Projected climate reshuffling based on multivariate climate-availability, climate-analog, and climate-velocity analyses: implications for community disaggregation

There is a need for biologically relevant metrics of climate risk for regional- to global-scale climate vulnerability assessments and adaptation planning. Here, we develop, combine, and compare univariate and multivariate forms of several metrics (climate-availability, climate-analog, and two forms of climate-velocity) used to assess the risks arising from future climate change, using downscaled climate projections for Wisconsin (USA) as a case study. Climate-availability and climate-analog analyses show little or no overlap between late-20th-century and projected late-21st-century climates for Wisconsin, and large differences among variables in the distance, bearing, and velocity of projected climate change. There is a strong negative correlation between geographic and climatic distances to closest analogs, creating a tradeoff when climate velocity is assessed using multivariate analog-based approaches: some locations have no good analogs anywhere in future climate space and so analog-based methods pick nearby locations, resulting in low velocity estimates. local velocities projected for Wisconsin are higher than global means. In this region, lake effects, not topographic heterogeneity, exert the strongest influences on regional patterns of climate-velocity and analogs. The multivariate analog-based velocities are correlated with univariate velocity measures that are scaled to local spatial heterogeneity, with the magnitude and correlation analog-based velocities estimates most similar to those of the intervariable mean of climate velocities. Because species are differentially sensitive to particular dimensions of climate change, and vary in their dispersal capacity, the strong differences among climate variables in the spatial direction, distance, and rate of projected climate change provide a powerful mechanism for community restructuring.     

The importance of population,climate change and CO2 plant physiological forcing in determining future global water stress

Future levels of water stress depend on changes in several key factors including population, climate-change driven water availability, and a carbon dioxide physiological-forcing effect on evaporation and run-off. In this study we use an ensemble of the HadCM3 climate model forced with a range of future emissions scenarios combined with a simple water scarcity index to assess the contribution of each of these factors to the projected population living in water stress over the 21st century.Population change only scenarios increase the number of people living in water stress such that at peak global population 65% of people experience some level of water stress. Globally, the climate model ensemble projects an increase in water availability which partially offsets some of the impacts of population growth. The result is 1 billion fewer people living in water stress by the 2080s under the high end emissions scenarios than if population increased in the absence of climate change.This study highlights the important role plant-physiological forcing has on future water resources. The effect of rising CO₂ is to increase available water and to reduce the number of people living in high water stress by around 200 million compared to climate only projections. This effect is of a similar order of magnitude to climate change.     

Why Indoor Climates Change: A Case Study

Indoor climates and climate change are an integral – but to date poorly integrated – element of climate and climate-change research more generally. They have been examined chiefly through the study of human thermal comfort, about which two conflicting schools of thought have emerged. One sees thermal comfort as governed by a common and fixed human preference and confined to a narrow range of conditions. The other sees it as strongly influenced by habit and expectations, which can differ greatly from one person, place, or period to another. This paper examines, in the light of these theories and what they imply, an episode of major and rapid indoor climate change – a sharp rise in winter temperatures thatoccurred in the northern United States in the first half of the nineteenth century. It finds support for both points of view and suggests that each is valid under particular circumstances. The results, if borne out by more research, will help to inform projections of future demand for heating and cooling and for outdoor climatic amenities, both significant elements of the human dimensions of global climatic change.     

Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale

Hydrological modeling for climate-change impact assessment implies using meteorological variables simulated by global climate models (GCMs). Due to mismatching scales, coarse-resolution GCM output cannot be used directly for hydrological impact studies but rather needs to be downscaled. In this study, we investigated the variability of seasonal streamflow and flood-peak projections caused by the use of three statistical approaches to downscale precipitation from two GCMs for a meso-scale catchment in southeastern Sweden: (1) an analog method (AM), (2) a multi-objective fuzzy-rule-based classification (MOFRBC) and (3) the Statistical DownScaling Model (SDSM). The obtained higher-resolution precipitation values were then used to simulate daily streamflow for a control period (1961–1990) and for two future emission scenarios (2071–2100) with the precipitation-streamflow model HBV. The choice of downscaled precipitation time series had a major impact on the streamflow simulations, which was directly related to the ability of the downscaling approaches to reproduce observed precipitation. Although SDSM was considered to be most suitable for downscaling precipitation in the studied river basin, we highlighted the importance of an ensemble approach. The climate and streamflow change signals indicated that the current flow regime with a snowmelt-driven spring flood in April will likely change to a flow regime that is rather dominated by large winter streamflows. Spring flood events are expected to decrease considerably and occur earlier, whereas autumn flood peaks are projected to increase slightly. The simulations demonstrated that projections of future streamflow regimes are highly variable and can even partly point towards different directions.     

