Regions of Strong Coupling Between Soil Moisture and Precipitation

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Science  20 Aug 2004:
Vol. 305, Issue 5687, pp. 1138-1140
DOI: 10.1126/science.1100217


Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.

Atmospheric chaos severely limits the predictability of precipitation on seasonal time scales. Weather forecasts, which rely heavily on atmospheric initialization, rarely demonstrate skill beyond about a week. Hope for accurate seasonal forecasts lies with simulating the atmospheric response to slowly varying states of the ocean and land surface— components of the Earth system that can be predicted weeks to months in advance. A systematic response of the atmosphere to these boundary components would contribute skill to seasonal prediction.

The critical importance of the ocean surface in this regard is well known (1). Ocean temperature anomalies can be predicted a year or more in advance (2). Furthermore, the atmosphere responds particularly strongly (and predictably) to ocean temperature anomalies in certain regions—in “hot spots” of ocean-atmosphere coupling. The eastern equatorial Pacific is the most famous oceanic hot spot, playing a key role in the El Niño–La Niña cycle (3).

Another potentially useful slowly varying component of the Earth system is soil moisture, which can influence weather through its impact on evaporation and other surface energy fluxes. Soil moisture anomalies can persist for months (4), and although a paucity of observations prevents an unambiguous demonstration of soil moisture impacts on precipitation (5), such impacts are often seen in atmospheric general circulation model (AGCM) studies (6, 7). Indeed, some AGCM studies suggest that in continental midlatitudes during summer, oceanic impacts on precipitation are small relative to soil moisture impacts (8).

This suggests a question: Are there specific locations on the Earth's surface for which soil moisture anomalies have a substantial impact on precipitation? The identification of such hot spots would have important implications for the design of seasonal prediction systems and for the associated development of ground-based and satellite-based strategies for monitoring soil moisture, if such impacts were found to be local. In a broader sense, such identification is critical for understanding Earth's climate system and the limits of predictability therein.

Although AGCM studies (912) and even numerical weather prediction model studies (13) have addressed this question, published results are based on different experimental designs and reflect distinctive features of different model parameterizations. The coupling question, however, was recently addressed en masse by a dozen AGCM groups (14), all performing the same highly controlled numerical experiment. The experiments were coordinated by GLACE, the Global Land-Atmosphere Coupling Experiment (15). Each model contributing to GLACE generated several ensembles of boreal summer (June through August) simulations designed to quantify that model's land-atmosphere coupling strength (16) for that season. By combining the results across these models, we eliminate much of the undesired individual model dependence. We obtain, in effect, a unique result: a multimodel average depiction of the global distribution of land-atmosphere coupling strength. Given the limitations of the observational data, both now and in the foreseeable future, such a multimodel estimate of coupling strength distribution is arguably the best estimate attainable.

Each GLACE participant performed an ensemble of 16 simulations in which soil moisture varied between the simulations, and another ensemble in which the geographically varying time series of subsurface soil moisture was forced to be the same across the 16 simulations (17). Coupling strength—the degree to which all prescribed boundary conditions affect some atmospheric quantity X— can be estimated (18) for each of the two ensembles with the diagnostic Ω: Math(1) where σ2X is the intraensemble variance of X and σ2<X> is the corresponding variance of the ensemble-mean time series—the single time series generated by averaging across the 16 ensemble members at each time interval, chosen here to be 6 days. We are interested, of course, in precipitation; to reduce noise, however, we take X to be the natural logarithm of the precipitation. Performing statistics on the logarithms of precipitation is a common practice in hydrology and meteorology, because unmodified precipitation distributions tend to be highly skewed (19, 20).

A study of the equation shows that outside of sampling error, Ω should vary from 0 to 1, with higher values implying a higher impact of the atmosphere's boundary conditions on precipitation. To isolate soil moisture's impact on precipitation from that of all other forcings, such as time-varying ocean temperatures and the seasonal variation of solar radiation, we compute the difference in the Ω values between the two ensembles. In simple terms, this Ω difference approximates the fraction of the precipitation variance explained by variations in soil moisture alone.

