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Global warming has become a critical environmental, social, and economic threat, with increasing frequency and intensity of extreme weather events. This study aims to analyse temperature trends and climate indices in the Po Valley, a significant economic and agricultural region in Italy, by examining data from two historical stations: the urban Modena Observatory and the rural Mount Cimone Observatory. The analysis extends previous studies to 2018, assessing the magnitude of climate changes since the 1950s and isolating the Urban Heat Island (UHI) effect in Modena. Significant warming trends were confirmed at both sites, with in maximum (TX) and minimum (TN) temperatures trends nearly doubling from 1981 to 2018 compared to 1951–2018. For example, TX trends reached 0.84°C·decade−1 in Modena and 0.62°C·decade−1 at Mount Cimone, while TN trends were 0.77 and 0.80°C·decade−1, respectively. Extreme climate indices showed a substantial increase in warm days and nights (TX90p and TN90p, respectively). Particularly we found TX90p of 27.5 days·decade−1 in Modena and 15 days·decade−1 at Mount Cimone while TN90p of 29.5 days·decade−1 in Modena, 22 days·decade−1 at Mount Cimone. The UHI effect significantly impacts Modena's temperature trends. Urbanization contributes up to 65% of the rise in warm nights. Specifically, frost days decreased by 1.88 days·decade−1 (37% of Urban Contribute, UC), tropical nights increased by 5.16 days·decade−1 (57% UC), warm nights increased by 12.7 days·decade−1 (65% UC), and cool nights decreased by 3.19 days·decade−1 (39% UC). Overall, the study underscores the importance of considering both global and local factors in regional climate trend analysis. [ABSTRACT FROM AUTHOR]

Extreme drought and intensification of land use pose substantial threats to ecosystem stability. However, existing studies that assess ecosystem stability focus primarily on the stability of aboveground net primary productivity (ANPP), while the stability of belowground net primary productivity (BNPP) is often not considered. Here, we examined the response patterns and drivers of ANPP stability and BNPP stability in an alpine meadow to drought and clipping (simulating grazing and mowing) by the implementation of five levels of simulated precipitation (1/12, 1/4, 1/2, 3/4 and ambient precipitation) and clipping (unclipped vs. clipped) treatments over 7 years (2016–2022). The results showed that extreme drought decreased ANPP stability, while moderate drought increased BNPP stability and net primary productivity stability. The findings highlight the importance of jointly considering above‐ and belowground processes when evaluating ecosystem stability. Moreover, clipping did not have an impact on ANPP stability but reduced BNPP stability, and clipping differentially affected the stability of ANPP and BNPP in response to extreme drought. Furthermore, ANPP stability was largely determined by species temporal asynchrony rather than richness, whereas dominant species stability strongly contributed to the variation in BNPP stability of this alpine meadow faced with drought and clipping. Synthesis: In general, these results provide experimental evidence for the decoupling of the responses of above‐ and belowground productivity stability to extreme drought and clipping. Our findings indicate that aboveground responses should not be used as the sole indicator of the whole ecosystem consequences of extreme drought and disturbance factors such as grazing or mowing, emphasizing the need to evaluate ecosystem stability from a whole ecosystem perspective. [ABSTRACT FROM AUTHOR]

Ecological simulations including forest and vegetation growth models require climate inputs that match the resolution and extent of the process being modelled. Climate inputs are often derived at resolutions coarser than the scale of many ecosystem processes. Machine learning models can be trained to spatially downscale climate data to fine (30 m) resolution using topographic variables such as elevation, aspect and other site‐specific factors. Statistically downscaled climate models will have spatially varying uncertainty that is not usually incorporated into downscaling techniques for error propagation into later models, are often applied on smaller areas, are not fine enough resolutions for many modelling techniques, or are not always scalable to large spatial extents. There remains opportunity to leverage machine learning advancements to downscale climate to very fine (30 m) resolutions with associated spatially explicit uncertainty to represent microclimatic variation in ecological models. In this study, we used quantile machine learning to produce 30 m downscaled temperature and precipitation data and associated model prediction uncertainty for the state of California. Temperature models were accurate at downscaling 4 km climate data to 30 m, performing better than the 4 km data at high and low slope positions and at high elevations, especially where there were fewer weather observations. Precipitation model predictions did not show global improvement over the 4 km scale, but were more accurate at high elevations, slopes with higher solar radiation and in valleys. For all climate variables, the added detail of spatial explicit uncertainty via 90% prediction intervals provides critical insight into the utility of empirically downscaled climate. The resulting 30 m spatially contiguous outputs can be used as ecological model inputs with uncertainty propagation, to illuminate climate trends over time as a function of fine‐scale spatial factors, and to highlight areas of spatially explicit uncertainty. [ABSTRACT FROM AUTHOR]

We present a suite of new atmospheric river (AR) metrics that are designed for quick analysis of AR characteristics and statistics in gridded climate datasets such as model output and reanalysis. This package is expected to be particularly useful for climate model evaluation. The metrics include mean bias and spatial pattern correlation, which are efficient for diagnosing systematic AR biases in climate models. For example, the package identifies that in CMIP5 and CMIP6 models, AR tracks in the south Atlantic are positioned farther poleward compared to the ERA5 reanalysis, while in the south Pacific, tracks are generally biased towards the equator. For the landfalling AR peak season, we find that most climate models simulate a completely opposite seasonal cycle over western Africa. This tool is also useful for identifying and characterizing structural differences among different AR detectors (ARDTs). For example, ARs detected with the Mundhenk algorithm exhibit systematically larger size, width and length compared to the TempestExtremes (TE) method. The AR metrics developed from this work can be routinely applied for model benchmarking and during the development cycle to trace performance evolution across model versions or generations and set objective targets for the improvement of models. They can also be used by operational centers to perform near real-time climate and extreme events impact assessment as part of their forecast cycle. [ABSTRACT FROM AUTHOR]

For clades originating in warm climates, the tropical niche conservatism hypothesis predicts that current biological assemblages in colder or drier climates are expected to have lower phylogenetic diversity, and species in colder or drier climates are expected to be more closely related to each other (i.e., higher phylogenetic clustering). Liverworts are one of the oldest clades of extant land plants. They originated about 500 Ma during a warm (“greenhouse”) period and experienced multiple major cycles of warm and cold periods. Here, I test the tropical niche conservatism hypothesis using liverwort assemblages distributed along an elevational gradient crossing about 5000 m of elevation in the central Himalaya. I found that, in general, phylogenetic diversity and dispersion decrease with increasing elevation and thus with decreasing temperature, which is consistent with the tropical niche conservatism hypothesis. Phylogenetic diversity decreases with elevation monotonically, but phylogenetic dispersion decreases with elevation in a triphasic (zig‐zag) pattern, which is generally consistent with the triphasic pattern found in angiosperms and polypod ferns along the same elevational gradient. Temperature‐related variables explained approximately the same amount of the variation in phylogenetic diversity and dispersion as did precipitation‐related variables, although mean annual temperature explained 9%−15% more variation than did annual precipitation. Climate extreme variables explained approximately the same amount of variation in phylogenetic diversity and dispersion as did climate seasonality variables. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Time Period Major Taxa Studied Methods Results Main Conclusions Climate extremes are becoming more frequent under global warming, with substantial repercussions for vegetation growth. The degree to which climate extremes increase the risk of high‐impact events on vegetation growth is of high concern.Northern Hemisphere (north of 30°N).From 2001 to 2022.Plants.We utilised solar‐induced chlorophyll fluorescence (SIF) and the normalised difference vegetation index (NDVI) as proxies for vegetation growth. We performed event coincidence and sensitivity analyses to attribute satellite‐derived vegetation growth extremes to diverse climate extremes (extreme heat, cold, wet and drought) in the Northern Hemisphere.Our results showed that extreme heat and cold were the main climatic extremes that induced positive and negative vegetation growth extremes north of 30°N, respectively, mainly in cold and humid ecosystems (boreal and temperate forests). Water‐related extreme events accounted for less than one‐third of vegetation extremes. The contribution of drought to positive vegetation growth extreme events (approximately 17%), mainly in cold and humid ecosystems, was even slightly higher than that of extreme wet (approximately 12%), which predominantly impacted relatively warm and arid ecosystems (croplands and temperate grasslands). We further identified potential climatic thresholds that could reverse vegetation growth responses to climate extremes (temperature is 12.5°C and climatic water deficit is ‐60 mm, approximately). We also showed that the past two decades of warming and precipitation changes did not induce a shift in the main climatic drivers of vegetation extremes across northern ecosystems.Our results emphasise the crucial role of background hydrothermal conditions in the attribution of vegetation growth extremes to diverse climate extremes across northern ecosystems and have substantial implications for predicting how Northern Hemisphere vegetation will respond to increasing climate extremes in the future. [ABSTRACT FROM AUTHOR]

