Impact of equatorial Atlantic variability on ENSO predictive skill.

El Niño-Southern Oscillation (ENSO) is a key mode of climate variability with worldwide climate impacts. Recent studies have highlighted the impact of other tropical oceans on its variability. In particular, observations have demonstrated that summer Atlantic Niños (Niñas) favor the development of Pacific Niñas (Niños) the following winter, but it is unclear how well climate models capture this teleconnection and its role in defining the seasonal predictive skill of ENSO. Here we use an ensemble of seasonal forecast systems to demonstrate that a better representation of equatorial Atlantic variability in summer and its lagged teleconnection mechanism with the Pacific relates to enhanced predictive capacity of autumn/winter ENSO. An additional sensitivity study further shows that correcting SST variability in equatorial Atlantic improves different aspects of forecast skill in the Tropical Pacific, boosting ENSO skill. This study thus emphasizes that new efforts to improve the representation of equatorial Atlantic variability, a region with long standing systematic model biases, can foster predictive skill in the region, the Tropical Pacific and beyond, through the global impacts of ENSO.

Transport pathways across the West African Monsoon as revealed by Lagrangian Coherent Structures.

The West African Monsoon (WAM) system is the main source of rainfall in the agriculturally based region of the Sahel. Understanding transport across the WAM is of crucial importance due to the strong impact of humidity and dust pathways on local cloud formation. However, the description of this transport is challenging due to its 3D complex nature. Lagrangian Coherent Structures (LCS) simplify transport description across the WAM by providing a geometrical partition of the troposphere into domains. Air parcels within each domain have similar dynamical characteristics. LCS make it possible to achieve an integrated vision of transport pathways across this system. Using this approach we unveil new connections in the WAM system. In particular, we identify transport pathways between the Tropical Easterly Jet (TEJ) and the African Easterly Jet (AEJ). Furthermore, the clockwise circulation associated with the divergent upper part of the Sahara heat low is clearly delimitated. Additionally, we show the presence of mixing regions in the AEJ and the lower part of the TEJ that are linked to pathways to sources of dust and humidity.

Dataset on the RETRO-BMC cruise onboard the R/V Hespérides, April 2017, Brazil-Malvinas Confluence.

This dataset, gathered during the RETRO-BMC cruise, reports multiple-scale measurements at the Confluence of the Brazil and Malvinas Currents. The cruise was carried out between 8 and 28 April 2017 onboard R/V Hespérides, departing from Ushuaia and arriving to Santos. Along its track, the vessel recorded near-surface temperature and salinity, as well as the horizontal flow from 20 m down to about 800 m. A total of 33 hydrographic stations were completed in a region off the Patagonian Shelf, within 41.2°S-35.9°S and out to 53.0°W. At each station, a multiparametric probe and velocity sensors were deployed inside the frame of a rosette used to collect water samples at selected depths; these samples were later used for several water analyses, including inorganic nutrient concentrations. Microstructure measurements were carried out in 11 of these hydrographic stations. In addition, two high-resolution three-dimensional surveys were conducted with an instrumented undulating vehicle between 40.6°S-39.0°S and 55.6°W-53.8°W. Lastly, eight high-frequency vertical profilers were deployed in the region and five position-transmitting drifters were launched. These data allow the description of the Confluence from the regional scale to the microscale, and provide a view of the variability of the frontal region on time scales from days to weeks.

Climate Variability and Malaria over West Africa.

Malaria is a major public health problem in West Africa. Previous studies have shown that climate variability significantly affects malaria transmission. The lack of continuous observed weather station data and the absence of surveillance data for malaria over long periods have led to the use of reanalysis data to drive malaria models. In this study, we use the Liverpool Malaria Model (LMM) to simulate spatiotemporal variability of malaria in West Africa using daily rainfall and temperature from the following: Twentieth Century Reanalysis (20th CR), National Center for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Twentieth Century (ERA20C), and interim ECMWF Re-Analysis (ERA-Interim). Malaria case data from the national surveillance program in Senegal are used for model validation between 2001 and 2016. The warm temperatures found over the Sahelian fringe of West Africa can lead to high malaria transmission during wet years. The rainfall season peaks in July to September over West Africa and Senegal, and the malaria season lasts from September to November, about 1-2 months after the rainfall peak. The long-term trends exhibit interannual and decadal variabilities. The LMM shows acceptable performance in simulating the spatial distribution of malaria incidence. However, some discrepancies are found. These results are useful for decision-makers who plan public health and control measures in affected West African countries. The study would have substantial implications for directing malaria surveillance activities and health policy. In addition, this malaria modeling framework could lead to the development of an early warning system for malaria in West Africa.

