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Human-induced (HI) warming increased Four Corners' vapor pressure deficits and reduced the Normalized Difference Vegetation Index by ~l8%-30%. Without HI warming, March snow water equivalent would have been ~20% higher. [...]

The state of the El Niño–Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts. [ABSTRACT FROM AUTHOR]

ABSTRACTS 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 [...]

Introduction. In December-February (DJF) of 2015/16, a strong El Nino (Nino-3.4 SST >29[degrees]C) contributed to a severe drought over southern Africa (SA; Funk et al. 2016). A 9-million ton cereal [...]

Predictions of drought onset and termination at subseasonal (from 2 weeks to 1 month) lead times could provide a foundation for more effective and proactive drought management. We used reforecasts archived in NOAA’s Subseasonal Experiment (SubX) to force the Noah Multiparameterization (Noah-MP), which produced forecasts of soil moisture from which we identified drought levels D0–D4. We evaluated forecast skill of major and more modest droughts, with leads from 1 to 4 weeks, and with particular attention to drought termination and onset. We find usable drought termination and onset forecast skill at leads 1 and 2 weeks for major D0–D2 droughts and limited skill at week 3 for major D0–D1 droughts, with essentially no skill at week 4 regardless of drought severity. Furthermore, for both major and more modest droughts, we find limited skill or no skill for D3–D4 droughts. We find that skill is generally higher for drought termination than for onset for all drought events. We also find that drought prediction skill generally decreases from north to south for all drought events. [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.

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]

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.