Range Of Future Climate Scenarios Research Articles

Can blue–green infrastructure counteract the effects of climate change on combined sewer overflows? Study of a swiss catchment

Abstract Combined sewer overflows (CSOs), the discharge of untreated sewage mixed with stormwater into surface waters, are expected to increase under climate change as a result of more extreme rainfall. Blue–green infrastructure (BGI), such as bioretention cells and porous pavements, can help to reduce the amount of stormwater entering combined sewer systems, thus reducing CSO discharge. However, our understanding of the potential for BGI to mitigate CSOs in a future climate is still lacking, as performance is typically evaluated for individual BGI elements with fixed implementation areas under historical climate conditions or limited future scenarios. In response, this study investigates the performance of 30 combinations of BGI elements and implementation rates to prevent increases in CSOs under a range of future climate scenarios in an urban catchment near Zurich, Switzerland. Median total annual rainfall, projected to increase by as much as 46%, could double the median annual CSO volume and increase median annual CSO frequency by up to 52%. Four BGI combinations that include bioretention cells show the most promise to prevent increases in CSO volume and frequency in a future climate; and given the diverse responses of BGI elements to distinct rainfall patterns, their combinations can enhance CSO discharge reduction across varying climate patterns. BGI is also likely to become more cost-effective under future climatic conditions as projected increases in total rainfall led to larger CSO volume reductions obtained through BGI. However, there is a trade-off between robustness to climate change and cost-effectiveness, since CSO volume reduction capacity scales with BGI implementation rate but cost-effectiveness declines. Our study illustrates the effectiveness of various BGI combinations to prevent increases in CSOs in a future climate, calling for a range of BGI elements and implementation areas to be considered for urban drainage adaptation.

Incorporating projected climate conditions to map future riparian refugia

AbstractIdentifying areas expected to remain buffered from climate change and maintain biodiversity and ecological function (i.e., climate refugia) is important for climate adaptation planning. As structurally diverse transitional zones between terrestrial and aquatic environments, riparian areas are often biological hotspots and provide critical corridors for species movement, particularly in arid and semi‐arid regions. In our study region in the western and central USA, identifying riparian areas that could serve as climate refugia is a priority for wildlife managers. We mapped areas with connected riparian habitats that, based on landscape diversity and projected changes in summer temperatures and landscape runoff, are expected to serve as climate refugia. To incorporate uncertainty and balance the need for near‐ and long‐term planning, we mapped potential refugia for 2 future time periods (2040–2069, 2070–2099) based on 2 climate models that represented divergent but plausible climate outcomes. The approach we developed is not constrained by physiology or behavior of target species and can be used to identify areas expected to fare comparatively well under a wide range of future climate scenarios. Our approach can also be used to identify areas where restoration could increase riparian connectedness and climate resilience.

How climate change and international trade will shape the future global soybean security pattern

Soybean planting is highly concentrated and sensitive to climate change, which makes global soybean security vulnerable to production fluctuations in the main producing countries. As an important trade commodity, the impacts of climate change on soybean production in the main producing countries can ripple globally through international trade. However, little is known about the spatial distribution of soybean security under future climate at the global scale. This study couples an ensemble of multi-crop models and a global economic model (GTAP, Global Trade Analysis Project) to evaluate variations in soybean yield of the top ten soybean-producing countries in the 2050s (2046–2055) and the consequent impacts on global soybean supply and price under a range of future climate scenarios. Our results show that (1) The top ten soybean-producing countries, accounting for nearly 97% of global soybean production, suffer an average soybean yield reduction of 0.82% (specific to each country: −5.81%∼7.05%), 0.43% (−23.5%∼11.72%) and 5.22% (−17.88%∼18.73%) under Representative Concentration Pathway (RCP) 2.6, RCP4.5 and RCP 8.5, respectively, when considering the fertilization effect of CO2. A substantial soybean yield loss of 19.51% (−28.86%∼2.97%) is projected under RCP8.5 without accounting for the CO2 fertilization effect. (2) Climate-induced yield reduction leads to insufficient soybean supply and higher price worldwide through the transmission of international trade, with the largest supply decline of 14.20% in Bolivia and the largest price increase of 91.19% in India. Regionally, soybean net-importing countries are more vulnerable because of the ripple effect of the soybean supply chain. For example, despite a simulated increase in soybean yield of 2.97%–18.73%, China's soybean supply will decline by 0.61%–2.27% due to import shortages from the United States and Brazil where severe soybean yield losses are witnessed. (3) Liberal trade does dampen the negative impact of climate change on global soybean supply and price by reducing trade costs and facilitating trade flows, but in soybean export-oriented regions, trade liberalization can result in increased exports at the expense of domestic soybean supply (e.g., Canada and Russia). Our findings highlight the necessity of developing a liberal trade policy that benefits all regions of the world and improving domestic soybean productivity to adapt to climate change.

