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In the western United States, climate warming poses a unique threat to water and snow hydrology because much of the snowpack accumulates at temperatures near 0°C. As the climate continues to warm, much of the region's precipitation is expected to switch from snow to rain, causing flashier hydrographs, earlier inlow to reservoirs, and reduced spring and summer snowpack. This study investigates historical variability in snow to precipitation proportion (Sf) and maps areas in the western United States that have demonstrated higher Sf sensitivity to warming in the past. Projected changes in Sf under 1.1, 1.8, and 3.0 °C future warming scenarios are presented in relation to historical variability and sensitivity. Our findings suggest that Sf in this region has primarily varied based on winter temperature rather than precipitation. The difference in Sf between cold and warm winters at low- and mid-elevations during 1916-2003 ranged from 31% in the Pacific Northwest to 40% in the California Sierra Nevada. In contrast, the difference in Sf between wet and dry winters was statistically not significant. Overall, in the northern Sierra, Klamath, and western slopes of the Cascade Mountains Ranges, Sf was most sensitive to temperature where winter temperature ranged between -5 to 5 °C. Results from our trend analysis show a regional shift in both Sf and signal-to-noise ratios during 1960-2003 as compared with 1916-2003. Our findings indicate that natural variability in Sf over 1916-2003 across all regions except for the Great Basin most closely resembles the projected 2040-warming scenario (+1.8 °C). [ABSTRACT FROM AUTHOR]

Annual precipitation in California is more variable than in any other state and is highly influenced by precipitation in winter months. A primary question among stakeholders is whether low precipitation in certain months is a harbinger of annual drought in California. Historical precipitation data from 1895 to 2013 are investigated to identify leading monthly indicators of annual drought in each of the seven climate divisions (CDs) as well as statewide. For this study, drought conditions are defined as monthly/annual (October–September) precipitation below the 20th/30th percentile, and a leading indicator is defined as a monthly drought preceding or during an annual drought that has the strongest association (i.e., joint probability of occurrence) with a statewide annual drought. Monthly precipitation variability and contributions to annual precipitation, along with joint probabilities of drought among the winter months, are first analyzed. Then the probabilities of annual drought and the variability in leading indicators are analyzed according to different climate phases and CDs. This study identified December within a water year as being the leading indicator that is most frequently associated with annual drought statewide (56%) and in most of the CDs (the highest was CD2 at 65%). Associated with its leading-indicator status, December drought was most frequently associated with drought in other winter months ( joint probability . 30%). Results from this study can help stakeholders to understand and assess the likelihood of annual drought events given monthly precipitation preceding or early in the water year. [ABSTRACT FROM AUTHOR]

The implementation of a multimodel drought monitoring system is described, which provides near-real-time estimates of surface moisture storage for the global land areas between 50°S and 50°N with a time lag of about 1 day. Near-real-time forcings are derived from satellite-based precipitation estimates and modeled air temperatures. The system distinguishes itself from other operational systems in that it uses multiple land surface models (Variable Infiltration Capacity, Noah, and Sacramento) to simulate surface moisture storage, which are then combined to derive a multimodel estimate of drought. A comparison of the results with other historic and current drought estimates demonstrates that near-real-time nowcasting of global drought conditions based on satellite and model forcings is entirely feasible. However, challenges remain because hydrological droughts are inherently defined in the context of a long-term climatology. Changes in observing platforms can be misinterpreted as droughts (or as excessively wet periods). This problem cannot simply be addressed through the addition of more observations or through the development of new observing platforms. Instead, it will require careful (re)construction of long-term records that are updated in near-real time in a consistent manner so that changes in surface meteorological forcings reflect actual conditions rather than changes in methods or sources. [ABSTRACT FROM AUTHOR]

As human exposure to hydroclimatic extremes increase and the number of in situ precipitation observations declines, precipitation estimates, such as those provided by the Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) mission, provide a critical source of information. Here, we present a new gauge-enhanced dataset [the Climate Hazards Center IMERG with Stations (CHIMES)] designed to support global crop and hydrologic modeling and monitoring. CHIMES enhances the IMERG Late Run product using an updated Climate Hazards Center (CHC) high-resolution climatology (CHPclim) and low-latency rain gauge observations. CHPclim differs from other products because it incorporates long-term averages of satellite precipitation, which increases CHPclim's fidelity in data-sparse areas with complex terrain. This fidelity translates into performance increases in unbiased IMERGlate data, which we refer to as CHIME. This is augmented with gauge observations to produce CHIMES. The CHC's curated rain gauge archive contains valuable contributions from many countries. There are two versions of CHIMES: preliminary and final. The final product has more copious and better-curated station data. Every pentad and month, bias-adjusted IMERGlate fields are combined with gauge observations to create pentadal and monthly CHIMESprelim and CHIMESfinal. Comparisons with pentadal, high-quality gridded station data show that IMERGlate performs well (r = 0.75), but has some systematic biases which can be reduced. Monthly cross-validation results indicate that unbiasing increases the variance explained from 50% to 63% and decreases the mean absolute error from 48 to 39 mm month−1. Gauge enhancement then increases the variance explained to 75%, reducing the mean absolute error to 27 mm month−1. [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]

Understanding the dynamics and physics of climate extremes will be a critical challenge for twenty-first-century climate science. Increasing temperatures and saturation vapor pressures may exacerbate heat waves, droughts, and precipitation extremes. Yet our ability to monitor temperature variations is limited and declining. Between 1983 and 2016, the number of observations in the University of East Anglia Climatic Research Unit (CRU) Tmax product declined precipitously (5900 → 1000); 1000 poorly distributed measurements are insufficient to resolve regional Tmax variations. Here, we show that combining long (1983 to the near present), high-resolution (0.05°), cloud-screened archives of geostationary satellite thermal infrared (TIR) observations with a dense set of ~15 000 station observations explains 23%, 40%, 30%, 41%, and 1% more variance than the CRU globally and for South America, Africa, India, and areas north of 50°N, respectively; even greater levels of improvement are shown for the 2011–16 period (28%, 45%, 39%, 52%, and 28%, respectively). Described here for the first time, the TIR Tmax algorithm uses subdaily TIR distributions to screen out cloud-contaminated observations, providing accurate (correlation ≈0.8) gridded emission Tmax estimates. Blending these gridded fields with ~15 000 station observations provides a seamless, high-resolution source of accurate Tmax estimates that performs well in areas lacking dense in situ observations and even better where in situ observations are available. Cross-validation results indicate that the satellite-only, station-only, and combined products all perform accurately (R ≈ 0.8–0.9, mean absolute errors ≈ 0.8–1.0). Hence, the Climate Hazards Center Infrared Temperature with Stations (CHIRTSmax) dataset should provide a valuable resource for climate change studies, climate extreme analyses, and early warning applications. [ABSTRACT FROM AUTHOR]

The article discusses whether sea surface temperatures (SST) caused poor rainfall in eastern Kenya and southern Somalia from March to May 2012 and the frequency of dry events from 2003 to 2012. It mentions the use of two Global Forecast System version 2 modeling ensembles to estimate fractions of attributable risk. Possible contributions to dry weather in East Africa include warming in the western Pacific and stronger western-to-central Pacific SST gradients.