Low Emission Scenarios Research Articles

Habitat suitability mapping for a high-value non-timber forest product: A case study of <i>Rauvolfia serpentina</i>

Non-timber forest products (NTFPs) are essential for rural livelihoods but are often threatened by deforestation and overexploitation. Rauvolfia serpentina is a critical NTFP species; however, there have been no studies on the impact of future climate change on its potential habitat distribution. We used the Maxent modeling approach to address this knowledge gap and determine this species’ current and future suitability under climate change scenarios. The areas more suitable to its occurrence were mainly located near the southwestern coastal regions of Myanmar and India. Based on our predictions, these ‘very high’ suitability areas are likely to decrease in 2070 under both low and high CO2 emission scenarios, indicating the future vulnerability of this species’ habitat to the impact of climate change. This species is a highly valued NTFP species in the study region; therefore, such a reduction in habitat suitability may result in reductions in NTFP production and economic benefits for the dependent populations. Our findings can help define and conserve areas containing the natural habitats of this high-valued NTFP species.

Spatio-temporal characteristics and driving factors of the meteorological drought across China based on CMIP6

Abstract The characteristics of meteorological drought in different river basins in China have clear spatio-temporal differences and the difference between watersheds is also reflected in the influence mechanism of meteorological drought. This study investigated the meteorological drought risk under different future emission scenarios, based on the Coupled Model Intercomparison Project 6 (CMIP6). Furthermore, we explored the influence of precipitation and temperature on meteorological drought in different basins of China in the future. Meanwhile, the uncertainty associated with CMIP6 in different watersheds is also considered. In the future, the frequency of meteorological drought events mainly shows a decreasing trend, but the drought intensity and duration increase. In China, the rate of probability change in drought's intensity was greater than drought duration and the probability distribution of drought characteristics is significantly different between the historical and the future periods. Under the low and medium emission scenarios (SSP126 and SSP245), the Joint Recurrence Period of future drought intensity and duration increases and the Recurrence Period decreases in the western and north-eastern basins. Precipitation plays a major role in the meteorological drought, especially in the northeast and southeast basins of China.

Dynamic downscaling of climate simulations and projected changes in tropical South America using RegCM4.7

AbstractHigh‐resolution (dx = 25 km) simulations of the regional climate model RegCM4.7 coupled with the Community Land Model version 4.5 (CLM4.5) under low (RCP2.6) and high (RCP8.5) emissions scenarios, which interact with the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (AR5‐IPCC), were carried out over tropical South America (TSA). These simulations were produced through boundary conditions from simulations driven by the general circulation model HadGEM2‐ES. With the goal of verifying the added value (AV) of RegCM4.7, reproducing in an adequate and coherent way the regional aspects of the historical period (1986–2005), as well as evaluating the regional aspects simulated by the model in the scope of the change projections for the far‐future (2080–2099). For this study, the climate in the TSA was characterized based on the variables of precipitation and near‐surface air temperature. For the evaluation of the spatial–temporal representation, frequency and distribution of the regional and global simulation, the high‐resolution observational dataset of the Climate Research Unit version ts4.02 (CRU) was used. Although some differences and biases still persist, RegCM4.7 presents AV in the spatial representation of precipitation and temperature over the northeast region of Brazil and part of the Andes, especially in winter. However, it does not adequately represent precipitation over the Amazon Basin, especially in summer. Results for future projections indicate that the more refined simulation of RegCM4.7 improves the projected change patterns of the coarser resolution simulation of HadGEM2‐ES and even modifies the precipitation signal in some cases. Both models project increase temperature with greater magnitude for RCP8.5. RegCM4.7 presents a much more refined and realistic spatial distribution. HadGEM2‐ES simulates the major aspects of climate enough to consider forcing RegCM4.7 to generate simulations with better performance and more realistic projections.

Quantifying the weekly cycle effect of air pollution in cities of China.

