Ensemble Of Regional Climate Models Research Articles

Droughts projection over the Niger and Volta River basins of West Africa at specific global warming levels

AbstractThis study investigates possible impacts of four global warming levels (GWLs: GWL1.5, GWL2.0, GWL2.5, and GWL3.0) on drought characteristics over Niger River basin (NRB) and Volta River basin (VRB). Two drought indices—Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI)—were employed in characterizing droughts in 20 multi‐model simulation outputs from the Coordinated Regional Climate Downscaling Experiment (CORDEX). The performance of the simulation in reproducing basic hydro‐climatological features and severe drought characteristics (i.e., magnitude and frequency) in the basins were evaluated. The projected changes in the future drought frequency were quantified and compared under the four GWLs for two climate forcing scenarios (RCP8.5 and RCP4.5). The regional climate model (RCM) ensemble gives a realistic simulation of historical hydro‐climatological variables needed to calculate the drought indices. With SPEI, the simulation ensemble projects an increase in the magnitude and frequency of severe droughts over both basins (NRB and VRB) at all GWLs, but the increase, which grows with the GWLs, is higher over NRB than over VRB. More than 75% of the simulations agree on the projected increase at GWL1.5 and all simulations agree on the increase at higher GWLs. With SPI, the projected changes in severe drought is weaker and the magnitude remains the same at all GWLs, suggesting that SPI projection may underestimate impacts of the GWLs on the intensity and severity of future drought. The results of this study have application in mitigating impact of global warming on future drought risk over the regional water systems.

Changes in climate extremes over Bangladesh at 1.5 °C, 2 °C, and 4 °C of global warming with high-resolution regional climate modeling

Global mean temperature is continuously rising and causing changes in the extreme climatic events. Following these changes, climate extremes—the rare events that reside in the tail of the distribution of essential climate variables—are expected to be further intensified, more frequent, and prolonged. Changes in extremes would vary spatially from region to region and thus need regional assessment for future adaptation planning. This study assesses the climate extremes at 1.5 °C, 2 °C, and 4 °C of global warming over Bangladesh which is one of the most vulnerable countries to climate change. Future changes in climate extremes are assessed using a subset of extreme temperature and precipitation indices devised by Expert Team on Climate Change Detection and Indices (ETCCDI). Projections from high-resolution regional climate model ensembles are used to derive extreme climate indices. Our analysis shows overall upward changes in warm indices and downward changes in cold indices at higher specific warming levels. We found a much higher increase in extreme rainfall compared with the annual total rainfall. Increasing variability of rainfall indices is found at higher specific warming levels. Our analysis also suggests a higher increase of temperature during the winter and post-monsoon seasons, as well as an increase in the 1-day and 5-day maximum rainfall during pre- and post-monsoon seasons. A significant regional difference is found in almost all the rainfall indices. The forecasted increase of extreme rainfall and consecutive dry days (CDD) over the northeast region indicates a possibility of an increase of flash floods in the future. Moreover, the increase in the extreme rainfall over the southeast region will increase the chances of landslides.

Climate change impacts on viticulture in Croatia: Viticultural zoning and future potential

AbstractSince changes in temperature and precipitation have different effects on (a) all developmental stages of grapevines in most of the wine regions worldwide (i.e., on their phenological characteristics) and (b) different varieties, a comprehensive database of bioclimatic indices has been calculated and analysed for Croatian wine producing regions. The database consists of the average growing season temperature, growing degree‐days, Huglin index, dryness index and cool night index that are based on all available meteorological measurements as well as the outputs of regional climate models (RCMs) from the EURO‐CORDEX database. The horizontal grid spacing of 0.11° from the RCM ensembles enabled a fine‐scale determination of bioclimatic indices for the present and future climate in Croatia. In addition, statistical analyses (standard statistical parameters and Bayesian method) were carried out to examine trends in sugar content, total acidity and date of harvest. Calculations were performed for the present and future climate on the basis of data from seven selected vineyards/wineries and four varieties (‘Graševina’, ‘Plavac mali’, ‘Chardonnay’ and ‘Merlot’). The results show whether the part of Croatia that is suitable for grape cultivation in the present climate will continue to be favourable in the future within the Mediterranean area. In general, projections suggest further warming and drying of the climate in Croatia and an earlier harvest, with some variations among varieties that show latitude dependence. Projections for the future climate also suggest that the existing viticultural zoning will be much less adequate for the Croatian territory because it reduces the economically sustainable production of wine in certain areas.

