Skip to content

Publications

David MacLeodErik W. KolstadKaterina MichaelidesMichael Bliss SingerSensitivity of Rainfall Extremes to Unprecedented Indian Ocean Dipole Events. DOI:  https://doi.org/10.1029/2023GL105258

 

Abstract

 

Strong positive Indian Ocean Dipole (pIOD) events like those in 1997 and 2019 caused significant flooding in East Africa. While future projections indicate an increase in pIOD events, limited historical data hinders a comprehensive understanding of these extremes, particularly for unprecedented events. To overcome this we utilize a large ensemble of seasonal reforecast simulations, which show that regional rainfall continues to increase with pIOD magnitude, with no apparent limit. In particular we find that extreme rain days are highly sensitive to the pIOD index and their seasonal frequency increases super-linearly with higher pIOD magnitudes. It is vital that socio-economic systems and infrastructure are able to handle not only the increasing frequency of events like 1997 and 2019 but also unprecedented seasons of extreme rainfall driven by as-yet-unseen pIOD events. Future studies should prioritize understanding the hydrological implications and population exposure to these unprecedented extremes in East Africa.

 

E. W. Kolstad, D. MacLeod, T. D. DemissieDrivers of Subseasonal Forecast Errors of the East African Short RainsGeophysical Research LettersGeophysical Research LettersDOI:https://doi.org/10.1029/2021GL093292

 

Abstract

 

The ‘short rains’ in East Africa from October to December have significant year-to-year variability. Their abundance or deficiency is often associated with floods or droughts for which early warning is crucial, though even in normal seasons skillful forecasts facilitate planning and preparedness. Here we study the relationship between initial-state sea surface temperatures and subseasonal rainfall forecast errors in the European Center for Medium-Range Weather Forecasts model in the region. We demonstrate that the initial mode of the Indian Ocean Dipole (IOD) is a partial control on the rainfall error in weeks 3–4. This relationship is also clear on the seasonal scale, exemplified by too-wet forecasts during the 2015 season when the IOD was positive, and too-dry forecasts in 2010 when it was negative. Our results provide an entry point for model improvement, and we show that a priori forecast corrections based on the initial IOD index are feasible.

 

Erik W. Kolstad, and David MacLeodLagged oceanic effects on the East African short rains. Climate Dynamics. DOI: https://doi.org/10.1007/s00382-022-06176-6

 

Abstract

 

The East African ‘short rains’ in October–December (OND) exhibit large interannual variability. Drought and flooding are not unusual, and long-range rainfall forecasts can guide planning and preparedness for such events. Although seasonal forecasts based on dynamical models are making inroads, statistical models based on sea surface temperature (SST) precursors are still widely used, making it important to better understand the strengths and weaknesses of such models. Here we define a simple statistical forecast model, which is used as a tool to shed light on the dynamics that link SSTs and rainfall across time and space, as well as on why such models sometimes fail. Our model is a linear regression, where the August states of El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) predict about 40% of the short rains variability in 1950–2020. The forecast errors are traced back to the initial SSTs: too-wet (too-dry) forecasts are linked linearly to positive (negative) initial ENSO and IOD states in August. The link to the initial IOD state is mediated by changes in the IOD between August and OND, highlighting a physical mechanism for prediction busts. We also identify asymmetry and nonlinearity: when ENSO and/or the IOD are positive in August, the range and variance of OND forecast errors are larger than when the SST indices are negative. Upfront adjustments of predictions conditional on initial SSTs would have helped in some years with large forecast busts, such as the dry 1987 season during a major El Niño, for which the model erroneously predicts copious rainfall, but it would have exacerbated the forecast in the wet 2019 season, when the IOD was strongly positive and the model predicts too-dry conditions.

 

Tamirat B. Jimma, Teferi Demissie, Gulilat T. Diro, Kassahun Ture, Tadesse Terefe & Dawit SolomonSpatiotemporal variability of soil moisture over Ethiopia and its teleconnections with remote and local driversTheoretical and Applied Climatology. DOI: https://doi.org/10.1007/s00704-022-04335-7

 

Abstract

 

