Quantification of uncertainty sources in a probabilistic climate change assessment of future water shortages

As the incorporation of probabilistic climate change information into UK water resource management gathers apace, understanding the relative scales of the uncertainty sources in projections of future water shortage metrics is necessary for the resultant information to be understood and used effectively. Utilising modified UKCP09 weather generator data and a multi-model approach, this paper represents a first attempt at extending an uncertainty assessment of future stream flows under forced climates to consider metrics of water shortage based on the triggering of reservoir control curves. It is found that the perturbed physics ensemble uncertainty, which describes climate model parameter error uncertainty, is the cause of a far greater proportion of both the overall flow and water shortage per year probability uncertainty than that caused by SRES emissions scenario choice in the 2080s. The methodology for producing metrics of future water shortage risk from UKCP09 weather generator information described here acts as the basis of a robustness analysis of the North Staffordshire WRZ to climate change, which provides an alternative approach for making decisions despite large uncertainties, which will follow.     

Improved regional scale processes reflected in projected hydrological changes over large European catchments

For the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the recent version of the coupled atmosphere/ocean general circulation model (GCM) of the Max Planck Institute for Meteorology has been used to conduct an ensemble of transient climate simulations These simulations comprise three control simulations for the past century covering the period 1860–2000, and nine simulations for the future climate (2001–2100) using greenhouse gas (GHG) and aerosol concentrations according to the three IPCC scenarios B1, A1B and A2. For each scenario three simulations were performed. The global simulations were dynamically downscaled over Europe using the regional climate model (RCM) REMO at 0.44° horizontal resolution (about 50 km), whereas the physics packages of the GCM and RCM largely agree. The regional simulations comprise the three control simulations (1950–2000), the three A1B simulations and one simulation for B1 as well as for A2 (2001–2100). In our study we concentrate on the climate change signals in the hydrological cycle and the 2 m temperature by comparing the mean projected climate at the end of the twenty-first century (2071–2100) to a control period representing current climate (1961–1990). The robustness of the climate change signal projected by the GCM and RCM is analysed focussing on the large European catchments of Baltic Sea (land only), Danube and Rhine. In this respect, a robust climate change signal designates a projected change that sticks out of the noise of natural climate variability. Catchments and seasons are identified where the climate change signal in the components of the hydrological cycle is robust, and where this signal has a larger uncertainty. Notable differences in the robustness of the climate change signals between the GCM and RCM simulations are related to a stronger warming projected by the GCM in the winter over the Baltic Sea catchment and in the summer over the Danube and Rhine catchments. Our results indicate that the main explanation for these differences is that the finer resolution of the RCM leads to a better representation of local scale processes at the surface that feed back to the atmosphere, i.e. an improved representation of the land sea contrast and related moisture transport processes over the Baltic Sea catchment, and an improved representation of soil moisture feedbacks to the atmosphere over the Danube and Rhine catchments.     

Future evolution of extreme precipitation in the Mediterranean

Mediterranean basins can be impacted by severe floods caused by extreme rainfall, and there is a growing awareness about the possible increase in these heavy rainfall events due to climate change. In this study, the climate change impacts on extreme daily precipitation in 102 catchments covering the whole Mediterranean basin are investigated using nonstationary extreme value model applied to annual maximum precipitation in an ensemble of high-resolution regional climate model (RCM) simulations from the Euro-CORDEX experiment. Results indicate contrasted trends, with significant increasing trends in Northern catchments and conversely decreasing trends in Southern catchments. For most cases, the time of signal emergence for these trends is before the year 2000. The same spatial pattern is obtained under the two climate scenarios considered (RCP4.5 and RCP8.5) and in most RCM simulations, suggesting a robust climate change signal. The strongest multi-model agreement concerns the positive trends, which can exceed +?20% by the end of the twenty-first century in some simulations, impacting South France, North Italy, and the Balkans. For these areas, society-relevant strong impacts of such Mediterranean extreme precipitation changes could be expected in particular concerning flood-related damages.     

