Abstract

Using high-resolution (1 km) hydrologic modeling of the 575 000-km² Red–Arkansas River basin, the impact of spatially aggregating soil moisture imagery up to the footprint scale (32–64 km) of spaceborne microwave radiometers on regional-scale prediction of surface energy fluxes is examined. While errors in surface energy fluxes associated with the aggregation of soil moisture are potentially large (>50 W m⁻²), relatively simple representations of subfootprint-scale variability are capable of substantially reducing the impact of soil moisture aggregation on land surface model energy flux predictions. This suggests that even crude representations of subgrid soil moisture statistics obtained from statistical downscaling procedures can aid regional-scale surface energy flux prediction. One possible soil moisture downscaling procedure, based on an assumption of spatial scaling (i.e., a power-law relationship between statistical moments and scale), is demonstrated to improve TOPmodel-based Land–Atmosphere Transfer Scheme (TOPLATS) prediction of grid-scale surface energy fluxes derived from coarse-resolution soil moisture imagery.

Abstract

Global and regional trends in drought for 1950–2000 are analyzed using a soil moisture–based drought index over global terrestrial areas, excluding Greenland and Antarctica. The soil moisture fields are derived from a simulation of the terrestrial hydrologic cycle driven by a hybrid reanalysis–observation forcing dataset. Drought is described in terms of various statistics that summarize drought duration, intensity, and severity. There is an overall small wetting trend in global soil moisture, forced by increasing precipitation, which is weighted by positive soil moisture trends over the Western Hemisphere and especially in North America. Regional variation is nevertheless apparent, and significant drying over West Africa, as driven by decreasing Sahel precipitation, stands out. Elsewhere, Europe appears to have not experienced significant changes in soil moisture, a trait shared by Southeast and southern Asia. Trends in drought duration, intensity, and severity are predominantly decreasing, but statistically significant changes are limited in areal extent, of the order of 1.0%–7.0% globally, depending on the variable and drought threshold, and are generally less than 10% of continental areas. Concurrent changes in drought spatial extent are evident, with a global decreasing trend of between −0.021% and −0.035% yr⁻¹. Regionally, drought spatial extent over Africa has increased and is dominated by large increases over West Africa. Northern and East Asia show positive trends, and central Asia and the Tibetan Plateau show decreasing trends. In South Asia all trends are insignificant. Drought extent over Australia has decreased. Over the Americas, trends are uniformly negative and mostly significant.

Within the long-term trends there are considerable interannual and decadal variations in soil moisture and drought characteristics for most regions, which impact the robustness of the trends. Analysis of detrended and smoothed soil moisture time series reveals that the leading modes of variability are associated with sea surface temperatures, primarily in the equatorial Pacific and secondarily in the North Atlantic. Despite the overall wetting trend there is a switch since the 1970s to a drying trend, globally and in many regions, especially in high northern latitudes. This is shown to be caused, in part, by concurrent increasing temperatures. Although drought is driven primarily by variability in precipitation, projected continuation of temperature increases during the twenty-first century indicate the potential for enhanced drought occurrence.

Abstract

The lack of observational data for use in evaluating the realism of model-based land–atmosphere feedback signal and strength has been deemed a major obstacle to future improvements to seasonal weather prediction by the Global Land–Atmosphere Coupling Experiment (GLACE). To address this need, a 7-yr (2002–09) satellite remote sensing data record is exploited to produce for the first time global maps of predominant coupling signals. Specifically, a previously implemented convective triggering potential (CTP)–humidity index (HI) framework for describing atmospheric controls on soil moisture–rainfall feedbacks is revisited and generalized for global application using CTP and HI from the Atmospheric Infrared Sounder (AIRS), soil moisture from the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E), and the U.S. Climate Prediction Center (CPC) merged satellite rainfall product (CMORPH). Based on observations taken during an AMSR-E-derived convective rainfall season, the global land area is categorized into four convective regimes: 1) those with atmospheric conditions favoring deep convection over wet soils, 2) those with atmospheric conditions favoring deep convection over dry soils, 3) those with atmospheric conditions that suppress convection over any land surface, and 4) those with atmospheric conditions that support convection over any land surface. Classification maps are produced using both the original and modified frameworks, and later contrasted with similarly derived maps using inputs from the National Aeronautics and Space Administration (NASA) Modern Era Retrospective Analysis for Research and Applications (MERRA). Both AIRS and MERRA datasets of CTP and HI are validated using radiosonde observations. The combinations of methods and data sources employed in this study enable evaluation of not only the sensitivity of the classification schemes themselves to their inputs, but also the uncertainty in the resultant classification maps. The findings are summarized for 20 climatic regions and three GLACE coupling hot spots, as well as zonally and globally. Globally, of the four-class scheme, regions for which convection is favored over wet and dry soils accounted for the greatest and least extent, respectively. Despite vast differences among the maps, many geographically large regions of concurrence exist. Through its ability to compensate for the latitudinally varying CTP–HI–rainfall tendency characteristics observed in this study, the revised classification framework overcomes limitations of the original framework. By identifying regions where coupling persists using satellite remote sensing this study provides the first observationally based guidance for future spatially and temporally focused studies of land–atmosphere interactions. Joint distributions of CTP and HI and soil moisture, rainfall occurrence, and depth demonstrate the relevance of CTP and HI in coupling studies and their potential value in future model evaluation, rainfall forecast, and/or hydrologic consistency applications.