Ectotherms are largely impacted by extreme climate events, essentially heatwaves. In Tunisia, the pine processionary moth (PPM), Thaumetopoea pityocampa, is a defoliator typically laying eggs in summer. Its geographical range is expanding northwards in Europe while retracting from southern Tunisia where summer temperatures can reach extreme heats. We explored the effects of climate change over the last 30 years on PPM at its southern range edge. We investigated variations of fecundity and causes of egg mortality over time using historical and contemporary collections of egg masses. Due to incomplete historical series, a clustering method was used to group climatically homologous sampling sites and perform allochronic analyses on clusters instead of individual localities. Our results suggest negative effects of summer heat on egg survival, down to 0% hatching rate in one site during a major heatwave. Such high hatching failures resulted partly from high egg sterility and aborted embryos, with little effects of parasitism rate, suggesting heat susceptibility during embryonic development, but our results do not allow distinguishing impeded mating success from failed egg maturation or early embryo death. Decreasing female fecundity was observed in regions where allochronic comparisons were possible, associated with a decrease in parasitism rate, while climatic variability increased. Previous studies have confirmed experimentally the thermal sensitivity of PPM early instars to temperatures observed in the present work, including one population from southern Tunisia. However, further work is required to evaluate the relative importance of warming summers among populations, since the risk of heat stress depends on the phenology of sensitive instars, and populations from the warmest areas may not necessarily be the most vulnerable to climate change if they already evolved phenological heat avoidance. In addition to heat‐induced mortality, the ultimate fitness of heat survivors should also be explored to determine potential carry‐over effects on subsequent life stages. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) and North Atlantic tripole sea surface temperature (SST_tri) are important modes in the atmosphere and ocean over the North Atlantic, respectively. The link between the two is well‐known. However, this link weakened during 1980–2001, which is particularly pronounced in late winter and was not detected in early winter. This phenomenon has not been well revealed. The role of NAO in the above correlation changes was discussed. In late winter, a significant eastward shift (up to 20° longitude) of NAO south center during 1980–2001 was observed in both observation and CMIP6, accompanied by the eastward expansion of NAO north center. Spatial shift of the NAO forced the region of strong air‐sea interactions to shift and resulting in the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale. Plain Language Summary: A significant correlation between the North Atlantic Oscillation (NAO) and the North Atlantic tripole sea surface temperature (SST_tri) is widely recognized. However, this study found that the correlation between the two was significantly weakened in late winter during 1980–2001 while no such change was detected in early winter. Therefore, this paper will focus on the late winter and discuss the role of NAO spatial structure in the above correlation changes. When the NAO south center is located over the North Atlantic, both observations and the climate models indicate that the corresponding North Atlantic SST shows a significant tripole pattern. However, during 1980–2001, the NAO south center shift significantly eastward from the North Atlantic toward Western Europe (up to 20° longitude). At the same time, the NAO north center expanded eastward significantly. The eastward shift of the NAO results in the significant eastward shift of the turbulent heat flux and wind stress anomalies. Shift in the region of strong air‐sea interaction led to the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale and also provide implications for subseasonal‐seasonal predictions of Eurasian climate extremes. Key Points: The link between NAO and North Atlantic tripole SST weakened obviously during 1980–2001 late winter, which was not detected in early winterThe eastward shift of ∼20° longitude in NAO south center forced the strong air‐sea interactions region to shift in observations and CMIP6NAO's spatial shift in late winter caused the changes in the link between NAO and North Atlantic tripole SST [ABSTRACT FROM AUTHOR]

Large‐scale topographies affect global extreme weather and climate though dynamic and thermal forcing. However, the impacts of typical topographies on heat waves in different drylands globally remain unclear. In this study, we find that heat waves are mainly occurred in global drylands during 1940–2022. The frequencies and intensities of heat waves in global drylands have significantly increased after 1980s. Multiple numerical model simulations reveal that the impact range of Asian topography on heat waves in global drylands is widest, not only in East Asia, Central, and west Asia locally, but also can reach as far as North Africa and North America. While the impact ranges of topographies in Africa, Arabian Peninsula, North America, and South America on heat waves in drylands are relatively narrow, which are concentrated in localized and their surroundings. These conclusions could provide clues to understand the influence of topography on global weather and climate extremes. Plain Language Summary: Topographies have important impacts on heat waves in global drylands. Through numerical model simulations, this study reveals that Asian topography contributes significantly to heat waves in global drylands, with the widest impact ranges which can cover drylands of East Asia, Central and west Asia, North Africa, even North America. The impacts of the East African Plateau, the Arabian Plateau, the Sierra Madre in North America and the Andes in South America on heat waves are mainly in the local and surrounding regions. Key Points: Heat waves are mainly occurred in global drylands and experience significant increases after 1980sThe impact range of Asian topography on heat waves in global drylands is widest, which can reach as far as North Africa and North AmericaThe topographies in Africa, Arabian Peninsula, North America, and South America can only affect heat waves in localized and surroundings [ABSTRACT FROM AUTHOR]

Global warming has increased the probability of extreme climate events, with compound extreme events having more severe impacts on socioeconomics and the environment than individual extremes. Utilizing the Coupled Model Intercomparison Project Phase 6 (CMIP6), we predicted the spatiotemporal variations of compound extreme precipitation-high temperature events in China under three Shared Socioeconomic Pathways (SSPs) across two future periods, and analyzed the changes in exposed populations and identified influencing factors. From the result, we can see that, the CMIP6 effectively reproduces precipitation patterns but exhibits biases. The frequency of compound event rises across SSPs, especially under high radiative forcing, with a stronger long-term upward trend. Furthermore, the economically developed areas, notably China's southeastern coast and North China Plain, will be hotspots for frequent and intense compound extreme events, while other regions will see reduced exposure. Finally, in the long-term future (2070–2100), there is a noteworthy shift in population exposure to compound events, emphasizing the increasing influence of population factors over climate factors. This highlights the growing importance of interactions between population and climate in shaping exposure patterns. [ABSTRACT FROM AUTHOR]

