Subselection of seasonal ensemble precipitation predictions for East Africa

Abstract

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) ‐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.

Advances, gaps and way forward in provision of climate services over the Greater Horn of Africa

“…With the increase in ensemble members from Global Climate Centers and the need to run high resolution models, there is need for methods of selecting ensemble members. Heinrich-Mertsching et al (2023b) showed that sub-selection of ensemble members leads to systematically higher skill than random sub-selection.…”

“…To tackle this issue, further research needs to be conducted to enhance the accuracy of Long rains forecasts. This could involve the integration of new statistical approaches, ensemble member selection (Heinrich-Mertsching et al, 2023b), ongoing research on exploring the utilization of machine learning techniques (Deman et al, 2022) and hybrid methods that take into consideration the different forecasting approaches.…”

“…High-performance computing facilities are limited over the region which limits the resolution and complexity of models that can be run (Kijazi et al, 2021;Heinrich-Mertsching et al, 2023b). Considering that the region has complex topography and large inland water bodies it is essential to run the models and resolutions that can resolve these local effects (Argent et al, 2015;Ntwali et al, 2016;Kerandi et al, 2017).…”