State-of-the-art global models underestimate impacts from climate extremes

Unravelling the neglected role of ultraviolet radiation on stomata: A meta-analysis with implications for modelling ecosystem-climate interactions

Stomata play a pivotal role in regulating gas exchange between plants and the atmosphere controlling water and carbon cycles. Accordingly, we investigated the impact of ultraviolet-B radiation, a neglected environmental factor varying with ongoing global change, on stomatal morphology and function by a Comprehensive Meta-Analysis. The overall UV effect at the leaf level is to decrease stomatal conductance, stomatal aperture and stomatal size, although stomatal density was increased. The significant decline in stomatal conductance is marked (6% in trees and >10% in grasses and herbs) in short-term experiments, with more modest decreases noted in long-term UV studies. Short-term experiments in growth chambers are not representative of long-term field UV effects on stomatal conductance. Important consequences of altered stomatal function are hypothesized. In the short term, UV-mediated stomatal closure may reduce carbon uptake but also water loss through transpiration, thereby alleviating deleterious effects of drought. However, in the long term, complex changes in stomatal aperture, size, and density may reduce the carbon sequestration capacity of plants and increase vegetation and land surface temperatures, potentially exacerbating negative effects of drought and/or heatwaves. Therefore, the expected future strength of carbon sink capacity in high-UV regions is likely overestimated.

Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years

Climate change-induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (Nw). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in Nw, ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme Nw. This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme Nw with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

Association between temperature and mortality: a multi-city time series study in Sichuan Basin, southwest China

BACKGROUND

There are few multi-city studies on the association between temperature and mortality in basin climates. This study was based on the Sichuan Basin in southwest China to assess the association of basin temperature with non-accidental mortality in the population and with the temperature-related mortality burden.

METHODS

Daily mortality data, meteorological and air pollution data were collected for four cities in the Sichuan Basin of southwest China. We used a two-stage time-series analysis to quantify the association between temperature and non-accidental mortality in each city, and a multivariate meta-analysis was performed to obtain the overall cumulative risk. The attributable fractions (AFs) were calculated to access the mortality burden attributable to non-optimal temperature. Additionally, we performed a stratified analyses by gender, age group, education level, and marital status.

RESULTS

A total of 751,930 non-accidental deaths were collected in our study. Overall, 10.16% of non-accidental deaths could be attributed to non-optimal temperatures. A majority of temperature-related non-accidental deaths were caused by low temperature, accounting for 9.10% (95% eCI: 5.50%, 12.19%), and heat effects accounted for only 1.06% (95% eCI: 0.76%, 1.33%). The mortality burden attributable to non-optimal temperatures was higher among those under 65 years old, females, those with a low education level, and those with an alternative marriage status.

CONCLUSIONS

Our study suggested that a significant association between non-optimal temperature and non-accidental mortality. Those under 65 years old, females, and those with a low educational level or alternative marriage status had the highest attributable burden.

Exploring interactions between socioeconomic context and natural hazards on human population displacement

Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone. For this purpose, a large, harmonized, global database of disaster-induced movements in the presence of floods, storms, and landslides during 2016-2021 is presented. We account for environmental, societal, and economic factors to predict the number of displaced persons per event in the affected regions. Here we show that displacements can be primarily attributed to the combination of poor household conditions and intense precipitation, as revealed through the interpretation of the trained models using both Shapley values and causality-based methods. We hence provide empirical evidence that differential or uneven vulnerability exists and provide a means for its quantification, which could help advance evidence-based mitigation and adaptation planning efforts.

Host-Parasitoid Phenology, Distribution, and Biological Control under Climate Change

Climate change raises a serious threat to global entomofauna-the foundation of many ecosystems-by threatening species preservation and the ecosystem services they provide. Already, changes in climate-warming-are causing (i) sharp phenological mismatches among host-parasitoid systems by reducing the window of host susceptibility, leading to early emergence of either the host or its associated parasitoid and affecting mismatched species' fitness and abundance; (ii) shifting arthropods' expansion range towards higher altitudes, and therefore migratory pest infestations are more likely; and (iii) reducing biological control effectiveness by natural enemies, leading to potential pest outbreaks. Here, we provided an overview of the warming consequences on biodiversity and functionality of agroecosystems, highlighting the vital role that phenology plays in ecology. Also, we discussed how phenological mismatches would affect biological control efficacy, since an accurate description of stage differentiation (metamorphosis) of a pest and its associated natural enemy is crucial in order to know the exact time of the host susceptibility/suitability or stage when the parasitoids are able to optimize their parasitization or performance. Campaigns regarding landscape structure/heterogeneity, reduction of pesticides, and modelling approaches are urgently needed in order to safeguard populations of natural enemies in a future warmer world.

Differential responses of CO2 and latent heat fluxes to climatic anomalies on two alpine grasslands on the northeastern Qinghai-Tibetan Plateau

