• Energy Research

Precipitable Water Vapor (PWV) has strong relations with extreme rainfall and their increments in a future warming world are typically associated. It is, however, unclear how different climatic conditions and orographic effects modulate these changes in the equatorial region. We investigate PWV and heavy rainfall over Ethiopia using Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX). An in-depth RCM evaluation is first provided by comparing the modeled annual cycle of PWV with those obtained from Global Positioning System observations and reanalysis, and, by investigating the changes in PWV before and after a heavy-rainfall event. Two characteristic timescales are found for the buildup and decline of PWV before and after such events: a short of about 2 days and a long timescale extending beyond ten days. Overall RCMs reproduce well the PWV annual cycle but substantial biases appear for some models in the very dry and in the tropical wet climate zones. CORDEX models simulate well the peak in PWV anomalies at the day of a heavy-rainfall event but strongly overestimate the timescales of buildup and decline. Future scenarios all point towards a PWV increase (up to 40%) for end-of-the-century RCP8.5 with limited spatial and seasonal variations. PWV changes align with near-surface temperature changes at a rate of 7.7% per degree warming. Changes in daily heavy rainfall, on the other hand, are lower especially in northwestern Ethiopia in the far future (RCP8.5), potentially caused by an overall drying.

Prompted with the ongoing and projected climate change, a wide range of cities have committed, not only to mitigate greenhouse gas emissions but also to implement different climate change adaptation measures. These measures serve to ensure the well-being of the urban population. In practice, however, the planning of realistic adaptation measures is a complex process. Prior to starting such endeavor, it may therefore be useful to explore the maximum potential benefit that can be gained through adaptation measures. In this work, simple, extreme yet realistic adaptation measures are proposed in terms of changes in albedo and vegetation fraction. The impact of these land-use scenarios is explored by use of the land surface model SURFEX on the summer climate in terms of heat waves and the urban heat island for the city of Brussels. This is done for different periods in the future using the greenhouse gas scenario RCP8.5.

This repository is linked to the paper "CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia" submitted to Annals of Forest Science and written by Louis DE WERGIFOSSE (corresponding author), Frédéric ANDRE, Hugues GOOSSE, Steven CALUWAERTS, Lesley DE CRUZ, Rozemien DE TROCH, Bert VAN SCHAEYBROECK and Mathieu JONARD. The files stored in the repository are the input files that should be used in the model HETEROFOR to retrieve the results displayed in the study and the corresponding results themselves. The source code of the model HETEROFOR can be freely accessed and downloaded (https://doi.org/10.5281/zenodo.3591348). Additional information on the model can be found in the following description papers: Jonard et al., 2020 (https://doi.org/10.5194/gmd-13-905-2020) and de Wergifosse et al., 2020 (https://doi.org/10.5194/gmd-13-1459-2020). The repository contains three directories. The first (HETEROFOR_input_files) comprises the additional files to those in the model repository presented in the previous paragraph needed to run the model for the purpose of this study. The second directory (Simulation_outputs_raw) contains the data directly provided by the model without any processing. The third directory (Simulation_outputs_raw) includes the model outputs after processing. The directory "HETEROFOR_input_files" is constituted of two directories called "Climate_files" and "Stand_files". "Climate_files" is subdivided in three sub-directories. Sub-directory "Original_downscaled_CORDEX_timeseries" contains the climate projections of the four sites and scenarios described in the study. These downscaled timeseries have been produced by the Royal Meteorological Institute of Belgium under the program CORDEX.be, which is part of EURO-CORDEX. A bias correction has been further applied to these climate timeseries that are stored in the "Bias_corrected_timeseries" sub-directory. The files of these two sub-directories should be used in HETEROFOR as "Meteorological data" input files. The "CO2_concentrations" sub-directory includes the yearly averaged projected concentrations for the three RCP scenarios described in the paper. In HETEROFOR, they should be put as input in the "Atmospheric CO2 concentration" part after selecting the option "Variable over time". The second directory called "Stand files" contain the six inventory files described in the study for which a thinning has been applied. They should be used in HETEROFOR as "Inventory data" input files. The directory "Simulation_outputs_raw" is divided similarly to the study into two simulation experiments. The "First simulation experiment" directory is further subdivided into constant and time-dependent CO2 concentrations like in the study and contains one file for the regular modality and one for the thinning modality. All the files are constructed the same way with, for each tree and site (or stand, soil and climate), annual values of Net Primary Production (NPP) in kg of carbon, transpiration and potential transpiration in L under the different climate scenarios. In addition, the "Phenology" directory contains, for each day and under all climate scenarios, the green proportion (proportion of green leaves comprised between 0 and 1) for the two tree species considered in the study (Common oak and European beech). Finally, the directory "Simulation_outputs_processed" is constructed similarly to "Simulation_outputs_raw" but all the data are integrated in one file at the yearly time step. However, the units change with the NPP expressed in gC/m2 and transpiration and potential transpiration in mm (or L/m2) while the vegetation period is averaged according to the percentage of species occurrence in the different stands. For more information concerning this repository or the study, please do not hesitate to contact Louis DE WERGIFOSSE (louis.dewergifosse@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).

