Monsoon variability over the Horn of Africa.

Over the past several decades, the Horn of Africa has been ravaged by catastrophic droughts and famines. In spite of the devastating frequent droughts and occasional floods in the region, our understanding of the region's weather and climate variability is limited. The present study represents the first exhaustive investigation of rainfall variability over the Horn of Africa at intraseasonal, seasonal, interannual, and multidecadal time-scales, and contributes substantially to the fundamental understanding of weather and climate variability in the region.This research involves observational and modeling investigations to explore and document the space-time distribution of the major elements of weather and climate and their variability in the Horn of Africa. In particular, an exhaustive diagnostic examination is performed to identify the dominant modes of rainfall variability and the large-scale atmospheric and oceanic features that affect rainfall in the region. Building on the results of the diagnostic study, dependable short- to long-range prediction models are developed. These models are capable of predicting rainfall amounts and anomalies at a specific location or region from a few days to seasons in advance. The modeling study has identified the roles of sea surface temperatures over the Mediterranean Sea, Atlantic, and Indian Oceans in shaping rainfall variability over the Horn of Africa. The impacts of depleted vegetation resulting from poor early rains on Horn of Africa summer rainfall also are investigated and identified through model sensitivity experiments

Frequent but Predictable Droughts in East Africa Driven by a Walker Circulation Intensification

During and after recent La Niña events, the decline of the eastern East African (EA) March-April-May (MAM) rains has set the stage for life-threatening sequential October-November-December (OND) and MAM droughts. The MAM 2022 drought was the driest on record, preceded by three poor rainy seasons, and followed by widespread starvation. Connecting these dry seasons is an interaction between La Niña and climate change. This interaction provides important opportunities for long-lead prediction and proactive disaster risk management, but needs exploration. Here, for the first time, we use observations, reanalyses, and climate change simulations to show that post-1997 OND La Niña events are robust precursors of: (a) strong MAM “Western V sea surface temperature Gradients” in the Pacific, which (b) help produce large increases in moisture convergence and atmospheric heating near Indonesia, which in turn produce (c) regional shifts in moisture transports and vertical velocities, which (d) help explain the increased frequency of dry EA MAM rainy seasons. We also show that, at 20-year time scales, increases in atmospheric heating in the Indo-Pacific Warm Pool region are attributable to warming Western V SST, which is in turn largely attributable to climate change. As energy builds up in the oceans and atmosphere, during and after La Niña events, we see stronger heating and heat convergence over warm tropical waters near Indonesia. The result of this causal chain is that increased Warm Pool atmospheric heating and moisture convergence sets the stage for dangerous sequential droughts in EA. These factors link EA drying to a stronger Walker Circulation and explain the predictable risks associated with recent La Niña events

Translating seasonal climate forecasts into water balance forecasts for decision making

Seasonal rainfall forecasts support early preparedness. These forecasts are typically disseminated at Regional Climate Outlook Forums (RCOFs), in the form of seasonal tercile probability categories—above normal, normal, below normal. However, these categories cannot be related directly to impacts on terrestrial water stores within catchments, since they are mediated by non-linear hydrological processes occurring on fine spatiotemporal scales, including rainfall partitioning into infiltration, evapotranspiration, runoff and groundwater recharge. Hydrological models are increasingly capable of capturing these processes, but there is no simple way to drive such models with a specific RCOF seasonal tercile rainfall forecast. Here we demonstrate a new method, “Quantile Bin Resampling” (QBR), for producing seasonal water forecasts for a drainage basin by integrating a tercile seasonal rainfall forecast with a hydrological model. QBR is based on mapping historical quantiles of basin-average rainfall to historical simulations of the water balance, and circumvents challenges associated with using climate model output to drive impact models directly. We evaluate QBR by generating 35 years of seasonal reforecasts for various water balance stores and fluxes for the Upper Ewaso Ng’iro basin in Kenya. Hindcasts indicate that when input tercile rainfall forecasts have skill, QBR provides accurate water forecasts at kilometre-scale resolution, which is relevant for anticipatory action down to village level. Pilot operational experimental water forecasts were produced for this basin using QBR for the 2022 March-May rainfall season, then disseminated to regional stakeholders at the Greater Horn of Africa Climate Outlook Forum (GHACOF). We discuss this initiative, along with limitations, plans and future potential of the method. Beyond the demonstrated application to water-related forecasts, QBR can be easily adapted to work with any rainfall-driven impact model. It can translate objective tercile climate probabilities into impact-relevant water balance forecasts at high spatial resolution in an efficient, transparent and flexible way

Using co-production to improve the appropriate use of sub-seasonal forecasts in Africa

Forecasts on sub-seasonal to seasonal (S2S) timescales have huge potential to aid preparedness and disaster risk reduction planning decisions in a variety of sectors. However, realising this potential depends on the provision of reliable information that can be appropriately applied in the decision-making context of users. This study describes the African SWIFT (Science for Weather Information and Forecasting Techniques) forecasting testbed which brings together researchers, forecast producers and users from a range of African and UK institutions. The forecasting testbed is piloting the provision of real-time, bespoke S2S forecast products to decision-makers in Africa. Drawing on data from the kick-off workshop and initial case study examples, this study critically reflects on the co-production process. Specifically, having direct access to real-time data has allowed user-guided iterations to the spatial scale, timing, visualisation and communication of forecast products to make them more actionable for users. Some key lessons for effective co-production are emerging. First, it is critical to ensure there is sufficient resource to support co-production, especially in the early co-exploration of needs. Second, all the groups in the co-production process require capacity building to effectively work in new knowledge systems. Third, evaluation should be ongoing and combine meteorological verification with decision-makers feedback. Ensuring the sustainability of project-initiated services within the testbed hinges on integrating the knowledge-exchanges between individuals in the co-production process into shaping sustainable pathways for improved operational S2S forecasting within African institutions

