High-resolution Climate Data Research Articles

Agricultural droughts and under-five mortality in Côte d’Ivoire: Differential impacts across social and demographic groups

Little research explores how climate extremes affect early childhood mortality in sub-Saharan Africa, despite great vulnerability to both climate extremes and high rates of early childhood mortality. Although there have been substantial improvements in early childhood mortality in recent decades, climate change threatens to offset this progress. Focusing on the case of Côte d’Ivoire, I combine individual-level data from the Demographic and Health Surveys with high-resolution climate data to investigate how and when in-utero exposure to severe agricultural droughts influences early childhood mortality. I find that in-utero exposure to severe agricultural droughts increases the probability of under-five mortality, and most recent droughts seem to exert the greatest impact. I also find that boys and children born into families with little to no formal education are highly vulnerable to drought exposure during gestation. Maternal education mitigates the negative impact of droughts on under-five survival, regardless of urban or rural residence, suggesting that its protective effects may be linked to specific knowledge, behaviours, and practices that highly educated mothers employ rather than their living environment. These findings carry important lessons for policymakers, emphasising the need for policies that enhance educational opportunities for parents and develop targeted interventions for boys and children from families with little formal education.

Sample Article for Pree Reviewer

Background Large research consortia can generate tremendous amounts of biological information, including high-resolution soil, vegetation, and climate data. While this knowledge stock holds invaluable potential for answering evolutionary and ecological questions, making these data exploitable for modelling remains a daunting task due to the many processing steps required for synthesis. This might result in many researchers to fall back to a handful of ready-to-use data sets, potentially at the expense of statistical power and scientific rigour. In a push for a more stringent approach, we introduce BEpipeR, an R pipeline that allows for the streamlined synthesis of plot-based Biodiversity Exploratories data. Methods BEpipeR was designed with flexibility and ease of use in mind. For instance, users simply choose between aggregating forest or grassland data, or a combination thereof, effectively allowing them to process any experimental plot data of this research consortium. Additionally, instead of coding, they parse most processing information in a user-friendly way through parameter sheets. Processing includes, among others, the creation of a spatially explicit plot-ID template, data wrangling, quality control, plot-wise aggregations, the calculation of derived metrics, data joining to a large composite data set, and metadata compilation. Results With BEpipeR, we provide a feature-rich pipeline that allows users to process Biodiversity Exploratories data in a flexible and reproducible way. This pipeline might serve as a starting point for aggregating the numerous data sets of this and potentially similar research consortia. In this way, it might be a primer for the construction of consortia-wide composite data sets that take full advantage of the consortia’s rich information stocks, ultimately boosting the visibility and participation of individual research projects. Conclusions The BEpipeR permits the user-friendly processing and plot-wise aggregation of Biodiversity Exploratories data. With modifications, this framework may be easily adopted by other research consortia.

BEpipeR: a user-friendly, flexible, and scalable data synthesis pipeline for the Biodiversity Exploratories and other research consortia

Abstract Background Large research consortia can generate tremendous amounts of biological information, including high-resolution soil, vegetation, and climate data. While this knowledge stock holds invaluable potential for answering evolutionary and ecological questions, making these data exploitable for modelling remains a daunting task due to the many processing steps required for synthesis. This might result in many researchers to fall back to a handful of ready-to-use data sets, potentially at the expense of statistical power and scientific rigour. In a push for a more stringent approach, we introduce BEpipeR, an R pipeline that allows for the streamlined synthesis of plot-based Biodiversity Exploratories data. Methods BEpipeR was designed with flexibility and ease of use in mind. For instance, users simply choose between aggregating forest or grassland data, or a combination thereof, effectively allowing them to process any experimental plot data of this research consortium. Additionally, instead of coding, they parse most processing information in a user-friendly way through parameter sheets. Processing includes, among others, the creation of a spatially explicit plot-ID template, data wrangling, quality control, plot-wise aggregations, the calculation of derived metrics, data joining to a large composite data set, and metadata compilation. Results With BEpipeR, we provide a feature-rich pipeline that allows users to process Biodiversity Exploratories data in a flexible and reproducible way. This pipeline might serve as a starting point for aggregating the numerous data sets of this and potentially similar research consortia. In this way, it might be a primer for the construction of consortia-wide composite data sets that take full advantage of the consortia’s rich information stocks, ultimately boosting the visibility and participation of individual research projects. Conclusions The BEpipeR pipeline permits the user-friendly processing and plot-wise aggregation of Biodiversity Exploratories data. With modifications, this framework may be easily adopted by other research consortia.

