Extended regional climate model projections for Europe until the mid-twentyfirst century: combining ENSEMBLES and CMIP3

This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling.     

Forest fire danger projections in the Mediterranean using ENSEMBLES regional climate change scenarios

We present future fire danger scenarios for the countries bordering the Mediterranean areas of Europe and north Africa building on a multi-model ensemble of state-of-the-art regional climate projections from the EU-funded project ENSEMBLES. Fire danger is estimated using the Canadian Forest Fire Weather Index (FWI) System and a related set of indices. To overcome some of the limitations of ENSEMBLES data for their application on the FWI System—recently highlighted in a previous study by Herrera et al. (Clim Chang 118:827–840, 2013)—we used an optimal proxy variable combination. A robust assessment of future fire danger projections is undertaken by disentangling the climate change signal from the uncertainty derived from the multi-model ensemble, unveiling a positive signal of fire danger potential over large areas of the Mediterranean. The increase in the fire danger signal is accentuated towards the latest part of the transient period, thus pointing to an elevated fire potential in the region with time. The fire-climate links under present and future conditions are further discussed building upon observed climate data and burned area records along a representative climatic gradient within the study region.     

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.     

ENSEMBLES-based assessment of regional climate effects in Luxembourg and their impact on vegetation

Projected future regional climate changes in Luxembourg are assessed based on a six-member ensemble of regional climate models (RCM) from the ENSEMBLES project. The key aspects are projected changes in air temperature and their impacts on vegetation. Up to now, there have been only few assessments of future climate conditions for Luxembourg. As agriculture is the dominant land use in Luxembourg, possible effects on crops and vegetation in general are highly relevant. Different RCMs at 25 km spatial and a daily temporal resolution, ranging from 1961 to 2100 based on the SRES A1B emission scenario are used. To reduce systematic biases in the RCM-derived time series, a bias correction is applied. Multi-model annual mean temperatures are projected to increase by 3.1 °C between the reference time span (1961 to 1990) and the far future (2069 to 2098). Clear change signals are found in seasonal bivariate frequency distributions of air temperature and precipitation. Derived impacts are an elongation of the thermal vegetation period by 6.2 days per decade due to an earlier onset in spring; growing degree day sums show a substantial increase leading to potentially better growth conditions; the earlier onset of the vegetation period causes an increase in late frost risk, especially in the near future (2021 to 2050) projections compared to the reference period.     

Going to the Extremes

Projections of changes in climate extremes are critical to assessing the potential impacts of climate change on human and natural systems. Modeling advances now provide the opportunity of utilizing global general circulation models (GCMs) for projections of extreme temperature and precipitation indicators. We analyze historical and future simulations of ten such indicators as derived from an ensemble of 9 GCMs contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR4), under a range of emissions scenarios. Our focus is on the consensus from the GCM ensemble, in terms of direction and significance of the changes, at the global average and geographical scale. The climate extremes described by the ten indices range from heat-wave frequency to frost-day occurrence, from dry-spell length to heavy rainfall amounts. Historical trends generally agree with previous observational studies, providing a basic sense of reliability for the GCM simulations. Individual model projections for the 21st century across the three scenarios examined are in agreement in showing greater temperature extremes consistent with a warmer climate. For any specific temperature index, minor differences appear in the spatial distribution of the changes across models and across scenarios, while substantial differences appear in the relative magnitude of the trends under different emissions rates. Depictions of a wetter world and greater precipitation intensity emerge unequivocally in the global averages of most of the precipitation indices. However, consensus and significance are less strong when regional patterns are considered. This analysis provides a first overview of projected changes in climate extremes from the IPCC-AR4 model ensemble, and has significant implications with regard to climate projections for impact assessments. An erratum to this article is available at . An erratum to this article can be found at     

Do weak AR4 models bias projections of future climate changes over Australia?

