Paleoclimate Research Articles

Large Reductions in Temperate Rainforest Biome Due to Unmitigated Climate Change

AbstractTemperate rainforests are rare ecosystems globally; restricted to cool, moist conditions that are sensitive to a changing climate. Despite their crucial conservation importance, a global assessment of how temperate rainforests will be impacted by climate change is lacking. We calculated historical (1970–2000) climate conditions for the temperate rainforest biome using ERA5 reanalysis data for three key bioclimatic variables: warmest quarter temperature, annual precipitation and proportion of rainfall during warmest quarter. We used high‐spatial resolution climate projections for these variables to identify regions likely to become unsuitable for temperate rainforests under four future shared socioeconomic pathway (SSP) scenarios. We predict unmitigated climate change (SSP 5–8.5) would lead to a 68.3 (95% confidence interval (95 CI): 53.4–81.3)% loss in the existing temperate rainforest biome by 2100 at a global scale with some national‐level reductions exceeding 90%. Restricting global warming to <2°C (consistent with SSP 1–2.6), limits loss of global temperate rainforest biome to 9.7 (95 CI: 7.8–13.3)% by 2100 and is crucial to ensuring temperate rainforest persistence. Deforestation has resulted in loss of up to 43% of the current temperate rainforest biome with only 37% of primary forest remaining, and some regions like Europe with virtually none. Protection and restoration of the temperate rainforest biome, along with emissions reductions, are vital to its climate future.

Nature vs. nurture: Drivers of site productivity in loblolly pine (Pinus taeda L.) forests in the southeastern US

Forest productivity is one of the most important aspects of forest management, landscape planning, and climate change assessment. However, although there are multiple elements known to affect productivity, most of them rely on the “nature” of the edaphic, climatic, and geographic conditions, and only some specific aspects can be modified through forest management or “nurture”. Through evaluation of site resource availability and an understanding of the main drivers of productivity, management can present solutions to overcome site resource limitations to productivity. Therefore, understanding the implications of a specific management regime requires understanding what drives productivity across large spatial extents and among different management regimes. In this study, we used data from over 1 million hectares of industrial forestland, covering over 6000 different soils and several management regimes of Pinus taeda L. plantations, as well as plot-based data from the Forest Inventory and Analysis (FIA) program, facilitating a comparison of planted and natural Pinus taeda stands. Combined with US Geological Survey LiDAR data, we computed site index and generated wall-to-wall productivity maps for planted Pinus taeda stands in the southeastern US, as well as point-based site index estimates for the FIA dataset. We modeled site index using a random forest algorithm considering edaphic, geologic, and physiographic province information based on the Forest Productivity Cooperative “SPOT” system, and also included climate and management history data. Our model predicted site index with an R2 of 0.701 and RMSE of 1.41 m on the industrial data and R2 of 0.417 and RMSE of 1.84 m for the FIA data. We found that year of establishment of the forest, physiographic province, and geology, were the most important drivers of site index. The soil classification modifier indicating root restrictions were the most important soil-specific variable. Additionally, we found an average increase in site index of 3.05 m since the 1950s for all FIA data, and an average increase of 4.73 m for all industrial data since the 1970s. For the latest period analyzed (2000–2012), average site index in planted FIA plots was 1.2 m higher than naturally regenerated FIA plots, and site index in all industrial forestland had a site index almost 3 m greater than planted FIA plots. Overall, we believe this work sets the foundation for better understanding of forest productivity and highlights the importance of intensive silviculture to improve productivity, and as an additional tool to achieve the economic, environmental, and social objectives.

