Future Temperature Changes Research Articles

Current and future distributions of main dermatitis-causing insects and risks of dermatitis across China

Insect-related dermatitis (IRD) has emerged as a significant public health concern. However, its risk distribution and epidemic dynamics remain poorly understood. Here, we project the current and future risk distributions of dermatitis-causing insects (DCIs) across China. The maximum entropy modeling was adopted to project the suitability distribution of eight main DCIs. Our findings reveal a significant concentration of risk distributions for both DCIs and IRD southeast of the Heihe-Tengchong Line in China. Furthermore, our projections indicate that most DCIs are expected to experience an increase in their suitability distribution under future climate change. These shifts are closely related to future changes of temperature and precipitation. Further field validation of key DCIs confirmed that the observed suitable regions would expand northward with growing temperature and precipitation. Collectively, the above results revealed the risk distribution of DCIs and IRD in China, providing valuable references for disease management and prevention of IRD.

Constraints on the Projected Tropical SST Response to Greenhouse Warming by the Observed Antarctic Sea Ice Concentration

AbstractThe future tropical sea surface temperature (SST) changes profoundly impact global and regional climate. Under greenhouse warming, the reduction of Antarctic sea ice concentration (SIC) acts as an extratropical energy perturbation, exerting a substantial influence on the spatial distribution of tropical SST change. This study reveals a strong correlation between the current Antarctic SIC and tropical SST change, especially the interhemispheric asymmetry and El Niño‐like pattern under greenhouse warming among CMIP6 models. Considering the commonly underestimated Antarctic SIC in CMIP6 models, this study applies an emergent constraint on the projected tropical SST response to greenhouse warming using the observed Antarctic SIC. The interhemispheric asymmetry in projected tropical SST warming can be markedly diminished in the multi‐model ensemble mean, with a 30% reduction in the intermodel uncertainty. The spatial constraints on the projected tropical Pacific SST change produce a more pronounced and westward‐extended El Niño‐like warming pattern.

Multiple GCM-Based Climate Change Projections Across Northwest Region of Bangladesh Using Statistical Downscaling Model

Bangladesh, one of the most vulnerable countries to climate change, has been experiencing significant climate change-induced risks. Particularly, the northwest region of the country has been severely affected by climate extremes, including droughts and heat waves. Therefore, proper understanding and assessment of future climate change scenarios is crucial for the adaptive management of water resources. The current study used the statistical downscaling model (SDSM) to downscale and analyze climate change-induced future changes in temperature and precipitation based on multiple global climate models (GCMs), including HadCM3, CanESM2, and CanESM5. A quantitative approach was adopted for both calibration and validation, showing that the SDSM is well-suited for downscaling mean temperature and precipitation. Furthermore, bias correction was applied to enhance the accuracy of the downscaled climate variables. The downscaled projections revealed an upward trend in mean annual temperatures, while precipitation exhibited a declining trend up to the end of the century for all scenarios. The observed data periods for the CanESM5, CanESM2, and HadCM3 GCMs used in SDSM were 1985–2014, 1975–2005, and 1975–2001, respectively. Based on the aforementioned periods, the projections for the next century indicate that under the CanESM5 (SSP5-8.5 scenario), temperature is projected to increase by 0.98 °C, with a 12.4% decrease in precipitation. For CanESM2 (RCP8.5 scenario), temperature is expected to rise by 0.94 °C, and precipitation is projected to decrease by 10.3%. Similarly, under HadCM3 (A2 scenario), temperature is projected to increase by 0.67 °C, with a 7.0% decrease in precipitation. These downscaled pathways provide a strong basis for assessing the potential impacts of future climate change across the northwestern region of Bangladesh.