Societal Impact Statement Summary Conserving frankincense trees (Boswellia) is crucial for both ecological and socio‐economic reasons. Surveying these trees in the field and using remote sensing unmanned aerial vehicles in the Socotra Archipelago, we found that Socotran frankincense trees are threatened by forest fragmentation, overgrazing, and increasingly frequent extreme climate events. A better understanding of the distribution and the threats of these important insular species will improve the conservation policy of the local authorities and benefit local communities in the Socotra Archipelago. At the same time, this work serves as a good practice example to guide conservation efforts for other culturally important threatened tree species around the world, therefore helping to sustain local livelihoods, fostering ecological resilience, and supporting socio‐economic stability. Globally, frankincense trees (Burseraceae: Boswellia) are increasingly under threat because of habitat deterioration, climate impacts, and the olibanum trade. Despite harboring nearly half of the species in the genus, up‐to‐date insights are lacking for the insular endemic frankincense trees of the Socotra Archipelago UNESCO (United Nations Educational, Scientific and Cultural Organization) World Heritage Site (Yemen). We combined georeferencing of individual trees in the field with remote sensing applying unmanned aerial vehicles (UAVs) to evaluate Boswellia distribution and (sub)population sizes in the entire Socotra Archipelago. We counted 17,253 trees across all 11 taxa and we surveyed almost 55% directly in the field, collecting individual information on threats and health indicators. We estimate that the current total population sizes of the relatively common Socotran Boswellia taxa (Boswellia elongata, Boswellia popoviana, and Boswellia ameero) consist of a few thousand mature individuals with fragmented distribution of which a large proportion occurs in highly disjunct relictual stands, while the more range‐restricted species survive only through a few hundred (Boswellia nana and Boswellia samhaensis) to fewer than a hundred trees (Boswellia scopulorum). Our field data show that the Socotran frankincense trees are threatened by fragmentation and overgrazing resulting in a lack of natural regeneration, in combination with effects of extreme climate events (e.g., higher frequency and intensity of cyclones and prolonged drought) and potential future infrastructure developments; the species are less impacted by resin collection. We provide recommendations to strategize urgent protection of the declining Socotran frankincense trees, and we update their conservation status, resulting in an endangered status for seven and a critically endangered status for four taxa. [ABSTRACT FROM AUTHOR]

Overshoot scenarios, in which the forcing reaches a peak before starting to decline, show non-symmetric changes during the CO2 increasing and decreasing phases, producing persistent changes on climate. Non-reversibility mechanisms, associated among others with lagged responses of climate components, changes in ocean circulation and heat transport and changes in the ice cover, bring hysteresis to the climate system. These mechanisms generally have an impact in global scales, potentially generating hemispheric temperature changes and alterations of the Intertropical Convergence Zone (ITCZ). This work analyzes simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to explore the relevance of these mechanisms in overshoot scenarios with different forcing conditions (SSP5-3.4OS and SSP1-1.9) and the impact of these large-scale mechanisms on regional climates, with a particular focus on the degree to which changes in regional extremes are reversible. Results show that non-reversibility of temperature and precipitation extremes mostly occurs during the transition period around the global temperature maximum, when a decoupling between regional extremes and global temperature generates persistent changes at regional level. These changes mainly impact temperature extremes in extratropical regions and precipitation extremes in tropical regions around the ITCZ. In scenarios with strong forcing changes like SSP5-3.4OS, regional non-reversibility can be mostly linked to a temperature asymmetry between Northern and Southern Hemisphere, associated with ITCZ shifts. This asymmetry may be associated with persistent changes in the heat transport and with a different thermal inertia depending on the region, leading regionally to a different timing of the temperature maximum. In scenarios with lower forcing changes like SSP1-1.9, the contribution of this mechanism is more limited and other factors like ice melting may also have a relevant role. [ABSTRACT FROM AUTHOR]

Climate change has increased the frequency and severity of weather extremes, including droughts, heat waves and compound drought and heatwave (CDHW) events. CDHW events profoundly impact water availability, agriculture, public health and energy production, particularly in the Horn of Africa (HOA). This study examined the historical spatiotemporal patterns of CDHW periods in the HOA during three periods (1951–1980, 1971–2000 and 1991–2020) using the ERA5 reanalysis dataset. This study utilized monthly Standardized Precipitation Evapotranspiration Index (SPEI) data to detect droughts and daily maximum temperature data to identify heatwaves for characterizing the duration, severity and magnitude of CDHW events. The results show a substantial increase in the duration of CDHW events in recent years, with durations reaching up to 25 days. The average duration of heat waves also increased from 7 days before 1993 to 18 days by 2011, culminating in a record‐breaking 43‐day heat wave in 2019. Most areas experienced a significant increase in heatwave duration by more than 12 days from the early period of 1951–1980 to the late period of 1991–2020. Although around 76% and 69% of the HOA exhibited insignificant heatwave trends in the first two periods, Ethiopia and Kenya experienced substantial increases of more than 18 days during the most recent period, with some durations exceeding 25 days in recent decades. The magnitude of CDHW events generally decreased as drought duration intensified, but specific areas, particularly southwest Kenya and Eritrea, exhibited higher CDHW values in the last period. These findings underscore the urgent need to understand and address CDHW events in the HOA. Targeted interventions for disaster risk reduction and resilience‐building are needed to mitigate the adverse effects of these events in this vulnerable region. This study provides a basis for future research and policy formulation in the HOA. [ABSTRACT FROM AUTHOR]

Recently, climate extremes have been grabbing attention as important drivers of environmental change. Here, we assemble an observational inventory of energy and mass fluxes to quantify the ice loss from glaciers on the Russian High Arctic archipelago of Novaya Zemlya. Satellite altimetry reveals that 70 ± 19% of the 149 ± 29 Gt mass loss between 2011 and 2022 occurred in just four high-melt years. We find that 71 ± 3% of the melt, including the top melt cases, are driven by extreme energy imports from atmospheric rivers. The majority of ice loss occurs on leeward slopes due to foehn winds. 45 of the 54 high-melt days (>1 Gt d−1) in 1990 to 2022 show a combination of atmospheric rivers and foehn winds. Therefore, the frequency and intensity of atmospheric rivers demand accurate representation for reliable future glacier melt projections for the Russian High Arctic. Moist, warm air from mid-latitudes lead to a speed-up of glacial ice loss in Novaya Zemlya, High Russian Arctic. Foehn winds enable especially high melt rates. These conditions became more frequent over the past decade. [ABSTRACT FROM AUTHOR]

Climate variability in the growing season is well suited for testing adaptation measures. Adaptation to adverse events, such as heatwaves and droughts, increases the capacity of players in agri-food systems, not only producers but also transporters and food manufacturers, to prepare for production disruptions due to seasonal extremes and climate change. Climate impact models (e.g., crop models) can be used to develop adaptation responses. To run these models, historical records and climate forecasts need to be combined as a single daily time series. We introduce the daily 0.5° global hybrid reanalysis-forecast meteorological forcing dataset from 2010 to 2021. The dataset consists of the Japanese 55-yr Reanalysis (JRA55) and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI-CPS2) 5-member ensemble forecast. Both are bias-corrected using the Delta method and integrated with a baseline climatology derived from the Environmental Research and Technology Development Fund's Strategic Research 14 Meteorological Forcing Dataset (S14FD). The dataset is called JCDS (JRA55-CPS2-Delta-S14FD) and offers a framework for monitoring and forecasting applications towards adaptation. [ABSTRACT FROM AUTHOR]