The weekend effect, i.e., the concentration of air pollutants is different from weekends to weekdays, has been explored since the 1970s. In most studies, the weekend effect is referred to the change of O , i.e., lower emission of NO on weekends leads to a higher concentration of O . Determining whether it is true can provide insight into the strategy of air pollution controlling. In this study, we explore the weekly cycle of cities of China based on the weekly cycle anomaly (WCA), which is proposed in this paper. The advantage of using WCA is that we can avoid the influence of other change components such as the daily cycle and the seasonal cycle. The p values of the significant tests from all cities are analyzed for obtaining a whole picture of the weekly cycle of air pollution. The result shows that the concept of the weekend effect is not proper for cities in China, as many cities reach the valley phase of the emission on weekdays but not weekends. Thus, researches should not pre-assume that the weekend is the low emission scenario. We focus on the anomaly of O on the peak and the valley of the emission scenario estimated by the NO concentration. Through the analysis of the distribution of the p values of all cities, we show that almost all cities in China have weekly cycle of O corresponding to the weekly cycle emission of NO , i.e., O of the peak time of NO is less than valley time. The cities of the strong weekly cycle are located in four regions, i.e., the Beijing-Tianjing-Hebei region, the Shandong Peninsula Delta, the Yangtze River Delta, and the Pearl River Delta, and these regions are also the regions with relatively severe pollution levels.

Artificial Upwelling—A Refined Narrative

AbstractThe current narrative of artificial upwelling (AU) is to translocate nutrient rich deep water to the ocean surface, thereby stimulating the biological carbon pump (BCP). Our refined narrative takes the response of the solubility pump and the CO2 emission scenario into account. Using global ocean‐atmosphere model experiments we show that the effectiveness of a hypothetical maximum AU deployment in all ocean areas where AU is predicted to lower surface pCO2, the draw down of CO2 from the atmosphere during years 2020–2100 depends strongly on the CO2 emission scenario and ranges from 1.01 Pg C/year (3.70 Pg CO2/year) under RCP 8.5 to 0.32 Pg C/year (1.17 Pg CO2/year) under RCP 2.6. The solubility pump becomes equally effective compared to the BCP under the highest emission scenario (RCP 8.5), but responds with CO2 outgassing under low CO2 emission scenarios.

Assessment of uncertainties in a complex modeling chain for predicting reservoir sedimentation under changing climate

Long-term predictions of reservoir sedimentation require an objective consideration of the preceding catchment processes. In this study, we apply a complex modeling chain to predict sedimentation processes in the Banja reservoir (Albania). The modeling chain consists of the water balance model WaSiM, the soil erosion and sediment transport model combination RUSLE-SEDD, and the 3d hydro-morphodynamic reservoir model SSIIM2 to accurately represent all relevant physical processes. Furthermore, an ensemble of climate models is used to analyze future scenarios. Although the capabilities of each model enable us to obtain satisfying results, the propagation of uncertainties in the modeling chain cannot be neglected. Hence, approximate model parameter uncertainties are quantified with the First-Order Second-Moment (FOSM) method. Another source of uncertainty for long-term predictions is the spread of climate projections. Thus, we compared both sources of uncertainties and found that the uncertainties generated by climate projections are 408% (for runoff), 539% (for sediment yield), and 272% (for bed elevation in the reservoir) larger than the model parameter uncertainties. We conclude that (i) FOSM is a suitable method for quantifying approximate parameter uncertainties in a complex modeling chain, (ii) the model parameter uncertainties are smaller than the spread of climate projections, and (iii) these uncertainties are of the same order of magnitude as the change signal for the investigated low-emission scenario. Thus, the proposed method might support modelers to communicate different sources of uncertainty in complex modeling chains, including climate impact models.

Cloudiness delays projected impact of climate change on coral reefs

The increasing frequency of mass coral bleaching and associated coral mortality threaten the future of warmwater coral reefs. Although thermal stress is widely recognized as the main driver of coral bleaching, exposure to light also plays a central role. Future projections of the impacts of climate change on coral reefs have to date focused on temperature change and not considered the role of clouds in attenuating the bleaching response of corals. In this study, we develop temperature- and light-based bleaching prediction algorithms using historical sea surface temperature, cloud cover fraction and downwelling shortwave radiation data together with a global-scale observational bleaching dataset observations. The model is applied to CMIP6 output from the GFDL-ESM4 Earth System Model under four different future scenarios to estimate the effect of incorporating cloudiness on future bleaching frequency, with and without thermal adaptation or acclimation by corals. The results show that in the low emission scenario SSP1-2.6 incorporating clouds into the model delays the bleaching frequency conditions by multiple decades in some regions, yet the majority (>70%) of coral reef cells still experience dangerously frequent bleaching conditions by the end of the century. In the moderate scenario SSP2-4.5, however, the increase in thermal stress is sufficient to overwhelm the mitigating effect of clouds by mid-century. Thermal adaptation or acclimation by corals could further shift the bleaching projections by up to 40 years, yet coral reefs would still experience dangerously frequent bleaching conditions by the end of century in SPP2-4.5. The findings show that multivariate models incorporating factors like light may improve the near-term outlook for coral reefs and help identify future climate refugia. Nonetheless, the long-term future of coral reefs remains questionable if the world stays on a moderate or higher emissions path.

