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Niger is highly vulnerable to rainfall variability, often with adverse socioeconomic consequences. This study examined observed subseasonal rainfall variability during Niger's monsoon season (May to September). Using k-means clustering of dekadal (ten-day) rainfall, a typology was developed for the annual evolution of the monsoon season. Year-to-year rainfall variability for each of the first few dekads of the season is modest, but the middle, or peak of the rainy season demonstrates large interannual variability. Clustering analysis of annual timeseries for each dekad of the season revealed two types of monsoon progression. The distinction between the two types is strongly dependent on differences during the latter half of the season. For the first and third ten-day periods in August, and the first ten days in September, the two groups of years are more distinct. These results imply that while reliable prediction of the timing of anomalous onsets will be challenging, due to the relatively narrow range of uncertainty historically, there are opportunities for further exploration of dynamic and or statistical predictors or precursors using this typology that could potentially provide better information for decision-makers, especially with respect to agriculture. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are responsible for up to 90% of major flood events along the U.S. West Coast. The time scale of subseasonal forecasting (from 2 weeks to 1 month) is a critical lead time for proactive mitigation of flood disasters. The NOAA Climate Testbed Subseasonal Experiment (SubX) is a research-to-operations project with almost immediate availability of forecasts. It has produced a reforecast database that facilitates evaluation of flood forecasts at these subseasonal lead times. Here, we examine the SubX-driven forecast skill of AR-related flooding out to 4-week lead using the Distributed Hydrology Soil Vegetation Model (DHSVM), with particular attention to the role of antecedent soil moisture (ASM), which modulates the relationship between meteorological and hydrological forecast skill. We study three watersheds along a transect of the U.S. West Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. We find that the SubX-driven flood forecast skill drops quickly after week 1, during which there is relatively high deterministic forecast skill. We find some probabilistic forecast skill relative to climatology as well as ensemble streamflow prediction (ESP) in week 2, but minimal skill in weeks 3–4, especially for annual maximum floods, notwithstanding some probabilistic skill for smaller floods in week 3. Using ESP and reverse-ESP experiments to consider the relative influence of ASM and SubX reforecast skill, we find that ASM dominates probabilistic forecast skill only for small flood events at week 1, while SubX reforecast skill dominates for large flood events at all lead times. [ABSTRACT FROM AUTHOR]

Since 2015, Sub-Saharan Africa (SSA) has experienced an unprecedented rise in acute food insecurity (AFI), and current projections for the year 2020 indicate that more than 100 million Africans are estimated to receive emergency food assistance. Climate-driven drought is one of the main contributing factors to AFI, and timely and appropriate actions can be taken to mitigate impacts of AFI on lives and livelihoods through early warning systems. To support this goal, we use observations of peak Normalized Difference Vegetation Index (NDVI) as an indicator of seasonal drought conditions following a rainy season to show that delays in the onset of the rainy season (onset date) can be an effective early indicator of seasonal drought conditions. The core of this study is an evaluation of the relationship of the onset dates and peak NDVI, stratified by AFI risks, calculated using AFI reports by the United States Agency of International Development (USAID)-funded Famine Early Warning Systems Network (FEWS NET). Several parts of SSA, mostly located in East Africa (EA), reported the "Crisis" phase of AFI—requiring emergency food assistance—at least one-third of the time between April 2011 to present. The results show that the onset date can effectively explain much of the interannual variability in peak NDVI in the regions with the highest AFI risk level, particularly in EA where the median of correlation (across all the Administrative Unit 2) varies between -0.42 to -0.68. In general, an onset date delay of at least 1 dekad (10 days) increases the likelihood of seasonal drought conditions. In the regions with highest risks of AFI, an onset delay of just 1 dekad doubles the chance of the standardized anomaly of peak NDVI being below -1, making a -1 anomaly the most probable outcome. In those regions, a 2-dekads delay in the onset date is associated with a very high probability (50%) of seasonal drought conditions (-1 standardized anomaly of NDVI). Finally, a multivariate regression analysis between standardized anomaly and onset date anomaly further substantiates the negative impacts of delay in onset date on NDVI anomaly. This relationship is statistically significant over the SSA as a whole, particularly in the EA region. These results imply that the onset date can be used as an additional critical tool to provide alerts of seasonal drought development in the most food-insecure regions of SSA. Early warning systems using onset date as a tool can help trigger effective mid-season responses to save human lives, livestock, and livelihoods, and, therefore, mitigate the adverse impacts of drought hazards. [ABSTRACT FROM AUTHOR]

Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development's (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing-based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET's current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1-5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods. [ABSTRACT FROM AUTHOR]

