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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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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, Rolf H. Reichle, and Sarith P. P. Mahanama

Abstract

NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

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Mu Xiao, Sarith P. Mahanama, Yongkang Xue, Fei Chen, and Dennis P. Lettenmaier

Abstract

When compared with differences in snow accumulation predicted by widely used hydrological models, there is a much greater divergence among otherwise “good” models in their simulation of the snow ablation process. Here, we explore differences in the performance of the Variable Infiltration Capacity model (VIC), Noah land surface model with multiparameterization options (Noah-MP), the Catchment model, and the third-generation Simplified Simple Biosphere model (SiB3) in their ability to reproduce observed snow water equivalent (SWE) during the ablation season at 10 Snowpack Telemetry (SNOTEL) stations over 1992–2012. During the ablation period, net radiation generally has stronger correlations with observed melt rates than does air temperature. Average ablation rates tend to be higher (in both model predictions and observations) at stations with a large accumulation of SWE. The differences in the dates of last snow between models and observations range from several days to approximately a month (on average 5.1 days earlier than in observations). If the surface cover in the models is changed from observed vegetation to bare soil in all of the models, only the melt rate of the VIC model increases. The differences in responses of models to canopy removal are directly related to snowpack energy inputs, which are further affected by different algorithms for surface albedo and energy allocation across the models. We also find that the melt rates become higher in VIC and lower in Noah-MP if the shrub/grass present at the observation sites is switched to trees.

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Randal D. Koster, Qing Liu, Sarith P. P. Mahanama, and Rolf H. Reichle

Abstract

The assimilation of remotely sensed soil moisture information into a land surface model has been shown in past studies to contribute accuracy to the simulated hydrological variables. Remotely sensed data, however, can also be used to improve the model itself through the calibration of the model’s parameters, and this can also increase the accuracy of model products. Here, data provided by the Soil Moisture Active Passive (SMAP) satellite mission are applied to the land surface component of the NASA GEOS Earth system model using both data assimilation and model calibration in order to quantify the relative degrees to which each strategy improves the estimation of near-surface soil moisture and streamflow. The two approaches show significant complementarity in their ability to extract useful information from the SMAP data record. Data assimilation reduces the ubRMSE (the RMSE after removing the long-term bias) of soil moisture estimates and improves the timing of streamflow variations, whereas model calibration reduces the model biases in both soil moisture and streamflow. While both approaches lead to an improved timing of simulated soil moisture, these contributions are largely independent; joint use of both approaches provides the highest soil moisture simulation accuracy.

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Rolf H. Reichle, Sujay V. Kumar, Sarith P. P. Mahanama, Randal D. Koster, and Q. Liu

Abstract

Land surface (or “skin”) temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) are assimilated into the Noah land surface model and Catchment land surface model (CLSM) using an ensemble-based, offline land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LSTs typically exhibit different mean values and variabilities. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation (“open loop”) are comparable to each other and superior to ISCCP retrievals. For LST, the RMSE values are 4.9 K (CLSM), 5.5 K (Noah), and 7.6 K (ISCCP), and the anomaly correlation coefficients (R) are 0.61 (CLSM), 0.63 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over an open loop, as indicated by nonoverlapping 95% confidence intervals) of up to 0.7 K in RMSE and 0.05 in the anomaly R. The skill of the latent and sensible heat flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.

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Sarith P. P. Mahanama, Randal D. Koster, Rolf H. Reichle, and Max J. Suarez

Abstract

Anomalous atmospheric conditions can lead to surface temperature anomalies, which in turn can lead to temperature anomalies in the subsurface soil. The subsurface soil temperature (and the associated ground heat content) has significant memory—the dissipation of a temperature anomaly may take weeks to months—and thus subsurface soil temperature may contribute to the low-frequency variability of energy and water variables elsewhere in the system. The memory may even provide some skill to subseasonal and seasonal forecasts.

This study uses three long-term AGCM experiments to isolate the contribution of subsurface soil temperature variability to variability elsewhere in the climate system. The first experiment consists of a standard ensemble of Atmospheric Model Intercomparison Project (AMIP)-type simulations in which the subsurface soil temperature variable is allowed to interact with the rest of the system. In the second experiment, the coupling of the subsurface soil temperature to the rest of the climate system is disabled; that is, at each grid cell, the local climatological seasonal cycle of subsurface soil temperature (as determined from the first experiment) is prescribed. Finally, a climatological seasonal cycle of sea surface temperature (SST) is prescribed in the third experiment. Together, the three experiments allow the isolation of the contributions of variable SSTs, interactive subsurface soil temperature, and chaotic atmospheric dynamics to meteorological variability. The results show that allowing an interactive subsurface soil temperature does, indeed, significantly increase surface air temperature variability and memory in most regions. In many regions, however, the impact is negligible, particularly during boreal summer.

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Randal D. Koster, Gregory K. Walker, Sarith P. P. Mahanama, and Rolf H. Reichle

Abstract

Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in large-scale seasonal streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, for example, through the assimilation of satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale seasonal streamflow forecasts only when evaporation variance is significant relative to precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface–modeling system as a tool for addressing the science of hydrological prediction.

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Aihui Wang, Theodore J. Bohn, Sarith P. Mahanama, Randal D. Koster, and Dennis P. Lettenmaier

Abstract

Retrospectively simulated soil moisture from an ensemble of six land surface/hydrological models was used to reconstruct drought events over the continental United States for the period 1920–2003. The simulations were performed at one-half-degree spatial resolution, using a common set of atmospheric forcing data and model-specific soil and vegetation parameters. Monthly simulated soil moisture was converted to percentiles using Weibull plotting position statistics, and the percentiles were then used to represent drought severities and durations. An ensemble method, based on an inverse mapping of the average of the individual model’s soil moisture percentiles, was also used to combine all models’ simulations. Major results are 1) all models and the ensemble reconstruct the known severe drought events during the last century. The spatial extents and severities of drought are plausible for the individual models although substantial among-model disparities exist. 2) The simulations are in more agreement with each other over the eastern than over the western United States. 3) Most of the models show that soil moisture memory is much longer over the western than over the eastern United States. The results provide some insights into how a hydrological nowcast system can be developed, and also early results from a test application within the University of Washington’s real-time national Surface Water Monitor and a review of the multimodel nowcasts during the southeastern drought beginning in summer 2007 are included.

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