Advances in giant clam (Tridacnidae spp.) sclerochronology and sclerochemistry

Understanding past environmental change through high-resolution palaeoenvironmental proxy reconstructions provides crucial baselines for understanding global environmental changes, particularly during the Quaternary Period. Such data are crucial for testing and refining climate models to enable better adaptations to current and future climate changes. Giant clams (Tridacnidae spp.), the largest bivalve mollusc in the Indo-Pacific region, possess distinctive features such as long lifespan, rapid growth, high-resolution growth patterns, wide distribution habitat, and stable shell geochemical composition. These attributes make giant clams a promising and dependable novel palaeoenvironmental archive, offering numerous available geochemical proxies. In this review, we examined preview studies on giant clams conducted for sclerochronology research, spanning publications from 1986 to 2023. The previous studies have proven that isotopic and trace elements proxies of the giant clam, including δ18O, δ13C, Sr/Ca, Mg/Ca, Ba/Ca, and Fe/Ca, can faithfully reconstruct palaeoclimate records including sea surface temperature, dissolved inorganic carbon, insolation, extreme weather events and primary productivity at ultra-high resolution. These proxies have been successfully employed for palaeoclimatic and palaeoenvironmental reconstructions from the Miocene to the present across a range of locations from South Japan to Northern Australia to the Red Sea. These studies have illuminated changes in interannual climate cycles, climate variation, and human migrations over thousands to millions of years. They have generated quantitative records that are valuable for testing and refining numerical climate models, understanding the relationship between past climate cycles and future climate variability, and providing insights into human-environment interactions over time. In the end, the review also addresses the challenges and provided recommendations for future research, highlighting persisting gaps in understanding the influence of microstructure on the shell, age dating of fossil samples, uncertainty in proxies, the need for tank experiments, and access to ultra-high resolution palaeoenvironmental records.

Preliminary investigation of the local marine reservoir effect in port JOLI harbour, NOVA scotia using archaeological M. arenaria shells

Port Joli Harbour, situated on the southwest coast of the maritime province of Nova Scotia, Canada, has the highest density of shell midden deposits in Atlantic Canada. The terrestrial radiocarbon chronology points to continuous human occupation of the harbour from 1685 cal yr BP to 250 cal yr BP. Despite the abundance of the soft-shell clams Mya arenaria at shell midden sites throughout the harbour they have not been incorporated into the chronology, nor have they been used to study the local marine reservoir effect in the harbour. We report 12 new 14C measurements on archived Mya arenaria from the same depositional contexts as previously dated charcoal and terrestrial mammal bone samples from two shell middens in Port Joli Harbour. From these shell-charcoal and shell-bone pairs, we present preliminary calculations of local marine reservoir corrections (ΔR) for the harbour. The ΔR values are highly variable and are overall more positive than the pre-bomb values reported for the Scotian Shelf. The variability of these values highlights the challenges in calculating ΔR values from archived archaeological samples but also the opportunities to include high-resolution analysis of ΔR variations in discussions on human-environment interactions in coastal settings during the late Holocene. We suggest that the chondrophore may be more favourable for calculating first order estimates of ΔR to be used in future archaeological calibrations, while high resolution analysis of the ventral margin region is useful for climate reconstructions. We recommend that more 14C measurements from M. arenaria shells be obtained from either distinct features or rapidly deposited middens in Port Joli Harbour. A suite of temporally and geographically precise ΔR values for the harbour will not only allow for the study of middens that are at risk of coastal erosion and lack charcoal for 14C dating but will also allow for the reconstruction of past climate conditions and refine our understanding of late Holocene human-environment interactions in the North Atlantic.

Predicting the Potential Distribution of the Endangered Pyrethrum tatsienense in China Using an Optimized Maxent Model Under Climate Change Scenarios.

