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Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. The results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes. [ABSTRACT FROM AUTHOR]

Flash droughts—uncharacteristically rapid dryings of the land system—are naturally associated with extreme precipitation deficits. Such precipitation deficits, however, do not tell the whole story, for land surface drying can be exacerbated by anomalously high evapotranspiration (ET) rates driven by anomalously high temperatures (e.g., during heat waves), anomalously high incoming radiation (e.g., from reduced cloudiness), and other meteorological anomalies. In this study, the relative contributions of precipitation and ET anomalies to flash drought generation in the Northern Hemisphere are quantified through the analysis of diagnostic fields contained within the MERRA-2 reanalysis product. Unique to the approach is the explicit treatment of soil moisture impacts on ET through relationships diagnosed from the reanalysis data; under this treatment, an ET anomaly that is negative relative to the local long-term climatological mean is still considered positive in terms of its contribution to a flash drought if it is high for the concurrent value of soil moisture. Maps produced in the analysis show the fraction of flash drought production stemming specifically from ET anomalies and illustrate how ET anomalies for some droughts are related to temperature and radiation anomalies. While ET is found to have an important impact on flash drought production in the central United States and in parts of Russia known from past studies to be prone to heat wave–related drought, and while this impact does appear stronger during the onset (first several days) of flash droughts, overall the contribution of ET to these droughts is small relative to the contribution of precipitation deficit. [ABSTRACT FROM AUTHOR]

Probabilistic forecasts of U.S. Drought Monitor (USDM) intensification over 2-, 4-, and 8-week time periods are developed based on recent anomalies in precipitation, evapotranspiration, and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. While recent precipitation, evapotranspiration, and soil moisture do provide skillful forecasts, it is found that additional information on the current state of the USDM adds significant skill to the forecasts. The USDM state information takes the form of a metric that quantifies the 'distance' from the next-higher drought category using a nondiscrete estimate of the current USDM state. This adds skill because USDM states that are close to the next-higher drought category are more likely to intensify than states that are farther from this threshold. The method shows skill over most of the United States but is most skillful over the north-central United States, where the cross-validated Brier skill score averages 0.20 for both 2- and 4-week forecasts. The 8-week forecasts are less skillful in most locations. The 2- and 4-week probabilities have very good reliability. The 8-week probabilities, on the other hand, are noticeably overconfident. For individual drought events, the method shows the most skill when forecasting high-amplitude flash droughts and when large regions of the United States are experiencing intensifying drought. [ABSTRACT FROM AUTHOR]

Flash drought refers to relatively short periods of warm surface temperature and anomalously low and rapid decreasing soil moisture (SM). Based on the physical mechanisms associated with flash droughts, these events are classified into two categories: heat wave and precipitation P deficit flash droughts. In previous work, the authors have defined heat wave flash droughts as resulting from the confluence of severe warm air temperature Tair, which increases evapotranspiration (ET), and anomalously low and decreasing SM. Here, a second type of flash drought caused by precipitation deficits is explored. The authors term these events P-deficit flash droughts, which they associate with lack of P. Precipitation deficits cause ET to decrease and temperature to increase. The P-deficit flash droughts are analyzed based on observations of P, Tair, and SM and ET reconstructed using land surface models for the period 1916-2013. The authors find that P-deficit flash droughts are more common than heat wave flash droughts. They are about twice as likely to occur as heat wave flash droughts over the conterminous United States. They are most prevalent over the southern United States with maxima over the southern Great Plains and the Southwest, in contrast to heat wave flash droughts that are mostly likely to occur over the Midwest and the Pacific Northwest, where the vegetation cover is dense. [ABSTRACT FROM AUTHOR]

This work discusses the land surface-atmosphere interactions during the severe drought of 2008 in southern South America, which was among the most severe in the last 50 years in terms of both intensity and extent. Once precipitation returned to normal values, it took about two months for the soil moisture content and vegetation to recover. The land surface effects were examined by contrasting long-term simulations using a consistent set of satellite-derived annually varying land surface biophysical properties against simulations using the conventional land-cover types in the Weather Research and Forecasting Model-Noah land surface model (WRF-Noah). The new land-cover dataset is based on ecosystem functional properties that capture changes in vegetation status due to climate anomalies and land-use changes. The results show that the use of realistic information of vegetation states enhances the model performance, reducing the precipitation biases over the drought region and over areas of excessive precipitation. The precipitation bias reductions are attributed to the corresponding changes in greenness fraction, leaf area index, stomatal resistance, and surface roughness. The temperature simulation shows a generalized increase, which is attributable to a lower vegetation greenness and a doubling of the stomatal resistance that reduces the evapotranspiration rate. The increase of temperature has a beneficial effect toward the eastern part of the domain with a notable reduction of the bias, but not over the central region where the bias is increased. The overall results suggest that an improved representation of the surface processes may contribute to improving the predictive skill of the model system. [ABSTRACT FROM AUTHOR]