Regime shifts of Mediterranean forest carbon uptake and reduced resilience driven by multidecadal ocean surface temperatures.

The mechanisms translating global circulation changes into rapid abrupt shifts in forest carbon capture in semi-arid biomes remain poorly understood. Here, we report unprecedented multidecadal shifts in forest carbon uptake in semi-arid Mediterranean pine forests in Spain over 1950-2012. The averaged carbon sink reduction varies between 31% and 37%, and reaches values in the range of 50% in the most affected forest stands. Regime shifts in forest carbon uptake are associated with climatic early warning signals, decreased forest regional synchrony and reduced long-term carbon sink resilience. We identify the mechanisms linked to ocean multidecadal variability that shape regime shifts in carbon capture. First, we show that low-frequency variations of the surface temperature of the Atlantic Ocean induce shifts in the non-stationary effects of El Niño Southern Oscillation (ENSO) on regional forest carbon capture. Modelling evidence supports that the non-stationary effects of ENSO can be propagated from tropical areas to semi-arid Mediterranean biomes through atmospheric wave trains. Second, decadal changes in the Atlantic Multidecadal Oscillation (AMO) significantly alter sea-air heat exchanges, modifying in turn ocean vapour transport over land and land surface temperatures, and promoting sustained drought conditions in spring and summer that reduce forest carbon uptake. Third, we show that lagged effects of AMO on the winter North Atlantic Oscillation also contribute to the maintenance of long-term droughts. Finally, we show that the reported strong, negative effects of ocean surface temperature (AMO) on forest carbon uptake in the last decades are unprecedented over the last 150 years. Our results provide new, unreported explanations for carbon uptake shifts in these drought-prone forests and review the expected impacts of global warming on the profiled mechanisms.

Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal.

The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal. The findings confirm that the risk of malaria transmission is mainly linked to climate variables such as rainfall and temperature as well as specific landscape characteristics. For the whole of Senegal, a lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November, corresponding to the second peak of temperature occurring in October. Observed malaria data from the Programme National de Lutte contre le Paludisme (PNLP, National Malaria control Programme in Senegal) and outputs from the meteorological data used in this study were compared. The malaria model outputs present some consistencies with observed malaria dynamics over Senegal, and further allow the exploration of simulations performed with reanalysis data sets over a longer time period. The simulated malaria risk significantly decreased during the 1970s and 1980s over Senegal. This result is consistent with the observed decrease of malaria vectors and malaria cases reported by field entomologists and clinicians in the literature. The main differences between model outputs and observations regard amplitude, but can be related not only to reanalysis deficiencies but also to other environmental and socio-economic factors that are not included in this mechanistic malaria model framework. The present study can be considered as a validation of the reliability of reanalysis to be used as inputs for the calculation of malaria parameters in the Sahel using dynamical malaria models.

Climate and health: observation and modeling of malaria in the Ferlo (Senegal).

The aim of this work, undertaken in the framework of QWeCI (Quantifying Weather and Climate Impacts on health in the developing countries) project, is to study how climate variability could influence malaria seasonal incidence. It will also assess the evolution of vector-borne diseases such as malaria by simulation analysis of climate models according to various climate scenarios for the next years. Climate variability seems to be determinant for the risk of malaria development (Freeman and Bradley, 1996 [1], Lindsay and Birley, 1996 [2], Kuhn et al., 2005 [3]). Climate can impact on the epidemiology of malaria by several mechanisms, directly, via the development rates and survival of both pathogens and vectors, and indirectly, through changes in vegetation and land surface characteristics such as the variability of breeding sites like ponds.