Climate change can significantly impact the ecological suitability and diversity of species. Pyrethrum tatsienense, a critically endangered species in China, requires a thorough understanding of its habitat distribution and the environmental factors that affect it in the context of climate change. The Maxent algorithm was used to examine the key factors influencing the distribution of P. tatsienense in China, using data from 127 species occurrences and environmental variables from the Last Interglacial (LIG), Last Glacial Maximum (LGM), Mid-Holocene (MH), current, and future scenarios. The Maxent model was optimized utilizing the R package ENMeval, providing the most accurate predictions for suitable habitats across various scenarios. Results show that suitable regions for P. tatsienense encompass approximately 15.02% (14.42 × 105 km2) of China, predominantly on the Qinghai-Tibetan Plateau. The mean UV-B of the highest month (UVB3: 39.7%), elevation (elev: 28.7%), and the warmest season of precipitation (Bio18: 17.4%) are the major limiting factors for suitable habitat. The optimal species distribution ranges are identified as > 7500 J m-2 day-1 for UVB3, 2700-5600 m for elev, and 150-480 mm for Bio18. Predictions for the historical climate indicate the presence of refugia at the junction of Sichuan, Tibet, and Qinghai. The MH predictions show an increase in climatic suitability for P. tatsienense compared to the LIG and LGM, with an expansion of suitable areas westward. Future climate change scenarios indicate that the potential suitable habitat for P. tatsienense is expected to increase with increasing radiative forcing, with higher latitude regions becoming new marginally suitable habitats. However, predicted environmental changes in western Tibet may drive the loss of highly suitable habitats in the future. These findings enhance our understanding of how environmental factors impact the habitat suitability of P. tatsienense and provide valuable insights for developing effective management and conservation strategies for this important species.

Climatic and environmental changes since last deglaciation in the steppe region of northern China and their impact on early population survival patterns

The northern Chinese steppe is situated in the periphery of the East Asian Summer Monsoon and exhibits high sensitivity to variations in monsoon intensity and global climate change. Therefore, comprehending the climatic evolution history of this region is pivotal for elucidating early human civilization development. The research focuses on a fully enclosed crater lake located in the northern Chinese steppe by utilizing n-alkanes, total organic carbon (TOC), total nitrogen (TN) in lake sediments, based on AMS-14C dating analysis, the climatic and environmental evolution history of this region from the last deglaciation to the middle Holocene has been reconstructed, and the impact of climate change on the evolution of early human civilization is elucidated. The findings indicate that the EASM driven by summer solar radiation, exerts control over the climate and hydrology in the steppe region of northern China. Since the last deglaciation, the gradual warming and humidification of the climate environment have significantly enhanced the animal and plant resources, thereby establishing a solid foundation for the transition of early human populations in northern Chinese steppe from high-mobility hunter-gatherers to settled communities. Additionally, this climatic shift has provided an optimal backdrop for the emergence of primitive agriculture. The changes of stone tool assemblages unearthed at related sites also confirm the above views. However, the climate model underwent rapid changes during the period of 7.2–6.4 cal kyr BP, leading to the occurrence of extreme arid climate events. Consequently, regional precipitation decreased, lake ecology became imbalanced, woody plants retreated in the basin, and vegetation coverage was greatly reduced. The aforementioned factor may have played a pivotal role in the disappearance of early populations within the region. Consequently, climate and environmental change not only serve as the primary driving force behind human civilization’s development but also act as an inexorable catalyst for its decline.

Multidisciplinarity in Geoscientific Research

The multidisciplinary approach of geoscientific research in the academic setting underscores the increasing necessity of integrating diverse fields of knowledge to tackle complex interconnected issues. This multidisciplinary field seeks to uncover the complex operations of the Earth, encompassing the analysis of its past movements of tectonic plates, historical climates, and valuable natural resources like minerals and hydrocarbons. In this context, geospatial intelligence plays a crucial role by enabling the effective analysis and interpretation of extensive geographic datasets, thus enhancing our understanding of environmental dynamics. Nevertheless, advancements in technology, globalization, and the difficulties posed by environmental and climatic transformations demand an even more profound collaboration among various disciplines, facilitating an expansive viewpoint and yielding more holistic solutions to contemporary predicaments.

