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Thresholds of soil moisture exist below which the atmosphere becomes hypersensitive to land surface drying, inducing thermal feedbacks that can exacerbate heatwaves. Realistic representation of threshold transitions in forecast models could improve extreme heat predictability and understanding of the role of land–atmosphere coupling. This study evaluates the performance of several forecast models from the Subseasonal Experiment (SubX) and several prototype versions of the Unified Forecast System (UFS) in their representation of threshold transitions by validation against reanalysis data. A metric of skill (true skill score) is applied to soil moisture breakpoint values, which mark the transition to heatwave hypersensitivity for drying soils. Forecast models have poor skill at being initialized on the correct side of the breakpoint, but show improvement when normalized to account for deficiencies in their soil moisture climatologies. Regionally, models performed best in the U.S. Northwest and worst in the Southwest. They effectively capture the tendency of western regions to spend more summer days in the hypersensitive regime than the eastern United States. Models represent well extreme heat as a consequence of atmospheric initial state for the first week of the forecast, but struggle to represent the soil moisture feedback regime. Forecast models generally perform better at extreme heat prediction when they are already dry and in the hypersensitive regime, even when erroneously so, implying that errors or biases exist in model parameterizations. Nevertheless, composite analysis shows encouraging model performance of the "hit" category, suggesting that an improvement in soil moisture initialization could further improve extreme heat forecast skill. [ABSTRACT FROM AUTHOR]

Extreme heat events are a threat to human health, productivity, and food supply, so understanding their drivers is critical to adaptation and resilience. Anticyclonic circulation and certain quasi-stationary Rossby wave patterns are well known to coincide with heatwaves, and soil moisture deficits amplify extreme heat in some regions. However, the relative roles of these two factors in causing heatwaves is still unclear. Here we use constructed circulation analogs to estimate the contribution of atmospheric circulation to heatwaves in the United States in the Community Earth System Model version 1 (CESM1) preindustrial control simulations. After accounting for the component of the heatwaves explained by circulation, we explore the relationship between the residual temperature anomalies and soil moisture. We find that circulation explains over 85% of heatwave temperature anomalies in the eastern and western United States but only 75%–85% in the central United States. In this region, there is a significant negative correlation between soil moisture the week before the heatwave and the strength of the heatwave that explains additional variance. Further, for the hottest central U.S. heatwaves, positive temperature anomalies and negative soil moisture anomalies are evident over a month before heatwave onset. These results provide evidence that positive land–atmosphere feedbacks may be amplifying heatwaves in the central United States and demonstrate the geographic heterogeneity in the relative importance of the land and atmosphere for heatwave development. Analysis of future circulation and soil moisture in the CESM1 Large Ensemble indicates that, over parts of the United States, both may be trending toward greater heatwave likelihood. [ABSTRACT FROM AUTHOR]

Diagnosis of rapidly developing springtime droughts in the central United States has mostly been made via numerous individual case studies rather than in an aggregate sense. This study investigates common aspects of subseasonal "meteorological drought" evolution, here defined as persistent precipitation minus evapotranspiration (P − ET) deficits, revealed in early (1 April–15 May) and late (16 May–30 June) spring composites of 5-day running mean JRA-55 reanalysis data for three different central U.S. regions during 1958–2018. On average, these droughts are initiated by a quasi-stationary Rossby wave packet (RWP), propagating from the western North Pacific, which arises about a week prior to drought onset. The RWP is related to a persistent ridge west of the incipient drought region and strong subsidence over it. This subsidence is associated with low-level divergent flow that dries the atmosphere and suppresses precipitation for roughly 1–2 weeks, and generally has a greater impact on the local moisture budget than does reduced poleward moisture transport. The resulting "dynamically driven" evaporative demand corresponds to a rapid drying of the root-zone soil moisture, which decreases around 40 percentiles within about 10 days. Anomalous near-surface warmth develops only after the P − ET deficit onset, as does anomalously low soil moisture that then lingers a month or more, especially in late spring. The horizontal scale of the RWPs, and of the related drought anomalies, decreases from early to late spring, consistent with the climatological change in the Pacific Rossby waveguide. Finally, while this composite analysis is based upon strong, persistent P − ET deficits, it still appears to capture much of the springtime development of "flash droughts" as well. [ABSTRACT FROM AUTHOR]

