Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset

Perceptions of environmental changes among a climate-vulnerable population from Bangladesh

Effective climate change adaptation requires a thorough understanding of whether and how affected populations perceive climatic and environmental changes. Existing research has been inconclusive regarding the consistency of these perceptions compared to objective meteorological indicators. Moreover, no systematic comparison has been done for the perception of discrete environmental events such as floods or erosion. This study relies on novel panel survey data of approximately 1700 households residing along the Jamuna River in Bangladesh as well as on unique individual-level, satellite-based erosion data. It compares respondents' perceptions of environmental events, namely riverbank erosion, and three climate change indicators, specifically long-term temperature change and changes in precipitation during wet and dry seasons, to objective measurements using satellite imagery and climatic time-series data (CRU TS). I find that long-term temperature change is perceived more accurately than long-term changes in precipitation. Given that educational attainment and climate change literacy among the study population are low, this indicates that global temperature increases are felt even by remote populations who have never heard the term climate change. Erosion is strongly overestimated, especially by those respondents who had been personally affected by it. Since human behavior is guided by perceptions rather than objective data, this has important policy implications, underlining the importance of considering people's perceptions if the goal is to assist them in adapting to environmental changes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10584-024-03678-6.

Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation

Yield gaps, here defined as the difference between actual and attainable yields, provide a framework for assessing opportunities to increase agricultural productivity. Previous global assessments, centred on a single year, were unable to identify temporal variation. Here we provide a spatially and temporally comprehensive analysis of yield gaps for ten major crops from 1975 to 2010. Yield gaps have widened steadily over most areas for the eight annual crops and remained static for sugar cane and oil palm. We developed a three-category typology to differentiate regions of 'steady growth' in actual and attainable yields, 'stalled floor' where yield is stagnated and 'ceiling pressure' where yield gaps are closing. Over 60% of maize area is experiencing 'steady growth', in contrast to ∼12% for rice. Rice and wheat have 84% and 56% of area, respectively, experiencing 'ceiling pressure'. We show that 'ceiling pressure' correlates with subsequent yield stagnation, signalling risks for multiple countries currently realizing gains from yield growth.

Rising minimum temperatures contribute to 50 years of occupancy decline among cold-adapted Arctic and boreal butterflies in North America

Global climate change has been identified as a potential driver of observed insect declines, yet in many regions, there are critical data gaps that make it difficult to assess how communities are responding to climate change. Poleward regions are of particular interest because warming is most rapid while biodiversity data are most sparse. Building on recent advances in occupancy modeling of presence-only data, we reconstructed 50 years (1970-2019) of butterfly occupancy trends in response to rising minimum temperatures in one of the most under-sampled regions of North America. Among 90 modeled species, we found that cold-adapted species are far more often in decline compared with their warm-adapted, more southernly distributed counterparts. Furthermore, in a post hoc analysis using species' traits, we find that species' range-wide average annual temperature is the only consistent predictor of occupancy changes. Species with warmer ranges were most likely to be increasing in occupancy. This trend results in the majority of butterflies increasing in occupancy probability over the last 50 years. Our results provide the first look at macroscale butterfly biodiversity shifts in high-latitude North America. These results highlight the potential of leveraging the wealth of presence-only data, the most abundant source of biodiversity data, for inferring changes in species distributions.

Quantifying the drought sensitivity of grassland under different climate zones in Northwest China

Grassland is essential for maintaining the stability and functionality of terrestrial ecosystems. Although previous research has explored how grassland responds to drought, the drought sensitivity of grassland (DSG) across climate zones and aridity gradients remains uncertain. In this study, we conducted a comprehensive investigation spanning 1982 to 2015 in Northwest China. To assess the time-cumulative effect (TCE) and the time-lag effect (TLE) of drought on grassland, we employed Spearman rank correlation analysis, utilizing long-term datasets of the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). This analysis allowed us to quantify the DSG in the region and further examine its variations across climate zones and aridity gradient. Our results revealed that 81.2 % and 99.7 % of the grassland in Northwest China was influenced by the TCE and TLE of drought, respectively, with 38.2 % and 60.9 % of these effects being statistically significant (p < 0.05). The mean accumulated and lagged timescales of drought on grassland were 7.89 and 9.41 months, respectively. Remarkably, the highest DSG was observed in the semi-arid zone (0.58), followed by the arid (0.54), sub-humid (0.51), and humid (0.44) zones. Furthermore, we identified significant nonlinear variation patterns of DSG along the aridity gradient, characterized by several discernible trend breaks. These findings contribute to our understanding of the impacts of drought on vegetation, particularly in ecologically fragile regions.

Forest understorey flowering phenology responses to experimental warming and illumination

Species are altering their phenology to track warming temperatures. In forests, understorey plants experience tree canopy shading resulting in light and temperature conditions, which strongly deviate from open habitats. Yet, little is known about understorey phenology responses to forest microclimates. We recorded flowering onset, peak, end and duration of 10 temperate forest understorey plant species in two mesocosm experiments to understand how phenology is affected by sub-canopy warming and how this response is modulated by illumination, which is related to canopy change. Furthermore, we investigated whether phenological sensitivities can be explained by species' characteristics, such as thermal niche. We found a mean advance of flowering onset of 7.1 d per 1°C warming, more than previously reported in studies not accounting for microclimatic buffering. Warm-adapted species exhibited greater advances. Temperature sensitivity did not differ between early- and later-flowering species. Experimental illumination did not significantly affect species' phenological temperature sensitivities, but slightly delayed flowering phenology independent from warming. Our study suggests that integrating sub-canopy temperature and light availability will help us better understand future understorey phenology responses. Climate warming together with intensifying canopy disturbances will continue to drive phenological shifts and potentially disrupt understorey communities, thereby affecting forest biodiversity and functioning.

Dynamics of rhizosphere microbial structure and function associated with the biennial bearing of moso bamboo

Moso bamboo (Phyllostachys edulis) is a valuable nontimber forestry product with a biennial cycle, producing abundant bamboo shoots within one year (on-year) and few shoots within the following year (off-year). Moso bamboo plants undergo clonal reproduction, resulting in similar genetic backgrounds. However, the number of moso bamboo shoots produced each year varies. Despite this variation, the impact of soil nutrients and the root microbiome on the biennial bearing of moso bamboo is poorly understood. We collected 139 soil samples and determined 14 major physicochemical properties of the rhizosphere, rhizoplane, and bulk soil in different seasons (i.e., the growing and deciduous seasons) and different years (i.e., on- and off-years). Based on 16S rRNA and metagenomic sequencing, major variations were found in the rhizospheric microbial composition during different seasons and years in the moso bamboo forest. Environmental driver analysis revealed that essential nutrients (i.e., SOC, TOC, TN, P, and NH4+) were the main drivers of the soil microbial community composition and were correlated with the on- and off-year cycles. Moreover, 19 MAGs were identified as important biomarkers that could distinguish on- and off-years. We found that both season and year influenced both the microbial community structure and functional pathways through the biosynthesis of nutrients that potentially interact with the moso bamboo growth rhythm, especially the on-year root-associated microbiome, which had a greater abundance of specific nutrients such as gibberellins and vitamin B6. This work provides a dynamic perspective of the differential responses of various on- and off-year microbial communities and enhances our understanding of bamboo soil microbiome biodiversity and stability.