Discrete extreme heat events, deluges, and droughts will become more frequent and disproportionately affect the processes and functions of grassland ecosystems. Here, we compared the responses of CO2 and heat fluxes to natural extreme events in 2016 in a lower alpine meadow and neighboring upper shrubland on the northeastern Qinghai-Tibetan Plateau. Unlike insensitive sensible heat flux, latent heat flux (LE) increased by 21.8 % in the meadow and by 56.4 % in the shrubland during a dry period and subsequent compound hot-dry period in August. Changes (Δ, data for 2016 minus the corresponding means from other years) in the heat flux at both sites were determined by changes in solar radiation (ΔSwin), as sufficient soil moisture was available. ΔLE was more sensitive to ΔSwin in the open-canopy shrubland, reflecting its greater capacity for evaporative cooling to buffer climate anomalies. CO2 fluxes responded weakly to extreme wet or dry events but strongly when those events were accompanied by exceptional heat. During single or compound hot events, the mean changes in total ecosystem respiration (ΔTER) increased by about 30 % in both grasslands, although ΔTER was more sensitive to changes in the topsoil temperature in the more productive meadow than in the shrubland. The mean changes in gross primary productivity (ΔGPP) fluctuated by <10 % in the warmer meadow but increased by 29.3 % in the cooler shrubland relative to the respective baseline, probably because of the differences in canopy structure and root depth and the consequent high-temperature stress on vegetation photosynthesis. The changes in net ecosystem CO2 exchange (ΔNEE) were significantly related to ΔTER in the meadow and increased by 55.8 %, whereas ΔNEE was controlled mainly by ΔGPP in the shrubland and decreased by 22.4 %. Overall, both alpine grasslands were resistant to rainfall anomalies but susceptible to exceptional warmth, with the differential responses being ascribed to canopy structure and root depth. Our results provide helpful insights based on which the carbon sequestration and water-holding functions of alpine grasslands during future climate change can be predicted.

Detect and attribute the extreme maize yield losses based on spatio-temporal deep learning

Providing accurate crop yield estimations at large spatial scales and understanding yield losses under extreme climate stress is an urgent challenge for sustaining global food security. While the data-driven deep learning approach has shown great capacity in predicting yield patterns, its capacity to detect and attribute the impacts of climatic extremes on yields remains unknown. In this study, we developed a deep neural network based multi-task learning framework to estimate variations of maize yield at the county level over the US Corn Belt from 2006 to 2018, with a special focus on the extreme yield loss in 2012. We found that our deep learning model hindcasted the yield variations with good accuracy for 2006-2018 (R2 = 0.81) and well reproduced the extreme yield anomalies in 2012 (R2 = 0.79). Further attribution analysis indicated that extreme heat stress was the major cause for yield loss, contributing to 72.5% of the yield loss, followed by anomalies of vapor pressure deficit (17.6%) and precipitation (10.8%). Our deep learning model was also able to estimate the accumulated impact of climatic factors on maize yield and identify that the silking phase was the most critical stage shaping the yield response to extreme climate stress in 2012. Our results provide a new framework of spatio-temporal deep learning to assess and attribute the crop yield response to climate variations in the data rich era.

Sustainable irrigation and climate feedbacks

Agricultural irrigation induces greenhouse gas emissions directly from soils or indirectly through the use of energy or construction of dams and irrigation infrastructure, while climate change affects irrigation demand, water availability and the greenhouse gas intensity of irrigation energy. Here, we present a scoping review to elaborate on these irrigation-climate linkages by synthesizing knowledge across different fields, emphasizing the growing role climate change may have in driving future irrigation expansion and reinforcing some of the positive feedbacks. This Review underscores the urgent need to promote and adopt sustainable irrigation, especially in regions dominated by strong, positive feedbacks.

Rice availability and stability in Africa under future socio-economic development and climatic change

As Africa is facing multiple challenges related to food security, frameworks integrating production and availability are urgent for policymaking. Attention should be given not only to gradual socio-economic and climatic changes but also to their temporal variability. Here we present an integrated framework that allows one to assess the impacts of socio-economic development, gradual climate change and climate anomalies. We apply this framework to rice production and consumption in Africa whereby we explicitly account for the continent's dependency on imported rice. We show that socio-economic development dictates rice availability, whereas climate change has only minor effects in the long term and is predicted not to amplify supply shocks. Still, rainfed-dominated or self-producing regions are sensitive to local climatic anomalies, while trade dominates stability in import-dependent regions. Our study suggests that facilitating agricultural development and limiting trade barriers are key in relieving future challenges to rice availability and stability.

Flood hazard potential reveals global floodplain settlement patterns

Flooding is one of the most common natural hazards, causing disastrous impacts worldwide. Stress-testing the global human-Earth system to understand the sensitivity of floodplains and population exposure to a range of plausible conditions is one strategy to identify where future changes to flooding or exposure might be most critical. This study presents a global analysis of the sensitivity of inundated areas and population exposure to varying flood event magnitudes globally for 1.2 million river reaches. Here we show that topography and drainage areas correlate with flood sensitivities as well as with societal behaviour. We find clear settlement patterns in which floodplains most sensitive to frequent, low magnitude events, reveal evenly distributed exposure across hazard zones, suggesting that people have adapted to this risk. In contrast, floodplains most sensitive to extreme magnitude events have a tendency for populations to be most densely settled in these rarely flooded zones, being in significant danger from potentially increasing hazard magnitudes given climate change.

Livestock exposure to future cumulated climate-related stressors in West Africa

A large range of climate change impacts is expected during the twenty-first century in vulnerable regions such as West Africa, where local populations largely rely on livestock systems as their main food production and income source. As climate change threatens livestock systems in various ways, here we assess how regional livestock could be exposed to cumulated climate-related stressors in the future. Using the world's largest multi-model climate impacts simulations database ISIMIP, we find that a large part of West Africa will experience at least 5-6 cumulated multiple climate stressors before the 2030s, including amplified severe heat stress conditions and flood risks. Consequently, about 30% of the current total regional livestock could be exposed to these cumulated stressors, with sheep and goat as the most affected species. This paper brings new quantifications that may help policy makers to prioritize decisions that could prepare local populations to face multiple climate-related impacts.