Scale-dependent regional climate predictability over North America inferred from CMIP3 and CMIP5 ensemble simulations

Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty (noise) in surface temperature predictions (represented by the spread among an ensemble of global climate model simulations) generally exceeds the ensemble mean (signal) at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.     

Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia

This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.     

The impacts of climate change on the winter hardiness zones of woody plants in Europe

The potential impacts of climate change on potatoes cropping in the Peruvian highlands (Altiplano) is assessed using climate projections for 2071–2100, obtained from the HadRM3P regional atmospheric model of the Hadley Centre. The atmospheric model is run under two different special report on emission scenarios: high CO₂ concentration (A2) and moderate CO₂ concentration (B2) for four locations situated in the surroundings of Lake Titicaca. The two main varieties of potato cultivated in the area are studied: the Andean potato (Solanum tuberosum) and the bitter potato (Solanum juzepczukii). A simple process-oriented model is used to quantify the climatic impacts on crops cycles and yields by combining the effects of temperature on phenology, of radiation and CO₂ on maximum yield and of water balance on yield deficit. In future climates, air temperature systematically increases, precipitation tends to increase at the beginning of the rainy season and slightly decreases during the rest of the season. The direct effects of these climatic changes are earlier planting dates, less planting failures and shorter crop cycles in all the four locations and for both scenarios. Consequently, the harvesting dates occur systematically earlier: roughly in January for the Andean potato instead of March in the current situation and in February for the bitter potato instead of April. Overall, yield deficits will be higher under climate change than in the current climate. There will be a strong negative impact on yields for S. tuberosum (stronger under A2 scenario than under B2); the impact on S. juzepczukii yields, however, appears to be relatively mixed and not so negative.     

Seasonal to yearly assessment of temperature and precipitation trends in the North Western Mediterranean Basin by dynamical downscaling of climate scenarios at high resolution (1971–2050)

The complex topography and high climatic variability of the North Western Mediterranean Basin (NWMB) require a detailed assessment of climate change projections at high resolution. ECHAM5/MPIOM global climate projections for mid-21st century and three different emission scenarios are downscaled at 10 km resolution over the NWMB, using the WRF-ARW regional model. High resolution improves the spatial distribution of temperature and precipitation climatologies, with Pearson's correlation against observation being higher for WRF-ARW (0.98 for temperature and 0.81 for precipitation) when compared to the ERA40 reanalysis (0.69 and 0.53, respectively). However, downscaled results slightly underestimate mean temperature (≈1.3 K) and overestimate the precipitation field (≈400 mm/year). Temperature is expected to raise in the NWMB in all considered scenarios (up to 1.4 K for the annual mean), and particularly during summertime and at high altitude areas. Annual mean precipitation is likely to decrease (around ?5 % to ?13 % for the most extreme scenarios). The climate signal for seasonal precipitation is not so clear, as it is highly influenced by the driving GCM simulation. All scenarios suggest statistically significant decreases of precipitation for mountain ranges in winter and autumn. High resolution simulations of regional climate are potentially useful to decision makers. Nevertheless, uncertainties related to seasonal precipitation projections still persist and have to be addressed.     

Regional hydrologic consequences of increases in atmospheric CO2 and other trace gases

Concern over changes in global climate caused by growing atmospheric concentrations of carbon dioxide and other trace gases has increased in recent years as our understanding of atmospheric dynamics and global climate systems has improved. Yet despite a growing understanding of climatic processes, many of the effects of human-induced climatic changes are still poorly understood. Major alterations in regional hydrologic cycles and subsequent changes in regional water availability may be the most important effects of such climatic changes. Unfortunately, these are among the least well-understood impact. Water-balance modeling techniques - modified for assessing climatic impacts - were developed and tested for a major watershed in northern California using climate-change scenarios from both state-of-the-art general circulation models and from a series of hypothetical scenarios. Results of this research suggest strongly that plausible changes in temperature and precipitation caused by increases in atmospheric trace-gas concentrations could have major impacts on both the timing and magnitude of runoff and soil moisture in important agricultural areas. Of particular importance are predicted patterns of summer soil-moisture drying that are consistent across the entire range of tested scenarios. The decreases in summer soil moisture range from 8 to 44%. In addition, consistent changes were observed in the timing of runoff-specifically dramatic increases in winter runoff and decreases in summer runoff. These hydrologic results raise the possibility of major environmental and socioeconomic difficulties and they will have significant implications for future water-resource planning and management.     