Interannual Variations in Terrestrial Net Ecosystem Productivity and Climate Attribution in the Southern Hilly Region of China

The vegetation ecosystem in the southern hilly region of China (SHRC) plays a crucial role in the country's carbon reservoir. Clarifying the dynamics of net primary productivity (NPP) in this area and its response to climate factors in the context of climate change is important for national forest ecology, management, and carbon neutrality efforts. This study, based on remote sensing and meteorological data spanning the period 2001 to 2021, aims to unveil the spatiotemporal patterns of vegetation productivity and climate factors in the southern hilly region, explore interannual variation characteristics of vegetation productivity with altitude, and investigate the response characteristics of NPP to various climate factors. The results indicate that from 2001 to 2021, the annual average NPP in the southern hilly region had a significant increasing trend of 2.13 ± 0.78 g m-2 a-1. The trend of NPP varies significantly with altitude. Despite a general substantial upward trend in vegetation NPP, regions at lower elevations exhibit a faster rate of increase, suggesting a diminishing difference in the NPP of different elevation ranges. The overall rise in average temperature has positive implications for the southern hilly region, while the impact of precipitation on vegetation NPP demonstrates noticeable spatial heterogeneity. Regions in which vegetation NPP is significantly negatively correlated with precipitation are mainly concentrated in the southern areas of Guangdong, Fujian, and Jiangxi provinces. In contrast, other regions further away from the southeastern coast tend to exhibit a positive correlation. Over the past two decades, there has been an asymmetry in the diurnal temperature variation in the SHRC, with the nighttime warming rate being 1.8 times that of the daytime warming rate. The positive impact of daytime warming on NPP of vegetation is more pronounced than the impact of nighttime temperature changes. Understanding the spatiotemporal patterns of NPP in the SHRC and the characteristics of its response to climate factors contributes to enhancing our ability to protect and manage vegetation resources amidst the challenges of global climate change.

Modification of the RothC model to evaluate the inconsistent effect of conservation tillage on SOC stock and a suggestion of a national-scale assessment framework

Simulation of conservation tillage effect on soil organic carbon (SOC) stock on the national scale is essential for Tier 3 level greenhouse gas inventory in the agricultural sector. However, the conservation tillage effects varied depending on different soil conditions, potentially leading to inaccurate national assessments. This study aimed to propose a framework for estimating the national scale impact of conservation tillage on SOC. As even in the most commonly used SOC dynamic model, the Rothamsted Carbon Model (RothC), does not reflect the conservation tillage effect in an explicit way, we modified it by developing the tillage rate modifiers (TRMs). First, we investigated the conditions for the inconsistent conservation tillage effects using the decision tree analysis based on 210 field experiment data from the mid-latitude region. The results highlighted that soil sand content and the existing SOC stock were the main factors driving the inconsistencies. After we categorized into four distinctive conditions, the TRMs for each condition were parameterized using a genetic algorithm. The average TRMs were 0.88 in the soils with sand content >37.6 % and 1.58 in the soils with sand content ≤37.6 %, indicating that conservation tillage is more effective in coarse-textured soil, and there is a risk of decreasing SOC stock in the latter condition. Using the modified RothC model, a three-step national-scale simulation framework was suggested: compiling country-specific data, establishing baseline and conservation tillage scenarios, and modeling conservation tillage effects with uncertainty analysis. Our approach also defined the maximum conservation tillage area, factoring in local cropping systems and soil conditions. Our refined RothC model with TRMs provides a nuanced understanding of conservation tillage effects, emphasizing the role of soil characteristics. The proposed national-scale simulation framework offers a reliable tool for evaluating conservation tillage impact on SOC, ensuring more accurate greenhouse gas inventories in agriculture.

High-resolution climate projection dataset based on CMIP6 for Peru and Ecuador: BASD-CMIP6-PE

Here, we present BASD-CMIP6-PE, a high-resolution (1d, 10 km) climate dataset for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 climate projections of 10 GCMs. This dataset includes both historical simulations (1850-2014) and future projections (2015-2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method. The BASD performance was evaluated using observational data and through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. Results demonstrated that BASD significantly reduced biases between CMIP6-GCM simulations and observational data, enhancing long-term statistical representations, including mean and extreme values, and seasonal patterns. Furthermore, the hydrological evaluation highlighted the appropriateness of adjusted GCM simulations for simulating streamflow, including mean, low, and high flows. These findings underscore the reliability of BASD-CMIP6-PE in assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.

Increasing tropical cyclone intensity in the western North Pacific partly driven by warming Tibetan Plateau

The increase in intense tropical cyclone (TC) activity across the western North Pacific (WNP) has often been attributed to a warming ocean. However, it is essential to recognize that the tropical WNP region already boasts high temperatures, and a marginal increase in oceanic warmth due to global warming does not exert a significant impact on the potential for TCs to intensify. Here we report that the weakened vertical wind shear is the primary driver behind the escalating trend in TC intensity within the summer monsoon trough of the tropical WNP, while local ocean surface and subsurface thermodynamic factors play a minor role. Through observational diagnoses and numerical simulations, we establish that this weakening of the vertical wind shear is very likely due to the increase in temperature of the Tibetan Plateau. With further warming of the Tibetan Plateau under the Representative Concentration Pathway 4.5 scenario, the projected TCs will likely become stronger.

Simulating long-term wildfire impacts on boreal forest structure in Central Yakutia, Siberia, since the Last Glacial Maximum

Background

Wildfires are recognized as an important ecological component of larch-dominated boreal forests in eastern Siberia. However, long-term fire-vegetation dynamics in this unique environment are poorly understood. Recent paleoecological research suggests that intensifying fire regimes may induce millennial-scale shifts in forest structure and composition. This may, in turn, result in positive feedback on intensifying wildfires and permafrost degradation, apart from threatening human livelihoods. Most common fire-vegetation models do not explicitly include detailed individual-based tree population dynamics, but a focus on patterns of forest structure emerging from interactions among individual trees may provide a beneficial perspective on the impacts of changing fire regimes in eastern Siberia. To simulate these impacts on forest structure at millennial timescales, we apply the individual-based, spatially explicit vegetation model LAVESI-FIRE, expanded with a new fire module. Satellite-based fire observations along with fieldwork data were used to inform the implementation of wildfire occurrence and adjust model parameters.

Results

Simulations of annual forest development and wildfire activity at a study site in the Republic of Sakha (Yakutia) since the Last Glacial Maximum (c. 20,000 years BP) highlight the variable impacts of fire regimes on forest structure throughout time. Modeled annual fire probability and subsequent burned area in the Holocene compare well with a local reconstruction of charcoal influx in lake sediments. Wildfires can be followed by different forest regeneration pathways, depending on fire frequency and intensity and the pre-fire forest conditions. We find that medium-intensity wildfires at fire return intervals of 50 years or more benefit the dominance of fire-resisting Dahurian larch (Larix gmelinii (Rupr.) Rupr.), while stand-replacing fires tend to enable the establishment of evergreen conifers. Apart from post-fire mortality, wildfires modulate forest development mainly through competition effects and a reduction of the model's litter layer.

Conclusion

With its fine-scale population dynamics, LAVESI-FIRE can serve as a highly localized, spatially explicit tool to understand the long-term impacts of boreal wildfires on forest structure and to better constrain interpretations of paleoecological reconstructions of fire activity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42408-023-00238-8.

Projected soil carbon loss with warming in constrained Earth system models

The soil carbon-climate feedback is currently the least constrained component of global warming projections, and the major source of uncertainties stems from a poor understanding of soil carbon turnover processes. Here, we assemble data from long-term temperature-controlled soil incubation studies to show that the arctic and boreal region has the shortest intrinsic soil carbon turnover time while tropical forests have the longest one, and current Earth system models overestimate intrinsic turnover time by 30 percent across active, slow and passive carbon pools. Our constraint suggests that the global soils will switch from carbon sink to source, with a loss of 0.22-0.53 petagrams of carbon per year until the end of this century from strong mitigation to worst emission scenarios, suggesting that global soils will provide a strong positive carbon feedback on warming. Such a reversal of global soil carbon balance would lead to a reduction of 66% and 15% in the current estimated remaining carbon budget for limiting global warming well below 1.5 °C and 2 °C, respectively, rendering climate mitigation much more difficult.