A method for incorporating climate variability in climate change impact assessments: Sensitivity of river flows in the Eden catchment to precipitation scenarios

Interest in the impacts of climate change is ever increasing. This is particularly true of the water sector where understanding potential changes in the occurrence of both floods and droughts is important for strategic planning. Climate variability has been shown to have a significant impact on UK climate and accounting for this in future climate change projections is essential to fully anticipate potential future impacts. In this paper a new resampling methodology is developed which includes the variability of both baseline and future precipitation. The resampling methodology is applied to 13 CMIP3 climate models for the 2080s, resulting in an ensemble of monthly precipitation change factors. The change factors are applied to the Eden catchment in eastern Scotland with analysis undertaken for the sensitivity of future river flows to the changes in precipitation. Climate variability is shown to influence the magnitude and direction of change of both precipitation and in turn river flow, which are not apparent without the use of the resampling methodology. The transformation of precipitation changes to river flow changes display a degree of non-linearity due to the catchment’s role in buffering the response. The resampling methodology developed in this paper provides a new technique for creating climate change scenarios which incorporate the important issue of climate variability.     

Past to future extreme events in Liverpool Bay: model projections from 1960–2100

Knowledge of the likely future wind, wave and surge climate in Liverpool Bay is of importance for coastal flood defence management. We examine a 140-year time series (1960–2100) of wind and wave model projections at the WaveNet buoy location in Liverpool Bay and also of surge model projection at two ports in Liverpool Bay, namely Liverpool and Heysham. To this end we use model projections from the UK Climate Projections 09 (UKCP09) programme. We use a medium emissions scenario ensemble from the HadCM3 climate model sensitivity tests. A continental shelf model (CS3) with ~12 km resolution was used to separately simulate the waves and the surge. The models are forced by hourly wind and pressure data from the Met Office (Hadley Centre) regional climate model (RCM). Swell wave boundary conditions are generated over the full Atlantic using global climate model (GCM) winds. Analysis of significant changes in the statistics over time shows that there is little change in extreme wave and surge conditions in Liverpool Bay. Although there is a slight increase in the severity of the most extreme events, the frequency of extreme wind and wave events is slightly reduced, while the frequency of extreme surge events slightly increases over the 140-year period. From the model projections, we find that the trends in the local wind are directly reflected in the wave field within Liverpool Bay. The trends in the skew surge projections deviate slightly from those in the wind patterns.     

A study on the water vapor transport trend and water vapor source of the Tibetan Plateau

This study presents results of the pilot experiments made with new parametric multi-site multi-variable stochastic daily weather generator (WG) SPAGETTA. The experiments are performed for eight European regions and we focus on spatial characteristics of temperature. The WG is calibrated using the gridded weather data E-OBS. In evaluating the generator, the spatial and temporal temperature autocorrelations derived from the synthetic series were found to perfectly fit the values derived from the calibration data. Next, the WG is validated in terms of the frequency of “spatial hot days” and the annual maximum length of “spatial hot spells”. The results indicate a very good correspondence between characteristics derived from synthetic and calibration data. As part of the validation tests, the performance of the WG is compared with a regional climate model (RCM), which shows a similar performance as the generator. In a final experiment, the use of the WG for the future climate is demonstrated, the WG parameters (including the temperature autocorrelations) calibrated with the observed data are modified according to the RCM-based changes in these parameters. While analyzing synthetic series produced with the modified generator, we discuss partial impacts due to changes in individual WG parameters on the spatial hot days and spells. We show that the impacts are mainly (but not only) due to changes in temperature averages. The projected changes in temperature autocorrelations have also some impacts, larger for the spatial hot spells than for the spatial hot days. Climate change impacts on spatial hot days/spells based on the WG are compared with impacts based on the RCM, and we conclude that the differences are mainly due to simplifying assumptions adopted in our pilot experiment.     