Creating regional climate change scenarios over southern South America for the 2020’s and 2050’s using the pattern scaling technique: validity and limitations

Dynamical downscaling of global climate simulations is the most adequate tool to generate regional projections of climate change. This technique involves at least a present climate simulation and a simulation of a future scenario, usually at the end of the twenty first century. However, regional projections for a variety of scenarios and periods, the 2020s or the 2050s, are often required by the impact community. The pattern scaling technique is used to estimate information on climate change for periods and scenarios not simulated by the regional model. We based our study on regional simulations performed over southern South America for present climate conditions and two emission scenarios at the end of the twenty first century. We used the pattern scaling technique to estimate mean seasonal changes of temperature and precipitation for the 2020s and the 2050s. The validity of the scalability assumptions underlying the pattern scaling technique for estimating near future regional climate change scenarios over southern South America is assessed. The results show that the pattern scaling works well for estimating mean temperature changes for which the regional changes are linearly related to the global mean temperature changes. For precipitation changes, the validity of the scalability assumption is weaker. The errors of estimating precipitation changes are comparable to those inherent to the regional model and to the projected changes themselves.     

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.     

Projected changes in drought occurrence under future global warming from multi-model,multi-scenario,IPCC AR4 simulations

Recent and potential future increases in global temperatures are likely to be associated with impacts on the hydrologic cycle, including changes to precipitation and increases in extreme events such as droughts. We analyze changes in drought occurrence using soil moisture data for the SRES B1, A1B and A2 future climate scenarios relative to the PICNTRL pre-industrial control and 20C3M twentieth century simulations from eight AOGCMs that participated in the IPCC AR4. Comparison with observation forced land surface model estimates indicates that the models do reasonably well at replicating our best estimates of twentieth century, large scale drought occurrence, although the frequency of long-term (more than 12-month duration) droughts are over-estimated. Under the future projections, the models show decreases in soil moisture globally for all scenarios with a corresponding doubling of the spatial extent of severe soil moisture deficits and frequency of short-term (4–6-month duration) droughts from the mid-twentieth century to the end of the twenty-first. Long-term droughts become three times more common. Regionally, the Mediterranean, west African, central Asian and central American regions show large increases most notably for long-term frequencies as do mid-latitude North American regions but with larger variation between scenarios. In general, changes under the higher emission scenarios, A1B and A2 are the greatest, and despite following a reduced emissions pathway relative to the present day, the B1 scenario shows smaller but still substantial increases in drought, globally and for most regions. Increases in drought are driven primarily by reductions in precipitation with increased evaporation from higher temperatures modulating the changes. In some regions, increases in precipitation are offset by increased evaporation. Although the predicted future changes in drought occurrence are essentially monotonic increasing globally and in many regions, they are generally not statistically different from contemporary climate (as estimated from the 1961–1990 period of the 20C3M simulations) or natural variability (as estimated from the PICNTRL simulations) for multiple decades, in contrast to primary climate variables, such as global mean surface air temperature and precipitation. On the other hand, changes in annual and seasonal means of terrestrial hydrologic variables, such as evaporation and soil moisture, are essentially undetectable within the twenty-first century. Changes in the extremes of climate and their hydrological impacts may therefore be more detectable than changes in their means.     

Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven with RCP8.5 climate projections from 19 GCMs