Cryptic degassing and protracted greenhouse climates after flood basalt events

Large igneous provinces erupt highly reactive, predominantly basaltic lavas onto Earth’s surface, which should boost the weathering flux leading to long-term CO2 drawdown and cooling following cessation of volcanism. However, throughout Earth’s geological history, the aftermaths of multiple Phanerozoic large igneous provinces are marked by unexpectedly protracted climatic warming and delayed biotic recovery lasting millions of years beyond the most voluminous phases of extrusive volcanism. Here we conduct geodynamic modelling of mantle melting and thermomechanical modelling of magma transport to show that rheologic feedbacks in the crust can throttle eruption rates despite continued melt generation and CO2 supply. Our results demonstrate how the mantle-derived flux of CO2 to the atmosphere during large igneous provinces can decouple from rates of surface volcanism, representing an important flux driving long-term climate. Climate–biogeochemical modelling spanning intervals with temporally calibrated palaeoclimate data further shows how accounting for this non-eruptive cryptic CO2 can help reconcile the life cycle of large igneous provinces with climate disruption and recovery during the Permian–Triassic, Mid-Miocene and other critical moments in Earth’s climate history. These findings underscore the key role that outgassing from intrusive magmas plays in modulating our planet’s surface environment.

Climate change is expected to reduce the potential distribution of Ceiba glaziovii in Caatinga, the largest area of dry tropical forest in South America

Ecological niche modeling is a widely used tool to predict species distribution considering current, past, or future climate change scenarios across different geographic areas. Modeling scenarios allow researchers to assess the impacts of climate change on species distribution and identify priority areas for conservation. This study aimed to model the current and future potential distribution of Ceiba glaziovii under different climate change scenarios in Brazil. The MaxEnt algorithm was used to correlate species occurrence points with bioclimatic variables in current and future climate scenarios. Four General Circulation Models (GCMs) from CMIP6 were employed: BCC-CSM2-MR, CNRM-CM6-1, IPSL-CM6A-LR, and MIROC6, considering optimistic and pessimistic projections. The contribution of variables and model accuracy were assessed using the Jackknife statistical test and the Area Under the Curve (AUC) parameter. AUC values for current and future scenarios demonstrated high accuracy. The bioclimatic variables of precipitation and temperature were the main contributors to determining areas with higher habitat suitability. In the future climate scenario, there was a reduction in areas with good climatic suitability for all four GCMs, considering optimistic and pessimistic projections. Among the areas with high habitat suitability, the IPSL-CM6A-1 model in the optimistic projection showed the smallest reduction, while in the pessimistic scenario, all areas with high suitability disappeared. The species' climatic niche is expected to decrease under all tested climate change scenarios. The central areas of the Caatinga and its transition zones exhibit the highest climatic suitability in current and future scenarios and should be prioritized for the species' conservation.

Evaluation of Clustering Techniques for Solar Irradiance Prediction for Optimal Design of Microgrids in Rural Communities

Abstract The electrification of rural communities is crucial from both social and economic perspectives, aligned with Sustainable Development Goal 7: “Affordable and Clean Energy”. This study presents a comprehensive comparison of clustering techniques, including k-means, Gaussian Mixture Models (GMM), Hierarchical Clustering, Density-based Spatial Clustering of Applications with Noise (DBSCAN), and Agglomerative Clustering, aimed at enhancing solar irradiance prediction. Leveraging historical climate data from a rural community in the coastal region of Ecuador, each technique is evaluated using error metrics such as Mean Absolute Error (MAE) and Coefficient of Determination R2. This assessment identifies the most effective clustering technique in this specific context. In order to delve deeper into these comparisons, simulations are conducted in AMPL to validate and refine the selection of techniques. In this process, it is considered the sizing and design of a microgrid within the Barcelona community, Ecuador, which integrates various energy sources, including solar. Additionally, a penalty system is introduced for unmet energy demands during less critical periods, thereby optimizing efficiency and enhancing energy availability within the community. In conclusion, this paper introduces a scalable methodology to improve solar irradiance prediction, emphasizing the significance of comparing clustering techniques as its main contribution. This advancement in prediction accuracy has the potential to enhance the feasibility and efficiency of renewable energy systems for rural communities, thereby fostering sustainable economic growth and bolstering efforts in climate change mitigation and adaptation.