Negative interaction effect of heat and drought stress at the warm end of species distribution

Geographic range limits of species are often a reflection of their ecological niche limits. In many organisms, important niche limits that coincide with distribution limits are warm and warm-dry conditions. We investigated the effects of heat and drought, as they can occur at the warm end of distribution. In a greenhouse experiment, we raised North American Arabidopsis lyrata from the centre of its distribution as well as from low- and high-latitude limits under average and extreme conditions. We assessed plant growth and development, as well as leaf and root functional traits, and tested for a decline in performance and selection acting on growth, leaf, and root traits. Drought and heat, when applied alone, lowered plant performance, while combined stress caused synergistically negative effects. Plants from high latitudes did not survive under combined stress, whereas plants originating from central and low latitudes had low to moderate survival, indicating divergent adaptation. Traits positively associated with survival under drought, with or without heat, were delayed and slowed growth, though plastic responses in these traits were generally antagonistic to the direction of selection. In line, higher tolerance of stress in southern populations did not involve aspects of growth but rather a higher root-to-shoot ratio and thinner leaves. In conclusion, combined heat and drought, as can occur at southern range edges and presumably more so under global change, seriously impede the long-term persistence of A. lyrata, even though they impose selection and populations may adapt, though under likely interference by considerable maladaptive plasticity.

Pathways and drivers of canopy accession across primary temperate forests of Europe

Canopy accession strategies reveal much about tree life histories and forest stand dynamics. However, the protracted nature of ascending to the canopy makes direct observation challenging. We use a reconstructive approach based on an extensive tree ring database to study the variability of canopy accession patterns of dominant tree species (Abies alba, Acer pseudoplatanus, Fagus sylvatica, Picea abies) in temperate mountain forests of Europe and elucidate how disturbance histories, climate, and topography affect canopy accession. All four species exhibited high variability of radial growth histories leading to canopy accession and indicated varying levels of shade tolerance. Individuals of all four species survived at least 100 years of initial suppression. Fir and particularly beech, however, survived longer periods of initial suppression, exhibited more release events, and reached the canopy later on average, with a larger share of trees accessing the canopy after initially suppressed growth. These results indicate the superior shade tolerance of beech and fir compared to spruce and maple. The two less shade-tolerant species conversely relied on faster growth rates, revealing their competitive advantage in non-suppressed conditions. Additionally, spruce from higher-elevation spruce-dominated forests survived shorter periods of initial shading and exhibited fewer releases, with a larger share of trees reaching the canopy after open canopy recruitment (i.e. in absence of suppression) and no subsequent releases compared to spruce growing in lower-elevation mixed forests. Finally, disturbance factors were identified as the primary driver of canopy accession, whereby disturbances accelerate canopy accession and consequently regulate competitive interactions. Intensifying disturbance regimes could thus promote shifts in species composition, particularly in favour of faster-growing, more light-demanding species.

1 km monthly precipitation and temperatures dataset for China from 1952 to 2019 based on new baseline climatology surfaces

Long-term climate data and high-quality baseline climatology surface with high resolution are essential to investigate climate change and its effect on hydrological processes and ecosystem functioning. However, large uncertainties remain in the climate products in China owing to lacking of high-density distribution network of weather stations. Here, the thin plate spline (TPS) algorithm was used to produce new baseline climatology surfaces (ChinaClim_baseline) using >2000 freely available weather stations. Then, climatologically aided interpolation (CAI) was employed to generate a 1 km monthly precipitation and temperatures dataset for China during 1952-2019 (ChinaClim_time-series) via superimposing ChinaClim_baseline and monthly anomaly surface. Our finding showed that ChinaClim_baseline performed exceptionally well in four climatic regions, with RMSEs for precipitation and temperature element estimation of 1.276-28.439 mm and 0.310-2.040 °C, respectively. The correlations among ChinaClim_baseline and WorldClim2 and CHELSA were high, but there were clearly spatial differences. For ChinaClim_time-series, precipitation and temperature elements had average RMSEs between 7.502- 52.307 mm, and 0.461-0.939 °C for all months, respectively. In comparison to Peng's climatic surface and CHELSAcruts, R2 increased by ~7 %, RMSE and MAE dropped by ~17 % for precipitation; R2 hardly increased, while RMSE and MAE decreased by ~50 % for temperature elements. Our findings indicated that ChinaClim_baseline improved the accuracy of time-series climatic elements estimation obviously, and the satellite-driven data can greatly improve the accuracy of time-series precipitation estimation, but not the accuracy of time-series temperature estimation. Overall, ChinaClim_baseline as an excellent baseline climatology surface can be used for obtaining high-quality and long-term climate datasets from past to future. Meantime, ChinaClim_time-series of 1 km spatial resolution is appropriate for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China.

Climate warming enhances chemical weathering in permafrost-dominated eastern Siberia

Understanding the impacts of climate warming on hydrogeochemical processes, particularly in areas dominated by permafrost, is crucial. However, the natural background levels of chemical components in eastern Siberian rivers from permafrost-dominated regions and their responses to climate warming have not been adequately quantified. This study aims to address this knowledge gap by using a comprehensive river water chemistry database (n = 1264) spanning from 1940 to 2019. Our results reveal that the concentration of total dissolved solids (TDS), a key parameter of drinking water quality, in river basins predominantly free of permafrost (194.6 ± 256.4 mg/L) is approximately 2.3 times higher than in permafrost-dominated river basins (83.7 ± 35.8 mg/L) in eastern Siberia. These observations imply potential shifts in freshwater quality resulting from permafrost degradation. We further detect that carbonate weathering, which plays a fundamental role in the global carbon cycle, is a predominant process controlling hydrogeochemical cycles. (Ca2+ + Mg2+) concentrations as a proxy for carbonate weathering intensity are sensitive to climate warming, increasing at a rate of 0.10 mmol/(L·°C). This finding provides evidence that the current acceleration of carbonate weathering, driven by climate warming, is already influencing local water quality. Additionally, (Ca2+ + Mg2+) concentrations and TDS are highly interrelated with temperature-dependent variables (e.g., active layer thickness of permafrost and leaf area index) and basin erosion-controlling factors (e.g., precipitation, elevation and slope of basin). Under a warming climate, river chemical fluxes (e.g., export of TDS and major ions) increase notably, especially during the winter, indicating an increase in mineral-laden groundwater discharge to rivers due to permafrost degradation. Our results demonstrate that climate warming is accelerating hydrogeochemical processes in permafrost-dominated Arctic basins.

A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) have facilitated highly accurate observations of changes in total water storage anomalies (TWSA). However, limited observations of TWSA derived from GRACE in the Yangtze River Basin (YRB) have hindered our understanding of its long-term variability. In this paper, we present a deep learning model called RecNet to reconstruct the climate-driven TWSA in the YRB from 1923 to 2022. The RecNet model is trained on precipitation, temperature, and GRACE observations with a weighted mean square error (WMSE) loss function. The performance of the RecNet model is validated and compared against GRACE data, water budget estimates, hydrological models, drought indices, and existing reconstruction datasets. The results indicate that the RecNet model can successfully reconstruct historical water storage changes, surpassing the performance of previous studies. In addition, the reconstructed datasets are utilized to assess the frequency of extreme hydrological conditions and their teleconnections with major climate patterns, including the El Niño-Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and North Atlantic Oscillation. Independent component analysis is employed to investigate individual climate patterns' unique or combined influence on TWSA. We show that the YRB exhibits a notable vulnerability to extreme events, characterized by a recurrent occurrence of diverse extreme dry/wet conditions throughout the past century. Wavelet coherence analysis reveals significant coherence between the climate patterns and TWSA across the entire basin. The reconstructed datasets provide valuable information for studying long-term climate variability and projecting future droughts and floods in the YRB, which can inform effective water resource management and climate change adaptation strategies.