The projected futures of water resources vulnerability under climate and socioeconomic change in the Yangtze River Basin, China

Despite being one of the most abundant water resources globally, the Yangtze River Basin (YRB) region is facing substantial risks aggravated by climatic and anthropogenic changes. Here, we adopted an integrated framework to investigate the plausible futures of water resource provisioning in YRB under current and future conditions on the sub-watershed level: (i) a process-based model (InVEST) was used to project the water yield of the whole of YRB in the nearest future (2040–2060) and distant future (2080–2100) and (ii) a socio-ecological index was developed to assess the spatio-temporal patterns of water resources vulnerability (WRV). Model projections indicated that several water-rich areas in the southeastern YRB would suffer declining water yield in the future. While water yield was projected to increase in some drier regions in the northwest. Future changes in the basin-level vulnerability were projected to decrease under low emissions scenarios (RCP2.6) combined with a sustainability socioeconomic scenario (SSP1). The greatest increase in vulnerability was projected under the medium-to-high end (RCP7.0) rather than high-end (RCP8.5) climate change scenario. The areas with high WRV were distributed in sub-watersheds near Taihu Lake and the source of the Yangtze River. Climate and socioeconomic change were projected to have different roles in shaping the future dynamics of WRV, with precipitation reduction and water consumption increase being likely to result in increased WRV levels in the southeastern lower reaches and the middle reaches, respectively. Our study added new spatial data on projected water resource dynamics for the region of vital ecological and economic importance in Asia. The areas prone to increased WRV should be prioritised in future water resources management practices. The assessment approach used in this study integrated concurrent measures from both subjective and objective perspectives and could be used in relevant studies on exploring how future vulnerability could respond to environmental changes.

Evolution of offshore wind resources in Northern Europe under climate change

Climate-change impacts on offshore wind resources in Northern Europe over the 21st century are investigated based on the most up-to-date narratives of societal development and land use, the Shared Socioeconomic Pathways (SSPs). Three scenarios are considered: a high-emissions (pessimistic) scenario, SSP5-8.5; an intermediate scenario, SSP2-4.5, in which current emissions do not vary notably; and, for the first time in this type of work, a low-emissions (optimistic) scenario, SSP1-1.9, representing the fulfilment of the ambitious goals of the EU's Green Deal and the Paris Agreement. A multi-model ensemble is constructed with the global climate models that are found to best reproduce the wind climate in Northern Europe. The results anticipate an overall decline in wind power density, especially in the high-emissions scenario and in certain regions (up to 30% off Western Ireland), which should be taken into account in planning future offshore wind deployments. As an exception, slight increases (around 10%) are projected in certain areas of the Baltic Sea. The general decline is less pronounced in the low-emissions scenario. Indeed, the results prove that reducing emissions as advocated by current climate objectives would not only weaken the declining trend but also lead to a more stable resource.

Climate change multi-model projections in CMIP6 scenarios in Central Hokkaido, Japan

Simulation of future climate changes, especially temperature and rainfall, is critical for water resource management, disaster mitigation, and agricultural development. Based on the category-wise indicator method, two preferred Global Climate Models (GCMs) for the Ishikari River basin (IRB), the socio-economic center of Hokkaido, Japan, were examined from the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6). Climatic variables (maximum/minimum temperature and precipitation) were projected by the Statistical DownScaling Model (SDSM) under all shared socioeconomic pathway-representative concentration pathway (SSP-RCP) scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4OS, and SSP5-8.5) in two phases: 2040–2069 (2040s) and 2070–2099 (2070s), with the period of 1985–2014 as the baseline. Predictors of SDSM were derived from CMIP6 GCMs and the reanalysis dataset NOAA-CIRES-DOE 20th Century Reanalysis V3 (20CRv3). Results showed that CMIP6 GCMs had a significant correlation with temperature measurements, but could not represent precipitation features in the IRB. The constructed SDSM could capture the characteristics of temperature and precipitation during the calibration (1985–1999) and validation (2000–2014) phases, respectively. The selected GCMs (MIROC6 and MRI-ESM-2.0) generated higher temperature and less rainfall in the forthcoming phases. The SSP-RCP scenarios had an apparent influence on temperature and precipitation. High-emission scenarios (i.e., SSP5-8.5) would project a higher temperature and lower rainfall than the low-emission scenarios (e.g., SSP1-1.9). Spatial–temporal analysis indicated that the northern part of the IRB is more likely to become warmer with heavier precipitation than the southern part in the future. Higher temperature and lower rainfall were projected throughout the late twenty-first century (2070s) than the mid-century (2040s) in the IRB. The findings of this study could be further used to predict the hydrological cycle and assess the ecosystem's sustainability.