Figure 1 shows the global map of the Ω difference averaged across all of the participating models in GLACE. This multimodel estimation of land atmosphere coupling strength reveals several distinct hot spots. Hot spots appear in the central Great Plains of North America, the Sahel, equatorial Africa, and India. Less intense hot spots appear in South America, central Asia, and China.

Fig. 1.

The land-atmosphere coupling strength diagnostic for boreal summer (the Ω difference, dimensionless, describing the impact of soil moisture on precipitation), averaged across the 12 models participating in GLACE. (Insets) Areally averaged coupling strengths for the 12 individual models over the outlined, representative hotspot regions. No signal appears in southern South America or at the southern tip of Africa.

The positions of the hot spots are not unexpected (8, 21), particularly if the soil moisture influence is presumed to be local rather than remote. Consider first that in wet climates, for which soil water is plentiful, evaporation is controlled not by soil moisture but by net radiative energy. This is illustrated in Fig. 2, which shows how the Ω difference diagnostic, applied to evaporation rather than precipitation, varies (on average) with soil moisture. The Ω difference—the fraction of the evaporation variance explained by soil moisture variations—is indeed lowest when soil moisture is high. Because evaporation in wet climates is not highly sensitive to soil moisture variations, precipitation should not be sensitive to them, either.

Fig. 2.

Average relationship between soil wetness (the degree of saturation in the soil) and two separate aspects of the land-surface energy budget: the Ω difference for evaporation (solid curve, in dimensionless units) and the average evaporation rate (dashed curve, in cm/day). Both aspects should have suitably high values to allow soil moisture anomalies to be translated into precipitation anomalies; the plot shows that this mostly occurs for intermediate values of soil wetness, i.e., in the transition zones between wet and dry climates. The curves are derived by averaging the soil wetness, Ω difference, and evaporation fields across the 12 models, constructing scatter plots for the two relationships with data from nonice land points, and then binning the data according to soil wetness value.

Now consider that in dry climates, evaporation rates are sensitive to soil moisture but are also, of course, generally small, as demonstrated for the models by the dashed curve in Fig. 2. Intuitively, small evaporation rates should have a limited ability to affect precipitation. The atmosphere in dry regions is, in any case, predisposed to limit precipitation. Only in the transition zones between wet and dry climates, where the atmosphere is amenable to precipitation generation [in particular, where boundary-layer moisture can trigger moist convection (22)] and where evaporation is suitably high but still sensitive to soil moisture, can we expect soil moisture to influence precipitation. The major hot spots shown lie mainly in such transition zones (23).

The insets in the map (Fig. 1) show that not all of the GLACE models place hot spots in the regions indicated. In North America, for example, only half of the models show a statistically significant (24) coupling strength in the outlined region. The 12 models agree slightly more in the Sahelian and Indian hotspot regions; nevertheless, throughout the world, there exists extensive intermodel variability in the strength and positioning of the hot spots, a reflection of ongoing uncertainty in the proper way to represent the physical processes defining land-atmosphere coupling strength. Indeed, some of the models showing a small coupling strength in the insets also show a low coupling strength everywhere else on the planet. The intermodel variability highlights the importance of the averaging process leading to Fig. 1. The insets support the idea, stated above, that any single-model analysis of coupling strength will provide model-specific results. The patterns revealed by the averaging process are valuable because they show where many independent models agree that the land-atmosphere coupling is important.

The plotted hot spots indicate where a global initialization of soil moisture may enhance precipitation prediction skill during Northern Hemisphere summer (25, 26). Under the assumption that the soil moisture impacts are predominantly local, the hot spots indicate where the routine monitoring of soil moisture, with both ground-based and space-based systems, will yield the greatest return in boreal summer seasonal forecasting. The hot spots are, in a sense, land-surface analogs to the ocean's “El Niño hot spot” in the eastern tropical Pacific.

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