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.     

Influence of climate factors on spatial distribution of Texas cattle breeds

This paper investigates the spatial distribution of cattle breeders in Texas to quantify how climate factors influence cattle breed selection. A multivariate probit model is employed to examine the county-level binary choices of Bos taurus, Bos indicus and composite breeds derived from cattle breed association membership data. The estimation results suggest that summer heat stress is a significant factor for breed selection: positive for Bos indicus and negative for Bos taurus and composite breeds, with the average marginal effects on breed membership probability being 9.7 %, ?26.5 % and ?7.9 %, respectively. The intensity of the summer heat impacts can lead to noteworthy changes in spatial distributions of Texas cattle breeds in the event of climate change.     

Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models

The uncertainties and sources of variation in projected impacts of climate change on agriculture and terrestrial ecosystems depend not only on the emission scenarios and climate models used for projecting future climates, but also on the impact models used, and the local soil and climatic conditions of the managed or unmanaged ecosystems under study. We addressed these uncertainties by applying different impact models at site, regional and continental scales, and by separating the variation in simulated relative changes in ecosystem performance into the different sources of uncertainty and variation using analyses of variance. The crop and ecosystem models used output from a range of global and regional climate models (GCMs and RCMs) projecting climate change over Europe between 1961–1990 and 2071–2100 under the IPCC SRES scenarios. The projected impacts on productivity of crops and ecosystems included the direct effects of increased CO₂ concentration on photosynthesis. The variation in simulated results attributed to differences between the climate models were, in all cases, smaller than the variation attributed to either emission scenarios or local conditions. The methods used for applying the climate model outputs played a larger role than the choice of the GCM or RCM. The thermal suitability for grain maize cultivation in Europe was estimated to expand by 30–50% across all SRES emissions scenarios. Strong increases in net primary productivity (NPP) (35–54%) were projected in northern European ecosystems as a result of a longer growing season and higher CO₂ concentrations. Changing water balance dominated the projected responses of southern European ecosystems, with NPP declining or increasing only slightly relative to present-day conditions. Both site and continental scale models showed large increases in yield of rain-fed winter wheat for northern Europe, with smaller increases or even decreases in southern Europe. Site-based, regional and continental scale models showed large spatial variations in the response of nitrate leaching from winter wheat cultivation to projected climate change due to strong interactions with soils and climate. The variation in simulated impacts was smaller between scenarios based on RCMs nested within the same GCM than between scenarios based on different GCMs or between emission scenarios.     

Use of general circulation model output in the creation of climate change scenarios for impact analysis

Many scientific studies warn of a rapid global climate change during the next century. These changes are understood with much less certainty on a regional scale than on a global scale, but effects on ecosystems and society will occur at local and regional scales. Consequently, in order to study the true impacts of climate change, regional scenarios of future climate are needed. One of the most important sources of information for creating scenarios is the output from general circulation models (GCMs) of the climate system. However, current state-of-the-art GCMs are unable to simulate accurately even the current seasonal cycle of climate on a regional basis. Thus the simple technique of adding the difference between 2 × CO₂ and 1 × CO₂ GCM simulations to current climatic time series cannot produce scenarios with appropriate spatial and temporal details without corrections for model deficiencies. In this study a technique is developed to allow the information from GCM simulations to be used, while accommodating for the deficiencies. GCM output is combined with knowledge of the regional climate to produce scenarios of the equilibrium climate response to a doubling of the atmospheric CO₂ concentration for three case study regions, China, Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general climate change calculated with several GCMs with the observed patterns of interannual climate variability, reasonable scenarios of temperature and precipitation variations can be created. Generalizations of this procedure to other regions of the world are discussed.     

Vulnerability and adaptation assessments of agriculturalcrops under climate change in the Southeastern USA

Summary  It is expected that a change in climatic conditions due to global warming will directly impact agricultural production. Most climate change studies have been applied at very large scales, in which regions were represented by only one or two weather stations, which were mainly located at airports of major cities. The objective of this study was to determine the potential impact of climate change at a local level, taking into account weather data recorded at remote locations. Daily weather data for a 30-year period were obtained for more than 500 sites, representing the southeastern region of the USA. Climate change scenarios, using transient and equilibrium global circulation models (GCM), were defined, created and applied to the daily historical weather data. The modified temperature, precipitation and solar radiation databases corresponding to each of the climate change scenarios were used to run the CERES v.3.5 simulation model for maize and winter wheat and the CROPGRO v.3.5 model for soybean and peanut. The GCM scenarios projected a shorter duration of the crop-growing season. Under the current level of CO₂, the GCM scenarios projected a decrease of crop yields in the 2020s. When the direct effects of CO₂ were assumed in the study, the scenarios resulted in an increase in soybean and peanut yield. Under equilibrium , the GCM climate change scenarios projected a decrease of maize and winter wheat yield. The indirect effects of climate change also tended to decrease soybean and peanut yield. However, when the direct effects of CO₂ were included, most of the scenarios resulted in an increase in legume yields. Possible changes in sowing data, hybrids and cultivar selection, and fertilization were considered as adaptation options to mitigate the potential negative impact of potential warming. Received July 20, 1999/Revised April 18, 2000     

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.     

