High-resolution dynamically downscaled projections of future extreme temperatures, heatwaves and exposure in Southeast Asia

Extreme heat and heatwaves driven by global warming pose escalating risks globally, particularly in Southeast Asia (SEA), home to 680 million people, with a high concentration in urban areas. This study made use of CMIP6-based convection-permitting dynamically downscaled simulations at 8 km resolution over SEA under the three Shared Socioeconomic Pathways (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Projected changes in daily maximum temperatures and key heat metrics including the frequency of hotter days, heatwaves, and cumulative heat intensity and exposure time were analyzed across the region, as well as for individual countries and cities, for the near future (2040-2059) and far future (2080-2099) relative to the historical baseline (1995-2014). Results indicate a substantial rise in daily maximum temperatures over SEA, with average increases of 1.0-2.1 °C by 2040-2059 and 1.1-4.0 °C by 2080-2099 across the three scenarios. The Mekong Delta, eastern Sumatra and southern Borneo are identified as hotspots with pronounced temperature increase. More frequent and prolonged heatwaves are also projected over SEA, with heatwave frequency and duration doubling around 2025 and 2040, respectively, across the three scenarios, and increasing fivefold around 2045 and 2070, respectively, under SSP5-8.5. Specifically, the Maritime Continent is projected to face a notably higher frequency of hotter days, establishing a new heat norm by the end of the century. Under SSP2-4.5 and SSP5-8.5, approximately 4 and 9 months/year, respectively, will be as hot as or hotter than the historical 5 % hottest days. Cities along the Strait of Malacca and on Java are expected to experience extreme heat with heightened cumulative intensity and longer durations. Mitigating emissions along a low-carbon pathway would provide substantial benefits for the Maritime Continent in the second half of the century, not only for human health but also for agriculture and ecosystems. This study provides the highest resolution and most updated projections of extreme heat over SEA to help inform targeted climate adaption strategies in this highly vulnerable region.

Divergent regional volume growth responses of Scots Pine and Oak stands to climate change in Europe

European climatic change has been proposed to induce many changes to forests, about factors such as tree species distributions, site productivity, groundwater availability, outbreaks of forest pests, and damage from wind-breakage of trees. Novel approaches to empirical tree growth modelling using re-measurements over large climatic gradients capture variability associated with long-term climatic conditions as well as weather during the growth period. Using the latest version of the individual tree-based forest simulator, PrognAus, which has been outfitted with a climate-sensitive basal area increment module, we forecast growth of trees in pure and mixed stands of Pinus sylvestris L. and Quercus spp. across a network of 23 European sites between 2017 and 2100 under current climate and RCP 4.5 and RCP 8.5 climatic scenarios. By training a stand-level static reduced model (SRM) from these local level results, we forecast widespread future growth changes for stands of Pinus sylvestris and Quercus spp. across Europe. Our SRM predicts stand gross-volume relative growth (ratio of the gross volume production in a given growth year to the gross volume production until the start of the growth year) with a generalized additive mixed model (GAMM). We decomposed overall growth into tensors capturing variation associated with stand species mixture type (pure P. sylvestris, mixed P. sylvestris- Q. spp., pure Q. spp.), age, and weather conditions during the growth year and the preceeding year. Wall-to-wall predictions based on the SRM are presented for a high-resolution 30-arcsecond grid spanning most parts of Europe.

Future projection of droughts in Morocco and potential impact on agriculture

The present study evaluates the future drought hazard in Morocco using a Multi-Model Ensemble (MME) approach. First, the artificial neural network-based MME is constructed using the General Circulation Models (GCMs) from the Climate Models Intercomparison Project phase 6 (CMIP6) which are most successful in representing the historical temperature and precipitation values. Next, the future changes in the precipitation, Potential EvapoTranspiration (PET) calculated using temperatures data, aridity index, and drought indices calculated via the Standardized Precipitation Evapotranspiration Index (SPEI) values were projected for the historical period 1980-2014, near future 2025-2050, mid future 2051-2075, and far future 2076-2100. The obtained results indicate that there will be a decrease in values of the precipitation and an increase in values of the PET, leading to an increase in aridity risk for Morocco. The future projections using the SPEI results show that the average index values will mostly be in the drought zone, indicating that the drought severity will increase. The spatial analysis of SPEI values in different regions of Morocco demonstrates that the northern part of the country has relatively more drought occurrences, and drought severity tends to increase with each passing period. The study also reveals that drought severity will significantly increase after 2050 in the Shared Socio-economic Pathways 5-8.5 (SSP5-8.5) scenario. The research concludes that the increase in drought severity will significantly impact Morocco's water resources, agriculture and food security among others.

Individual and combined impacts of urbanization and climate change on catchment runoff in Southeast Queensland, Australia

Assessing the impacts of climate change and land-use change is of critical importance, particularly for urbanized catchments. In this study, a novel framework was used to examine and quantify these impacts on the runoff in six catchments in Southeast Queensland, Australia. For each catchment, temporal variations in impervious areas were derived from six satellite images using a sub-pixel classification technique and incorporated into the SIMHYD hydrological model. This model was satisfactorily calibrated and validated with daily runoff observations (0.63 ≤ Nash-Sutcliffe efficiency coefficient ≤ 0.94, percent bias ≤ ±18 %) and was used to produce baseline runoff for 1986-2005 in these six catchments. The projected population increase was used to predict future imperviousness based on the linear relationship between the two. The projected rainfall and evapotranspiration were derived from the ensemble means of the eight general circulation models. Catchment runoff was projected under two climate change scenarios (RCP4.5 and 8.5), three urbanization scenarios (low, medium, and high), and six combined scenarios for two future periods (2026-2045 and 2046-2065). Comparing with the baseline, it was found that (1) climate change alone would lead to a -3.8 % to -17.6 % reduction in runoff among the six catchments, for all scenarios and both future periods; (2) a 11.8 % to 78 % increase in runoff was projected under the three urbanization scenarios, and (3) a decrease in runoff due to climate change would moderate the increase in runoff caused by urbanization. For example, the combined effect would be a 54 % increase in runoff, with a -17.2 % decrease due to climate change and 78 % increase due to urbanization. Overall, runoff in the six catchments may be significantly affected by urban expansion. From this study, decision makers could gain a better understanding of the relative importance of the effects of climate and land-use change, which can be applied when developing future long-term water management plans at the catchment scale.

