1-month-lagged autocorrelation of soil moisture for boreal summer were computed. An additional global run with the NSIPP Catchment LSM employing the Mosaic LSM's water holding capacities was also performed. These three global runs show that while some of the intermodel difference in memory can be explained (following traditional interpretations) in terms of differences in water holding capacity and potential evaporation, much of the intermodal difference stems from differences in the parameterizations of evaporation and runoff.

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Sarith P. P. Mahanama and Randal D. Koster

Abstract

Because precipitation and net radiation in an atmospheric general circulation model (AGCM) are typically biased relative to observations, the simulated evaporative regime of a region may be biased, with consequent negative effects on the AGCM’s ability to translate an initialized soil moisture anomaly into an improved seasonal prediction. These potential problems are investigated through extensive offline analyses with the Mosaic land surface model (LSM). The LSM was first forced globally with a 15-yr observation-based dataset. The simulation was then repeated after imposing a representative set of GCM climate biases onto the forcings—the observational forcings were scaled so that their mean seasonal cycles matched those simulated by the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1; NASA Global Modeling and Assimilation Office) AGCM over the same period. The AGCM’s climate biases do indeed lead to significant biases in evaporative regime in certain regions, with the expected impacts on soil moisture memory time scales. Furthermore, the offline simulations suggest that the biased forcing in the AGCM should contribute to overestimated feedback in certain parts of North America—parts already identified in previous studies as having excessive feedback. The present study thus supports the notion that the reduction of climate biases in the AGCM will lead to more appropriate translations of soil moisture initialization into seasonal prediction skill.

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Rolf H. Reichle and Randal D. Koster

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.

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Leonard M. Druyan and Randal D. Koster

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.

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Randal D. Koster and Max J. Suarez

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.

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Randal D. Koster and Max J. Suarez

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.

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