California agriculture will undergo significant transformations over the next few decades in response to climate extremes, environmental regulation and policy encouraging environmental justice, and economic pressures that have long driven agricultural changes. With several local climates suited to a variety of crops, periodically abundant nearby precipitation, and public investments that facilitated abundant low-priced irrigation water, California hosts one of the most diverse and productive agroecosystems in the world. California farms supply nearly half of the high-nutrient fruit, tree nut, and vegetable production in the United States. Climate change impacts on productivity and profitability of California agriculture are increasing and forebode problems for standard agricultural practices, especially water use norms. We highlight many challenges California agriculture confronts under climate change through the direct and indirect impacts on the biophysical conditions and ecosystem services that drive adaptations in farm practices and water accessibility and availability. In the face of clear conflicts among competing interests, we consider ongoing and potential sustainable and equitable solutions, with particular attention to how technology and policy can facilitate progress. [ABSTRACT FROM AUTHOR]

Background: Heat and drought stresses usually occur together in nature, and both are expected to increase in frequency and intensity as a result of climate change. The synergistic impacts of these compound climate extremes on potatoes are far from the effects of individual stresses. However, the dynamics of the effects of combined heat and drought stresses on potato physiology and biochemistry have yet to be thoroughly assessed. To elucidate this point, we set up a pot experiment using 'Atlantic' potato seedlings as test material. A total of six treatments were set up: CK (normal growth conditions: 21 ℃, 0 PEG), A1B1 (31 ℃, 20% PEG), A1B2 (31 ℃, 10% PEG), A1B3 (31 ℃, 0 PEG), A2B1 (21 ℃, 20% PEG), and A2B2 (21 ℃, 10% PEG), and 15 physiological indices were determined with the stress time of 0, 6, 12 and 18 days. Results: After 18 days of stress, the phenotype of potato seedlings was significantly different. Compared with CK, the thickness of potato leaves and palisade tissue increased under heat and drought stress, and the combined stress reduced the photosynthetic efficiency of potato leaves. In all treatments except CK, the chlorophyll content decreased significantly, the antioxidant enzyme activity increased first and then decreased, and the relative conductivity and malondialdehyde content increased significantly. The heat and combined treatment made the content of the osmotic regulator first increase and then decrease, while the treatment of 21 ℃ had no significant change. According to the correlation, principal component and interaction analysis, both heat and drought treatment had significant effects on each index, and the longer the stress time, the greater the effect, and the effect of combined stress was greater than that of single stress. However, after 6 days of stress, the activity of antioxidant enzymes and the content of transparent regulatory substances increased. Conclusions: In conclusion, potato can cope with heat, drought and combined stress by adjusting leaf tissue structure, antioxidant enzyme activity and osmotic regulatory substances in a short time. [ABSTRACT FROM AUTHOR]

High-resolution numerical ocean models can be used to help interpret sparse observations in the nearshore and to help understand the impacts of climate change and extreme events on these dynamically complex coastal areas. However, these high-resolution ocean models require inputs with comparably high resolution, which is particularly difficult to achieve for freshwater discharge. Here, we explored a simple rain-based hydrological model as input into a high-resolution (≳ 13 m) model of Quatsino Sound – a fjord system located on the northwestern coast of Vancouver Island, British Columbia, Canada. Through a series of sensitivity tests using an application of the Finite Volume Community Ocean Model (FVCOM version 4.1), we found that model performance was hindered by the lack of knowledge of ungauged rivers and streams. In this case study, including the only major gauged river implied ignoring 538 other watersheds of various sizes and accounted for only about a quarter of the total estimated freshwater discharge. We found that including at least 60 % and ideally closer to 75 %–80 % of total freshwater fluxes gave similar model performance to including all possible 539 freshwater sources; in our model simulations, this percentage of freshwater flux meant including rivers with watersheds greater than 20–50 km2 , corresponding to 7–19 total rivers. Further sensitivity tests also indicated that knowing the main outpour locations into the nearshore ocean is an important factor, but it is not as important as the total freshwater discharge included. Overall, this study illustrates the complexities of studying the land–ocean connection and offers a simple and accessible tool to help address a common problem in nearshore modelling. [ABSTRACT FROM AUTHOR]

Simple Summary: The Sanmartinero creole bovine (SM) is found in the department of Meta (Colombian Orinoquia) and derives from the bullfighting cattle introduced by the Spanish in the 15th century. Its adaptation process has allowed it to develop adaptive characteristics to live in extreme climatic environments. Although there are studies on its reproductive and productive characteristics, little is known about possible asymmetries in some biological structures. Fluctuating asymmetry allows us to detect a possible developmental instability that may be caused by stressful conditions. The aim of our study was to present the current state of developmental instability in two bilateral cephalic characters of the SM creole bovine. The results showed a fluctuating asymmetry biased to the left for ear and horn length, and to the right for ear width and horn perimeter. We consider that the environment in which it lives may have an impact on the development of cephalic structures, creating stress in the animal, which causes the detected asymmetry. However, the authors are not aware of any studies on this topic. Asymmetric studies can indicate disturbances in the developmental process. Fluctuating asymmetry (FA) is considered an indicator of stress. The Sanmartinero (SM) creole bovine is native to the department of Meta (Colombian Orinoquia) and its adaptation process has allowed it to live in extreme tropical environments. The aim of this cross-sectional and descriptive study was to present the current state of the knowledge of asymmetries in some cephalic characters of the SM creole bovine. A total of 94 animals were studied (18 uncastrated males and 76 females) from three different farms, with an age range of 0.5–10 years. For each animal, two measurements of the ear (width and length) and two measurements of the horn (perimeter and length) were obtained in vivo. The degree of asymmetry was calculated as (R − L)/(R + L). Bilateral differences pointed towards a fluctuating asymmetry (e.g., a random variation in the trait that is expected to be perfectly symmetrical) biased towards right for ear width and horn perimeter, and towards left for ear and horn length. Since the development of these structures—ears and horns—is under the control of the same set of genes, the fluctuating asymmetry could constitute a reflection of a normal condition. [ABSTRACT FROM AUTHOR]

This article explores the challenges posed by extreme weather and climate-related events and the inadequacy of current resilience and adaptation frameworks. It discusses the "Exploring Unprecedented Extremes" workshop held in Montreal, Canada, which aimed to address the research gap in understanding and preparing for these events. The workshop emphasized the need for transformational changes in governance and the integration of diverse perspectives to effectively respond to climate extremes. It also highlighted the importance of effective communication and coproduction of knowledge in bridging the gap between scientific research and decision-making. The article provides examples of recent extreme events and emphasizes the urgency of addressing these challenges. It discusses the importance of understanding different ways of knowing and establishing trust in research collaborations, as well as exploring various modeling approaches used to study climate change and extreme weather events. The text concludes by emphasizing the need for a shift in perspective and the adoption of transdisciplinary approaches to effectively address climate extremes. The workshop served as a foundation for examining the impacts of climate change and identifying strategies for adaptation and transformation, and the text emphasizes the urgency of taking action and promoting understanding among individuals, communities, and organizations. [Extracted from the article]