Statistical-dynamical modeling of the maize yield response to future climate change in West, East and Central Africa using the regional climate model REMO

Africa is vulnerable to the impacts of climate change, particularly in terms of its agriculture and crop production. The majority of climate models project a negative impact of future climate change on crop production, with maize being particularly vulnerable. However, the magnitude of this change remains uncertain. Therefore, it is important to reduce the uncertainties related to the anticipated changes to guide adaptation options. This study uses a combination of local and large-scale empirical orthogonal function (EOF) predictors as a novel approach to model the impacts of future climate change on crop yields in West, East and Central Africa. Here a cross-validated Bayesian model was developed using predictors derived from the regional climate model REMO for the period 1982-2100. On average, the combined local and large-scale EOF predictors explained around 28 % of maize yield variability from 1982 to 2016 of the entire study regions. Notably, climate predictors played a significant role in West Africa, explaining up to 51 % of the maize yield variability. Large-scale climate EOF predictors contributed most to the explained variance, reflecting the role of regional climate in future maize yield variability. Under a high-emissions scenario (RCP8.5), maize yield is projected to decrease over the entire study region by 20 % by the end of the century. However, a minor increase is projected in eastern Africa. This study highlights the importance of incorporating climate predictors at various scales into crop yield modeling. Furthermore, the findings will offer valuable guidance to decision-makers in shaping adaptation options.

Increasing forest carbon sinks in cold and arid northeastern Tibetan Plateau

Arid forest lands account for 6 % of the world's forest area, but their carbon density and carbon storage capacity have rarely been assessed. Forest inventories provide estimates of forest stock and biomass carbon density, improve our understanding of the carbon cycle, and help us develop sustainable forest management policies in the face of climate change. Here, we carried out three forest inventories at five-year intervals from 2006 to 2016 in 104 permanent sample plots covering the Qinghai spruce (Picea crassifolia) distribution in the north slope of Qilian Mountains, northeastern Tibetan Plateau. Results shows that mean biomasses for Qinghai spruce were 133.80, 144.89, and 157.01 Mg ha-1 while biomass carbon densities were 65.52, 70.92, and 76.88 Mg C ha-1, in 2006, 2011, and 2016, respectively. This shows an increase in the Qinghai spruce carbon density of 17.34 % from 2006 to 2016. Both the precipitation and temperature play crucial roles on the increase of aboveground carbon density. The average carbon densities were different among forests with different ages and were higher for older forests. Our results show that the carbon sequestration rate for Qinghai spruce in the Qilian Mountains is significantly higher than the average rates of national forest parks in China, suggesting that this spruce forest has the potential to sequester a significant amount of carbon despite the general harsh growing conditions of cold and arid ecoregions. Our findings provide important insights that are helpful for the assessment of forest carbon for cold and arid lands.

The phenological response of European vegetation to urbanisation is mediated by macrobioclimatic factors

Plant phenology is a crucial component of ecosystem functioning and is affected by multiple elements of global change; we therefore need to quantify the current phenological changes associated to human activities and understand their impacts on ecosystems. Urbanisation and the intensification of anthropogenic activities alter meteorological conditions and cause phenological changes in urban vegetation worldwide. We used remote sensing data to evaluate the phenological response (start of season date SOS, length of season LOS and end of season date EOS) of five main vegetation types (evergreen forests, deciduous forests, mixed forests, sparse woody vegetation and grasslands) to urbanisation in the 69 most populated pan-European metropolitan areas (i.e., those that include cities with a population over 450,000 inhabitants) for the period 2002-2021. In general, SOS advanced and LOS increased with urbanisation intensity across European metropolitan areas. We found that macrobioclimatic factors strongly determined the strength and direction of the phenological response to urbanisation intensity. The greatest advances in SOS with increasing urbanisation were registered in metropolitan areas in the Mediterranean region, where there was also more uncertainty in this relationship. The EOS advanced with urbanisation in metropolitan areas in the Mediterranean macrobioclimate, whereas in areas with higher precipitation during summer the opposite trend was observed suggesting water availability mediates the response between urbanisation and autumn phenophases. Our results suggest that macrobioclimatic constraints operating at the continental scale are crucial to understand the relationship between plant phenology and urbanisation intensity.

Impacts of ground-level ozone on sugarcane production

Sugarcane is a vital commodity crop often grown in (sub)tropical regions which have been experiencing a recent deterioration in air quality. Unlike for other commodity crops, the risk of air pollution, specifically ozone (O3), to this C4 crop has not yet been quantified. Yet, recent work has highlighted both the potential risks of O3 to C4 bioenergy crops, and the emergence of O3 exposure across the tropics as a vital factor determining global food security. Given the large extent, and planned expansion of sugarcane production in places like Brazil to meet global demand for biofuels, there is a pressing need to characterize the risk of O3 to the industry. In this study, we sought to a) derive sugarcane O3 dose-response functions across a range of realistic O3 exposure and b) model the implications of this across a globally important production area. We found a significant impact of O3 on biomass allocation (especially to leaves) and production across a range of sugarcane genotypes, including two commercially relevant varieties (e.g. CTC4, Q240). Using these data, we calculated dose-response functions for sugarcane and combined them with hourly O3 exposure across south-central Brazil derived from the UK Earth System Model (UKESM1) to simulate the current regional impact of O3 on sugarcane production using a dynamic global vegetation model (JULES vn 5.6). We found that between 5.6 % and 18.3 % of total crop productivity is likely lost across the region due to the direct impacts of current O3 exposure. However, impacts depended critically on the substantial differences in O3 susceptibility observed among sugarcane genotypes and how these were implemented in the model. Our work highlights not only the urgent need to fully elucidate the impacts of O3 in this important bioenergetic crop, but the potential implications air quality may have upon tropical food production more generally.

Quercus ilex L. dieback is genetically determined: Evidence provided by dendrochronology, δ13C and SSR genotyping

Quercus ilex L. dieback has been reported in several Mediterranean forests, revealing different degree of crown damages even in close sites, as observed in two Q. ilex forest stands in southern Tuscany (IT). In this work, we applied a novel approach combining dendrochronological, tree-ring δ13C and genetic analysis to test the hypothesis that different damage levels observed in a declining (D) and non-declining (ND) Q. ilex stands are connected to population features linked to distinct response to drought. Furthermore, we investigated the impact of two major drought events (2012 and 2017), that occurred in the last fifteen years in central Italy, on Q. ilex growth and intrinsic water use efficiency (WUEi). Overall, Q. ilex showed slightly different ring-width patterns between the two stands, suggesting a lower responsiveness to seasonal climatic variations for trees at D stand, while Q. ilex at ND stand showed changes in the relationship between climatic parameters and growth across time. The strong divergence in δ13C signals between the two stands suggested a more conservative use of water for Q. ilex at ND compared to D stand that may be genetically driven. Q. ilex at ND resulted more resilient to drought compared to trees at D, probably thanks to its safer water strategy. Genotyping analysis based on simple-sequence repeat (SSR) markers revealed the presence of different Q. ilex populations at D and ND stands. Our study shows intraspecific variations in drought response among trees grown in close. In addition, it highlights the potential of combining tree-ring δ13C data with SSR genotyping for the selection of seed-bearing genotypes aimed to preserve Mediterranean holm oak ecosystem and improve its forest management.

