Abstract

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

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 T _air, 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, T _air, 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

The current generation of drought monitors uses physically based indices, such as the standardized precipitation index (SPI), total soil moisture (SM) percentiles, and the standardized runoff index (SRI) to monitor precipitation, soil moisture, and runoff deficits, respectively. Because long-term observations of soil moisture and, to a lesser extent, spatially distributed runoff are not generally available, SRI and SMP are more commonly derived from land surface model–derived variables, where the models are forced with observed quantities such as precipitation, surface air temperature, and winds. One example of such a system is the North American Land Data Assimilation System (NLDAS). While monitoring systems based on sources like NLDAS are able to detect droughts, they are challenged by classification of drought into, for instance, the D0–D4 categories used by the U.S. Drought Monitor (USDM), in part because of uncertainties among multiple drought indicators, models, and assimilation systems. An objective scheme for drawing boundaries between the D0–D4 classes used by the USDM is explored here. The approach is based on multiple SPI, SM, and SRI indices, from which an ensemble mean index is formed. The mean index is then remapped to a uniform distribution by using the climatology of the ensemble (percentile) averages. To assess uncertainties in the classification, a concurrence measure is used to show the extent to which the different indices agree. An approach to drought classification that uses both the mean of the ensembles and its concurrence measure is described. The classification scheme gives an idea of drought severity, as well as the representativeness of the ensemble mean index.

Abstract

The authors analyzed the skill of monthly and seasonal soil moisture (SM) and runoff (RO) forecasts over the United States performed by driving the Variable Infiltration Capacity (VIC) hydrologic model with forcings derived from the National Multi-Model Ensemble hindcasts (NMME_VIC). The grand ensemble mean NMME_VIC forecasts were compared to ensemble streamflow prediction (ESP) forecasts derived from the VIC model forced by resampling of historical observations during the forecast period (ESP_VIC), using the same initial conditions as NMME_VIC. The forecast period is from 1982 to 2010, with the forecast initialized on 1 January, 1 April, 5 July, and 3 October. Overall, forecast skill is seasonally and regionally dependent. The authors found that 1) the skill of the grand ensemble mean NMME_VIC forecasts is comparable with that of the individual model that has the highest skill; 2) for all forecast initiation dates, the initial conditions play a dominant role in forecast skill at 1-month lead, and at longer lead times, forcings derived from NMME forecasts start to contribute to forecast skill; and 3) the initial conditions dominate contributions to skill for a dry climate regime that covers the western interior states for all seasons and the north-central part of the country for January. In this regime, the forecast skill for both methods is high even at 3-month lead. This regime has low mean precipitation and precipitation variations, and the influence of precipitation on SM and RO is weak. In contrast, a wet regime covers the region from the Gulf states to the Tennessee and Ohio Valleys for forecasts initialized in January and April, the Southwest monsoon region, the Southeast, and the East Coast in summer. In these dynamically active regions, where rainfall depends on the path of the moisture transport and atmospheric forcing, forecast skill is low. For this regime, the climate forecasts contribute to skill. Skillful precipitation forecasts after lead 1 have the potential to improve SM and RO forecast skill, but it was found that this mostly was not the case for the NMME models.

Abstract

We examine reforecasts of flash droughts over the United States for the late spring (April–May), midsummer (June–July), and late summer/early autumn (August–September) with lead times up to 3 pentads based on the NOAA second-generation Global Ensemble Forecast System reforecasts version 2 (GEFSv2). We consider forecasts of both heat wave and precipitation deficit (P deficit) flash droughts, where heat wave flash droughts are characterized by high temperature and depletion of soil moisture and P deficit flash droughts are caused by lack of precipitation that leads to (rather than being the cause of) high temperature. We find that the GEFSv2 reforecasts generally capture the frequency of occurrence (FOC) patterns. The equitable threat score (ETS) of heat wave flash drought forecasts for late spring in the regions where heat wave flash droughts are most likely to occur over the north-central and Pacific Northwest regions is statistically significant up to 2 pentads. The GEFSv2 reforecasts capture the basic pattern of the FOC of P-deficit flash droughts and also are skillful up to lead about 2 pentads. However, the reforecasts overestimate the P-deficit flash drought FOC over parts of the Southwest in late spring, leading to large false alarm rates. For autumn, the reforecasts underestimate P-deficit flash drought occurrence over California and Nevada. The GEFSv2 reforecasts are able to capture the approximately linear relationship between evaporation and soil moisture, but the lack of skill in precipitation forecasts limits the skill of P-deficit flash drought forecasts.

Abstract

We examined drought variability and trends over the last century (1916–2013) over the conterminous United States (CONUS) using observed precipitation P, temperature T, and reconstructed total moisture percentiles (TMP) and runoff from four land surface models. We used an integrated drought index (IDI), which we defined as the equally weighted mean of the 3-month standardized runoff index (SRI3) and TMP from four land surface models mapped onto a uniform probability distribution. Using a definition of drought as IDI less than 0.3 for 6 months or longer, we identified 16 drought events, which we termed great droughts that covered more than 50% of the CONUS during our study period. We examined the properties of great droughts and compared these with the 2012 event. The great droughts were located at least partially over the central United States (30°–42°N, 85°–110°W). We found that 12 of these great droughts occurred when cold sea surface temperature anomalies (SSTAs) were located in the tropical Pacific with warm SSTAs in the North Atlantic. We also found a predominance of decreasing trends in IDI; droughts occurred less often and events were less severe as time progressed. In particular, only 2 of the 16 great droughts (2012 and 1988) occurred in the second half of the record.

Abstract

Precipitation forecasts from six climate models in the North American Multi-Model Ensemble (NMME) are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for global land areas, and their skill was evaluated over the period 1982–2010. The skill of monthly precipitation forecasts from the NMME is also assessed. The value-added utility in using the NMME models to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on the inherent persistence characteristics of the SPI itself. As expected, skill of the NMME-generated SPI forecasts depends on the season, location, and specific index considered (the 3- and 6-month SPI were evaluated). In virtually all locations and seasons, statistically significant skill is found at lead times of 1–2 months, although the skill comes largely from initial conditions. Added skill from the NMME is primarily in regions exhibiting El Niño–Southern Oscillation (ENSO) teleconnections. Knowledge of the initial drought state is critical in SPI prediction, and there are considerable differences in observed SPI values between different datasets. Root-mean-square differences between datasets can exceed typical thresholds for drought, particularly in the tropics. This is particularly problematic for precipitation products available in near–real time. Thus, in the near term, the largest advances in the global prediction of meteorological drought are obtainable from improvements in near-real-time precipitation observations for the globe. In the longer term, improvements in precipitation forecast skill from dynamical models will be essential in this effort.

Abstract

The Environmental Modeling Center (EMC) at the National Centers for Environmental Prediction (NCEP) and the University of Washington (UW) run parallel drought monitoring systems over the continental United States based on the North American Land Data Assimilation System (NLDAS). The NCEP system uses four land surface models (LSMs): Variable Infiltration Capacity (VIC), Noah, Mosaic, and Sacramento (SAC). The UW system uses VIC, SAC, Noah, and the Community Land Model (CLM). An assessment of differences in drought characteristics using both systems for the period 1979–2008 was performed. For soil moisture (SM) percentiles and runoff indices, differences are relatively small among different LSMs in the same system. However, the ensemble mean differences between the two systems are large over the western United States—in some cases exceeding 20% for SM and runoff percentile differences. These differences are most apparent after 2002 when the NCEP system transitioned to use the real-time North American Regional Reanalysis (NARR) and its precipitation gauge station data. (The UW system went into real-time operation in 2005.) Experiments were performed to address the sources of uncertainties. Comparison of simulations using the two systems with different model forcings indicates that the precipitation forcing differences are the primary source of the SM and runoff differences. While temperature, shortwave and longwave radiation, and wind speed forcing differences are also large after 2002, their contributions to SM and runoff differences are much smaller than precipitation.

Abstract

The large-scale atmospheric hydrologic cycle over the United States and Mexico derived from the 23-yr NCEP regional reanalysis (RR) was evaluated by comparing the RR products with satellite estimates, independent sounding data, and the operational Eta Model three-dimensional variational data assimilation (3DVAR) system (EDAS).

In general, the winter atmospheric transport and precipitation are realistic. The climatology and interannual variability of the Pacific, subtropical jet streams, and low-tropospheric moisture transport are well captured. During the summer season, the basic features and the evolution of the North American monsoon (NAM) revealed by the RR compare favorably with observations. The RR also captures the out-of-phase relationship of precipitation as well as the moisture flux convergence between the central United States and the Southwest. The RR is able to capture the zonal easterly Caribbean low-level jet (CALLJ) and the meridional southerly Great Plains low-level jet (GPLLJ). Together, they transport copious moisture from the Caribbean to the Gulf of Mexico and from the Gulf of Mexico to the Great Plains, respectively. The RR systematically overestimates the meridional southerly Gulf of California low-level jet (GCLLJ). A comparison with observations suggests that the meridional winds from the RR are too strong, with the largest differences centered over the northern Gulf of California. The strongest winds over the Gulf in the RR extend above 700 hPa, while the operational EDAS and station soundings indicate that the GCLLJ is confined to the boundary layer.

Abstract

Drought indices derived from the Climate Forecast System Reanalysis (CFSR) are compared with indices derived from the ensemble North American Land Data Assimilation System (NLDAS) and the North American Regional Reanalysis (NARR) over the United States. Uncertainties in soil moisture, runoff, and evapotranspiration (E) from three systems are assessed by comparing them with limited observations, including E from the AmeriFlux data, soil moisture from the Oklahoma Mesonet and the Illinois State Water Survey, and streamflow data from the U.S. Geological Survey (USGS). The CFSR has positive precipitation (P) biases over the western mountains, the Pacific Northwest, and the Ohio River valley in winter and spring. In summer, it has positive biases over the Southeast and large negative biases over the Great Plains. These errors limit the ability to use the standardized precipitation indices (SPIs) derived from the CFSR to measure the severity of meteorological droughts. To compare with the P analyses, the Heidke score for the 6-month SPI derived from the CFSR is on average about 0.5 for the three-category classification of drought, floods, and neutral months. The CFSR has positive E biases in spring because of positive biases in downward solar radiation and high potential evaporation. The negative E biases over the Great Plains in summer are due to less P and soil moisture in the root zone. The correlations of soil moisture percentile between the CFSR and the ensemble NLDAS are regionally dependent. The correlations are higher over the area east of 100°W and the West Coast. There is less agreement between them over the western interior region.