Soil moisture is one of the essential climate variables with a potential impact on local climate variability. Despite the importance of soil moisture, studies on soil moisture characteristics in Ethiopia are less documented. In this study, the spatiotemporal variability of Ethiopian soil moisture (SM) has been characterized, and its local and remote influential driving factors are investigated. An empirical orthogonal function (EOF) and KMeans clustering algorithm have been employed to classify the large domain into homogeneous zones. Complex maximum covariance analysis (CMCA) is applied to evaluate the covariability between SM and selected local and remote variables such as rainfall (RF), evapotranspiration (ET), and sea surface temperature (SST). Inter-comparison among SM datasets highlight that the FLDAS dataset better depicts the country’s SM spatial and temporal distribution (i.e., a correlation coefficient , with observations). Results also indicate that regions located in northeastern Ethiopia are drier irrespective of the season (JJAS, MAM, and OND) considered. In contrast, the western part of the country consistently depicted a wetter condition in all seasons. During summer (JJAS), the soil moisture variability is characterized by a strong east–west spatial contrast. The highest and lowest soil moisture values were observed across the country’s central western and eastern parts, respectively. Furthermore, analyses indicate that interannual variability of SM is dictated substantially by RF, though the impact on some regions is weaker. It is also found that ET likely drives the SM in the eastern part of Ethiopia due to a higher atmospheric moisture demand that ultimately invokes changes in surface humidity and rainfall. A composite analysis based on the extreme five wettest and driest SM years revealed a similar spatial distribution of wet SM with positive anomalies of RF across the country and ET over the southern regions. Remote SSTs are also found to have a significant influence on SM distribution. In particular, equatorial central Pacific and western Indian oceans SST anomalies are predominant factors for spatiotemporal SM variations over the country. Major global oceanic indices: Oceanic Nino Index (ONI), Indian Ocean Dipole (IOD), Pacific warm pool (PACWARMPOOL), and Pacific Decadal Oscillations (PDO) are found to be closely associated with the SM anomalies in various parts of the country. The associationship between these remote SST anomalies and local soil moisture is via large-scale atmospheric circulations that are linked to regional factors such as precipitation and temperature anomalies.

  

Joël Arnault, Anthony Musili Mwanthi, Tanja Portele, Lu Li, Thomas Rummler, Benjamin Fersch, Mohammed Abdullahi Hassan, Titike Kassa Bahaga, Zhenyu Zhang, Eric Mensah Mortey, Ifeany Chukwudi Achugbu8,9, Hassane Moutahir, Souleymane Sy, Jianhui Wei,Patrick Laux, Stefan Sobolowski and Harald KunstmannRegional water cycle sensitivity to afforestation: synthetic numerical experiments for tropical AfricaFrontiers in Climatehttps://doi.org/10.3389/fclim.2023.1233536

 

Abstract

 

Afforestation as a climate change mitigation option has been the subject of intense debate and study over the last few decades, particularly in the tropics where agricultural activity is expanding. However, the impact of such landcover changes on the surface energy budget, temperature, and precipitation remains unclear as feedbacks between various components are difficult to resolve and interpret. Contributing to this scientific debate, regional climate models of varying complexity can be used to test how regional climate reacts to afforestation. In this study, the focus is on the gauged Nzoia basin (12,700 km2) located in a heavily farmed region of tropical Africa. A reanalysis product is dynamically downscaled with a coupled atmospheric-hydrological model (WRF-Hydro) to finely resolve the land-atmosphere system in the Nzoia region. To overcome the problem of Nzoia river flooding over its banks we enhance WRF-Hydro with an overbank flow routing option, which improves the representation of daily discharge based on the Nash-Sutcliffe efficiency and Kling-Gupta efficiency (from −2.69 to 0.30, and −0.36 to 0.63, respectively). Changing grassland and cropland areas to savannas, woody savannas, and evergreen broadleaf forest in three synthetic numerical experiments allows the assessment of potential regional climate impacts of three afforestation strategies. In all three cases, the afforestation-induced decrease in soil evaporation is larger than the afforestation-induced increase in plant transpiration, thus increasing sensible heat flux and triggering a localized negative feedback process leading to more precipitation and more runoff. This effect is more pronounced with the woody savannas experiment, with 7% less evapotranspiration, but 13% more precipitation, 8% more surface runoff, and 12% more underground runoff predicted in the Nzoia basin. This study demonstrates a potentially large impact of afforestation on regional water resources, which should be investigated in more detail for policy making purposes.

