Potential impact of climate change on ecosystems of the Barents Sea Region

The EU project BALANCE (Global Change Vulnerabilities in the Barents region: Linking Arctic Natural Resources, Climate Change and Economies) aims to assess vulnerability to climate change in the Barents Sea Region. As a prerequisite the potential impact of climate change on selected ecosystems of the study area has to be quantified, which is the subject of the present paper. A set of ecosystem models was run to generate baseline and future scenarios for 1990, 2020, 2050 and 2080. The models are based on data from the Regional Climate Model (REMO), driven by a GCM which in turn is forced by the IPCC-B2 scenario. The climate change is documented by means of the Köppen climate classification. Since the multitude of models requires the effect of climate change on individual terrestrial and marine systems to be integrated, the paper concentrates on a standardised visualisation of potential impacts by use of a Geographical Information System for the timeslices 2050 and 2080. The resulting maps show that both terrestrial and marine ecosystems of the Barents region will undergo significant changes until both 2050 and 2080.     

A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom

Strategic-scale assessments of climate change impacts are often undertaken using the change factor (CF) methodology whereby future changes in climate projected by General Circulation Models (GCMs) are applied to a baseline climatology. Alternatively, statistical downscaling (SD) methods apply climate variables from GCMs to statistical transfer functions to estimate point-scale meteorological series. This paper explores the relative merits of the CF and SD methods using a case study of low flows in the River Thames under baseline (1961–1990) and climate change conditions (centred on the 2020s, 2050s and 2080s). Archived model outputs for the UK Climate Impacts Programme (UKCIP02) scenarios are used to generate daily precipitation and potential evaporation (PE) for two climate change scenarios via the CF and SD methods. Both signal substantial reductions in summer precipitation accompanied by increased PE throughout the year, leading to reduced flows in the Thames in late summer and autumn. However, changes in flow associated with the SD scenarios are generally more conservative and complex than that arising from CFs. These departures are explained in terms of the different treatment of multidecadal natural variability, temporal structuring of daily climate variables and large-scale forcing of local precipitation and PE by the two downscaling methods.     

Comparison of the use of alternative UKCP09 products for modelling the impacts of climate change on flood frequency

Climate change could have significant impacts on hydrology. This paper uses UK Climate Projections 09 (UKCP09) products to assess the impacts on flood frequency in Britain. The main UKCP09 product comprises conditional probabilistic information on changes in a number of climate variables on a 25?×?25?km grid across the UK (the Sampled Data change factors). A second product is a Weather Generator which produces time-series of current weather variables and future weather variables based on the Sampled Data and consistent with the change factors. A third product comprises time-series from a Regional Climate Model (RCM) ensemble which were used to downscale Global Climate Models (GCMs) on which the projections are based and whose outputs were used in the production of the Sampled Data. This paper compares the use of Sampled Data change factors, Weather Generator time-series, RCM-derived change factors and RCM time-series. Each is used to provide hydrological model inputs for nine catchments, to assess impacts for the 2080s (A1B emissions). The results show relatively good agreement between methods for most catchments, with the four median values for a catchment generally being within 10% of each other. There are also some clear differences, with the use of time-series generally leading to a greater uncertainty range than the use of change factors because the latter do not allow for the effects of, or changes in, natural variability. Also, the use of Weather Generator time-series leads to much greater impacts than the other methods for one catchment. The results suggest that climate impact studies should not necessarily rely on the application of just one UKCP09 product, as each has different strengths and weaknesses.     