Regional climate projections using climate models commonly use an “all-model” ensemble based on data sets such as the Intergovernmental Panel on Climate Change’s (IPCC) 4th Assessment (AR4). Some regional assessments have omitted models based on specific criteria. We use a criteria based on the capacity of climate models to simulate the observed probability density function calculated using daily data, model-by-model and region-by-region for each of the AR4 models over Australia. We demonstrate that by omitting those climate models with relatively weak skill in simulating the observed probability density functions of maximum and minimum temperature and precipitation, different regional projections are obtained. Differences include: larger increases in the mean maximum and mean minimum temperatures, but smaller increases in the annual maximum and minimum temperatures. There is little impact on mean precipitation but the better models simulate a larger increase in the annual rainfall event combined with a larger decrease in the number of rain days. The weaker models bias the amount of mean warming towards lower increases, bias annual maximum temperatures to excessive warming and bias precipitation such that the amount of the annual rainfall event is under-estimated. We suggest that omitting weak models from regional scale estimates of future climate change helps clarify the nature and scale of the projected impacts of global warming.     

Applying probabilistic projections of climate change with impact models: a case study for sub-arctic palsa mires in Fennoscandia

A comparison of two approaches for determining probabilistic climate change impacts is presented. In the first approach, ensemble climate projections are applied directly as inputs to an impact model and the risk of impact is computed from the resulting ensemble of outcomes. As this can involve large numbers of projections, the approach may prove to be impractical when applied to complex impact models with demanding input requirements. The second approach is to construct an impact response surface based on a sensitivity analysis of the impact model with respect to changes in key climatic variables, and then to superimpose probabilistic projections of future climate onto the response surface to assess the risk of impact. To illustrate this comparison, an impact model describing the spatial distribution of palsas in Fennoscandia was applied to estimate the risk of palsa disappearance. Palsas are northern mire complexes with permanently frozen peat hummocks, located at the outer limit of the permafrost zone and susceptible to rapid decline due to regional warming. Probabilities of climate changes were derived from an ensemble of coupled atmosphere–ocean general circulation model (AOGCM) projections using a re-sampling method. Results indicated that the response surface approach, though introducing additional uncertainty, gave risk estimates of area decline for palsa suitability that were comparable to those obtained using multiple simulations with the original palsa model. It was estimated as very likely (>90% probability) that a decline of area suitable for palsas to less than half of the baseline distribution will occur by the 2030s and likely (>66%) that all suitable areas will disappear by the end of the twenty-first century under scenarios of medium (A1B) and moderately high (A2) emissions. For a low emissions (B1) scenario, it was more likely than not (>50%) that conditions over a small fraction of the current palsa distribution would remain suitable until the end of the twenty-first century.     

The role of boundary organizations in climate change adaptation from the perspective of municipal practitioners

In climate change impact research it is crucial to carefully select the meteorological input for impact models. We present a method for model selection that enables the user to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity. This is done in three steps: First, using principal component analysis for a multitude of meteorological parameters, to find common patterns of climate change within the multi-model ensemble. Second, detecting model similarities with regard to these multivariate patterns using cluster analysis. And third, sampling models from each cluster, to generate a subset of representative simulations. We present an application based on the ENSEMBLES regional multi-model ensemble with the aim to provide input for a variety of climate impact studies. We find that the two most dominant patterns of climate change relate to temperature and humidity patterns. The ensemble can be reduced from 25 to 5 simulations while still maintaining its essential characteristics. Having such a representative subset of simulations reduces computational costs for climate impact modeling and enhances the quality of the ensemble at the same time, as it prevents double-counting of dependent simulations that would lead to biased statistics.     

