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Heinrich-Mertsching, C., Sørland, S.L., Gudoshava, M., Koech, E., Bahaga, T.K. & Sobolowski, S.P. (2023): Subselection of seasonal ensemble precipitation predictions for East Africa. Quarterly Journal of the Royal Meteorological Society. DOI: 10.1002/qj.4525
The Greater Horn of Africa (GHA) is highly vulnerable to climate and weather hazards such as drought, heat waves, and floods. There is a need for accurate seasonal forecasts to prepare for risks (such as crop failure and reduced grazing opportunities) and take advantage of favorable conditions (rains arrive on time and where they are needed) when they arise. As such, information at finer spatial scales than current state-of-the-art global prediction models can provide is needed. Dynamical downscaling is one method employed to obtain information at finer scales. However, providers of seasonal forecasts over the GHA are hampered by limited computational resources and time constraints that restrict the number of global model ensemble members that can be downscaled. Some ensemble subselection criteria must be employed. Currently, providers take an uninformed (or random) approach. Specifically, forecasters simply take the first ensemble member of the global model seasonal forecast ensemble. However, recent work, focused on decadal prediction, has shown that subselecting global model ensemble members in an informed way, that is, according to their ability to reproduce key features of the climate system, results in improved predictions. This emerges from the fact that the climate system is likely more predictable than our models would have us believe. Seeing an opportunity for improvement, we apply the same thinking to the seasonal context and assess several procedures for subselecting ensemble members from seasonal predictions with exchangeable members. Such informed subselections have the potential to take advantage of information in an ensemble of global simulations that might be missed by random selection. Three subselection methods are investigated, with a focus on seasonal predictions for rainfall over GHA. We demonstrate that informed subselection leads to systematically higher skill than random subselection. We find that (1) for small subsample sizes, such as would be chosen for dynamical downscaling and/or downstream impact modeling, informed subselection nearly always outperforms random subselection, (2) subselecting based on well-known teleconnections benefits those seasons in which such pathways are active, such as OND and JJAS, and (3) k-means subselection outperforms random selection for small ensemble sizes throughout all seasons, including the notoriously difficult to predict MAM season. These techniques require only input that is available at the time of the forecast release and are easy to apply operationally.
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
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
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
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.