This paper describes a publicly available, long-term (1915-2011), hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915-2011 versus 1950-2000), 2) increased the spatial resolution from ⅛° to , and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community. [ABSTRACT FROM AUTHOR]

This study investigates the land-atmosphere coupling strength during summer over the United States using the Regional Climate Model version 4 (RegCM4)-Community Land Model version 3.5 (CLM3.5). First, a 10-yr simulation driven with reanalysis lateral boundary conditions (LBCs) is conducted to evaluate the model performance. The model is then used to quantify the land-atmosphere coupling strength, predictability, and added forecast skill (for precipitation and 2-m air temperature) attributed to realistic land surface initialization following the Global Land-Atmosphere Coupling Experiment (GLACE) approaches. Similar to previous GLACE results using global climate models (GCMs), GLACE-type experiments with RegCM4 identify the central United States as a region of strong land-atmosphere coupling, with soil moisture-temperature coupling being stronger than soil moisture-precipitation coupling, and confirm that realistic soil moisture initialization is more promising in improving temperature forecasts than precipitation forecasts. At a 1-15-day lead, the added forecast skill reflects predictability (or land-atmosphere coupling strength) indicating that that model can capture the realistic land-atmosphere coupling at a short time scale. However, at a 16-30-day lead, predictability cannot translate to added forecast skill, implying that the coupling at the longer time scale may not be represented well in the model. In addition, comparison of results from GLACE2-type experiments with RegCM4 driven by reanalysis LBCs and those driven by GCM LBCs suggest that the intrinsic land-atmosphere coupling strength within the regional model is the dominant factor for the added forecast skill at a 1-15-day lead, while the impact of LBCs from the GCM may play a dominant role in determining the signal of added forecast skill in the regional model at a 16-30-day lead. It demonstrates the complexities of using regional climate model for GLACE-type studies. [ABSTRACT FROM AUTHOR]

The atmospheric moisture budget and surface interactions for the southern Great Plains are evaluated for contrasting May-June periods (1998, 2002, 2006, and 2007) as background for the Cloud and Land Surface Interaction Campaign (CLASIC) of (wet) 7-30 June 2007. Budget components [flux divergence (MFD), storage change (dPW), and inflow (IF/ A)] are estimated from North American Regional Reanalysis data. Precipitation ( P) is calculated from NCEP daily gridded data, evapotranspiration ( E) is obtained as moisture budget equation residual, and the recycling ratio ( PE/ P) is estimated using a new equation. Regional averages are presented for months and five daily P categories. Monthly budget results show that E and E − P are strongly positively related to P; E − P generally is positive and balanced by positive MFD that results from its horizontal velocity divergence component (HD, positive) exceeding its horizontal advection component (HA, negative). An exception is 2007 (CLASIC), when E − P and MFD are negative and supported primarily by negative HA. These overall monthly results characterize low P days (≤0.6 mm), including for nonanomalous 2007, but weaken as daily P approaches 4 mm. In contrast, for 4 < P ≤ 8 mm day−1 E − P and MFD are moderately negative and balanced largely by negative HD except in 2007 (negative HA). This overall pattern was accentuated (including for nonanomalous 2007) when daily P > 8 mm. Daily P E/ P ratios are small and of limited range, with P category averages 0.15-0.19. Ratios for 2007 are above average only for daily P ≤ 4 mm. CLASIC wetness principally resulted from distinctive MFD characteristics. Solar radiation, soil moisture, and crop status/yield information document surface interactions. [ABSTRACT FROM AUTHOR]