Effects of landscape simplicity on crop yield: A reanalysis of a global database

Ecological theory on diversity suggests that agriculture requires sufficient biodiversity, ecological function, and critical ecosystem services to remain sustainable and resilient. As such, research related to the effect of ecosystem services and diversity on crop yields has increased significantly in the past decade. One such study by Dainese and colleagues that presented a global synthesis of a compiled database of 1,475 crop experiments related to pollination and pest control ecosystem services and crop yields quickly garnered attention in the literature with more than 540 citations since its publication in 2019. Given the strong influence of this study on the research on diversity and agricultural production, we conduct a reanalysis on the publicly available dataset from the global synthesis study to test the robustness of findings to modeling approach and assumptions. In our reanalysis we apply ordinary least squares regression methods rather than Bayesian path analysis to the same data to examine the robustness of observed field-scale landscape diversity-ecosystem services-crop yield relationships. The result of our reanalysis supports the findings of Dainese and colleagues, illustrating the robustness of findings that suggest that increasing landscape simplicity is associated with lower rates of pollination and pest control ecosystem service provisioning and lower crop yields. However, our analyses also suggest that provisioning of pollination and pest control services account for only a small fraction of the total effect of landscape simplicity on crop yields. Furthermore, we find that management and soil health may mediate the effects of landscape simplicity on ecosystem services and crop yields. While our results complement previous findings for landscape simplicity and ecosystem services, they also indicate that above and below ground ecosystem services are not mutually exclusive but concurrently contribute to support crop production in agriculture.

Forgotten giants: Robust climate signal in pollarded trees

Tree ring records are among the most valuable resources to create high-resolution climate reconstructions. Most climate reconstructions are based on old trees growing in inaccessible mountainous areas with low human activity. Therefore, reconstruction of climate conditions in lowlands is usually based on data from distant mountains. Albeit old trees can be common in humanized areas, they are not used for climate reconstructions. Pollarding was a common traditional management in Europe that enabled trees to maintain great vitality for periods exceeding the longevity of unmanaged trees. We evaluate the potential of pollarded deciduous oaks to record past climate signal. We sampled four pollarded woodlands in Central Spain under continental Mediterranean climate. We hypothesized that pollarded trees have a strong response to water availability during current period without pollarding management, but also in the period under traditional management if pruning was asynchronous among trees. Moreover, we hypothesized that if climate is a regional driver of oak secondary growth, chronologies from different woodlands will be correlated. Pollard oaks age exceeded 500 years with a strong response to Standardized Precipitation-Evapotranspiration Index (SPEI) from 9 to 11 months. Climate signal was exceptionally high in three of the sites (r2 = 0.443-0.655) during low management period (1962-2022). The largest fraction of this climate signal (≈70 %) could be retrieved during the traditional management period (1902-1961) in the three sites where pollarding was asynchronous. Chronologies were significantly correlated since the 19th century for all the studied period, highlighting a shared climate forcing. We identified critical points to optimize pollard tree sampling schema. Our results show the enormous potential of pollarded woodlands to reconstruct hydroclimate conditions in the Mediterranean with a fine spatial grain. Studying pollarded trees is an urgent task, since the temporal window to retrieve the valuable information in pollarded trees is closing as these giants collapse and their wood rots.

Climate-dependent responses of root and shoot biomass to drought duration and intensity in grasslands-a meta-analysis

Understanding the effects of altered precipitation regimes on root biomass in grasslands is crucial for predicting grassland responses to climate change. Nonetheless, studies investigating the effects of drought on belowground vegetation have produced mixed results. In particular, root biomass under reduced precipitation may increase, decrease or show a delayed response compared to shoot biomass, highlighting a knowledge gap in the relationship between belowground net primary production and drought. To address this gap, we conducted a meta-analysis of nearly 100 field observations of grassland root and shoot biomass changes under experimental rainfall reduction to disentangle the main drivers behind grassland responses to drought. Using a response-ratio approach we tested the hypothesis that water scarcity would induce a decrease in total biomass, but an increase in belowground biomass allocation with increased drought length and intensity, and that climate (as defined by the aridity index of the study location) would be an additional predictor. As expected, meteorological drought decreased root and shoot biomass, but aboveground and belowground biomass exhibited contrasting responses to drought duration and intensity, and their interaction with climate. In particular, drought duration had negative effects on root biomass only in wet climates while more intense drought had negative effects on root biomass only in dry climates. Shoot biomass responded negatively to drought duration regardless of climate. These results show that long-term climate is an important modulator of belowground vegetation responses to drought, which might be a consequence of different drought tolerance and adaptation strategies. This variability in vegetation responses to drought suggests that physiological plasticity and community composition shifts may mediate how climate affects carbon allocation in grasslands, and thus ultimately carbon storage in soil.

Product level dataset on embodied human appropriation of net primary production

This dataset includes data on the embodied human appropriation of net primary production (eHANPP) associated with products derived from agriculture and forestry. The human appropriation of net primary production (HANPP) is an indicator of changes in the yearly availability of biomass energy from photosynthesis that remains available in terrestrial ecosystems after harvest, under current land use, compared to the net primary production of the potential natural vegetation. HANPP is an indicator of land-use intensity that is relevant for biodiversity and biogeochemical cycles. The eHANPP indicator allocates HANPP to products and allows tracing trade flows from origin (the country where production takes place) to consumption (the country where products are consumed), thereby underpinning research into the telecouplings in global land use. The datasets described in this article trace eHANPP associated with the bilateral trade flows between 222 countries. It covers 161 primary crops, 13 primary animal products and 4 primary forestry products, as well as the end uses of these products for the years 1986 to 2013.

High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset

Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981-2020). The dataset development involves four main steps: (i) quality control; (ii) gap-filling; (iii) homogenisation of weather stations, and (iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adequately captures complex climatology at high spatiotemporal resolution and therefore provides a substantial improvement for numerous applications at local-regional level. This is particularly useful in view of data scarcity and urgently needed model-based decision making for climate change, water balance and ecosystem assessment studies in Peru.

Quantifying the influence of climate, host and change in land-use patterns on occurrence of Crimean Congo Hemorrhagic Fever (CCHF) and development of spatial risk map for India

Crimean Congo Hemorrhagic Fever (CCHF), is an emerging zoonosis globally and in India. The present study focused on identifying the risk factors for occurrence of CCHF in the Indian state of Gujarat and development of risk map for India. The past CCHF outbreaks in India were collated for the analyses. Influence of land use change and climatic factors in determining the occurrence of CCHF in Gujarat was assessed using Bayesian spatial models. Change in maximum temperature in affected districts was analysed to identify the significant change points over 110 years. Risk map was developed for Gujarat using Bayesian Additive Regression Trees (BART) model with remotely sensed environmental variables and host (livestock and human) factors. We found the change in land use patterns and maximum temperature in affected districts to be contributing to the occurrence of CCHF in Gujarat. Spatial risk map developed using CCHF occurrence data for Gujarat identified density of buffalo, minimum land surface temperature and elevation as risk determinants. Further, spatial risk map for the occurrence of CCHF in India was developed using selected variables. Overall, we found that combination of factors such as change in land-use patterns, maximum temperature, buffalo density, day time minimum land surface temperature and elevation led to the emergence and further spread of the disease in India. Mitigation measures for CCHF in India could be designed considering disease epidemiology and initiation of surveillance strategies based on the risk map developed in this study.

Increasing volatility of reconstructed Morava River warm-season flow, Czech Republic

Study region

The Morava River basin, Czech Republic, Danube Basin, Central Europe.