National Climate Change Risk Assessments to inform adaptation policy priorities and environmental sustainability outcomes: a knowledge systems perspective

National Climate Change Risk Assessments (CCRAs) have a key role in informing priorities for adaptation policy but face significant challenges due to multiple facets of risk and adaptation. Issues are especially pronounced for meeting goals of environmental sustainability due to the complex dynamics of socio-ecological systems. In practice, a CCRA can therefore differ from its original conceptual blueprint. These challenges are explored from a knowledge systems perspective, focusing on the role of stakeholders/policymakers, risk descriptors, methods, evidence sources, and scientists. A UK case study evaluates recent developments (CCRA3) including identification of policy urgency through adaptation shortfalls and its application to the natural environment. Important science-policy issues are also highlighted regarding inclusion of opportunities, systemic risks, residual risks, and risk tolerance. A general conclusion is that CCRAs inevitably leave open questions which lead back to their evolving role in the science-policy interface. A knowledge systems perspective identifies CCRAs as open, adaptive, reflexive processes that help redefine interpretations of risk and adaptation, rather than just providing a specific policy-relevant product. This perspective identifies scope for progressive refinement of CCRAs to enhance collective science-policy adaptive capacity whilst also engaging wider society. For environmental sustainability, this open process can be used to iteratively redefine robust future pathways and system reference conditions that also better reflect evolving societal perceptions and tolerance on sustainability risk in the face of climate change.

Hardship at birth alters the impact of climate change on a long-lived predator

Climate change is increasing the frequency of extreme events, such as droughts or hurricanes, with substantial impacts on human and wildlife communities. Extreme events can affect individuals through two pathways: by altering the fitness of adults encountering a current extreme, and by affecting the development of individuals born during a natal extreme, a largely overlooked process. Here, we show that the impact of natal drought on an avian predator overrode the effect of current drought for decades, so that individuals born during drought were disadvantaged throughout life. Incorporation of natal effects caused a 40% decline in forecasted population size and a 21% shortening of time to extinction. These results imply that climate change may erode populations more quickly and severely than currently appreciated, suggesting the urgency to incorporate “penalties” for natal legacies in the analytical toolkit of impact forecasts. Similar double impacts may apply to other drivers of global change.

The long-term effects of extreme climate events in early life are largely overlooked in forecasts of climate change impacts. Here, the authors show that raptorial red kites born during drought are disadvantaged throughout life, and including this climate legacy leads to substantial decreases in forecasted population size and time to extinction.

Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21^st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr⁻¹, reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr⁻¹) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake.

Accurate estimates of carbon fluxes are important to our understanding of the carbon cycle. Here, via model-data integration, the authors disentangle anthropogenic and environmental carbon flux contributions of terrestrial woody vegetation, and find that environmental processes are weaker and more susceptible to interannual variations and extreme events in the 21st century than previously estimated.

Implications of changes in climate and human development on 21st-century global drought risk

Climate change is expected to exacerbate drought conditions over many global regions. However, the future risk posed by droughts depends not only on the climate-induced changes but also on the changes in societal exposure and vulnerability to droughts. Here we illustrate how the consideration of human vulnerability alters global drought risk associated with runoff (hydrological) and soil moisture (agriculture) droughts during the 21st-century. We combine the changes in drought frequency, population growth, and human development as a proxy of vulnerability to project global drought risk under plausible climate and socioeconomic development pathways. Results indicate that the shift toward a pathway of high greenhouse gas emissions and socioeconomic inequality leads to i) increased population exposure to runoff and soil moisture droughts by 81% and seven folds, respectively, and ii) a stagnation of human development. These consequences are more pronounced for populations living in low than in very high human development countries. In particular, Sub-Saharan Africa and South Asia, where the majority of the world's less developed countries are located, fare the worst in terms of future drought risk. The disparity in risk between low and very high human development countries can be substantially reduced in the presence of a shift toward a world of rapid and sustainable development that actively reduces social inequality and emissions. Our results underscore the importance of rapid human development in hotspots of drought risk where effective adaptation is most needed to reduce future drought impacts.

Response of Spruce Forest Ecosystem CO2 Fluxes to Inter-Annual Climate Anomalies in the Southern Taiga

Climate extremes and anomalies modify the CO2 ecosystem–atmosphere exchange of the boreal forests and consequently alter the terrestrial carbon stocks and the atmospheric greenhouse gas concentrations. The effects of the anomalous weather conditions on the CO2 net ecosystem exchange (NEE), total ecosystem respiration (TER), and gross primary production (GPP) of the typical southern taiga nemorose spruce forest were analyzed using continuous eddy covariance flux measurements in the 2015–2020 period. The forest was found to be a source of atmospheric CO2 in 2016, 2017, 2019, and 2020 (the mean annual GPP/TER ratio was between 0.87 and 0.97). In 2018, the forest was found to act as a CO2 sink (GPP/TER = 1.47) when a positive temperature anomaly in the growing season was accompanied by a negative precipitation anomaly and increased global radiation. The early start of the CO2 uptake in the anomalously warm winter of 2019/2020 did not result in an increase in the annual GPP/TER ratio (0.90). The comparison of the flux data obtained from the nemorose spruce forest on the well-drained soils with the data obtained from paludified spruce forest in the same landscape showed that the mean annual GPP/TER ratio of the sites alternatively responded to the mentioned anomalies. This study suggests that a variety of soil moisture regimes across the southern taiga spruce forests provide a non-uniformity in the response reactions of the CO2 ecosystem–atmosphere exchange on the climate anomalies.

Regional and seasonal partitioning of water and temperature controls on global land carbon uptake variability

Global fluctuations in annual land carbon uptake (NEE_IAV) depend on water and temperature variability, yet debate remains about local and seasonal controls of the global dependences. Here, we quantify regional and seasonal contributions to the correlations of globally-averaged NEE_IAV against terrestrial water storage (TWS) and temperature, and respective uncertainties, using three approaches: atmospheric inversions, process-based vegetation models, and data-driven models. The three approaches agree that the tropics contribute over 63% of the global correlations, but differ on the dominant driver of the global NEE_IAV, because they disagree on seasonal temperature effects in the Northern Hemisphere (NH, >25°N). In the NH, inversions and process-based models show inter-seasonal compensation of temperature effects, inducing a global TWS dominance supported by observations. Data-driven models show weaker seasonal compensation, thereby estimating a global temperature dominance. We provide a roadmap to fully understand drivers of global NEE_IAV and discuss their implications for future carbon–climate feedbacks.