Long-term (1901-2002) diurnal temperature range (DTR) data are evaluated to examine their spatial and temporal variability across the United States; the early century origin of the DTR declines; and the relative regional contributions to DTR variability among cloud cover, precipitation, soil moisture, and atmosphere/ocean teleconnections. Rotated principal component analysis (RPCA) of the Climatic Research Unit (CRU) Time Series (TS) 2.1 dataset identifies five regions of unique spatial U.S. DTR variability. RPCA creates regional orthogonal indices of cloud cover, soil moisture, precipitation, and the teleconnections used subsequently in stepwise multiple linear regression to examine their regional impact on DTR, maximum temperature (Tmax), and minimum temperature (Tmin). The southwestern United States has the smallest DTR and cloud cover trends as both Tmax and Tmin increase over the century. The Tmin increases are the primary influence on DTR trend in other regions, except in the south-central United States, where downward Tmax trend largely affects its DTR decline. The Tmax and DTR tend to both exhibit simultaneous decadal variations during unusually wet and dry periods in response to cloud cover, soil moisture, and precipitation variability. The widely reported post-1950 DTR decline began regionally at various times ranging from around 1910 to the 1950s. Cloud cover alone accounts for up to 63.2% of regional annual DTR variability, with cloud cover trends driving DTR in northern states. Cloud cover, soil moisture, precipitation, and atmospheric/oceanic teleconnection indices account for up to 80.0% of regional variance over 1901-2002 (75.4% in detrended data), although the latter only account for small portions of this variability. [ABSTRACT FROM AUTHOR]

This study examines the land-atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)-Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)-Community Land Model, version 3 (CLM3) and CAM4-CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land-atmosphere coupling; 2) soil moisture-precipitation coupling is weaker than soil moisture-surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4-CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4-CLM4 is weaker than in CAM3-CLM3, which is further supported by Global Land-Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM-CLM models do not seem to overestimate the land-atmosphere coupling strength. [ABSTRACT FROM AUTHOR]

The North American Regional Reanalysis (NARR) is a state-of-the-art land-atmosphere reanalysis product that provides improved representation of the terrestrial hydrologic cycle compared to previous global reanalyses, having the potential to provide an enhanced picture of hydrologic extremes such as floods and droughts and their driving mechanisms. This is partly because of the novel assimilation of observed precipitation, state-of-the-art land surface scheme, and higher spatial resolution. NARR is evaluated in terms of the terrestrial water budget and its depiction of drought at monthly to annual time scales against two offline land surface model [Noah v2.7.1 and Variable Infiltration Capacity (VIC)] simulations and observation-based runoff estimates over the continental United States for 1979-2003. An earlier version of the Noah model forms the land component of NARR and so the offline simulation provides an opportunity to diagnose NARR land surface variables independently of atmospheric feedbacks. The VIC model has been calibrated against measured streamflow and so provides a reasonable estimate of large-scale evapotranspiration. Despite similar precipitation, there are large differences in the partitioning of precipitation into evapotranspiration and runoff. Relative to VIC, NARR and Noah annual evapotranspiration is biased high by 28% and 24%, respectively, and the runoff ratios are 50% and 40% lower. This is confirmed by comparison with observation-based runoff estimates from 1130 small, relatively unmanaged basins across the continental United States. The overestimation of evapotranspiration by NARR is largely attributed to the evapotranspiration component of the Noah model, whereas other factors such as atmospheric forcings or biases induced by precipitation assimilation into NARR play only a minor role. A combination of differences in the parameterization of evapotranspiration and in particular low stomatal resistance values in NARR, the seasonality of vegetation characteristics, the near-surface radiation and meteorology, and the representation of soil moisture dynamics, including high infiltration rates and the relative coupling of soil moisture with baseflow in NARR, are responsible for the differences in the water budgets. Large-scale drought as quantified by soil moisture percentiles covaries closely over the continental United States between the three datasets, despite large differences in the seasonal water budgets. However, there are large regional differences, especially in the eastern United States where the VIC model shows higher variability in drought dynamics. This is mostly due to increased frequency of completely dry conditions in NARR that result from differences in soil depth, higher evapotranspiration, early snowmelt, and early peak runoff. In the western United States, differences in the precipitation forcing contribute to large discrepancies between NARR and Noah/VIC simulations in the representation of the early 2000s drought. [ABSTRACT FROM AUTHOR]