Cause and Characteristics of Changes in Mesoscale Convective Systems within a Convection-Permitting Regional Climate Model

Abstract Changes in the frequency and intensity of mesoscale convective systems (MCS) are assessed using convection-permitting regional climate model simulations. We present a novel classification method that relates MCSs to the magnitude of their large-scale forcing environments to better understand the changing nature of MCSs across different forcing environments in a possible future climate scenario. Overall, the annual frequency, intensity, and amount of precipitation associated with MCSs are projected to increase for broad portions of the eastern conterminous United States (CONUS). Furthermore, changes in the characteristics of MCSs show larger, longer-lived, and faster MCSs particularly over the Midwest and Southeast. Seasonal examination of this response reveals a robust intensification of March–May (MAM) MCSs. The higher frequency of MAM MCSs are found to occur in weakly forced synoptic environments. Increased rainfall amounts are explained by an intensification of MCSs due to changing thermodynamics and enhanced lower-tropospheric moisture transport into the central CONUS by the North Atlantic Subtropical High. Differences in mid-latitude storm tracks are favorable towards the enhanced development of MCSs associated with strong baroclinic forcing within the Midwest and Northern Plains, but are only realized in the presence of strong changes in lower-tropospheric moisture transport. Overall, these results suggest a shift in the behavior of MAM MCSs that more closely corresponds to the behavior of June–August (JJA) MCSs in the historical climate. The increased occurrence of MCSs in weakly forced environments, particularly in MAM, deserves further investigation.

Water quality–fisheries tradeoffs in a changing climate underscore the need for adaptive ecosystem–based management

Changes driven by both unanticipated human activities and management actions are creating wicked management landscapes in freshwater and marine ecosystems that require new approaches to support decision-making. By linking a predictive model of nutrient- and temperature-driven bottom hypoxia with observed commercial fishery harvest data from Lake Erie (United States-Canada) over the past century (1928-2022) and climate projections (2030-2099), we show how simple, yet robust models and routine monitoring data can be used to identify tradeoffs associated with nutrient management and guide decision-making in even the largest of aquatic ecosystems now and in the future. Our approach enabled us to assess planned nutrient load reduction targets designed to mitigate nutrient-driven hypoxia and show why they appear overly restrictive based on current fishery needs, indicating tradeoffs between water quality and fisheries management goals. At the same time, our temperature results show that projected climate change impacts on hypoxic extent will require more stringent nutrient regulations in the future. Beyond providing a rare example of bottom hypoxia driving changes in fishery harvests at an ecosystem scale, our study illustrates the need for adaptive ecosystem-based management, which can be informed by simple predictive models that can be readily applied over long time periods, account for tradeoffs across multiple management sectors (e.g., water quality, fisheries), and address ecosystem nonstationarity (e.g., climate change impacts on management targets). Such approaches will be critical for maintaining valued ecosystem services in the many aquatic systems worldwide that are vulnerable to multiple drivers of environmental change.

Seasonality in cave dripwater and air properties – implications for speleothem palaeoclimatology, Nova Grgosova Cave (Croatia)