Constrained Projections of Extreme Low Temperatures in Eastern China

AbstractThe latest two generations of climate models (CMIP5 and CMIP6, Coupled Model Intercomparison Projects Phase 5 and 6) show a clear discrepancy in the projected future changes in mean temperature. Different methods have been proposed to reduce this difference, however, very limited studies are focused on extreme low temperatures (ELT). Here we propose a new method to constrain the projection of ELT changes by establishing their quasi‐linear relationships with mean temperature (Tmean) in Eastern China. The results show that the Tmean weighting coefficients considering model performance and independence can effectively reduce the uncertainty range of future ELT. Before constraint, there are substantial differences in the projected ranges of Tmean and ELT between CMIP5 and CMIP6 models. After constraint, the projected ranges of CMIP6 models are considerably reduced, particularly at the warmer end, thus showing better consistency with CMIP5. Under the SSP5‐8.5 scenario, at the end of the 21st century (2081–2100), the original projected changes in Tmean are constrained from 5.1 (3.5–7.9)°C to 5.0 (3.5–6.5)°C, with the warmer end of the projected range decreased by 1.4°C. For the ELT, the decreases of the warmer ends are 1.9°C and 1.2°C for the annual minima of daily minimum (TNn) and maximum temperature (TXn), respectively. The reliability evaluation shows that the differences between pseudo‐observations represented by two large ensemble models and constrained projections are smaller than those for unconstrained projections, thereby confirming the reliability of the weighted method employed in this study.

Assessing Future Changes in Mean Radiant Temperature: Considering Climate Change and Urban Development Impacts in Fredericton, New Brunswick, Canada, by 2050

Urban development and climate change are two main impacting factors in the thermal environment of cities. This study aims to analyze future changes in Mean Radiant Temperature (MRT), one of the main contributors to human thermal comfort and the concept of Urban Heat Island (UHI), considering climate change and urban development scenarios in the study area, Fredericton, New Brunswick, by 2050. The analysis utilizes the SOLWEIG (Solar and Longwave Environmental Irradiance Geometry) model from the Urban Multi-scale Environmental Predictor (UMEP) platform to calculate MRT values. By integrating these two impacting factors, this research provides insights into the potential future changes in MRT levels and the resulting thermal conditions and geohazards in the study area. The analysis enables the identification of areas susceptible to increased radiant heat exchange due to the proposed changes in land cover, urban morphology, and air temperature. Furthermore, this study contributes to a better understanding of the complex interactions between climate change, urbanization, and urban microclimates. By incorporating MRT assessments and prioritizing thermal comfort, cities can develop strategies to mitigate the negative effects of UHI and create sustainable and livable urban environments for future generations.

Ambient temperature and female infertility prevalence: an ecological study based on the 2019 global burden of disease study

BackgroundThe impact of climate change on human health is well established; however, its effect on the prevalence of female infertility is poorly understood. In this study, we aimed to investigate the association between ambient temperature changes and the prevalence of female infertility.MethodsIn this ecological study, 174 countries and regions were included. We utilized 2000–2019 data on the age-standardized prevalence rate (ASPR) of female infertility and temperature data from Global Burden of Disease, ERA5 (fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis for the global climate), and Coupled Model Intercomparison Project Phase 6 databases. Temperature over 12 months was averaged to express the annual temperature estimates, and the deviance percentage of temperature (DPT) was calculated based on the 20-year average temperature. Three-node restricted cubic spline curves were used to evaluate the association between temperature and the ASPR of female infertility. Linear mixed-effects models, with country code as a random effect, were used to estimate the effect size (β) and 95% confidence interval (CI) for DPT and the ASPR of female infertility. Adjusted linear mixed-effects models were used to predict the impact of future temperature changes (2020–2030) on the ASPR of female infertility.ResultsBetween 2000 and 2019, a U-shaped relationship was observed between temperature and the ASPR of female infertility, with the lowest ASPR occurring at 15 ℃. Increased DPT was associated with an increased ASPR of female infertility, with an adjusted β (95% CI) of 78.952 (10.514, 147.710). Future temperature increases will further elevate the ASPR of female infertility.ConclusionGlobally, temperature changes may be associated with an increase in the ASPR of female infertility.

