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The article examines the reasons behind the extreme precipitation in the Cevennes Mountain in France in the fall of 2014. Topics covered include daily precipitation level of 300 millimeters, the influence of moist air from the Mediterranean Sea on the extreme precipitation and the findings that the probability of the Cevennes extreme rainfall tripled since 1950.

Observations show that, in contrast to the Arctic, the area of Antarctic sea ice has increased since 1979. A potential driver of this significant increase relates to the mass loss of the Antarctic ice sheet. Subsurface ocean warming causes basal ice-shelf melt, freshening the surface waters around Antarctica, which leads to increases in sea-ice cover. With climate warming ongoing, future mass-loss rates are projected to accelerate, which has the potential to affect future Antarctic sea-ice trends. Here we investigate to what extent future sea-ice trends are influenced by projected increases in Antarctic freshwater flux due to subsurface melt, using a state-of-the-art global climate model (EC-Earth) in standardized Climate Model Intercomparison Project phase 5 (CMIP5) climate-change simulations. Virtually all CMIP5 models disregard ocean-ice-sheet interactions and project strongly retreating Antarctic sea ice. Applying various freshwater flux scenarios, we find that the additional fresh water significantly offsets the decline in sea-ice area and is even able to reverse the trend in the strongest freshwater forcing scenario that can reasonably be expected, especially in austral winter. The model also simulates decreasing sea surface temperatures (SSTs), with the SST trends exhibiting strong regional variations that largely correspond to regional sea-ice trends. [ABSTRACT FROM AUTHOR]

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, NGOs and companies and relies on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961-2013 are validated using correlation and skill scores. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known ENSO teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis. [ABSTRACT FROM AUTHOR]

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, NGOs and companies and relies on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predict and. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hind casts for the period 1961-2013 are validated using correlation and skill scores. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known ENSO teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis. [ABSTRACT FROM AUTHOR]

Changes in sea ice significantly modulate climate change because of its high reflective and strong insulating nature. In contrast to Arctic sea ice, sea ice surrounding Antarctica has expanded, with record extent in 2010. This ice expansion has previously been attributed to dynamical atmospheric changes that induce atmospheric cooling. Here we show that accelerated basal melting of Antarctic ice shelves is likely to have contributed significantly to sea-ice expansion. Specifically, we present observations indicating that melt water from Antarctica's ice shelves accumulates in a cool and fresh surface layer that shields the surface ocean from the warmer deeper waters that are melting the ice shelves. Simulating these processes in a coupled climate model we find that cool and fresh surface water from ice-shelf melt indeed leads to expanding sea ice in austral autumn and winter. This powerful negative feedback counteracts Southern Hemispheric atmospheric warming. Although changes in atmospheric dynamics most likely govern regional sea-ice trends, our analyses indicate that the overall sea-ice trend is dominated by increased ice-shelf melt. We suggest that cool sea surface temperatures around Antarctica could offset projected snowfall increases in Antarctica, with implications for estimates of future sea-level rise. [ABSTRACT FROM AUTHOR]