Terrestrial Net Ecosystem Productivity in China during 1900–2100

Terrestrial ecosystems are a critical carbon dioxide (CO 2 ) sink for achieving carbon (C) neutrality before 2060 in China. Here, we used the Coupled Model Intercomparison Project phase 6 (CMIP6) model outputs to quantify the spatiotemporal patterns of net ecosystem productivity (NEP) and its major environmental controls, as well as the dominant C pools for NEP during 1900–2100. We found that (a) according to CMIP6 NEP spatiotemporal characteristics, terrestrial ecosystems in China sequestered 0.310 ± 0.058 Pg C·year –1 during 1980–2014 and will act as a strong C sink [from 0.515 ± 0.075 Pg C·year –1 under Shared Socioeconomic Pathway 126 (SSP126) to 0.631 ± 0.089 Pg C·year –1 under SSP585] in the future (2015–2100). Pronounced turning points were found for the temporal trends of NEP during the historical (1984) and under 4 future emissions scenarios (2057 for SSP126, 2053 for SSP245, 2038 for SSP370, and 2044 for SSP585). (b) The positive effect of temperature on NEP appears to weaken after each turning point of future scenarios. (c) The enlarged vegetation C pool size dominates the growing terrestrial ecosystem C storage. The CMIP6 projection shows that the total C storage in Chinese terrestrial ecosystems increases continuously and peaks in the 2040s to 2050s under each scenario. Future afforestation in the northeast, southeast, and southwestern regions, as well as soil C pool management in the northwest and middle north regions, will greatly contribute to achieving C neutrality in China, particularly under low emission scenario (SSP126).

Warming-Induced Growth Inhibition Weakens the Resilience of Low-Latitude Tidal Marshes to Sea-Level Rise

Sea-level rise (SLR) is expected to suppress the growth of tidal marshes due to prolonged inundation, but this can be compensated by increasing surface elevation with SLR. Warming-induced growth regulation could make it more complicated. We applied a spatially explicit individual-based model (IBM) with dynamic parameterization to predict the range expansion of Spartina alterniflora by 2100 over a low-latitude wetland, under one low-emission scenario (Shared Socioeconomic Pathway 1 [SSP1]: 0.28 m SLR and 0.55 °C warming) and one high-emission scenario (SSP5: 1.01 m and 3.55 °C). The results showed that (a) the simulations of an IBM with life-history-mediated biophysical feedbacks (IBM 1 ) better tracked the actual range expansion of S. alterniflora over 2014 to 2017 (90.1% accuracy) than that without the feedbacks (IBM 0 ) (83.0%); (b) under SSP1, most marshes were predicted to survive SLR by 2100 in both IBM 0 and IBM 1 , while, under SSP5, the marshes were predicted to disappear much more in IBM 0 (93.2% drowned) than IBM 1 (31.9%); and (c) warming-induced growth inhibition of S. alterniflora leads to exacerbated disappearance and even collapse of the marshes under SSP5 in IBM 1 . This study highlights the importance of life-history-mediated biophysical and physiological feedbacks in regulating the response of tidal marshes to climate change. Warming-induced growth inhibition weakens low-latitude tidal marsh resilience to SLR.