The dominant driver of variations in global land carbon sink remains unclear. Here the authors show that the seasonal compensation of temperature effects on land carbon sink in the Northern Hemisphere could induce a global water dominance.

Monitoring the Water Mass Balance Variability of Small Shallow Lakes by an ERA5-Land Reanalysis and Water Level Measurement-Based Model. An Application to the Trasimeno Lake, Italy

Climate change has a strong impact on inland water bodies such as lakes. This means that the increase in lake temperature recorded in recent decades-in Europe as well-can change the evaporation regime of the lakes. This, together with the variation of the water cycle, in particular precipitation, implies that the water mass balance of lakes may vary due to climate change. Water mass balance modeling is therefore of paramount importance to monitor lakes in the context of global warming. Although many studies have focused on such a modeling, there is no shared approach that can be used for any lake across the globe, irrespective of the size. This becomes even more problematic for shallow and small lakes, for which few studies exist. For this reason, in this paper the use of reanalysis data, in particular ERA5-Land provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), is proposed for the mass balance modeling. In fact, ERA5-Land has a global coverage and it is the only data source comprising a specific model for lakes, the Fresh-water Lake model (FLake). The chosen case study is the Trasimeno lake, a small and shallow lake located in Central Italy. The use of the reanalysis was preceded by data validation by considering both ground-based and satellite observations. The results show that there is a good agreement between the observed monthly variation of the lake level, ΔH, and the corresponding values of the water storage, δ, computed by means of the ERA5-Land data (Pearson coefficient larger than 70%). Discrepancies between observations and the ERA5-Land data happen in periods characterized in Europe by an extreme climate anomaly. This promising result encourages the use of ERA5-Land for other lakes.

Isolating two sources of variability of subcortical stimulation to quantify fluctuations of corticospinal tract excitability

OBJECTIVE

Investigate the variability previously found with cortical stimulation and handheld transcranial magnetic stimulation (TMS) coils, criticized for its high potential of coil position fluctuations, bypassing the cortex using deep brain electrical stimulation (DBS) of the corticospinal tract with fixed electrodes where both latent variations of the coil position of TMS are eliminated and cortical excitation fluctuations should be absent.

METHODS

Ten input-output curves were recorded from five anesthetized cats with implanted DBS electrodes targeting the corticospinal tract. Goodness of fit of regressions with a conventional single variability source as well as a dual variability source model was quantified using a Schwarz Bayesian Information approach to avoid overfitting.

RESULTS

Motor evoked potentials (MEPs) through DBS of the corticospinal tract revealed short-term fluctuations in excitability of the targeted neuron pathway reflecting endogenous input-side variability at similar magnitude as TMS despite bypassing cortical networks.

CONCLUSION

Input-side variability, i.e., variability resulting in changing MEP amplitudes as if the stimulation strength was modulated, also emerges in electrical stimulation at a similar degree and is not primarily a result of varying stimulation, such as minor coil movements in TMS. More importantly, this variability component is present, although the cortex is bypassed. Thus, it may be of spinal origin, which can include cortical input from spinal projections. Further, the nonlinearity of the compound variability entails complex heteroscedastic non-Gaussian distributions and typically does not allow simple linear averages in statistical analysis of MEPs. As the average is dominated by outliers, it risks bias. With appropriate regression, the net effects of excitatory and inhibitory inputs to the targeted neuron pathways become noninvasively observable and quantifiable.

SIGNIFICANCE

The neural responses evoked by artificial stimulation in the cerebral cortex are variable. For example, MEPs in response to repeated presentations of the same stimulus can vary from no response to saturation across trials. Several sources of such variability have been suggested, and most of them may be technical in nature, but localization is missing.

Radiative Cooling and Solar Heating Janus Films for Personal Thermal Management

Hot and cold seasonal temperature fluctuations pose a serious public health threat. Radiative thermal management has been shown to be an effective method for personal thermal management. However, the currently available materials cannot maintain human thermal comfort against the hot and cold seasonal temperature fluctuations, such as heating in cold weather or cooling in hot weather. Here, a Janus film that integrates the two opposite requirements of heating and cooling into one functional dual-mode film is fabricated. In cooling mode, the Al backing and embedded silicon dioxide (SiO₂) microparticle can achieve a high solar reflectivity (∼0.85) and high IR emissivity (∼0.95) to induce a temperature drop of ∼2 °C. In contrast, the embedded carbon nanotubes (CNTs) can improve solar absorption (∼0.95) and induce a temperature increase of ∼7 °C. Owing to its radiative cooling and solar heating capability and compatibility with large-scale production, this Janus film is promising to bring new insights into the design of the next-generation functional textiles.