 

Claudio Heinrich-Mertsching, Silje Lund Sørland, Masilin Gudoshava, Eunice Koech, Titike K. Bahaga, Stefan Pieter SobolowskiSubselection of seasonal ensemble precipitation predictions for East Africa.Quarterly Journal of the Royal Meteorological Society Volume 149, Issue 755https://doi.org/10.1002/qj.4525

 

Abstract

 

This works proposes a probabilistic framework for rainy season onset forecasts over Greater Horn of Africa derived from bias-corrected, long range, multi-model ensemble precipitation forecasts. A careful analysis of the contribution of the different forecast systems to the overall multi-model skill shows that the improvement over the best performing individual model can largely be explained by the increased ensemble size. An alternative way of increasing ensemble size by blending a single model ensemble with climatology is explored and demonstrated to yield better probabilistic forecasts than the multi-model ensemble. Both reliability and skill of the probabilistic forecasts are better for OND onset than for MAM and JJAS onset where forecasts are found to be late biased and have only minimal skill relative to climatology. The insights gained in this study will help enhance operational subseasonal-to-seasonal forecasting in the GHA region.

 

Michael Scheuerer, Titike K. Bahaga, Zewdu T. Segele & Thordis L. ThorarinsdottirProbabilistic rainy season onset prediction over the greater horn of africa based on long-range multi-model ensemble forecasts.Climate Dynamicshttps://doi.org/10.1007/s00382-023-07085-y

 

Abstract

 

This works proposes a probabilistic framework for rainy season onset forecasts over Greater Horn of Africa derived from bias-corrected, long range, multi-model ensemble precipitation forecasts. A careful analysis of the contribution of the different forecast systems to the overall multi-model skill shows that the improvement over the best performing individual model can largely be explained by the increased ensemble size. An alternative way of increasing ensemble size by blending a single model ensemble with climatology is explored and demonstrated to yield better probabilistic forecasts than the multi-model ensemble. Both reliability and skill of the probabilistic forecasts are better for OND onset than for MAM and JJAS onset where forecasts are found to be late biased and have only minimal skill relative to climatology. The insights gained in this study will help enhance operational subseasonal-to-seasonal forecasting in the GHA region.

 

 

Jima, W.L., Bahaga, T.K. & Tsidu, G.M. Fidelity of CMIP6 Models in Simulating June–September Rainfall Climatology, Spatial and Trend Patterns Over Complex Topography of Greater Horn of AfricaPure and Applied Geophysicshttps://doi.org/10.1007/s00024-023-03414-8

 

Abstract

This study focuses on evaluating the High-Resolution Model Inter-comparison Project (HighResMIP), Atmospheric Model Intercomparison Project (AMIP), and Coupled Model simulations within the framework of the Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6). We used fifteen Models to explore how CMIP6 reproduced the June–September (JJAS) precipitation features over the Greater Horn of Africa (GHA) during the 1979–2014 historical simulation periods. Rainfall from the Global Precipitation Climatology Center (GPCC) and Climatic Research Unit (CRU) are used to validate the model simulations. Overall, the AMIP multi-model ensemble mean (MME) is able to reproduce the observed seasonal mean, the annual cycle, the frequency distribution of cumulative rainfall, spatial and trend patterns of precipitation over GHA. Particularly, long-term mean of JJAS season precipitation is well reproduced over the western part of Sudan Republic, much of South Sudan, over some isolated parts of north-western Uganda, Ethiopian Highlands, and western Ethiopia. However, consistent with previous studies, coupled models MME shows substantial discrepancies compared to AMIP in simulating JJAS rainfall climatology by exhibiting dry bias relative to both GPCC and CRU rainfall. In contrast, the HighresMIP experiments reveal wet bias over most parts of the GHA. The annual cycles of observed rainfall are well captured in AMIP, CMIP, and HighresMIP experiments and with further improvement in MMEs mean. In addition, the spatial rainfall pattern correlation between GPCC (CRU) and model simulations is as high as 0.89 (0.94), whereas the maximum trend pattern correlation is 0.47(0.72) with GPCC (CRU) respectively. Employing a multicriteria decision-making algorithm (MCDM) based on eight performance metrics as the selection criterion, we identified four, three, and two models and their MMEs out of AMIP, CMIP, and HighresMIP experiments, respectively, having superior skills over Ethiopian Highlands. In contrast, the study shows substantial biases in a number of models from AMIP, CMIP and HighresMIP experiments over GHA relative to GPCC and CRU observations that need to be improved with either bias correction or through further tuning of the models to improve their skills.