Circulatory-system diseases (CSDs) are responsible for 50-60% of all deaths in Romania. Due to its continental climate, with cold winters and very warm summers, there is a strong temperature dependence of the CSD mortality. Additionally, within its capital Bucharest, the urban heat island (UHI) is expected to enhance (reduce) heat (cold)-related mortality. Using distributed lag non-linear models, we establish the relation between temperature and CSD mortality in Bucharest and its surroundings. A striking finding is the strong temperature-related response to high urban temperatures of women in comparison with men from the total CSDs mortality. In the present climate, estimates of the CSDs attributable fraction (AF) of mortality at high temperatures is about 66% higher in Bucharest than in its rural surroundings for men, while it is about 100% times higher for women. Additionally, the AF in urban areas is also significantly higher for elderly people, and for those with hypertensive and cerebrovascular diseases than in the rural surroundings. On the other hand, in rural areas, men but especially women are currently more vulnerable with respect to low temperatures than in the urban environment. In order to project future thermal-related mortality, we have used five bias-corrected climate projections from regional circulation models under two climate-change scenarios, RCP4.5 and RCP8.5. Analysis of the temperature-mortality associations for future climate reveals the strongest signal under the scenario RCP8.5 for women, elderly people as well as for groups with hypertensive and cerebrovascular diseases. The net AF increase is much larger in urban agglomeration for women (8.2 times higher than in rural surroundings) and elderly people (8.5 times higher than in rural surroundings). However, our estimates of thermal attributable mortality are most likely underestimated due to the poor representation of UHI and future demography.

Convection-permitting models (CPMs) have been proven successful in simulating extreme precipitation statistics. However, when such models are used to study climate change, contrasting sensitivities with respect to resolution (CPM vs. models with parameterized convection) are found for different parts of the world. In this study, we explore to which extent this contrasting sensitivity is due to the specific characteristics of the model or due to the characteristics of the region. Therefore, we examine the results of 360 years of climate model data from two different climate models (COSMO-CLM driven by EC-EARTH and ALARO-0 driven by CNRM ARPEGE) both at convection-permitting scale (CPS, ~ 3 km resolution) and non-convection-permitting scale (non-CPS, 12.5 km resolution) over two distinct regions (flatland vs. hilly region) in Belgium. We found that both models show an overall consistent scale-dependency of the future increase in hourly extreme precipitation for day-time. More specifically, both models yield a larger discrepancy in the day-time climate change signal between CPS and non-CPS for extreme precipitation over flatland (Flanders) than for orographically induced extreme precipitation (Ardennes). This result is interesting, since both RCMs are very different (e.g., in terms of model physics and driving GCM) and use very different ways to represent deep convection processes. Despite those model differences, the scale-dependency of projected precipitation extremes is surprisingly similar in both models, suggesting that the this scale-dependency is more dependent on the characteristics of the region, than on the model used.