Independence of Future Changes of River Runoff in Europe from the Pathway to Global Warming

The outcomes of the 2015 Paris Agreement triggered a number of climate impact assessments, such as for floods and droughts, to focus on future time frames corresponding to the years of reaching specific levels of global warming. Yet, the links between the timing of the warming levels and the corresponding greenhouse gas concentration pathways to reach them remain poorly understood. To address this gap, we compared projected changes of annual mean, extreme high, and extreme low river discharges in Europe at 1.5 °C and 2 °C under Representative Concentration Pathways RCP8.5 and RCP4.5 from an ensemble of regional climate model (RCM) simulations. The statistical significance of the difference between the two scenarios for both warming levels was then evaluated. The results show that in the majority of Europe (>95% of the surface area for the annual mean discharge, >98% for high and low extremes), the changes projected in the two pathways were statistically indistinguishable. These results suggest that in studies of changes at global warming levels, the projections of the two pathways can be merged into a single ensemble without major loss of information. With regard to the uncertainty of the unified ensemble, the findings show that the projected changes of annual mean, extreme high, and extreme low river discharge were statistically significant in large portions of Europe.

Evolution of Mediterranean Sea water properties under climate change scenarios in the Med-CORDEX ensemble

Twenty-first century projections for the Mediterranean water properties have been analyzed using the largest ensemble of regional climate models (RCMs) available up to now, the Med-CORDEX ensemble. It is comprised by 25 simulations, 10 historical and 15 scenario projections, from which 11 are ocean–atmosphere coupled runs and 4 are ocean forced simulations. Three different emissions scenarios are considered: RCP8.5, RCP4.5 and RCP2.6. All the simulations agree in projecting a warming across the entire Mediterranean basin by the end of the century as a result of the decrease of heat losses to the atmosphere through the sea surface and an increase in the net heat input through the Strait of Gibraltar. The warming will affect the whole water column with higher anomalies in the upper layer. The temperature change projected by the end of the century ranges between 0.81 and 3.71 °C in the upper layer (0–150 m), between 0.82 and 2.97 °C in the intermediate layer (150–600 m) and between 0.15 and 0.18 °C in the deep layer (600 m—bottom). The intensity of the warming is strongly dependent on the choice of emission scenario and, in second order, on the choice of Global Circulation Model (GCM) used to force the RCM. On the other hand, the local structures reproduced by each simulation are mainly determined by the regional model and not by the scenario or the global model. The salinity also increases in all the simulation due to the increase of the freshwater deficit (i.e. the excess of evaporation over precipitation and river runoff) and the related increase in the net salt transport at the Gibraltar Strait. However, in the upper layer this process can be damped or enhanced depending upon the characteristics of the inflowing waters from the Atlantic. This, in turn, depends on the evolution of salinity in the Northeast Atlantic projected by the GCM. Thus a clear zonal gradient is found in most simulations with large positive salinity anomalies in the eastern basin and a freshening of the upper layer of the western basin in most simulations. The salinity changes projected for the whole basin range between 0 and 0.34 psu in the upper layer, between 0.08 and 0.37 psu in the intermediate layer and between − 0.05 and 0.33 in the deep layer. These changes in the temperature and salinity modify in turn the characteristics of the main water masses as the new waters become saltier, warmer and less dense along the twenty-first century. There is a model consensus that the intensity of the deep water formation in the Gulf of Lions is expected to decrease in the future. The rate of decrease remains however very uncertain depending on the scenario and model chosen. At the contrary, there is no model consensus concerning the change in the intensity of the deep water formation in the Adriatic Sea and in the Aegean Sea, although most models also point to a reduction.