The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates

The majority of climate change impacts assessments account for climate change uncertainty by adopting the scenario-based approach. This typically involves assessing the impacts for a small number of emissions scenarios but neglecting the role of climate model physics uncertainty. Perturbed physics ensemble (PPE) climate simulations offer a unique opportunity to explore this uncertainty. Furthermore, PPEs mean it is now possible to make risk-based impacts estimates because they allow for a range of estimates to be presented to decision-makers, which spans the range of climate model physics uncertainty inherent from a given climate model and emissions scenario, due to uncertainty associated with the understanding of physical processes in the climate model. This is generally not possible with the scenario-based approach. Here, we present the first application of a PPE to estimate the impact of climate change on heat-related mortality. By using the estimated impacts of climate change on heat-related mortality in six cities, we demonstrate the benefits of quantifying climate model physics uncertainty in climate change impacts assessment over the more common scenario-based approach. We also show that the impacts are more sensitive to climate model physics uncertainty than they are to emissions scenario uncertainty, and least sensitive to whether the climate change projections are from a global climate model or a regional climate model. The results demonstrate the importance of presenting model uncertainties in climate change impacts assessments if the impacts are to be placed within a climate risk management framework.     

Projecting long-term armed conflict risk: An underappreciated field of inquiry?

Little research has been done on projecting long-term conflict risks. Such projections are currently neither included in the development of socioeconomic scenarios or climate change impact assessments nor part of global agenda-setting policy processes. In contrast, in other fields of inquiry, long-term projections and scenario studies are established and relevant for both strategical agenda-setting and applied policies. Although making projections of armed conflict risk in response to climate change is surrounded by uncertainty, there are good reasons to further develop such scenario-based projections. In this perspective article we discuss why quantifying implications of climate change for future armed conflict risk is inherently uncertain, but necessary for shaping sustainable future policy agendas. We argue that both quantitative and qualitative projections can have a purpose in future climate change impact assessments and put out the challenges this poses for future research.     

Drivers of mean climate change around the Netherlands derived from CMIP5

For the construction of regional climate change scenarios spanning a relevant fraction of the spread in climate model projections, an inventory of major drivers of regional climate change is needed. For the Netherlands, a previous set of regional climate change scenarios was based on the decomposition of local temperature/precipitation changes into components directly linked to the level of global warming, and components related to changes in the regional atmospheric circulation. In this study this decomposition is revisited utilizing the extensive modelling results from the CMIP5 model ensemble in support for the 5th IPCC assessment. Rather than selecting a number of GCMs based on performance metrics or relevant response features, a regression technique was developed to utilize all available model projections. The large number of projections allows a quantification of the separate contributions of emission scenarios, systematic model responses and natural variability to the total likelihood range. Natural variability plays a minor role in modelled differences in the global mean temperature response, but contributes for up to 50 % to the range of mean sea level pressure responses and local precipitation. Using key indicators (“steering variables”) for the temperature and circulation response, the range in local seasonal mean temperature and precipitation responses can be fairly well reproduced.     

Spatial variations of sea-level rise and impacts: An application of DIVA

Due to complexities of creating sea-level rise scenarios, impacts of climate-induced sea-level rise are often produced from a limited number of models assuming a global uniform rise in sea level. A greater number of models, including those with a pattern reflecting regional variations would help to assure reliability and a range of projections, indicating where models agree and disagree. This paper determines how nine new patterned-scaled sea-level rise scenarios (plus the uniform and patterned ensemble mean rises) influence global and regional coastal impacts (wetland loss, dry land loss due to erosion and the expected number of people flooded per year by extreme sea levels). The DIVA coastal impacts model was used under an A1B scenario, and assumed defences were not upgraded as conditions evolved. For seven out of nine climate models, impacts occurred at a proportional rate to global sea-level rise. For the remaining two models, higher than average rise in sea level was projected in northern latitudes or around populated coasts thus skewing global impact projections compared with the ensemble global mean. Regional variability in impacts were compared using the ensemble mean uniform and patterned scenarios: The largest relative difference in impacts occurred around the Mediterranean coast, and the largest absolute differences around low-lying populated coasts, such as south, south-east and east Asia. Uniform projections of sea-level rise impacts remain a useful method to determine global impacts, but improved regional scale models of sea-level rise, particularly around semi-enclosed seas and densely populated low-lying coasts will provide improved regional impact projections and a characterisation of their uncertainties.     

