Abstract:The atmospheric seasonal cycle of the North Atlantic region is dominated by meridional movements of the circulation systems: from the tropics, where the West African Monsoon and extreme tropical weather events take place, to the extratropics, where the circulation is dominated by seasonal changes in the jetstream and extratropical cyclones. Climate variability over the North Atlantic is controlled by various mechanisms. Atmospheric internal variability plays a crucial role in the mid-latitudes. However, El Niño-Southern Oscillation (ENSO) is still the main source of predictability in this region situated far away from the Pacific. Although the ENSO influence over tropical and extra-tropical areas is related to different physical mechanisms, in both regions this teleconnection seems to be non-stationary in time and modulated by multidecadal changes of the mean flow. Nowadays, long observational records (greater than 100 years) and modeling projects (e.g., CMIP) permit detecting non-stationarities in the influence of ENSO over the Atlantic basin, and further analyzing its potential mechanisms. The present article reviews the ENSO influence over the Atlantic region, paying special attention to the stability of this teleconnection over time and the possible modulators. Evidence is given that the ENSO-Atlantic teleconnection is weak over the North Atlantic. In this regard, the multidecadal ocean variability seems to modulate the presence of teleconnections, which can lead to important impacts of ENSO and to open windows of opportunity for seasonal predictability.

West African societies are highly dependent on the West African Monsoon (WAM). Thus, a correct representation of the WAM in climate models is of paramount importance. In this article, the ability of 8 CMIP5 historical General Circulation Models (GCMs) and 4 CORDEX-Africa Regional Climate Models (RCMs) to characterize the WAM dynamics and variability is assessed for the period July-August-September 1979-2004. Simulations are compared with observations. Uncertainties in RCM performance and lateral boundary conditions are assessed individually. Results show that both GCMs and RCMs have trouble to simulate the northward migration of the Intertropical Convergence Zone in boreal summer. The greatest bias improvements are obtained after regionalization of the most inaccurate GCM simulations. To assess WAM variability, a Maximum Covariance Analysis is performed between Sea Surface Temperature and precipitation anomalies in observations, GCM and RCM simulations. The assessed variability patterns are: El Niño-Southern Oscillation (ENSO); the eastern Mediterranean (MED); and the Atlantic Equatorial Mode (EM). Evidence is given that regionalization of the ENSO-WAM teleconnection does not provide any added value. Unlike GCMs, RCMs are unable to precisely represent the ENSO impact on air subsidence over West Africa. Contrastingly, the simulation of the MED-WAM teleconnection is improved after regionalization. Humidity advection and convergence over the Sahel area are better simulated by RCMs. Finally, no robust conclusions can be determined for the EM-WAM teleconnection, which cannot be isolated for the 1979-2004 period. The novel results in this article will help to select the most appropriate RCM simulations to study WAM teleconnections.

The atmospheric response to global sea surface temperatures is the leading cause of rainfall variability in the West African Sahel. On interannual periodicities, El Niño–Southern Oscillation, the Atlantic equatorial mode, and Mediterranean warm/cold events primarily drive variations of summer rainfall over the Sahel. Nevertheless, the rainfall response to these modes of interannual SST variability has been suggested to be unstable throughout the observational record. This study explores changes in the leading patterns of covariability between Sahel rainfall and SSTs, analyzing the dynamical mechanisms at work to explain the nonstationary relationship between anomalies in these two fields. A new network of rain gauge stations across West Africa is used for the first time to investigate these instabilities during the period 1921–2010. A hypothesis is raised that the underlying SST background seems to favor some interannual teleconnections and inhibit others in terms of the cross-equatorial SST gradients and associated impacts on the location of the intertropical convergence zone. Results of this study are relevant for improving the seasonal predictability of summer rainfall in the Sahel.

Sea surface temperature is the key variable when tackling seasonal to decadal climate forecasts. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. In addition, recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability and investigate possible teleconnections, the sea surface temperature based statistical seasonal foreCAST model (S 4 CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of the S 4 CAST model whose operation is focused on studying the impacts of sea surface temperature on any climaterelated variable. Two applications focused on analyzing the predictability of different climatic events have been implemented as benchmark examples. Published by Copernicus Publications on behalf of the European Geosciences Union.Geosci. Model Dev., 8, 3639-3658, 2015 www.geosci-model-dev.net/8/3639/2015/ R. Suárez-Moreno and B. Rodríguez-Fonseca: S 4 CAST v2.0 3641 hindcast and forecast calculations and validation. Section 4 describes two case studies concerning the predictability of Sahelian rainfall and tropical Pacific SSTA.

Climate variability is a key factor in driving malaria outbreaks. As shown in previous studies, climate-driven malaria modeling provides a better understanding of malaria transmission dynamics, generating malaria-related parameters validated as a reliable benchmark to assess the impact of climate on malaria. In this framework, the present study uses climate observations and reanalysis products to evaluate the predictability of malaria incidence in West Africa. Sea surface temperatures (SSTs) are shown as a skillful predictor of malaria incidence, which is derived from climate-driven simulations with the Liverpool Malaria Model (LMM). Using the S4CAST tool, we find robust modes of anomalous SST variability associated with skillful predictability of malaria incidence Accordingly, significant SST anomalies in the tropical Pacific and Atlantic Ocean basins are related to a significant response of malaria incidence over West Africa. For the Mediterranean Sea, warm (cold) SST anomalies are responsible for increased (decreased) surface air temperatures and precipitation over West Africa, resulting in higher (lower) malaria incidence. Our results put forward the key role of SST variability as a source of predictability of malaria incidence, being of paramount interest to decision-makers who plan public health measures against malaria in West Africa. Accordingly, SST anomalies could be used operationally to forecast malaria risk over West Africa for early warning systems.

Abstract. Sea Surface Temperature is the key variable when tackling seasonal to decadal climate forecast. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. Recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability, the Sea Surface Temperature based Statistical Seasonal foreCAST model (S4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of S4CAST model whose operation is focused on the study of the predictability of any variable related to sea surface temperature. An application focused on West African rainfall predictability has been implemented as a benchmark example.

In this paper, the sea surface temperature (SST) based statistical seasonal forecast model (S4CAST) is utilized to examine the spatial and temporal prediction skill of Sahel heavy and extreme daily precipitation events. As in previous studies, S4CAST points out the Mediterranean Sea and El Niño Southern Oscillation (ENSO) as the main drivers of Sahel heavy/extreme daily rainfall variability at interannual timescales (period 1982–2015). Overall, the Mediterranean Sea emerges as a seasonal short-term predictor of heavy daily rainfall (1 month in advance), while ENSO returns a longer forecast window (up to 3 months in advance). Regarding the spatial skill, the response of heavy daily rainfall to the Mediterranean SST forcing is significant over a widespread area of the Sahel. Contrastingly, with the ENSO forcing, the response is only significant over the southernmost Sahel area. These differences can be attributed to the distinct physical mechanisms mediating the analyzed SST-rainfall teleconnections. This paper provides fundamental elements to develop an operational statistical-seasonal forecasting system of Sahel heavy and extreme daily precipitation events.

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