Heat stress may cause a significant reduction of rice yield in China under future climate scenarios

Extreme heat events have become more frequent and severe under climate change and seriously threaten rice growth. Most existing crop models tend to underestimate the impacts of heat stress on rice yields. Heat stress modules in crop models have not been extensively explored, particularly on a large scale. This study modeled rice growth under heat stress at the flowering and filling stages through two heat stress models which coupled into the CERES-Rice model. We evaluated the advanced model with provincial statistics and Gridded Observed Rice Yield. Our improved CERES-Rice model produced more accurate estimates on rice yield than the original model evidenced by an increased correlation coefficient (R) of 12.72% and d-index of 0.02%. The RMSE and MAE decreased by 5.94% and 6.01%, respectively. Most pseudo positive correlations between rice yield and the number of heat days were corrected to the negative ones by the improved model. The future projections from the improved model signifies multi-model ensemble yield projection without CO₂ effect (MME-I-NOCO₂) has an apparent fall from 2020 to 2099 under RCP4.5, RCP6.0 and RCP8.5 with the decreasing percentages of 6%, 14%, and 37%, respectively, whereas the decreasing trend (12%) only occurs under RCP8.5 with CO₂ effect (MME-I-CO₂). The apparently decreasing trends of yield projection from MME-I-NOCO₂ will occur in most rice-planted regions of China with the decreasing rate < 50 kg/ha/a especially in the central-south and southern cropping regions, and this decreasing trend will be slowed down for MME-I-CO₂. Relative to rice yield of historical period, rice yield variations of MME-I-NOCO₂ for different growing seasons show a downward trend with the decrease of approximately 54%, 60%, and 43%, respectively. Our study highlights the importance of modeling crop yields under heat stress to food security, agricultural adaptation and mitigation to climate change.

How the Black Swan damages the harvest: Extreme weather events and the fragility of agriculture in development countries

Climate change constitutes a rising challenge to the agricultural base of developing countries. Most of the literature has focused on the impact of changes in the means of weather variables on mean changes in production and has found very little impact of weather upon agricultural production. Instead, we focus on the relationship between extreme events in weather and extreme losses in crop production. Indeed, extreme events are of the greatest interest for scholars and policy makers only when they carry extraordinary negative effects. We build on this idea and for the first time, we adopt a conditional dependence model for multivariate extreme values to understand the impact of extreme weather on agricultural production. Specifically, we look at the probability that an extreme event drastically reduces the harvest of any of the major crops. This analysis, which is run on data for six different crops and four different weather variables in a vast array of countries in Africa, Asia and Latin America, shows that extremes in weather and yield losses of major staples are associated events. We find a high heterogeneity across both countries and crops and we are able to predict per country and per crop the risk of a yield reduction above 90% when extreme events occur. As policy implication, we can thus assess which major crop in each country is less resilient to climate shocks.

Quantifying the Occurrence of Multi-Hazards Due to Climate Change

This paper introduces a climatic multi-hazard risk assessment for Greece, as the first-ever attempt to enhance scientific knowledge for the identification and definition of hazards, a critical element of risk-informed decision making. Building on an extensively validated climate database with a very high spatial resolution (5 × 5 km2), a detailed assessment of key climatic hazards is performed that allows for: (a) the analysis of hazard dynamics and their evolution due to climate change and (b) direct comparisons and spatial prioritization across Greece. The high geographical complexity of Greece requires that a large number of diverse hazards (heatwaves—TX, cold spells—TN, torrential rainfall—RR, snowstorms, and windstorms), need to be considered in order to correctly capture the country’s susceptibility to climate extremes. The current key findings include the dominance of cold-temperature extremes in mountainous regions and warm extremes over the coasts and plains. Extreme rainfall has been observed in the eastern mainland coasts and windstorms over Crete and the Aegean and Ionian Seas. Projections of the near future reveal more warm extremes in northern areas becoming more dominant all over the country by the end of the century.

Atmosphere similarity patterns in boreal summer show an increase of persistent weather conditions connected to hydro-climatic risks

Recent studies have shown that hydro-climatic extremes have increased significantly in number and intensity in the last decades. In the Northern Hemisphere such events were often associated with long lasting persistent weather patterns. In 2018, hot and dry conditions prevailed for several months over Central Europe leading to record-breaking temperatures and severe harvest losses. The underlying circulation processes are still not fully understood and there is a need for improved methodologies to detect and quantify persistent weather conditions. Here, we propose a new method to detect, compare and quantify persistence through atmosphere similarity patterns by applying established image recognition methods to day to day atmospheric fields. We find that persistent weather patterns have increased in number and intensity over the last decades in Northern Hemisphere mid-latitude summer, link this to hydro-climatic risks and evaluate the extreme summers of 2010 (Russian heat wave) and of 2018 (European drought). We further evaluate the ability of climate models to reproduce long-term trend patterns of weather persistence and the result is a notable discrepancy to observed developments.

Improving weather forecasting by assimilation of water vapor isotopes

Stable water isotopes, which depend on meteorology and terrain, are important indicators of global water circulation. During the past 10 years, major advances have been made in general circulation models that include water isotopes, and the understanding of water isotopes has greatly progressed as a result of innovative, improved observation techniques. However, no previous studies have combined modeled and observed isotopes using data assimilation, nor have they investigated the impacts of real observations of isotopes. This is the first study to assimilate real satellite observations of isotopes using a general circulation model, then investigate the impacts on global dynamics and local phenomena. The results showed that assimilating isotope data improved not only the water isotope field but also meteorological variables such as air temperature and wind speed. Furthermore, the forecast skills of these variables were improved by a few percent, compared with a model that did not assimilate isotope observations.

Change of extreme snow events shaped the roof of traditional Chinese architecture in the past millennium

As a symbol of civilization and culture, architecture was originally developed for sheltering people from unpleasant weather or other environmental conditions. Therefore, architecture is expected to be sensitive to climate change, particularly to changes in the occurrence of extreme weather events. However, although meteorological factors are widely considered in modern architecture design, it remains unclear whether and how ancient people adapted to climate change from the perspective of architecture design, particularly on a millennium time scale. Here, we show periodic change and a positive trend in roof slope of traditional buildings in the northern part of central and eastern China and demonstrate climate change adaptation in traditional Chinese architecture, driven by fluctuations in extreme snowfall events over the past thousand years. This study provides an excellent example showing how humans have long been aware of the impact of climate change on daily life and learned to adapt to it.