A large-scale view of marine heatwaves revealed by archetype analysis

Marine heatwaves can have disastrous impacts on ecosystems and marine industries. Given their potential consequences, it is important to understand how broad-scale climate variability influences the probability of localised extreme events. Here, we employ an advanced data-mining methodology, archetype analysis, to identify large scale patterns and teleconnections that lead to marine extremes in certain regions. This methodology is applied to the Australasian region, where it identifies instances of anomalous sea-surface temperatures, frequently associated with marine heatwaves, as well as the broadscale oceanic and atmospheric conditions associated with those extreme events. Additionally, we use archetype analysis to assess the ability of a low-resolution climate model to accurately represent the teleconnection patterns associated with extreme climate variability, and discuss the implications for the predictability of these impactful events.

The mechanism linking the variability of the Antarctic sea ice extent in the Indian Ocean sector to Indian summer monsoon rainfall

The study investigates the mechanism of teleconnection between the variability of sea ice extent (SIE) in the Indian Ocean sector of the Southern Ocean and the variability of Indian summer monsoon rainfall. We utilized reanalysis, satellite, in-situ observation data, and model output from the coupled model intercomparison project phase 5 (CMIP5) from 1979 to 2013. The empirical orthogonal function (EOF) and correlation analysis show that the first and third modes of principal component (PC1 and PC3) of SIE in the Indian Ocean sector during April-May-June (AMJ) are significantly correlated with the second mode of principal component (PC2) of Indian summer monsoon rainfall. The reanalysis data revealed that the changes in the SIE in the Indian Ocean sector excite meridional wave train responses along the Indian Ocean for both principal component modes. Positive (negative) SIE anomalies based on first and third EOFs (EOF1 and EOF3), contribute to the strengthening (weakening) of the Polar, Ferrel, and Hadley cells, inducing stronger (weaker) convective activity over the Indian latitudes. The stronger (weaker) convective activity over the Indian region leads to more (less) rainfall over the region during high (low) ice phase years. Furthermore, a stronger (weaker) polar jet during the high (low) ice phase is also noted. The selected CMIP5 models captured certain atmospheric teleconnection features found in the reanalysis. During AMJ, the SIE simulated by the NorESM1-M model was significantly positively correlated with Indian summer monsoon rainfall, whereas the IPSL-CM54-LR model showed a negative correlation.

A Kernel Density Estimation Approach and Statistical Generalized Additive Model of Western North Pacific Typhoon Activities

This paper presents a development of a statistical model of typhoon genesis, tracks based on kernel density estimation and a generalized additive model (GAM). Modeling of typhoon activity is ultimately beneficial to the people living in coastal zones, insurance/re-insurance companies, policy, planning and decision departments. A 50-year record (1972–2021) of typhoon track observations from the International Best Track Archive Climate Stewardship have been used to observe the distribution of typhoon genesis by kernel density estimation. The tracks are simulated through the development of a GAM. It reproduces the observation well. A distance calculation approach between observed and simulated tracks’ landfall have been used to validate the model and the model shows a very good skill (approximately 75%).

Skill and Intercomparison of Global Climate Models in Simulating Wind Speed, and Future Changes in Wind Speed over South Asian Domain

Investigating the role of complex dynamical components of a global climate model (GCM) in improving near-surface wind speed (WS) simulation is vital for the climate community in building reliable future WS projections. The relative skill of GCMs in representing WS at diverse climate variable scales (daily, monthly, seasonal, and annual) over land and ocean areas of the South Asian domain is not clear yet. With this in mind, this paper evaluated the skill of 28 Coupled Model Intercomparison Project phase five GCMs in reproducing the WS using a devised relative score approach. It is recommended to use the mean ensemble of MPI-ESM-MR, CSIRO-Mk3.6.0, and GFDL-ESM2G GCMs for understanding future changes in wind–wave climate and offshore wind energy potential. The inter-comparison of GCMs shows that the GCM with high or low atmospheric resolution does not necessarily exhibit the best or worst performance, respectively, whereas the dynamic components in the model configuration play the major role, especially the atmosphere component relative to other dynamical components. The strengthening of annual and seasonal mean WS is observed over coastal plains of the United Republic of Tanzania, Oman, eastern Thailand, eastern Gulf of Thailand and Sumatra, and weakening over the central northern equatorial region of the Indian Ocean in the 21st century for RCP4.5 and RCP8.5 emission scenarios.

Cool microrefugia accumulate and conserve biodiversity under climate change

A major challenge in climate change biology is to explain why the impacts of climate change vary around the globe. Microclimates could explain some of this variation, but climate change biologists often overlook microclimates because they are difficult to map. Here, we map microclimates in a freshwater rock pool ecosystem and evaluate how accounting for microclimates alters predictions of climate change impacts on aquatic invertebrates. We demonstrate that average maximum temperature during the growing season can differ by 9.9-11.6°C among microclimates less than a meter apart and this microclimate variation might increase by 21% in the future if deeper pools warm less than shallower pools. Accounting for this microclimate variation significantly alters predictions of climate change impacts on aquatic invertebrates. Predictions that exclude microclimates predict low future occupancy (0.08-0.32) and persistence probabilities (2%-73%) for cold-adapted taxa, and therefore predict decreases in gamma richness and a substantial shift toward warm-adapted taxa in local communities (i.e., thermophilization). However, predictions incorporating microclimates suggest cool locations will remain suitable for cold-adapted taxa in the future, no change in gamma richness, and 825% less thermophilization. Our models also suggest that cool locations will become suitable for warm-adapted taxa and will therefore accumulate biodiversity in the future, which makes cool locations essential for biodiversity conservation. Simulated protection of the 10 coolest microclimates (9% of locations on the landscape) results in a 100% chance of conserving all focal taxa in the future. In contrast, protecting the 10 currently most biodiverse locations, a commonly employed conservation strategy, results in a 3% chance of conserving all focal taxa in the future. Our study suggests that we must account for microclimates if we hope to understand the future impacts of climate change and design effective conservation strategies to limit biodiversity loss.