Due to the wealth of climate-sensitive properties, speleothems have been propelled into prominence as one of the most powerful continental archives of past climate changes. However, the multitude of processes that operate in the unsaturated karst zone and in the cave atmosphere can make palaeoclimate interpretations of the speleothem proxies challenging and site specific. Hence, to better understand the climate-proxy relationship, cave monitoring studies are usually undertaken. Here, we present the first results of an ongoing cave monitoring study in the Nova Grgosova Cave in Croatia covering an eighteen-month long monitoring period. The driving mechanisms for Mg/Ca and Sr/Ca variability in dripwater samples that feed ten stalagmites are discussed. The results reveal high variability in infiltration among the monitored sites as well as strong seasonal variability in cave air carbon dioxide (CO2) concentrations. A strong positive correlation between dripwater Mg/Ca and Sr/Ca suggests that prior calcite precipitation (PCP) is taking place at this site affecting the chemical composition of dripwater. Principal component analysis furthermore reveals that dripwater Mg/Ca and Sr/Ca are in strong negative correlation with cave air CO2 concentrations, while there is a weak correlation with dripwater quantity. Cave ventilation is a primary process leading to the PCP at the Nova Grgosova Cave. The seasonality revealed in this study suggests the possibility that the Mg/Ca and Sr/Ca ratio in the speleothems from this cave site can be used to aid seasonal reconstructions of past climate conditions in central Croatia and beyond.

Next-generation public health surveillance: extreme heat event prediction and monitoring system

Abstract Background Extreme Heat Events (EHEs) are a growing threat to public health. The increase in the frequency of occurrence of once rarer EHEs and the rise in their average temperatures are dangerously drastic for public health outcomes. Despite this, existing public health surveillance (PHS) systems fail to leverage IoT and AI technologies to facilitate real-time, continuous monitoring of EHE indicators and its associated health risks. This study closes this gap by proposing a comprehensive PHS system that can, in real-time monitor and predict EHE indicators to provide timely alerts to public health authorities. Methods The EHE PHS system collects EHE detection metrics, including indoor temperature and humidity levels, from IoT sensors and thermostats to map them across Canada, primarily focusing on low-income communities. The system also integrates historical climate data (surface temperature, humidity, wind speed and direction, and air quality indicators) from Environment Canada. The system uses the data to train initial prediction models including CNN, LSTM and GNN. The validated model will be integrated into the system, enabling real-time EHE prediction. The output will be displayed in user-friendly visualizations on the EHE PHS system’s dashboard capabilities. Results The system allows for the analysis of real-time data through dashboards and provides alerts when certain indicators (e.g. excessive or prolonged heat) detrimental to public health outcomes are detected. The alerts enable valuable lead time for public health authorities to implement proactive measures, such as issuing heat advisories and deploying resources to vulnerable areas. Conclusions The EHE PHS system will serve as a blueprint for global public health researchers, utilizing IoT and AI technologies for proactive crisis prevention. This knowledge will inform the development of heat-resilient policies and set a precedent for global public health crisis prevention. Key messages • The proposed EHE PHS system uses AI and IoT technologies, to enhance EHE prediction, risk identification, and real-time monitoring for effective public health interventions. • By integrating advanced predictive models and environmental data, the system facilitates early detection of EHE risk, which can significantly mitigate the health impact on vulnerable populations.

Integrating Machine Learning and Genetic Algorithms to Optimize Building Energy and Thermal Efficiency Under Historical and Future Climate Scenarios