Hot weather and heat extremes: health risks

Hot ambient conditions and associated heat stress can increase mortality and morbidity, as well as increase adverse pregnancy outcomes and negatively affect mental health. High heat stress can also reduce physical work capacity and motor-cognitive performances, with consequences for productivity, and increase the risk of occupational health problems. Almost half of the global population and more than 1 billion workers are exposed to high heat episodes and about a third of all exposed workers have negative health effects. However, excess deaths and many heat-related health risks are preventable, with appropriate heat action plans involving behavioural strategies and biophysical solutions. Extreme heat events are becoming permanent features of summer seasons worldwide, causing many excess deaths. Heat-related morbidity and mortality are projected to increase further as climate change progresses, with greater risk associated with higher degrees of global warming. Particularly in tropical regions, increased warming might mean that physiological limits related to heat tolerance (survival) will be reached regularly and more often in coming decades. Climate change is interacting with other trends, such as population growth and ageing, urbanisation, and socioeconomic development, that can either exacerbate or ameliorate heat-related hazards. Urban temperatures are further enhanced by anthropogenic heat from vehicular transport and heat waste from buildings. Although there is some evidence of adaptation to increasing temperatures in high-income countries, projections of a hotter future suggest that without investment in research and risk management actions, heat-related morbidity and mortality are likely to increase.

Impacts of climate change on the livestock food supply chain; a review of the evidence

The potential impacts of climate change on current livestock systems worldwide are a major concern, and yet the topic is covered to a limited extent in global reports such as the ones produced by the Intergovernmental Panel on Climate Change. In this article, we review the risk of climate-related impacts along the land-based livestock food supply chain. Although a quantification of the net impacts of climate change on the livestock sector is beyond the reach of our current understanding, there is strong evidence that there will be impacts throughout the supply chain, from farm production to processing operations, storage, transport, retailing and human consumption. The risks of climate-related impacts are highly context-specific but expected to be higher in environments that are already hot and have limited socio-economic and institutional resources for adaptation. Large uncertainties remain as to climate futures and the exposure and responses of the interlinked human and natural systems to climatic changes over time. Consequently, adaptation choices will need to account for a wide range of possible futures, including those with low probability but large consequences.

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Risk results from the interaction of climate-related hazards with the exposure and vulnerability of human and natural systems.

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Climate change will impact the livestock sector throughout the food supply chain⁠—from farm production to human consumption.

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Key hazards relate to climate change trends but also, and importantly, to climate variability and climate extremes.

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Large uncertainties remain as to climate futures and the exposure and responses of the interlinked human and natural systems.

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Adaptation choices will need to account for a wide range of possible futures.

A review of the combined effects of climate change and other local human stressors on the marine environment

Climate change (CC) is a key, global driver of change of marine ecosystems. At local and regional scales, other local human stressors (LS) can interact with CC and modify its effects on marine ecosystems. Understanding the response of the marine environment to the combined effects of CC and LS is crucial to inform marine ecosystem-based management and planning, yet our knowledge of the potential effects of such interactions is fragmented. At a global scale, we explored how cumulative effect assessments (CEAs) have addressed CC in the marine realm and discuss progress and shortcomings of current approaches. For this we conducted a systematic review on how CEAs investigated at different levels of biological organization ecological responses, functional aspects, and the combined effect of CC and HS. Globally, the effects of 52 LS and of 27 CC-related stressors on the marine environment have been studied in combination, such as industrial fisheries with change in temperature, or sea level rise with artisanal fisheries, marine litter, change in sediment load and introduced alien species. CC generally intensified the effects of LS at species level. At trophic groups and ecosystem levels, the effects of CC either intensified or mitigated the effects of other HS depending on the trophic groups or the environmental conditions involved, thus suggesting that the combined effects of CC and LS are context-dependent and vary among and within ecosystems. Our results highlight that large-scale assessments on the spatial interaction and combined effects of CC and LS remain limited. More importantly, our results strengthen the urgent need of CEAs to capture local-scale effects of stressors that can exacerbate climate-induced changes. Ultimately, this will allow identifying management measures that aid counteracting CC effects at relevant scales.

Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks

Gridded datasets provide spatially and temporally consistent runoff estimates that serve as reliable sources for assessing water resources from regional to global scales. This study presents LSTM-REG, a regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks. LSTM-REG covers the period from 1980 to 2016 at a 0.5° spatial and daily temporal resolution. LSTM-REG has been extensively validated and benchmarked against GR4J-REG, a gridded runoff dataset based on a parsimonious regionalization scheme and the GR4J hydrological model. While both datasets provide runoff estimates with reliable prediction efficiency, LSTM-REG outperforms GR4J-REG for most basins in the independent evaluation set. Thus, the results demonstrate a higher generalization capacity of LSTM-REG than GR4J-REG, which can be attributed to the higher efficiency of the proposed LSTM-based regionalization scheme. The developed datasets are freely available in open repositories to foster further regional hydrology research in northwest Russia.

Climate change/variability and hydrological modelling studies in Zimbabwe: a review of progress and knowledge gaps

This paper reviews developments in climate science and hydrological modelling studies in Zimbabwe over the past 29 years in an effort to expose knowledge gaps within this research domain. We initially give a global and regional overview and then follow a systematic thematic approach in reviewing specifically online published, peer-reviewed journal articles on climate change/variability and hydrological modelling in Zimbabwe. The state and progress towards advanced integrated climate and hydrological modelling research are assessed, tracking benchmarks in the research methodologies (tools and techniques) used therein including geographic information systems and remote sensing. We present descriptive summaries of key findings, highlighting the main study themes (categories) and general conclusions arising from these studies while examining their implications for future climate and hydrological modelling research in Zimbabwe. Challenges associated with climate and hydrological modelling research in Zimbabwe are also briefly discussed and the main knowledge gaps in terms of research scope and methodologies employed in the reviewed studies also exposed. We conclude by presenting plausible potential areas of focus in updating and advancing scientific knowledge to better understand the climate-land use-hydrology nexus in Zimbabwe. While this paper is primarily relevant for researchers, the general findings are also important for policy-makers since it exposes potential areas for policy intervention or agenda setting in as far as climate and hydrology science research is concerned so as to effectively address pertinent questions in this domain in Zimbabwe.