Sensitivity Studies of the Impacts of Climate Change on White Nile Flows

Several exploratory studies are presented on the sensitivity of the water balance of the White Nile to climate change, using both observed and stochastic time series to drive the models. Example results are presented using various assumed climate change scenarios and results from a General Circulation Model (GCM). The relative merits and shortcomings of each modelling approach are also discussed. A simple analytical model for Lake Victoria is also used to illustrate some of the overall features of the lake's likely response. Particular difficulties with the White Nile system are that, due to the huge area of open water in the basin, transient responses to short-lived events can occur over timescales comparable with those for which long term climate change impacts are being studied, and predicted changes in flows are extremely sensitive to estimates for the rainfall and evaporation at lake and swamp surfaces. Of the modelling approaches considered, the network simulation approach with stochastic inputs is recommended as a way of smoothing out these transient effects, and assessing the uncertainty in the results due to inaccuracies in the data, the model parameters and the climate change predictions. The paper concludes with a brief discussion of some other areas of uncertainty in the hydrological modelling of White Nile flows and possible alternative external forcing mechanisms for flows in the next few decades.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers.     

The Vulnerability of the Australian Beef Industry to Impacts of the Cattle Tick (Boophilus microplus) under Climate Change

An integrated assessment is presented of the potential impacts of the cattle tick (Boophilus microplus Canestrini) on the Australian beefindustry under climate change. The project was carried out as a case study to test an impact assessment approach that was designed to integrate biological, production and socio-economic impacts on managed and natural systems. A climate-driven, tick population model was run for European, zebu and crossbred cattle breeds having different levels of resistance to cattle ticks. A geographical information system (GIS) was used to organise spatial data on climate scenarios and industry statistics and to undertake regional analyses.A comparison was made of the two available approaches to conducting impact assessments, namely a bottom-up approach using sensitivity analysis and a top-down approach using climate change scenarios from a global circulation model (GCM) (CSIRO, 1996). The output, in terms of the abundance of tick populations and reductions in cattle productivity for each breed showed significant expansions in potential geographical impacts. In the absence of any adaptation measures, the results indicated changes in the losses in live weight gain of cattle tick ranging from 7780 tonnes per year by 2030 to 21637 tonnes per year by 2100, in comparison with estimates for current losses of 6594 tonnes per year.The principal adaptation options available to the beef industry are to switch to breeds that are more resistant to cattle ticks, or to increase the frequency of treatments with various tick control products. In this paper we focus on switching breeds as an adaptive measure when appropriate damage thresholds are triggered under the climate change scenarios. When adaptation measures were put in place, the losses ranged from 4962 tonnes in 2030 to 5619 tonnes in 2100 compared with 2636 tonnes at present if all producers adopted the optimal breed structure. Optimal breed structure was defined as one that would prevent tick numbers per animal exceeding 100 ticks per animal for European and 700 ticks per animal for crossbred breeds of cattle in any week of the year under a tick control strategy that was suitable for present climatic conditions. The lower threshold for European breeds reflects their vulnerability to explosive increases in numbers because of their low resistance to ticks. The results of the analyses using the GCM scenarios were used in an economic model to calculate costs of lost live-weight gain for 2030, 2070 and 2100. The greatest increases in costs were incurred in the southern parts of the current distribution in Queensland and potentially in northern New South Wales if the present quarantine barrier failed.Given the great uncertainty of the nature of possible regional changes in climate, analyses of the sensitivity of losses in live weight gain to changes in climatic variables were also undertaken. The analyses included a measure of likely impacts of cattle tick on the beef cattle industry, in the absence of adaptation measures, as a baseline measure of sensitivity. The likely impacts on crossbred cattle were insensitive to the climatic variables.When adaptive breed changes were allowed, the economic impacts on the industry were insensitive to the GCM scenarios. This suggests that, at least in this instance, reducing the uncertainties in climate change scenarios is not a priority if the adaptation strategies can be implemented in a cost-effective manner. Finally we made a qualitative assessment of the sustainability and robustness of alternative approaches to adaptation and assessed regional vulnerability to cattle tick under climate change. The conclusions were so strongly dependent on assumptions about the future of other global changes, in particular the ability to maintain quarantine barriers and to retain effective acaricides at comparable costs to the present, that we strongly recommend that risk assessments of climate change extend to all relevant variables in involved in global change where possible.     