Stagg:: A data pre-processing R package for climate impacts analysis

The increasing availability of high-resolution climate data has greatly expanded the study of how the climate impacts humans and society. However, the processing of these multi-dimensional datasets poses significant challenges for researchers in this growing field, most of whom are social scientists. This paper introduces stagg, or “space-time aggregator”, a new R package that streamlines three critical components of climate data processing for impacts analysis: nonlinear transformation, spatial and temporal aggregation, and spatial weighting by social or economic variables. The package consolidates the data processing pipeline into a few lines of code, lowering barriers to entry for researchers and facilitating a larger and more diverse research community. The paper provides an overview of stagg's functions, followed by an applied example demonstrating the package's utility in climate impacts research. stagg has the potential to be a valuable tool in generating evidence-based estimates of the likely impacts of future climate change.

Archival Aerial Photographs of Africa: Present Potential and Imagining a Machine-Learning Future

AbstractArchival aerial photographs are a unique but underused and potentially game-changing source to study twentieth-century environmental and climate change dynamics. While satellite imagery with comparable high resolution appeared only in the early twenty-first century, archival aerial imagery with native sub-1-meter resolution became ubiquitous in the 1940s. Archival aerial photography therefore quadruples the time depth of high-resolution analysis to eighty years, allowing for a more reliable identification of structural trends. Moreover, the greater time-depth brings into focus the Great Acceleration that started in the 1940s, and virtually in real time. The article uses a human manual analysis of a sample from two time series (1943 and 1971) of archival photographs of the Oshikango area of Namibia (see Figure 1) to demonstrate how aerial photography complements conventional datasets. Namibia was one of the first places in colonial Africa where what subsequently became the standard protocol for “aerial mapping” was used and for which the imagery and the “flight plans” have survived. The standard protocol makes the imagery compatible with any archival aerial photography from the 1940s to 1990s and the flight plans contain key information to identify, interpret, and combine the individual photographs into orthomosaics. Although the use of manual analysis of aerial photography is not new, unlocking the full explanatory potential of high-resolution mass data requires machine reading and analysis. Current machine reading methods, however, are based on the pixel method, which identifies such features as farms, water holes, and trees only as low-resolution pixel aggregates. In contrast, the object method of machine analysis, combined with Geographical Information Systems (GIS) technology to unlock the sub-1-meter native resolution of historical aerial photography, renders visible individual trees and other features, including their precise location and size, allowing for the dimensions of trees and other features to be measured between different time series of images. The interrelationships between different features in the environment can thus be assessed more precisely in space and over time, for example comparing tree growth and surface water sources. A major challenge is that the object method used for high resolution geospatial imagery cannot be easily applied to monochromatic archival aerial photography because it has been designed for analyzing multispectral satellite imagery. As discussed in the article, using the manual sample as a training data set for an experimental machine-learning protocol demonstrates proof of concept for automatically extracting such features as farms, water holes and trees as individual objects from archival aerial photography. This increases the time depth of available high-resolution land use, environmental, and climate data from 2000 back to the 1940s and provides a base line for the Great Acceleration and brings the massive changes from the 1940s through the 1990s in focus as captured in aerial photography.