Lake heatwaves under climate change

Lake ecosystems, and the organisms that live within them, are vulnerable to temperature change^1-5, including the increased occurrence of thermal extremes⁶. However, very little is known about lake heatwaves-periods of extreme warm lake surface water temperature-and how they may change under global warming. Here we use satellite observations and a numerical model to investigate changes in lake heatwaves for hundreds of lakes worldwide from 1901 to 2099. We show that lake heatwaves will become hotter and longer by the end of the twenty-first century. For the high-greenhouse-gas-emission scenario (Representative Concentration Pathway (RCP) 8.5), the average intensity of lake heatwaves, defined relative to the historical period (1970 to 1999), will increase from 3.7 ± 0.1 to 5.4 ± 0.8 degrees Celsius and their average duration will increase dramatically from 7.7 ± 0.4 to 95.5 ± 35.3 days. In the low-greenhouse-gas-emission RCP 2.6 scenario, heatwave intensity and duration will increase to 4.0 ± 0.2 degrees Celsius and 27.0 ± 7.6 days, respectively. Surface heatwaves are longer-lasting but less intense in deeper lakes (up to 60 metres deep) than in shallower lakes during both historic and future periods. As lakes warm during the twenty-first century^7,8, their heatwaves will begin to extend across multiple seasons, with some lakes reaching a permanent heatwave state. Lake heatwaves are likely to exacerbate the adverse effects of long-term warming in lakes and exert widespread influence on their physical structure and chemical properties. Lake heatwaves could alter species composition by pushing aquatic species and ecosystems to the limits of their resilience. This in turn could threaten lake biodiversity⁹ and the key ecological and economic benefits that lakes provide to society.

Fingerprint of climate change in precipitation aggressiveness across the central Mediterranean (Italian) area

Rainfall erosivity and its derivative, erosivity density (ED, i.e., the erosivity per unit of rain), is a main driver of considerable environmental damages and economic losses worldwide. This study is the first to investigate the interannual variability, and return periods, of both rainfall erosivity and ED over the Mediterranean for the period 1680–2019. By capturing the relationship between seasonal rainfall, its variability, and recorded hydrological extremes in documentary data consistent with a sample (1981–2015) of detailed Revised Universal Soil Loss Erosion-based data, we show a noticeable decreasing trend of rainfall erosivity since about 1838. However, the 30-year return period of ED values indicates a positive long-term trend, in tandem with the resurgence of very wet days (> 95th percentile) and the erosive activity of rains during the past two decades. A possible fingerprint of recent warming is the occurrence of prolonged wet spells in apparently more erratic and unexpected ways.

Impacts of extreme summers on European ecosystems: a comparative analysis of 2003, 2010 and 2018

In Europe, three widespread extreme summer drought and heat (DH) events have occurred in 2003, 2010 and 2018. These events were comparable in magnitude but varied in their geographical distribution and biomes affected. In this study, we perform a comparative analysis of the impact of the DH events on ecosystem CO₂ fluxes over Europe based on an ensemble of 11 dynamic global vegetation models (DGVMs), and the observation-based FLUXCOM product. We find that all DH events were associated with decreases in net ecosystem productivity (NEP), but the gross summer flux anomalies differ between DGVMs and FLUXCOM. At the annual scale, FLUXCOM and DGVMs indicate close to neutral or above-average land CO₂ uptake in DH2003 and DH2018, due to increased productivity in spring and reduced respiration in autumn and winter compensating for less photosynthetic uptake in summer. Most DGVMs estimate lower gross primary production (GPP) sensitivity to soil moisture during extreme summers than FLUXCOM. Finally, we show that the different impacts of the DH events at continental-scale GPP are in part related to differences in vegetation composition of the regions affected and to regional compensating or offsetting effects from climate anomalies beyond the DH centres. This article is part of the theme issue 'Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale'.

Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity

In summer 2018, central and northern Europe were stricken by extreme drought and heat (DH2018). The DH2018 differed from previous events in being preceded by extreme spring warming and brightening, but moderate rainfall deficits, yet registering the fastest transition between wet winter conditions and extreme summer drought. Using 11 vegetation models, we show that spring conditions promoted increased vegetation growth, which, in turn, contributed to fast soil moisture depletion, amplifying the summer drought. We find regional asymmetries in summer ecosystem carbon fluxes: increased (reduced) sink in the northern (southern) areas affected by drought. These asymmetries can be explained by distinct legacy effects of spring growth and of water-use efficiency dynamics mediated by vegetation composition, rather than by distinct ecosystem responses to summer heat/drought. The asymmetries in carbon and water exchanges during spring and summer 2018 suggest that future land-management strategies could influence patterns of summer heat waves and droughts under long-term warming.