The effect of modeling choices on updating intensity-duration-frequency curves and stormwater infrastructure designs for climate change

Intensity-duration-frequency (IDF) curves, commonly used in stormwater infrastructure design to represent characteristics of extreme rainfall, are gradually being updated to reflect expected changes in rainfall under climate change. The modeling choices used for updating lead to large uncertainties; however, it is unclear how much these uncertainties affect the design and cost of stormwater systems. This study investigates how the choice of spatial resolution of the regional climate model (RCM) ensemble and the spatial adjustment technique affect climate-corrected IDF curves and resulting stormwater infrastructure designs in 34 US cities for the period 2020 to 2099. In most cities, IDF values are significantly different between three spatial adjustment techniques and two RCM spatial resolutions. These differences have the potential to alter the size of stormwater systems designed using these choices and affect the results of climate impact modeling more broadly. The largest change in the engineering decision results when the design storm is selected from the upper bounds of the uncertainty distribution of the IDF curve, which changes the stormwater pipe design size by five increments in some cases, nearly doubling the cost. State and local agencies can help reduce some of this variability by setting guidelines, such as avoiding the use of the upper bound of the future uncertainty range as a design storm and instead accounting for uncertainty by tracking infrastructure performance over time and preparing for adaptation using a resilience plan.

Climate change impacts on summer flood frequencies in two mountainous catchments in China and Switzerland

Abstract Flood events are difficult to characterize if available observation records are shorter than the recurrence intervals, and the non-stationarity of the climate adds additional uncertainty. In this study, we use a hydrological model coupled with a stochastic weather generator to simulate the summer flood regime in two mountainous catchments located in China and Switzerland. The models are set up with hourly data from only 10–20 years of observations but are successfully validated against 30–40-year long records of flood frequencies and magnitudes. To assess the climate change impacts on flood frequencies, we re-calibrate the weather generator with the climate statistics for 2021–2050 obtained from ensembles of bias-corrected regional climate models. Across all assessed return periods (10–100 years) and two emission scenarios, nearly all model chains indicate an intensification of flood extremes. According to the ensemble averages, the potential flood magnitudes increase by more than 30% in both catchments. The unambiguousness of the results is remarkable and can be explained by three factors rarely combined in previous studies: reduced statistical uncertainty due to a stochastic modelling approach, hourly time steps and the focus on headwater catchments where local topography and convective storms are causing runoff extremes within a confined area.

Preliminary long-term predictive modelling of groundwater resources in view of climate change – a case study from eastern Poland

The paper discusses an effective and simple approach to preliminary long-term predictive modelling to the estimation of the effects of predicted climate change on groundwater resources in aquifer recharged by rain infiltration for the end of the 21st century. The groundwater resources in an analysed catchment were assessed based on predicted precipitation and air temperature from seven climate change projections in two sets of the Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission scenarios (SRES), associated with various regional climate models (RCM). The predicted groundwater resources were obtained by diminishing the predicted renewable resources by recent environmental flows in a river dewatering the catchment. The predicted reserve was assessed taking into account the forecasted groundwater abstraction. The study revealed that the predicted groundwater reserve depended on the assumed prediction model, based on particular SRES and RCM ensembles. The groundwater resources in the study area at the close of the 21st century are expected to considerably decrease when compared to the reference period 1971–1990. The future groundwater reserve assessed by the climate change model based on IPCC emission scenario B2 connected with the regional climate model HIRHAM and regional climate model RCAO, may decrease when compared to the reference period, by 51 or 92%, respectively. In view of the IPCC emission scenario A2 assumptions, this preliminary predictive modelling shows that there may be a shortage of groundwater resources in the analysed catchment in the final decades of the 21st century.