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.     

Climate change scenarios for precipitation and potential evapotranspiration over central Belgium

In this article, we examine climate model estimations for the future climate over central Belgium. Our analysis is focused mainly on two variables: potential evapotranspiration (PET) and precipitation. PET is calculated using the Penman equation with parameters appropriately calibrated for Belgium, based on RCM data from the European project PRUDENCE database. Next, we proceed into estimating the model capacity to reproduce the reference climate for PET and precipitation. The same analysis for precipitation is also performed based on GCM data from the IPCC AR4 database. Then, the climate change signal is evaluated over central Belgium using RCM and GCM simulations based on several SRES scenarios. The RCM simulations show a clear shift in the precipitation pattern with an increase during winter and a decrease during summer. However, the inclusion of another set of SRES scenarios from the GCM simulations leads to a less clear climate change signal.     

The integration of PESETA sectoral economic impacts into the GEM-E3 Europe model: methodology and results

The PESETA project has estimated the physical effects of climate change in Europe for the following impact categories with a market valuation: agriculture, river floods, coastal systems and tourism. Four alternative scenarios of future climate change have been considered. The computable general equilibrium (CGE) GEM-E3 model for Europe has been used to integrate the PESETA damages under a consistent economic framework. The approach followed has been to assess the effects of future climate (as of 2080s) on today’s economy. This article details the way each sectoral impact has been integrated into the CGE model. The EU welfare loss is estimated to be in a range of 0.2% to 1%, depending on the climate future and the projected sea level rise. Results show that the Southern Europe region appears as the most vulnerable area to climate change. Impacts in coastal systems, agriculture and river floods determine the overall and regional pattern of impacts within Europe.     

Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.     

Time of emergence of anthropogenic warming signals in the Northeast Asia assessed from multi-regional climate models

Time of Emergence (ToE) is the time at which the signal of climate change emerges from the background noise of natural climate variability, and can provide useful information for climate change impacts and adaptations. This study examines future ToEs for daily maximum and minimum temperatures over the Northeast Asia using five Regional Climate Models (RCMs) simulations driven by single Global Climate Model (GCM) under two Representative Concentration Pathways (RCP) emission scenarios. Noise is defined based on the interannual variability during the present-day period (1981-2010) and warming signals in the future years (2021-2100) are compared against the noise in order to identify ToEs. Results show that ToEs of annual mean temperatures occur between 2030s and 2040s in RCMs, which essentially follow those of the driving GCM. This represents the dominant influence of GCM boundary forcing on RCM results in this region. ToEs of seasonal temperatures exhibit larger ranges from 2030s to 2090s. The seasonality of ToE is found to be determined majorly by noise amplitudes. The earliest ToE appears in autumn when the noise is smallest while the latest ToE occurs in winter when the noise is largest. The RCP4.5 scenario exhibits later emergence years than the RCP8.5 scenario by 5-35 years. The significant delay in ToEs by taking the lower emission scenario provides an important implication for climate change mitigation. Daily minimum temperatures tend to have earlier emergence than daily maximum temperature but with low confidence. It is also found that noise thresholds can strongly affect ToE years, i.e. larger noise threshold induces later emergence, indicating the importance of noise estimation in the ToE assessment.     

Comparison of uncertainty sources for climate change impacts: flood frequency in England

This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.     

