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East Africa is highly vulnerable to extreme weather events, such as droughts and floods. Skillful seasonal forecasts exist for the October–November–December short rains, enabling informed decisions, whereas seasonal forecasts for the March–April–May (MAM) long rains have historically had low skill, limiting preparation capacity. Therefore, improved long rains prediction is a high priority and would contribute to climate change resilience in the region. Recent work has highlighted how lower‐troposphere Congo zonal winds in MAM strongly impact regional moisture fluxes and the long rains total precipitation. We therefore approach long rains predictability through the predictability of the Congo winds. We analyze a set of hindcasts from a dynamical prediction system that is able to reproduce the long rains—Congo winds relationship in its individual ensemble members. Encouragingly, in observations, the strength of MAM Congo zonal winds and East Africa rainfall show substantial correlation with the MAM Atlantic (including North Atlantic Oscillation, NAO) and Indo‐Pacific variability, suggestive of ocean influence and potential predictability. However, these features are replaced by different teleconnections in the hindcast ensemble mean fields. This is also true for NAO linkage to Congo winds, despite correct representation in individual members, and good skill in hindcasting the NAO itself. The net effect is strongly negative skill for the Congo winds. We explore statistical correction methods, including using the Congo zonal wind as an anchor index in a signal‐to‐noise calibration for the long rains. This is considered a demonstration of concept, for subsequent implementation using models with better Congo zonal wind skill. Indeed, the clear signals found in the Atlantic (including Mediterranean) and Indo‐Pacific, studied here both in observations and a dynamical prediction system, motivate evaluation of these features across other prediction systems, and offer the prospect of improved physically‐informed long rains dynamical predictions. [ABSTRACT FROM AUTHOR]

The Met Office Global Coupled Model (GC) and the NCEP Climate Forecast System (CFSv2) are both widely used for predicting and simulating the Indian summer monsoon (ISM), and previous studies have demonstrated similarities in the biases in both systems at a range of time scales from weather forecasting to climate simulation. In this study, ISM biases are studied in seasonal forecasting setups of the two systems, in order to provide insight into how they develop across time scales. Similarities are found in the development of the biases between the two systems, with an initial reduction in precipitation followed by a recovery associated with an increasingly cyclonic wind field to the north-east of India. However, this occurs on longer time scales in CFSv2, with a much stronger recovery followed by a second reduction associated with sea surface temperature (SST) biases, so that the bias at longer lead times is of a similar magnitude to that in GC. In GC, the precipitation bias is almost fully developed within a lead time of just eight days, suggesting that carrying out simulations with short time integrations may be sufficient for obtaining substantial insight into the biases in much longer simulations. The relationship between the precipitation and SST biases in GC seems to be more complex than in CFSv2, and is different during the early part of the monsoon season from during the later part of the monsoon season. The relationship of the bias with large-scale drivers is also investigated, using the Boreal Summer IntraSeasonal Oscillation (BSISO) index as a measure of whether the large-scale dynamics favours increasing, active, decreasing or break monsoon conditions. Both models simulate decreasing conditions the best and increasing conditions the worst, in agreement with previous studies and extending these previous results to include CFSv2 and multiple BSISO cycles. [ABSTRACT FROM AUTHOR]

In boreal winter, strong upper‐level midlatitude troughs across the Atlantic–Africa–southwestern Asia sector generate substantial tropical–extratropical interaction and have become recognized as important factors in some extreme weather events. As such, they represent important dynamic features to understand and capture in weather forecasts, as well as in climate models for projections on longer timescales. Here, we empirically study the 20% of winter days with strongest trough signatures during 1982–2020 at each longitude across the sector, and show that the trough impact over northern Africa, most notably in central parts, is particularly strong in magnitude, low‐latitude extent and persistence, leading to the characterization of a northern Africa mode of several‐days weather fluctuation. Weather conditions that follow strong troughs from the eastern Atlantic to the Central Mediterranean include: (i) a warming tendency across much of northern Africa, generally of several Celsius magnitude ahead of the trough, and >1°C even extending to the south of 10° N in central parts and continuing eastward until the Ethiopian Highlands; (ii) precipitation development further north than normal across northern tropical Africa, especially strong over longitudes corresponding to a northward extension of the main Congo rain belt. The intertropical discontinuity and low‐level heat low are also shifted significantly north, with the complex of anomalies persisting for several days, beyond the timescale of the trough. For context, at all other trough longitudes across the sector, a warming signal does emerge (statistically significant), but with much shorter persistence (2–3 days), smaller magnitude and extending southward clearly only to 15–20° N. Mid‐level tropical plumes of moisture are also typically present for strong troughs from the eastern Atlantic to southwestern Asia, and these alone can lead to weather extremes. However, low‐level warming and mid‐level moistening are uniquely juxtaposed at low latitudes over central Africa, where a near‐equatorial signature develops. [ABSTRACT FROM AUTHOR]

Motivated by the wide range of projections of regional rainfall (or precipitation, pr) based on the CMIP5 ensemble of global climate models, we investigate the atmospheric moisture budget of idealized rising‐CO2 (1pctCO2) simulations from 10 models submitted to CMIP6. We use the new CMIP6 standard variables, the vertically integrated eastward and northward moisture flux, and provide a multi‐model assessment of these, their convergence (conv), along with water vapour path (prw). Seasonal climatologies of a 20‐year base climate for pr, prw, flux, and conv match well those from the ECMWF's Reanalysis 5. In addition, composites 'H' of quantities for months in the top decile of monthly rainfall, calculated at each model grid point, produce a field of heavy rainfall, on average double that of the mean. Spatial correlation coefficients (r) between the pr and conv fields are typically.7 in each season. This rises to.9 for the corresponding H fields, due to the important link between moisture convergence and heavy rainfall. Fields of standardized change (Δ), or change per degree of global warming, were calculated. The ensemble‐mean Δpr is only weakly correlated with the base climate pr, and likewise for conv. However, the prw change is highly correlated with the base prw, pointing to the importance of the "thermodynamic" change in that variable. Globally, the net Δconv is highly correlated with Δpr, around.9. This rises to.95 for the heavy‐rain composites. Relationships between changes in pr and other components hold at grid points also, when the individual model results are correlated. Clearly, there is a dependence in the change in pr with that in conv, particularly for heavy rainfall. Assessment of a larger ensemble from CMIP6, using the new flux variables, would improve the evaluation of these processes, and potentially allow more confident projections in future rainfall changes. [ABSTRACT FROM AUTHOR]