Extreme sea levels in the Baltic Sea under climate change scenarios – Part 1: Model validation and sensitivity

Abstract. We analyze extreme sea levels (ESLs) and related uncertainty in an ensemble of regional climate change scenarios for the Baltic Sea. The ERA-40 reanalysis and five Coupled Model Intercomparison Project phase 5 (CMIP5) global general circulation models (GCMs) have been dynamically downscaled with the coupled atmosphere–ice–ocean model RCA4-NEMO (Rossby Centre regional atmospheric model version 4 – Nucleus for European Modelling of the Ocean). The 100-year return levels along the Swedish coast in the ERA-40 hindcast are within the 95 % confidence limits of the observational estimates, except those on the west coast. The ensemble mean of the 100-year return levels averaged over the five GCMs shows biases of less than 10 cm. A series of sensitivity studies explores how the choice of different parameterizations, open boundary conditions and atmospheric forcing affects the estimates of 100-year return levels. A small ensemble of different regional climate models (RCMs) forced with ERA-40 shows the highest uncertainty in ESLs in the southwestern Baltic Sea and in the northeastern part of the Bothnian Bay. Some regions like the Skagerrak, Gulf of Finland and Gulf of Riga are sensitive to the choice of the RCM. A second ensemble of one RCM forced with different GCMs uncovers a lower sensitivity of ESLs against the variance introduced by different GCMs. The uncertainty in the estimates of 100-year return levels introduced by GCMs ranges from 20 to 40 cm at different stations and includes the estimates based on observations. It is of similar size to the 95 % confidence limits of 100-year return levels from tide gauge records.

Climate futures for Western Nepal based on regional climate models in the CORDEX‐SA

AbstractWith the objective to provide a basis for regional climate models (RCMs) selection and ensemble generation for climate impact assessments, we perform the first ever analysis of climate projections for Western Nepal from 19 RCMs in the Coordinated Regional Downscaling Experiment for South Asia (CORDEX‐SA). Using the climate futures (CF) framework, projected changes in annual total precipitation and average minimum/maximum temperature from the RCMs are classified into 18 CF matrices for two representative concentration pathways (RCPs: 4.5/8.5), three future time frames (2021–2045/2046–2070/2071–2095), three geographic regions (mountains/hills/plains) and three representative CF (low‐risk/consensus/high‐risk). Ten plausible CF scenario ensembles were identified to assess future water availability in Karnali basin, the headwaters of the Ganges. Comparison of projections for the three regions with literature shows that spatial disaggregation possible using RCMs is important, as local values are often higher with higher variability than values for South Asia. Characterization of future climate using raw and bias‐corrected data shows that RCM projections vary most between mountain and Tarai plains with increasing divergence for higher future and RCPs. Warmer temperatures, prolonged monsoon and sporadic rain events even in drier months are likely across all regions. Highest fluctuations in precipitation are projected for the hills and plains while highest changes in temperature are projected for the mountains. Trends in change in annual average discharge for the scenarios vary across the basin with both precipitation and temperature change influencing the hydrological cycle. CF matrices provide an accessible and simplified basis to systematically generate application‐specific plausible climate scenario ensembles from all available RCMs for a rigorous impact assessment.

User tailored results of a regional climate model ensemble to plan adaption to the changing climate in Germany

Abstract. In the German regional climate modeling project ReKliEs-De the existing EURO-CORDEX simulations have been systematically complemented by new simulations to derive more robust ranges of possible future climate change. The focus of the project lay on user tailored results, which are required for the planning of measures to adapt to the changing climate. Changes in temperature and precipitation indices are calculated from a multi model ensemble for the end of the 21st century. The results for the mitigation scenario RCP2.6 are compared to the results of the “business as usual” scenario RCP8.5. Averaged over Germany the increase of mean annual temperature and of the number of summer days will be around 3 times higher for RCP8.5 than for RCP2.6. In summer, the increase of dry days could be twice as high in RCP8.5 compared to RCP2.6.

Assessment of multi-model climate projections of water resources over South America CORDEX domain