Future changes of terrestrial ecosystems due to changes in atmospheric CO₂ concentration and climate are subject to a large degree of uncertainty, especially for vegetation in the Tropics. Here, we evaluate the natural vegetation response to projected future changes using an improved version of a dynamic vegetation model (CLM-CN-DV) driven with climate change projections from 19 global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The simulated equilibrium vegetation distribution under historical climate (1981–2000) has been compared with that under the projected future climate (2081–2100) scenario for Representative Concentration Pathway 8.5 (RCP8.5) to qualitatively assess how natural potential vegetation might change in the future. With one outlier excluded, the ensemble average of vegetation changes corresponding to climates of 18 GCMs shows a poleward shift of forests in northern Eurasia and North America, which is consistent with findings from previous studies. It also shows a general “upgrade” of vegetation type in the Tropics and most of the temperate zones, in the form of deciduous trees and shrubs taking over C3 grass in Europe and broadleaf deciduous trees taking over C4 grasses in Central Africa and the Amazon. LAI and NPP are projected to increase in the high latitudes, southeastern Asia, southeastern North America, and Central Africa. This results from CO₂ fertilization, enhanced water use efficiency, and in the extra-tropics warming. However, both LAI and NPP are projected to decrease in the Amazon due to drought. The competing impacts of climate change and CO₂ fertilization lead to large uncertainties in the projection of future vegetation changes in the Tropics.     

Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.     

Simulating the hydrological response to predicted climate change on a watershed in southern Alberta, Canada

The current body of research in western North America indicates that water resources in southern Alberta are vulnerable to climate change impacts. The objective of this research was to parameterize and verify the ACRU agro-hydrological modeling system for a small watershed in southern Alberta and subsequently simulate the change in future hydrological responses over 30-year simulation periods. The ACRU model successfully simulated monthly streamflow volumes (r ²?=?0.78), based on daily simulations over 27 years. The delta downscaling technique was used to perturb the 1961?C1990 baseline climate record from a range of global climate model (GCM) projections to provide the input for future hydrological simulations. Five future hydrological regimes were compared to the 1961?C1990 baseline conditions to determine the average net effect of change scenarios on the hydrological regime of the Beaver Creek watershed over three 30-year time periods (starting in 2010, 2040 and 2070). The annual projections of a warmer and mostly wetter climate in this region resulted in a shift of the seasonal streamflow distribution with an increase in winter and spring streamflow volumes and a reduction of summer and fall streamflow volumes over all time periods, relative to the baseline conditions (1961?C1990), for four of the five scenarios. Simulations of actual evapotranspiration and mean annual runoff showed a slight increase, which was attributed to warmer winters, resulting in more winter runoff and snowmelt events.     

Potential Impacts of Climate Change on Agriculture: A Case of Study of Coffee Production in Veracruz, Mexico

This paper explores the relation between coffee production and climatic and economic variables in Veracruz in order to estimate the potential impacts of climate change. For this purpose, an econometric model is developed in terms of those variables. The model is validated by means of statistical analysis, and then used to project coffee production under different climatic conditions. Climate change scenarios are produced considering that the observed trends of climate variables will continue to prevail until the year 2020. An approach for constructing simple probability scenarios for future climate variability is presented and used to assess possible impacts of climate change beyond what is expected from changes in mean values. The model shows that temperature is the most relevant climatic factor for coffee production, since production responds significantly to seasonal temperature patterns. The results for the projected climate change conditions for year 2020 indicate that coffee production might not be economically viable for producers, since the model indicates a reduction of 34% of the current production. Although different economic variables (the state and international coffee prices, a producer price index for raw materials for coffee benefit, the national and the USA coffee stocks) were considered as potentially relevant, our model suggests that the state real minimum wage could be regarded as the most important economic variable. Real minimum wage is interpreted here as a proxy for the price of labor employed for coffee production. This activity in Mexico is very labor intensive representing up to 80% of coffee production costs. As expected, increments in the price of such an important production factor increase production costs and have strong negative effects on production. Different assumptions on how real minimum wage could evolve for the year 2020 are considered for developing future production scenarios.     

Exploring temporality in socio-ecological resilience through experiences of the 2015–16 El Niño across the Tropics

In a context of both long-term climatic changes and short-term climatic shocks, temporal dynamics profoundly influence ecosystems and societies. In low income contexts in the Tropics, where both exposure and vulnerability to climatic fluctuations is high, the frequency, duration, and trends in these fluctuations are important determinants of socio-ecological resilience. In this paper, the dynamics of six diverse socio-ecological systems (SES) across the Tropics – ranging from agricultural and horticultural systems in Africa and Oceania to managed forests in South East Asia and coastal systems in South America – are examined in relation to the 2015–16 El Niño, and the longer context of climatic variability in which this short-term ‘event’ occurred. In each case, details of the socio-ecological characteristics of the systems and the climate phenomena experienced during the El Niño event are described and reflections on the observed impacts of, and responses to it are presented. Drawing on these cases, we argue that SES resilience (or lack of) is, in part, a product of both long-term historical trends, as well as short-term shocks within this history. Political and economic lock-ins and dependencies, and the memory and social learning that originates from past experience, all contribute to contemporary system resilience. We propose that the experiences of climate shocks can provide a window of insight into future ecosystem responses and, when combined with historical perspectives and learning from multiple contexts and cases, can be an important foundation for efforts to build appropriate long-term resilience strategies to mediate impacts of changing and uncertain climates.     