Vegetation canopy height shapes bats’ occupancy: a remote sensing approach

ABSTRACT Anthropogenic activities have significantly altered land cover on a global scale. These changes often have a negative effect on biodiversity limiting the distribution of species. The extent of the effect on species’ distribution depends on the landscape composition and configuration at a local and landscape level. To better understand this effect on a large scale, we evaluated how land cover and vegetation structure shape bat species’ occurrence while considering species’ imperfect detection. We hypothesize that intensification of anthropogenic activities in agriculture, for example, reduces heterogeneity of land cover and vegetation structure, and thereby, limits bat occurrence. To investigate this, we conducted acoustic bat sampling across 59 locations in southern Portugal, each with three spatial replicates. We derived fine-scale vegetation structural metrics by combining spaceborne LiDAR (GEDI) and synthetic aperture radar data (Sentinel-1 and ALOS/PALSAR-2). Additionally, we included land cover metrics and high-resolution climate data from CHELSA. Our findings revealed an important relationship between bat species’ occupancy and vegetation structure, particularly with vegetation canopy height. Moreover, forest and shrubland proportions were the main land cover types influencing bat species responses. All species’ best-ranking occupancy models included at least one climatic variable (temperature, humidity, or potential evapotranspiration), demonstrating the importance of climate when predicting bat distribution. Our acoustic surveys had a species’ detection probability varying from 0.19 to 0.86, and it was influenced by night conditions. These findings underscore the importance of modeling imperfect detection, especially for highly vagile and elusive organisms like bats. Our results demonstrate the effectiveness of using vegetation and landscape metrics derived from high-resolution remote sensing data to model species distribution in the context of biodiversity monitoring and conservation.

Six years of high-resolution climatic data collected along an elevation gradient in the Italian Alps

The complex meso- and microclimatic heterogeneity inherent to mountainous regions, driven by both topographic and biotic factors, and the lack of observations, poses significant challenges to using climate models to predict and understand impacts at various scales. We present here a six-year dataset (2017–2022) of continuous climatic measurements collected at five elevations from 983 m to 2705 m above sea level in the Val Mazia - Matschertal valley in the Italian Alps. The measurements include the air temperature, relative humidity, wind speed and direction, solar radiation, soil properties, precipitation, and snow height. Collected within the European Long-Term Ecological Research program (LTER), this dataset is freely available in an open access repository. The time series may be valuable for the validation of regional climate models, atmospheric exchange modelling, and providing support for hydrological models and remote sensing products in mountain environments. Additionally, our data may be useful for research on the influence of elevation on ecological processes such as vegetation growth, plant composition, and soil biology. Beyond its utility in advancing such fundamental research, meteorological monitoring data contribute to informed socio-political decisions on climate adaptation strategies, land management, and water resource planning, enhancing the safety and resilience of mountain communities and biodiversity.

Climate, weather, and child health: quantifying health co-benefits

Climate change affects human health negatively in a number of complex ways, and children are particularly vulnerable. Quantifying the negative impacts of climate change on health, and identifying locations where children are at greater risk, can aid evidence-based policy making. We combine high-resolution climatic data with a dataset on infant and child mortality, wasting, and stunting, from more than a hundred countries, to estimate the effects of both gradual and acute climate change, focusing on drought and heatwaves, to plausibly attribute changing child health outcomes to historical climate change. Our results suggest a non-linear relationship between temperature and children’s health, adverse effects of increases in acute events, and a strong regional heterogeneity in these impacts. Our findings also highlight the importance of poverty reduction. Greater wealth is associated with better child health outcomes, and partially mitigates the negative impacts of climate change on child health. Finally, using updated warming scenarios, our projections show that there are substantial health co-benefits from achieving low emissions scenarios.

Determining the Climatic Drivers for Wine Production in the Côa Region (Portugal) Using a Machine Learning Approach

The Côa region in inner-northern Portugal heavily relies on viticulture, which is a cornerstone of its economy and cultural identity. Understanding the intricate relationship between climatic variables and wine production (WP) is crucial for adapting management practices to changing climatic conditions. This study employs machine learning (ML), specifically random forest (RF) regression, to predict grapevine yields in the Côa region using high-resolution climate data for 2004–2020. SHAP (SHapley Additive exPlanations) values are used to potentially explain the non-linear relationships between climatic factors and WP. The results reveal a complex interplay between predictors and WP, with precipitation emerging as a key determinant. Higher precipitation levels in April positively impact WP by replenishing soil moisture ahead of flowering, while elevated precipitation and humidity levels in August have a negative effect, possibly due to late-season heavy rainfall damaging grapes or creating more favorable conditions for fungal pathogens. Moreover, warmer temperatures during the growing season and adequate solar radiation in winter months favor higher WP. However, excessive radiation during advanced growth stages can lead to negative effects, such as sunburn. This study underscores the importance of tailoring viticultural strategies to local climatic conditions and employing advanced analytical techniques such as SHAP values to interpret ML model predictions effectively. Furthermore, the research highlights the potential of ML models in climate change risk reduction associated with viticulture, specifically WP. By leveraging insights from ML and interpretability techniques, policymakers and stakeholders can develop adaptive strategies to safeguard viticultural livelihoods and stable WP in a changing climate, particularly in regions with a rich agrarian heritage, such as the Côa region.