Future climate projections focusing on precipitation and water resource trends over South America (SA) are investigated using two ensembles. One of them is composed of three global climate models (GCMs), and the other of eight regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX). The present (1970–2005) and the future (2006–2100) climate trends are analyzed for representative pathway scenarios 4.5 (RCP4.5) and 8.5 (RCP8.5). For the most pessimistic scenario (RCP8.5), trends in water resources are assessed considering the terrestrial branch of the hydrologic cycle by analyzing the precipitation minus evapotranspiration (P-ET). For the present climate, RCMs added value to the GCMs in simulating more realistic precipitation fields in several regions. GCMs and RCMs project, in general, the same precipitation change signal for the end of the 21st century over SA, which is stronger in RCP8.5 than in RCP4.5. For RCP8.5 in most regions, GCMs and RCMs ensembles have the same precipitation trend signal, but a great spread between the ensemble members, which is greater in austral summer than winter, can be noted. In winter a negative trend in rainfall in most members and regions predominates. At the end of the 21st century, relative changes in rainfall in RCP8.5 are in the range of +14% (over northeastern Brazil in summer) to − 36% (over the Andes Mountains in winter). In RCP8.5, the ensembles project an increase in air temperature with a similar magnitude, while in RCP4.5 the trends are weaker. For air temperature, there is small spread between members, and the positive trend is statistically significant for all ensemble members in the RCP8.5 scenario. In terms of water resources, on an annual scale, for RCP8.5 the RCM ensemble projects a larger area with wetter conditions in the future than GCMs. Regionally, it is expected a decrease in water availability in the Amazon basin and an increase over northeast Brazil and southeast SA during the summer. In other regions (northern Amazon, the Andes Mountains and Patagonia) the ensembles indicate drier conditions in the future winter, except in southern Amazon. It is expected that such information could be useful for devising adaptation and mitigation policies due to climate change over the SA.

The impact of climate change on wind power abundance and variability in China

The abundance of wind resources and its variability are the essential character for its exploitability. A multi-ensemble of regional climate model projections was used to explore the change of wind resources and its variability of mainland China on multiple time-scales. We found the wind resources from an ensemble of regional climate models are highly consistent with previous studies based on station observations, reanalysis data and model simulations. The most abundant wind resources are located in Tibet Plateau, Hexi Corridor, Inner Mongolia and part of the three provinces of north-east. In these regions, wind resources show slightly decrease (3–4%) under Representative Concentration Pathway 4.5 and 8.5 scenarios, but this decrease is negligible with respect to the abundance of wind resources. While wind power in Tibet Plateau is rich, a high variability of wind power on monthly time-scale was identified. Hence, a caution should be taken while exploiting wind power in this region as larger variability on monthly time-scale require higher backup energy to secure electrical safety. The present study is not conclusive about causes of such wind shift to south of power and its increasing variability, for further studies should be carried out to explore the mechanisms of wind power change.

Projecting Wet Season Rainfall Extremes Using Regional Climate Models Ensemble and the Advanced Delta Change Model: Impact on the Streamflow Peaks in Mkurumudzi Catchment, Kenya

Each year, many African countries experience natural hazards such as floods and, because of their low adaptative capabilities, they hardly have the means to face the consequences, and therefore suffer huge economic losses. Extreme rainfall plays a key role in the occurrence of these hazards. Therefore, climate projection studies should focus more on extremes in order to provide a wider range of future scenarios of extremes which can aid policy decision making in African societies. Some researchers have attempted to analyze climate extremes through indices reflecting extremes in climate variables such as rainfall. However, it is difficult to assess impacts on streamflow based on these indices alone, as most hydrological models require daily data as inputs. Others have analyzed climate projections through general circulation models (GCMs) but have found their resolution too coarse for regional studies. Dynamic downscaling using regional climate models (RCMs) seem to address the limitation of GCMs, although RCMs might still lack accuracy due to the fact that they also contain biases that need to be eliminated. Given these limitations, the current study combined both dynamic and statistical downscaling methods to correct biases and improve the reproduction of high extremes by the models. This study’s aim was to analyze extreme high flows under the projection of extreme wet rainfall for the horizon of 2041 of a Kenyan South Coast catchment. The advanced delta change (ADC) method was applied on observed data (1982–2005), control (1982–2005) and near future (2018–2041) from an ensemble mean of multiple regional climate models (RCMs). The created future daily rainfall time series was introduced in the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) hydrological model and the generated future flow were compared to the baseline flow at the gaging station 3KD06, where the observed flow was available. The findings suggested that in the study area, the RCMs, bias corrected by the ADC method, projected an increase in rainfall wet extremes in the first rainy season of the year MAMJ (March–April–May–June) and a decrease in the second rainy season OND (October–November–December). The changes in rainfall extremes, induced a similar change pattern in streamflow extremes at the gaging station 3KD06, meaning that an increase/decrease in rainfall extremes generated an increase/decrease in the streamflow extremes. Due to lack of long-term good quality data, the researchers decided to perform a frequency analysis for up to a 50 year return period in order to assess the changes induced by the ADC method. After getting a longer data series, further analysis could be done to forecast the maximum flow to up to 1000 years, which could serve as design flow for different infrastructure.