Corrigendum to “Meeting the food security challenge for nine billion people in 2050: What impact on forests?” [Global Environ. Change 62 (2020) 102056]

As the world’s population continues to grow, agricultural expansion is expected to increase to meet future food demand often at the expense of other land uses. However, there are limited studies examining the degree to which forest cover will change and the underlying assumptions driving these projections. Focusing on food and forest scenarios for the middle to the end of the current century, we review 63 main scenarios and 28 global modelling studies to address variations in land use projections and evaluate the potential outcomes on forest cover. Further, their potential impacts on greenhouse gases (GHG) emission/sequestration and global temperature are explored. A majority (59%) of scenarios expected a reduction in both forests and pasturelands to make way for agricultural expansion (particularly reference and no mitigation scenarios). In most scenarios, the extent of forest loss is proportional to that of crop gain, which is associated with higher GHG emission and global temperature, loss of carbon sequestration potential and increase in soil erosion. However, 32% of scenarios predicted that meeting food security objectives is possible without leading to further deforestation if there is a global reduction in the demand for energy intensive foods, and improvements in crop yields. Forest gain and lower rates of deforestation are needed to achieve ambitious climate targets over the next decade. Our analysis also highlights carbon taxes (prices), reforestation/afforestation and bioenergy as important variables that can contribute to maintaining or increasing global forest area in the future.     

Projections of climate change impacts on central America tropical rainforest

Tropical rainforest plays an important role in the global carbon cycle, accounting for a large part of global net primary productivity and contributing to CO₂ sequestration. The objective of this work is to simulate potential changes in the rainforest biome in Central America subject to anthropogenic climate change under two emissions scenarios, RCP4.5 and RCP8.5. The use of a dynamic vegetation model and climate change scenarios is an approach to investigate, assess or anticipate how biomes respond to climate change. In this work, the Inland dynamic vegetation model was driven by the Eta regional climate model simulations. These simulations accept boundary conditions from HadGEM2-ES runs in the two emissions scenarios. The possible consequences of regional climate change on vegetation properties, such as biomass, net primary production and changes in forest extent and distribution, were investigated. The Inland model projections show reductions in tropical forest cover in both scenarios. The reduction of tropical forest cover is greater in RCP8.5. The Inland model projects biomass increases where tropical forest remains due to the CO₂ fertilization effect. The future distribution of predominant vegetation shows that some areas of tropical rainforest in Central America are replaced by savannah and grassland in RCP4.5. Inland projections under both RCP4.5 and RCP8.5 show a net primary productivity reduction trend due to significant tropical forest reduction, temperature increase, precipitation reduction and dry spell increments, despite the biomass increases in some areas of Costa Rica and Panama. This study may provide guidance to adaptation studies of climate change impacts on the tropical rainforests in Central America.     

Climate change hotspots in the CMIP5 global climate model ensemble

We use a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5 climate model ensemble. Our hotspot metric extends previous work through the inclusion of extreme seasonal temperature and precipitation, which exert critical influence on climate change impacts. The results identify areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau as persistent regional climate change hotspots throughout the 21st century of the RCP8.5 and RCP4.5 forcing pathways. In addition, areas of southern Africa, the Mediterranean, the Arctic, and Central America/western North America also emerge as prominent regional climate change hotspots in response to intermediate and high levels of forcing. Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2 °C of global warming (relative to the late-20th-century baseline), but not at the higher levels of global warming that occur in the late-21st-century period of the RCP8.5 pathway, with areas of southern Africa, the Mediterranean, and the Arctic exhibiting particular intensification of relative aggregate climate change in response to high levels of forcing. Although specific impacts will clearly be shaped by the interaction of climate change with human and biological vulnerabilities, our identification of climate change hotspots can help to inform mitigation and adaptation decisions by quantifying the rate, magnitude and causes of the aggregate climate response in different parts of the world.