Weather research and forecasting model simulations of extended warm-season heavy precipitation episode over the US southern great plains: Data assimilation and microphysics sensitivity experiments

This study examines eight microphysics schemes (Lin, WSM5, Eta, WSM6, Goddard, Thompson, WDM5, WDM6) in the Advanced Research Weather Research and Forecasting Model (WRF-ARW) for their reproduction of observed strong convection over the US Southern Great Plains (SGP) for three heavy precipitation events of 27-31 May 2001. It also assesses how observational analysis nudging (OBNUD), threedimensional (3DVAR) and four-dimensional variational (4DVAR) data assimilation (DA) affect simulated cloud properties relative to simulations with no DA (CNTRL). Primary evaluation data were cloud radar reflectivity measurements by the millimetre cloud radar (MMCR) at the Central Facility (CF) of the SGP site of the ARM Climate Research Facility (ACRF). All WRF-ARW microphysics simulations reproduce the intensity and vertical structure of the first two major MMCR-observed storms, although the first simulated storm initiates a few hours earlier than observed. Of three organised convective events, the model best identifies the timing and vertical structure of the second storm more than 50 hours into the simulation. For this wellsimulated cloud structure, simulated reflectivities are close to the observed counterparts in the mid- and upper troposphere, and only overestimate observed cloud radar reflectivity in the lower troposphere by less than 10 dBZ. Based on relative measures of skill, no single microphysics scheme excels in all aspects, although the WDM schemes show much-improved frequency bias scores (FBSs) in the lower troposphere for a range of reflectivity thresholds. The WDM6 scheme has improved FBSs and high simulated-observed reflectivity correlations in the lower troposphere, likely due to its large production of liquid water immediately below the melting level. Of all the DA experiments, 3DVAR has the lowest mean errors (MEs) and root mean-squared errors (RMSEs), although both the 3DVAR and 4DVAR simulations reduced noticeably the MEs for seven of eight microphysics schemes relative to CNTRL. Lower-tropospheric θe and convective available potential energy (CAPE) also are closer to the observations for the 4DVAR than CNTRL simulations. © 2013 Z. T. Segele et al

Classifying Drought in Ethiopia Using Machine Learning

© 2016 The Authors. This study applies machine learning to the rapidly growing societal problem of drought. Severe drought exists in Ethiopia with crop failures affecting about 90 million people. The Ethiopian famine of 1983-85 caused a loss of ∼400,000-1,000,000 lives. The present drought was triggered by low precipitation associated with the current El Niño and long-term warming, enhancing the potential for a catastrophe. In this study, the roles of temperature, precipitation and El Niño are examined to characterize both the current and previous droughts. Variable selection, using genetic algorithms with 10-fold cross-validation, was used to reduce a large number of potential predictors (27) to a manageable set (7). Variables present in ≥ 70% of the folds were retained to classify drought (no drought). Logistic regression and Primal Estimated sub-GrAdient SOlver for SVM (Pegasos) using both hinge and log cost functions, were used to classify drought. Logistic regression (Pegasos) produced correct classifications for 81.14% (83.44%) of the years tested. The variable weights suggest that El Niño plays an important role but, since the region has undergone a steady warming trend of ∼1.6°C since the 1950s, the larger weights associated with positive temperature anomalies are critical for correct classification

Seasonal-to-interannual variability of ethiopia/horn of Africa monsoon. Part II: Statistical multimodel ensemble rainfall predictions

© 2015 American Meteorological Society. An ensemble-based multiple linear regression technique is developed to assess the predictability of regional and national June-September (JJAS) anomalies and local monthly rainfall totals for Ethiopia. The ensemble prediction approach captures potential predictive signals in regional circulations and global sea surface temperatures (SSTs) two to three months in advance of the monsoon season. Sets of 20 potential predictors are selected from visual assessments of correlation maps that relate rainfall with regional and global predictors. Individual predictors in each set are utilized to initialize specific forward stepwise regression models to develop ensembles of equal number of statistical model estimates, which allow quantifying prediction uncertainties related to individual predictors and models. Prediction skill improvement is achieved through error minimization afforded by the ensemble. For retroactive validation (RV), the ensemble predictions reproduce well the observed all-Ethiopian JJAS rainfall variability two months in advance. The ensemble mean prediction outperforms climatology, with mean square error reduction (SSClim) of 62%. The skill of the prediction remains high for leave-one-out cross validation (LOOCV), with the observed-predicted correlation r (SSClim) being +0.81 (65%) for 1970-2002. For tercile predictions (below, near, and above normal), the ranked probability skill score is 0.45, indicating improvement compared to climatological forecasts. Similarly high prediction skill is found for local prediction of monthly rainfall total at Addis Ababa (r = +0.72) and Combolcha (r = +0.68), and for regional prediction of JJAS standardized rainfall anomalies for northeastern Ethiopia (r = +0.80). Compared to the previous generation of rainfall forecasts, the ensemble predictions developed in this paper show substantial value to benefit society

Circulation Patterns Associated with Current and Future Rainfall over Ethiopia and South Sudan from a Convection-Permitting Model