Climate change impact on water and salt balances: an assessment of the impact of climate change on catchment salt and water balances in the Murray-Darling Basin, Australia

Climate change has potentially significant implications for hydrology and the quantity and quality of water resources. This study investigated the impacts of climate change and revegetation on water and salt balance, and stream salt concentration for catchments within the Murray-Darling Basin, Australia. The Biophysical Capacity to Change model was used with climate change scenarios obtained using the CSIRO DARLAM 125 (125 km resolution) and Cubic Conformal (50 km resolution) regional climate models. These models predicted up to 25% reduction in mean annual rainfall and a similar magnitude of increase in potential evapotranspiration by 2070. Relatively modest changes in rainfall and temperature can lead to significant reductions in mean annual runoff and salt yield and increases in stream salt concentrations within the Basin. The modelled reductions in mean annual runoff were up to 45% in the wetter/cooler southern catchments and up to 64% in the drier/hotter western and northern catchments. The maximum reductions in salt yield were estimated to be up to 34% in the southern catchments and up to 49% in the northern and western catchments. These changes are associated with average catchment rainfall decreases of 13 to 21%. The results suggest that percentage changes in rainfall will be amplified in runoff. This study demonstrates that climate change poses significant challenges to natural resource management in Australia.     

Climate change and river flooding: Part 2 sensitivity characterisation for british catchments and example vulnerability assessments

This paper is the second of a series describing a scenario-neutral methodology to assess the sensitivity and vulnerability of British catchments to changes in flooding due to climate change. In paper one, nine flood sensitivity types were identified from response surfaces generated for 154 catchments. The response surfaces describe changes in 20-year return period flood peaks (RP20) in response to a large set of changes in precipitation, temperature and potential evapotranspiration. In this paper, a recursive partitioning algorithm is used to link families of sensitivity types to catchment properties, via a decision tree. The tree shows 85 % success characterising the four sensitivity families, using five properties and nine paths. Catchment annual average rainfall is the primary partitioning factor, with drier catchments having a more variable response to climate (precipitation) change than wetter catchments and higher catchment losses and permeability being aggravating factors. The full sensitivity-exposure-vulnerability methodology is illustrated for two catchments: sensitivity is estimated by using the decision tree to identify the sensitivity family (and its associated average response surface); exposure is defined from a set of climate model projections and combined with the response surface to estimate the resulting impacts (changes in RP20); vulnerability under a range of adaptive capacity thresholds is estimated from the set of impacts. Even though they are geographically close, the two catchments show differing vulnerability to climate change, due to their differing properties. This demonstrates that generalised response surfaces characterised by catchment properties are useful screening tools to quantify the vulnerability of catchments to climate change without the need to undertake a full climate change impact study.     

A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km²) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.     

Modeling climate change and biophysical impacts of crop production in the Austrian Marchfeld Region

Climate change affects major biophysical processes in agricultural crop production (e.g. evaporation of plants and soils, nutrient cycles, and growth of plants). This analysis aims to assess some of these effects by simulating regional climate projections that are integrated in the biophysical process model EPIC (Environmental Policy Integrated Climate). Statistical climate models have been developed for six weather parameters based on daily weather records of a weather station in the Austrian Marchfeld region from 1975 to 2006. These models have been used to estimate daily weather parameters for the period 2007–2038. The resulting projections have been compared to climate scenarios provided from the TYNDALL Centre for Climate Change Research, which are based on General Circulation Models (GCMs). The comparison indicates some differences, namely a smaller temperature increase and a higher precipitation amount in the TYNDALL data. Both climate datasets have been used to simulate impacts of climate change on crop yields, topsoil organic carbon content, and nitrate leaching with EPIC and thus to perform a sensitivity analysis of EPIC. Yield impacts have been assessed for four simulated crops, i.e. 6.2?t/ha for winter wheat for statistical climate projections compared to 5.7?t/ha for TYNDALL scenarios, 10.6?t/ha for corn compared to 10.5?t/ha, 3.9?t/ha for sunflower compared to 3.7?t/ha, and 4.5?t/ha for spring barley compared to 4.3?t/ha—all values as an average over the period 2007–2038. Smaller differences have been simulated for topsoil organic carbon content i.e. 55.1?t/ha for the statistical climate projections compared to 55.3?t/ha for the TYNDALL scenarios and nitrate leaching i.e. 7.1?kg/ha compared to 11.1?kg/ha. All crop yields as well as topsoil organic carbon content and nitrate leaching show highest sensitivity to temperature and solar radiation.     