An economic analysis of potential impacts of climate change in Egypt

Projections of climate impacts on crop yields simulated for different General Circulation Model (GCM) scenarios are used, in a recursively dynamic general equilibrium framework, to account for potential economy-wide impacts of climate change in Egypt. Comparing these impact projections to those obtained under a reference, business-as-usual, scenario assuming some moderate changes in the political, economic or technological spheres, indicates that global warming has potentially negative effects. The analysis is based on a global assessment of potential climate change-induced variations in world commodity production and trade. The Egyptian agricultural sector, and the non-agricultural sector to a lesser extent, are projected to be increasingly less self-sufficient. Specific potential adverse impacts are identified. The simulation results show that high-cost adaptation measures involving major changes in the agricultural system and practices may mitigate these adverse impacts. Stimulating economic development of the rural areas and creating appropriate conditions for effective diffusion and development of technologies — particularly for the agricultural sector — would seem a desirable strategy. Perhaps, more importantly, the simulation results show that the assumption of exogenously determined technological progress may be inappropriate, in which case the potential adverse impacts of a future warming of the global climate are likely to be fewer than is indicated in this study — if prevailing constraints on productivity growth in the major food and feed grains are ‘released’ by endogenous advances in technology.     

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.     

Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M

The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical downscaling and hydrological modeling is illustrated through its application to the basin. Prior to statistical downscaling of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based downscaling models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from downscaled outputs have been reduced after downscaling process. Finally, the corrected downscaled outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.     

REGIONAL CLIMATE CHANGE SCENARIOS UNDER GLOBAL WARMING IN KAZAKHSTAN

The aim of this paper is to report on the development of regional climate change scenarios for Kazakhstan as the result of increasing of CO₂ concentration in the global atmosphere. These scenarios are used in the assessment of climate change impacts on the agricultural, forest and water resources of Kazakhstan. Climate change scenarios for Kazakhstan to assess both long-term (2× CO₂ in 2075) and short-term (2000, 2010 and 2030) impacts were prepared. The climate conditions under increasing CO₂ concentration were estimated from three General Circulation Models (GCM) outputs: the model of the Canadian Climate Center Model (CCCM), the model of the Geophysical Fluid Dynamics Laboratory (GFDL) and the 1% transient version of the GFDL model (GFDL-T). The near-term climate scenarios were obtained using the probabilistic forecast model (PFM) to the year 2010 and the results of GFDL-T for years 2000 and 2030. A baseline scenario representing the current climate conditions based on observations from 1951 to 1980 was developed. The assessment of climate change in Kazakhstan based on the analysis of 100-years observations is given too. As a result of comparisons of the current climate (based on observed climate) the 1× CO₂ output from GCMs showed that the GFDL model best matches the observed climate. The GFDL model suggests that the minimum increase in temperature is expected in winter, when most of the territory is expected to have temperatures 2.3–4.5 °C higher. The maximum (4.3 to 8.2 °C) is expected to be in spring. CCCM scenario estimates an extreme worming above 11 °C in spring months. GFDL-T outputs provide an intermediate scenario.     

Probabilistic assessment of projected climatological drought characteristics over the Southeast USA

The study makes a probabilistic assessment of drought risks due to climate change over the southeast USA based on 15 Global Circulation Model (GCM) simulations and two emission scenarios. The effects of climate change on drought characteristics such as drought intensity, frequency, areal extent, and duration are investigated using the seasonal and continuous standard precipitation index (SPI) and the standard evapotranspiration index (SPEI). The GCM data are divided into four time periods namely Historical (1961–1990), Near (2010–2039), Mid (2040–2069), and Late (2070–2099), and significant differences between historical and future time periods are quantified using the mapping model agreement technique. Further, the kernel density estimation approach is used to derive a novel probability-based severity-area-frequency (PBS) curve for the study domain. Analysis suggests that future increases in temperature and evapotranspiration will outstrip increases in precipitation and significantly affect future droughts over the study domain. Seasonal drought analysis suggest that the summer season will be impacted the most based on SPI and SPEI. Projections based on SPI follow precipitation patterns and fewer GCMs agree on SPI and the direction of change compared to the SPEI. Long-term and extreme drought events are projected to be affected more than short-term and moderate ones. Based on an analysis of PBS curves, especially based on SPEI, droughts are projected to become more severe in the future. The development of PBS curves is a novel feature in this study and will provide policymakers with important tools for analyzing future drought risks, vulnerabilities and help build drought resilience. The PBS curves can be replicated for studies around the world for drought assessment under climate change.     