This study examines the impact of sea surface temperature (SST) and soil moisture on summer precipitation over two regions of the United States (the upper Mississippi River basin and the Great Plains) based on data from observation and observation-forced model simulations (in the case of soil moisture). Results from SST-precipitation correlation analysis show that spatially averaged SST of identified oceanic areas are better predictors than derived SST patterns from the EOF analysis and that both predictors are strongly associated with the Pacific Ocean. Results from conditioned soil moisture-precipitation correlation analysis show that the impact of soil moisture on precipitation differs between the outer-quartiles years (with summer precipitation amount in the first and fourth quartiles) and inner-quartiles years (with summer precipitation amount in the second and third quartiles), and also between the high- and low-skill SST years (categorized according to the skill of SST-based precipitation prediction). Specifically, the soil moisture-precipitation feedback is more likely to be positive and significant in the outer-quartiles years and in the years when the skill of precipitation prediction based on SST alone is low. This study indicates that soil moisture should be included as a useful predictor in precipitation prediction, and the resulting improvement in prediction skills will be especially substantial during years of large precipitation anomalies. It also demonstrates the complexity of the impact of SST and soil moisture on precipitation, and underlines the important complementary roles both SST and soil moisture play in determining precipitation. [ABSTRACT FROM AUTHOR]

Several land surface datasets, such as the observed Illinois soil moisture dataset; three retrospective offline run datasets from the Noah land surface model (LSM), Variable Infiltration Capacity (VIC) LSM, and Climate Prediction Center leaky bucket soil model; and three reanalysis datasets (North American Regional Reanalysis, NCEP/Department of Energy Global Reanalysis, and 40-yr ECMWF Re-Analysis), are used to study the spatial and temporal variability of soil moisture and its response to the major components of land surface hydrologic cycles: precipitation, evaporation, and runoff. Detailed analysis was performed on the evolution of the soil moisture vertical profile. Over Illinois, model simulations are compared to observations, but for the United States as a whole some impressions can be gained by comparing the multiple soil moisture-precipitation-evaporation-runoff datasets to one another. The magnitudes and partitioning of major land surface water balance components on seasonal-interannual time scales have been explored. It appears that evaporation has the most prominent annual cycle but its interannual variability is relatively small. For other water balance components, such as precipitation, runoff, and surface water storage change, the amplitudes of their annual cycles and interannual variations are comparable. This study indicates that all models have a certain capability to reproduce observed soil moisture variability on seasonal-interannual time scales, but offline runs are decidedly better than reanalyses (in terms of validation against observations) and more highly correlated to one another (in terms of intercomparison) in general. However, noticeable differences are also observed, such as the degree of simulated drought severity and the locations affected-this is due to the uncertainty in model physics, input forcing, and mode of running (interactive or offline), which continue to be major issues for land surface modeling. [ABSTRACT FROM AUTHOR]

This study assesses the impact of satellite rainfall error structure on soil moisture simulations with the NASA Catchment land surface model. Specifically, the study contrasts a complex satellite rainfall error model (SREM2D) with the standard rainfall error model used to generate ensembles of rainfall fields as part of the Land Data Assimilation System (LDAS) developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region, which offers good coverage by weather radars and in situ meteorological and soil moisture measurement stations. The authors used high-resolution (25 km, 3-hourly) satellite rainfall fields derived from the NOAA/Climate Prediction Center morphing (CMORPH) global satellite product and rain gauge--calibrated radar rainfall fields (considered as the reference rainfall). The LDAS simulations are evaluated in terms of rainfall and soil moisture error. Comparisons of rainfall ensembles generated by SREM2D and LDAS against reference rainfall show that both rainfall error models preserve the satellite rainfall error characteristics across a range of spatial scales. The error structure in SREM2D is shown to generate rainfall replicates with higher variability that better envelop the reference rainfall than those generated by the LDAS error model. Likewise, the SREM2D-generated soil moisture ensemble shows slightly higher spread than the LDAS-generated ensemble and thus better encapsulates the reference soil moisture. Soil moisture errors, however, are less sensitive than precipitation errors to the complexity of the precipitation error modeling approach because soil moisture dynamics are dissipative and nonlinear. [ABSTRACT FROM AUTHOR]

