Climate change and heat-related mortality in six cities Part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change

Previous assessments of the impacts of climate change on heat-related mortality use the “delta method” to create temperature projection time series that are applied to temperature–mortality models to estimate future mortality impacts. The delta method means that climate model bias in the modelled present does not influence the temperature projection time series and impacts. However, the delta method assumes that climate change will result only in a change in the mean temperature but there is evidence that there will also be changes in the variability of temperature with climate change. The aim of this paper is to demonstrate the importance of considering changes in temperature variability with climate change in impacts assessments of future heat-related mortality. We investigate future heat-related mortality impacts in six cities (Boston, Budapest, Dallas, Lisbon, London and Sydney) by applying temperature projections from the UK Meteorological Office HadCM3 climate model to the temperature–mortality models constructed and validated in Part 1. We investigate the impacts for four cases based on various combinations of mean and variability changes in temperature with climate change. The results demonstrate that higher mortality is attributed to increases in the mean and variability of temperature with climate change rather than with the change in mean temperature alone. This has implications for interpreting existing impacts estimates that have used the delta method. We present a novel method for the creation of temperature projection time series that includes changes in the mean and variability of temperature with climate change and is not influenced by climate model bias in the modelled present. The method should be useful for future impacts assessments. Few studies consider the implications that the limitations of the climate model may have on the heat-related mortality impacts. Here, we demonstrate the importance of considering this by conducting an evaluation of the daily and extreme temperatures from HadCM3, which demonstrates that the estimates of future heat-related mortality for Dallas and Lisbon may be overestimated due to positive climate model bias. Likewise, estimates for Boston and London may be underestimated due to negative climate model bias. Finally, we briefly consider uncertainties in the impacts associated with greenhouse gas emissions and acclimatisation. The uncertainties in the mortality impacts due to different emissions scenarios of greenhouse gases in the future varied considerably by location. Allowing for acclimatisation to an extra 2°C in mean temperatures reduced future heat-related mortality by approximately half that of no acclimatisation in each city.     

Indicator plant species selection for monitoring the impact of climate change based on prediction uncertainty

To develop a long-term volunteer-based system for monitoring the impacts of climate change on plant distributions, potential indicator plants and monitoring sites were assessed considering habitat prediction uncertainty. We used species distribution models (SDMs) to project potential habitats for 19 popular edible wild plants in Japan. Prediction uncertainties of SDMs were assessed using three high-performance modeling algorithms and 19 simulated future climate data. SDMs were developed using presence/absence records, four climatic variables, and five non-climatic variables. The results showed that prediction uncertainties for future climate simulations were greater than those from the three different modeling algorithms. Among the 19 edible wild plant species, six had highly accurate SDMs and greater changes in occurrence probabilities between current and future climate conditions. The potential habitats of these six plants under future climate simulations tended to shift northward and upward, with predicted losses in potential southern habitats. These results suggest that these six plants are candidate indicators for long-term biological monitoring of the impacts of climate change. If temperature continuously increases as predicted, natural populations of these plants will decline in Kyushu, Chugoku and Shikoku districts, and in low altitudes of Chubu and Tohoku districts. These results also indicate the importance of occurrence probability and prediction uncertainty of SDMs for selecting target species and site locations for monitoring programs. Sasa kurilensis, a very popular and widespread dominant scrub bamboo in the cool-temperate regions of Japan, was found to be the most effective plant for monitoring.     

Incorporating climate change into ecosystem service assessments and decisions: a review

Climate change is having a significant impact on ecosystem services and is likely to become increasingly important as this phenomenon intensifies. Future impacts can be difficult to assess as they often involve long timescales, dynamic systems with high uncertainties, and are typically confounded by other drivers of change. Despite a growing literature on climate change impacts on ecosystem services, no quantitative syntheses exist. Hence, we lack an overarching understanding of the impacts of climate change, how they are being assessed, and the extent to which other drivers, uncertainties, and decision making are incorporated. To address this, we systematically reviewed the peer‐reviewed literature that assesses climate change impacts on ecosystem services at subglobal scales. We found that the impact of climate change on most types of services was predominantly negative (59% negative, 24% mixed, 4% neutral, 13% positive), but varied across services, drivers, and assessment methods. Although uncertainty was usually incorporated, there were substantial gaps in the sources of uncertainty included, along with the methods used to incorporate them. We found that relatively few studies integrated decision making, and even fewer studies aimed to identify solutions that were robust to uncertainty. For management or policy to ensure the delivery of ecosystem services, integrated approaches that incorporate multiple drivers of change and account for multiple sources of uncertainty are needed. This is undoubtedly a challenging task, but ignoring these complexities can result in misleading assessments of the impacts of climate change, suboptimal management outcomes, and the inefficient allocation of resources for climate adaptation.     