Implications of climate change due to the enhanced greenhouse effect on floods and droughts in Australia

Potential impacts of climate change on heavy rainfall events and flooding in the Australian region are explored using the results of a general circulation model (GCM) run in an equilibrium enhanced greenhouse experiment. In the doubled CO₂ simulation, the model simulates an increase in the frequency of high-rainfall events and a decrease in the frequency of low-rainfall events. This result applies over most of Australia, is statistically more significant than simulated changes in total rainfall, and is supported by theoretical considerations. We show that this result implies decreased return periods for heavy rainfall events. The further implication is that flooding could increase, although we discuss here the many difficulties associated with assessing in quantitative terms the significance of the modelling results for the real world.The second part of the paper assesses the implications of climate change for drought occurrence in Australia. This is undertaken using an off-line soil water balance model driven by observed time series of rainfall and potential evaporation to determine the sensitivity of the soil water regime to changes in rainfall and temperature, and hence potential evaporation. Potential impacts are assessed at nine sites, representing a range of climate regimes and possible climate futures, by linking this sensitivity analysis with scenarios of regional climate change, derived from analysis of enhanced greenhouse experiment results from five GCMs. Results indicate that significant drying may be limited to the south of Australia. However, because the direction of change in terms of the soil water regime is uncertain at all sites and for all seasons, there is no basis for statements about how drought potential may change.     

CLIMATE CHANGE IMPACTS ON THE HYDROLOGIC RESOURCES OF EUROPE: A SIMPLIFIED CONTINENTAL SCALE ANALYSIS

U.S. Country Studies supported analyses of climate change impacts on water resources have been completed or are underway in the following Central and Eastern European nations: Czech Republic, Slovakia, Poland, Romania, Estonia, Russian Federation, and the Ukraine. Climate change impacts on the hydrologic resources of these countries is being performed at the river basin scale using monthly water balance models using GCM-based climate scenarios. The authors have performed a regional analysis of climate change impacts on the Hydrologic Resources of Europe using the Turc Annual Model. The regional analysis was done with GIS methodolgies using regional climate databases. The regional results were compared to the U.S. Country Studies hydrologic assessmnent results to validiate the use of this simplified methodolgy for making regional climate change assessment. Results from three countries showed acceptable performace of the annual approach . Using GCM-based climate scenarios regional analysis of potential climate change impacts on the hydrologic resources of Europe was conducted and national and regional results are presented.     

Climate change and future populations at risk of malaria

Global estimates of the potential impact of climate change on malaria transmission were calculated based on future climate scenarios produced by the HadCM2 and the more recent HadCM3 global climate models developed by the UK Hadley Centre. This assessment uses an improved version of the MIASMA malaria model, which incorporates knowledge about the current distributions and characteristics of the main mosquito species of malaria.The greatest proportional changes in potential transmission are forecast to occur in temperate zones, in areas where vectors are present but it is currently too cold for transmission. Within the current vector distribution limits, only a limited expansion of areas suitable for malaria transmission is forecast, such areas include: central Asia, North America and northern Europe. On a global level, the numbers of additional people at risk of malaria in 2080 due to climate change is estimated to be 300 and 150 million for P. falciparum and P. vivax types of malaria, respectively, under the HadCM3 climate change scenario. Under the HadCM2 ensemble projections, estimates of additional people at risk in 2080 range from 260 to 320 million for P. falciparum and from 100 to 200 million for P. vivax. Climate change will have an important impact on the length of the transmission season in many areas, and this has implications for the burden of disease. Possible decreases in rainfall indicate some areas that currently experience year-round transmission may experience only seasonal transmission in the future. Estimates of future populations at risk of malaria differ significantly between regions and between climate scenarios.