Skillful subseasonal prediction of extreme heat and precipitation greatly benefits multiple sectors, including water management, public health, and agriculture, in mitigating the impact of extreme events. A statistical model is developed to predict the weekly frequency of extreme warm days and 14-day standardized precipitation index (SPI) during boreal summer in the United States. We use a leading principal component of U.S. soil moisture and an index based on the North Pacific sea surface temperature (SST) as predictors. The model outperforms the NCEP Climate Forecast System, version 2 (CFSv2), at weeks 3–4 in the eastern United States. It is found that the North Pacific SST anomalies persist for several weeks and are associated with a persistent wave train pattern, which leads to increased occurrences of blocking and extreme temperature over the eastern United States. Extreme dry soil moisture conditions persist into week 4 and are associated with an increase in sensible heat flux and a decrease in latent heat flux, which may help to maintain the overlying anticyclone. The clear-sky conditions associated with blocking anticyclones further decrease soil moisture and increase the frequency of extreme warm days. This skillful statistical model has the potential to aid in irrigation scheduling, crop planning, and reservoir operation and to provide mitigation of impacts from extreme heat events. [ABSTRACT FROM AUTHOR]

Increased heat-wave frequency across the United States has led to the need for improved predictability of heat-wave events. A detailed understanding of land–atmosphere interactions and the relationship between soil moisture and temperature extremes could provide useful information for prediction. This study identifies, for many locations, a threshold of soil moisture below which there is an increase in the sensitivity of atmospheric temperature to declining soil moisture. This shift to a hypersensitive regime causes the atmosphere to be more susceptible to atmospherically driven heat-wave conditions. The soil moisture breakpoint where the regime shift occurs is estimated using segmented regression applied to observations and reanalysis data. It is shown that as the soil gets drier, there is a concomitant change in the rate of decrease in latent heat flux and increase in sensible heat flux leading to a strong positive feedback of increased air temperature near the surface, which further dries out the soil. Central, southwestern, and southeastern parts of the United States seem to have regions of clear regime shifts, while the eastern part of the United States generally does not get dry enough to reveal significant breakpoints. Sensible heat flux is seen to be a primary driver of this increased temperature sensitivity aided by the drop in latent heat flux. An investigation of flux tower sites verifies the breakpoint–flux relationships found in reanalysis data. Accurate estimation of these breakpoints can contribute to improved heat-wave prediction. [ABSTRACT FROM AUTHOR]

The semiarid U.S. Great Plains is prone to severe droughts having major consequences for agricultural production, livestock health, and river navigation. The recent 2012 event was accompanied by record deficits in precipitation and high temperatures during the May-August growing season. Here the physics of Great Plains drought are explored by addressing how meteorological drivers induce soil moisture deficits during the growing season. Land surface model (LSM) simulations driven by daily observed meteorological forcing from 1950 to 2013 compare favorably with satellite-derived terrestrial water anomalies and reproduce key features found in the U.S. Drought Monitor. Results from simulations by two LSMs reveal that precipitation was directly responsible for between 72% and 80% of the soil moisture depletion during 2012, and likewise has accounted for the majority of Great Plains soil moisture variability since 1950. Energy balance considerations indicate that a large fraction of the growing season temperature variability is itself driven by precipitation, pointing toward an even larger net contribution of precipitation to soil moisture variability. To assess robustness across a larger sample of drought events, daily meteorological output from 1050 years of climate simulations, representative of conditions in 1979-2013, are used to drive two LSMs. Growing season droughts, and low soil moisture conditions especially, are confirmed to result principally from rainfall deficits. Antecedent meteorological and soil moisture conditions are shown to affect growing season soil moisture, but their effects are secondary to forcing by contemporaneous rainfall deficits. This understanding of the physics of growing season droughts is used to comment on plausible Great Plains soil moisture changes in a warmer world. [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]

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 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]

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]