Description This dataset contains a set of 13 climatological variables (Variable, VariableName) at a spatial resolution of 1x1km for Europe (nx = 13147, ny = 6071) for historical (ClimatePeriod) and future climate conditions. These variables are a subset of the so-called bioclimatic variables that are often part of global gridded datasets (e.g. WorldClim, CHELSA) that have been specifically developed for species distribution modelling and ecological applications. The climatological data correspond to 35-year (Startyear_Endyear = 1971_2005) and 30-year (Startyear_Endyear = 2041_2070) mean values representing respectively historical and future climate conditions. To account for the future climate conditions, three possible emission scenarios of greenhouse gases as defined by the Intergovernmental Panel on Climate Change (IPCC) are used (ClimatePeriod = rcp26, rcp45, rcp85). The complete set of variables (var[1-13]) for which historical and future climate data layers are produced are given below. The source data for the climate layers were assembled from the EURO-CORDEX archive (Kotlarski et al., 2014). More specifically, we have used the regional climate model simulations for Europe at a spatial resolution of 12.5x12.5km on which a three-step statistical downscaling approach has been applied: Processing (averaging, totals, …) of all available time series of the EURO-CORDEX model experiments (ClimatePeriod = evaluation, historical, rcp) for the climatological variables. Interpolation of the data layers from the 12.5x12.5km EURO-CORDEX grid to a 1x1km spatial CHELSA (Karger et al., 2017) reference grid (see files lat_1km.csv and lon_1km.csv). Calculate differences between the 1x1km-interpolated variables (Variable = only for var[1-9]) from the evaluation model experiments (or ClimatePeriod) and the corresponding reference bioclimatic CHELSA variables. In order to account for possible biases present in the EURO-CORDEX climate models, these differences (or biases) are then subtracted from the respective 1x1-km-interpolated variables for the historical and rcp model experiments (ClimatePeriod). The dimensions of the 1x1km grid (excl. the first row and column): y-dimension = number of columns = 6071 x-dimension = number of rows = 13147 The longitudes and latitudes of respectively the southwest and northeast corner of the grid are: longitude -44.592; latitude 21.991 (southwest corner) longitude 64.967; latitude 72.583 (northeast corner) The climatological variables are used as input data for the species distribution modelling of Invasive Alien Species for the Tracking Invasive Alien Species (TrIAS) project. Variables Variable (VariableName): Unit var1 (AnnualMeanTemperature): °C var2 (AnnualAmountPrecipitation): mm year-1 var3 (AnnualVariationPrecipitation): coefficient of variation var4 (AnnualVariationTemperature): stdev var5 (MaximumTemperatureWarmestMonth): °C var6 (MinimumTemperatureColdestMonth): °C var7 (TemperatureAnnualRange): °C var8 (PrecipitationWettestMonth): mm var9 (PrecipitationDriestMonth): mm var10 (30yrMeanAnnualCumulatedGDDAbove5degreesC): °C days var11 (AnnualMeanPotentialEvapotranspiration): mm day-1 var12 (AnnualMeanSolarRadiation): W m-2 var13 (AnnualVariationSolarRadiation): stdev Files varX_VariableName_ClimatePeriod_Startyear_Endyear.csv: climatological data layers for the 13 variables listed above lon_1km.csv: longitudes for the 1x1km grid lat_1km.csv: latitudes for the 1x1km grid {"references": ["Kotlarski et al. (2014). Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. https://doi.org/10.5194/gmd-7-1297-2014", "Karger et al. (2017). Climatologies at high resolution for the earth's land surface areas. https://doi.org/10.1038/sdata.2017.122"]} This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS). We also acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5.

AbstractThe Clausius‐Clapeyron (CC) relation expresses the exponential increase in the moisture‐holding capacity of air of approximately 7%/°C. Earlier studies show that extreme hourly precipitation increases with daily mean temperature, consistent with the CC relation. Recent studies at specific locations found that for temperatures higher than around 12 °C, hourly precipitation extremes scale at rates higher than the CC scaling, a phenomenon that is often referred to as “super‐CC scaling.” These scalings are typically estimated by collecting rainfall data in temperature bins, followed by a linear fit or a visual inspection of the precipitation quantiles in each bin. In this study, a piecewise linear quantile regression model is presented for a more flexible, and robust estimation of the scaling parameters, and their associated uncertainties. Moreover, we use associated information criteria to prove statistically whether or not a pronounced super‐CC scaling exists. The techniques were tested on stochastically simulated data and, when applied to hourly station data across Western Europe and Scandinavia, revealed large uncertainties in the scaling rates. Finally, goodness‐of‐fit measures indicated that the dew point temperature is a better scaling predictor than temperature.

This study aimed to simulate oak and beech forest growth under various scenarios of climate change and to evaluate how the forest response depends on site properties and particularly on stand characteristics using the individual process-based model HETEROFOR. First, this model was evaluated on a wide range of site conditions. We used data from 36 long-term forest monitoring plots to initialize, calibrate, and evaluate HETEROFOR. This evaluation showed that HETEROFOR predicts individual tree radial growth and height increment reasonably well under different growing conditions when evaluated on independent sites. In our simulations under constant CO2 concentration ([CO2]cst) for the 2071-2100 period, climate change induced a moderate net primary production (NPP) gain in continental and mountainous zones and no change in the oceanic zone. The NPP changes were negatively affected by air temperature during the vegetation period and by the annual rainfall decrease. To a lower extent, they were influenced by soil extractable water reserve and stand characteristics. These NPP changes were positively affected by longer vegetation periods and negatively by drought for beech and larger autotrophic respiration costs for oak. For both species, the NPP gain was much larger with rising CO2 concentration ([CO2]var) mainly due to the CO2 fertilisation effect. Even if the species composition and structure had a limited influence on the forest response to climate change, they explained a large part of the NPP variability (44% and 34% for [CO2]cst and [CO2]var, respectively) compared to the climate change scenario (5% and 29%) and the inter-annual climate variability (20% and 16%). This gives the forester the possibility to act on the productivity of broadleaved forests and prepare them for possible adverse effects of climate change by reinforcing their resilience.