 

David MacLeod, Erik W. Kolstad, Katerina Michaelides, Michael B. SingerSensitivity of Rainfall Extremes to Unprecedented Indian Ocean Dipole Eventshttps://doi.org/10.1029/2023GL105258

 

 Abstract

 

Strong positive Indian Ocean Dipole (pIOD) events like those in 1997 and 2019 caused significant flooding in East Africa. While future projections indicate an increase in pIOD events, limited historical data hinders a comprehensive understanding of these extremes, particularly for unprecedented events. To overcome this we utilize a large ensemble of seasonal reforecast simulations, which show that regional rainfall continues to increase with pIOD magnitude, with no apparent limit. In particular we find that extreme rain days are highly sensitive to the pIOD index and their seasonal frequency increases super-linearly with higher pIOD magnitudes. It is vital that socio-economic systems and infrastructure are able to handle not only the increasing frequency of events like 1997 and 2019 but also unprecedented seasons of extreme rainfall driven by as-yet-unseen pIOD events. Future studies should prioritize understanding the hydrological implications and population exposure to these unprecedented extremes in East Africa.

Tamirat B. J., Teferi D., Gulilat T. D., Kassahun T., Tadesse T. (2023): Spatiotemporal variability of soil moisture over Ethiopia and its teleconnections with remote and local drivers. Climate Dynamics. DOI: 10.1007/s00704-022-04335-7
Abstract
Soil moisture is one of the essential climate variables with a potential impact on local climate variability. Despite the importance of soil moisture, studies on soil moisture characteristics in Ethiopia are less documented. In this study, the spa- tiotemporal variability of Ethiopian soil moisture (SM) has been characterized, and its local and remote influential driving factors are investigated. An empirical orthogonal function (EOF) and KMeans clustering algorithm have been employed to classify the large domain into homogeneous zones. Complex maximum covariance analysis (CMCA) is applied to evaluate the covariability between SM and selected local and remote variables such as rainfall (RF), evapotranspiration (ET), and sea surface temperature (SST). Inter-comparison among SM datasets highlight that the FLDAS dataset better depicts the country’s SM spatial and temporal distribution (i.e., a correlation coefficient. Results also indicate that regions located in northeastern Ethiopia are drier irrespective of the season (JJAS, MAM, and OND) considered.
Kolstad, E. W., & MacLeod, D. (2022): Lagged Effects on the East African Short Rains. Climate Dynamics. DOI: 10.1007/s00382-022-06176-6
Abstract
The East African ‘short rains’ in October–December (OND) exhibit large interannual variability. Drought and flooding are not unusual, and long-range rainfall forecasts can guide planning and preparedness for such events. Although seasonal forecasts based on dynamical models are making inroads, statistical models based on sea surface temperature (SST) precursors are still widely used, making it important to better understand the strengths and weaknesses of such models. Here we define a simple statistical forecast model, which is used as a tool to shed light on the dynamics that link SSTs and rainfall across time and space, as well as on why such models sometimes fail. Our model is a linear regression, where the August states of El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) predict about 40% of the short rains variability in 1950–2020. The forecast errors are traced back to the initial SSTs: too-wet (too-dry) forecasts are linked linearly to positive (negative) initial ENSO and IOD states in August. The link to the initial IOD state is mediated by changes in the IOD between August and OND, highlighting a physical mechanism for prediction busts. We also identify asymmetry and nonlinearity: when ENSO and/or the IOD are positive in August, the range and variance of OND forecast errors are larger than when the SST indices are negative. Upfront adjustments of predictions conditional on initial SSTs would have helped in some years with large forecast busts, such as the dry 1987 season during a major El Niño, for which the model erroneously predicts copious rainfall, but it would have exacerbated the forecast in the wet 2019 season, when the IOD was strongly positive and the model predicts too-dry conditions.
Kolstad, E. W., MacLeod, D., & Demissie, T. D (2021): Drivers of subseasonal forecasting of the East African short rains. Geophysical Research Letters. DOI: 10.1029/2021GL093292
Abstract
The ‘short rains’ in East Africa from October to December have significant year-to-year variability. Their abundance or deficiency is often associated with floods or droughts for which early warning is crucial, though even in normal seasons skillful forecasts facilitate planning and preparedness. Here we study the relationship between initial-state sea surface temperatures and subseasonal rainfall forecast errors in the ECMWF model in the region. We demonstrate that the initial mode of the Indian Ocean Dipole (IOD) is a partial control on the rainfall error in weeks 3–4. This relationship is also clear on the seasonal scale, exemplified by too-wet forecasts during the 2015 season when the IOD was positive, and too dry forecasts in 2010 when it was negative. Our results provide an entry point for model improvement, and we show that a priori forecast corrections based on the initial IOD index are feasible.