Climatological Changes in Soil Moisture during the 21st Century over the Indian Region Using CMIP5 and Satellite Observations

Climate data records of soil moisture (SM) are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. However, many of these studies rely on models for which the errors are not yet fully understood over a region. This may have a considerable impact on the economic growth of the country if the model’s future predictions are used for studying long-term trends. Here we examined the spatial distribution of past, present, and future predictions of SM studied using the Coupled Model Intercomparison Project Phase5 (CMIP5) simulations for the historical period (1850–2005) and future climate projections (2006–2099) based on Representative Concentration Pathways (RCP-RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Furthermore, the performance of modeled SM with the satellite AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) was studied. The modeled SM variations of 38 Global Climate Models (GCMs) show discreteness but still we observed that CESM1-CM5, CSIRO-MK3-6-0, BCC-CSM1-1, and also BCC-CSM1-1-M, NorESM1-M models performed better spatially as well as temporally in all future scenarios. However, from the spatial perspective, a large deviation was observed in the interior peninsula during the monsoon season from model to model. In addition, the spatial distribution of trends was highly diversified from model to model, while the Taylor diagram presents a clear view of the model’s performance with observations over the region. Skill score statistics also give the accuracy of model predictions in comparison with observations. The time series was estimated for the future trend of the SM along with the past few decades, whereas the preindustrial and industrial period changes were involved. Significant positive anomaly trends are noticed in the whole time series of SM during the future projection period of 2021–2099 using CMIP5 SM model datasets.

Climate Change Impact on Peruvian Biomes

The biodiversity present in Peru will be affected by climatic and anthropogenic changes; therefore, understanding these changes will help generate biodiversity conservation policies. This study analyzes the potential distributions of biomes (B) in Peru under the effects of climate change. The evaluation was carried out using the random forest (RF) method, six bioclimatic variables, and digital topography for the classification of current B in Peru. Subsequently, the calibrated RF model was assimilated to three downscaled regional climate models to project future B distributions for the 2035–2065 horizon. We evaluated possible changes in extension and elevation as well as most susceptible B. Our projections show that future scenarios agreed that 82% of current B coverage will remain stable. Approximately 6% of the study area will change its current conditions to conditions of higher humidity; 4.5% will maintain a stable physiognomy, but with an increase in humidity; and finally, 6% will experience a decrease in humidity but maintain its appearance. Additionally, glaciers and swamps are indicated as the most vulnerable B, with probable losses greater than 50% of their current area. These results demonstrate the need to generate public policies for the adaptation and mitigation of climate effects on B at a national scale.

Assessing the impact of suppressing Southern Ocean SST variability in a coupled climate model

The Southern Ocean exerts a strong influence on global climate, regulating the storage and transport of heat, freshwater and carbon throughout the world’s oceans. While the majority of previous studies focus on how wind changes influence Southern Ocean circulation patterns, here we set out to explore potential feedbacks from the ocean to the atmosphere. To isolate the role of oceanic variability on Southern Hemisphere climate, we perform coupled climate model experiments in which Southern Ocean variability is suppressed by restoring sea surface temperatures (SST) over 40°–65°S to the model’s monthly mean climatology. We find that suppressing Southern Ocean SST variability does not impact the Southern Annular Mode, suggesting air–sea feedbacks do not play an important role in the persistence of the Southern Annular Mode in our model. Suppressing Southern Ocean SST variability does lead to robust mean-state changes in SST and sea ice. Changes in mixed layer processes and convection associated with the SST restoring lead to SST warming and a sea ice decline in southern high latitudes, and SST cooling in midlatitudes. These results highlight the impact non-linear processes can have on a model’s mean state, and the need to consider these when performing simulations of the Southern Ocean.

Role of Surface Gravity Waves in Aquaplanet Ocean Climates

We present a set of idealized numerical experiments of a solstitial aquaplanet ocean and examine the thermodynamic and dynamic implications of surface gravity waves (SGWs) upon its mean state. The aquaplanet's oceanic circulation is dominated by an equatorial zonal jet and four Ekman driven meridional overturning circulation (MOC) cells aligned with the westerly atmospheric jet streams and easterly trade winds in both hemispheres. Including SGW parameterization (representing modulations of air-sea momentum fluxes, Langmuir circulation, and Stokes-Coriolis force) increases mixed layer vertical momentum diffusivity by ∼40% and dampens surface momentum fluxes by ∼4%. The correspondingly dampened MOC impacts the oceanic density structure to 1 km depth by lessening the large-scale advective transports of heat and salt, freshening the equatorial latitudes (where evaporation minus precipitation [E - P] is negative) and increasing salinity in the subtropics (where E - P is positive) by ∼1%. The midlatitude pycnocline in both hemispheres is deepened by the inclusion of SGWs. Including SGWs into the aquaplanet ocean model acts to increase mixed layer depth by ∼10% (up to 20% in the wintertime in midlatitudes), decrease vertical shear in the upper 200 m and alter local midlatitude buoyancy frequency. Generally, the impacts of SGWs upon the aquaplanet ocean are found to be consistent across cooler and warmer climates. We suggest that the implications of these simulations could be relevant to understanding future projections of SGW climate, exoplanetary oceans, and the dynamics of the Southern Ocean mixed layer.

Landscape Conservation Forecasting for Data-Poor at-Risk Species on Western Public Lands, United States

Managing vast federal public lands governed by multiple land use policies creates challenges when demographic data on at-risk species are lacking. The U.S. Bureau of Land Management Cedar City Field Office used this project in the Black Mountains (Utah) to inform vegetation management supporting at-risk greater sage-grouse and Utah prairie dog planning. Ecological systems were mapped from satellite remote sensing imagery and used to model species habitat suitability under two levels of management activity (custodial, preferred) and climate scenarios for historic and two global circulation models. Spatial state-and-transition models of ecological systems were simulated for all six scenarios up to 60 years while coupled with expert-developed habitat suitability indices. All ecological systems are at least moderately departed from reference conditions in 2012, whereas habitat suitability was 50.5% and 48.4% for sage-grouse and prairie dog, respectively. Management actions replaced non-native annual grasslands with perennial grasses, removed conifers, and controlled exotic forbs. The drier climate most affected ecological departure and prairie dog habitat suitability at 30 years only. Different climates influenced spatial patterns of sage-grouse habitat suitability, but nonspatial values were unchanged. Climate impacts on fire, vegetation succession, and restoration explain many results. Front-loading restoration is predicted to benefit under future drier climate.