Assessing controls on ice dynamics at Crane Glacier, Antarctic Peninsula, using a numerical ice flow model

AbstractThe Antarctic Peninsula's widespread glacier retreat and ice shelf collapse have been attributed to atmospheric and oceanic warming. Following the initial post-collapse period of retreat, several former tributary glaciers of the Larsen A and B ice shelves have been slowly re-advancing for more than a decade. Here, we use a flowline model of Crane Glacier to gauge the sensitivity of former tributary glaciers to future climate change following this period of long-term dynamic adjustment. The glacier's long-term geometry and speed changes are similar to those of other former Larsen A and B tributaries, suggesting that Crane Glacier is a reasonable representation of regional dynamics. For the unperturbed climate simulations, discharge remains nearly unchanged in 2018–2100, indicating that dynamic readjustment to shelf collapse took ~15 years. Despite large uncertainties in Crane Glacier's past and future climate forcing, a wide range of future climate scenarios leads to a relatively modest range in grounding line discharge (0.8–1.5 Gt a−1) by 2100. Based on the model results for Crane, we infer that although former ice shelf tributaries may readvance following collapse, similar to the tidewater glacier cycle, their dynamic response to future climate perturbations should be less than their response to ice shelf collapse.

Tropical forest restoration under future climate change

One of the most promising ways to rapidly remove CO2 from the atmosphere is through the restoration of tropical forests. Ongoing and future climate change may, however, threaten the permanence of carbon stored through restoration. Excessive heat, drought or increased disturbances such as wildfire could all negatively impact the integrity of restored carbon. To investigate these risks to tropical forest restoration, we performed 221 simulations with a dynamic global vegetation model (LPJ-LMfire) driven by a range of future climate scenarios and ecophysiological responses to CO2 concentrations. We show that carbon in restored tropical forests is largely preserved under the entire range of potential future climates, regardless of assumptions we make about the potential for CO2 fertilization of photosynthesis. Restoring even half of the potential area can account for 56–69% of the carbon storage, depending on whether areas are selected for low cost or high carbon gain.

Estimating PM2.5-related premature mortality and morbidity associated with future wildfire emissions in the western US

Wildfire activity in the western United States (US) has been increasing, a trend that has been correlated with changing patterns of temperature and precipitation associated with climate change. Health effects associated with exposure to wildfire smoke and fine particulate matter (PM2.5) include short- and long-term premature mortality, hospital admissions, emergency department visits, and other respiratory and cardiovascular incidents. We estimate PM2.5 exposure and health impacts for the entire continental US from current and future western US wildfire activity projected for a range of future climate scenarios through the 21st century. We use a simulation approach to estimate wildfire activity, area burned, fine particulate emissions, air quality concentrations, health effects, and economic valuation of health effects, using established and novel methodologies. We find that climatic factors increase wildfire pollutant emissions by an average of 0.40% per year over the 2006–2100 period under Representative Concentration Pathway (RCP) 4.5 (lower emissions scenarios) and 0.71% per year for RCP8.5. As a consequence, spatially weighted wildfire PM2.5 concentrations more than double for some climate model projections by the end of the 21st century. PM2.5 exposure changes, combined with population projections, result in a wildfire PM2.5-related premature mortality excess burden in the 2090 RCP8.5 scenario that is roughly 3.5 times larger than in the baseline period. The combined effect of increased wildfire activity, population growth, and increase in the valuation of avoided risk of premature mortality over time results in a large increase in total economic impact of wildfire-related PM2.5 mortality and morbidity in the continental US, from roughly $7 billion per year in the baseline period to roughly $36 billion per year in 2090 for RCP4.5, and $43 billion per year in RCP8.5. The climate effect alone accounts for a roughly 60% increase in wildfire PM2.5-related premature mortality in the RCP8.5 scenario, relative to baseline conditions.