A way forward on adaptation to climate change in Colombian agriculture: perspectives towards 2050

Policy measures regarding adaptation to climate change include efforts to adjust socio-economic and ecologic systems. Colombia has undertaken various measures in terms of climate change mitigation and adaptation since becoming a party of the Kyoto protocol in 2001 and a party of the United Nations Framework Convention on Climate Change (UNFCCC) in 1995. The first national communication to the UNFCCC stated how Colombian agriculture will be severely impacted under different emission scenarios and time frames. The analyses in this document further support that climate change will severely threaten the socioeconomics of Colombian agriculture. We first query national data sources to characterize the agricultural sector. We then use 17 Global Circulation Model (GCM) outputs to quantify how Colombian agricultural production may be affected by climate change, and show the expected changes to years 2040–2069 (“2050”) under the A2 scenario of the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES-A2) and the overall trends in both precipitation and temperature to 2100. We then evaluate expected changes within different regions and measure the proportion of area affected within each crop’s distributional range. By 2050, climatic change in Colombia will likely impact 3.5 million people, 14?% of national GDP corresponding to agriculture, employment of 21?% of the population, agro-industries, supply chains, and food and nutritional security. If no adaptation measures are taken, 80?% of crops would be impacted in more than 60?% of their current areas of cultivation, with particularly severe impacts in high value perennial and exportable crops. Impacts also include soil degradation and organic matter losses in the Andes hillsides; likely flooding in the Caribbean and Pacific coasts; niche losses for coffee, fruit, cocoa, and bananas; changes in prevalence of pests and diseases; and increases in the vulnerabilities of non-technically developed smallholders. There is, however, still time to change the current levels of vulnerability if a multidisciplinary focus (i.e., agronomic, economic, and social) in vulnerable sectors is undertaken. Each sub-sector and the Government need to invest in: (1) data collection, (2) detailed, regionally-based impact assessments, (3) research and development, and (4) extension and technology transfer. Support to vulnerable smallholders should be given by the state in the form of agricultural insurance systems contextualized under the phenomenon of climate change. A national coordination scheme led by (but not restricted to) the Ministry of Agriculture and Rural Development (MADR) with the contributions of national and international institutions is needed to address agricultural adaptation.     

Projections of uncertainties in climate change scenarios into expected winter wheat yields

Summary The crop model CERES-Wheat in combination with the stochastic weather generator were used to quantify the effect of uncertainties in selected climate change scenarios on the yields of winter wheat, which is the most important European cereal crop. Seven experimental sites with the high quality experimental data were selected in order to evaluate the crop model and to carry out the climate change impact analysis. The analysis was based on the multi-year crop model simulations run with the daily weather series prepared by the stochastic weather generator. Seven global circulation models (GCMs) were used to derive the climate change scenarios. In addition, seven GCM-based scenarios were averaged in order to derive the average scenario (AVG). The scenarios were constructed for three time periods (2025, 2050 and 2100) and two SRES emission scenarios (A2 and B1). The simulated results showed that: (1) Wheat yields tend to increase (40 out of 42 applied scenarios) in most locations in the range of 7.5–25.3% in all three time periods. In case of the CCSR scenario that predicts the most severe increase of air temperature, the yields would be reduced by 9.6% in 2050 and by 25.8% in 2100 if the A2 emission scenario would become reality. Differences between individual scenarios are large and statistically significant. Particularly for the time periods 2050 and 2100 there are doubts about the trend of the yield shifts. (2) The site effect was caused by the site-specific soil and climatic conditions. Importance of the site influence increases with increasing severity of imposed climatic changes and culminates for the emission scenario A2 and the time period 2100. The sustained tendency benefiting two warmest sites has been found as well as more positive response to the changed climatic conditions of the sites with deeper soil profiles. (3) Temperature variability proved to be an important factor and influenced both mean and standard deviation of the yields. Change of temperature variability by more than 25% leads to statistically significant changes in yield distribution. The effect of temperature variability decreases with increased values of mean temperature. (4) The study proved that the application of the AVG scenarios – despite possible objections of physical inconsistency – might be justifiable and convenient in some cases. It might bring results comparable to those derived from averaging outputs based on number of scenarios and provide more robust estimate than the application of only one selected GCM scenario.     