Unprecedented precipitation deficits in the 2022-2023 growing season across the primary wheat-producing region in the United States caused delays in winter wheat emergence and poor crop growth. Using an integrated approach, we quantitatively unraveled a 37% reduction in wheat production as being attributable to both per-harvested acre yield loss and severe crop abandonment, reminiscent of the Dust Bowl years in the 1930s. We used random forest machine learning and game theory analytics to show that the main driver of yield loss was spring drought, whereas fall drought dominated abandonment rates. Furthermore, results revealed, across the US winter wheat belt, the La Niña phase of the El Niño Southern Oscillation (ENSO), increased abandonment rates compared to the El Niño phase. These findings underscore the necessity of simultaneously addressing crop abandonment and yield decline to stabilize wheat production amid extreme climatic conditions and provide a holistic understanding of global-scale ENSO dynamics on wheat production. [ABSTRACT FROM AUTHOR]

Climate changes and global warming increase risk of recurrent extreme and complex climatic features. It necessitates accurate modeling and forecasting of climate phenomena for sustainable development goals. However, machine learning algorithms and advanced statistical models are extensively employed to analyze complex data and make predictions related to climate phenomena. It is important to have comprehensive knowledge to use these models and consider their potential implications. This study aims to evaluate and compare some popular machine learning and probabilistic methods by analyzing various time series indices associated with precipitation and temperature. For application, time series data of Standardized Precipitation Temperature Index (SPTI), Standardized Temperature Index (STI), Standardized Compound Drought and Heat Index (SCDHI), and Biased Diminished Weighted Regional Drought Index (BDWRDI) are used from various meteorological regions of Pakistan. The performance of each algorithm is compared using Residual Mean Square Error (RMSE) and Mean Average Error (MAE). The outcomes associated with this research indicate a higher preference of neural networks over machine learning methods in the training sets. However, the efficiency varies from model to model, indicator to indicator, time scale to time scale, and location to location during the testing phase. The most appropriate models are found by considering a list of candidates forecasting models and investigating the performance of each model. [ABSTRACT FROM AUTHOR]

The Huaihe River basin (HRB) of China located in the climate transition zone between warm temperate and subtropical areas is highly sensitive to climatic change. However, the changes in future climate extreme events under anthropogenic warming and the population exposure to these climate extremes in HRB remain unexplored. Here, using the eight commonly used extreme climate indices and based on the bias‐corrections of 16 global climate models (GCMs) in CMIP6, we present a projection and uncertainty analysis of extreme events and investigate the corresponding population exposure risk in HRB under three shared socioeconomic pathways (SSP1‐2.6, SSP2‐4.5, SSP5‐8.5). The 16‐GCM ensemble mean projects an evident warming trend under all three scenarios with a total increase of 25.6–68.0 days in summer days (>25°C) by the end of the century in HRB. Larger increases (decreases) in maximum and minimum temperatures (frost days) are projected in the western HRB. Very heavy rain days (R20mm), maximum 5‐day precipitation (RX5day) and simple daily intensity index (SDII) will experience intensification across most of HRB (especially in southern and western HRB). The consecutive dry days is projected to decrease in northwestern HRB and increase in southern HRB. However, there is a large spatial variability in GCM uncertainty with a higher SSP scenario generally having higher uncertainty. Increases in summer days and R20mm exacerbate population exposure in HRB in near future (2030–2059), but in far future (2070–2099) although summer days (R20mm) continues to rise, population exposure is expected to decrease due to the rapid decline in population density. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change induced weather and climate extremes have led to frequent heat waves, droughts and floods threatening water resources and food security for an agricultural country like Pakistan. Despite their significance, the trends and variability of extreme temperature and precipitation indices and associated large‐scale drivers in the agro‐ecological zones (AEZs) of Pakistan remain unknown and need urgent attention because of abrupt climate change. The present study documents the spatiotemporal variations of climate change indices together with the elevation‐dependent variability trends over various AEZs in Pakistan for the period of 42 years (1979–2020). Nonparametric Mann–Kendall (MK) and Sen's slope (SS) estimator tests have been employed for trend estimation. Results indicated linearly increasing (warming) and statistically significant trends in Tmean, TNx, WSDI and TR20 whereas significant decreasing (cooling) trends in cool nights (−1.73 days·decade−1) and cold spells (−1.28 days·decade−1). The spatial distribution of temperature indices trends depicts robust warming over southwestern and central zones while cooling trends over northern zones. Regarding precipitation extremes, all indices have shown increasing (wetter) trends with a significant increase in PRCPTOT and RX5day. The stations in northern and subhumid AEZs received more precipitation compared to other zones. Elevation‐dependent trends in temperature indices exhibited a statistically significant positive (negative) relationship with cold (warm) tails. Most of the extreme precipitation indices have a weak, but positive association with elevation except SDII. The weakening of South Asian subtropical upper‐level jet by a high‐pressure system over northeast Pakistan resulted in amplified land surface temperatures. However, the spatial patterns of zonal winds indicate a trough over Pakistan's southern and central parts, with warmer sea‐surface temperature, low sea‐level pressure and easterly anomalies, favour moisture transport and precipitation in Pakistan. The outcomes of present study will be useful in addressing various climate‐induced disasters occurring in various AEZs of Pakistan. [ABSTRACT FROM AUTHOR]

The study focuses on understanding the future exposure to rainfall and temperature extremes in one of the world's most populous islands, Java, Indonesia. We use the high‐resolution climate projections from the Coordinated Regional Climate Downscaling Experiment (CORDEX) simulation for Southeast Asia by 2100 under RCP4.5 and RCP8.5 scenarios. Results show that the island will likely experience drying in the lowlands due to annual rainfall decline by approximately 13%–18%, potentially exposing around 27%–73% of the population (varying under different scenarios) during the end of the century (2060–2099). Additionally, the future drying condition may be exacerbated by extreme temperatures with a 1.7–3.1°C increase in maximum daily temperature, linked with more than half of the population (63%) likely to experience at least an unprecedented temperature of 3°C under RCP8.5. Our seasonal analysis also suggests that dry seasons get even drier, and wet seasons get wetter. In terms of landuse areas exposed, we show a higher fraction of crop and forest areas may face both drying and warming, which can potentially lead to crop failure and wildfire. Our study indicates that compound drought and heat may be a common threat in lowland Java in the future, while intensifying rainfall extremes in the uplands may lead to flash flooding downstream and landslides. These findings highlight the urgent need for adaptation and mitigation strategies to reduce the risks associated with climate change in Java as one of Indonesia's most critical regions in the future. [ABSTRACT FROM AUTHOR]