A bridge scour risk management approach to deal with uncertain climate future

Riverine bridges are under a substantial threat of scour due to the magnitude and frequency of floods arising due to climate change. Infrequent inspections, inadequate data on foundation depths, and the lack of consideration of hydrologic and climate parameters often result in uncertainties within current scour risk assessments. This paper presents an approach for assessing the risk progression of local scour in peak flow conditions that consider uncertainties associated with location, downscaling of climate predictions, and hydrologic, hydraulic and scour prediction models. A rational scour risk rating based on warning time to failure is introduced that can provide a useful addition to the existing bridge condition indexes. The case study on a set of railway bridges in Southeast England shows that climate change could accelerate the scour risk progression, even in the low-emission scenario. The approach and results form a vital basis for scour risk mitigation and climate adaptation planning.

Regional non-stationary future extreme rainfall under changing climate over Asian Monsoon Region

An increase in the Intensity-Duration-Frequency (IDF) of rainfall extremes questions the assumption of stationarity in the design and planning of water infrastructures under changing climate. To address this, non-stationary-based approaches are widely used for assessing the extremes over time. However, the trends associated with future extreme rainfall would differ from that of the historical dataset. Therefore, the main objective of this study is to develop a non-stationary modelling approach to generate future regional IDF curves by incorporating the trend in the rainfall over Asian Monsoon Region (AMR). Daily rainfall data from (i) APHRODITE and (ii) baseline period (1951–2005) and future projections (2020−2100) simulated from 27 GCMs are used in this study. The proposed framework includes (i) selection of GCM models, (ii) Stationary (S) and Non-Stationary (NS) Generalized Extreme Value Distribution (GEVD) Models, (iii) delineation of extreme precipitation zones, (iv) performance evaluation of zone-wise models, and (v) estimation of future regional IDF curves for near-future (2020–2060), and far-future (2061–2100). Results show that the eight models and their corresponding GCM covariates cover around 90% of grids across the AMR compared to S-GEVD models among the 27 GCMs. Six extreme precipitation zones were obtained, and fitted regional models could mimic the individual station models. The change in extreme rainfall intensity for the future climate is higher for zone-1 (low extreme rainfall region), with an increase of 87–98% across different emission scenarios using the non-stationary models, whereas for zone-6 (higher extreme rainfall region) has a 60–68% increase from its stationary counterparts. For high and low emission scenarios, intensity increases up to 98% and 87% respectively for a lower return period (5-year), and 65% and 58% for a higher return period (100-year) by the end of the century.

Extreme Coastal Water Levels Evolution at Dakar (Senegal, West Africa)

Increasingly, it is reported that the coastline of the Dakar region is affected by coastal flooding due to extreme water levels during wave events. Here, we quantify the extreme coastal water levels as well as the different factors contributing to coastal flooding during the period 1994–2015. Severe water levels reach values of 1.78 m and increase by 8.4 mm/year. The time spent above this threshold has already increased by 1.7 over the study period and will increase by 2100 to 8 times with 0.4 m mean sea level rise and up to 20 times with 0.8 m in the IPCC low and high greenhouse gas emission scenarios, respectively. Tide is the main contributor to the extremes when combined with large wave runup, due to wave breaking which contributes to 38% of the increase in extreme events while sea level rises to 44%. Our results show that because of its prominent location, Dakar region is affected by waves coming from the Northern and Southern Hemispheres with contrasted evolutions: wave runup events increase faster (7 mm/year) during austral winter due to a maximum of the South Atlantic storm activity, and have a decreasing trend (−3 mm/year) during boreal winter (December, January, February) driven by the evolution of corresponding climate modes.

Dietary sterol availability modulates heat tolerance of Daphnia

Abstract The increasing frequency and intensity of summer heatwaves are pushing freshwater zooplankton towards their upper thermal tolerance limits. At the same time, higher temperatures and prolonged water column stratification can favour the dominance of cyanobacteria in phytoplankton. Even when not toxic or grazing resistant, these prokaryotes lack phytosterols as essential precursors for cholesterol, the main sterol in animal tissues. Cholesterol plays a crucial role in the physiological adaptation of ectotherms to high temperature. Therefore, the shift to cyanobacteria‐dominated systems may increase the vulnerability of zooplankton to heatwaves by intensifying cholesterol limitation. Here, we used death time curves that take into consideration the intensity and duration of a thermal challenge and a dynamic model to study the effects of cholesterol limitation on the heat tolerance of the keystone species Daphnia magna and to simulate the cumulative mortality that could occur in a fluctuating environment over several days of heatwave. We show that increasing cholesterol limitation does not affect the slope between time‐to‐immobilisation and temperature, but does decrease the maximal temperature that Daphnia can withstand by up to 0.74°C. This seemingly small difference is sufficient to halve the time individuals can survive heat stress. Our simulations predicted that, when facing heatwaves over several days, the differences in survival caused by cholesterol limitation build up rapidly. Considering the anticipated intensity and duration of future (2070–2099) heatwaves, cholesterol limitation could increase mortality by up to 45% and 72% under low and medium greenhouse gas emission scenarios, respectively. These results suggest that the increasing risk of cholesterol limitation due to more frequent cyanobacterial blooms could compromise the resistance of zooplankton populations to future heatwaves. More generally, this study shows the importance of considering the nutritional context in any attempt to predict ectotherm mortality with increasing temperatures in the field.