Development of indicators of ecosystem functioning in a temperate shelf sea: a combined fieldwork and modelling approach

A conceptual model of the main carbon and nitrogen flows through pelagic and benthic food webs was used to identify the key biogeochemical processes representing ecosystem functioning, and to select indicators of each of these processes. A combined fieldwork and modelling approach was used to provide the data required to evaluate the indicators in terms of their suitability for assessing and managing the impacts of climate change and demersal trawling. Four of our 16 proposed indicators (phytoplankton production and productivity, near-bed oxygen concentrations and oxygen penetration of the seabed) met the majority of criteria we used for evaluating indicators. Five indicators (depth of anoxic sediment, zoobenthos biomass, production, productivity and bioturbation potential) did not comply with sufficient criteria to be considered as good indicators. Six of our proposed indicators (zooplankton biomass, size structure, production and productivity; ecosystem productivity; ecosystem balance) could not be assessed for sensitivity and specificity using our models, and therefore need to be addressed in future work aimed at improving both the models and the fieldwork. Our results indicate that evaluation of indicators is difficult, because of the number and variety of human pressures which need to be considered in reality, and the interactions between these pressures and the ecosystem components which they affect. The challenge will be to establish if there are indeed any indicators which are able to meet the majority of criteria for good indicators in holistic ecosystem-based assessments.     

Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems

Forest ecosystems are critical to mitigating greenhouse gas emissions through carbon sequestration. However, climate change has affected forest ecosystem functioning in both negative and positive ways, and has led to shifts in species/functional diversity and losses in plant species diversity which may impair the positive effects of diversity on ecosystem functioning. Biodiversity may mitigate climate change impacts on (I) biodiversity itself, as more‐diverse systems could be more resilient to climate change impacts, and (II) ecosystem functioning through the positive relationship between diversity and ecosystem functioning. By surveying the literature, we examined how climate change has affected forest ecosystem functioning and plant diversity. Based on the biodiversity effects on ecosystem functioning (B→EF), we specifically address the potential for biodiversity to mitigate climate change impacts on forest ecosystem functioning. For this purpose, we formulate a concept whereby biodiversity may reduce the negative impacts or enhance the positive impacts of climate change on ecosystem functioning. Further B→EF studies on climate change in natural forests are encouraged to elucidate how biodiversity might influence ecosystem functioning. This may be achieved through the detailed scrutiny of large spatial/long temporal scale data sets, such as long‐term forest inventories. Forest management strategies based on B→EF have strong potential for augmenting the effectiveness of the roles of forests in the mitigation of climate change impacts on ecosystem functioning.     

Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (gamma, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966-1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of gamma near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.     

A Synthesis of Climate and Vegetation Cover Effects on Biogeochemical Cycling in Shrub-Dominated Drylands

Semi-arid and arid ecosystems dominated by shrubs (“dry shrublands”) are an important component of the global C cycle, but impacts of climate change and elevated atmospheric CO₂ on biogeochemical cycling in these ecosystems have not been synthetically assessed. This study synthesizes data from manipulative studies and from studies contrasting ecosystem processes in different vegetation microsites (that is, shrub or herbaceous canopy versus intercanopy microsites), to assess how changes in climate and atmospheric CO₂ affect biogeochemical cycles by altering plant and microbial physiology and ecosystem structure. Further, we explore how ecosystem structure impacts on biogeochemical cycles differ across a climate gradient. We found that: (1) our ability to project ecological responses to changes in climate and atmospheric CO₂ is limited by a dearth of manipulative studies, and by a lack of measurements in those studies that can explain biogeochemical changes, (2) changes in ecosystem structure will impact biogeochemical cycling, with decreasing pools and fluxes of C and N if vegetation canopy microsites were to decline, and (3) differences in biogeochemical cycling between microsites are predictable with a simple aridity index (MAP/MAT), where the relative difference in pools and fluxes of C and N between vegetation canopy and intercanopy microsites is positively correlated with aridity. We conclude that if climate change alters ecosystem structure, it will strongly impact biogeochemical cycles, with increasing aridity leading to greater heterogeneity in biogeochemical cycling among microsites. Additional long-term manipulative experiments situated across dry shrublands are required to better predict climate change impacts on biogeochemical cycling in deserts.     