Assessing the robustness of projected precipitation changes over central Africa on the basis of a multitude of global and regional climate projections

It is well accepted within the scientific community that a large ensemble of different projections is required to achieve robust climate change information for a specific region. For this purpose we have compiled a state-of-the-art multi-model multi-scenario ensemble of global and regional precipitation projections. This ensemble combines several global projections from the CMIP3 and CMIP5 databases, along with some recently downscaled regional CORDEX-Africa projections. Altogether daily precipitation data from 77 different climate change projections is analysed; separated into 31 projections for a high and 46 for a low emission scenario. We find a robust indication that, independent of the underlying emission scenario, annual total precipitation amounts over the central African region are not likely to change severely in the future. However some robust changes in precipitation characteristics, like the intensification of heavy rainfall events as well as an increase in the number of dry spells during the rainy season are projected for the future. Further analysis shows that over some regions the results of the climate change assessment clearly depend on the size of the analyzed ensemble. This indicates the need of a “large-enough” ensemble of independent climate projections to allow for a reliable climate change assessment.     

Improved confidence in climate change projections of precipitation further evaluated using daily statistics from ENSEMBLES models

Probability density functions for daily precipitation data are used as a validation tool comparing station measurements to seven transient regional climate model runs, with a horizontal resolution of 25 km and driven by the SRES A1B scenario forcing, within the ENSEMBLES project. The validation is performed for the control period 1961–1990 for eight predefined European subregions, and a ninth region enclosing all eight subregions, with different climate characteristics. Models that best match the observations are then used for making climate change projections of precipitation distributions during the twenty-first century for each subregion separately. We find, compared to the control period, a distinct decrease in the contribution to the total precipitation for days with moderate precipitation and a distinct increase for days with more intense precipitation. This change in contribution to the total precipitation is found to amplify with time during all of the twenty-first century with an average rate of 1.1% K^?1. Furthermore, the crossover point separating the decreasing from the increasing contributions does not show any significant change with time for any specific subregion. These results are a confirmation and a specification of the results from a previous study using the same station measurements but with a regional climate model ensemble within the PRUDENCE project.     

Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments

Ensembles of historical climate simulations and climate projections from the World Climate Research Programme’s (WCRP’s) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset were investigated to determine how model credibility affects apparent relative scenario likelihoods in regional risk assessments. Methods were developed and applied in a Northern California case study. An ensemble of 59 twentieth century climate simulations from 17 WCRP CMIP3 models was analyzed to evaluate relative model credibility associated with a 75-member projection ensemble from the same 17 models. Credibility was assessed based on how models realistically reproduced selected statistics of historical climate relevant to California climatology. Metrics of this credibility were used to derive relative model weights leading to weight-threshold culling of models contributing to the projection ensemble. Density functions were then estimated for two projected quantities (temperature and precipitation), with and without considering credibility-based ensemble reductions. An analysis for Northern California showed that, while some models seem more capable at recreating limited aspects twentieth century climate, the overall tendency is for comparable model performance when several credibility measures are combined. Use of these metrics to decide which models to include in density function development led to local adjustments to function shapes, but led to limited affect on breadth and central tendency, which were found to be more influenced by “completeness” of the original ensemble in terms of models and emissions pathways.     

A multi-hazard regional level impact assessment for Europe combining indicators of climatic and non-climatic change

To better prioritise adaptation strategies to a changing climate that are currently being developed, there is a need for quantitative regional level assessments that are systematic and comparable across multiple weather hazards. This study presents an indicator-based impact assessment framework at NUTS-2 level for the European Union that quantifies potential regional changes in weather-related hazards: heat stress in relation to human health, river flood risk, and forest fire risk. This is done by comparing the current (baseline) situation with two future time periods, 2011–2040 and 2041–2070. The indicator values for the baseline period are validated against observed impact data. For each hazard, the method integrates outcomes of a set of coherent high-resolution regional climate models from the ENSEMBLES project based on the SRES A1B emission scenario, with current and projected non-climatic drivers of risk, such as land use and socio-economic change. An index of regional adaptive capacity has been developed and compared with overall hazard impact in order to identify the potentially most vulnerable regions in Europe. The results show strongest increases in impacts for heat stress, followed by forest fire risk, while for flood risk the sign and magnitude of change vary across regions. A major difference with previous studies is that heat stress risk could increase most in central Europe, which is due to the ageing population there. An overall assessment combining the three hazards shows a clear trend towards increasing impact from climaterelated natural hazards for most parts of Europe, but hotspot regions are found in eastern and southern Europe due to their low adaptive capacities. This spatially explicit assessment can serve as a basis for discussing climate adaptation mainstreaming, and priorities for regional development in the EU.     