Amplification of the discrepancy between simplified and physics-based wet-bulb globe temperatures in a warmer climate

The Simplified Wet Bulb Globe Temperature (sWBGT) is widely used in heat stress assessments for climate-change studies, but its limitations have not been thoroughly explored. Building on recent critiques of sWBGT's use for current climate on global scale, this study examines sWBGT's biases using dynamically-downscaled sub-daily climate projections under multiple future emission scenarios. The analysis is aimed at understanding caveats in the application of sWBGT and the uncertainties in existing climate change analysis dependent on sWBGT. Results indicate sWBGT's biases are heavily influenced by local near-surface air temperature, with overestimation of heat stress in East Asia regions, particularly hot and humid areas, due to static assumptions of radiation and wind speed. This overestimation is amplified in warmer climates, leading to exaggerated projected heat stress increases in future. In contrast, underestimations are found for heat stress levels attributed to low wind speeds and strong radiations, such as over the Tibetan Plateau and certain extreme events. Additionally, sWBGT underestimates variability in extreme heatwave events compared to WBGT in both current and future climates, irrespective of overestimation in absolute heatwave intensities. This study emphasizes the limitations of sWBGT, especially in future warmer climates. Importance of sub-daily data for capturing daily maximum heat stress level and reflecting diurnal variations in different components is also discussed. In conclusion, we recommend using Liljegren's model (i.e., physics-based calculation) with high-resolution sub-daily climate data for more accurate outdoor heat stress assessments in climate change studies.

High-resolution climate data reveal an increasing risk of warming-driven activity restriction for diurnal and nocturnal lizards.

Widespread species experience a variety of climates across their distribution, which can structure their thermal tolerance, and ultimately, responses to climate change. For ectotherms, activity is highly dependent on temperature, its variability and availability of favourable microclimates. Thermal exposure and tolerance may be structured by the availability and heterogeneity of microclimates for species living along temperature and/or precipitation gradients - but patterns and mechanisms underlying such gradients are poorly understood. We measured critical thermal limits (CTmax and CTmin) for five populations of two sympatric lizard species, a nocturnal gecko (Chondrodactylus bibronii) and a diurnal skink (Trachylepis variegata) and recorded hourly thermal variation for a year in three types of microclimate relevant to the activity of lizards (crevice, full sun and partial shade) for six sites across a precipitation gradient. Using a combination of physiological and modelling approaches, we derived warming tolerance for the present and the end of the century. In the present climate, we found an overall wider thermal tolerance for the nocturnal species relative to the diurnal species, and no variation in CTmax but variable CTmin along the precipitation gradient for both species. However, warming tolerances varied significantly over the course of the day, across months and microhabitats. The diurnal skink was most restricted in its daily activity in the three driest sites with up to six daily hours of restricted activity in the open (i.e. outside refugia) during the summer months, while the impacts for the nocturnal gecko were less severe, due to its higher CTmax and night activity. With climate change, lizards will experience more months where activity is restricted and increased exposure to high temperatures even within the more sheltered microhabitats. Together our results highlight the importance of considering the relevant spatiotemporal scale and habitat for understanding the thermal exposure of diurnal and nocturnal species.

Imprints of Holocene aridity variability in the Aegean Sea and interconnections with north-latitude areas