Gradient in the climate change signal of European discharge predicted by a multi-model ensemble

In order to perform hydrological studies on the PRUDENCE regional climate model (RCM) simulations, a special focus was put on the discharge from large river catchments located in northern and central Europe. The discharge was simulated with a simplified land surface (SL) scheme and the Hydrological Discharge (HD) model. The daily fields of precipitation, 2 m temperature and evapotranspiration from the RCM simulations were used as forcing. Therefore the total catchment water balances are constrained by the hydrological cycle of the different RCMs. The validation of the simulated hydrological cycle from the control simulations shows that the multi-model ensemble mean is closer to the observations than each of the models, especially if different catchments and hydrological variables are considered. Therefore, the multi-model ensemble mean can be used to largely reduce the uncertainty that is introduced by a single RCM. This also provides more confidence in the future projections for the multi-model ensemble means. The scenario simulations predict a gradient in the climate change signal over Northern and Central Europe. Common features are the overall warming and the general increase of evapotranspiration. But while in the northern parts the warming will enhance the hydrological cycle leading to an increased discharge, the large warming, especially in the summer, will slow down the hydrological cycle caused by a drying in the central parts of Europe which is accompanied by a reduction of discharge. The comparison of the changes predicted by the multi-model ensemble mean to the changes predicted by the driving GCM indicates that the RCMs can compensate problems that a driving GCM may have with local scale processes or parameterizations.     

Assessing the site-specific impacts of climate change on hydrology, soil erosion and crop yields in the Loess Plateau of China

Climate changes may have great impacts on the fragile agro-ecosystems of the Loess Plateau of China, which is one of the most severely eroded regions in the world. We assessed the site-specific impacts of climate change during 2010?C2039 on hydrology, soil loss and crop yields in Changwu tableland region in the Loess Plateau of China. Projections of four climate models (CCSR/NIES, CGCM2, CSIRO-Mk2 and HadCM3) under three emission scenarios (A2, B2 and GGa) were used. A simple spatiotemporal statistical method was used to downscale GCMs monthly grid outputs to station daily weather series. The WEPP (Water and Erosion Prediction Project) model was employed to simulate the responses of agro-ecosystems. Compared with the present climate, GCMs projected a ?2.6 to 17.4% change for precipitation, 0.6 to 2.6°C and 0.6 to 1.7°C rises for maximum and minimum temperature, respectively. Under conventional tillage, WEPP predicted a change of 10 to 130% for runoff, ?5 to 195% for soil loss, ?17 to 25% for wheat yield, ?2 to 39% for maize yield, ?14 to 18% for plant transpiration, ?8 to 13% for soil evaporation, and ?6 to 9% for soil water reserve at two slopes during 2010?C2039. However, compared with conventional tillage under the present climate, conservation tillage would change runoff by ?34 to 71%, and decrease soil loss by 26 to 77% during 2010?C2039, with other output variables being affected slightly. Overall, climate change would have significant impacts on agro-ecosystems, and adoption of conservation tillage has great potential to reduce the adverse effects of future climate changes on runoff and soil loss in this region.     

Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We find a negative relationship between these controls that weakens the forecast skills, nevertheless there is a middle ground between both controls in several catchments, as shown by our results.     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

Agricultural risk management policies under climate uncertainty

Climate change is forecasted to increase the variability of weather conditions and the frequency of extreme events. Due to potential adverse impacts on crop yields it will have implications for demand of agricultural risk management instruments and farmers’ adaptation strategies. Evidence on climate change impacts on crop yield variability and estimates of production risk from farm surveys in Australia, Canada and Spain, are used to analyse the policy choice between three different types of insurance (individual, area-yield and weather index) and ex post payments. The results are found to be subject to strong uncertainties and depend on the risk profile of different farmers and locations; the paper provides several insights on how to analyse these complexities. In general, area yield performs best more often across our countries and scenarios, in particular for the baseline and marginal climate change (without increases in extreme events). However, area yield can be very expensive if farmers have limited information on how climate change affects yields (misalignment in expectations), and particularly so under extreme climate change scenarios. In these more challenging cases, ex post payments perform well to increase low incomes when the risk is systemic like in Australia; Weather index performs well to reduce the welfare costs of risks when the correlation between yields and index is increased by the extreme events. The paper also analyses the robustness of different instruments in the face of limited knowledge of the probabilities of different climate change scenarios; highlighting that this added layer of uncertainty could be overcome to provide sound policy advice under uncertainties introduced by climate change. The role of providing information to farmers on impacts of climate change emerges as a crucial result of this paper as indicated by the significantly higher budgetary expenditures occurring across all instruments when farmers’ expectations are misaligned relative to actual impacts of climate change.     

Estimation methods for monthly humidity from dynamical downscaling data for quantitative assessments of climate change impacts

Methods are proposed to estimate the monthly relative humidity and wet bulb temperature based on observations from a dynamical downscaling coupled general circulation model with a regional climate model (RCM) for a quantitative assessment of climate change impacts. The water vapor pressure estimation model developed was a regression model with a monthly saturated water vapor pressure that used minimum air temperature as a variable. The monthly minimum air temperature correction model for RCM bias was developed by stepwise multiple regression analysis using the difference in monthly minimum air temperatures between observations and RCM output as a dependent variable and geographic factors as independent variables. The wet bulb temperature was estimated using the estimated water vapor pressure, air temperature, and atmospheric pressure at ground level both corrected for RCM bias. Root mean square errors of the data decreased considerably in August.     

Assessing climate change impacts on hydrology from an ensemble of regional climate models,model scales and linking methods – a case study on the Lule River basin

This paper investigates how using different regional climate model (RCM) simulations affects climate change impacts on hydrology in northern Europe using an offline hydrological model. Climate change scenarios from an ensemble of seven RCMs, two global climate models (GCMs), two global emissions scenarios and two RCMs of varying resolution were used. A total of 15 climate change simulations were included in studies on the Lule River basin in Northern Sweden. Two different approaches to transfer climate change from the RCMs to hydrological models were tested. A rudimentary estimate of change in hydropower potential on the Lule River due to climate change was also made. The results indicate an overall increase in river flow, earlier spring peak flows and an increase in hydropower potential. The two approaches for transferring the signal of climate change to the hydrological impacts model gave similar mean results, but considerably different seasonal dynamics, a result that is highly relevant for other types of climate change impacts studies.     

Robust projections of Fire Weather Index in the Mediterranean using statistical downscaling