Study focus

Hydrological summer extremes represent a prominent natural hazard in Central Europe. River low flows constrain transport and water supply for agriculture, industry and society, and flood events are known to cause material damage and human loss. However, understanding changes in the frequency and magnitude of hydrological extremes is associated with great uncertainty due to the limited number of gauge observations. Here, we compile a tree-ring network to reconstruct the July-September baseflow variability of the Morava River from 1745 to 2018 CE. An ensemble of reconstructions was produced to assess the impact of calibration period length and trend on the long-term mean of reconstruction estimates. The final estimates represent the first baseflow reconstruction based on tree rings from the European continent. Simulated flows and historical documentation provide quantitative and qualitative validation of estimates prior to the 20th century.

New hydrological insights for the region

The reconstructions indicate an increased variability of warm-season flow during the past 100 years, with the most extreme high and low flows occurring after the start of instrumental observations. When analyzing the entire reconstruction, the negative trend in baseflow displayed by gauges across the basin after 1960 is not unprecedented. We conjecture that even lower flows could likely occur in the future considering that pre-instrumental trends were not primarily driven by rising temperature (and the evaporative demand) in contrast to the recent trends.

Greening of human-dominated ecosystems in India

Satellite data show the Earth has been greening and identify croplands in India as one of the most prominent greening hotspots. Though India's agriculture has been dependent on irrigation enhancement to reduce crop water stress and increase production, the spatiotemporal dynamics of how irrigation influenced the satellite observed greenness remains unclear. Here, we use satellite-derived leaf area data and survey-based agricultural statistics together with results from state-of-the-art Land Surface Models (LSM) to investigate the role of irrigation in the greening of India's croplands. We find that satellite observations provide multiple lines of evidence showing strong contributions of irrigation to significant greening during dry season and in drier environments. The national statistics support irrigation-driven yield enhancement and increased dry season cropping intensity. These suggest a continuous shift in India's agriculture toward an irrigation-driven dry season cropping system and confirm the importance of land management in the greening phenomenon. However, the LSMs identify CO2 fertilization as a primary driver of greening whereas land use and management have marginal impacts on the simulated leaf area changes. This finding urges a closer collaboration of the modeling, Earth observation, and land system science communities to improve representation of land management in the Earth system modeling.

A new approach to estimate soil organic carbon content targets in European croplands topsoils

Adopting land management practices that increase the stock of soil organic carbon (SOC) in croplands is widely promoted as a win-win strategy to enhance soil health and mitigate climate change. In this context, the definition of reference SOC content and stock values is needed to provide reliable targets to farmers, policymakers, and stakeholders. In this study, we used the LUCAS dataset to compare different methods for evaluating reference SOC content and stock values in European croplands topsoils (0-20 cm depth). Methods gave generally similar estimates although being built on very different assumptions. In the absence of an objective criterion to establish which approach is the most suitable to determine SOC reference values, we propose an ensemble modelling approach that consists in extracting the estimates using different relevant methods and retaining the median value among them. Interestingly, this approach led us to select values from the three different approaches with similar frequencies. Using estimated bulk density values, we obtained a first rough estimate of 3.5 Gt C of SOC storage potential in the cropland topsoils that we interpret as a long-term aspirational target that would be reachable only under extreme changes in agricultural practices. The use of additional methods in the ensemble modelling approach and more valid statistical spatial estimates may further refine our approach designed for the estimation of SOC reference values for croplands.

Increased aerosol scattering contributes to the recent monsoon rainfall decrease over the Gangetic Plain

The climate effects of atmospheric aerosols remain highly uncertain. Part of the uncertainty arises from the fact that scattering and absorbing aerosols have distinct or even opposite effects. Thus their relative fraction is critical in determining the overall aerosol climate effect. This study combines observations and global model simulations to demonstrate that changes in the fraction of scattering and absorbing aerosols play an important role in driving the monsoon precipitation decrease over northern India since the 1980s, especially over the Gangetic Basin. Increased aerosol scattering, or decreased aerosol absorption, manifested as a significant increase of aerosol single scattering albedo (SSA), causes strong cooling in the upper atmosphere. This suppresses vertical convection and thus reduces precipitation. Further analysis of the Couple Model Intercomparison Project Phase 6 multi-model-mean historical simulation shows that failing to capture the SSA increase over northern India is likely an important cause of the simulated precipitation trend bias in this area.

Historical and projected evolutions of glaciers in response to climate change in High Mountain Asia

Glacier changes are regarded as the conspicuous icon of climate change in High Mountain Asia (HMA) alpine environments. Multi-temporal glacier coverage is essential for mass balance estimations and understanding glacial changes in response to climate variability. However, consistent multi-temporal glacier area datasets across the HMA are limited due to challenges posed by seasonal snow and cloud cover in remote sensing satellite images. In this study, a new method is proposed to estimate glacier and nonseasonal snow (GNS) areas by combining the normalized difference snow index (NDSI) and bright temperature bands from Landsat remote sensing images in 1990s, 2000s, 2010s and 2020s. Meanwhile, the integrated ice dynamic Open Global Glacier Model (OGGM) is applied to simulate nine typical glaciers and project their changes using 15 general circulation models (GCMs) under four shared socioeconomic pathways (SSPs). The results showed that total GNS areas in HMA were estimated at 71.26 × 103 km2 in the 1990s, and decreased to 53.17 × 103 km2 in the 2020s. Massive proportions of GNS were mainly distributed in the southeast and northwest region. Tipping points of GNS indicating a transition from mass balance gain to loss during four periods were detected in the Upper Tarim basin, Inner Tibetan Plateau, Upper Yangtze river basin, Upper Brahmaputra river basin, and Upper Salween river basin. The reduction of total GNS area was attributed to significantly rising temperature and decreasing solid precipitation. The projections indicate that glacier shrinkages will be the dominant trend in the future, and most typical glaciers will experience a mass loss of 60% under SSP245 (SSP2-RCP4.5) and over 90% under SSP585 (SSP5-RCP8.5) by the end of the century. The response of glaciers to climate change is also found to be influenced by factors including local topography and elevation ranges. The findings of this study provides a better understanding of glacial dynamics in response to climate change and highlights the importance of making water resources management strategies to mitigate the influence of glacier retreat.

Reversed asymmetric warming of sub-diurnal temperature over land during recent decades

In the latter half of the twentieth century, a significant climate phenomenon "diurnal asymmetric warming" emerged, wherein global land surface temperatures increased more rapidly during the night than during the day. However, recent episodes of global brightening and regional droughts and heatwaves have brought notable alterations to this asymmetric warming trend. Here, we re-evaluate sub-diurnal temperature patterns, revealing a substantial increase in the warming rates of daily maximum temperatures (Tmax), while daily minimum temperatures have remained relatively stable. This shift has resulted in a reversal of the diurnal warming trend, expanding the diurnal temperature range over recent decades. The intensified Tmax warming is attributed to a widespread reduction in cloud cover, which has led to increased solar irradiance at the surface. Our findings underscore the urgent need for enhanced scrutiny of recent temperature trends and their implications for the wider earth system.