Past and future changes in maximum air temperature and cold days in winter in Poland

Past and future changes in maximum air temperature and cold days in winter in Poland

Modelled Water Temperature Patterns and Energy Balance of a Threatened Coastal Lagoon Ecosystem

ABSTRACTCoastal water temperatures control physical, chemical, and biological processes and are expected to rise due to future changes in freshwater temperature and flow rates, heat exchange with the warming atmosphere, and thermal interactions with a changing ocean. However, the thermal sensitivity of transitional, coastal water bodies to climate change remains poorly understood, due partly to a lack of knowledge on present‐day thermal controls in these settings. Accordingly, we applied a coastal hydrodynamic model (MIKE 3 FM), with a coupled thermal module to simulate hydrodynamics and water temperature variability in the Basin Head lagoon, a federally protected coastal ecosystem in the Canadian province of Prince Edward Island. Field data from the lagoon were used to calibrate and assess the numerical model, while atmospheric, oceanic, and hydrologic data were used to form the thermal and hydrodynamic boundary conditions. The model successfully reproduced tidal water level oscillations as well as diurnal and semi‐diurnal (tidal) temperature fluctuations. Model results show longitudinal, cross‐shore, and vertical thermal variability within the lagoon, including pronounced thermal variability near the bed and near the inlet due to tidal pumping. Model results and field data highlight the thermal sensitivity of the lagoon during heat waves; however, distinct cold‐water plumes at freshwater inputs (springs and groundwater‐dominated streams) persisted, with temporally averaged temperatures in these zones up to 18 °C colder than the ambient lagoon. Although, these freshwater inflows can dominate local energy budgets, the surface heat fluxes, especially shortwave radiation, exert the dominant control on the lagoon‐wide energy budget. Collectively, the model findings emphasise the interacting effects of atmospheric, hydrologic, and oceanic forcing on the spatiotemporal patterns of water temperatures in this threatened coastal ecosystem.

Analysis of past and future temperature variability and change in Southern Ethiopia

Purpose This study aims to analyze the temperature variability and change for the past 30 years (1990–2019) and the future 60 years (2030s, 2050s and 2070s) in Wolaita Zone and the surroundings, in Southern Ethiopia. Design/methodology/approach The temperature (maximum and minimum) data of the past 30 years (1990–2019) of ten meteorological stations and the future (2021–2080) data of regional climate models (RCMs) under two representative concentration pathways (RCP4.5 and RCP8.5) were used in this study. The accuracy of RCMs in representing observed temperature data was evaluated against mean absolute error, root-mean-square error, percent bias, Nash–Sutcliffe measure of efficiency, index of agreement (d) and coefficient of determination (R2). The temperature variability was analyzed using the coefficient of variation, and the trend was determined using the Mann–Kendall trend and Sen’s slope tests. Findings The results indicate that the past maximum (Tmax) and minimum (Tmin) temperatures showed low variability (CV = 4.3%) with consistently increasing trends. Similarly, Tmax and Tmin are projected to have low variability in the future years, with upward trends. The Tmax and Tmin are projected to deviate by 0.7°C–1.2°C, 1.3°C–2.2°C and 1.5°C–3.2°C by 2030s, 2050s and 2070s, respectively, under RCP4.5 and RCP8.5, from the baseline. Thus, it can be concluded that temperature has low variability in all periods, with consistently increasing trends. The increasing temperature could have been affecting agricultural production systems in Southern Ethiopia. Research limitations/implications This research did not remove the uncertainties of models (inherited errors of models) in future temperature projections. However, this study did not have any limitation. Therefore, individuals or organizations working on agricultural productivity, food security and sustainable development can use the results and recommendations. Practical implications The globe has been warming due to the increasing temperature; as a result, many adaptation and mitigation measures have been suggested globally and nationally (IPCC, 2021). FAO (2017) indicates that the level of vulnerability to the impacts of climate change varies with geographic location, economy and demography; the adaptation measures need to be local. The detailed information on temperature variability and change in the past and future helps to understand the associated negative impacts on agriculture, hydrology, biodiversity, environment and human well-being, among others. Social implications The projected future climate pattern helps the country devise proactive adaptation and mitigation measures for the associated damages at different levels (from local to national). This could improve the resilience of farmers and the country to climate change impacts. This contributes to achieving sustainable development goals (e.g. no poverty, zero hunger and climate action). This is because the agriculture sector in Ethiopia accounts for 80% of employment, 33% of the gross domestic product and 76% of exports (EPRSS, 2023). Originality/value Temperature is one of the major climate elements affecting agricultural production in rain-fed production systems. Despite this, past studies in Southern Ethiopia considered only the past temperature but not the future climate. Thus, generating detailed information about past and future temperatures is very important to take proactive adaptation measures for reducing climate-associated damages in the agriculture sector in Ethiopia.