This paper examines hydrological variability and its changes in two different versions of a coupled ocean–atmosphere general circulation model developed at the National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory and forced with estimates of future increases of greenhouse gas and aerosol concentrations. This paper is the second part, documenting potential changes in variability as greenhouse gases increase. The variance changes are examined using an ensemble of 8 transient integrations for an older model version and 10 transient integrations for a newer model. Monthly and annual data are used to compute the mean and variance changes. Emphasis is placed on computing and analyzing the variance changes for the middle of the twenty-first century and compared with those found in the respective control integrations. The hydrologic cycle intensifies because of the increase of greenhouse gases. In general, precipitation variance increases in most places. This is the case virtually everywhere the mean precipitation rate increases and many places where the precipitation decreases. The precipitation rate variance decreases in the subtropics, where the mean precipitation rate also decreases. The increased precipitation rate and variance, in middle to higher latitudes during late fall, winter, and early spring leads to increased runoff and its variance during that period. On the other hand, the variance changes of soil moisture are more complicated, because soil moisture has both a lower and upper bound that tends to reduce its fluctuations. This is particularly true in middle to higher latitudes during winter and spring, when the soil moisture is close to its saturation value at many locations. Therefore, changes in its variance are limited. Soil moisture variance change is positive during the summer, when the mean soil moisture decreases and is close to the middle of its allowable range. In middle to high northern latitudes, an increase in runoff and its variance during late winter and spring plus the decrease in soil moisture and its variance during summer lend support to the hypothesis stated in other publications that a warmer climate can cause an increasing frequency of both excessive discharge and drier events, depending on season and latitude. [ABSTRACT FROM AUTHOR]

This study examines a lagged soil moisture–precipitation (S–P) correlation for 24 yr of boreal summer (1979–2002) from the 40-yr ECMWF Re-Analysis (ERA-40), the NCEP–Department of Energy (DOE) reanalysis 2 (R-2), the North American Regional Reanalysis (NARR), 10 yr (1986–95) of data from phase 2 of the Global Soil Wetness Project (GSWP-2), and two 24-yr model simulations with the NCAR Community Atmosphere Model version 3.1 (CAM3). The different datasets and model simulations all show a similar negative-dominant S–P correlation pattern with wet areas having more significantly negative correlations than the dry areas. The experiments with CAM3 show that this correlation pattern is not caused by the soil moisture feedback. Rather, the combined effect of the precipitation variability and the memory of soil moisture is the main reason for this correlation pattern. Theoretical analysis confirms this conclusion and shows that the correlation pattern is related to both the precipitation spectrum and the time scale of soil moisture retention. This study suggests that the attribution of lagged correlations of precipitation with soil moisture or related variables should be done cautiously. [ABSTRACT FROM AUTHOR]

Drought indices derived from the North American Land Data Assimilation System (NLDAS) Variable Infiltration Capacity (VIC) and Noah models from 1950 to 2000 are intercompared and evaluated for their ability to classify drought across the United States. For meteorological drought, the standardized precipitation index (SPI) is used to measure precipitation deficits. The standardized runoff index (SRI), which is similar to the SPI, is used to classify hydrological drought. Agricultural drought is measured by monthly-mean soil moisture (SM) anomaly percentiles based on probability distributions (PDs). The PDs for total SM are regionally dependent and influenced by the seasonal cycle, but the PDs for SM monthly-mean anomalies are unimodal and Gaussian. Across the eastern United States (east of 95°W), the indices derived from VIC and Noah are similar, and they are able to detect the same drought events. Indices are also well correlated. For river forecast centers (RFCs) across the eastern United States, different drought indices are likely to detect the same drought events. The monthly-mean soil moisture (SM) percentiles and runoff indices between VIC and Noah have large differences across the western interior of the United States. For small areas with a horizontal resolution of 0.5° on the time scales of one to three months, the differences of SM percentiles and SRI between VIC and Noah are larger than the thresholds used to classify drought. For the western RFCs, drought events selected according to SM percentiles or SRI derived from different NLDAS systems do not always overlap. [ABSTRACT FROM AUTHOR]

Proper partitioning of the surface heat fluxes that drive the evolution of the planetary boundary layer in numerical weather prediction models requires an accurate specification of the initial state of the land surface. The National Centers for Environmental Prediction (NCEP) operational Eta Model is used to produce land surface analyses by continuously cycling soil temperature and moisture fields. These fields previously evolved only in response to radiation budget constraints and modeled precipitation, but NCEP recently upgraded the self-cycling process so that soil fields respond instead to the radiation budget and observed precipitation. A comparison of 0000 and 1200 UTC Eta Model analyses of soil temperature and moisture at several soil depths with observations from the Oklahoma Mesonet during 2004 and 2005 shows that there are strong biases in soil temperature and a severe underestimation of soil moisture at all depths. After the change to a new assimilation scheme, there is notable improvement in the magnitude of the analyzed soil moisture fields, although a strong dry bias persists in the soil moisture field. A simple one-layer slab soil model quantifies the effect of such soil moisture errors on the diurnal cycle of soil temperature and reveals that these soil moisture errors alone may account for only 1.6°C increases in predicted maximum soil temperatures during the day and temperature reductions of the same magnitude at night. The much larger remaining soil temperature errors possibly stem from documented problems with the solar radiation and longwave parameterizations within the Eta Model. [ABSTRACT FROM AUTHOR]