Size-focused conservation may fail to protect the world's oldest trees

Old trees are irreplaceable natural resources that provide multifaceted benefits to humans. Current conservation strategies focus primarily on large-sized trees that were often considered old. However, some studies have demonstrated that small trees can be more than thousands of years old, suggesting that conventional size-focused perceptions may hamper the efficiency of current conservation strategies for old trees. Here, we compiled paired age and diameter data using tree-ring records sampled from 121,918 trees from 269 species around the world to detect whether tree size is a strong predictor of age for old trees and whether the spatial distribution of small old trees differs from that of large old trees. We found that tree size was a weak predictor of age for old trees, and diameter explained only 10% of the total age variance of old trees. Unlike large-sized trees that are mainly in warm, wet environments and protected, small old trees are predominantly in cold, dry environments and mostly unprotected, indicating that size-focused conservation failed to protect some of the oldest trees. To conserve old trees, comprehensive old-tree recognition systems are needed that consider not only tree size but also age and external characteristics. Protected areas designed for small old trees are urgently needed.

Southern Hemisphere dominates recent decline in global water availability

Global land water underpins livelihoods, socioeconomic development, and ecosystems. It remains unclear how water availability has changed in recent decades. Using an ensemble of observations, we quantified global land water availability over the past two decades. We show that the Southern Hemisphere has dominated the declining trend in global water availability from 2001 to 2020. The significant decrease occurs mainly in South America, southwestern Africa, and northwestern Australia. In the Northern Hemisphere, the complex regional increasing and decreasing trends cancel each other, resulting in a negligible hemispheric trend. The variability and trend in water availability in the Southern Hemisphere are largely driven by precipitation associated with climate modes, particularly the El Niño-Southern Oscillation. This study highlights their dominant role in controlling global water availability.

Widespread synchronous decline of Mediterranean-type forest driven by accelerated aridity

Large-scale, abrupt ecosystem change in direct response to climate extremes is a critical but poorly documented phenomenon1. Yet, recent increases in climate-induced tree mortality raise concern that some forest ecosystems are on the brink of collapse across wide environmental gradients2,3. Here we assessed climatic and productivity trends across the world's five Mediterranean forest ecosystems from 2000 to 2021 and detected a large-scale, abrupt forest browning and productivity decline in Chile (>90% of the forest in <100 days), responding to a sustained, acute drought. The extreme dry and warm conditions in Chile, unprecedented in the recent history of all Mediterranean-type ecosystems, are akin to those projected to arise in the second half of the century4. Long-term recovery of this forest is uncertain given an ongoing decline in regional water balance. This dramatic plummet of forest productivity may be a spyglass to the future for other Mediterranean ecosystems.

SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies

A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global product that estimates rainfall from satellite soil moisture observations. Previous studies have demonstrated the SM2RAIN products' high potential in estimating rainfall around the world. This manuscript describes the SM2RAIN-Climate rainfall product, which uses the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture v06.1 to provide monthly global rainfall for the 24-year period 1998-2021 at 1-degree spatial resolution. The assessment of the proposed rainfall dataset against different existing state-of-the-art rainfall products exhibits the robust performance of SM2RAIN-Climate in most regions of the world. This performance is indicated by correlation coefficients between SM2RAIN-Climate and state-of-the-art products, consistently exceeding 0.8. Moreover, evaluation results indicate the potential of SM2RAIN-Climate as an independent rainfall product from other satellite rainfall products in capturing the pattern of global rainfall trend.

Were climatic forcings the main driver for mid-holocene changes in settlement dynamics on the Varamin Plain (Central Iranian Plateau)?

Settlement crises in ancient cultures of Western Asia are commonly thought to be caused by climatic events such as severe droughts. However, the insufficient climate proxy situation in this region challenges the inference of clear relationships between climate and settlement dynamics. We investigate the Holocene climatic changes on the Varamin Plain in the context of the climatic history of Western Central Asia by using a transient comprehensive Earth System Model simulation (8 ka BP to pre-industrial), a high-resolution regional snapshot simulation and a synthesis of pollen-based climate reconstructions. In line with the reconstructions, the models reveal only slightly varying mean climatic conditions on the Varamin Plain but indicate substantial changes in seasonality during the Holocene. Increased precipitation during spring, combined with lower temperature and potentially stronger snow accumulation on the upstream Alborz mountains may have led to an increased water supply on the alluvial fan during the vegetation period and thus to more favourable conditions for agricultural production during the Mid-Holocene compared to modern times. According to the model, dry periods on the Central Iranian Plateau are related to particularly weak Westerly winds, fostering the subsidence in the mid-troposphere and hampering precipitation over the region. The model reveals that dry periods have spatially heterogenous manifestations, thus explaining why they do not appear in all proxy records in the wider study region. In fact, the climatic signal may depend on local environmental conditions. The interaction of the topography with the atmospheric circulation leads to additional spatial heterogeneity. Although our results provide several indications for a connection between climate and settlement dynamics, the small overall changes in moisture call into question whether climate is the main driver for settlement discontinuities on the Central Iranian Plateau. To shed further light on this issue, more high-resolution long-term proxy records are needed.

Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution

A fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.

Dry growing seasons predicted Central American migration to the US from 2012 to 2018

Controlling for factors such as criminal violence and poverty, we tested if drier than usual growing season weather was a predictor of emigration from El Salvador, Guatemala, and Honduras to the US between 2012 and 2018. We focus on growing season weather because agriculture is a primary transmission pathway from the effects of climate change upon migration. We secured the migration apprehensions data for our analysis through a FOIA request to US Customs and Border Protection. Border Patrol intake interviews recorded the original home location of families that arrived at the southern US border. We used this geographic information to measure recent weather patterns and social circumstances in the area that each family departed. We found 70.7% more emigration to the US when local growing seasons in Central America were recently drier than the historical average since 1901.

Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones

Unprecedented global vegetation greening during past decades is well known to affect annual and seasonal land surface temperatures (LST). However, the impact of observed vegetation cover change on diurnal LST across global climatic zones is not well understood. Using global climatic time-series datasets, we investigated the long-term growing season daytime and nighttime LST changes globally and explored associated dominant contributors including vegetation and climate factors including air temperature, precipitation, and solar radiation. Results revealed asymmetric growing season mean daytime and nighttime LST warming (0.16 °C/10a and 0.30 °C/10a, respectively) globally from 2003 to 2020, as a result, the diurnal LST range (DLSTR) declined at 0.14 °C/10a. The sensitivity analysis indicated the LST response to changes in LAI, precipitation, and SSRD mainly concentrated during daytime instead of nighttime, however, which showed comparable sensitivities for air temperature. Combining the sensitivities results and the observed LAI and climate trends, we found rising air temperature contributes to 0.24 ± 0.11 °C/10a global daytime LST warming and 0.16 ± 0.07 °C/10a nighttime LST warming, turns to be the dominant contributor to the LST changes. Increased LAI cooled global daytime LST (-0.068 ± 0.096 °C/10a) while warmed nighttime LST (0.064 ± 0.046 °C/10a); hence LAI dominates declines in DLSTR trends (-0.12 ± 0.08 °C/10a), despite some day-night process variations across climate zones. In Boreal regions, reduced DLSTR was due to nighttime warming from LAI increases. In other climatic zones, daytime cooling, and DLSTR decline, was induced by increased LAI. Biophysically, the pathway from air temperature heats the surface through sensible heat and increased downward longwave radiation during day and night, while the pathway from LAI cools the surface by enhancing energy redistribution into latent heat rather than sensible heat during the daytime. These empirical findings of diverse asymmetric responses could help calibrate and improve biophysical models of diurnal surface temperature feedback in response to vegetation cover changes in different climate zones.