Lessons from a high-CO<sub>2</sub> world: an ocean view from ∼ 3 million years ago

Abstract. A range of future climate scenarios are projected for high atmospheric CO2 concentrations, given uncertainties over future human actions as well as potential environmental and climatic feedbacks. The geological record offers an opportunity to understand climate system response to a range of forcings and feedbacks which operate over multiple temporal and spatial scales. Here, we examine a single interglacial during the late Pliocene (KM5c, ca. 3.205±0.01 Ma) when atmospheric CO2 exceeded pre-industrial concentrations, but were similar to today and to the lowest emission scenarios for this century. As orbital forcing and continental configurations were almost identical to today, we are able to focus on equilibrium climate system response to modern and near-future CO2. Using proxy data from 32 sites, we demonstrate that global mean sea-surface temperatures were warmer than pre-industrial values, by ∼2.3 ∘C for the combined proxy data (foraminifera Mg∕Ca and alkenones), or by ∼3.2–3.4 ∘C (alkenones only). Compared to the pre-industrial period, reduced meridional gradients and enhanced warming in the North Atlantic are consistently reconstructed. There is broad agreement between data and models at the global scale, with regional differences reflecting ocean circulation and/or proxy signals. An uneven distribution of proxy data in time and space does, however, add uncertainty to our anomaly calculations. The reconstructed global mean sea-surface temperature anomaly for KM5c is warmer than all but three of the PlioMIP2 model outputs, and the reconstructed North Atlantic data tend to align with the warmest KM5c model values. Our results demonstrate that even under low-CO2 emission scenarios, surface ocean warming may be expected to exceed model projections and will be accentuated in the higher latitudes.

Climate change decreases the cooling effect from postfire albedo in boreal North America.

Fire is a primary disturbance in boreal forests and generates both positive and negative climate forcings. The influence of fire on surface albedo is a predominantly negative forcing in boreal forests, and one of the strongest overall, due to increased snow exposure in the winter and spring months. Albedo forcings are spatially and temporally heterogeneous and depend on a variety of factors related to soils, topography, climate, land cover/vegetation type, successional dynamics, time since fire, season, and fire severity. However, how these variables interact to influence albedo is not well understood, and quantifying these relationships and predicting postfire albedo becomes increasingly important as the climate changes and management frameworks evolve to consider climate impacts. Here we developed a MODIS-derived 'blue sky' albedo product and a novel machine learning modeling framework to predict fire-driven changes in albedo under historical and future climate scenarios across boreal North America. Converted to radiative forcing (RF), we estimated that fires generate an annual mean cooling of -1.77±1.35W/m2 from albedo under historical climate conditions (1971-2000) integrated over 70years postfire. Increasing postfire albedo along a south-north climatic gradient was offset by a nearly opposite gradient in solar insolation, such that large-scale spatial patterns in RF were minimal. Our models suggest that climate change will lead to decreases in mean annual postfire albedo, and hence a decreasing strength of the negative RF, a trend dominated by decreased snow cover in spring months. Considering the range of future climate scenarios and model uncertainties, we estimate that for fires burning in the current era (2016) the cooling effect from long-term postfire albedo will be reduced by 15%-28% due to climate change.

Conservation planning at the intersection of landscape and climate change: brook trout in the Chesapeake Bay watershed

AbstractWe developed a multi‐scale conservation planning framework for brook trout (Salvelinus fontinalis) within the Chesapeake Bay watershed that incorporates both land use and climate stressors. Our specific objectives were to (1) construct a continuous spatial model of brook trout distribution and habitat quality at the stream reach scale; (2) characterize brook trout vulnerability to climate change under a range of future climate scenarios; and (3) identify multi‐scale restoration and protection priorities for brook trout across the Chesapeake Bay watershed. Boosted regression tree analysis predicted brook trout occurrence at the stream reach scale with a high degree of accuracy (CV AUC = 0.92) as a function of both natural (e.g., water temperature and precipitation) and anthropogenic (e.g., agriculture and urban development) landscape and climatic attributes. Current land use activities result in a predicted loss of occurrence in over 11,000 stream segments (40% of suitable habitat) and account for over 15,000 km (45% of current value) of lost functional brook trout fishery value (i.e., length‐weighted occurrence probability) in the Chesapeake Bay watershed. Climate change (increased ambient temperatures and altered precipitation) is projected to result in a loss of occurrence in at least 3000 additional segments (19% of current value) and at least 3000 km of functional fishery value (9% of current value) by 2062. Model outcomes were used to identify low‐ and high‐quality stream segments within relatively intact and degraded sub‐watersheds as restoration and protection priorities, respectively, and conservation priorities were targeted in watersheds with high projected resilience to climate change. Our results suggest that traditional restoration activities, such as habitat enhancement, riparian management, and barrier removal, may be able to recover a substantial amount of brook trout habitat lost to historic landscape change. However, restoration efforts must be designed within the context of expected impacts from climate change or those efforts may not produce long‐term benefits to brook trout in this region.