The analysis of the ASTC1 sediment core from the south Aegean Sea region offers critical insights into the complex interplay of geological and climatic factors over the Holocene period. The data reveals fluctuating climatic conditions during the last 8.7 ka as seen through the elemental concentrations obtained by XRF core scanning combined with a qualitative mineral analysis within a robust chronological framework. Short-term fluctuations in both Ti/Al and Zr/Si ratios suggest brief oscillations of increased aridity which partially coincide with the Holocene “Rapid Climate Change” events (RCCs). Among them, the most pronounced in our record are those centered between 8.5–8 ka, 3–2.5 ka (Greek Dark Ages), and 0.6–0.3 ka (Little Ice Age). The arid and humid events identified in the sediment record align with major archaeological periods in Greece, suggesting a potential influence of climatic conditions on the development and decline of civilizations in the region. Moreover, a general arid trend as of 6 ka toward the present was evidenced in our record and aligns with other high-resolution climatic data from the Northern Hemisphere, suggesting climatic teleconnections. Spectral analysis of the ASTC1 record reveals cyclical climate patterns with periodicities of approximately 2500, 1200, and 550 years, which coincide with the Bond and Hallstatt cycles. The phase relation of these cycles in our record, the Greenland ice record, and the North Atlantic Drift ice indices show that colder conditions in the higher latitudes are expressed as events of enhanced aridity in our record and generally in the lower latitudinal regions.

Bioclimatic zoning for building performance using tailored clustering method and high-resolution climate data

Building environments are specific and complex bioclimatic systems. Thus, well-suited climate classification methods for buildings are essentially needed to develop building design guidelines and standards. To address it, the present research introduces a novel fit-for-purpose clustering method for bioclimatic zoning based on the hygrothermal and energy performance of buildings. This bioclimatic zoning was developed to update the Brazilian standard and has been validated across various climates and building typologies (residential and commercial) in Brazil. In a preliminary analysis, three classification methods were developed using K-means and Decision Tree to classify climates according to building performance. Subsequently, a final bioclimatic zoning method was developed using a tailored version of the best method designed for real-world applications (Decision Tree) in the Brazilian context. The performance of the bioclimatic zoning achieved was compared with three existing climate classifications: Köppen-Geiger, ASHRAE 169-2020, and ABNT-NBR 15220-3 (Brazilian Standard). The results showed that the new bioclimatic zoning method outperformed the existing ones to cluster the building performance indicators. Moreover, high-resolution spatial climate databases, such as NASA-POWER, CRU, and ERA5-Land, were processed and analyzed to be employed in locations without properly measured data. Three metamodels of climate indicators were developed and compared with these databases to select the most accurate climate data sources. Finally, these databases were employed to classify all 5570 Brazilian municipalities according to the final bioclimatic zoning, which enabled the development of an accurate and high-resolution map.

Mapping the race between crop phenology and climate risks for wheat in France under climate change.

Climate change threatens food security by affecting the productivity of major cereal crops. To date, agroclimatic risk projections through indicators have focused on expected hazards exposure during the crop's current vulnerable seasons, without considering the non-stationarity of their phenology under evolving climatic conditions. We propose a new method for spatially classifying agroclimatic risks for wheat, combining high-resolution climatic data with a wheat's phenological model. The method is implemented for French wheat involving three GCM-RCM model pairs and two emission scenarios. We found that the precocity of phenological stages allows wheat to avoid periods of water deficit in the near future. Nevertheless, in the coming decades the emergence of heat stress and increasing water deficit will deteriorate wheat cultivation over the French territory. Projections show the appearance of combined risks of heat and water deficit up to 4years per decade under the RCP 8.5 scenario. The proposed method provides a deep level of information that enables regional adaptation strategies: the nature of the risk, its temporal and spatial occurrence, and its potential combination with other risks. It's a first step towards identifying potential sites for breeding crop varieties to increase the resilience of agricultural systems.

Implementing policies to mitigate urban heat islands: Analyzing urban development factors with an innovative machine learning approach

Taiwan is located in a hot and humid subtropical climate. Urban development, building heat emissions, and human activities lead to the urban heat island (UHI) effect in which urban air temperatures are significantly higher than those in the surrounding suburban areas. In this study, the factors contributing to the UHI effect were investigated in a case study of Taichung City, Taiwan by using high-resolution geographic climate data. Geographic Information System tools were used for grid-based spatial data analysis, and a physiological equivalent temperature index map was calculated and overlaid to identify heat zones. The results revealed that environmental factors have complex, nonlinear effects on temperatures in in urban areas; hence, the analysis was challenging. The decision tree algorithm was used to identified key environmental factors contributing to urban heat in areas with different levels of development; a weighting analysis was then performed to select effective urban cooling strategies. Finally, backpropagation neural network models tailored to different development levels were used for simulations to validate the proposed cooling policies. The results of this study were incorporated into the urban planning policies of Taichung City, Taiwan to mitigate the UHI effect and create a more suitable urban living environment.