USE OF A STOCHASTIC WEATHER GENERATOR IN THE DEVELOPMENT OF CLIMATE CHANGE SCENARIOS

Climate change scenarios with a high spatial and temporal resolution are required in the evaluation of the effects of climate change on agricultural potential and agricultural risk. Such scenarios should reproduce changes in mean weather characteristics as well as incorporate the changes in climate variability indicated by the global climate model (GCM) used. Recent work on the sensitivity of crop models and climatic extremes has clearly demonstrated that changes in variability can have more profound effects on crop yield and on the probability of extreme weather events than simple changes in the mean values. The construction of climate change scenarios based on spatial regression downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translated the coarse resolution GCM grid-box predictions of climate change to site-specific values. These values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather data. This approach permits the incorporation of changes in the mean and variability of climate in a consistent and computationally inexpensive way. The stochastic weather generator used in this study, LARS-WG, has been validated across Europe and has been shown to perform well in the simulation of different weather statistics, including those climatic extremes relevant to agriculture. The importance of downscaling and the incorporation of climate variability are demonstrated at two European sites where climate change scenarios were constructed using the UK Met. Office high resolution GCM equilibrium and transient experiments.     

Potential climate change impacts on the probability of wind damage in a south Swedish forest

We estimated how the possible changes in wind climate and state of the forest due to climate change may affect the probability of exceeding critical wind speeds expected to cause wind damage within a forest management unit located in Southern Sweden. The topography of the management unit was relatively gentle and the forests were dominated by Norway spruce (Picea abies (L.) Karst.). We incorporated a model relating the site index (SI) to the site productivity into the forest projection model FTM. Using estimated changes in the net primary production (NPP) due to climate change and assuming a relative change in NPP equal to a relative change in the site productivity, we simulated possible future states of the forest under gradual adjustment of SI in response to climate change. We estimated changes in NPP by combining the boreal-adapted BIOMASS model with four regional climate change scenarios calculated using the RCAO model for the period 2071–2100 and two control period scenarios for the period 1961–1990. The modified WINDA model was used to calculate the probability of wind damage for individual forest stands in simulated future states of the forest. The climate change scenarios used represent non-extreme projections on a 100-year time scale in terms of global mean warming. A 15–40% increase in NPP was estimated to result from climate change until the period 2071–2100. Increasing sensitivity of the forest to wind was indicated when the management rules of today were applied. A greater proportion of the calculated change in probability of wind damage was due to changes in wind climate than to changes in the sensitivity of the forest to wind. While regional climate scenarios based on the HadAM3H general circulation model (GCM) indicated no change (SRES A2 emission scenario) or a slightly reduced (SRES B2 emission scenario) probability of wind damage, scenarios based on the ECHAM4/OPYC3 GCM indicated increased probability of wind damage. The assessment should, however, be reviewed as the simulation of forest growth under climate change as well as climate change scenarios are refined.     

长江口海平面上升预测及其对滨海湿地影响

选择吴淞站和吕四站2个验潮站数据,通过统计学方法进行长江口海平面上升预测,从而构建了一套长江口地区较完备的海平面上升情景库：以2013年为基准年份,其最佳预测值的范围在2030年、2050年、2100年分别为50~217 mm,118~430 mm,256~1215 mm。以此情景库为基础,探究海平面上升变化对长江口滨海湿地的影响,结果表明：随着海平面上升值的增加,长江口滨海湿地的面积不断减少;在基于验潮站数据作趋势外推得到的情景下,湿地面积减少较平缓,而在考虑全球变暖背景的情景下,湿地面积减少迅速;且不论在何种情景下,时间尺度越大,湿地减少的面积越大。     

Climate change adaptation advantage for African road infrastructure

The African continent is facing the potential of a $183.6 billion USD liability to repair and maintain roads damaged from temperature and precipitation changes directly related to predicted climate change through 2100. This cost is strictly to retain the current road inventory. This cost does not include costs associated with impacts to critically needed new roads. In many African countries, limited or non-existent funds for adaptation and mitigation are challenging these countries to identify the threats that are posed by climate change, develop adaptation approaches to the predicted changes, incorporate changes into mid-range and long-term development plans, and secure funding for the proposed and necessary adaptations. Existing studies have attempted to quantify the impact of climate change on infrastructure assets that will be affected by climate change in the coming decades. The current study extends these efforts by specifically addressing the effect of climate change on the African road infrastructure. The study identifies both total costs and opportunity costs of repairing and maintaining infrastructure due to increased stressors from climate change. Proactive and reactive costs are examined for six climate scenarios, with costs ranging, respectively, from an average of $22 million USD to $54 million USD annually per country. A regional analysis shows contrast between impacts in five areas of the continent, with impacts ranging from 22 % opportunity cost to 168 %. These costs have the potential to delay critical infrastructure development on the continent and present a challenge to policy makers balancing short-term needs with long-term planning.     