Modeling of historical and future changes in temperature and precipitation in the Panj River Basin in Central Asia under the CMIP5 RCP and CMIP6 SSP scenarios

This study examines the complexities of climate modeling, specifically in the Panj River Basin (PRB) in Central Asia, to evaluate the transition from CMIP5 to CMIP6 models. The research aimed to identify differences in historical simulations and future predictions of rainfall and temperature, examining the accuracy of eight General Circulation Models (GCMs) used in both CMIP5 (RCP4.5 and 8.5) and CMIP6 (SSP2–4.5 and 5–8.5). The evaluation metrics demonstrated that the GCMs have a high level of accuracy in reproducing maximum temperature (Tmax) with a correlation coefficient of 0.96. The models also performed well in replicating minimum temperature (Tmin) with a correlation coefficient of 0.94. This suggests that the models have improved modeling capabilities in both CMIPs. The performance of Max Plank Institute (MPI) across all variables in CMIP6 models was exceptional. Within the CMIP5 domain, Geophysical Fluid Dynamics (GFDL) demonstrated outstanding skill in reproducing maximum temperature (Tmax) and precipitation (KGE 0.58 and 0.34, respectively), while (Institute for Numerical Mathematics) INMCM excelled in replicating minimum temperature (Tmin) (KGE 0.28). The uncertainty analysis revealed a significant improvement in the CMIP6 precipitation bias bands, resulting in a more precise depiction of diverse climate zones compared to CMIP5. Both CMIPs consistently tended to underestimate Tmax in the Csa zone and overestimate it in the Bwk zone throughout all months. Nevertheless, the CMIP6 models demonstrated a significant decrease in uncertainty, especially in ensemble simulations, suggesting improvements in forecasting PRB climate dynamics. The projections revealed a complex story, as the CMIP6 models predict a relatively small increase in temperature and a simultaneous drop in precipitation. This indicates a trend towards more uniform temperature patterns across different areas. Nevertheless, the precipitation forecasts exhibited increased variability, highlighting the intricate interaction of climate dynamics in the PRB area under the impact of global warming scenarios. Hydrological components in global climate models can be further improved and developed with the theoretical reference provided by this study.

Projecting and Downscaling Future Temperature and Precipitation Based on CMIP6 Models Using Machine Learning in Hatay Province, Türkiye

