Southern African summer-rainfall variability, and its teleconnections, on interannual to interdecadal timescales in CMIP5 models

23 pagesInternational audienceThis study provides the first assessment of CMIP5 model performances in simulating southern Africa (SA) rainfall variability in austral summer (Nov–Feb), and its teleconnections with large-scale climate variability at different timescales. Observed SA rainfall varies at three major timescales: interannual (2–8 years), quasi-decadal (8–13 years; QDV) and interdecadal (15–28 years; IDV). These rainfall fluctuations are, respectively, associated with El Niño Southern Oscillation (ENSO), the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO), interacting with climate anomalies in the South Atlantic and South Indian Ocean. CMIP5 models produce their own variability, but perform better in simulating interannual rainfall variability, while QDV and IDV are largely underestimated. These limitations can be partly explained by spatial shifts in core regions of SA rainfall variability in the models. Most models reproduce the impact of La Niña on rainfall at the interannual scale in SA, in spite of limitations in the representation of ENSO. Realistic links between negative IPO are found in some models at the QDV scale, but very poor performances are found at the IDV scale. Strong limitations, i.e. loss or reversal of these teleconnections, are also noted in some simulations. Such model errors, however, do not systematically impact the skill of simulated rainfall variability. This is because biased SST variability in the South Atlantic and South Indian Oceans strongly impact model skills by modulating the impact of Pacific modes of variability. Using probabilistic multi-scale clustering, model uncertainties in SST variability are primarily driven by differences from one model to another, or comparable models (sharing similar physics), at the global scale. At the regional scale, i.e. SA rainfall variability and associated teleconnections, while differences in model physics remain a large source of uncertainty, the contribution of internal climate variability is increasing. This is particularly true at the QDV and IDV scales, where the individual simulations from the same model tend to differentiate, and the sampling error increase

The Transcriptional Regulator CzcR Modulates Antibiotic Resistance and Quorum Sensing in Pseudomonas aeruginosa

The opportunistic pathogen Pseudomonas aeruginosa responds to zinc, cadmium and cobalt by way of the CzcRS two-component system. In presence of these metals the regulatory protein CzcR induces the expression of the CzcCBA efflux pump, expelling and thereby inducing resistance to Zn, Cd and Co. Importantly, CzcR co-regulates carbapenem antibiotic resistance by repressing the expression of the OprD porin, the route of entry for these antibiotics. This unexpected co-regulation led us to address the role of CzcR in other cellular processes unrelated to the metal response. We found that CzcR affected the expression of numerous genes directly involved in the virulence of P. aeruginosa even in the absence of the inducible metals. Notably the full expression of quorum sensing 3-oxo-C12-HSL and C4-HSL autoinducer molecules is impaired in the absence of CzcR. In agreement with this, the virulence of the czcRS deletion mutant is affected in a C. elegans animal killing assay. Additionally, chromosome immunoprecipitation experiments allowed us to localize CzcR on the promoter of several regulated genes, suggesting a direct control of target genes such as oprD, phzA1 and lasI. All together our data identify CzcR as a novel regulator involved in the control of several key genes for P. aeruginosa virulence processes

Southern Africa Climate Over the Recent Decades:Description, Variability and Trends

South of 15°S, southern Africa has a subtropical climate, which is affected by temperate and tropical weather systems and comes under the influence of the Southern Hemisphere high-pressure systems. Most rainfall occurs in austral summer, but the southwest experiences winter rainfall. Much of the precipitation in summer is of convective origin forced by large-scale dynamics. There is a marked diurnal cycle in rainfall in summer. The El Niño Southern Oscillation (ENSO) influences interannual rainfall variability. In austral summer, drought tends to occur during El Niño, while above-normal rainfall conditions tend to follow La Niña. During El Niño, higher than normal atmospheric pressure anomalies, detrimental to rainfall, occur due to changes in the global atmospheric circulation. This also weakens the moisture transport from the Indian Ocean to the continent. The opposite mechanisms happen during La Niña. On top of the variability related to ENSO, the Pacific Ocean also influences the decadal variability of rainfall. Additionally, the Angola Current, the Agulhas Current, the Mozambique Channel and the southwest Indian Ocean affect rainfall variability. Over the last 40 to 60 years, near-surface temperatures have increased over almost the whole region, summer precipitation has increased south of 10°S, and winter precipitation has mostly decreased in South Africa. Meanwhile, the Agulhas Current and the Angola Current have warmed, and the Benguela Current has cooled

Variabilité des précipitations au sahel Central et Recherche du forcage climatique par analyse du signal : la station de Maïne-Soroa (SE Niger) entre 1950 et 2005. Rainfall variability in the Central Sahel and climate forcing by signal analysis: Maïné-Soroa station (SE Niger) over the period 1950-2005