Trait‐based climate vulnerability assessments in data‐rich systems: An application to eastern Bering Sea fish and invertebrate stocks

Trait‐based climate vulnerability assessments based on expert evaluation have emerged as a rapid tool to assess biological vulnerability when detailed correlative or mechanistic studies are not feasible. Trait‐based assessments typically view vulnerability as a combination of sensitivity and exposure to climate change. However, in some locations, a substantial amount of information may exist on system productivity and environmental conditions (both current and projected), with potential disparities in the information available for data‐rich and data‐poor stocks. Incorporating this level of detailed information poses challenges when conducting, and communicating uncertainty from, rapid vulnerability assessments. We applied a trait‐based vulnerability assessment to 36 fish and invertebrate stocks in the eastern Bering Sea (EBS), a data‐rich ecosystem. In recent years, the living marine resources of the EBS and Aleutian Islands have supported fisheries worth more than US $1 billion of annual ex‐vessel value. Our vulnerability assessment uses projections (to 2039) from three downscaled climate models, and graphically characterizes the variation in climate projections between climate models and between seasons. Bootstrapping was used to characterize uncertainty in specific biological traits and environmental variables, and in the scores for sensitivity, exposure, and vulnerability. The sensitivity of EBS stocks to climate change ranged from “low” to “high,” but vulnerability ranged between “low” and “moderate” due to limited exposure to climate change. Comparison with more detailed studies reveals that water temperature is an important variable for projecting climate impacts on stocks such as walleye pollock (Gadus chalcogrammus), and sensitivity analyses revealed that modifying the rule for determining vulnerability increased the vulnerability scores. This study demonstrates the importance of considering several uncertainties (e.g., climate projections, biological, and model structure) when conducting climate vulnerability assessments, and can be extended in future research to consider the vulnerability of user groups dependent on these stocks.     

Ecosystem Model Skill Assessment. Yes We Can!

Need to Assess the Skill of Ecosystem Models

Accelerated changes to global ecosystems call for holistic and integrated analyses of past, present and future states under various pressures to adequately understand current and projected future system states. Ecosystem models can inform management of human activities in a complex and changing environment, but are these models reliable? Ensuring that models are reliable for addressing management questions requires evaluating their skill in representing real-world processes and dynamics. Skill has been evaluated for just a limited set of some biophysical models. A range of skill assessment methods have been reviewed but skill assessment of full marine ecosystem models has not yet been attempted.

Northeast US Atlantis Marine Ecosystem Model

We assessed the skill of the Northeast U.S. (NEUS) Atlantis marine ecosystem model by comparing 10-year model forecasts with observed data. Model forecast performance was compared to that obtained from a 40-year hindcast. Multiple metrics (average absolute error, root mean squared error, modeling efficiency, and Spearman rank correlation), and a suite of time-series (species biomass, fisheries landings, and ecosystem indicators) were used to adequately measure model skill. Overall, the NEUS model performed above average and thus better than expected for the key species that had been the focus of the model tuning. Model forecast skill was comparable to the hindcast skill, showing that model performance does not degenerate in a 10-year forecast mode, an important characteristic for an end-to-end ecosystem model to be useful for strategic management purposes.

Skill Assessment Is Both Possible and Advisable

We identify best-practice approaches for end-to-end ecosystem model skill assessment that would improve both operational use of other ecosystem models and future model development. We show that it is possible to not only assess the skill of a complicated marine ecosystem model, but that it is necessary do so to instill confidence in model results and encourage their use for strategic management. Our methods are applicable to any type of predictive model, and should be considered for use in fields outside ecology (e.g. economics, climate change, and risk assessment).     