Land–atmosphere interactions in midlatitude continental regions are particularly active during the warm season. It is still unclear whether and under what circumstances these interactions may involve positive or negative feedbacks between soil moisture conditions and rainfall occurrence. Assessing such feedbacks is crucially important to a better understanding of the role of land surface conditions on the regional dynamics of the water cycle. This work investigates the relationship between soil moisture and subsequent precipitation at the daily time scale in a midlatitude continental region. Sounding data from 16 locations across the midwestern United States are used to calculate two indices of atmospheric instability—namely, the convective available potential energy (CAPE) and the convective inhibition (CIN). These indices are used to classify rainfall as convective or stratiform. Correlation analyses and uniformity tests are then carried out separately for these two rainfall categories, to assess the dependence of rainfall occurrence on antecedent soil moisture conditions, using simulated soil moisture values. The analysis suggests that most of the positive correlation observed between soil moisture and subsequent precipitation is due to the autocorrelation of long stratiform events. The authors found both areas with positive and areas with negative feedback on convective precipitation. This behavior is likely due to the contrasting effects of soil moisture conditions on convective phenomena through changes in surface temperature and the supply of water vapor to the overlying air column. No significant correlation is found between daily rainfall intensity and antecedent simulated soil moisture conditions either for convective or stratiform rainfall. [ABSTRACT FROM AUTHOR]

Estimating the water budgets in a small-scale basin is a challenge, especially in the mountainous western United States, where the terrain is complex and observational data in the mountain areas are sparse. This manuscript reports on research that downscaled 5-yr (1999–2004) hydrometeorological fields over the upper Rio Grande basin from a 2.5° NCEP–NCAR reanalysis to a 4-km local scale using a regional climate model [fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3]. The model can reproduce the terrain-related precipitation distribution—the trend of diurnal, seasonal, and interannual precipitation variability—although poor snow simulation caused it to overestimate precipitation and evapotranspiration in the cold season. The outcomes from the coupled model are also comparable to offline Variable Infiltration Capacity (VIC) and Land Data Assimilation System (LDAS)/Mosaic land surface simulations that are driven by observed and/or analyzed surface meteorological data. [ABSTRACT FROM AUTHOR]

A physically based hydrology model is used to produce time series for the period 1916–2003 of evapotranspiration (ET), runoff, and soil moisture (SM) over the western United States from which long-term trends are evaluated. The results show that trends in ET in spring and summer are determined primarily by trends in precipitation and snowmelt that determine water availability. From April to June, ET trends are mostly positive due primarily to earlier snowmelt and earlier emergence of snow-free ground, and secondarily to increasing trends in spring precipitation. From July to September trends in ET are more strongly influenced by precipitation trends, with the exception of areas (most notably California) that receive little summer precipitation and have experienced large changes in snowmelt timing. Trends in the seasonal timing of ET are modest, but during the period 1947–2003 when temperature trends are large, they reflect a shift of ET from midsummer to early summer and late spring. As in other studies, it is found that runoff is occurring earlier in spring, a trend that is related primarily to increasing temperature, and is most apparent during 1947–2003. Trends in the annual runoff ratio, a variable critical to western water management, are determined primarily by trends in cool season precipitation, rather than changes in the timing of runoff or ET. It was found that the signature of temperature-related trends in runoff and SM is strongly keyed to mean midwinter [December–February (DJF)] temperatures. Areas with warmer winter temperatures show increasing trends in the runoff fraction as early as February, and colder areas as late as June. Trends toward earlier spring SM recharge are apparent and increasing trends in SM on 1 April are evident over much of the region. The 1 July SM trends are less affected by snowmelt changes and are controlled more by precipitation trends. [ABSTRACT FROM AUTHOR]