Projections for future changes in precipitation and temperature are essential for decision-makers to understand climate change impacts on any region in the world. General circulation models (GCMs) are widely used tools to assess the future impacts of climate change. However, since they are produced at global scales, they cannot provide reliable information at local scales. For this reason, downscaling applications have been applied in recent years. In this study, support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) methods were evaluated to improve the forecast accuracy of EC-EARTH3 CMIP6 GCM outputs for the Hatay province of Türkiye. The results obtained from the models were compared with meteorological observation data on a monthly time scale. As a result of the study, RF (RMSE = 19.19–45.41) for precipitation projections and SVR for maximum temperature (RMSE = 1.49–2.23) and minimum temperature (RMSE = 1.44–1.69) projections were found successful compared to other methods. These methods were applied to GCM’s future outputs. According to the results, it was determined that there could be a significant increase in the annual average temperature in Hatay province under the SSP2-4.5 and SSP5-8.5 scenarios. It is also estimated that there may be an increase in temperature between 2.1 and 2.9 °C for the SSP2-4.5 scenario and 2.4 °C and 5.2 °C for the SSP5-8.5 scenario in the near (2020–2060) and far (2060–2100) future periods, respectively. It is also estimated that by the end of the 21st century, annual precipitation in Hatay province may decrease by approximately 10% for SSP2-4.5 and by approximately 20% for SSP5-8.5 scenarios.

Disentangling the effects of temperature and rainfall on the population dynamics of Kalahari meerkats

In arid habitats, recent increases in summer temperatures associated with global warming are adversely affecting many animal populations. However, annual rainfall also varies widely in many of these areas, and we do not yet fully understand the relative impact of variation in temperature and rainfall on the demography of arid‐zone species. Here, we examine the effects of temperature and rainfall variation on the demography of meerkats Suricata suricatta in the southern Kalahari over the last 25 years. During this period, average maximum monthly air temperatures at our study site increased by around 1.5°C to 3.2°C, while annual rainfall fluctuated without a consistent trend. We show that annual changes in female fecundity and recruitment were more closely correlated with variation in rainfall. Increasing air temperatures were associated with reductions in the recruitment of pups and the survival of some age classes but, in most cases, the demographic consequences of high temperatures were modest compared to the effects of low rainfall, which in some years led to the near cessation of successful reproduction and the extinction of many smaller groups. For instance, exceptionally low rainfall in 2012–2013 was associated with low recruitment and with declines in group size and population density, which fell by over 50%. Unusually hot years did not have similar consequences. Following the 2012–2013 drought, intermittent years of low rainfall and frequent droughts continued to suppress recruitment and slowed the population's recovery. Future changes in temperature may affect the dynamics and size of the meerkat population, but our work suggests that over the last 25 years, annual changes in rainfall have exerted a stronger influence on meerkat demography. Our study demonstrates the importance of long‐term, individual‐based data for determining how changes in climate affect the dynamics of animal populations, especially in arid environments where bottom–up processes often dominate.

Hybrid CNN-LSTM Model for Accurate Long-Term and Short-Term Temperature Prediction: A Case Study for Bingöl and Tunceli

Extreme and sudden weather events experienced with global warming and climate change reveal the importance of accurate air temperature prediction. For this reason, it can be used to optimize decision-making processes for a wide range of applications from health and agricultural planning to energy consumption strategies. Artificial intelligence methods are successfully applied in many application areas due to their flexibility and efficiency. Traditional weather forecasting models are inefficient in detecting sudden fluctuations and complex, irregular patterns in data. Artificial in-telligence methods overcome these limitations thanks to their ability to process big data and capture long-term temporal dependencies. In this study, the aim is to predict future temperature changes more accurately by capturing patterns in past data with the developed CNN-LSTM hybrid model. The developed hybrid model is compared in detail with RF, SVM, CNN, and LSTM. The compared models were tested using daily average temperature data between 1961-2024 and hourly temperature data between 2020-2024. Experiments have shown that CNN-LSTM outperforms the compared models with R2 value above 0.97 in all scenarios.