Impact of Reference Dataset Selection on RCM Evaluation, Bias Correction, and Resulting Climate Change Signals of Precipitation

Abstract Gridded datasets of precipitation are of great importance to evaluate recent climate models and are frequently applied to select a subset of available models. As climate models are still prone to biases on the regional scale, gridded datasets are also essential to correct or adjust these biases. Various studies revealed considerable differences, that is, observational uncertainty, in the available gridded datasets of precipitation, especially over complex terrain. This study focuses on the impacts of observational uncertainty on the evaluation, selection, and bias correction of 15 regional climate model (RCM) simulations provided through the EURO-CORDEX initiative over the alpine Adige catchment located in northern Italy. Nine reference datasets originating from observations, reanalysis, and remote sensing are applied to evaluate the performance of RCMs and select a subset based on validity. These reference datasets are then applied to bias correct the RCM ensemble using a standard quantile mapping method, and the resulting changes in the projections are assessed. The presented results show a selection of similar RCMs, indicating that observational uncertainty is lower than model uncertainty. The influence of the choice of the reference dataset on bias correction is negligible for the climate change signals. Small differences in projected change signals can be attributed to model selection. As expected, the choice of the reference dataset strongly influences future projections of precipitation even more pronounced for the extremes. The findings of this study highlight the need to account for observational uncertainty for bias correction of RCM simulations for impact modeling studies.

Large ensemble flood loss modelling and uncertainty assessment for future climate conditions for a Swiss pre-alpine catchment.

Information on possible changes in future flood risk is essential for successful adaptation planning and risk management. However, various sources of uncertainty arise along the model chains used for the assessment of flood risk under climate change. Knowledge on the importance of these different sources of uncertainty can help to design future assessments of flood risk, and to identify areas of focus for further research that aims to reduce existing uncertainties. Here we investigate the role of four sources of epistemic uncertainty affecting the estimation of flood loss for changed climate conditions for a meso-scale, pre-alpine catchment. These are: the choice of a scenario-neutral method, climate projection uncertainty, hydrological model parameter sets, and the choice of the vulnerability function. To efficiently simulate a large number of loss estimates, a surrogate inundation model was used. 46,500 loss estimates were selected according to the change in annual mean precipitation and temperature of an ensemble of regional climate models, and considered for the attribution of uncertainty. Large uncertainty was found in the estimated loss for a 100-year flood event with losses ranging from a decrease of loss compared to estimations for present day climate, to more than a 7-fold increase. The choice of the vulnerability function was identified as the most important source of uncertainty explaining almost half of the variance in the estimates. However, uncertainty related to estimating floods for changed climate conditions contributed nearly as much. Hydrological model parametrisation was found to be negligible in the present setup. For our study area, these results highlight the importance of improving vulnerability function formulation even in a climate change context where additional major sources of uncertainty arise.

What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models

We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models’ agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM–RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result.

Future changes in extreme weather and pyroconvection risk factors for Australian wildfires

Extreme wildfires have recently caused disastrous impacts in Australia and other regions of the world, including events with strong convective processes in their plumes (i.e., strong pyroconvection). Dangerous wildfire events such as these could potentially be influenced by anthropogenic climate change, however, there are large knowledge gaps on how these events might change in the future. The McArthur Forest Fire Danger Index (FFDI) is used to represent near-surface weather conditions and the Continuous Haines index (CH) is used here to represent lower to mid-tropospheric vertical atmospheric stability and humidity measures relevant to dangerous wildfires and pyroconvective processes. Projected changes in extreme measures of CH and FFDI are examined using a multi-method approach, including an ensemble of global climate models together with two ensembles of regional climate models. The projections show a clear trend towards more dangerous near-surface fire weather conditions for Australia based on the FFDI, as well as increased pyroconvection risk factors for some regions of southern Australia based on the CH. These results have implications for fields such as disaster risk reduction, climate adaptation, ecology, policy and planning, noting that improved knowledge on how climate change can influence extreme wildfires can help reduce future impacts of these events.