On a planet with a population of more than 7 billion, how do we identify the millions of drought-afflicted people who face a real threat of livelihood disruption or death without humanitarian assistance? Typically, these people are poor and heavily dependent on rainfed agriculture and livestock. Most live in Africa, Central America, or Southwest Asia. When the rains fail, incomes diminish while food prices increase, cutting off the poorest (most often women and children) from access to adequate nutrition. As seen in Ethiopia in 1984 and Somalia in 2011, food shortages can lead to famine. Yet these slow-onset disasters also provide opportunities for effective intervention, as seen in Ethiopia in 2015 and Somalia in 2017. Since 1985, the U.S. Agency for International Development's Famine Early Warning Systems Network (FEWS NET) has been providing evidence-based guidance for effective humanitarian relief efforts. FEWS NET depends on a Drought Early Warning System (DEWS) to help understand, monitor, model, and predict food insecurity. Here we provide an overview of FEWS NET's DEWS using examples from recent climate extremes. While drought monitoring and prediction provides just one part of FEWS NET's monitoring system, it draws from many disciplines—remote sensing, climate prediction, agroclimatic monitoring, and hydrologic modeling. Here we describe FEWS NET's multiagency multidisciplinary DEWS and Food Security Outlooks. This DEWS uses diagnostic analyses to guide predictions. Midseason droughts are monitored using multiple cutting-edge Earth-observing systems. Crop and hydrologic models can translate these observations into impacts. The resulting information feeds into FEWS NET reports, helping to save lives by motivating and targeting timely humanitarian assistance. [ABSTRACT FROM AUTHOR]

The Famine Early Warning Systems Network (FEWS NET) team provides food insecurity outlooks for several developing countries in Africa, central Asia, and Central America. This study describes development of a new global reference evapotranspiration (ET0) seasonal reforecast and skill evaluation with a particular emphasis on the potential use of this dataset by FEWS NET to support food insecurity early warning. The ET0 reforecasts span the 1982-2009 period and are calculated following the American Society for Civil Engineers formulation of the Penman-Monteith method driven by seasonal climate forecasts of monthly mean temperature, humidity, wind speed, and solar radiation from the National Centers for Environmental Prediction CFSv2 model and the National Aeronautics and Space Administration GEOS-5 model. The skill evaluation, using deterministic and probabilistic scores, focuses on the December-February (DJF), March-May (MAM), June-August (JJA), and September-November seasons. The results indicate that ET0 forecasts are a promising tool for early warning of drought and food insecurity. Globally, the regions where forecasts are most skillful (correlation > 0.35 at leads of 2 months) include the western United States, northern parts of South America, parts of the Sahel region, and southern Africa. The FEWS NET regions where forecasts are most skillful (correlation > 0.35 at lead 3) include northern sub-Saharan Africa (DJF; dry season), Central America (DJF; dry season), parts of East Africa (JJA; wet season), southern Africa (JJA; dry season), and central Asia (MAM; wet season). A case study over parts of East Africa for the JJA season shows that ET0 forecasts in combination with the precipitation forecasts would have provided early warning of recent severe drought events (e.g., in 2002, 2004, 2009) that contributed to substantial food insecurity in the region. [ABSTRACT FROM AUTHOR]

The article examines the some of the possible causes of drought in East Africa during the period April-June 2014 using the Variable Infiltration Capacity model. Topics covered include the influence of warm Eastern African air temperatures, reduction in rainfall associated with intense West Pacific Index and the effect of anthropogenic warming in reducing soil moisture, evapotranspiration and rainfall.

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]

Southwest Asia, defined as the region containing the countries of Afghanistan, Iran, Iraq, and Pakistan, is water scarce and receives nearly 75% of its annual rainfall during the boreal cold season of November-April. The forcing of southwest Asia precipitation has been previously examined for the entire boreal cold season from the perspective of climate variability originating over the Atlantic and tropical Indo-Pacific Oceans. This study examines the intermonthly differences in precipitation variability over southwest Asia and the atmospheric conditions directly responsible in forcing monthly November-April precipitation. Seasonally averaged November-April precipitation over southwest Asia is significantly correlated with sea surface temperature (SST) patterns consistent with Pacific decadal variability (PDV), El Niño-Southern Oscillation (ENSO), and the long-term change of global SST (LT). In contrast, the precipitation variability during the individual months of November-April is unrelated and is correlated with SST signatures that include PDV, ENSO, and LT in different combinations. Despite strong intermonthly differences in precipitation variability during November-April over southwest Asia, similar atmospheric circulations, highlighted by a stationary equivalent barotropic Rossby wave centered over Iraq, force the monthly spatial distributions of precipitation. Tropospheric flow on the eastern side of the equivalent barotropic Rossby wave modifies the flux of moisture and advects the mean meridional temperature gradient, resulting in temperature advection that is balanced by vertical motions over southwest Asia. The forcing of monthly southwest Asia precipitation by equivalent barotropic Rossby waves is different from the forcing by baroclinic Rossby waves associated with tropically forced-only modes of climate variability. [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]