A Protocol for Integrated Management, Monitoring, and Enhancement of Degraded Themeda triandra Grasslands based on Plantings of Indicator Species

Abstract Lowland temperate grasslands dominated by Themeda triandra (kangaroo grass) are an endangered ecosystem in southeastern Australia. Grass biomass must be removed frequently to maintain plant diversity, but few studies of the impacts of different biomass removal techniques have been undertaken, and no rapid monitoring schemes have been developed. Low species densities in many reserves (due to past stock grazing) make it difficult to assess the effects of management regimes on plant diversity. Management impacts could be assessed by planting indicator species in replicated enhancement plots and subjecting these plots to adaptive management trials. A protocol for selecting potential indicator species is described, based on a regional quadrat database, using clearly defined criteria. Potential indicator species need to be conspicuous, easy to identify and abundant in high quality diverse grassland remnants, to have relatively broad ecological tolerances, to occur in sites that are relatively species rich and have a comparatively low cover of dominant exotic species, to commonly persist at low densities in long‐grazed reserves, to be responsive to changes in management, and to have been studied ecologically. Only three species from western Victorian grasslands satisfied these criteria: Calocephalus citreus (lemon beauty‐heads), Chrysocephalum apiculatum (common everlasting), and Leptorhynchos squamatus (scaly buttons). All are widespread, herbaceous, hemicryptophytic daisies. Despite a number of caveats, the scheme has the potential to provide a more clearly focused framework for grassland ecosystem management than currently exists.     

植物功能性状对生态系统服务影响研究进展

全面认识和理解生态系统服务的形成机制是维持其持续供给的前提。植物功能性状直接参与多种生态系统过程, 影响生态系统服务供给, 探讨植物功能性状与生态系统服务的关系是揭示生态系统服务形成机制的重要途径。该文采用系统的文献综述方法, 分析了植物功能性状与生态系统服务关系的研究特点, 总结了影响不同生态系统服务的主要植物功能性状, 阐述了可能的影响途径。结果表明: 植物功能性状与生态系统服务关系研究以草地和森林等自然生态系统为主; 大部分研究集中在生态系统供给服务和支持服务, 包括生物量、净初级生产力、土壤肥力等; 根据植物功能性状对不同生态系统服务的影响程度, 植物功能性状可以聚类为土壤保持服务相关性状、水分循环相关性状、多功能相关性状、产品提供服务与养分循环相关性状以及授粉与生物控制服务相关性状; 并阐述了植物功能性状指标影响不同的生态系统服务途径。围绕植物功能性状对生态系统服务的影响, 今后尚需进一步探讨生态系统多功能性、植物功能性状相关性、气候变化和人类活动不确定性、时空尺度差异等因素对二者关系的影响。     

Crop yield response to climate change varies with cropping intensity

Projections of the response of crop yield to climate change at different spatial scales are known to vary. However, understanding of the causes of systematic differences across scale is limited. Here, we hypothesize that heterogeneous cropping intensity is one source of scale dependency. Analysis of observed global data and regional crop modelling demonstrate that areas of high vs. low cropping intensity can have systematically different yields, in both observations and simulations. Analysis of global crop data suggests that heterogeneity in cropping intensity is a likely source of scale dependency for a number of crops across the globe. Further crop modelling and a meta‐analysis of projected tropical maize yields are used to assess the implications for climate change assessments. The results show that scale dependency is a potential source of systematic bias. We conclude that spatially comprehensive assessments of climate impacts based on yield alone, without accounting for cropping intensity, are prone to systematic overestimation of climate impacts. The findings therefore suggest a need for greater attention to crop suitability and land use change when assessing the impacts of climate change.     

Implications of climate change for agricultural productivity in the early twenty-first century

This paper reviews recent literature concerning a wide range of processes through which climate change could potentially impact global-scale agricultural productivity, and presents projections of changes in relevant meteorological, hydrological and plant physiological quantities from a climate model ensemble to illustrate key areas of uncertainty. Few global-scale assessments have been carried out, and these are limited in their ability to capture the uncertainty in climate projections, and omit potentially important aspects such as extreme events and changes in pests and diseases. There is a lack of clarity on how climate change impacts on drought are best quantified from an agricultural perspective, with different metrics giving very different impressions of future risk. The dependence of some regional agriculture on remote rainfall, snowmelt and glaciers adds to the complexity. Indirect impacts via sea-level rise, storms and diseases have not been quantified. Perhaps most seriously, there is high uncertainty in the extent to which the direct effects of CO₂ rise on plant physiology will interact with climate change in affecting productivity. At present, the aggregate impacts of climate change on global-scale agricultural productivity cannot be reliably quantified.     