Application of Regional Climate Models for Updating Intensity-duration-frequency Curves under Climate Change

Global Climate Models (GCMs) are currently the most powerful tools for accessing changes in the hydrological regime at the watershed scale due to climate change and variability. GCMs, however, have limitations due to their coarse spatial and temporal resolutions. Regional Climate Models (RCMs) are often referred to as suitable alternatives due to their higher resolution of the long-term climate projections. It is expected that RCMs are better for simulating extreme conditions than the GCMs. This present work, investigate the difference in updated IDF (Intensity-Duration-Frequency) relationships developed using GCMs and RCMs. The IDF updating method implemented with the IDF_CC tool for Canada has been used for comparison. The analyses are conducted using 369 selected Environment and Climate Change Canada hydro-meteorological stations from the IDF_CC tool database with record length longer than 20 years. Results for the future period (2020-2100), are based on multi-model ensembles of (i) the RCMs from the NA-CORDEX (North-American Coordinated Regional Climate Downscaling Experiment) project (ensemble 1) (ii) a sub-set of six GCMs from the GCMs available in the IDF_CC tool used as drivers for the RCMs (ensemble 2) and (iii) all 24 GCMs from the IDF_CC tool database (ensemble 3). One representative concentration pathway (RCP), RCP 8.5, is used in the analysis. The RCMs from the NA-CORDEX project selected for this study use six GCMs as drivers to produce the future predictions for the North American continent, including Canada. Two metrics are applied for the comparison of results: (i) the difference in projected precipitation using the multi-model ensemble median; and (ii) the difference in uncertainty range. The uncertainty range is defined in this study as the percentage projected change in future, 25 to 75 quantiles obtained using the RCMs a GCMs ensembles. The regional models from the NA-CORDEX project generated lower extreme precipitation projections than the GCMs for the stations located in the Canadian prairies (provinces of Alberta, Saskatchewan, Manitoba). Stations located at the East and West coasts of Canada show a smaller difference in the projected extremes obtained using GCMs and RCMs. The use of RCMs shows increase in uncertainty when compared to GCMs. This result indicates that even when using regional climate models, it’s advisable to extend the analyses and include as many as possible models from different climate centers.

Near‐surface mean wind in Switzerland: Climatology, climate model evaluation and future scenarios

AbstractNear‐surface seasonal and annual mean wind speed in Switzerland is investigated using homogenized observations, Twentieth Century Reanalysis (20CRv2c) data and raw model output of a 75 member EURO‐COoRdinated Downscaling EXperiment regional climate model (RCM) ensemble for present day and future scenarios. The wind speed observations show a significant decrease in the Alps and on the southern Alpine slopes in the period 1981–2010. However, the 20CRv2c data reveal that the recent trends lie well within the decadal variability over longer time periods and no clear signs of a systematic wind stilling can be found for Switzerland. The ensemble of RCMs shows large biases in the annual mean wind speed over the Jura mountains, and some members also show large biases in the Alps compared to station observations. The spatial distribution of the model biases varies strongly between the RCMs, while the resolution and the driving global model have less impact on the pattern of the model bias. The RCMs are mostly able to represent the seasonality of wind speed on the Plateau but miss important details in complex terrain related to local wind systems. Most models show no significant changes in near‐surface mean wind speed until the end of the 21st century. The model ensemble changes range from a 7% decrease to a 6% increase with an ensemble mean decrease of 1 to 2%. Due to model biases, the scale mismatch between model grid and station observations and the missing representation of local winds in the simulations, the changes need to be interpreted with utmost care. Future assessments might lead to major revisions even for the sign of the projected changes, in particular over complex terrain.