A drought monitoring system (DMS) can help to detect and characterize drought conditions and reduce adverse drought impacts. The authors evaluate how a DMS for Washington State, based on a land surface model (LSM), would perform. The LSM represents current soil moisture (SM), snow water equivalent (SWE), and runoff over the state. The DMS incorporates the standardized precipitation index (SPI), standardized runoff index (SRI), and soil moisture percentile (SMP) taken from the LSM. Four historical drought events (1976--77, 1987--89, 2000--01, and 2004--05) are constructed using DMS indicators of SPI/SRI-3, SPI/SRI-6, SPI/SRI-12, SPI/SRI-24, SPI/SRI-36, and SMP, with monthly updates, in each of the state''s 62 Water Resource Inventory Areas (WRIAs). The authors also compare drought triggers based on DMS indicators with the evolution of drought conditions and management decisions during the four droughts. The results show that the DMS would have detected the onset and recovery of drought conditions, in many cases, up to four months before state declarations. [ABSTRACT FROM AUTHOR]

A seamless and effective early warning system is critical for mitigating the most adverse impacts of water and food insecurity in Africa. The U.S. Agency for International Development (USAID) supported Famine Early Warning Systems Network (FEWS NET) is tasked with providing early warning and analysis on acute food and water insecurity to help government decision-makers and relief agencies plan for and respond to resulting humanitarian crises. This presentation describes a recently developed multimodel based seasonal scale hydrologic forecasting system, which has been operationally supporting the early warning efforts of FEWS NET since August 2018. Since then, this system has helped identify flood risk potential in parts of Eastern and Western Africa, and development and progression of drought conditions in Southern Africa. Both of these types of extremes contribute to food and water insecurity. At the core of this system is NASA's Land Information System (LIS) (a hydrologic modeling framework), which supports seasonal scale monitoring and forecasts of runoff and soil moisture by combining satellite and reanalysis based atmospheric forcings datasets and dynamical climate forecasts. Specifically, the LIS-based seasonal scale hydrologic forecasting system incorporates NASA/GMAO's Catchment and the Noah Multi-Physics (Noah-MP) land surface models (LSMs). It is driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), and the UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall dataset to provide a long historic record for monitoring (1982-present) of hydrologic variables. Currently, the seasonal scale hydrological forecasts are generated by using NASA's Goddard Earth Observing System Model, version 5 (GEOS-5).Long-term availability of monitoring and forecasts of hydrologic variables allows for historical validation of this dataset against independent hydrologic observations and reported crop yields (up to the extent they are available). Validation results thus far have demonstrated agreement of hydrologic monitoring simulations with independent satellite based observations in Eastern Africa and the value of this system in supporting food insecurity early warning in Southern Africa.This presentation will first describe the development of this system, followed by its validation for drought early warning and subsequently the process of stakeholder engagement and operational application of the products derived from this system by the FEWS NET. [ABSTRACT FROM AUTHOR]

Southern Africa precipitation during December-March (DJFM), the height of the rainy season, is closely related with two modes of climate variability, El Niño-Southern Oscillation (ENSO) and the subtropical Indian Ocean dipole (SIOD). Recent research has found that the combined effects of ENSO and SIOD phasing are linked with changes to the regional southern Africa atmospheric circulation beyond the individual effects of either ENSO or SIOD alone. Here, the authors extend the recent research and examine the southern Africa land surface hydrology associated with the synchronous effects of ENSO and SIOD events using a macroscale hydrologic model, with particular emphasis on the evolution of the hydrologic conditions over three critical Transfrontier Conservation Areas: the Kavango-Zambezi Conservation Area, the Greater Limpopo Transfrontier Park, and the Kgalagadi Transfrontier Park. A better understanding of the climatic effects of ENSO and SIOD phase combinations is important for regional-scale transboundary conservation planning, especially for southern Africa, where both humans and wildlife are dependent on the timing and amount of precipitation. Opposing ENSO and SIOD phase combinations (e.g., El Niño and a negative SIOD or La Niña and a positive SIOD) result in strong southern Africa climate impacts during DJFM. The strong instantaneous regional precipitation and near-surface air temperature anomalies during opposing ENSO and SIOD phase combinations lead to significant soil moisture and evapotranspiration anomalies in the year following the ENSO event. By contrast, when ENSO and SIOD are in the same phase (e.g., El Niño and a positive SIOD or La Niña and a negative SIOD), the southern Africa climate impacts during DJFM are minimal. [ABSTRACT FROM AUTHOR]

The article focuses on the history of anthropogenic warming and drought in Eutophia and Southern Africa in 2015 brought by El Niño and local air temperatures. Topics discussed include recurrent soil moisture and runoff deficits experienced by Ethiopian highlands since 1990, warm phase of El Niño-Southern Oscillation (ENSO) from 1997-2013, and rainfall variance observed from 2015-2016.

