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Après la sécheresse et la désertification qui ont fortement frappé la Mauritanie dans les années 1970 et 1980, la ville de Nouakchott a connu un développement démographique spectaculaire. En effet, la population de la capitale mauritanienne est passée de 134 704 habitants en 1977 à 899 887 habitants recensés en mars 2013. Depuis 2000, Nouakchott voit sa population augmenter de 125 personnes par semaine ; une croissance absolue jamais atteinte par le passé. La ville s'est donc agrandie considérablement et les nouveaux arrivants ont construit leurs maisons dans des zones marginales sans aménagement préalable. Plusieurs quartiers se sont installés soit dans des zones dépressionnaires à sols salés sous forme de sebkha, soit dans des zones dunaires fortement ensablées.

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

Un ensemble d’indicateurs a été compilé pour vérifier si la fréquence et/ou l’intensité des précipitations a significativement évolué au cours des dernières décennies dans la République Islamique de Mauritanie. Cette étude s’appuie sur des indices nationaux basés les séries quotidiennes de précipitations de neuf stations synoptiques qui couvrent la période 1933-2010. L’analyse des précipitations a été réalisée en calculant annuellement treize indices pluviométriques : le total pluviométrique (PTOT), le nombre total de jours humides (précipitations ≥1 mm, JP), la lame d’eau moyenne précipitée par jour humide (Simple day intensity index, SDII), la pluviométrie maximale enregistrée sur 1 jour (Px1J), la fréquence des événements pluviométriques ≥ 10 mm (P10), ≥ 20 mm (P20), intenses (P95) et extrêmes (P99). Le poids relatif des cinq derniers indices dans le total pluviométrique annuel étant également apprécié. Les résultats montrent que les indices PTOT, JP, P10 et P20 présentent une tendance à la baisse significative sur la période 1933-2010. Dans le même temps, la fréquence des précipitations intenses et extrêmes évolue peu. De facto, la lame d’eau moyenne précipitée par jour humide (SDII) augmente de manière significative. Les résultats obtenus vont dans le sens des conclusions du Groupe d’expert intergouvernemental sur l’évolution du climat (Giec) à l’échelle globale, à savoir des précipitations extrêmes inchangées dans un contexte global de dessiccation. Selon de nombreux modèles, la dégradation pluviométrique pourrait s’amplifier dans les décennies à venir. Dès lors, des stratégies d’adaptation transfrontalières devraient être envisagées d’urgence car le processus de réchauffement de la planète n'est pas susceptible de diminuer dans les prochaines décennies.

Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

We investigate European regional climate change for time periods when the global mean temperature has increased by 1.5 and 2 °C compared to pre-industrial conditions. Results are based on regional downscaling of transient climate change simulations for the 21st century with global climate models (GCMs) from the fifth-phase Coupled Model Intercomparison Project (CMIP5). We use an ensemble of EURO-CORDEX high-resolution regional climate model (RCM) simulations undertaken at a computational grid of 12.5 km horizontal resolution covering Europe. The ensemble consists of a range of RCMs that have been used for downscaling different GCMs under the RCP8.5 forcing scenario. The results indicate considerable near-surface warming already at the lower 1.5 °C of warming. Regional warming exceeds that of the global mean in most parts of Europe, being the strongest in the northernmost parts of Europe in winter and in the southernmost parts of Europe together with parts of Scandinavia in summer. Changes in precipitation, which are less robust than the ones in temperature, include increases in the north and decreases in the south with a borderline that migrates from a northerly position in summer to a southerly one in winter. Some of these changes are already seen at 1.5 °C of warming but are larger and more robust at 2 °C. Changes in near-surface wind speed are associated with a large spread among individual ensemble members at both warming levels. Relatively large areas over the North Atlantic and some parts of the continent show decreasing wind speed while some ocean areas in the far north show increasing wind speed. The changes in temperature, precipitation and wind speed are shown to be modified by changes in mean sea level pressure, indicating a strong relationship with the large-scale circulation and its internal variability on decade-long timescales. By comparing to a larger ensemble of CMIP5 GCMs we find that the RCMs can alter the results, leading either to attenuation or amplification of the climate change signal in the underlying GCMs. We find that the RCMs tend to produce less warming and more precipitation (or less drying) in many areas in both winter and summer.

HELIX - High-End cLimate Impacts and eXtremes

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere - the "global carbon budget" - is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. CO2 emissions from fossil fuels and industry (E-FF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (E-LUC), mainly deforestation, are based on land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S-OCEAN) and terrestrial CO2 sink (S-LAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B-IM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as +/- 1 sigma. For the last decade available (2007-2016), E-FF was 9.4 +/- 0.5 GtC yr(-1), E-LUC 1.3 +/- 0.7 GtC yr(-1), G(ATM) 4.7 +/- 0.1 GtC yr(-1), S-OCEAN 2.4 +/- 0.5 GtC yr(-1), and S-LAND 3.0 +/- 0.8 GtC yr(-1), with a budget imbalance B-IM of 0.6 GtC yr(-1) indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in E-FF was approximately zero and emissions remained at 9.9 +/- 0.5 GtC yr(-1). Also for 2016, E-LUC was 1.3 +/- 0.7 GtC yr(-1), G(ATM) was 6.1 +/- 0.2 GtC yr(-1), S-OCEAN was 2.6 +/- 0.5 GtC yr(-1), and S-LAND was 2.7 +/- 1.0 GtC yr(-1), with a small B-IM of 0.3 GtC. G(ATM) continued to be higher in 2016 compared to the past decade (2007-2016), reflecting in part the high fossil emissions and the small S-LAND consistent with El Nino conditions. The global atmospheric CO2 concentration reached 402.8 +/- 0.1 ppm averaged over 2016. For 2017, preliminary data for the first 6-9 months indicate a renewed growth in E-FF of +2.0% (range of 0.8 to 3.0 %) based on national emissions projections for China, USA, and India, and projections of gross domestic product (GDP) corrected for recent changes in the carbon intensity of the economy for the rest of the world. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quere et al., 2016, 2015b, a, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017 (GCP, 2017).

The JULES-crop model (Osborne et al., 2015) is a parametrisation of crops within the Joint UK Land Environment Simulator (JULES), which aims to simulate both the impact of weather and climate on crop productivity and the impact of croplands on weather and climate. In this evaluation paper, observations of maize at three FLUXNET sites in Nebraska (US-Ne1, US-Ne2 and US-Ne3) are used to test model assumptions and make appropriate input parameter choices. JULES runs are performed for the irrigated sites (US-Ne1 and US-Ne2) both with the crop model switched off (prescribing leaf area index (LAI) and canopy height) and with the crop model switched on. These are compared against GPP and carbon pool FLUXNET observations. We use the results to point to future priorities for model development and describe how our methodology can be adapted to set up model runs for other sites and crop varieties.