This study investigates likely changes in mean and extreme precipitation over southern Africa in response to changes in radiative forcing using an ensemble of global climate models prepared for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Extreme seasonal precipitation is defined in terms of 10-yr return levels obtained by inverting a generalized Pareto distribution fitted to excesses above a predefined high threshold. Both present (control) and future climate precipitation extremes are estimated. The future-to-control climate ratio of 10-yr return levels is then used as an indicator for the likely changes in extreme seasonal precipitation. A Bayesian approach to multimodel ensembling is adopted. The relative weights assigned to each of the model simulations is determined from bias, convergence, and correlation. Using this method, the probable limits of the changes in mean and extreme precipitation are estimated from their posterior distribution. Over the western parts of southern Africa, an increase in the severity of dry extremes parallels a statistically significant decrease in mean precipitation during austral summer months. A notable delay in the onset of the rainy season is found in almost the entire region. An early cessation is found in many parts. This implies a statistically significant shortening of the rainy season. A substantial reduction in moisture influx from the southwestern Indian Ocean during austral spring is projected. This and the preaustral spring moisture deficits are possible mechanisms delaying the rainfall onset in southern Africa. A possible offshore (northeasterly) shift of the tropical–temperate cloud band is consistent with more severe droughts in the southwest of southern Africa and enhanced precipitation farther north in Zambia, Malawi, and northern Mozambique. This study shows that changes in the mean vary on relatively small spatial scales in southern Africa and differ between seasons. Changes in extremes often, but not always, parallel changes in the mean precipitation. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates how climate change affects the daily extreme precipitation events that occur in the autumn in Cévennes mountain range (South of France). We use an ensemble of 10 EURO‐CORDEX model simulations with two horizontal resolutions (0.11° and 0.44°). Those data sets, after pooling all models together, are fitted by stationary generalized extreme value and empirical distributions for several periods to estimate a climate change signal in the tail of distribution of extreme rainfall. We find that the exceedance probability of a 1‐in‐100‐year event in the historical climate has increased by a factor of 2.5 ± 0.8 under the current climate. The results show that higher‐resolution simulations with bias adjustment provide a robust and confident increase in the intensity and likelihood of occurrence of the events in the current climate in comparison with the historical climate. These changes are in agreement with an observations‐based analysis in a previous study. [ABSTRACT FROM AUTHOR]

A new set of climate change scenarios for 2050 for the Netherlands was produced recently. The scenarios span a wide range of possible future climate conditions, and include climate variables that are of interest to a broad user community. The scenario values are constructed by combining output from an ensemble of recent General Climate Model (GCM) simulations, Regional Climate Model (RCM) output, meteorological observations and a touch of expert judgment. For temperature, precipitation, potential evaporation and wind four scenarios are constructed, encompassing ranges of both global mean temperature rise in 2050 and the strength of the response of the dominant atmospheric circulation in the area of interest to global warming. For this particular area, wintertime precipitation is seen to increase between 3.5 and 7% per degree global warming, but mean summertime precipitation shows opposite signs depending on the assumed response of the circulation regime. Annual maximum daily mean wind speed shows small changes compared to the observed (natural) variability of this variable. Sea level rise in the North Sea in 2100 ranges between 35 and 85 cm. Preliminary assessment of the impact of the new scenarios on water management and coastal defence policies indicate that particularly dry summer scenarios and increased intensity of extreme daily precipitation deserves additional attention in the near future. [ABSTRACT FROM AUTHOR]

This study proposes an objective integrated seasonal forecasting system for producing well-calibrated probabilistic rainfall forecasts for South America. The proposed system has two components: (i) an empirical model that uses Pacific and Atlantic sea surface temperature anomalies as predictors for rainfall and (ii) a multimodel system composed of three European coupled ocean–atmosphere models. Three-month lead austral summer rainfall predictions produced by the components of the system are integrated (i.e., combined and calibrated) using a Bayesian forecast assimilation procedure. The skill of empirical, coupled multimodel, and integrated forecasts obtained with forecast assimilation is assessed and compared. The simple coupled multimodel ensemble has a comparable level of skill to that obtained using a simplified empirical approach. As for most regions of the globe, seasonal forecast skill for South America is low. However, when empirical and coupled multimodel predictions are combined and calibrated using forecast assimilation, more skillful integrated forecasts are obtained than with either empirical or coupled multimodel predictions alone. Both the reliability and resolution of the forecasts have been improved by forecast assimilation in several regions of South America. The Tropics and the area of southern Brazil, Uruguay, Paraguay, and northern Argentina have been found to be the two most predictable regions of South America during the austral summer. Skillful rainfall forecasts are generally only possible during El Niño or La Niña years rather than in neutral years. [ABSTRACT FROM AUTHOR]