Decreases in global beer supply due to extreme drought and heat

Beer is the most popular alcoholic beverage in the world by volume consumed, and yields of its main ingredient, barley, decline sharply in periods of extreme drought and heat. Although the frequency and severity of drought and heat extremes increase substantially in range of future climate scenarios by five Earth System Models, the vulnerability of beer supply to such extremes has never been assessed. We couple a process-based crop model (decision support system for agrotechnology transfer) and a global economic model (Global Trade Analysis Project model) to evaluate the effects of concurrent drought and heat extremes projected under a range of future climate scenarios. We find that these extreme events may cause substantial decreases in barley yields worldwide. Average yield losses range from 3% to 17% depending on the severity of the conditions. Decreases in the global supply of barley lead to proportionally larger decreases in barley used to make beer and ultimately result in dramatic regional decreases in beer consumption (for example, -32% in Argentina) and increases in beer prices (for example, +193% in Ireland). Although not the most concerning impact of future climate change, climate-related weather extremes may threaten the availability and economic accessibility of beer.

Trade‐offs between temporal stability and level of forest ecosystem services provisioning under climate change

The ability of forests to continuously provide ecosystem services (ES) is threatened by rapid changes in climate and disturbance regimes. Consequently, these changes present a considerable challenge for forest managers. Management of forests often focuses on maximizing the level of ES provisioning over extended time frames (i.e., rotation periods of more than 100 yr). However, temporal stability is also crucial for many ES, for example, in the context of a steady provisioning of resources to the industry, or the protection of human infrastructure against natural hazards. How temporal stability and the level of ES provisioning are related is of increasing interest, particularly since changing climate and disturbance regimes amplify temporal variability in forest ecosystems. In this simulation study, we investigated whether forest management can simultaneously achieve high levels and temporal stability of ES provisioning. Specifically, we quantified (1) trade‐offs between ES stability and level of ES provisioning, and (2) the effect of tree species diversity on ES stability. Simulating a wide range of future climate scenarios and management strategies, we found a negative relationship between temporal stability and level of ES provisioning for timber production, carbon cycling, and site protection in a landscape in the Austrian Alps. Tree species diversity had a predominantly positive effect on ES stability. We conclude that attempts to maximize the level of ES provisioning may increase its temporal variability, and thus threaten the continuity of ES supply. Consequently, considerations of stability need to be more explicitly included in forest management planning under increasingly variable future conditions.

Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change.

To identify areas on the landscape that may contribute to a robust network of conservation areas, we modeled the probabilities of occurrence of several en route migratory shorebirds and wintering waterfowl in the southern Great Plains of North America, including responses to changing climate. We predominantly used data from the eBird citizen‐science project to model probabilities of occurrence relative to land‐use patterns, spatial distribution of wetlands, and climate. We projected models to potential future climate conditions using five representative general circulation models of the Coupled Model Intercomparison Project 5 (CMIP5). We used Random Forests to model probabilities of occurrence and compared the time periods 1981–2010 (hindcast) and 2041–2070 (forecast) in “model space.” Projected changes in shorebird probabilities of occurrence varied with species‐specific general distribution pattern, migration distance, and spatial extent. Species using the western and northern portion of the study area exhibited the greatest likelihoods of decline, whereas species with more easterly occurrences, mostly long‐distance migrants, had the greatest projected increases in probability of occurrence. At an ecoregional extent, differences in probabilities of shorebird occurrence ranged from −0.015 to 0.045 when averaged across climate models, with the largest increases occurring early in migration. Spatial shifts are predicted for several shorebird species. Probabilities of occurrence of wintering Mallards and Northern Pintail are predicted to increase by 0.046 and 0.061, respectively, with northward shifts projected for both species. When incorporated into partner land management decision tools, results at ecoregional extents can be used to identify wetland complexes with the greatest potential to support birds in the nonbreeding season under a wide range of future climate scenarios.

Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia.

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long-term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat-induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long-term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range - with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot-spells, in driving species-climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species.