The Potential Impact of Global Warming on Hail Losses to Winter Cereal Crops in New South Wales

This study was undertaken to determine the impact of potential global warming on the magnitude of hail losses to winter cereal crops within two areas situated on the western slopes of New South Wales, Australia. A model relating historical crop hail losses to climatic variables was developed for each area. These models included seasonal measures of vertical instability, low-level moisture and the height of the freezing level. In both areas, windshear was not found to be an important factor influencing seasonal crop hail losses. The two crop hail loss models were then used in conjunction with upper-air climatic data from three single mixed-layer global climate models (GCMs). Each GCM was run for 1 × CO₂ conditions and for 2 × CO₂ conditions. The enhanced greenhouse effect on climatic variables was taken to be the difference between their values for these two runs. Changes to climatic variables were then translated directly into changes in the percentage value of the winter cereal crop lost due to hail. In both areas, the three GCMs agreed concerning the direction of change in each of the variables used in the crop hail loss model. GCM simulations of the greenhouse effect resulted in a decline in winter cereal crop hail losses, with the exception of one GCM simulation at one location where losses increased slightly. None of the changes due to the enhanced greenhouse effect, however, were significant owing to a large observed seasonal variability of crop hail losses. Also, the simulated seasonal variability of crop hail losses did not change significantly due to the enhanced greenhouse effect. These results depended on two important assumptions. Firstly, it was assumed that the dominant relationships between climatic variables and crop hail losses in the past would remain the same in a future climate. Secondly, it was assumed that the single mixed-layer GCMs used in the study were correctly predicting climate change under enhanced greenhouse conditions.     

The Shared Socioeconomic Pathways and their energy,land use,and greenhouse gas emissions implications: An overview

This paper presents the overview of the Shared Socioeconomic Pathways (SSPs) and their energy, land use, and emissions implications. The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature. A multi-model approach was used for the elaboration of the energy, land-use and the emissions trajectories of SSP-based scenarios. The baseline scenarios lead to global energy consumption of 400–1200 EJ in 2100, and feature vastly different land-use dynamics, ranging from a possible reduction in cropland area up to a massive expansion by more than 700 million hectares by 2100. The associated annual CO₂ emissions of the baseline scenarios range from about 25 GtCO₂ to more than 120 GtCO₂ per year by 2100. With respect to mitigation, we find that associated costs strongly depend on three factors: (1) the policy assumptions, (2) the socio-economic narrative, and (3) the stringency of the target. The carbon price for reaching the target of 2.6 W/m² that is consistent with a temperature change limit of 2 °C, differs in our analysis thus by about a factor of three across the SSP marker scenarios. Moreover, many models could not reach this target from the SSPs with high mitigation challenges. While the SSPs were designed to represent different mitigation and adaptation challenges, the resulting narratives and quantifications span a wide range of different futures broadly representative of the current literature. This allows their subsequent use and development in new assessments and research projects. Critical next steps for the community scenario process will, among others, involve regional and sectoral extensions, further elaboration of the adaptation and impacts dimension, as well as employing the SSP scenarios with the new generation of earth system models as part of the 6th climate model intercomparison project (CMIP6).     

Pattern Scaling: An Examination of the Accuracy of the Technique for Describing Future Climates

A fully probabilistic, or risk, assessment of future regional climate changeand its impacts involves more scenarios of radiative forcing than can besimulated by a general (GCM) or regional (RCM) circulation model. Additionalscenarios may be created by scaling a spatial response pattern from a GCM bya global warming projection from a simple climate model. I examine thistechnique, known as pattern scaling, using a particular GCM (HadCM2).Thecritical assumption is that there is a linear relationship between the scaler(annual global-mean temperature) and the response pattern. Previous studieshave found this assumption to be broadly valid for annual temperature; Iextend this conclusion to precipitation and seasonal (JJA) climate. However,slight non-linearities arise from the dependence of the climatic response onthe rate, not just the amount, of change in the scaler. These non-linearitiesintroduce some significant errors into the estimates made by pattern scaling,but nonetheless the estimates accurately represent the modelled changes. Aresponse pattern may be made more robust by lengthening the period from whichit is obtained, by anomalising it relative to the control simulation, and byusing least squares regression to obtain it. The errors arising from patternscaling may be minimised by interpolating from a stronger to a weaker forcingscenario.     