New Zealand's National Institute of Water and Atmospheric Research publishes climate normals for New Zealand that are used for reporting the regional state of the climate, climate extremes and variability. Temperature and precipitation patterns are affected by both anthropogenic climate change and natural climate variability which in turn affect the climatological normal values calculated every decade. This study investigates how New Zealand's normals for temperature and precipitation have shifted over time at the national, regional and seasonal scales from the 1941–1970 period to the 1991–2020 period. Contrary to WMO recommendations, but aligned with many other countries, New Zealand's climate normals have traditionally not undergone homogenisation. The impact of introducing some homogenisation in the latest 1991–2020 normals, in contrast to historical non‐homogenized station normals, has been assessed using a new homogenized "Seventeen‐Station" temperature series from 1941 to 2020. We demonstrate that interpolating sparse non‐homogenized normals spatially to a grid can produce significant erroneous patterns and therefore undermine the accuracy of the conclusions drawn when using such normals. We find that the historical non‐homogenized temperature normals have a consistent negative bias in the long‐term trend at national, regional and seasonal scales. Our analysis of homogeneity tested precipitation showed consistent decreases at a national scale across all normal periods relative to the 1951–1980 precipitation normal. We also highlight how fixed period temperature and precipitation normals do not fully reflect the current state of a climate that is influenced by decadal variability and global warming. To derive normals fit for use in a changing climate it is suggested that automated methods for broad data homogenisation be developed along with alternative methods to derive normals that account for a non‐stationary climate. [ABSTRACT FROM AUTHOR]

Climate change affects biodiversity in a variety of ways, necessitating the exploration of multiple climate dimensions using appropriate metrics. Despite the existence of several climate change metrics tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed 'climetrics' which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are implemented, including 1) standardized local anomalies; 2) changes in probabilities of local climate extremes; 3) changes in areas of analogous climates; 4) novel climates; 5) changes in distances to analogous climates; and 6) climate change velocity. For climate change velocity, three different algorithms are implemented in the package including; 1) distanced‐based velocity ('dVe'); 2) threshold‐based velocity ('ve'); and 3) gradient‐based velocity ('gVe'). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen‐Geiger (KG) climate zones. The 'climetrics' R package is integrated with the 'rts' package for efficient handling of raster time‐series data. The functions in 'climetrics' are designed to be user‐friendly, making them suitable for less‐experienced R users. Detailed descriptions in help pages and vignettes of the package facilitate further customization by advanced users. In summary, the 'climetrics' R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns. [ABSTRACT FROM AUTHOR]

Populist radical right parties are often considered to be the most extreme opponents of climate protection in Western Europe. Others predict a 'climate nationalism' among populist radical right parties combining nativism with a pro-climate agenda. Based on a new data set on party positions on climate change, including 485 party manifestos – 76 from populist radical right parties – from the 1990s to 2022 in 10 Western European countries, we find that populist radical right parties are divided but generally less likely to speak out for climate protection than other parties, which rather contradicts the climate nationalism argument. We find that populist radical right parties only became more aware of the issue since 2019 in the face of the mass mobilizations of Fridays for Future and, to a lesser extent, when it became a visible issue within the party system. Thus, we argue that populist radical right parties are forced to talk about the climate when the issue is emphasized by organized actors. [ABSTRACT FROM AUTHOR]

The availability of reliable information on local climatic-tourism conditions is a growing need due to the influence it exerts on the quality of the organizational strategy of tourist destination's, and travel experience. Evaluations of the tourism potential of the climate have been carried out on a daily or monthly resolution, thus limiting the collection of detailed information that makes it possible to fine-tune tourism management and operational decision-making on an intraday scale. This research is the first case study to analyse the climatic suitability for nature tourism, using the weather types method at hourly resolution. The study applies to arid tourist destinations in Isfahan province (Iran). The detailed resolution has made it possible to identify the time slots favourable to the development of nature tourism in those periods of the year recognized as critical in the daily resolution analyses. In the same way, the hourly resolution has also identified critical bands in those periods indicated as favourable in the evaluations to daily resolution. The hourly resolution provides detailed information that can allow tourists and also tourism managers to establish intraday adaptation strategies that make it possible to develop the activity even in places with extreme climates. [ABSTRACT FROM AUTHOR]

Central America and the Caribbean are regularly battered by megadroughts, heavy rainfall, heat waves, and tropical cyclones. Although 21st‐century climate change is expected to increase the frequency, intensity, and duration of these extreme weather events (EWEs), their incidence in regional protected areas (PAs) remains poorly explored. We examined historical and projected EWEs across the region based on 32 metrics that describe distinct dimensions (i.e., intensity, duration, and frequency) of heat waves, cyclones, droughts, and rainfall and compared trends in PAs with trends in unprotected lands. From the early 21st century onward, exposure to EWEs increased across the region, and PAs were predicted to be more exposed to climate extremes than unprotected areas (as shown by autoregressive model coefficients at p < 0.05 significance level). This was particularly true for heat waves, which were projected to have a significantly higher average (tested by Wilcoxon tests at p < 0.01) intensity and duration, and tropical cyclones, which affected PAs more severely in carbon‐intensive scenarios. PAs were also predicted to be significantly less exposed to droughts and heavy rainfall than unprotected areas (tested by Wilcoxon tests at p < 0.01). However, droughts that could threaten connectivity between PAs are increasingly common in this region. We estimated that approximately 65% of the study area will experience at least one drought episode that is more intense and longer lasting than previous droughts. Collectively, our results highlight that new conservation strategies adapted to threats associated with EWEs need to be tailored and implemented promptly. Unless urgent action is taken, significant damage may be inflicted on the unique biodiversity of the region. [ABSTRACT FROM AUTHOR]

Cities in South America face many of the urban water supply challenges experienced globally including increasingly extreme future hydroclimatic conditions and rapid population growth. These challenges are further exacerbated by historical socioeconomic inequity, informal land occupation, and poor water services management. In recent years, decision support tools that aid in structuring water supply management and infrastructure pathway policies that remain robust under deeply uncertain future scenarios have been developed. However, within the context of developing countries, failing to acknowledge the complex social and institutional dynamics and stark differences in residents' experiences of climate extremes may lead to uneven adaptation capacities across socioeconomic strata. Toward this end, our study extends the deeply uncertain pathways framework by applying multiobjective optimization, disaggregated service area-level assessments of performance and vulnerability across time, and exploratory visual analytics in the Federal District of Brazil (FDB). We highlight the performance and robustness disparities between two water supply service areas in the FDB that differ in socioeconomic standing to reveal the impacts of deeply uncertain future hydroclimatic and socioeconomic scenarios on vulnerable populations. We further show that historical inequity renders poorer residents significantly more vulnerable to deeply uncertain future conditions without urgent and significant infrastructure investments. Overall, the outcomes of our study are largely applicable to urban water utilities in regions with high levels of historical regional inequity seeking to develop water management and infrastructure planning policies that are robust, adaptive, and equitable. [ABSTRACT FROM AUTHOR]

Objective: To investigate the impact of extreme climate on geological disasters in Shanxi and propose effective disaster prevention and mitigation strategies. Methods: Using daily temperature and precipitation data from 27 meteorological stations in Shanxi Province from 1975 to 2020, 32 extreme climate indices were calculated. Combined with geological disaster site data, the distribution characteristics of extreme climates and their relationship with geological disasters were analyzed, and a regression model for geological disaster risk zones was constructed. Results: Sixteen extreme climate indices in Shanxi Province showed significant changes, especially TMAXmean (100% significant). Indices related to negative precipitation effects showed a declining trend, with 77.78% being significant, while 96.3% of positive temperature effect indices showed an increasing trend, with 73.6% being significant. Geological disaster hotspots were concentrated in the mid-altitude (500–1500 m) hilly and low mountain areas along the central north–south axis and on Q and Pz strata. Extreme high-temperature indices were significantly positively correlated with geological disaster hotspots, while extreme low-temperature indices were negatively correlated. Indices related to extreme heavy precipitation (e.g., R99p.Slope, RX5day.Slope) were associated with an increase in geological disaster hotspots, whereas higher total precipitation and frequent heavy precipitation events were associated with a decrease in disaster hotspots. The grey relational degree between the Z-score and TXn.Slope, TXx.Slope, GSL.Slope, and TX90P.Slope was greater than 0.8. The random forest model performed best in evaluation metrics such as MAE, RMSE, and R2. Conclusions: Shanxi is likely to experience more extreme high-temperature and precipitation events in the future. The low-altitude hilly and terraced areas in Zones III and VII are key regions for geological disaster prevention and control. High temperatures and extreme rainfall events generally increase the disaster risk, while higher total precipitation reduces it. The random forest model is the optimal tool for predicting geological disaster risks in Shanxi Province. [ABSTRACT FROM AUTHOR]