Climate change and annual survival in a temperate passerine: partitioning seasonal effects and predicting future patterns

Predicting climate change impacts on population size requires detailed understanding of how climate influences key demographic rates, such as survival. This knowledge is frequently unavailable, even in well‐studied taxa such as birds. In temperate regions, most research into climatic effects on annual survival in resident passerines has focussed on winter temperature. Few studies have investigated potential precipitation effects and most assume little impact of breeding season weather. We use a 19‐year capture–mark–recapture study to provide a rare empirical analysis of how variation in temperature and precipitation throughout the entire year influences adult annual survival in a temperate passerine, the long‐tailed tit Aegithalos caudatus. We use model averaging to predict longer‐term historical survival rates, and future survival until the year 2100. Our model explains 73% of the interannual variation in survival rates. In contrast to current theory, we find a strong precipitation effect and no effect of variation in winter weather on adult annual survival, which is correlated most strongly to breeding season (spring) weather. Warm springs and autumns increase annual survival, but wet springs reduce survival and alter the form of the relationship between spring temperature and annual survival. There is little evidence for density dependence across the observed variation in population size. Using our model to estimate historical survival rates indicates that recent spring warming has led to an upward trend in survival rates, which has probably contributed to the observed long‐term increase in the UK long‐tailed tit population. Future climate change is predicted to further increase survival, under a broad range of carbon emissions scenarios and probabilistic climate change outcomes, even if precipitation increases substantially. We demonstrate the importance of considering weather over the entire annual cycle, and of considering precipitation and temperature in combination, in order to develop robust predictive models of demographic responses to climate change.SynthesisPrediction of climate change impacts demands understanding of how climate influences key demographic rates. In our 19‐year mark‐recapture study of long‐tailed tits Aegithalos caudatus, weather explained 73% of the inter‐annual variation in adult survival; warm springs and autumns increased survival, wet springs reduced survival, but winter weather had little effect. Robust predictions thus require consideration of the entire annual cycle and should not focus solely on temperature. Unexpectedly, survival appeared not to be strongly density‐dependent, so we use historical climate data to infer that recent climate change has enhanced survival over the four decades in which the UK long‐tailed tit population has more than doubled. Furthermore, survival rates in this species are predicted to further increase under a wide range of future climate scenarios.

Designing high‐yielding wheat ideotypes for a changing climate

AbstractGlobal warming is characterized by shifts in weather patterns and increases in climatic variability and extreme events. New wheat cultivars will be required for a rapidly changing environment, putting severe pressure on breeders who must select for climate conditions which can only be predicted with a great degree of uncertainty. To assist breeders to identify key wheat traits for improvements under climate change, wheat ideotypes can be designed and tested in silico using a wheat simulation model for a wide range of future climate scenarios predicted by global climate models. A wheat ideotype is represented by a set of cultivar parameters in a model, which could be optimized for best wheat performance under projected climate change. As an example, high‐yielding wheat ideotypes were designed at two contrasting European sites for the 2050 (A1B) climate scenario. Simulations showed that wheat yield potential can be substantially increased for new ideotypes compared with current wheat varieties under climate change. The main factors contributing to yield increase were improvement in light conversion efficiency, extended duration of grain filling resulting in a higher harvest index, and optimal phenology.

Statistically Downscaled Climate Change Projections for the Animas River Basin, Colorado, USA

Mountain river basins provide the majority of western North America with snowmelt runoff water resources throughout the late spring–early summer snowmelt season. However, this snowmelt water resource is extremely vulnerable to any changes in air temperature and precipitation. Studies of larger mountain river basins have projected potentially warmer and drier climates in the future, but the resolution of these studies is often incompatible with smaller basins and subsequent water resources planning. The purpose of this study was to test the potential of increasing the resolution of future climate projections by combining a series of surface and upper-level atmospheric datasets using a statistical downscaling technique and to then project how the future climate could change for a typical small snowmelt fed mountain basin in western North America, the Animas River Basin, Colorado, over the course of the 21st century. Results indicated that, in general, a warmer and drier climate may occur, with this technique more effectively capturing changes in air temperature over precipitation. With this kind of data at hand, increasing levels of sustainable water resource planning for a range of future climate scenarios may be achieved for mountain river basins of a similar scale.