Making local futures tangible—Synthesizing, downscaling, and visualizing climate change scenarios for participatory capacity building

Local in its causes and global in its impacts, climate change still poses an unresolved challenge for scientists, politicians, entrepreneurs, and citizens. Climate change research is largely global in focus, aims at enhanced understanding, and is driven by experts, all of which seem to be insufficient to anchor climate change action in regional and local contexts. We present results from a participatory scenario study conducted in collaboration with the municipality of Delta in SW British Columbia, Canada. This study applies a participatory capacity building approach for climate change action at the local level where the sources of emissions and the mechanisms of adaptation reside and where climate change is meaningful to decision-makers and stakeholders alike. The multi-scale scenario approach consists of synthesizing global climate change scenarios, downscaling them to the regional and local level, and finally visualizing alternative climate scenarios out to 2100 in 3D views of familiar, local places. We critically discuss the scenarios produced and the strengths and weaknesses of the approach applied.     

Adaptation of agriculture to climate change

Preparing agriculture for adaptation to climate change requires advance knowledge of how climate will change and when. The direct physical and biological impacts on plants and animals must be understood. The indirect impacts on agriculture's resource base of soils, water and genetic resources must also be known. We lack such information now and will, likely, for some time to come. Thus impact assessments for agriculture can only be conjectural at this time. How-ever, guidance can be gotten from an improved understanding of current climatic vulnerabilities of agriculture and its resource base, from application of a realistic range of climate change scenarios to impact assessment, and from consideration of the complexity of current agricultural systems and the range of adaptation techniques and policies now available and likely to be available in the future.     

CLIMATE CHANGE VULNERABILITY AND RESPONSE STRATEGIES FOR THE COASTAL ZONE OF POLAND

Four accelerated sea level rise scenarios, 30 and 100 cm by the year 2100, and 10 and 30 cm by the year 2030, have been assumed as boundary conditions (along with some wind climate changes) for the entire Polish coast, under two recent programmes completed in 1992 and 1995. Three adaptation strategies, i.e., retreat, limited protection and full protection have been adopted and compared in physical and socio-economic terms. Over 2,200 km² and 230,000 people are found vulnerable in the most severe case of 100-cm rise by 2100. The total cost of land at loss in that case is estimated at nearly 30 USD billion (plus some 18 USD billion at risk of flooding), while the cost of full protection reaches 6 USD billion. Particular features of vulnerability and adaptation schemes have been examined as well, including specific sites and the effects of not only sea level rise but also other climate change factors, and interactions with other climate change studies in Poland. Planning of coastal zone management facing climate change can be facilitated by the use of a GIS-supported coastal information and analysis system. An example of the application of such a system for a selected Polish coastal site is shown to illustrate the most recent smaller-scale research activities undertaken in the wake of the overall assessment of the vulnerability to climate change for the entire Polish coastal zone.     

Risk-Based Reanalysis of the Effects of Climate Change on U.S. Cold-water Habitat

Summary A probabilistic risk assessment was conducted for the effects of future climate change on U.S. cold-water habitat. Damage functions for the loss of current cold-water fish habitat in the United States and the Rocky Mountain region were integrated with probability distributions for U.S. June/July/August (JJA) temperature change using Monte Carlo techniques. Damage functions indicated temperature thresholds for incipient losses (≥5%) of cold-water habitat in the United States and the Rocky Mountains of 0.6 and 0.4 ^∘C, respectively. Median impacts associated with different temperature distributions suggested habitat loss in 2025, 2050, and 2100 of approximately 10, 20, and 30%, respectively, for the United States and 20, 35, and 50%, respectively, in the Rocky Mountains. However, 2100 losses in excess of 60% and 90% were possible for the United States and the Rocky Mountains, respectively, albeit at low probabilities. The implementation of constraints on greenhouse gas emissions conforming to the WRE750/550/350 stabilization scenarios had little effect on reducing habitat loss out to 2050, but median effects in 2100 were reduced by up to 20, 30, and 60%, respectively. Increased focus on probabilistic risk assessment may be a profitable mechanism for enhancing understanding and communication of climate change impacts and, subsequently, risk management.