CMIP5-Based Projection of Decadal and Seasonal Sea Surface Temperature Variations in East China Shelf Seas

The East China Shelf Seas, comprising the Bohai Sea, the Yellow Sea, and the shelf region of East China Sea, play significant roles among the shelf seas of the Western North Pacific Ocean. The projection of sea surface temperature (SST) changes in these regions is a hot research topic in marine science. However, this is a very difficult task due to the lack of available long-term projection data. Recently, with the high development of simulation technology based on numerical models, the model intercomparison projects, e.g., Phase 5 of the Climate Model Intercomparison Project (CMIP5), have become important ways of understanding climate changes. CMIP5 provides multiple models that can be used to estimate SST changes by 2100 under different representative concentration pathways (RCPs). This paper developed a CMIP5-based SST investigation framework for the projection of decadal and seasonal variation of SST in East China Shelf Seas by 2100. Since the simulation results of CMIP5 models may have degrees of errors, this paper uses hydrological observation data from World Ocean Atlas 2018 (WOA18) for model validation and correction. This paper selects seven representative ones including ACCESS1.3, CCSM4, FIO-ESM, CESM1-CAM5, CMCC-CMS, NorESM1-ME, and Max Planck Institute Earth System Model of medium resolution (MPI-ESM-MR). The decadal and seasonal SST changes in the next 100 years (2030, 2060, 2090) are investigated by comparing with the present analysis in 2010. The experimental results demonstrate that SST will increase significantly by 2100: the decadal SST will increase by about 1.55 °C, while the seasonal SST will increase by 1.03–1.95 °C.

Impact of climate change on navel orangeworm, a major pest of tree nuts in California

California's agricultural sector is the highest valued agricultural sector in the United States. It is also a global leader in the production of various specialty crops, including three major tree nuts - almond, walnut, and pistachio. These three nut crops accounted for approximately 16% of the state's total agricultural economy. Current and future changes in climate pose many challenges in agriculture and impacts related to increased pest pressure in agriculture due to elevated temperatures are significant. The navel orangeworm, Amyelois transitella (Walker), is the most challenging pest of tree nuts in California and often cause a significant economic loss despite the careful implementation of multiple pest control tactics. Temperature variations can directly affect the developmental rates, behavior, and overall population dynamics of this pest, and it is critically important to understand these dynamics with respect to climate change. The objective of this study was to quantify changes in the timing and number of navel orangeworm generations in almonds, walnuts, and pistachios for the entire Central Valley of California using projections from ten general circulation models (GCMs) under two emission scenarios. The results suggest that navel orangeworm is likely to complete its life cycle much faster under climate change due to projected temperature increases. The results also suggest that under future climate change, navel orangeworm can complete one additional generation within the growing season and likely going to pose significant risks to these major nut industries in the future. Quantifying navel orangeworm generations and assessing risks to tree nuts under climate change can help facilitate and strategize integrated pest management (IPM) practices to the sustainability of the production systems by minimizing risks.

Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method

Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate the predictive capacity of 11 multi-model ensemble methods (MMEs), including random forest (RF), to estimate reference evapotranspiration (ET0) using 10 AR5 models for the scenarios RCP4.5 and RCP8.5. The study was carried out in the Segura Hydrographic Demarcation (SE of Spain), a typical Mediterranean semiarid area. ET0 was estimated in the historical scenario (1970–2000) using a spatially calibrated Hargreaves model. MMEs obtained better results than any individual model for reproducing daily ET0. In validation, RF resulted more accurate than other MMEs (Kling–Gupta efficiency (KGE) M=0.903, SD=0.034 for KGE and M=3.17, SD=2.97 for absolute percent bias). A statistically significant positive trend was observed along the 21st century for RCP8.5, but this trend stabilizes in the middle of the century for RCP4.5. The observed spatial pattern shows a larger ET0 increase in headwaters and a smaller increase in the coast.

Past and future potential range changes in one of the last large vertebrates of the Australian continent, the emu Dromaius novaehollandiae

In Australia, significant shifts in species distribution have occurred with the loss of megafauna, changes in indigenous Australian fire regime and land-use changes with European settlement. The emu, one of the last megafaunal species in Australia, has likely undergone substantial distribution changes, particularly near the east coast of Australia where urbanisation is extensive and some populations have declined. We modelled emu distribution across the continental mainland and across the Great Dividing Range region (GDR) of eastern Australia, under historical, present and future climates. We predicted shifts in emu distribution using ensemble modelling, hindcasting and forecasting distribution from current emu occurrence data. Emus have expanded their range northward into central Australia over the 6000 years modelled here. Areas west of the GDR have become more suitable since the mid-Holocene, which was unsuitable then due to high precipitation seasonality. However, the east coast of Australia has become climatically sub-optimal and will remain so for at least 50 years. The north east of NSW encompasses the range of the only listed endangered population, which now occurs at the margins of optimal climatic conditions for emus. Being at the fringe of suitable climatic conditions may put this population at higher risk of further decline from non-climatic anthropogenic disturbances e.g. depredation by introduced foxes and pigs. The limited scientific knowledge about wild emu ecology and biology currently available limits our ability to quantify these risks.

Local decadal prediction according to statistical/dynamical approaches

Dynamical climate models present an initialization problem due to the poor availability of deep oceanic data, which is required for the model assimilation process. In this sense, teleconnection indices, defined from spatial and temporal patterns of climatic variables, are conceived as useful tools to complement them. In this work, the near-term climate predictability of 35 temperature and 36 precipitation time series of three cities (Barcelona, Bristol and Lisbon) was analysed using two approaches: (a) a statistical-dynamical combination of self-predictable teleconnection indices and long-term climate projections on a local scale and (b) dynamical model outputs obtained from drift-corrected decadal experiments. Fourier and wavelet analyses were used to assess the predictability of seven teleconnection indices thanks to a cross-validation process (with differentiated training and validation periods). The standardized absolute error of teleconnection-based prediction was compared with that obtained from a (9) multi-model ensemble based on the Coupled Model Intercomparison Project Phase 5. Results showed that decadal predictions at horizons between 20 and 30 years are adequate for temperature and precipitation if a teleconnection-based approach is used, while temperature is better predicted from a 5-year horizon using drift-corrected dynamical outputs.

Evaluation of the impact of heat on wheat dormancy, late maturity α-amylase and grain size under controlled conditions in diverse germplasm

In the Australian wheat belts, short episodes of high temperatures or hot spells during grain filling are becoming increasingly common and have an enormous impact on yield and quality, bringing multi-billion losses annually. This problem will become recurrent under the climate change scenario that forecast increasing extreme temperatures, but so far, no systematic analysis of the resistance to hot spells has yet been performed in a diverse genetic background. We developed a protocol to study the effects of heat on three important traits: grain size, grain dormancy and the presence of Late Maturity α-Amylase (LMA), and we validated it by analysing the phenotypes of 28 genetically diverse wheat landraces and exploring the potential variability existing in the responses to hot spells. Using controlled growth environments, the different genotypes were grown in our standard conditions until 20 days after anthesis, and then moved for 10 days into a heat chamber. Our study showed that our elevated temperature treatment during mid-late filling triggered multiple detrimental effects on yield and quality. We observed a reduction in grain size, a reduction in grain dormancy and increased LMA expression in most of the tested genotypes, but potential resistant lines were identified for each analyzed trait opening new perspectives for future genetic studies and breeding for heat-insensitive commercial lines.