The grass may not always be greener: projected reductions in climatic suitability for exotic grasses under future climates in Australia

Climate change presents a new challenge for the management of invasive exotic species that threaten both biodiversity and agricultural productivity. The invasion of exotic perennial grasses throughout the globe is particularly problematic given their impacts on a broad range of native plant communities and livelihoods. As the climate continues to change, pre-emptive long-term management strategies for exotic grasses will become increasingly important. Using species distribution modelling we investigated potential changes to the location of climatically suitable habitat for some exotic perennial grass species currently in Australia, under a range of future climate scenarios for the decade centred around 2050. We focus on eleven species shortlisted or declared as the Weeds of National Significance or Alert List species in Australia, which have also become successful invaders in other parts of the world. Our results indicate that the extent of climatically suitable habitat available for all of the exotic grasses modelled is projected to decrease under climate scenarios for 2050. This reduction is most severe for the three species of Needle Grass (genus Nassella) that currently have infestations in the south-east of the continent. Combined with information on other aspects of establishment risk (e.g. demographic rates, human-use, propagule pressure), predictions of reduced climatic suitability provide justification for re-assessing which weeds are prioritised for intensive management as the climate changes.

National housing and impervious surface scenarios for integrated climate impact assessments

Understanding the impacts of climate change on people and the environment requires an understanding of the dynamics of both climate and land use/land cover changes. A range of future climate scenarios is available for the conterminous United States that have been developed based on widely used international greenhouse gas emissions storylines. Climate scenarios derived from these emissions storylines have not been matched with logically consistent land use/cover maps for the United States. This gap is a critical barrier to conducting effective integrated assessments. This study develops novel national scenarios of housing density and impervious surface cover that are logically consistent with emissions storylines. Analysis of these scenarios suggests that combinations of climate and land use/cover can be important in determining environmental conditions regulated under the Clean Air and Clean Water Acts. We found significant differences in patterns of habitat loss and the distribution of potentially impaired watersheds among scenarios, indicating that compact development patterns can reduce habitat loss and the number of impaired watersheds. These scenarios are also associated with lower global greenhouse gas emissions and, consequently, the potential to reduce both the drivers of anthropogenic climate change and the impacts of changing conditions. The residential housing and impervious surface datasets provide a substantial first step toward comprehensive national land use/land cover scenarios, which have broad applicability for integrated assessments as these data and tools are publicly available.

Potential benefits of early vigor and changes in phenology in wheat to adapt to warmer and drier climates

Developing crop cultivars with novel traits could help agriculture adapt to climate change. As introducing new traits into crops is expensive and time consuming, it is helpful to develop methods which can test whether a potential new plant trait increases or maintains production in future climates. We used a crop-soil simulation model (APSIM-Nwheat) to test whether changes in physiological traits, related to early vigor and flowering time, would result in increased yield when compared to traditional cultivars of wheat grown at higher temperatures, elevated atmospheric CO 2 and lower rainfall in a Mediterranean climate. Early vigor was simulated by changing four different plant traits. The impact of each trait on grain yield varied with climate scenario and soil type. Higher specific leaf area had minimal effect on yield for the historical climate, but it could increase production in future warmer climates. Increased rooting depth generally had a positive impact on yield, while lower radiation use efficiency and earlier flowering tended to reduce yield. The interaction between these traits was generally positive, and our results indicate that early vigor may improve yield for a range of future climate scenarios. However, in the low rainfall regions, early vigor is unlikely to compensate for rainfall reductions of ⩾30%. Yield gains for early vigor are likely to be larger on sandy loam than on heavier clay soil. The simulation of cultivars differing in flowering time showed that in drier climates earlier flowering cultivars increase potential yield while in warming climates later cultivars increase yield. In conclusion, our analyses suggest that there is great potential for adapting wheat systems to climate change by introducing cultivars with new traits. Our results also show how simulation analyses can assist plant breeders in determining which traits could be important for crop production in future climates.

Introduction: Assessing the effects of climate change on Chicago and the Great Lakes

This paper introduces the analyses of the potential impacts of climate change on the city of Chicago and the Great Lakes region and potential response options that provide the basis for this special issue. Covering projected changes in climate and hydrology, this collection of studies first estimates the potential impacts of climate change on human health, natural ecosystems, water resources, energy, and infrastructure in the city of Chicago and the surrounding Great Lakes region. A consistent set of future climate projections have been used as the basis for each analysis, which together provide a vivid impression of the consequences likely to result under the SRES higher (A1FI) as compared to the lower (B1) emission scenarios. These findings highlight the importance of the next set of analyses, which focus on quantifying Chicago's greenhouse gas emissions and prioritizing emissions reduction and adaptation options in light of the projected impacts. Together, these analyses form the basis for the City of Chicago's Climate Action Plan, the first such plan to be based on a thorough climate change impact assessment exploring the implications of a range of future climate scenarios. Although focused on Chicago and the Great Lakes region, the methods described here are generally applicable across North America and many other parts of the world, serving as a pattern for future regional assessments to directly inform adaptation and mitigation policy at the local to regional scale.