To what extent can we predict variation of bryophyte and tracheophyte community composition at fine spatial scale along an elevation gradient?

Mounting evidence points to the need for high-resolution climatic data in biodiversity analyses under global change. As we move to finer resolution, other factors than climate, including other abiotic variables and biotic interactions play, however, an increasing role, raising the question of our ability to predict community composition at fine scales. Focusing on two lineages of land plants, bryophytes and tracheophytes, we determine the relative contribution of climatic, non-climatic environmental drivers, spatial effects, community architecture and composition of one lineage to predict community composition of the other lineage, and how our ability to predict community composition varies along an elevation gradient. The relationship between community composition of one lineage and 68 environmental variables at 2-25 m spatial resolution, architecture and composition of the other lineage, and spatial factors, was investigated by hierarchical and variance partitioning across 413 2x2m plots in the Swiss Alps. Climatic data, although significant, contributed less to the model than any other variable considered. Community composition of one lineage, reflecting both direct interactions and unmeasured (hidden) abiotic factors, was the best predictor of community composition of the other lineage. Total explained variance substantially varied with elevation, underlining the fact that the strength of the species composition-environment relationship varies depending on environmental conditions. Total variance explained increased towards high elevation up to 50 %, with an increasing importance of spatial effects and vegetation architecture, pointing to increasing positive interactions and aggregated species distribution patterns in alpine environments. In tracheophytes, an increase of the contribution of non-climatic environmental factors was also observed at high elevation, in line with the hypothesis of a stronger environmental control under harsher conditions. Further improvements of our ability to predict changes in plant community composition may involve the implementation of historical variables and higher-resolution climatic data to better describe the microhabitat conditions actually experienced by organisms.

Temperature anomalies undermine the health of reproductive-age women in low- and middle-income countries

Climate change is expected to undermine population health and well-being in low- and middle-income countries, but relatively few analyses have directly examined these effects using individual-level data at global scales, particularly for reproductive-age women. To address this lacuna, we harmonize nationally representative data from the Demographic and Health Surveys on reproductive health, body mass index (BMI), and temporary migration from 2.5 million adult women (ages 15 to 49) in approximately 109,000 sites across 59 low- and middle-income countries, which we link to high-resolution climate data. We use this linked dataset to estimate fixed-effect logistic regression models of demographic and health outcomes as a function of climate exposures, woman-level and site-level characteristics, seasonality, and regional time trends, allowing us to plausibly isolate climate effects from other influences on health and migration. Specifically, we measure the effects of recent exposures to temperature and precipitation anomalies on the likelihood of having a live birth in the past year, desire for another child, use of modern contraception, underweight (BMI < 18.5), and temporary migration, and subsequently allow for nonlinearity as well as heterogeneity across education, rural/urban residence, and baseline climate. This analysis reveals that exposures to high temperatures increase live births, reduce desire for another child, increase underweight, and increase temporary migration, particularly in rural areas. The findings represent clear evidence that anthropogenic temperature increases contribute to temporary migration and are a significant threat to women's health and reproductive autonomy in low- and middle-income countries.

Ambient heat and acute kidney injury: case-crossover analysis of 1 354 675 automated e-alert episodes linked to high-resolution climate data

As global temperatures continue to rise, the effects of ambient heat on acute kidney injury (AKI) are of growing concern. We used a novel nationwide electronic alert (e-alert) system to detect increases in AKI risk associated with high temperatures. We used a case-crossover design to link 1 354 675 AKI episodes occurring in England between April and September in years 2017-2021 to daily maximum temperature data at postcode sector level. AKI episode data were obtained from the UK Renal Registry. There were no further inclusion or exclusion criteria. Conditional logistic regression employing distributed lag non-linear models was used to assess odds of AKI episode on case days compared with day-of-week matched control days. Effects during heatwaves were also assessed using heat-episode analysis. There were strongly increased odds of AKI episode associated with high temperatures, with odds ratio (OR) 1·623 (95% CI 1·319-1·997) on a day of temperature 32°C compared with one of 17°C, the effects being strongest on a lag of 1 day. There was an OR of 1·020 (1·019-1·020) per 1°C increase in temperature above 17°C. The odds of a heat-related AKI episode were similar between AKI stages 1 and 2, but considerably lower for stage 3 events. A 7-day heatwave in July 2021 was associated with a 28·6% increase in AKI counts (95% CI 26·5-30·7). Heat-related AKI is a growing public health challenge. As even small changes in renal function can affect patient outcomes, susceptible individuals should be advised to take preventive measures whenever hot weather is forecast. Use of an e-alert system allows effects in milder cases that do not require secondary care to also be detected. National Institute for Health and Care Research (NIHR).