As the global energy demand rises and climate change creates more challenges, optimizing the performance of non-residential buildings becomes essential. Traditional simulation-based optimization methods often fall short due to computational inefficiency and their time-consuming nature, limiting their practical application. This study introduces a new optimization framework that integrates Bayesian optimization, XGBoost algorithms, and multi-objective genetic algorithms (GA) to enhance building performance metrics—total energy (TE), indoor overheating degree (IOD), and predicted percentage dissatisfied (PPD)—for historical (2020), mid-future (2050), and future (2080) scenarios. The framework employs IOD as a key performance indicator (KPI) to optimize building design and operation. While traditional indices such as the predicted mean vote (PMV) and the thermal sensation vote (TSV) are widely used, they often fail to capture individual comfort variations and the dynamic nature of thermal conditions. IOD addresses these gaps by providing a comprehensive and objective measure of thermal discomfort, quantifying both the frequency and severity of overheating events. Alongside IOD, the energy use intensity (EUI) index is used to assess energy consumption per unit area, providing critical insights into energy efficiency. The integration of IOD with EUI and PPD enhances the overall assessment of building performance, creating a more precise and holistic framework. This combination ensures that energy efficiency, thermal comfort, and occupant well-being are optimized in tandem. By addressing a significant gap in existing methodologies, the current approach combines advanced optimization techniques with modern simulation tools such as EnergyPlus, resulting in a more efficient and accurate model to optimize building performance. This framework reduces computational time and enhances practical application. Utilizing SHAP (SHapley Additive Explanations) analysis, this research identified key design factors that influence performance metrics. Specifically, the window-to-wall ratio (WWR) impacts TE by increasing energy consumption through higher heat gain and cooling demand. Outdoor temperature (Tout) has a complex effect on TE depending on seasonal conditions, while indoor temperature (Tin) has a minor impact on TE. For PPD, Tout is a major negative factor, indicating that improved natural ventilation can reduce thermal discomfort, whereas higher Tin and larger open areas exacerbate it. Regarding IOD, both WWR and Tin significantly affect internal heat gains, with larger windows and higher indoor temperatures contributing to increased heat and reduced thermal comfort. Tout also has a positive impact on IOD, with its effect varying over time. This study demonstrates that as climate conditions evolve, the effects of WWR and open areas on TE become more pronounced, highlighting the need for effective management of building envelopes and HVAC systems.

Evaluating the tradeoffs between water conservation, aesthetic value, evaporative cooling and CO2 emissions in St. augustinegrass and buffalograss

Urban lawns comprise a significant portion of urban greenery and provide several ecosystem services. Nevertheless, maintaining lawns comes with significant water costs in semi-arid inland southern California, as they require consistent irrigation to stay healthy and productive. The main objective of this study was to evaluate the effect of a wide range of irrigation rates and frequencies applied autonomously by a smart ET-based controller on the aesthetic value, cooling potential, and CO2 efflux of two warm-season turfgrass species. For three years, we studied the responses of Buffalograss and St. Augustinegrass to six irrigation rates and two irrigation frequencies in Riverside, California. Under historical average climate conditions, the minimum irrigation rate for Buffalograss was 93 % ETo, and for St. Augustinegrass, it was 74 % ETo. Under projected future climate conditions, the estimated minimum irrigation rate for Buffalograss did not change, but for St. Augustinegrass, it increased by 4 % and 7 % in 2100 under low emission (RCP 4.5) and high emission (RCP 8.5) scenarios, respectively. On average, canopy minus air temperature in Buffalograss was 6.2 ℃, and in St. Augustinegrass, it was 1.1 ℃. The average CO2 efflux in Buffalograss was 122.3 µg CO2-C m−2 s−1, and in St. Augustinegrass, it was 182.8 µg CO2-C m−2 s−1. Our results showed that turfgrass aesthetic values, cooling potential, and CO2 efflux diminished as the irrigation rate decreased, but at different rates for each turfgrass species.

Artificial intelligence reveals past climate extremes by reconstructing historical records

The understanding of recent climate extremes and the characterization of climate risk require examining these extremes within a historical context. However, the existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century. In this work, we use artificial intelligence to reconstruct observations of European climate extremes (warm and cold days and nights) by leveraging Earth system model data from CMIP6 through transfer learning. Our method surpasses conventional statistical techniques and diffusion models, showcasing its ability to reconstruct past extreme events and reveal spatial trends across an extensive time span (1901-2018) that is not covered by most reanalysis datasets. Providing our dataset to the climate community will improve the characterization of climate extremes, resulting in better risk management and policies.

Coastal fisheries adaptations to increasing climate change exposure in Japan

Abstract Climate change‐driven ocean warming is altering the geographic ranges of species, leading to a poleward shift of tropical species into temperate regions (tropicalisation). This threatens the native marine community assemblages by changing the availability of traditional marine resources, thus impacting the livelihood and well‐being of coastal fisheries. Fishers' individual responses are expected to transition from remaining to coping and from adapting to transforming as climate change exposure increases, yet empirical evidence is limited. The existing strong thermal gradient along the western coast of Shikoku (Japan), an area experiencing tropicalisation, provides a unique opportunity to analyse how regions facing different climate change exposure levels (North: low, Centre: medium and South: high) affect fishers' responses in relation to their adaptive capacity. Data from 92 face‐to‐face interviews with small‐scale fishers from 25 locations revealed that the largest proportion of coping and adaptive responses is in the Central region, while remaining responses predominated in the Northern and Southern regions. Results from a multinomial logistic model indicate that the exposure level is a good predictor for coping and adaptive responses but not for transformative ones. This possibly reflects the fact that these fishers might have exited coastal fisheries prior to our study as suggested by the strong fisher population decrease in the communities from the Southern region over the last three decades. This study provides novel empirical evidence on how climate change exposure influences fishers' responses to past and current climate change impacts, highlighting the intricate interplay between exposure level and individual adaptive capacity. Read the free Plain Language Summary for this article on the Journal blog.

Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity

Abstract The success of plant species under climate change will be determined, in part, by their phenological responses to temperature. Despite the growing need to forecast such outcomes across entire species ranges, it remains unclear how phenological sensitivity to temperature might vary across individuals of the same species. In this study, we harnessed community science data to document intraspecific patterns in phenological temperature sensitivity across the multicontinental range of six herbaceous plant species. Using linear models, we correlated georeferenced temperature data with 23,220 plant phenological records from iNaturalist to generate spatially-explicit estimates of phenological temperature sensitivity across the shared range of species. We additionally evaluated the geographic association between local historic climate conditions (i.e. mean annual temperature and interannual variability in temperature) and the temperature sensitivity of plants. We found that plant temperature sensitivity varied substantially at both the interspecific and intraspecific level, demonstrating that phenological responses to climate change have the potential to vary both within and among species. Additionally, we provide evidence for a strong geographic association between plant temperature sensitivity and local historic climate conditions. Plants were more sensitive to temperature in hotter climates (i.e., regions with high mean annual temperature), but only in regions with high interannual temperature variability. In regions with low interannual temperature variability, plants displayed universally weak sensitivity to temperature, regardless of baseline annual temperature. This evidence suggests that pheno-climatic forecasts may be improved by accounting for intraspecific variation in phenological temperature sensitivity. Broad climatic factors such as mean annual temperature and interannual temperature variability likely serve as useful predictors for estimating temperature sensitivity across species’ ranges.

A Global Analysis of Historical and Future Changes in Mediterranean Climate‐Type Regions

ABSTRACTMediterranean climate‐type regions (MCRs) are characterised by warm‐to‐hot dry summers and mild‐wet winters. These regions are typically found on the western or southern edges of continents, for example, in the Mediterranean Basin, the west coast of North and South America, southern Africa and southwest Australia. The MCRs are vulnerable to climate variability and change related to their unique characteristics, such as pronounced rainfall seasonality and prolonged hot and dry summers. Based on historical observations and CMIP6 climate projections, we apply an empirical bio‐climatic assessment of how the geographic distribution of MCRs has changed during the last century and how these zones will be further impacted under continued warming. Results indicate a poleward and eastward expansion of MCRs in the Mediterranean Basin, North America‐California and South America‐Central Chile regions. For parts of Southern Africa and Southern Australia, a retreat of the MCR margins and an expansion of more arid climate zones are projected. These shifts are particularly profound according to high emission and radiative forcing pathways and future scenarios. The warming in MCRs is projected to accelerate (e.g., mean regional warming of up to 5.5°C under a 4°C global warming scenario), and precipitation will decrease by about 5%–10% for every additional degree of global warming. One exception is the California MCR, where rainfall will likely increase. Such changes can challenge water resources, food security and other aspects of human livelihood and ecosystems in these unique geographical zones.