Ethiopia and South Sudan contain several population centers and important ecosystems that depend on July–August rainfall. Here we use two models to understand current and future rainfall: the first ever pan-African numerical model of climate change with explicit convection and a parameterized model that resembles a typical regional climate model at 4.5 and 25 km horizontal grid-spacing, respectively. The explicit convection and higher resolution of the first model offer a greatly improved representation of both the frequency and intensity of rainfall, when compared to the parametrized convection model. Furthermore, only this model has success in capturing the east–west propagation of rainfall over the full diurnal cycle. Enhanced low-level westerlies were found for extremely wet days, though this response was weaker in the explicit convection model. The increased orographic detail in the explicit model resulted in the splitting of the low-level Turkana Jet core into smaller cores, and inhibited its penetration far into South Sudan. Some projected changes were found to be independent of model, such as changes in the strength of Somali and Turkana jets, as well as the shifting of Turkana jet core to lower levels. However, the explicit model end-of-century projections showed a larger and clearer decrease in wet days, accompanied by an increase in wet day intensity and extreme rainfall. This study highlights serious limitations of relying solely on simulations which parameterize convection to inform decisions in the region of South Sudan and Ethiopia

Atmospheric and oceanic conditions associated with early and late onset for Eastern Africa short rains

Timing of the rainy season is essential for a number of climate sensitive sectors over Eastern Africa. This is particularly true for the agricultural sector, where most activities depend on both the spatial and temporal distribution of rainfall throughout the season. Using a combination of observational and reanalysis datasets, the present study investigates the atmospheric and oceanic conditions associated with early and late onset for Eastern Africa short rains season (October–December). Our results indicate enhanced rainfall in October and November during years with early onset and rainfall deficit in years with late onset for the same months. Early onset years are found to be associated with warmer sea surface temperatures (SSTs) in the western Indian Ocean, and an enhanced moisture flux and anomalous low-level flow into Eastern Africa from as early as the first dekad of September. The late onset years are characterized by cooler SSTs in the western Indian Ocean, anomalous westerly moisture flux and zonal flow limiting moisture supply to the region. The variability in onset date is separated into the interannual and decadal components, and the links with SSTs and low-level circulation over the Indian Ocean basin are examined separately for both timescales. Significant correlations are found between the interannual variability of the onset and the Indian Ocean dipole mode index. On decadal timescales the onset is shown to be partly driven by the variability of the SSTs over the Indian Ocean. Understanding the influence of these potentially predictable SST and moisture patterns on onset variability has huge potential to improve forecasts of the East African short rains. Improved prediction of the variability of the rainy season onset has huge implications for improving key strategic decisions and preparedness action in many sectors, including agriculture

A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying eastern Africa

Observations and simulations link anthropogenic greenhouse and aerosol emissions with rapidly increasing Indian Ocean sea surface temperatures (SSTs). Over the past 60 years, the Indian Ocean warmed two to three times faster than the central tropical Pacific, extending the tropical warm pool to the west by ~40° longitude (><4,000 km). This propensity toward rapid warming in the Indian Ocean has been the dominant mode of interannual variability among SSTs throughout the tropical Indian and Pacific Oceans (55°E–140°W) since at least 1948, explaining more variance than anomalies associated with the El Niño-Southern Oscillation (ENSO). In the atmosphere, the primary mode of variability has been a corresponding trend toward greatly increased convection and precipitation over the tropical Indian Ocean. The temperature and rainfall increases in this region have produced a westward extension of the western, ascending branch of the atmospheric Walker circulation. Diabatic heating due to increased mid-tropospheric water vapor condensation elicits a westward atmospheric response that sends an easterly flow of dry air aloft toward eastern Africa. In recent decades (1980–2009), this response has suppressed convection over tropical eastern Africa, decreasing precipitation during the ‘long-rains’ season of March–June. This trend toward drought contrasts with projections of increased rainfall in eastern Africa and more ‘El Niño-like’ conditions globally by the Intergovernmental Panel on Climate Change. Increased Indian Ocean SSTs appear likely to continue to strongly modulate the Warm Pool circulation, reducing precipitation in eastern Africa, regardless of whether the projected trend in ENSO is realized. These results have important food security implications, informing agricultural development, environmental conservation, and water resource planning

Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale

Skill of dynamical and GHACOF consensus seasonal forecasts of East African rainfall

Seasonal forecasts of rainfall are considered the priority timescale by many users in the tropics. In East Africa, the primary operational seasonal forecast for the region is produced by the Greater Horn of Africa Climate Outlook Forum (GHACOF), and issued ahead of each rainfall season. This study evaluates and compares the GHACOF consensus forecasts with dynamical model forecasts from the UK Met Office GloSea5 seasonal prediction system for the two rainy seasons. GloSea demonstrates positive skill (r = 0.69) for the short rains at 1 month lead. In contrast, skill is low for the long rains due to lack of predictability of driving factors. For both seasons GHACOF forecasts show generally lower levels of skill than GloSea. Several systematic errors within the GHACOF forecasts are identified; the largest being the tendency to over-estimate the likelihood of near normal rainfall, with over 70% (80%) of forecasts giving this category the highest probability in the short (long) rains. In a more detailed evaluation of GloSea, a large wet bias, increasing with forecast lead time, is identified in the short rains. This bias is attributed to a developing cold SST bias in the eastern Indian Ocean, driving an easterly wind bias across the equatorial Indian Ocean. These biases affect the mean state moisture availability, and could act to reduce the ability of the dynamical model in predicting interannual variability, which may also be relevant to predictions from coupled models on longer timescales