Soil water storage capacity and soil nutrients drive tree ring growth of six European tree species across a steep environmental gradient

Tree growth depends on tree-intrinsic attributes, synecological interactions, atmospheric conditions and soil properties. While the influence of tree factors and climate are analysed in detail in many studies, the effect of soil properties is less investigated and compensatory soil effects are often not quantified. In this study, we use a comprehensive dataset of 1659 tree increment cores from six common Central European tree species (Abies alba, Fagus sylvatica, Larix decidua, Picea abies, Pinus sylvestris, Quercus spp.) sampled at 1562 locations across large environmental gradients in the Eastern Alps. Soil data was available from an extensive soil survey including soil pits, laboratory analysis and the application of pedotransfer-functions. Up to three main tree species per site were sampled and the tree ring widths were dendrochronologically measured and synchronized. Topographic information from a high-resolution Digital Terrain Model, high-resolution climate data and biometric measures were available at each site. To determine the influence of soil water storage capacity and soil nutrient status, we used generalized additive models to expand standard models of well-known drivers of tree growth, including age, climatic water balance and temperature. For the time span of 38 years from 1981 until 2018, we found species-specific impacts of soil properties on tree ring growth. Specifically, soil water storage acted as a buffer to overcome drought periods, in particular for deep rooting tree species like Quercus spp. and Abies alba. In addition, we found species-specific growth reactions to soil nutrient status for nutrient-demanding species likes Fagus sylvatica but no effects for less demanding tree species like Pinus sylvestris. Our results show the magnitude of the effects of soil properties in relation to other growth factors on radial growth of six Central European tree species. Therefore, we posit that, while age and climate do have a stronger influence on tree growth, it is important to consider soil as a growth factor, particularly at the distribution margins along climatic gradients.

Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts

Abstract. Global climate models (GCMs) are important tools for understanding the climate system and how it is projected to evolve under scenario-driven emissions pathways. Their output is widely used in climate impacts research for modeling the current and future effects of climate change. However, climate model output remains coarse in relation to the high-resolution climate data needed for climate impacts studies, and it also exhibits biases relative to observational data. Treatment of the distribution tails is a key challenge in existing bias-adjusted and downscaled climate datasets available at a global scale; many of these datasets used quantile mapping techniques that were known to dampen or amplify trends in the tails. In this study, we apply the Quantile Delta Mapping (QDM) method (Cannon et al., 2015) for bias adjustment. After bias adjustment, we apply a new spatial downscaling method called Quantile-Preserving Localized-Analog Downscaling (QPLAD), which is designed to preserve trends in the distribution tails. Both methods are integrated into a transparent and reproducible software pipeline, which we apply to global, daily GCM surface variable outputs (maximum and minimum temperature and total precipitation) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments (O'Neill et al., 2016) for the historical experiment and four future emissions scenarios ranging from aggressive mitigation to no mitigation, namely SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 (Riahi et al., 2017). We use the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 (Hersbach et al., 2020) temperature and precipitation reanalysis as the reference dataset over the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) reference period of 1995–2014. We produce bias-adjusted and downscaled data over the historical period (1950–2014) and the future emissions pathways (2015–2100) for 25 GCMs in total. The output dataset is the Global Downscaled Projections for Climate Impacts Research (GDPCIR), a global, daily, 0.25∘ horizontal-resolution product which is publicly available and hosted on Microsoft AI for Earth's Planetary Computer (https://planetarycomputer.microsoft.com/dataset/group/cil-gdpcir/, last access: 23 October 2023).