A novel methodology is introduced for quantifying the added value of remotely sensed soil moisture products for global land surface modeling applications. The approach is based on the assimilation of soil moisture retrievals into a simple surface water balance model driven by satellite-based precipitation products. Filter increments (i.e., discrete additions or subtractions of water suggested by the filter) are then compared to antecedent precipitation errors determined using higher-quality rain gauge observations. A synthetic twin experiment demonstrates that the correlation coefficient between antecedent precipitation errors and filter increments provides an effective proxy for the accuracy of the soil moisture retrievals themselves. Given the inherent difficulty of directly validating remotely sensed soil moisture products using ground-based observations, this assimilation-based proxy provides a valuable tool for efforts to improve soil moisture retrieval strategies and quantify the novel information content of remotely sensed soil moisture retrievals for land surface modeling applications. Using real spaceborne data, the approach is demonstrated for four different remotely sensed soil moisture datasets along two separate transects in the southern United States. Results suggest that the relative superiority of various retrieval strategies varies geographically. [ABSTRACT FROM AUTHOR]

Droughts can be characterized by their severity, frequency and duration, and areal extent. Depth–area–duration analysis, widely used to characterize precipitation extremes, provides a basis for the evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. Gridded precipitation and temperature data were used to force a physically based macroscale hydrologic model at 1/2° spatial resolution over the continental United States, and construct a drought history from 1920 to 2003 based on the model-simulated soil moisture and runoff. A clustering algorithm was used to identify individual drought events and their spatial extent from monthly summaries of the simulated data. A series of severity–area–duration (SAD) curves were constructed to relate the area of each drought to its severity. An envelope of the most severe drought events in terms of their SAD characteristics was then constructed. The results show that (a) the droughts of the 1930s and 1950s were the most severe of the twentieth century for large areas; (b) the early 2000s drought in the western United States is among the most severe in the period of record, especially for small areas and short durations; (c) the most severe agricultural droughts were also among the most severe hydrologic droughts, however, the early 2000s western U.S. drought occupies a larger portion of the hydrologic drought envelope curve than does its agricultural companion; and (d) runoff tends to recover in response to precipitation more quickly than soil moisture, so the severity of hydrologic drought during the 1930s and 1950s was dampened by short wet spells, while the severity of the early 2000s drought remained high because of the relative absence of these short-term phenomena. [ABSTRACT FROM AUTHOR]

The availability of long-term gridded datasets of precipitation, temperature, and other surface meteorological variables offers the potential for deriving a range of land surface conditions that have not been directly observed. These include, for instance, soil moisture, snow water equivalent, evapotranspiration, runoff, and subsurface moisture transport. However, gridding procedures can themselves introduce artificial trends due to incorporation of stations with different record lengths and locations. Hence, existing gridded datasets are in general not appropriate for estimation of long-term trends. Methods are described here for adjustment of gridded daily precipitation and temperature maxima and minima over the continental United States based on newly available (in electronic form) U.S. Cooperative Observer station data archived at the National Climatic Data Center from the early 1900s on. The intent is to produce gridded meteorological datasets that can be used, in conjunction with hydrologic modeling, for long-term trend analysis of simulated hydrologic variables. [ABSTRACT FROM AUTHOR]

The potential predictability of surface-air temperature and precipitation over the United States was assessed for a GCM forced by observed sea surface temperatures and an estimate of observed soil-moisture content. The latter was obtained by substituting the GCM-simulated precipitation, which is used to drive the GCM's land surface component, with observed pentad-mean precipitation at each time step of the model's integration. With this substitution, the simulated soil moisture correlates well with an independent estimate of observed soil moisture in all seasons over the entire U.S. continent. Significant enhancements for the predictability of surface-air temperature and precipitation were found in boreal late spring and summer over the U.S. continent. Anomalous pattern correlations of precipitation and surface-air temperature over the U.S. continent in the June–August season averaged for the 1979–2000 period increased from 0.01 and 0.06 for the GCM simulations without precipitation substitution to 0.23 and 0.31, respectively, for the simulations with precipitation substitution. The results provide an estimate for the limits of potential predictability if soil-moisture variability is to be perfectly predicted. However, this estimate may be model dependent and needs to be substantiated by other modeling groups. [ABSTRACT FROM AUTHOR]