Here we present a first look at the Gross Primary Production (GPP) forecast skill levels achievable with a state‐of‐the‐art subseasonal‐to‐seasonal (S2S) forecast system. Using NASA's retrospective S2S ensemble forecast in conjunction with a terrestrial biosphere model, and using an independent, remote sensing‐based data set for validation, we demonstrate an ability to accurately forecast spring‐summer carbon uptake at multi‐month leads. Averaged across mid‐ and high latitudes of the Northern Hemisphere land, the GPP forecast initialized on January 1 produces statistically significant skill through summer. The skill achieved, however, is spatially variable, with some regions appearing to extract skill from accurate forecasts of snowpack removal and others extracting skill from the initialization of carbon and nitrogen states. Our results reveal some heretofore unexplored facets of climate predictability and provide a look at what might be possible with future S2S forecast systems that are fully integrated with biogeochemical cycles. Plain Language Summary: Seasonal forecasts of meteorology can support a variety of applications that can help manage precious natural resources. As an integral part of the Earth system, the land ecosystem plays an important role in seasonal forecasts; however, our ability to predict variations in the land's carbon cycle, which is tied strongly to the land's water and energy cycles, is not well understood. This study is a first look at the ability of a state‐of‐the‐art seasonal forecast system to predict year‐to‐year variations in the land's carbon uptake. We demonstrate that the system can accurately forecast spring‐summer carbon uptake several months ahead of time across many mid‐ and high latitude land areas within the Northern Hemisphere. This skill appears to be related both to accurate forecasts of snow removal timing and to the proper specification of land carbon and nitrogen conditions at the beginning of the forecasts. The consideration of carbon and nitrogen dynamics will undoubtedly be standard someday in seasonal forecast systems; this study provide a glimpse at what we might expect from such enhancements. Key Points: We demonstrate skillful forecasts of land carbon uptake in mid‐ to high latitudes at multi‐month leads (winter into summer)During spring, Gross Primary Production (GPP) forecast skill in some regions appears to stem from skillful forecasts of snow removal dateIn certain other regions, initialization of the carbon and nitrogen reservoirs appears to contribute to GPP forecast skill [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]

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 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 focuses on a study which aims to examine the influence of anthropogenic enhancement of moderate-to-strong El Niño weather event on drought as well as poor harvests in southern Africa during 2016. It highlights observational uncertainties using four datasets such as ERSSTv4, HadISST, Kaplan SST, and Hurrell.

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.

Summertime (June-September) precipitation in the Ethiopian highlands, serving as the main source for the Blue Nile River, exhibits a characteristically high level of inter-annual variability. Precipitation received during this season has explicit effects on rain-fed and irrigated agriculture in addition to hydropower infrastructure, not the least of which is the massive, soon-to-be online Grand Ethiopian Renaissance Dam. Season-ahead precipitation and streamflow predictions have accordingly been developed over the past decades in an attempt to inform sectoral decision-making, including both empirical and dynamical modeling approaches. Variables describing ENSO, the Tropical Easterly Jet, Indian Ocean sea-surface temperatures, pressure systems, etc. have previously been identified as significantly correlated with summertime precipitation, and thus commonly included as predictors in empirical models. Preliminary evaluation of these models and individual teleconnections, however, illustrate very different relationships, as pre-2000 predictors correlate with summertime precipitation on the order of 0.6 on average, whereas for post-2000 this drops to 0.2. Although seasonal precipitation amounts have remained relatively stable across both periods according to many observational and reanalysis data products, nearly all predictors identified in literature show a significant decline in relationship. This change is not immediately evident in an assessment of dynamical model relationships. Further analysis reveals that this phenomenon appears to not be isolated strictly to the Ethiopian highlands, and may be connected to global circulation changes including the AMO and western Pacific warm pool circulation anomalies. This begs the question of whether this may be attributable to a regime shift, global warming, or simply within the scope of long-term, low frequency climate variability. This apparent shift in the relationship of previously identified teleconnections with Ethiopian highland precipitation warrants further investigation, as the current set of prediction models may be over-representing expected prediction skill, resulting in direct implications to societal welfare. [ABSTRACT FROM AUTHOR]