International audienceUne régression polynomiale non-paramétrique (méthode LOESS) appliquée aux précipitations annuelles de Maïné-Soroa (SE Niger) montre trois périodes : humide (1950-1967), aride (1968-1993), semi-aride (1994-2005). L'utilisation de la transformée en ondelettes continues permet de séparer la variabilité interne (haute fréquence) et la variabilité forcée (basse fréquence). Nous avons pu mettre en évidence sept modes de fréquence localisés dans le temps : deux modes intra-saisonniers (4-9jrs, 16-20jrs), deux modes saisonniers (6 mois, 1an), deux modes pluriannuels (2-4ans et 5-8ans) et un mode quasi-décennal (12-18ans). Lors du régime humide, on retrouve une forte variance des modes saisonniers liés à la ZCIT et des modes pluriannuels se succédant dans le temps (5-8ans puis 2-4ans) en liaison avec les températures de l'Atlantique Tropical Nord (TNA) puis avec l'Anticyclone des Açores. Ces modes basses fréquences sont synchrones avec une activité convective mature (mode 4-9jrs) et l'apparition d'une perturbation (mode 16-20jrs) lors de la transition entre la phase océanique et la phase continentale de la mousson. Au contraire, lors des années arides on retrouve le mode quasi-décennal, en lien avec les températures de l'Atlantique Tropical Sud (TSA), forçant une activité convective peu développée et perturbée au coeur de la phase continentale. A non-parametric polynomial regression applied to the Maine-Soroa annual rainfall timeseries reveals three periods: wet (1950-1967), arid (1968-1993) and semi-arid (1994-2005). Continuous Wavelet Transform allows crossing internal variability (high frequency) and forcing variability (low frequency). We could reveal seven frequency modes highly localized in time: two intraseasonal modes (4-9 days, 16-20 days), two seasonal modes (6mo, 1yr), two interannuals modes (2- 4yrs, 5-8yrs) and one quasidecadal mode (12-18yrs). During wet period, we notice a strong variance across seasonal modes in relation to ITCZ and the two interannual modes succeeding in time (5-8yrs then 2-4yrs) in connection with the Tropical North Atlantic temperatures (TNA) then Azores high. At the same time, we notice great convective activity (4-9days) and the oceanic/continental transition phase disturbation (16-20days). During the arid period, we notice the quasidecadal mode in relation with the Tropical South Atlantic temperatures (TSA), forcing a weak convective activity and disturbed in the medium of the continental phase

Spatiotemporal and cross-scale interactions in hydroclimate variability:a case-study in France

International audienceUnderstanding how water resources vary in response to climate at different temporal and spatial scales is crucial to inform long-term management. Climate change impacts and induced trends may indeed be substantially modulated by low-frequency (multi-year) variations, whose strength varies in time and space, with large consequences for risk forecasting systems. In this study, we present a spatial classification of precipitation, temperature, and discharge variability in France, based on a fuzzy clustering and wavelet spectra of 152 near-natural watersheds between 1958 and 2008. We also explore phase–phase and phase–amplitude causal interactions between timescales of each homogeneous region. A total of three significant timescales of variability are found in precipitation, temperature, and discharge, i.e., 1, 2–4, and 5–8 years. The magnitude of these timescales of variability is, however, not constant over the different regions. For instance, southern regions are markedly different from other regions, with much lower (5–8 years) variability and much larger (2–4 years) variability. Several temporal changes in precipitation, temperature, and discharge variability are identified during the 1980s and 1990s. Notably, in the southern regions of France, we note a decrease in annual temperature variability in the mid 1990s. Investigating cross-scale interactions, our study reveals causal and bi-directional relationships between higher- and lower-frequency variability, which may feature interactions within the coupled land–ocean–atmosphere systems. Interestingly, however, even though time frequency patterns (occurrence and timing of timescales of variability) were similar between regions, cross-scale interactions are far much complex, differ between regions, and are not systematically transferred from climate (precipitation and temperature) to hydrological variability (discharge). Phase–amplitude interactions are indeed absent in discharge variability, although significant phase–amplitude interactions are found in precipitation and temperature. This suggests that watershed characteristics cancel the negative feedback systems found in precipitation and temperature. This study allows for a multi-timescale representation of hydroclimate variability in France and provides unique insight into the complex nonlinear dynamics of this variability and its predictability

Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what’s the best way to leverage regionalised information?

In this study, we used deep learning models with recurrent structure neural networks to simulate large-scale groundwater level (GWL) fluctuations in northern France. We developed a multi-station collective training for GWL simulations, using both “dynamic” variables (i.e. climatic) and static aquifer characteristics. This large-scale approach offers the possibility of incorporating dynamic and static features to cover more reservoir heterogeneities in the study area. Further, we investigated the performance of relevant feature extraction techniques such as clustering and wavelet transform decomposition, intending to simplify network learning using regionalised information. Several modelling performance tests were conducted. Models specifically trained on different types of GWL, clustered based on the spectral properties of the data, performed significantly better than models trained on the whole dataset. Clustering-based modelling reduces complexity in the training data and targets relevant information more efficiently. Applying multi-station models without prior clustering can lead the models to learn the dominant station behavior preferentially, ignoring unique local variations. In this respect, wavelet pre-processing was found to partially compensate clustering, bringing out common temporal and spectral characteristics shared by all available time series even when these characteristics are “hidden” because of too small amplitude. When employed along with prior clustering, thanks to its capability of capturing essential features across all time scales (high and low), wavelet decomposition used as a pre-processing technique provided significant improvement in model performance, particularly for GWLs dominated by low-frequency variations. This study advances our understanding of GWL simulation using deep learning, highlighting the importance of different model training approaches, the potential of wavelet preprocessing, and the value of incorporating static attributes

Trend and variability in a new, reconstructed streamflow dataset for West and Central Africa, and climatic interactions, 1950 – 2005

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