How repeatable is the Environmental Impact Classification of Alien Taxa (EICAT)? Comparing independent global impact assessments of amphibians

The magnitude of impacts some alien species cause to native environments makes them targets for regulation and management. However, which species to target is not always clear, and comparisons of a wide variety of impacts are necessary. Impact scoring systems can aid management prioritization of alien species. For such tools to be objective, they need to be robust to assessor bias. Here, we assess the newly proposed Environmental Impact Classification for Alien Taxa (EICAT) used for amphibians and test how outcomes differ between assessors. Two independent assessments were made by Kraus (Annual Review of Ecology Evolution and Systematics, 46, 2015, 75‐97) and Kumschick et al. (Neobiota, 33, 2017, 53‐66), including independent literature searches for impact records. Most of the differences between these two classifications can be attributed to different literature search strategies used with only one‐third of the combined number of references shared between both studies. For the commonly assessed species, the classification of maximum impacts for most species is similar between assessors, but there are differences in the more detailed assessments. We clarify one specific issue resulting from different interpretations of EICAT, namely the practical interpretation and assigning of disease impacts in the absence of direct evidence of transmission from alien to native species. The differences between assessments outlined here cannot be attributed to features of the scheme. Reporting bias should be avoided by assessing all alien species rather than only the seemingly high‐impacting ones, which also improves the utility of the data for management and prioritization for future research. Furthermore, assessments of the same taxon by various assessors and a structured review process for assessments, as proposed by Hawkins et al. (Diversity and Distributions, 21, 2015, 1360), can ensure that biases can be avoided and all important literature is included.     

Beyond just sea‐level rise: considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change

Due to their position at the land‐sea interface, coastal wetlands are vulnerable to many aspects of climate change. However, climate change vulnerability assessments for coastal wetlands generally focus solely on sea‐level rise without considering the effects of other facets of climate change. Across the globe and in all ecosystems, macroclimatic drivers (e.g., temperature and rainfall regimes) greatly influence ecosystem structure and function. Macroclimatic drivers have been the focus of climate change‐related threat evaluations for terrestrial ecosystems, but largely ignored for coastal wetlands. In some coastal wetlands, changing macroclimatic conditions are expected to result in foundation plant species replacement, which would affect the supply of certain ecosystem goods and services and could affect ecosystem resilience. As examples, we highlight several ecological transition zones where small changes in macroclimatic conditions would result in comparatively large changes in coastal wetland ecosystem structure and function. Our intent in this communication is not to minimize the importance of sea‐level rise. Rather, our overarching aim is to illustrate the need to also consider macroclimatic drivers within vulnerability assessments for coastal wetlands.     

A critical analysis of regulated river ecosystem responses to managed environmental flows from reservoirs

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Potential use of an arthropod database to support the non-target risk assessment and monitoring of transgenic plants

Worldwide, plants obtained through genetic modification are subject to a risk analysis and regulatory approval before they can enter the market. An area of concern addressed in environmental risk assessments is the potential of genetically modified (GM) plants to adversely affect non-target arthropods and the valued ecosystem services they provide. Environmental risk assessments are conducted case-by-case for each GM plant taking into account the plant species, its trait(s), the receiving environments into which the GM plant is to be released and its intended uses, and the combination of these characteristics. To facilitate the non-target risk assessment of GM plants, information on arthropods found in relevant agro-ecosystems in Europe has been compiled in a publicly available database of bio-ecological information during a project commissioned by the European Food Safety Authority (EFSA). Using different hypothetical GM maize case studies, we demonstrate how the information contained in the database can assist in identifying valued species that may be at risk and in selecting suitable species for laboratory testing, higher-tier studies, as well as post-market environmental monitoring.     