Abstract

The skill of instantaneous Atmospheric Infrared Sounder (AIRS) retrieved near-surface meteorology, including surface skin temperature (T_s ), air temperature (T_a ), specific humidity (q), and relative humidity (RH), as well as model-derived surface pressure (P _surf) and 10-m wind speed (w), is evaluated using collocated National Climatic Data Center (NCDC) in situ observations, offline data from the North American Land Data Assimilation System (NLDAS), and geostationary remote sensing (RS) data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Such data are needed for RS-based water cycle monitoring in areas without readily available in situ data. The study is conducted over the continental United States and Africa for a period of more than 6 years (2002–08). For both regions, it provides for the first time the geographic distribution of AIRS retrieval performance. Through conditional sampling, attribution of retrieval errors to scene atmospheric and surface conditions is performed. The findings support previous assertions that performance degrades with cloud fraction and that (positive) bias enhances with altitude. In general AIRS is biased warm and dry. In certain regions, strong AIRS–NCDC correlation suggests that bias-driven errors, which can be substantial, are correctable. The utility of the error characteristics for prescribing the input-induced uncertainty of RS retrieval models is demonstrated through two applications: a microwave soil moisture retrieval algorithm and the Penman–Monteith evapotranspiration model. An important side benefit of this study is the verification of NLDAS forcing.

Abstract

Part I of this series of studies developed procedures to implement the multiscale filtering algorithm for land surface hydrology and performed assimilation experiments with rainfall ensembles from a climate model. However, a most important application of the multiscale technique is to assimilate satellite-based remote sensing observations into a land surface model—and this has not been realized. This paper focuses on enabling the multiscale assimilation system to use remotely sensed precipitation data. The major challenge is the generation of a rainfall ensemble given one satellite rainfall map. An acceptable rainfall ensemble must contain a proper multiscale spatial correlation structure, and each ensemble member presents a realistic rainfall process in both space and time. A pattern-based sampling approach is proposed, in which random samples are drawn from a historical rainfall database according to the pattern of the satellite rainfall and then a cumulative distribution function matching procedure is applied to ensure the proper statistics for the pixel-level rainfall intensity. The assimilation system is applied using Tropical Rainfall Measuring Mission real-time satellite rainfall over the Red–Arkansas River basin. Results show that the ensembles so generated satisfy the requirements for spatial correlation and realism and the multiscale assimilation works reasonably well. A number of limitations also exist in applying this generation method, mainly stemming from the high dimensionality of the problem and the lack of historical records.

Abstract

The effects of small-scale heterogeneity in land-surface characteristics on the large-scale fluxes of water and energy in the land-atmosphere system have become a central focus of many of the climatology research experiments. The acquisition of high-resolution land-surface data through remote sensing and intensive land-climatology field experiments (like HAPEX and EIFE) has provided data to investigate the interactions between microscale land-atmosphere interactions and macroscale models. One essential research question is how to account for the small-scale heterogeneities and whether “effective” parameters can be used in the macroscale models. To address this question of scaling, three modeling experiments were performed and are reviewed in the paper. The first is concerned with the land-surface hydrology during rain events and between rain events. The second experiment applies the Simple Biosphere Model (SiB) to a heterogeneous domain and the spatial and temporal latent beat flux is analyzed. The third experiment uses thermatic mapper (TM) data to look at the scaling of the normalized vegetation index (NDVI), latent heat flux, and sensible heat flux through either scaling of the TM-derived fields using the TM data or the fields derived from aggregated TM data.

In all three experiments it was found that the surface fluxes and land characteristics can be sealed, and that macroscale models based on elective parameters are sufficient to account for the small-scale heterogeneities investigated. The paper also suggests that the scale at which a macroscale model becomes valid, the representative elementary scale (REA), is on the order 1.5–3 correlation lengths, which for land processes investigated appears to be about 1000–1500 m. At scales less than the REA scale, exact patterns of subgrid heterogeneities are needed for accurate small-scale modeling.