High resolution climate change projections for the Pyrenees region

Abstract. The Pyrenees, located in the transition zone of Atlantic and Mediterranean climates, constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions, with potential impact on the availability of water resources, mainly for downstream populations. High-resolution probabilistic climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this line, we have generated high–resolution climate projections for 21st century by applying two statistical downscaling methods (regression for max and min temperatures, and analogue for precipitation) over the Pyrenees region in the frame of the CLIMPY project over a new high-resolution (5 km × 5 km) observational grid using 24 climate models from CMIP5. The application of statistical downscaling to such a high resolution observational grid instead of station data partially circumvent the problems associated to the non-uniform distribution of observational in situ data. This new high resolution projections database based on statistical algorithms complements the widely used EUROCORDEX data based on dynamical downscaling and allows to identify features that are dependent on the particular downscaling method. In our analysis, we not only focus on maximum and minimum temperatures and precipitation changes but also on changes in some relevant extreme indexes, being 1986–2005 the reference period. Although climate models predict a general increase in temperature extremes for the end of the 21st century, the exact spatial distribution of changes in temperature and much more in precipitation remains uncertain as they are strongly model dependent. Besides, for precipitation, the uncertainty associated to models can mask – depending on the zones- the signal of change. However, the large number of downscaled models and the high resolution of the used grid allow us to provide differential information at least at massif level. The impact of the RCP becomes significant for the second half of the 21st century, with changes – differentiated by massifs – of extreme temperatures and analysed associated extreme indexes for RCP8.5 at the end of the century.

Genetic data and climate niche suitability models highlight the vulnerability of a functionally important plant species from south-eastern Australia

Habitat fragmentation imperils the persistence of many functionally important species, with climate change a new threat to local persistence due to climate niche mismatching. Predicting the evolutionary trajectory of species essential to ecosystem function under future climates is challenging but necessary for prioritizing conservation investments. We use a combination of population genetics and niche suitability models to assess the trajectory of a functionally important, but highly fragmented, plant species from south-eastern Australia (Banksia marginata, Proteaceae). We demonstrate significant genetic structuring among, and high level of relatedness within, fragmented remnant populations, highlighting imminent risks of inbreeding. Population simulations, controlling for effective population size (N e), suggest that many remnant populations will suffer rapid declines in genetic diversity due to drift in the absence of intervention. Simulations were used to demonstrate how inbreeding and drift processes might be suppressed by assisted migration and population mixing approaches that enhance the size and connectivity of remnant populations. These analyses were complemented by niche suitability models that predicted substantial reductions of suitable habitat by 2080; ~30% of the current distribution of the species climate niche overlaps with the projected distribution of the species climate niche in the geographic region by the 2080s. Our study highlights the importance of conserving remnant populations and establishing new populations in areas likely to support B. marginata in the future, and adopting seed sourcing strategies that can help populations overcome the risks of inbreeding and maladaptation. We also argue that ecological replacement of B. marginata using climatically suited plant species might be needed in the future to maintain ecosystem processes where B. marginata cannot persist. We recommend the need for progressive revegetation policies and practices to prevent further deterioration of species such as B. marginata and the ecosystems they support.

Indicator-Based Assessment of Resilience and Vulnerability in the Indian Himalayan Region: A Case Study on Socio-Economy under Different Scenarios

The Indian Himalayan region is vulnerable to climate change because of its geospatial fragility. The present study gives a framework for the analysis of household and village-level resilience and vulnerability in the Bhagirathi Basin of Indian Western Himalayan region under different climate change scenarios. Villages were selected depending on different biophysical criteria to have a good representation of the study area. Household-level survey using the household economy approach was done in 646 households of 30 villages to collect information on indicators of natural, physical, financial and human capital assets and scores were generated for each category. A cumulative resilience score was obtained for each household and village. Future climate projections on mean annual temperature were also accessed under Representative Concentration Pathway (RCP) 4.5 to estimate the change in mean temperature of the studied villages and probable change in agricultural production. The result shows that most of the villages of Tehri Garhwal are clustered in vulnerable classes in comparison to Uttarkashi villages and vulnerability scores of 11 and 8 villages changed under climate shock and future agricultural production change scenarios, respectively. The study has manifold implications on further research and policy implementation under socioeconomic vulnerability in the Himalayan region.

Evaluation of Historical CMIP5 GCM Simulation Results Based on Detected Atmospheric Teleconnections

Atmospheric teleconnections are characteristic to the climate system and exert major impacts on the global and regional climate. Accurate representation of teleconnections by general circulation models (GCMs) is indispensable given their fundamental role in the large scale circulation patterns. In this study a statistical method is introduced to evaluate historical GCM outputs of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with respect to teleconnection patterns. The introduced method is based on the calculation of correlations between gridded time series of the 500 hPa geopotential height fields in the Northern Hemisphere. GCMs are quantified by a simple diversity index. Additionally, potential action centers of the teleconnection patterns are identified on which the local polynomial regression model is fitted. Diversity fields and regression curves obtained from the GCMs are compared against the NCEP/NCAR Reanalysis 1 and the ERA-20C reanalysis datasets. The introduced method is objective, reproducible, and reduces the number of arbitrary decisions during the analysis. We conclude that major teleconnection patterns are positioned in the GCMs and in the reanalysis datasets similarly, however, spatial differences in their intensities can be severe in some cases that could hamper the applicability of the GCM results for some regions. Based on the evaluation method, best-performing GCMs can be clearly distinguished. Evaluation of the GCMs based on the introduced method might help the modeling community to choose GCMs that are the most applicable for impact studies and for regional downscaling exercises.

Current and future spatial assessment of biological control as a mechanism to reduce economic losses and carbon emissions: the case of Solanum sisymbriifolium in Africa

BACKGROUND

Solanum sisymbriifolium is a native plant of South America introduced into Africa, which has detrimental effects on crop yields, and on the environment due to weed control treatment by burning. In South America, S. sisymbriifolium is naturally controlled by the beetle Gratiana spadicea, making this a potential option for its control in Africa. Here, we aim to generate current and future scenarios for the introduction of G. spadicea as a biocontrol agent in Africa, analysing: (i) current and future effective biocontrol areas; (ii) potentially avoided economic losses (AEL), and chemical control costs and savings; and (iii) avoided carbon emissions (ACE) associated with the non-burning of crop fields. We combine species distribution models (SDM) with land cover maps to estimate current and future effective biocontrol considering Representative Concentration Pathways (RCP) 4.5 and 8.5 climate change scenarios. We then estimate AEL and ACE using biocontrol, and chemical control costs and savings.