This paper describes a methodology for coupling the Simplified Simple Biosphere Model (SSiB) to the regional Eta Model of the National Centers for Environmental Prediction (NCEP), and presents the application of the coupled system in regional simulation studies. The coupled Eta–SSiB model is used to study the impact of land surface processes and land surface initialization on the regional water and energy cycle in an extreme climate event, by comparing the results from the Eta–SSiB with those from the Eta–bucket model. Simulations from both models spanned 3 months via a succession of 48-hr simulations over June, July, and August 1993, a summer of heavy flooding in the United States. The monthly and seasonal means from the simulations in both model runs are compared. The Eta–SSiB model produces more realistic monthly mean precipitation over the United States and the flood areas. The improvements are mainly manifested in the intensity of the heavy rainfall and its spatial distribution. The results demonstrate that even with a short-term simulation, a more realistic representation of land surface processes and land surface initialization improves the monthly and seasonal means of the simulated regional precipitation for the summer of 1993. In addition to precipitation, the simulations of surface air temperature are also evaluated and they show that the Eta–SSiB model produces reasonable results over most of the United States, with the exception of a cold bias at night in the mountainous western region of the United States. To understand the mechanisms of land surface–atmosphere interactions and the causes for the differences in the Eta–SSiB and the Eta–bucket simulations, the water cycle in the atmosphere–land system and the energy balance at the land surface are analyzed. The changes in (a) spatial distribution and diurnal cycle of surface latent and sensible heat, and (b) low-level moisture flux... [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the dependence of summertime daily maximum temperature on antecedent soil moisture using daily surface observations from a selection of stations in the contiguous United States and daily time series of soil moisture computed with a simple local water balance model. The computed soil moisture time series are offered as an alternative to Palmer's soil moisture anomaly (Z) index, the Palmer Drought Severity Index (PDSI), and other such time series. In contrast to other water balance models that have been designed for the computation of soil moisture time series, the model herein is driven by daily rather than monthly data, uses the Priestley-Taylor method in lieu of Thornthwaite's method to calculate potential evapotranspiration, allows for runoff during dry periods as well as when soil moisture is not at field capacity, includes a crude scheme for taking into account the effects of snowmelt on the water balance, and permits geographical variations in soil water capacity. The Priestley-Taylor method is considered to yield more realistic estimates of evapo-transpiration than Thornthwaite's method since it accounts for net radiation and represents a special case of the widely used Penman-Monteith method. Total runoff is parameterized according to the Variable Infiltration Capacity model. Based on a comparison with soil moisture measurements at Peoria, Illinois, the model appears to simulate the variability of soil moisture anomalies (W[sup']) reasonably well. Analysis of the relationship between W[sup'] and daily maximum temperatures (T[submax]) shows that in the central and eastern United States during the summer, the entire frequency distribution of standardized T[submax] is shifted toward higher values following a 'low-W[sup']' day (i.e., a day on which W[sup'] falls into the bottom quartile of its frequency distribution). The shift is most pronounced at the high end of the temperature distribution, indicating that as the... [ABSTRACT FROM AUTHOR]

Recent results show disagreement between global and limited-area models as to the role of soil moisture feedback during the summer of 1993 in the central United States. July precipitation totals increase by 50% in the European Centre for Medium-Range Weather Forecasts global model when soil moisture is initialized "wet," but two separate regional modeling groups [University of Utah Limited Area Model group and National Center for Atmospheric Research Regional Climate Model (RegCM) group] have found very different responses to soil moisture, indicating that drier soil moisture conditions might actually lead to increased precipitation via an increase in convective instability and an enhancement of the low-level jet from the Gulf of Mexico. To further evaluate the sensitivity results of RegCM in this context, a new suite of simulations, driven by analyses of observations for May--July of 1988 and 1993 is performed. The model domain is larger than in the previous experiments and the sensitivity of predicted seasonal rainfall to "wet" and "dry" initial soil moisture is analyzed. In comparing the new simulations with the earlier results, it is found that the simulation of seasonal precipitation as well as its sensitivity to initial soil moisture are affected by domain size and location of the lateral boundaries in both the 1988 and 1993 experiments. The smaller domain captures observed precipitation better in the upper Mississippi basin; however, the sensitivity of precipitation to initial soil moisture appears to be more realistic in the larger domain. While the lateral boundary forcing in the small domain experiments constrains the model to a better overall simulation, it also yields an unrealistic response to internal forcings, which are not consistent with the applied large-scale forcing. These results demonstrate that the domain of a regional climate model must be carefully selected for its specific application. In particular, domains much large. [ABSTRACT FROM AUTHOR]