Climate change scenarios of precipitation extremes in Central Europe from ENSEMBLES regional climate models

The study examines future scenarios of precipitation extremes over Central Europe in an ensemble of 12 regional climate model (RCM) simulations with the 25-km resolution, carried out within the European project ENSEMBLES. We apply the region-of-influence method as a pooling scheme when estimating distributions of extremes, which consists in incorporating data from a ‘region’ (set of gridboxes) when fitting an extreme value distribution in any single gridbox. The method reduces random variations in the estimates of parameters of the extreme value distribution that result from large spatial variability of heavy precipitation. Although spatial patterns differ among the models, most RCMs simulate increases in high quantiles of precipitation amounts when averaged over the area for the late-twenty-first century (2070–2099) climate in both winter and summer. The sign as well as the magnitude of the projected change vary only little for individual parts of the distribution of daily precipitation in winter. In summer, on the other hand, the projected changes increase with the quantile of the distribution in all RCMs, and they are negative (positive) for parts of the distribution below (above) the 98% quantile if averaged over the RCMs. The increases in precipitation extremes in summer are projected in spite of a pronounced drying in most RCMs. Although a rather general qualitative agreement of the models concerning the projected changes of precipitation extremes is found in both winter and summer, the uncertainties in climate change scenarios remain large and would likely further increase considerably if a more complete ensemble of RCM simulations driven by a larger suite of global models and with a range of possible scenarios of the radiative forcing is available.     

The Effects of Spatial Scale of Climate Scenarios on Economic Assessments: An Example from U.S. Agriculture

The appropriate level of spatial resolution for climate scenarios is a key uncertainty in climate impact studies and regional integrated assessments. To the extent that such uncertainty may affect the magnitude of economic estimates of climate change, it has implications for the public policy debates concerning the efficiency of CO₂ control options. In this article, we investigate the effects that different climate scenario resolutions have on economic estimates of the impacts of future climate changeon agriculture in the United States. These results are derived via a set of procedures and an analytical model that has been used previously in climate change assessments. The results demonstrate that the spatial scale of climate scenarios affects the estimates of both regional changes in crop yields and the economic impact on the agricultural sector as a whole. An assessment based on the finer scale climatological information consistently yielded a less favorable assessment of the implications of climate change. Regional indicators of economic activity were of opposite sign in some regions, based on the scenario scale. Such differences in economic magnitudes or signs are potentially important in examining whether past climate change assessments may misstate the economic consequences of such changes. The results reported here suggest that refinement of the spatial scale of scenarios should be carefully considered in future impacts research.     

未来20年中国气温变化预估

利用大约40余个气候模式和模式集合,考虑多种人类排放情景,预估到2025年前相对于1961－1990年中国的气温变化。只考虑未来人类排放增加多模式集成预估结果表明,中国年平均气温自2006到2025年的20 a期间将继续变暖0.55 ℃,至2010年年平均气温平均变暖大约为1.08 ℃（平均变暖范围为 0.73-1.54 ℃）,至2020年年平均变暖约为1.43 ℃（平均变暖范围为1.10-2.09 ℃）,至2025年平均变暖约为1.39 ℃（平均变暖范围为0.94-2.19 ℃）。 对1990－2005年已经出现观测事实的近16 a气候模式预估结果进行检验表明,多模式考虑多种排放情景集成,一致预估出这16 a的明显变暖趋势,但是变暖幅度略低于实际观测值。经检测证实,对2006－2025年中国气温的预估具有一定的可信度。需要指出的是,目前的预估没有考虑未来的自然变化,只考虑人类排放继续增加的影响。