RESULTS

The effective biocontrol area reached 392 405 km² in 18 countries, representing 40% of potentially affected croplands. Climate change induced a decrease in affected croplands and effective biocontrol. The estimated AEL reached US$45 447.2 ± 5617.3 billion distributed across 16 countries, while the estimated chemical control costs and savings reached US$1988.5 billion and 1411.8 billion, respectively. Potential ACE reached 36.3 ± 5.4 Tg.

CONCLUSIONS

Our study provides evidence for the potential benefits of biological controllers on economic losses and carbon emissions, which can be incorporated into sustainable development in low-income countries.

Impact of projected sea surface temperature biases on tropical cyclones projections in the South Pacific

Climate model projections generally indicate fewer but more intense tropical cyclones (TCs) in response to increasing anthropogenic emissions. However these simulations suffer from long-standing biases in their Sea Surface Temperature (SST). While most studies investigating future changes in TC activity using high-resolution atmospheric models correct for the present-day SST bias, they do not consider the reliability of the projected SST changes from global climate models. The present study illustrates that future South Pacific TC activity changes are strongly sensitive to correcting the projected SST changes using an emergent constraint method. This additional correction indeed leads to a strong reduction of the cyclogenesis (−55%) over the South Pacific basin, while no statistically significant change arises in the uncorrected simulations. Cyclogenesis indices suggest that this strong reduction in the corrected experiment is caused by stronger vertical wind shear in response to a South Pacific Convergence Zone equatorward shift. We thus find that uncertainty in the projected SST patterns could strongly hamper the reliability of South Pacific TC projections. The strong sensitivity found in the current study will need to be investigated with other models, observational constraint methods and in other TC basins in order to assess the reliability of regional TC projections.

Climate Change Implications for Water Availability: A Case Study of Barcelona City

Barcelona city has a strong dependence on the Ter and Llobregat reservoir system to provide drinking water. One main concern for the next century is a potential water scarcity triggered by a severe and persistent rainfall shortage. This is one of the climate-driven impacts studied within the EU funded project RESCCUE. To evaluate potential drought scenarios, the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model reproduces the water contributions by month that have reached the reservoirs, regarding the accumulated rainfall over each sub-basin, representing the available historical-observed water levels. For future scenarios, we adjusted the input data set using climate projections of rainfall time series data of the project RESCCUE. Local outputs from 9 different climate models were applied to simulate river basins’ responses to reservoirs’ incoming water volume. Analyzing these results, we obtained average trends of the models for each scenario, hypothetical extreme values, and quantification for changes in water availability. Future water availability scenarios for Barcelona central water sources showed a mean decrease close to 11% in comparison with the period 1971–2015, considering the representative concentration pathway 8.5 (RCP8.5) climate change scenario in the year 2100. This research forecasts a slight downward trend in water availability from rainfall contributions from the mid-21st century. This planned future behavior does not mean that the annual water contributions are getting lower than the current ones, but rather, identifies an escalation in the frequency of drought cycles.

High-resolution and bias-corrected CMIP5 projections for climate change impact assessments

Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment.

Measurement(s)	climate change • precipitation process • precipitation amount • consecutive dry months index per time period • temperature of air
Technology Type(s)	computational modeling technique
Factor Type(s)	spatial region
Sample Characteristic - Environment	climate system
Sample Characteristic - Location	Earth (planet)

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11353664

What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?

The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.

Spatial global assessment of the pest Bagrada hilaris (Burmeister) (Heteroptera: Pentatomidae): current and future scenarios

BACKGROUND

The insect Bagrada hilaris (Burmeister) an important pest worldwide, mainly due to the serious economic losses incurred and the large number of zones invaded. However, current and future spatial distributions of this pest, and the total area of cropland potentially affected have not been estimated. Here, we aim to: (1) estimate the potential geographic distribution of B. hilaris; (2) quantify the total area of cropland potentially affected worldwide, and in two recently colonized zones (California and Chile); and (3) estimate future changes in distribution under different climate change scenarios.

RESULTS

We found that B. hilaris shows high environmental suitability in Mediterranean and arid regions, potentially affecting 1 108 184.1 km² of cropland worldwide. The most affected continents were Asia and America, with 309 659.8 and 294 638.6 km² of cropland at risk. More than 50% of cropland areas are at risk in seven countries. In California and central Chile, 43.7% and 50% of susceptible crops are at a high level of risk, respectively. Climate change scenarios predict an increase in the potential distribution of B. hilaris worldwide; America being the most affected continent.

CONCLUSIONS

Our results provide a spatially explicit baseline from which to focus efforts on the prevention, management and control of this pest worldwide. © 2018 Society of Chemical Industry.

Assessing Hydrological Ecosystem Services in a Rubber-Dominated Watershed under Scenarios of Land Use and Climate Change

Land use and climate change exert pressure on ecosystems and threaten the sustainable supply of ecosystem services (ESS). In Southeast-Asia, the shift from swidden farming to permanent cash crop systems has led to a wide range of impacts on ESS. Our study area, the Nabanhe Reserve in Yunnan province (PR China), saw the loss of extensive forest areas and the expansion of rubber (Hevea brasiliensis Müll. Arg.) plantations. In this study, we model water yield and sediment export for a rubber-dominated watershed under multiple scenarios of land use and climate change in order to assess how both drivers influence the supply of these ESS. For this we use three stakeholder-validated land use scenarios, varying in their degree of rubber expansion and land management rules. As projected climate change varies remarkably between different climate models, we combined the land use scenarios with datasets of temperature and precipitation changes, derived from nine General Circulation Models (GCMs) of the Fifth Assessment Report of the IPCC (Intergovernmental Panel on Climate Change) in order to model water yield and sediment export with InVEST (Integrated Valuation of Ecosystem Services and Trade-offs). Simulation results show that the effect of land use and land management decisions on water yield in Nabanhe Reserve are relatively minor (4% difference in water yield between land use scenarios), when compared to the effects that future climate change will exert on water yield (up to 15% increase or 13% decrease in water yield compared to the baseline climate). Changes in sediment export were more sensitive to land use change (15% increase or 64% decrease) in comparison to the effects of climate change (up to 10% increase). We conclude that in the future, particularly dry years may have a more pronounced effect on the water balance as the higher potential evapotranspiration increases the probability for periods of water scarcity, especially in the dry season. The method we applied can easily be transferred to regions facing comparable land use situations, as InVEST and the IPCC data are freely available.