Improvement of the Irrigation Scheme in the ORCHIDEE Land Surface Model and Impacts of Irrigation on Regional Water Budgets Over China

In China, irrigation is widespread in 40.7% cropland to sustain crop yields. By its action on water cycle, irrigation affects water resources and local climate. In this study, a new irrigation module, including flood and paddy irrigation technologies, was developed in the ORCHIDEE-CROP land surface model which describes crop phenology and growth in order to estimate irrigation demands over China from 1982 to 2014. Three simulations were performed including NI (no irrigation), IR (with irrigation limited by local water resources), and FI (with irrigation demand fulfilled). Observations and census data were used to validate the simulations. Results showed that the estimated irrigation water withdrawal ( W ) based on IR and FI scenarios bracket statistical W with fair spatial agreements ( r = 0 . 68 ± 0 . 07 ; p < 0 . 01 ). Improving irrigation efficiency was found to be the dominant factor leading to the observed W decrease. By comparing simulated total water storage (TWS) with GRACE observations, we found that simulated TWS with irrigation well explained the TWS variation over China. However, our simulation overestimated the seasonality of TWS in the Yangtze River Basin due to ignoring regulation of artificial reservoirs. The observed TWS decrease in the Yellow River Basin caused by groundwater depletion was not totally captured in our simulation, but it can be inferred by combining simulated TWS with census data. Moreover, we demonstrated that land use change tended to drive W locally but had little effect on total W over China due to water resources limitation.

The Influence of Environmental Change (Crops and Water) on Population Redistribution in Mexico and Ethiopia

This paper discusses the effects of long-term environmental change (represented by the abundance or scarcity relative to the long-term average level of crop yield/river flow) and short-term environmental shock (represented by the maximum number of consecutive years below the median crop yield/river flow per decade) on population redistribution in Mexico and Ethiopia. Crop production and water resources, which are affected by climate change and influence human survival and activities, were selected as research variables. Two developing countries, namely, Mexico and Ethiopia, were selected as comparison cases. The results showed that short-term environmental shocks had no correlation with population redistribution. Short-term environmental shocks might fail to influence migration decisions or cause only temporary displacements that cannot be detected by demographic statistics. Among the long-term environmental change factors, only crop yield deviation was found to have a significant positive correlation with population redistribution. Based on two different datasets and two different decades, crop yield deviation is positively correlated with population redistribution; the correlation coefficients between crop yield deviation and population redistribution were 0.134 to 0.162 in Mexico and 0.102 to 0.235 in Ethiopia. When urbanization was considered as the control variable, the correlation coefficient between crop yield deviation and population redistribution in Mexico dropped by half, while it was almost the same in Ethiopia. However, Ethiopia’s population redistribution was more clearly influenced by the population itself. Crop yield deviation relative to water flow deviation meant changes in livelihoods. Population redistribution is a possible means of adapting to changes in livelihood. Mexico exhibited high resilience to changes in livelihoods caused by long-term environmental change, especially in its densely populated areas. In contrast, Ethiopia was characterized mainly by high population growth and low population migration. People in some areas of Ethiopia were forced to endure hardship of livelihood deterioration or to stay where they were due to the difficulty of obtaining sufficient resources to afford the cost of migration.

Integrated Solutions for the Water-Energy-Land Nexus: Are Global Models Rising to the Challenge?

Increasing human demands for water, energy, food and materials, are expected to accentuate resource supply challenges over the coming decades. Experience suggests that long-term strategies for a single sector could yield both trade-offs and synergies for other sectors. Thus, long-term transition pathways for linked resource systems should be informed using nexus approaches. Global integrated assessment models can represent the synergies and trade-offs inherent in the exploitation of water, energy and land (WEL) resources, including the impacts of international trade and climate policies. In this study, we review the current state-of-the-science in global integrated assessment modeling with an emphasis on how models have incorporated integrated WEL solutions. A large-scale assessment of the relevant literature was performed using online databases and structured keyword search queries. The results point to the following main opportunities for future research and model development: (1) improving the temporal and spatial resolution of economic models for the energy and water sectors; (2) balancing energy and land requirements across sectors; (3) integrated representation of the role of distribution infrastructure in alleviating resource challenges; (4) modeling of solution impacts on downstream environmental quality; (5) improved representation of the implementation challenges stemming from regional financial and institutional capacity; (6) enabling dynamic multi-sectoral vulnerability and adaptation needs assessment; and (7) the development of fully-coupled assessment frameworks based on consistent, scalable, and regionally-transferable platforms. Improved database management and computational power are needed to address many of these modeling challenges at a global-scale.

High ecosystem stability of evergreen broadleaf forests under severe droughts

Global increase in drought occurrences threatens the stability of terrestrial ecosystem functioning. Evergreen broadleaf forests (EBFs) keep leaves throughout the year, and therefore could experience higher drought risks than other biomes. However, the recent temporal variability of global vegetation productivity or land carbon sink is mainly driven by non-evergreen ecosystems, such as semiarid grasslands, croplands, and boreal forests. Thus, we hypothesize that EBFs have higher stability than other biomes under the increasingly extreme droughts. Here we use long-term Standardized Precipitation and Evaporation Index (SPEI) data and satellite-derived Enhanced Vegetation Index (EVI) products to quantify the temporal stability (ratio of mean annual EVI to its SD), resistance (ability to maintain its original levels during droughts), and resilience (rate of EVI recovering to pre-drought levels) at biome and global scales. We identified significantly increasing trends of annual drought severity (SPEI range: -0.08 to -1.80), area (areal fraction range: 2%-19%), and duration (month range: 7.9-9.1) in the EBF biome over 2000-2014. However, EBFs showed the highest resistance of EVI to droughts, but no significant differences in resilience of EVI to droughts were found among biomes (forests, grasslands, savannas, and shrublands). Global resistance and resilience of EVI to droughts were largely affected by temperature and solar radiation. These findings suggest that EBFs have higher stability than other biomes despite the greater drought exposure. Thus, the conservation of EBFs is critical for stabilizing global vegetation productivity and land carbon sink under more-intense climate extremes in the future.