Future changes in sea surface temperature in the East Asian Marginal Seas projected by CMIP6 models

Future global-mean warming and its intermodel spreads have shown to be greater in the coupled model intercomparison project phase 6 (CMIP6) models than in the earlier generation CMIP5 models, mainly due to increases in both forcing and climate sensitivity. However, regional future changes and their intermodel difference in CMIP6 models have been less known. In this study, we assessed biases in the sea surface temperature (SST) simulated from 30 CMIP6 models and then estimated future SST changes in the near-term (2021-2040) and the mid-term(2041-2060) periods under the scenarios of the low (SSP1-2.6) and high (SSP5-8.5) emissions, by using selected eight CMIP6 models with superior performance in the East Asian Marginal Seas (EAMS). The SST changes in the EAMS are projected to be more pronounced in the mid-term compared to the near-term under global warming. SST is expected to increase by approximately 1.8°C under SSP1-2.6 and by 4.5°C under SSP5-8.5, with the largest increases occurring in the Yellow Sea, East Sea, and East China Sea. Seasonal variations are significant, with summer (August) warming projected to be about 50% greater than winter (February) warming under SSP5-8.5, primarily due to the shallowing of the summer mixed layer. Uncertainty in future SST projections is higher under SSP1-2.6 than SSP5-8.5 because of the stronger signal in the SSP5-8.5 scenario. Spatially, uncertainty is lower in the coastal areas and the Yellow Sea, but higher in the East Sea and the mixed water region (interfrontal zone) between the Kuroshio and Oyashio currents, where ocean currents contribute to greater intermodel variation. In conclusion, the projected future surface warming in the East Asian Marginal Seas (EAMS) is estimated to be approximately twice the global mean, indicating that EAMS is a climate change hotspot with a high level of vulnerability to future global warming.

Analysis of Future Drought Risk and Wheat Meteorological Disaster in Ningxia (Northwest China) Based on CMIP6 and SPEI

In arid areas, droughts caused by climate change seriously impact wheat production. Therefore, research on spatial and temporal variability of dry and hot wind events and drought risk under different development patterns of future climate can provide a reference for wheat cultivation planning in the study area. Based on meteorological data under three scenarios of the CMIP6 (Sixth International Coupled Model Comparison Program) shared socio-economic path (SSP), we introduced wheat dry hot wind discrimination criteria and calculated the Standardized Precipitation–Evapotranspiration Index (SPEI). Future temperature changes within the Ningxia Province were consistent, increasing at a rate of 0.037, 0.15 and 0.45 °C·(10 a−1) under SSP126, 245 and 585 scenarios, respectively. Simultaneously, average annual precipitation would increase by 17.77, 38.73 and 32.12 mm, respectively. Dry hot wind frequency differed spatially, being higher in northern Ningxia and western Ningxia, and lower in southern Ningxia and eastern Ningxia. During the wheat growing period, there is an obvious increasing drought risk trend under the SSP585 model in May, and the possibility of drought risk in the middle period was highest under the SSP126 model. In June, SPEI was generally higher than in May, and the risk of alternating drought and flood was greater under the SSP585 model, while near-medium drought risk was lower under the SSP126 and SSP245 models. The influence of DHW (dry and hot wind) on wheat yield will increase with the increase of warming level. However, when DHW occurs, effective irrigation can mitigate the harm. Irrigation water can be sourced from various channels, including rainfall, diversion, and groundwater. These results provide scientific reference for sustainable agricultural production, drought risk and wheat meteorological disaster forecast in inland arid areas affected by climate change.

Simulation and Prediction of Thermokarst Lake Surface Temperature Changes on the Qinghai–Tibet Plateau

Thermokarst lakes are shallow bodies of freshwater that develop in permafrost regions, and they are an essential focus of international permafrost research. However, research regarding the mechanisms driving temperature fluctuations in thermokarst lakes and the factors that influence these changes is limited. We aimed to analyze seasonal variations in the surface water temperature, clarify historical trends in the phenological characteristics of lake ice, and predict future temperature changes in surface water of the thermokarst lakes using the air2water model. The results indicated that in comparison with air temperature, the thermokarst lake’s surface water temperature showed a certain lag and significantly higher values in the warm season. The warming rate of the thermokarst lake’s average surface water temperature based on historical data from 1957 to 2022 was 0.21 °C per decade, with a notably higher rate in August (0.42 °C per decade) than in other months. Furthermore, the ice-covered period steadily decreased by 2.12 d per decade. Based on the Coupled Model Intercomparison Project 6 projections, by 2100, the surface water temperatures of thermokarst lakes during the warm season are projected to increase by 0.38, 0.46, and 2.82 °C (under scenarios SSP126, SSP245, and SSP585), respectively. Compared with typical tectonic lakes on the Qinghai–Tibet Plateau, thermokarst lakes have higher average surface water temperatures during ice-free periods, and they exhibit a higher warming rate (0.21 °C per decade). These results elucidate the response mechanisms of thermokarst lakes’ surface water temperature and the phenological characteristics of lake ice in response to climate change.