Strategy for generation of climate change projections feeding Spanish impact community

Abstract. Over the past decades, the successive Coupled Model Intercomparison Projects (CMIPs) have produced a huge amount of global climate model simulations. Along these years, the climate models have advanced and can thus provide credible evolution of climate at least at continental or global scales since they are better representing physical processes and feedbacks in the climate system. Nevertheless, due to the coarse horizontal resolution of global climate models, it is necessary to downscale these results for their use to assess possible future impacts of climate change in climate sensitive ecosystems and sectors and to adopt adaptation strategies at local and national level. In this vein, the Spanish State Meteorological Agency (AEMET) has been producing since 2006 a set of reference downscaled climate change projections over Spain either applying statistical downscaling techniques to the outputs of the Global Climate Models (GCMs) or making use of the information generated by dynamical downscaling techniques through European projects or international initiatives such as PRUDENCE, ENSEMBLES and EURO-CORDEX. The AEMET strategy aims at exploiting all the available sources of information on climate change projections. The generalized use of statistical and dynamical downscaling approaches allow us to encompass a great number of global models and therefore to provide a better estimation of uncertainty. Most impact climate change studies over Spain make use of this reference downscaled projections emphasizing the estimation of uncertainties. Additionally to the rationale and history behind the AEMET generation of climate change scenarios, we focus on some preliminary analysis of the dependency of estimated uncertainties on the different sources of data.

North China Plain threatened by deadly heatwaves due to climate change and irrigation

North China Plain is the heartland of modern China. This fertile plain has experienced vast expansion of irrigated agriculture which cools surface temperature and moistens surface air, but boosts integrated measures of temperature and humidity, and hence enhances intensity of heatwaves. Here, we project based on an ensemble of high-resolution regional climate model simulations that climate change would add significantly to the anthropogenic effects of irrigation, increasing the risk from heatwaves in this region. Under the business-as-usual scenario of greenhouse gas emissions, North China Plain is likely to experience deadly heatwaves with wet-bulb temperature exceeding the threshold defining what Chinese farmers may tolerate while working outdoors. China is currently the largest contributor to the emissions of greenhouse gases, with potentially serious implications to its own population: continuation of the current pattern of global emissions may limit habitability in the most populous region, of the most populous country on Earth.

Irrigation increases the intensity of heatwaves over the North China Plain but how this will be exacerbated by climate change has not been quantified. Here the authors show that irrigation enhances magnitude of extreme wet-bulb temperature and intensity of heatwaves in this region.

Stationary Rossby waves dominate subduction of anthropogenic carbon in the Southern Ocean

The Southern Ocean has taken up more than 40% of the total anthropogenic carbon (C_ant) stored in the oceans since the preindustrial era, mainly in subantarctic mode and intermediate waters (SAMW-AAIW). However, the physical mechanisms responsible for the transfer of C_ant into the ocean interior remain poorly understood. Here, we use high resolution (1/10°) ocean simulations to investigate these mechanisms at the SAMW-AAIW subduction hotspots. Mesoscale Stationary Rossby Waves (SRWs), generated where the Antarctic Circumpolar Current interacts with topography, make the dominant contribution to the C_ant transfer in SAMW-AAIW in the Indian and Pacific sectors (66% and 95% respectively). Eddy-resolving simulations reproduce the observed C_ant sequestration in these layers, while lower spatial resolution models, that do not reproduce SRWs, underestimate the inventory of C_ant in these layers by 40% and overestimate the storage in denser layers. A key implication is that climate model simulations, that lack sufficient resolution to represent sequestration by SRWs, are therefore likely to overestimate the residence time of C_ant in the ocean, with implications for simulated rates of climate change.

Practice and philosophy of climate model tuning across six U.S. modeling centers

Model calibration (or "tuning") is a necessary part of developing and testing coupled ocean-atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major U.S. climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present day radiative imbalance vs. the implied balance in the pre-industrial as a target.

Deadly heat waves projected in the densely populated agricultural regions of South Asia

The risk associated with any climate change impact reflects intensity of natural hazard and level of human vulnerability. Previous work has shown that a wet-bulb temperature of 35°C can be considered an upper limit on human survivability. On the basis of an ensemble of high-resolution climate change simulations, we project that extremes of wet-bulb temperature in South Asia are likely to approach and, in a few locations, exceed this critical threshold by the late 21st century under the business-as-usual scenario of future greenhouse gas emissions. The most intense hazard from extreme future heat waves is concentrated around densely populated agricultural regions of the Ganges and Indus river basins. Climate change, without mitigation, presents a serious and unique risk in South Asia, a region inhabited by about one-fifth of the global human population, due to an unprecedented combination of severe natural hazard and acute vulnerability.

Towards More Comprehensive Projections of Urban Heat-Related Mortality: Estimates for New York City under Multiple Population, Adaptation, and Climate Scenarios

BACKGROUND

High temperatures have substantial impacts on mortality and, with growing concerns about climate change, numerous studies have developed projections of future heat-related deaths around the world. Projections of temperature-related mortality are often limited by insufficient information to formulate hypotheses about population sensitivity to high temperatures and future demographics.

OBJECTIVES

The present study derived projections of temperature-related mortality in New York City by taking into account future patterns of adaptation or demographic change, both of which can have profound influences on future health burdens.

METHODS

We adopted a novel approach to modeling heat adaptation by incorporating an analysis of the observed population response to heat in New York City over the course of eight decades. This approach projected heat-related mortality until the end of the 21st century based on observed trends in adaptation over a substantial portion of the 20th century. In addition, we incorporated a range of new scenarios for population change until the end of the 21st century. We then estimated future heat-related deaths in New York City by combining the changing temperature-mortality relationship and population scenarios with downscaled temperature projections from the 33 global climate models (GCMs) and two Representative Concentration Pathways (RCPs).

RESULTS

The median number of projected annual heat-related deaths across the 33 GCMs varied greatly by RCP and adaptation and population change scenario, ranging from 167 to 3,331 in the 2080s compared with 638 heat-related deaths annually between 2000 and 2006.

CONCLUSIONS

These findings provide a more complete picture of the range of potential future heat-related mortality risks across the 21st century in New York City, and they highlight the importance of both demographic change and adaptation responses in modifying future risks. Citation: Petkova EP, Vink JK, Horton RM, Gasparrini A, Bader DA, Francis JD, Kinney PL. 2017. Towards more comprehensive projections of urban heat-related mortality: estimates for New York City under multiple population, adaptation, and climate scenarios. Environ Health Perspect 125:47-55; http://dx.doi.org/10.1289/EHP166.