The effect of climate change on wildfires constitutes a serious concern in fire-prone regions with complex fire behavior such as the Mediterranean. The coarse resolution of future climate projections produced by General Circulation Models (GCMs) prevents their direct use in local climate change studies. Statistical downscaling techniques bridge this gap using empirical models that link the synoptic-scale variables from GCMs to the local variables of interest (using e.g. data from meteorological stations). In this paper, we investigate the application of statistical downscaling methods in the context of wildfire research, focusing in the Canadian Fire Weather Index (FWI), one of the most popular fire danger indices. We target on the Iberian Peninsula and Greece and use historical observations of the FWI meteorological drivers (temperature, humidity, wind and precipitation) in several local stations. In particular, we analyze the performance of the analog method, which is a convenient first choice for this problem since it guarantees physical and spatial consistency of the downscaled variables, regardless of their different statistical properties. First we validate the method in perfect model conditions using ERA-Interim reanalysis data. Overall, not all variables are downscaled with the same accuracy, with the poorest results (with spatially averaged daily correlations below 0.5) obtained for wind, followed by precipitation. Consequently, those FWI components mostly relying on those parameters exhibit the poorest results. However, those deficiencies are compensated in the resulting FWI values due to the overall high performance of temperature and relative humidity. Then, we check the suitability of the method to downscale control projections (20C3M scenario) from a single GCM (the ECHAM5 model) and compute the downscaled future fire danger projections for the transient A1B scenario. In order to detect problems due to non-stationarities related to climate change, we compare the results with those obtained with a Regional Climate Model (RCM) driven by the same GCM. Although both statistical and dynamical projections exhibit a similar pattern of risk increment in the first half of the 21st century, they diverge during the second half of the century. As a conclusion, we advocate caution in the use of projections for this last period, regardless of the regionalization technique applied.     

Possible impacts of climate change on extreme weather events at local scale in south–central Canada

Synoptic weather typing and regression-based downscaling approaches have become popular in evaluating the impacts of climate change on a variety of environmental problems, particularly those involving extreme impacts. One of the reasons for the popularity of these approaches is their ability to categorize a complex set of meteorological variables into a coherent index, facilitating the projection of changes in frequency and intensity of future daily extreme weather events and/or their impacts. This paper illustrated the capability of the synoptic weather typing and regression methods to analyze climatic change impacts on a number of extreme weather events and environmental problems for south–central Canada, such as freezing rain, heavy rainfall, high-/low-streamflow events, air pollution, and human health. These statistical approaches are helpful in analyzing extreme events and projecting their impacts into the future through three major steps or analysis procedures: (1) historical simulation modeling to identify extreme weather events or their impacts, (2) statistical downscaling to provide station-scale future hourly/daily climate data, and (3) projecting changes in the frequency and intensity of future extreme weather events and their impacts under a changing climate. To realize these steps, it is first necessary to conceptualize the modeling of the meteorology, hydrology and impacts model variables of significance and to apply a number of linear/nonlinear regression techniques. Because the climate/weather validation process is critical, a formal model result verification process has been built into each of these three steps. With carefully chosen physically consistent and relevant variables, the results of the verification, based on historical observations of the outcome variables simulated by the models, show a very good agreement in all applications and extremes tested to date. Overall, the modeled results from climate change studies indicate that the frequency and intensity of future extreme weather events and their impacts are generally projected to significantly increase late this century over south–central Canada under a changing climate. The implications of these increases need be taken into consideration and integrated into policies and planning for adaptation strategies, including measures to incorporate climate change into engineering infrastructure design standards and disaster risk reduction measures. This paper briefly summarized these climate change research projects, focusing on the modeling methodologies and results, and attempted to use plain language to make the results more accessible and interesting to the broader informed audience. These research projects have been used to support decision-makers in south–central Canada when dealing with future extreme weather events under climate change.     

Hydrological Impacts of Climate Change on Inflows to Perth, Australia

The effects of climate change due to increasing atmospheric CO₂ onthe major tributaries to the Swan River (Perth, Western Australia) have been investigated. The climate scenarios are based on results from General Circulation Models (GCMs) and 1000 year time series are produced using a stochastic weather generator. The hydrological implications of these scenarios are then examined using a conceptual rainfall-runoff model, CMD-IHACRES, to model the response of six catchments, which combine to represent almost 90% of the total flow entering the upper Swan River,and hence the Perth city urban area. The changes in streamflow varies considerably between catchments, exhibiting a strong dependence on the physical attributes of the catchment in question. The increase in the magnitudes of rare flood events despite significant decreases in mean streamflow levels found in some catchments emphasizes the importance of estimating changes in the nature of the precipitation (variance, length of storm and interstorm periods), along with changes in the mean, in climate change scenarios.