Sensitivity of Horn of Africa Rainfall to Regional Sea Surface Temperature Forcing

The Abdus Salam International Center for Theoretical Physics (ICTP) version 4.4 Regional Climate Model (RegCM4) is used to investigate the rainfall response to cooler/warmer sea surface temperature anomaly (SSTA) forcing in the Indian and Atlantic Oceans. The effect of SSTA forcing in a specific ocean basin is identified by ensemble, averaging 10 individual simulations in which a constant or linearly zonally varying SSTA is prescribed in individual basins while specifying the 1971–2000 monthly varying climatological sea surface temperature (SST) across the remaining model domain. The nonlinear rainfall response to SSTA amplitude also is investigated by separately specifying +1K, +2K, and +4K SSTA forcing in the Atlantic and Indian Oceans. The simulation results show that warm SSTs over the entire Indian Ocean produce drier conditions across the larger Blue Nile catchment, whereas warming ≥ +2K generates large positive rainfall anomalies exceeding 10 mm·day−1 over drought prone regions of Northeastern Ethiopia. However, the June–September rainy season tends to be wetter (drier) when the SST warming (cooling) is limited to either the Northern or Southern Indian Ocean. Wet rainy seasons generally are characterized by deepening of the monsoon trough, east of 40°E, intensification of the Mascarene high, strengthening of the Somali low level jet and the tropical easterly jet, enhanced zonal and meridional vertically integrated moisture fluxes, and steeply vertically decreasing moist static energy. The opposite conditions hold for dry monsoon seasons

Influence of a Hailstreak on Boundary Layer Evolution

Severe thunderstorms developed on 20 June 1997 and produced heavy precipitation, damaging winds, and large hail over two swaths in southeastern South Dakota. Calculations of fractional vegetation coverage (scaled from 0 to 1) based upon composite satellite data indicate that, within the hailstreak region, vegetation coverage decreased from 0.50 to near 0.25 owing to the damaging effects of hail on the growing vegetation. The northern edge of the larger hailstreak was located a few kilometers south of Chamberlain, South Dakota, a National Weather Service surface observation site. Hourly observations from Chamberlain and several nearby surface sites in South Dakota are averaged over 7 days both before and after this hail event. These observations illustrate that the late-afternoon (nighttime) temperatures are 2°C higher (2°C lower) near the hailstreak after the event than before the event. Similarly, daily average dewpoint temperatures after the event are 2.6°C lower near the hailstreak. These changes are consistent with the influences of a recently devegetated zone on changes to the surface energy budget. To explore how these hailstreaks further affected the evolution of the planetary boundary layer in this region, two model simulations are performed using the fifth-generation Pennsylvania State University– National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). In the control run, climatology is used for the land surface characteristics, and hence the simulation is independent of the hailstreaks. In the hailstreak simulation (HSS), the fractional vegetation coverage and soil moisture in the hailstreak regions are modified to reflect the likely conditions within the hailstreaks. Two different days are simulated: one with low surface wind speeds and one with stronger surface wind speeds. For the low surface wind speed case, the HSS simulation produces a sea-breeze-like circulation in the boundary layer by midmorning. For the stronger surface wind speed case, this sea-breeze-like circulation does not develop in the HSS, but the simulated low-level temperatures are modified over a larger area. These results suggest that to capture and reasonably simulate the evolution of boundary layer structures, there is a need for routine daily updates of land surface information. Hailstreaks also are important to consider in the future as the focus for observational studies on nonclassical mesoscale circulations

Influence of a Hailstreak on Boundary Layer Evolution

Severe thunderstorms developed on 20 June 1997 and produced heavy precipitation, damaging winds, and large hail over two swaths in southeastern South Dakota. Calculations of fractional vegetation coverage (scaled from 0 to 1) based upon composite satellite data indicate that, within the hailstreak region, vegetation coverage decreased from 0.50 to near 0.25 owing to the damaging effects of hail on the growing vegetation. The northern edge of the larger hailstreak was located a few kilometers south of Chamberlain, South Dakota, a National Weather Service surface observation site. Hourly observations from Chamberlain and several nearby surface sites in South Dakota are averaged over 7 days both before and after this hail event. These observations illustrate that the late-afternoon (nighttime) temperatures are 2°C higher (2°C lower) near the hailstreak after the event than before the event. Similarly, daily average dewpoint temperatures after the event are 2.6°C lower near the hailstreak. These changes are consistent with the influences of a recently devegetated zone on changes to the surface energy budget. To explore how these hailstreaks further affected the evolution of the planetary boundary layer in this region, two model simulations are performed using the fifth-generation Pennsylvania State University– National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). In the control run, climatology is used for the land surface characteristics, and hence the simulation is independent of the hailstreaks. In the hailstreak simulation (HSS), the fractional vegetation coverage and soil moisture in the hailstreak regions are modified to reflect the likely conditions within the hailstreaks. Two different days are simulated: one with low surface wind speeds and one with stronger surface wind speeds. For the low surface wind speed case, the HSS simulation produces a sea-breeze-like circulation in the boundary layer by midmorning. For the stronger surface wind speed case, this sea-breeze-like circulation does not develop in the HSS, but the simulated low-level temperatures are modified over a larger area. These results suggest that to capture and reasonably simulate the evolution of boundary layer structures, there is a need for routine daily updates of land surface information. Hailstreaks also are important to consider in the future as the focus for observational studies on nonclassical mesoscale circulations