Moving beyond the Total Sea Ice Extent in Gauging Model Biases

Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. In this study we describe several approaches to improve how model biases in total sea ice distribution are quantified, and apply them to historically forced simulations contributed to the Coupled Model Intercomparison Project phase 5 (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent is often used to evaluate model performance. We introduce a new approach which investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using several observational data sets, we apply several new methods to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. We show that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. Our results suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the fine scale structure of sea ice characteristics, however, our "sector scale" metric aids to reduce the impact of compensating errors in hemispheric integrals.

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.

Incorporating Field Studies into Species Distribution and Climate Change Modelling: A Case Study of the Koomal Trichosurus vulpecula hypoleucus (Phalangeridae)

Species distribution models (SDMs) are an effective way of predicting the potential distribution of species and their response to environmental change. Most SDMs apply presence data to a relatively generic set of predictive variables such as climate. However, this weakens the modelling process by overlooking the responses to more cryptic predictive variables. In this paper we demonstrate a means by which data gathered from an intensive animal trapping study can be used to enhance SDMs by combining field data with bioclimatic modelling techniques to determine the future potential distribution for the koomal (Trichosurus vulpecula hypoleucus). The koomal is a geographically isolated subspecies of the common brushtail possum, endemic to south-western Australia. Since European settlement this taxon has undergone a significant reduction in distribution due to its vulnerability to habitat fragmentation, introduced predators and tree/shrub dieback caused by a virulent group of plant pathogens of the genus Phytophthora. An intensive field study found: 1) the home range for the koomal rarely exceeded 1 km in in length at its widest point; 2) areas heavily infested with dieback were not occupied; 3) gap crossing between patches (>400 m) was common behaviour; 4) koomal presence was linked to the extent of suitable vegetation; and 5) where the needs of koomal were met, populations in fragments were demographically similar to those found in contiguous landscapes. We used this information to resolve a more accurate SDM for the koomal than that created from bioclimatic data alone. Specifically, we refined spatial coverages of remnant vegetation and dieback, to develop a set of variables that we combined with selected bioclimatic variables to construct models. We conclude that the utility value of an SDM can be enhanced and given greater resolution by identifying variables that reflect observed, species-specific responses to landscape parameters and incorporating these responses into the model.

An observational radiative constraint on hydrologic cycle intensification

Intensification of the hydrologic cycle is a key dimension of climate change, with substantial impacts on human and natural systems. A basic measure of hydrologic cycle intensification is the increase in global-mean precipitation per unit surface warming, which varies by a factor of three in current-generation climate models (about 1-3 per cent per kelvin). Part of the uncertainty may originate from atmosphere-radiation interactions. As the climate warms, increases in shortwave absorption from atmospheric moistening will suppress the precipitation increase. This occurs through a reduction of the latent heating increase required to maintain a balanced atmospheric energy budget. Using an ensemble of climate models, here we show that such models tend to underestimate the sensitivity of solar absorption to variations in atmospheric water vapour, leading to an underestimation in the shortwave absorption increase and an overestimation in the precipitation increase. This sensitivity also varies considerably among models due to differences in radiative transfer parameterizations, explaining a substantial portion of model spread in the precipitation response. Consequently, attaining accurate shortwave absorption responses through improvements to the radiative transfer schemes could reduce the spread in the predicted global precipitation increase per degree warming for the end of the twenty-first century by about 35 per cent, and reduce the estimated ensemble-mean increase in this quantity by almost 40 per cent.

Response of the endangered tropical dry forests to climate change and the role of Mexican Protected Areas for their conservation

Assuming that co-distributed species are exposed to similar environmental conditions, ecological niche models (ENMs) of bird and plant species inhabiting tropical dry forests (TDFs) in Mexico were developed to evaluate future projections of their distribution for the years 2050 and 2070. We used ENM-based predictions and climatic data for two Global Climate Models, considering two Representative Concentration Pathway scenarios (RCP4.5/RCP8.5). We also evaluated the effects of habitat loss and the importance of the Mexican system of protected areas (PAs) on the projected models for a more detailed prediction of TDFs and to identify hot spots that require conservation actions. We identified four major distributional areas: the main one located along the Pacific Coast (from Sonora to Chiapas, including the Cape and Bajío regions, and the Balsas river basin), and three isolated areas: the Yucatán peninsula, central Veracruz, and southern Tamaulipas. When considering the effect of habitat loss, a significant reduction (~61%) of the TDFs predicted area occurred, whereas climate-change models suggested (in comparison with the present distribution model) an increase in area of 3.0-10.0% and 3.0-9.0% for 2050 and 2070, respectively. In future scenarios, TDFs will occupy areas above its current average elevational distribution that are outside of its present geographical range. Our findings show that TDFs may persist in Mexican territory until the middle of the XXI century; however, the challenges about long-term conservation are partially addressed (only 7% unaffected within the Mexican network of PAs) with the current Mexican PAs network. Based on our ENM approach, we suggest that a combination of models of species inhabiting present TDFs and taking into account change scenarios represent an invaluable tool to create new PAs and ecological corridors, as a response to the increasing levels of habitat destruction and the effects of climate change on this ecosystem.

Modeling the Present and Future Geographic Distribution of the Lone Star Tick, Amblyomma americanum (Ixodida: Ixodidae), in the Continental United States

The Lone star tick (Amblyomma americanum L.) is the primary vector for pathogens of significant public health importance in North America, yet relatively little is known about its current and potential future distribution. Building on a published summary of tick collection records, we used an ensemble modeling approach to predict the present-day and future distribution of climatically suitable habitat for establishment of the Lone star tick within the continental United States. Of the nine climatic predictor variables included in our five present-day models, average vapor pressure in July was by far the most important determinant of suitable habitat. The present-day ensemble model predicted an essentially contiguous distribution of suitable habitat extending to the Atlantic coast east of the 100th western meridian and south of the 40th northern parallel, but excluding a high elevation region associated with the Appalachian Mountains. Future ensemble predictions for 2061-2080 forecasted a stable western range limit, northward expansion of suitable habitat into the Upper Midwest and western Pennsylvania, and range contraction along portions of the Gulf coast and the lower Mississippi river valley. These findings are informative for raising awareness of A. americanum-transmitted pathogens in areas where the Lone Star tick has recently or may become established.