The potential of green manure to increase soil carbon sequestration and reduce the yield-scaled carbon footprint of rice production in southern China

Green manure (GM) has been used to support rice production in southern China for thousands of years. However, the effects of GM on soil carbon sequestration (CS) and the carbon footprint (CF) at a regional scale remain unclear. Therefore, we combined the datasets from long-term multisite experiments with a meta-analysis approach to quantify the potential of GM to increase the CS and reduce the CF of paddy soils in southern China. Compared with the fallow–rice practice, the GM–rice practice increased the soil C stock at a rate of 1.62 Mg CO2-eq ha–1 yr–1 and reduced chemical N application by 40% with no loss in the rice yield. The total CF varied from 7.51 to 13.66 Mg CO2-eq ha–1 yr–1 and was dominated by CH4 emissions (60.7–81.3%). GM decreased the indirect CF by 31.4% but increased the direct CH4 emissions by 19.6%. In the low and high CH4 emission scenarios, the CH4 emission factors of GM (EFgc) were 5.58 and 21.31%, respectively. The greater soil CS offset the increase in GM-derived CF in the low CH4 scenario, but it could not offset the CF increase in the high CH4 scenario. A trade-off analysis also showed that GM can simultaneously increase the CS and reduce the total CF of the rice production system when the EFgc was less than 9.20%. The variation in EFgc was mainly regulated by the GM application rates and water management patterns. Determining the appropriate GM application rate and drainage pattern warrant further investigation to optimize the potential of the GM–rice system to increase the CS and reduce the total CF in China.

Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios

Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree-grass competition. Enhanced productivity due to CO2 fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.

Temperature and urban heat island effect in Lublin city in Poland under changing climate

This study investigates the characteristics of the temperature regimes at an urban station (Litewski square) in Lublin city in Poland and a nearby rural station (Radawiec), and the Urban Heat Island (UHI) effect in Lublin city. In winter, spring, summer, and autumn at both urban and rural stations frequency distributions of daily minimum (Tmin), and maximum (Tmax) air temperature in 1998–2020 have shifted towards a warmer climate compared to the frequency distributions in 1974–1997. At both stations in 1974–2020, in all seasons, the annual Tmin and Tmax display increasing trends. At Litewski square and Radawiec, Tmax shows increasing trends of 0.083 and 0.088 ºC/year in summer, respectively. This is the largest increase in all four seasons. Furthermore, it is revealed that the heatwaves at both the urban and rural stations have increased in number over time. However, cold waves at both stations show a declining trend. The UHI effect in Lublin city has not increased significantly during 1974–2020. Population in Lublin city has declined over the period 1995–2020, but the population in the surrounding rural counties has increased. It is speculated that this is one of the causes of no clear increase in the UHI intensity. Apart from that, the city’s large green coverage (about 40%) is probably acting as a heating inhibitor. The annual Tmin and Tmax projected by 15 Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs indicate that the temperature regimes at both urban and rural stations show significant increasing trends during 2015–2100 under the selected SSPs, with the highest increase under high emission scenario (SSP5-8.5) and the lowest increase under the low emission scenario (SSP1-2.6). During 2015–2100, the UHI effect in Lublin city does not show any significant increasing or decreasing trends for the majority of the GCM–SSP combinations.

Predicting habitat suitability for Townsend's big-eared bats across California in relation to climate change.

Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend's big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species' distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. The estimated distribution differed between the combined (full dataset) and phenologically explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scenarios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Comparison of phenologically explicit models with combined models indicates the combined models better predict the extent of the known range of C. townsendii in California. However, life-history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species' responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions.