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Since drought and excessive rainfall can have significant socioeconomic impacts, it is important to have accurate high-resolution gridded datasets that can help improve analysis and forecasting of these conditions. One such widely used dataset is the Parameter-Elevation Regressions on Independent Slopes Model (PRISM). PRISM uses a digital elevation model (DEM) to obtain gridded elevation analyses and then uses a regression analysis along with approximately 15 000 surface precipitation measurements to produce a 4-km resolution daily precipitation product over the conterminous United States. The U.S. Climate Reference Network (USCRN) consists of 114 stations that take highly accurate meteorological measurements across all regions of the United States. A comparison between the USCRN and PRISM was performed using data from 2006 to 2018. There were good comparisons between the two datasets across nearly all seasons and regions; most mean daily differences were ,1 mm, with most absolute daily differences ;5 mm. The most general characteristics were for a net dry bias in the PRISM data in the Southwest and a net moist bias in the southern United States. Verifying the PRISM dataset provides us with confidence it can be used with estimates of evapotranspiration, highresolution gridded soil properties, and vegetation datasets to produce a daily gridded soil moisture product for operational use in the analyses and prediction of drought and excessive soil moisture conditions. [ABSTRACT FROM AUTHOR]

The spring dry season occurring in an arid region of the southwestern United States, which receives both winter storm track and summer monsoon precipitation, is investigated. Bimodal precipitation and vegetation growth provide an opportunity to assess multiple climate mechanisms and their impact on hydroclimate and ecosystems. We detect multiple shifts from wet to drier conditions in the observational record and land surface model output. Focusing on the recent dry period, a shift in the late 1990s resulted in earlier and greater spring soil moisture draw down, and later and reduced spring vegetation green-up, compared to a prior wet period (1979–97). A simple soil moisture balance model shows this shift is driven by changes in winter precipitation. The recent post-1999 dry period and an earlier one from 1948 to 1966 are both related to the cool tropics phase of Pacific decadal variability, which influences winter precipitation. In agreement with other studies for the southwestern United States, we find the recent drought cannot be explained in terms of precipitation alone, but also is due to the rising influence of temperature, thus highlighting the sensitivity of this region to warming temperatures. Future changes in the spring dry season will therefore be affected by how tropical decadal variability evolves, and also by emerging trends due to human-driven warming. [ABSTRACT FROM AUTHOR]

A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980-2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic data availability. [ABSTRACT FROM AUTHOR]

This is Part II of a two-part study introducing the heated condensation framework (HCF), which quantifies the potential convective state of the atmosphere in terms of land-atmosphere interactions. Part I introduced the full suite of HCF variables and applied them to case studies with observations and models over a single location in the southern Great Plains. It was shown in Part I that the HCF was capable of identifying locally initiated convection and quantifying energetically favorable pathways for initiation. Here, the HCF is applied to the entire conterminous United States and the climatology of convective initiation (CI) in relation to local land-atmosphere coupling (LoCo) is explored for 34 summers (June-August) using the North American Regional Reanalysis (NARR) and observations. NARR is found to be capable of capturing the convective threshold (buoyant mixing potential temperature θBM) and energy advantage transition (energy advantage potential temperature θadv) for most of the United States. However, there are compensating biases in the components of moisture qmix and temperature q*, resulting in low θBM biases for the wrong reason. The HCF has been used to show that local CI occurred over the Rocky Mountains and the southern Great Plains 35%-65% of the time. Finally, the LoCo process chain has been recast in light of the HCF. Both positive and negative soil moisture-convective feedbacks are possible, with negative feedbacks producing a stronger response in CI likelihood under weak convective inhibition. Positive feedbacks are present but weaker. [ABSTRACT FROM AUTHOR]

The article discusses anomalies in anticyclonic circulation, soil moisture and surface temperature in the contiguous U.S. during July 2012, described as the warmest on record. It mentions how the temperature anomalies were over the bounds of the 1979-2011 reanalysis of most of the northern Great Plains, Midwest and Northeast. It is suggested that the likelihood of extreme July temperature anomalies is greater in current forcing than in preindustrial forcing.

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. [ABSTRACT FROM AUTHOR]

Understanding the occurrence and variability of drought events in historic and projected future climate is essential to managing natural resources and setting policy. The Midwest region is a key contributor in corn and soybean production, and the occurrence of droughts may affect both quantity and quality of these crops. Soil moisture observations play an essential role in understanding the severity and persistence of drought. Considering the scarcity of the long-term soil moisture datasets, soil moisture observations in Illinois have been one of the best datasets for studies of soil moisture. In the present study, the authors use the existing observational dataset and then reconstruct long-term historic time series (1916–2007) of soil moisture data using a land surface model to study the effects of historic climate variability and projected future climate change on regional-scale (Illinois and Indiana) drought. The objectives of this study are to (i) estimate changes and trends associated with climate variables in historic climate variability (1916–2007) and in projected future climate change (2009–99) and (ii) identify regional-scale droughts and associated severity, areal extent, and temporal extent under historic and projected future climate using reconstructed soil moisture data and gridded climatology for the period 1916–2007 using the Variable Infiltration Capacity (VIC) model. The authors reconstructed the soil moisture for a long-term (1916–2007) historic time series using the VIC model, which was calibrated for monthly streamflow and soil moisture at eight U.S. Geological Survey (USGS) gauge stations and Illinois Climate Network’s (ICN) soil moisture stations, respectively, and then it was evaluated for soil moisture, persistence of soil moisture, and soil temperature and heat fluxes. After calibration and evaluation, the VIC model was implemented for historic (1916–2007) and projected future climate (2009–99) periods across the study domain. The nonparametric Mann–Kendall test was used to estimate trends using the gridded climatology of precipitation and air temperature variables. Trends were also estimated for annual anomalies of soil moisture variables, snow water equivalent, and total runoff using a long-term time series of the historic period. Results indicate that precipitation, minimum air temperature, total column soil moisture, and runoff have experienced upward trends, whereas maximum air temperature, frozen soil moisture, and snow water equivalent experienced downward trends. Furthermore, the decreasing trends were significant for the frozen soil moisture in the study domain. The results demonstrate that retrospective drought periods and their severity were reconstructed using model-simulated data. Results also indicate that the study region is experiencing reduced extreme and exceptional droughts with lesser areal extent in recent decades. [ABSTRACT FROM AUTHOR]

Composites based on observations and model outputs from the Climate Variability and Predictability (CLIVAR) drought experiments were used to examine the impact of El Niño–Southern Oscillation (ENSO) and the Atlantic multidecadal oscillation (AMO) on drought over the United States. Because drought implies persistent dryness, the 6-month standardized precipitation index, standardized runoff index, and soil moisture anomalies are used to represent drought. The experiments were performed by forcing an AGCM with prescribed sea surface temperature anomalies (SSTAs) superimposed on the monthly mean SST climatology. Four model outputs from the NCEP Global Forecast System (GFS), NASA’s Seasonal-to-Interannual Prediction Project, version 1 (NSIPP1), GFDL’s global atmospheric model, version 2.1 (AM2.1), and the Lamont-Doherty Earth Observatory (LDEO)/NCAR Community Climate System Model, version 3 (CCM3) were analyzed in this study. Each run lasts from 36 to 51 yr. The impact of ENSO on drought over the United States is concentrated over the Southwest, the Great Plains, and the lower Colorado River basin, with cold (warm) ENSO events favoring drought (wet spells). Over the East Coast and the Southeast, the impact of ENSO is small because the precipitation responses to ENSO are opposite in sign for winter and summer. For these areas, a prolonged ENSO does not always favor either drought or wet spells. The direct influence of the AMO on drought is small. The major influence of the AMO is to modulate the impact of ENSO on drought. The influence is large when the SSTAs in the tropical Pacific and in the North Atlantic are opposite in phase. A cold (warm) event in a positive (negative) AMO phase amplifies the impact of the cold (warm) ENSO on drought. The ENSO influence on drought is much weaker when the SSTAs in the tropical Pacific and in the North Atlantic are in phase. [ABSTRACT FROM AUTHOR]

While most projections of climate change and its regional impacts focus on overall changes in the state of the climate system, useful information can also be found in the evolution of the climate system from one state to another. Here the authors introduce one method for identifying regions in which significant and systematic long-term nonlinear evolutions may be present, even given quasi-linear anthropogenic forcing. Using climate change projections taken from simulations of NCAR’s Community Climate System Model, version 3 (CCSM3), the authors then employ the technique to isolate systematic nonlinear behavior of soil moisture variations over the United States. While the projections presented here only represent the results from one model system, it is argued that such nonlinear behavior is an important characteristic of future climate change that should be considered when discussing both short-term and long-term impacts of anthropogenic climate forcing. [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]

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]

This paper describes important characteristics of an uncoupled high-resolution land data assimilation system (HRLDAS) and presents a systematic evaluation of 18-month-long HRLDAS numerical experiments, conducted in two nested domains (with 12- and 4-km grid spacing) for the period from 1 January 2001 to 30 June 2002, in the context of the International H2O Project (IHOP_2002). HRLDAS was developed at the National Center for Atmospheric Research (NCAR) to initialize land-state variables of the coupled Weather Research and Forecasting (WRF)–land surface model (LSM) for high-resolution applications. Both uncoupled HRDLAS and coupled WRF are executed on the same grid, sharing the same LSM, land use, soil texture, terrain height, time-varying vegetation fields, and LSM parameters to ensure the same soil moisture climatological description between the two modeling systems so that HRLDAS soil state variables can be used to initialize WRF–LSM without conversion and interpolation. If HRLDAS is initialized with soil conditions previously spun up from other models, it requires roughly 8–10 months for HRLDAS to reach quasi equilibrium and is highly dependent on soil texture. However, the HRLDAS surface heat fluxes can reach quasi-equilibrium state within 3 months for most soil texture categories. Atmospheric forcing conditions used to drive HRLDAS were evaluated against Oklahoma Mesonet data, and the response of HRLDAS to typical errors in each atmospheric forcing variable was examined. HRLDAS-simulated finescale (4 km) soil moisture, temperature, and surface heat fluxes agreed well with the Oklahoma Mesonet and IHOP_2002 field data. One case study shows high correlation between HRLDAS evaporation and the low-level water vapor field derived from radar analysis. [ABSTRACT FROM AUTHOR]

Three independent surface soil moisture datasets for the period 1979–87 are compared: 1) global retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), 2) global soil moisture derived from observed meteorological forcing using the NASA Catchment Land Surface Model, and 3) ground-based measurements in Eurasia and North America from the Global Soil Moisture Data Bank. Time-average soil moisture fields from the satellite and the model largely agree in the global patterns of wet and dry regions. Moreover, the time series and anomaly time series of monthly mean satellite and model soil moisture are well correlated in the transition regions between wet and dry climates where land initialization may be important for seasonal climate prediction. However, the magnitudes of time-average soil moisture and soil moisture variability are markedly different between the datasets in many locations. Absolute soil moisture values from the satellite and the model are very different, and neither agrees better with ground data, implying that a “correct” soil moisture climatology cannot be identified with confidence from the available global data. The discrepancies between the datasets point to a need for bias estimation and correction or rescaling before satellite soil moisture can be assimilated into land surface models. [ABSTRACT FROM AUTHOR]