Historical and Future Changes in Water Temperature in the Pilica River (Central Europe) in Response to Global Warming

This study analyzes changes in the water temperature in the Pilica River (Poland), encompassing both historical data (1958–2023) and projections extending to the year 2100. We use multi-model ensembles (MMEs) with Bayesian Model Averaging (BMA) to integrate various Global Climate Model (GCM) datasets for current and projected climate data. Additionally, a Random Forest (RF) machine learning method is applied to project future water temperatures in the Pilica River. It has been demonstrated that over a period of more than sixty years, the average annual water temperature has increased by nearly 2 °C. Further changes are expected to continue in a similar direction with a gradual rise in this parameter, reaching a temperature increase of 3 °C by the end of the 21st century (SSP585). In the distant future, with average monthly water temperature changes at the Przedbórz station ranging from 0.27 °C to 0.87 °C·decade−1 and at the Białobrzegi station from 0.22 °C to 1.06 °C·decade−1. The results of these changes are concerning, especially considering the crucial role of water temperature in shaping seasonality and the dynamics of processes occurring within the river. In the context of the sustainability of the river itself, but also of the entire catchment area, strategies developed by relevant public administration bodies are needed to mitigate the impacts of global warming observed in the thermal regime of the Pilica River.

Future impacts of reduced freshwater inflow and sea level rise on forage fish and their predators in Apalachicola Bay, Florida

Future impacts of reduced freshwater inflow and sea level rise on forage fish and their predators in Apalachicola Bay, Florida

Optimization of temperature prediction algorithms and simulation of future neighborhood-scale thermal environments in Chongqing

Optimization of temperature prediction algorithms and simulation of future neighborhood-scale thermal environments in Chongqing

Identifying temperature refuges in Utah using temperature, biota, and habitat data

AbstractUnderstanding where on landscapes to make investments, such as designating protected areas, is a critical component of biodiversity management. Locations for management actions should achieve current management objectives while also having the best chance of continued success in the future. Climate change has the potential to undermine biodiversity management, as it may lead to substantial changes in environmental conditions that are outside local managers' control. Following changes in environmental conditions, areas on the landscape may become unsuitable for the species or habitats that the initial actions were intended to benefit. The potential for local actions to be undermined by global‐scale threats makes it essential to account for and minimize exposure to temperature change. We present a series of analyses identifying priority areas for wildlife and habitat management. We conducted our analyses using a systematic landscape planning approach that identifies areas within species' ranges or current distributions of key habitats that are predicted to be less affected by future temperature change. We used the ranges of 142 animal and 149 plant species identified as species of greatest conservation need (SGCN) together with the distributions of 14 terrestrial and 19 aquatic key habitats in Utah, USA. We measured temperature change in 2 ways: as changes in mean annual temperature between 2020 and the year 2100 (temperature difference) and by quantifying how far a species range or habitat would have to shift to maintain its current temperature envelope (climate velocity). We identified the sub‐watersheds with hydrologic unit code 12 (HUC 12) that collectively encompassed the ranges of our SGCNs and key habitats while minimizing overall exposure to temperature change. These high priority HUC 12s represented areas that were not only hotspots for SGCNs and key habitats but also acted as temperature refugia, where management actions are likely to be robust to temperature change. We hope that our identification of high‐priority HUC 12s will help inform and guide future management actions to improve their long‐term outcomes.