Historical disconnection from floodplain alters riparian forest composition, tree growth and deadwood amount

Riparian forests are among the most dynamic but threatened terrestrial ecosystems. Their dynamism and conservation depend on historical changes in river geomorphology, which can be evaluated through changes in channel sinuosity. However, we lack long-term assessments on sinuosity and how they impact riparian forest composition, tree growth and deadwood amount. To fill this research gap, we reconstructed river sinuosity in 14 sites across the middle Ebro basin, north-eastern Spain, using historical aerial photographs taken in 1927, 1956, 1998-2003 and 2014-2015. Relationships between sinuosity, stand composition and deadwood amount and decay degree were calculated. We also reconstructed radial growth of the major tree species (Populus alba, Populus nigra, Fraxinus angustifolia, Salix alba and Ulmus minor) in two sites to evaluate how coupled it was with changes in river flow after dam building. From 1927 to 2015, sinuosity decreased passing from 1.39 to 1.20. The river dynamics were altered in the 1950s and 1960s after dam and dyke building. Sites with high sinuosity values in 1956 corresponded to mature stands with large P. nigra individuals. Sinuosity was negatively related to F. angustifolia (rs = -0.83, p < 0.001) and P. alba (rs = -0.64, p = 0.02) abundance, whereas sites dominated by P. alba and U. minor presented abundant decayed deadwood. A loss of sinuosity and a contraction of the riverbank gradient increased disconnection of active channel from floodplain, with a mixing of more (e.g., P. nigra) and less phreatophytic species (e.g., U. minor). River flow diversion reduced growth and increased the tree-to-tree P. alba growth coherence. Hydrological droughts contributed to growth decline and dieback of U. minor, which is sensitive to spring river flow. Conservation and restoration of riparian forests must consider historical changes in river geomorphology related to human activities.

Relationships of stomatal morphology to the environment across plant communities

The relationship between stomatal traits and environmental drivers across plant communities has important implications for ecosystem carbon and water fluxes, but it has remained unclear. Here, we measure the stomatal morphology of 4492 species-site combinations in 340 vegetation plots across China and calculate their community-weighted values for mean, variance, skewness, and kurtosis. We demonstrate a trade-off between stomatal density and size at the community level. The community-weighted mean and variance of stomatal density are mainly associated with precipitation, while that of stomatal size is mainly associated with temperature, and the skewness and kurtosis of stomatal traits are less related to climatic and soil variables. Beyond mean climate variables, stomatal trait moments also vary with climatic seasonality and extreme conditions. Our findings extend the knowledge of stomatal trait-environment relationships to the ecosystem scale, with applications in predicting future water and carbon cycles.

Water-energy-vegetation nexus explain global geographical variation in surface urban heat island intensity

Surface urban heat island (SUHI) is a key climate risk associated with urbanization. Previous case studies have suggested that precipitation (water), radiation (energy), and vegetation have important effects on urban warming, but there is a lack of research that combines these factors to explain the global geographic variation in SUHI intensity (SUHII). Here, we utilize remotely sensed and gridded datasets to propose a new water-energy-vegetation nexus concept that explains the global geographic variation of SUHII across four climate zones and seven major regions. We found that SUHII and its frequency increase from arid zones (0.36 ± 0.15 °C) to humid zones (2.28 ± 0.10 °C), but become weaker in the extreme humid zones (2.18 ± 0.15 °C). We revealed that from semi-arid/humid to humid zones, high precipitation is often coupled with high incoming solar radiation. The increased solar radiation can directly enhance the energy in the area, leading to higher SUHII and its frequency. Although solar radiation is high in arid zones (mainly in West, Central, and South Asia), water limitation leads to sparse natural vegetation, suppressing the cooling effect in rural areas and resulting in lower SUHII. In extreme humid regions (mainly in tropical areas), incoming solar radiation tends to flatten out, which, coupled with increased vegetation as hydrothermal conditions become more favorable, leads to more latent heat and reduces the intensity of SUHI. Overall, this study offers empirical evidence that the water-energy-vegetation nexus highly explains the global geographic variation of SUHII. The results can be used by urban planners seeking optimal SUHI mitigation strategies and for climate change modeling work.

Vegetation as the catalyst for water circulation on global terrestrial ecosystem

Global climate change is expected to further intensify the global water cycle, leading to more rapid evaporation and more intense precipitation. At the same time, the growth and expansion of natural vegetation caused by climate change and human activities create potential conflicts between ecosystems and humans over available water resources. Clarifying how terrestrial ecosystem evapotranspiration responds to global precipitation and vegetation facilitates a better understanding of and prediction for the responses of global ecosystem energy, water, and carbon budgets under climate change. Relying on the spatial and temporal distribution of evapotranspiration, precipitation, and solar-induced chlorophyll fluorescence (SIF) from remote sensing platforms, we decouple the interaction mechanism of evapotranspiration, precipitation, and vegetation in linear and nonlinear scenarios using correlation and partial correlation analysis, multiple linear regression analysis, and binning. Major conclusions are as follows: (1) As a natural catalyst of the global water cycle, vegetation plays a crucial role in regulating the relationship between climate change and the water‑carbon-energy cycle. (2) Vegetation, a key parameter affecting the water cycle, participates in the entire water cycle process. (3) The increase in vegetation productivity and photosynthesis plays a dominant role in promoting evapotranspiration in vegetated areas, while the increase in precipitation dominates the promotion of evapotranspiration in non-vegetated areas.

Ambient temperatures associated with reduced cognitive function in older adults in China

The cognitive function status of older adults determines the social function and living quality of older adults, which is related to the healthy development and stability of the society. However, the impact of high or low ambient temperature on cognitive function in older adults remains unclear. Based on data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), we comprehensively assessed the impact of ambient temperature on the cognitive function of older adults in this study. The findings exhibited that for each 1 °C ascent in monthly temperature of high temperature, the examination score of global cognitive function of older adults decreased by 0.48 (95% CI 0.21-0.74), which was greater than that of 0.14 (95% CI 0.06-0.25) for each 1 °C reduction in low temperature. Overall, the detrimental effect of high temperature on cognitive function in older adults was more significant than that of low temperature, including on the five sub-cognitive functions involved. Our research provides vital technical guidance and reference for the health protection and prevention of cognitive function of older adults in specific external environmental conditions under the current climatic variation and temperature rise.

Key tropical crops at risk from pollinator loss due to climate change and land use

Insect pollinator biodiversity is changing rapidly, with potential consequences for the provision of crop pollination. However, the role of land use-climate interactions in pollinator biodiversity changes, as well as consequent economic effects via changes in crop pollination, remains poorly understood. We present a global assessment of the interactive effects of climate change and land use on pollinator abundance and richness and predictions of the risk to crop pollination from the inferred changes. Using a dataset containing 2673 sites and 3080 insect pollinator species, we show that the interactive combination of agriculture and climate change is associated with large reductions in insect pollinators. As a result, it is expected that the tropics will experience the greatest risk to crop production from pollinator losses. Localized risk is highest and predicted to increase most rapidly, in regions of sub-Saharan Africa, northern South America, and Southeast Asia. Via pollinator loss alone, climate change and agricultural land use could be a risk to human well-being.

Multidimensional responses of grassland stability to eutrophication

Eutrophication usually impacts grassland biodiversity, community composition, and biomass production, but its impact on the stability of these community aspects is unclear. One challenge is that stability has many facets that can be tightly correlated (low dimensionality) or highly disparate (high dimensionality). Using standardized experiments in 55 grassland sites from a globally distributed experiment (NutNet), we quantify the effects of nutrient addition on five facets of stability (temporal invariability, resistance during dry and wet growing seasons, recovery after dry and wet growing seasons), measured on three community aspects (aboveground biomass, community composition, and species richness). Nutrient addition reduces the temporal invariability and resistance of species richness and community composition during dry and wet growing seasons, but does not affect those of biomass. Different stability measures are largely uncorrelated under both ambient and eutrophic conditions, indicating consistently high dimensionality. Harnessing the dimensionality of ecological stability provides insights for predicting grassland responses to global environmental change.