Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

Four recurrent weather regimes are identified over North America from October to March through a k-means clustering applied to MERRA daily 500-hPa geopotential heights over the 1982–2014 period. Three regimes resemble Rossby wave train patterns with some baroclinicity, while one is related to an NAO-like meridional pressure gradient between eastern North America and western regions of the North Atlantic. All regimes are associated with distinct rainfall and surface temperature anomalies over North America. The four-cluster partition is well reproduced by ECMWF week-1 reforecasts over the 1995–2014 period in terms of spatial structures, daily regime occurrences, and seasonal regime counts. The skill in forecasting daily regime sequences and weekly regime counts is largely limited to 2 weeks. However, skill relationships with the MJO, ENSO, and SST variability in the Atlantic and Indian Oceans suggest further potential for subseasonal predictability based on wintertime large-scale weather regimes.

Probabilistic forecasts of weekly and week 3–4 averages of precipitation are constructed using extended logistic regression (ELR) applied to three models (ECMWF, NCEP, and CMA) from the Subseasonal-to-Seasonal (S2S) project. Individual and multimodel ensemble (MME) forecasts are verified over the common period 1999–2010. The regression parameters are fitted separately at each grid point and lead time for the three ensemble prediction system (EPS) reforecasts with starts during January–March and July–September. The ELR produces tercile category probabilities for each model that are then averaged with equal weighting. The resulting MME forecasts are characterized by good reliability but low sharpness. A clear benefit of multimodel ensembling is to largely remove negative skill scores present in individual forecasts. The forecast skill of weekly averages is higher in winter than summer and decreases with lead time, with steep decreases after one and two weeks. Week 3–4 forecasts have more skill along the U.S. East Coast and the southwestern United States in winter, as well as over west/central U.S. regions and the intra-American sea/east Pacific during summer. Skill is also enhanced when the regression parameters are fit using spatially smoothed observations and forecasts. The skill of week 3–4 precipitation outlooks has a modest, but statistically significant, relation with ENSO and the MJO, particularly in winter over the southwestern United States.

International audienceMoisture exchange between the South Atlantic and southern Africa is examined in this study through zonal moisture transport. Along the west coast of southern Africa, a multivariate analysis of the zonal flow of moisture computed from NCEP-DOE AMIP II Re-analyses reveals a primary mode of variability typical of variations in intensity and of the latitudinal migration of the circulation associated with the midlatitude westerlies and the South Atlantic anticyclone. In austral summer (January–February), this mode, referred to as the South Atlantic midlatitude mode, is found to be well correlated with rainfall over southern Africa (i.e. to the south of the upper lands surrounding the Congo basin). Its positive/negative phases are found to correspond with surface pressures changes over the South Atlantic region in austral summer when the South Atlantic anticyclone is shifted northward/southward respectively. Such changes are accompanied by dipole-like SST anomalies in the midlatitude South Atlantic Ocean, while simultaneous SST anomalies with a similar structure are also found over South Indian Ocean regions. In January–February, positive/negative events linked to the South Atlantic midlatitude mode are marked by meridional shifts (northward/southward) and weakening/strengthening of the ITCZ over the southern tropics, together with modulations in intensity (weakened/sustained) of the Angola low, which could act as a tropical source of moisture for Tropical Temperate Troughs (TTTs). In association with a strengthened/weakened zonal component of the southern extension of the African Easterly Jet (AEJ), this could modulate the meridional transfer of moisture south of 15°S to the advantage/detriment of Angolan coastal regions, where above/below rainfall are expected. Variations in the latitudinal position (northward/southward) of the South Atlantic anticyclone, and thus of the midlatitude westerlies, are also found to reduce/favour moisture advection towards southern Africa subtropics allowing the southern Indian trades to penetrate less/more over the subcontinent south of 25°S. This would create a situation where convection processes are inhibited/supported within the SICZ/TTTs region resulting in drier/wetter conditions locally for positive/negative events respectively

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Since 1999, the increased frequency of dry conditions over East Africa, particularly during the March–May (MAM) season, has heightened concerns in a region already highly insecure about food. The underlying mechanisms, however, are still not yet fully understood. This article analyses a proxy for daily convection variations over a large region encompassing East Africa and the whole Indian Ocean basin by applying a cluster analysis to more than 30 years of daily outgoing longwave radiation (OLR). Focusing on the MAM season to investigate relationships with East African long rains, four recurrent convection regimes associated with wet/dry conditions in East Africa are identified. Interestingly, all four regimes are related to western/central Pacific sea surface temperatures (SSTs) and rainfall. Wet regimes are associated with cool and dry/warm and wet conditions over the Maritime Continent (MC)/tropical Pacific east of the date line. Dry regimes exhibit opposite SST/rainfall dipole patterns in the Pacific compared to wet regimes, with the Indian Ocean found to modulate impacts on East African rainfall. Significant relationships between off‐equatorial warming in the west Pacific and a more frequent dry regime in May since 1998–1999 suggest an earlier onset of the monsoon and Somali jet, consistent with the recent abrupt shift observed in East African long rains and their modulation at multi‐decadal time scales of the Pacific.

ABSTRACT:The cumulative distribution function transform (CDF-t) is used to downscale daily precipitation and surface temperatures from a set of Global climate model (GCM) climatic projections over southern India. To deal with the full annual cycle, the approach has been applied by months, allowing downscaled projections for all seasons. First, CDF-t is validated over a historical period using observation from the Indian Meteorological Department (IMD). Resulting high resolution fields show substantial improvements compared to original GCM outputs in terms of distribution, seasonal cycle and monsoon means for arid, semi-arid and wetter regions of the subcontinent. Then, CDF-t is applied to GCM large-scale fields to project rainfall and surface temperature changes for the 21st century under the IPCC SRES A2 scenario. The results obtained show an increase of rainfall, mostly during the monsoon season, while winter precipitation is reduced, and suggest a widespread warming especially in the winter and post-monsoon season.

The skill of submonthly forecasts of rainfall over the East Africa–West Asia sector is examined for starts during the extended boreal winter season (September–April) using three ensemble prediction systems (EPSs) from the Subseasonal-to-Seasonal (S2S) project. Forecasts of tercile category probabilities over the common period 1999–2010 are constructed using extended logistic regression (ELR), and a multimodel forecast is formed by averaging individual model probabilities. The calibration of each model separately produces reliable probabilistic weekly forecasts, but these lack sharpness beyond a week lead time. Multimodel ensembling generally improves skill by removing negative skill scores present in individual models. In addition, the multimodel ensemble week-3–4 forecasts have a higher ranked probability skill score and reliability compared to week-3 or week-4 forecasts for starts in February–April, while skill gain is less pronounced for other seasons. During the 1999–2010 period, skill over continental subregions is the highest for starts in February–April and for starts during El Niño conditions and MJO phase 7, which coincides with enhanced forecast probabilities of above-normal rainfall. Overall, these results indicate notable opportunities for the application of skillful subseasonal predictions over the East Africa–West Asia sector during the extended boreal winter season.