Abstract

Drought has significant social and economic impacts that could be reduced by preparations made possible through seasonal prediction. During the convective season, when the potential of extreme drought is the highest, the soil moisture can provide a means of improved predictability through land–atmosphere interactions. In the past decade, there has been a significant amount of work aimed at better understanding the predictability of land–atmosphere interactions. One such approach classifies the interactions between the land and the atmosphere into coupling states. The coupling states have been shown to be persistent and were used to demonstrate the existence of strong biases in the coupling of the NCEP Climate Forecast System, version 2 (CFSv2). In this work, the attribution of the coupling state on the seasonal prediction of precipitation and temperature and the extent to which the bias in the coupling state hinders the prediction of drought is analyzed. This analysis combines the predictions from statistical models with the predictions from CFSv2 as a means to isolate and attribute the predictability. The results indicate that the intermountain region is a hotspot for seasonal prediction because of local persistence of initial conditions. In addition, the local persistence of initial conditions provides some level of drought prediction; however, accounting for the spatial interactions provides a more complete prediction. Furthermore, the statistical models provide more skillful predictions of precipitation during drought than the CFSv2; however, the CFSv2 predictions are more skillful for daily maximum temperature during drought. The implication, limitations, and extensions of this work are also discussed.

Abstract

Skillful seasonal hydrologic predictions are useful in managing water resources, preparing for droughts and their impacts, energy planning, and many other related sectors. In this study, a seasonal hydrologic ensemble prediction system is developed and evaluated over the eastern United States, with a focus on the Ohio River basin. The system uses a hydrologic model (i.e., the Variable Infiltration Capacity model) as the central element for producing ensemble predictions of soil moisture, snow, and streamflow with lead times up to six months. One unique feature of this system is in the method for generating ensemble atmospheric forcings for the forecast period. It merges seasonal climate forecasts from multiple climate models with observed climatology in a Bayesian framework, such that the uncertainties related to the atmospheric forcings can be better quantified while the signals from individual models are combined. Simultaneously, climate model forecasts are downscaled to an appropriate spatial scale for hydrologic predictions. When generating daily meteorological forcing, the system uses the rank structures of selected historical forcing records to ensure reasonable weather patterns in space and time.

Seasonal hydrologic predictions are made with this system, using seasonal climate forecast from the NCEP Climate Forecast System (CFS), and from a combination of the NCEP CFS and seven climate models in the European Union’s Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (CFS+DEMETER). Forecasts of these two types are made for the summer periods (May to October) of 1981–99 and are compared to forecasts produced with the traditional Ensemble Streamflow Prediction (ESP) approach used in operational seasonal streamflow predictions. The forecasts from this system for the summer of 1988 show very promising skill in precipitation, soil moisture, and streamflow over the Ohio River basin, especially the multimodel CFS+DEMETER forecast. The evaluation with all 19 summer forecasts shows that the multimodel CFS+DEMETER forecast is significantly better than the ESP forecast during the first two months of the forecasts. The advantage is marginal to moderate when only the CFS forecast is used. This study validates the approach of using seasonal climate predictions from dynamic climate models in hydrological predictions, and it also emphasizes the need for international collaborations to develop multimodel seasonal predictions.

Abstract

A procedure is developed to incorporate equality constraints in Kalman filters, including the ensemble Kalman filter (EnKF), and is referred to as the constrained ensemble Kalman filter (CEnKF). The constraint is carried out as a two-step filtering approach, with the first step being the standard (ensemble) Kalman filter. The second step is the constraint step carried out by another Kalman filter that optimally redistributes any imbalance from the first step. The CEnKF is implemented over a 75 000 km² domain in the southern Great Plains region of the United States, using the terrestrial water balance as the constraint. The observations, consisting of gridded fields of the upper two soil moisture layers from the Oklahoma Mesonet system, Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM-CART) energy balance Bowen ratio (EBBR) latent heat estimates, and U.S. Geological Survey (USGS) streamflow from unregulated basins, are assimilated into the Variable Infiltration Capacity (VIC) land surface model. The water balance was applied at the domain scale, and estimates of the water balance components for the domain are updated from the data assimilation step so as to assure closure.

Abstract

Understanding the variability of the terrestrial hydrologic cycle is central to determining the potential for extreme events and susceptibility to future change. In the absence of long-term, large-scale observations of the components of the hydrologic cycle, modeling can provide consistent fields of land surface fluxes and states. This paper describes the creation of a global, 50-yr, 3-hourly, 1.0° dataset of meteorological forcings that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature, and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation, which have been found to exhibit a spurious wavelike pattern in high-latitude wintertime. Wind-induced undercatch of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0° by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3 hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave radiation, specific humidity, surface air pressure, and wind speed) are downscaled in space while accounting for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project (GSWP2). The final product provides a long-term, globally consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and interannual variability and for the evaluation of coupled models and other land surface prediction schemes.