The soil moisture state simulated by a land surface model is a highly model-dependent quantity, meaning that the direct transfer of one model’s soil moisture into another can lead to a fundamental, and potentially detrimental, inconsistency. This is first illustrated with two recent examples, one from the National Centers for Environmental Prediction (NCEP) involving seasonal precipitation forecasting and another from the realm of ecological modeling. The issue is then further addressed through a quantitative analysis of soil moisture contents produced as part of a global offline simulation experiment in which a number of land surface models were driven with the same atmospheric forcing fields. These latter comparisons clearly demonstrate, on a global scale, the degree to which model-simulated soil moisture variables differ from each other and that these differences extend beyond those associated with model-specific layer thicknesses or soil texture. The offline comparisons also show, however, that once the climatological statistics of each model’s soil moisture variable are accounted for (here, through a simple scaling using the first two moments), the different land models tend to produce very similar information on temporal soil moisture variability in most parts of the world. This common information can perhaps be used as the basis for successful mappings between the soil moisture variables in different land models. [ABSTRACT FROM AUTHOR]

Retrospectively simulated soil moisture from an ensemble of six land surface/hydrological models was used to reconstruct drought events over the continental United States for the period 1920–2003. The simulations were performed at one-half-degree spatial resolution, using a common set of atmospheric forcing data and model-specific soil and vegetation parameters. Monthly simulated soil moisture was converted to percentiles using Weibull plotting position statistics, and the percentiles were then used to represent drought severities and durations. An ensemble method, based on an inverse mapping of the average of the individual model’s soil moisture percentiles, was also used to combine all models’ simulations. Major results are 1) all models and the ensemble reconstruct the known severe drought events during the last century. The spatial extents and severities of drought are plausible for the individual models although substantial among-model disparities exist. 2) The simulations are in more agreement with each other over the eastern than over the western United States. 3) Most of the models show that soil moisture memory is much longer over the western than over the eastern United States. The results provide some insights into how a hydrological nowcast system can be developed, and also early results from a test application within the University of Washington’s real-time national Surface Water Monitor and a review of the multimodel nowcasts during the southeastern drought beginning in summer 2007 are included. [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 statistical model based on canonical correlation analysis (CCA) was used to explore climatic associations and predictability of June–August (JJA) maximum and minimum surface air temperatures (Tmax and Tmin) as well as the frequency of Tmax daily extremes (Tmax90) in the central and western United States (west of 90°W). Explanatory variables are monthly and seasonal Pacific Ocean SST (PSST) and the Climate Division Palmer Drought Severity Index (PDSI) during 1950–2001. Although there is a positive correlation between Tmax and Tmin, the two variables exhibit somewhat different patterns and dynamics. Both exhibit their lowest levels of variability in summer, but that of Tmax is greater than Tmin. The predictability of Tmax is mainly associated with local effects related to previous soil moisture conditions at short range (one month to one season), with PSST providing a secondary influence. Predictability of Tmin is more strongly influenced by large-scale (PSST) patterns, with PDSI acting as a short-range predictive influence. For both predictand variables (Tmax and Tmin), the PDSI influence falls off markedly at time leads beyond a few months, but a PSST influence remains for at least two seasons. The maximum predictive skill for JJA Tmin, Tmax, and Tmax90 is from May PSST and PDSI. Importantly, skills evaluated for various seasons and time leads undergo a seasonal cycle that has maximum levels in summer. At the seasonal time frame, summer Tmax prediction skills are greatest in the Midwest, northern and central California, Arizona, and Utah. Similar results were found for Tmax90. In contrast, Tmin skill is spread over most of the western region, except for clusters of low skill in the northern Midwest and southern Montana, Idaho, and northern Arizona. [ABSTRACT FROM AUTHOR]

Observed vegetation feedbacks on temperature and precipitation are assessed across the United States using satellite-based fraction of photosynthetically active radiation (FPAR) and monthly climate data for the period of 1982–2000. This study represents the first attempt to spatially quantify the observed local impact of vegetation on temperature and precipitation over the United States for all months and by season. Lead–lag correlations and feedback parameters are computed to determine the regions where vegetation substantially impacts the atmosphere and to quantify this forcing. Temperature imposes a significant instantaneous forcing on FPAR, while precipitation's impact on FPAR is greatest at one-month lead, particularly across the prairie. An increase in vegetation raises the surface air temperature by absorbing additional radiation and, in some cases, masking the high albedo of snow cover. Vegetation generally exhibits a positive forcing on temperature, strongest in spring and particularly across the northern states. The local impact of FPAR on precipitation appears to be spatially inhomogeneous and relatively weak, potentially due to the atmospheric transport of transpired water. The computed feedback parameters can be used to evaluate vegetation–climate interactions simulated by models with dynamic vegetation. [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]