Competitive and demographic leverage points of community shifts under climate warming

Accelerating rates of climate change and a paucity of whole-community studies of climate impacts limit our ability to forecast shifts in ecosystem structure and dynamics, particularly because climate change can lead to idiosyncratic responses via both demographic effects and altered species interactions. We used a multispecies model to predict which processes and species'' responses are likely to drive shifts in the composition of a space-limited benthic marine community. Our model was parametrized from experimental manipulations of the community. Model simulations indicated shifts in species dominance patterns as temperatures increase, with projected shifts in composition primarily owing to the temperature dependence of growth, mortality and competition for three critical species. By contrast, warming impacts on two other species (rendering them weaker competitors for space) and recruitment rates of all species were of lesser importance in determining projected community changes. Our analysis reveals the importance of temperature-dependent competitive interactions for predicting effects of changing climate on such communities. Furthermore, by identifying processes and species that could disproportionately leverage shifts in community composition, our results contribute to a mechanistic understanding of climate change impacts, thereby allowing more insightful predictions of future biodiversity patterns.     

Projecting species’ vulnerability to climate change: Which uncertainty sources matter most and extrapolate best?

Species distribution models (SDMs) are commonly used to assess potential climate change impacts on biodiversity, but several critical methodological decisions are often made arbitrarily. We compare variability arising from these decisions to the uncertainty in future climate change itself. We also test whether certain choices offer improved skill for extrapolating to a changed climate and whether internal cross‐validation skill indicates extrapolative skill. We compared projected vulnerability for 29 wetland‐dependent bird species breeding in the climatically dynamic Prairie Pothole Region, USA. For each species we built 1,080 SDMs to represent a unique combination of: future climate, class of climate covariates, collinearity level, and thresholding procedure. We examined the variation in projected vulnerability attributed to each uncertainty source. To assess extrapolation skill under a changed climate, we compared model predictions with observations from historic drought years. Uncertainty in projected vulnerability was substantial, and the largest source was that of future climate change. Large uncertainty was also attributed to climate covariate class with hydrological covariates projecting half the range loss of bioclimatic covariates or other summaries of temperature and precipitation. We found that choices based on performance in cross‐validation improved skill in extrapolation. Qualitative rankings were also highly uncertain. Given uncertainty in projected vulnerability and resulting uncertainty in rankings used for conservation prioritization, a number of considerations appear critical for using bioclimatic SDMs to inform climate change mitigation strategies. Our results emphasize explicitly selecting climate summaries that most closely represent processes likely to underlie ecological response to climate change. For example, hydrological covariates projected substantially reduced vulnerability, highlighting the importance of considering whether water availability may be a more proximal driver than precipitation. However, because cross‐validation results were correlated with extrapolation results, the use of cross‐validation performance metrics to guide modeling choices where knowledge is limited was supported.     

Downy mildew (Plasmopara viticola) epidemics on grapevine under climate change

As climate is a key agro‐ecosystem driving force, climate change could have a severe impact on agriculture. Many assessments have been carried out to date on the possible effects of climate change (temperature, precipitation and carbon dioxide concentration changes) on plant physiology. At present however, likely effects on plant pathogens have not been investigated deeply. The aim of this work was to simulate future scenarios of downy mildew (Plasmopara viticola) epidemics on grape under climate change, by combining a disease model to output from two general circulation models (GCMs). Model runs corresponding to the SRES‐A2 emissions scenario, characterized by high projections of both population and greenhouse gas emissions from present to 2100, were chosen in order to investigate impacts of worst‐case scenarios, among those currently available from IPCC. Three future decades were simulated (2030, 2050, 2080), using as baseline historical series of meteorological data collected from 1955 to 2001 in Acqui Terme, an important grape‐growing area in the north‐west of Italy. Both GCMs predicted increase of temperature and decrease of precipitation in this region. The simulations obtained by combining the disease model to the two GCM outputs predicted an increase of the disease pressure in each decade: more severe epidemics were a direct consequence of more favourable temperature conditions during the months of May and June. These negative effects of increasing temperatures more than counterbalanced the effects of precipitation reductions, which alone would have diminished disease pressure. Results suggested that, as adaptation response to future climate change, more attention would have to be paid in the management of early downy mildew infections; two more fungicide sprays were necessary under the most negative climate scenario, compared with present management regimes. At the same time, increased knowledge on the effects of climate change on host–pathogen interactions will be necessary to improve current predictions.