This study presented an assessment of climate extremes in the Southeast Asia (SEA) region, utilizing downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The study outputs uncovered statistically significant trends indicating a rise in extreme precipitation and temperature events throughout SEA for both the near-term (2021–2060) and long-term (2061–2100) future under both SSP245 and SSP585 scenarios, in comparison to the historical period (1950–2014). Moreover, we investigated the seasonal fluctuations in rainfall and temperature distributions, accentuating the occurrence of drier dry seasons and wetter rainy seasons in particular geographic areas. The focused examination of seven prominent cities in SEA underscored the escalating frequency of extreme rainfall events and rising temperatures, heightening the urban vulnerability to urban flooding and heatwaves. This study's findings enhance our comprehension of potential climate extremes in SEA, providing valuable insights to inform climate adaptation, mitigation strategies, and natural disaster preparedness efforts within the region. [ABSTRACT FROM AUTHOR]

The Arabian Peninsula, with its rugged mountains, wadis, alluvial plains, sand dune deserts, and diverse coastlines, spans over 3 million km2. The Peninsula is situated at the crossroads of Africa and Asia and is a meeting point for diverse biogeographic realms, including the Palearctic, Afrotropical, and Indomalayan regions. This convergence of biogeographic zones has resulted in a remarkably diverse flora and fauna, which is adapted to the harsh and varied climates found throughout the Peninsula. Each of the countries of the Arabian Peninsula are biologically diverse and unique in their own right, but Yemen, Saudi Arabia, and Oman are the most diverse in terms of their landforms and biological diversity. The mountainous regions support a cooler and more moderate climate compared to the surrounding lowlands, thus forming unique ecosystems that function as refugia for plant and animal species, and have a high endemism of plant species. The desert ecosystems support a variety of lifeforms that are specially adapted to an extreme arid climate. Due to its long history of human habitation and subsistence agriculture, particularly in the mountainous areas, the Arabian Peninsula possesses unique crop varieties adapted to extreme arid climates, making them important genetic resources for the future in the face of climate change. The Arabian Peninsula, though rich and diverse in its biological diversity, has been greatly affected by human activities, especially in the last 50 years, including urbanization, habitat destruction, overgrazing, and climate change, which pose significant threats to the biodiversity of the region. This review presents the biogeography and background of conservation efforts made in the countries in the Arabian Peninsula and gives the progress made in botanical research and conservation practices throughout the Peninsula. [ABSTRACT FROM AUTHOR]

Extreme climate change is an existential threat to humanity and infrastructure development. At the same time, the construction and operation of carbon-intense public–private partnership (PPP) infrastructure such as road transport, water, public houses, energy supply and sanitation unleash most of the greenhouse gas emissions that impacts negatively on the climate. Increasingly, there is a heightened interests in the development and financing of climate-smart PPP solutions to promote resilient and sustainable public infrastructures. Therefore, this article aims at identifying the critical solutions to the provision of climate-smart PPP infrastructure projects together with the driving factors and challenges of its implementation in public facilities. The paper utilized a systematic literature review method where data were sourced from prominent academic databases of Scopus, Web of Science, Google Scholar, and PubMed. The outcomes of the review demonstrate that the adoption of climate finance, renewable energy, and maintaining resilient infrastructures are prominent solutions to attain low-carbon infrastructure development. Key drivers such as the global call to reduce huge emissions from construction projects and transition to sustainable green construction management account for the shift toward climate-smart PPP projects. The barriers identified include poor and unconcerted practice and policy directions to resolve emission problems in the construction industry. The outcomes of this article provide incentives for the development and management of climate-smart public projects. Researchers can harness the results to investigate and develop adaptation and mitigation strategies for low-carbon PPP projects. [ABSTRACT FROM AUTHOR]

Significant increase in wintertime air temperature, especially the reduced cold extremes under climate change, might be beneficial to the winter survival of perennial crops. However, climate warming could result in less snowfall, reduced snow cover, as well as changes in climate conditions for fall hardening and winter thaws. How these changes might impact the risks of winter damages to overwintering crops, such as perennial forage crops requires a comprehensive assessment for proactively adapting to climate change in the agricultural sector, especially the beef and dairy industries. Based on the most up-to-date climate projections from a set of global climate models, we used a snow model and a suite of agroclimatic indices for perennial forage crops to assess potential changes in the risks of winter injury to perennial forage crops across Canada in the near-term (2030s), the mid-term (2050s), and the distant future (2070s). Our results show that the risk of exposure to extremely low temperatures (daily Tmin ≤ −15 °C) without snow protection is projected to decrease across Canada with improved conditions for fall hardening. However, winter thaws and rainfall are projected to increase, and this would increase the risk of winter injury due to loss of hardiness together with potential soil heaving and ice encasement. [ABSTRACT FROM AUTHOR]

Extreme climatic events such as droughts, floods, cyclones, and landslides have adversely affected a homegarden that consists of its inherited characteristics as a secondary forest along with alterations made by human. Households, as the major beneficiary of homegardens, have the responsibility to maintain the homegarden incorporating its structure and composition with functions of the household to make the homegarden resilient to adverse effects. To assess the current climate resilience level of a homegarden, data were collected through an in-depth survey from 435 homegardens in Hambantota, Jaffna, Matale, and Ratnapura districts in Sri Lanka. Climate Resilience Index (CRI) of homegarden was constructed based on absorptive and adaptive capacities using the 13 variables. The mean value of the CRI was 0.375 (± 0.005) which ranged from 0.019 to 0.616. As per the multiple regression analysis performed taking the CRI as the dependent variable, the constructed models were complex, and all the variables were required to determine the level of resilience of a homegarden. Based on the CRI values five categories of homegardens were identified as very low, low, moderate, high and very high resilient. Accordingly, key variables affecting the level of resilience of a homegarden were; application of soil and water conservation methods, requirement of soil conservation, presence of woody trees, above-ground biomass, number of canopy strata, experience in farming and technical knowledge on farming, species richness of tree were selected as resilient variables that indicate absorptive and adaptive capacities. The resilience level of a homegarden can be changed, by altering these variables together. [ABSTRACT FROM AUTHOR]

Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts. The study employed the Coupled Model Intercomparison Project 6 models in simulating climate extreme indices under the Shared Socioeconomic Pathway scenarios (SSPs) over West Africa between 1979 and 2059 as exemplified by the Greater Accra region. The study observed a generally weak drought in the historical period and expected to intensify especially under SSP585 in Greater Accra. For instance, continuous dry days (CDD) reveal an increasing trend under the SSPs. Similarly, the overall projected trend of CDD over West Africa reveals an increase signifying a more frequent and longer drought in the future. The flood indices revealed a surge in the intensity and duration of extreme precipitation events under the SSPs in the region. For instance, R99pTOT and Rx5days are expected to significantly increase under the SSPs with intensification under the SSP245, SSP370, and SSP585. A similar trend has been projected across West Africa, especially along the Guinean coast. The study foresees a gradual and intensifying rise in heatwave indices over the Greater Accra region. The warming and cooling indices reveal an increasing and decreasing trend respectively in the historical period as well as under the SSPs particularly within urban centers like Accra and Tema. Most West African countries are projected to observe more frequent warm days and nights with cold nights and days becoming less frequent. Expected effects of future climate extreme indices pose potential threats to the water, food, and energy systems as well as trigger recurrent floods and droughts over Greater Accra. The findings of the study are expected to inform climate policies and the nationally determined contribution of the Paris Agreement as well as address the sustainable development goal 11 (Sustainable cities) and 13 (Climate action) in West Africa. [ABSTRACT FROM AUTHOR]

Background: Tree rings play an important role in reconstructing past climate. Growth differences among individual trees due to microclimatic conditions and local disturbances are averaged in developing tree-ring chronologies. Here, we addressed the problem of averaging by investigating growth synchrony in individual trees. We used tree-ring data of 1046 juniper trees from 32 sites on the Tibetan Plateau and 538 pine trees from 20 sites in the subtropical region of eastern China and calculated the tree-growth synchrony index (TGS). Results: Our results showed that both the TGS index and tree-ring index could be indicators of interannual variation of climatic factors. The TGS index identified 20% more climatic extremes than tree-ring index over the last 50 years that high synchrony indicates extreme climate forcing in controlling forest growth. Conclusions: The TGS index can identify extreme climatic events effectively than tree-ring index. This study provides a novel perspective for climate reconstruction, especially in the realm of tree growth response to extreme climate. Our findings contribute to understanding of the spatiotemporal dynamics and the causes of historical climate extremes and provide guidance for protecting trees from climate extremes in the future. [ABSTRACT FROM AUTHOR]

Changing climatic conditions threaten forest ecosystems. Drought, disease and infestation, are leading to forest die-offs which cause substantial economic and ecological losses. In central Europe, this is especially relevant for commercially important coniferous tree species. This study uses climate envelope exceedance (CEE) to approximate species risk under different future climate scenarios. To achieve this, we used current species presence-absence and historical climate data, coupled with future climate scenarios from various Earth System Models. Climate scenarios tended towards drier and warmer conditions, causing strong CEEs especially for spruce. However, we show that annual averages of temperature and precipitation obscure climate extremes. Including climate extremes reveals a broader increase in CEEs across all tree species. Our study shows that the consideration of climate extremes, which cannot be adequately reflected in annual averages, leads to a different assessment of the risk of forests and thus the options for adapting to climate change. [ABSTRACT FROM AUTHOR]

Accurate prediction of regional terrestrial water storage change (TWSA) is of great significance for water resources planning and management, and early warning of extreme climate disasters. Aiming at the problem that the conventional methods on prediction of TWSA time series are difficult to be accurate, the six typical regions are selected in China as examples, including the upper reaches of the Yangtze River (UYR), the southwest region (SWR), the Liaohe River Basin (LRB), the North China Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl River Basin (PRB). The mascon product from GRACE/GRACE-FO provided by CSR is used to extract TWSA time series in six typical areas. The improved Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) neural network and the latest Bidirectional LSTM (BiLSTM-attention) neural network model based on attention mechanism are proposed to predict and analyze the regional TWSA. In the experiment, the selection of the optimal model parameters such as the number of hidden layer nodes and the number of hidden units of the neural network model is tested and analyzed in detail. Meanwhile, the model prediction results are compared with the traditional least squares method and random forest (RF) prediction method. The root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) were used to evaluate the accuracy of the predicted results. The results show that the improved BP, LSTM and Bi-LSTM-attention neural network models all achieve higher prediction accuracy in UYR and SWR areas. RMSE is less than 2.641 cm, R2 is as high as 0.8 or more, NSE is above 0.6, and MAPE is within 0.1. Compared with the least square method, the RMSE of the predicted results from three neural network decreased by 0.998 cm, 0.700 cm and 0.7563 on average, and the R2 increased by 81.75%, 69.89% and 72% on average. Compared with RFML method, the RMSE from three neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and R2 is increased by 38.20%, 24.60% and 27.06% on average. NSE and RMSE are improved to varying degrees in the above regions. It shows that the improved BP, LSTM and BiLSTM-attention model used can effectively predict TWSA. The research methods and results in this paper can provide important reference for the rational utilization of regional water resources and disaster risk assessment. [ABSTRACT FROM AUTHOR]

Agriculture is one of the major economic sectors in Africa, and it predominantly depends on the climate. However, extreme climate changes do have a negative impact on agricultural production. The damage resulting from extreme climate change can be mitigated if farmers have access to accurate weather forecasts, which can enable them to make the necessary adjustments to their farming practices. To improve weather prediction amidst extreme climate change, we propose a novel prediction model based on a hybrid of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), local mean decomposition (LMD), and artificial neural networks (NN). A detailed comparison of the performance metrics for the short- and long-term prediction results with other prediction models reveals that the three-phase hybrid CEEMDAN-LMD-NN model is optimal in terms of the evaluation metrics used. The study's findings demonstrate the efficiency of the three-phase hybrid CEEMDAN-LMD-NN prediction model in decision-system design, particularly for large-scale commercial farmers, small-holder farmers, and the agricultural index insurance industry that require reliable forecasts generated at multi-step horizons. [ABSTRACT FROM AUTHOR]

Droughts can affect ecosystem CO2 fluxes directly or indirectly by changing plant community composition. However, it is unknown whether shifts in plant community composition buffer or amplify the response of ecosystem CO2 fluxes to droughts with different seasonal timing, as plant phenology and physiology of the different plant functional types respond differently to droughts. To identify the interaction of drought timing and plant community composition in regulating ecosystem CO2 fluxes, we conducted a three‐year manipulative experiment in which extreme droughts occurring in the early, mid and late growing seasons were separately imposed on experimental plot communities comprising graminoids, shrubs and their combination in a semi‐arid grassland of Inner Mongolia, China. Overall, mid‐season drought caused the largest negative effects regardless of plant community composition. In addition to decreasing aboveground biomass, mid‐season drought suppressed fluxes by reducing leaf photosynthetic rate, while early‐season and late‐season drought reduced fluxes mainly by shortening growing season length. All three community compositions had consistent responses to early‐season and mid‐season droughts. However, ecosystem CO2 fluxes in the combination community were less negatively affected by late‐season drought than in either shrub or graminoid communities because the growing season length was shortened less. Synthesis. Our results highlight that it is important to account for interactions of seasonal timing and plant community composition when predicting magnitude and pathways of drought effects on ecosystem carbon cycling. [ABSTRACT FROM AUTHOR]

Global climate change is leading to increased frequency of extreme climatic events, higher temperatures and water scarcity. Tef (Eragrostis tef (Zucc.) Trotter) is an underutilized C4 cereal crop that harbors a rich gene pool for stress resilience and nutritional quality. Despite gaining increasing attention as an "opportunity" crop, physiological responses and adaptive mechanisms of tef to drought stress have not been sufficiently investigated. This study was aimed to characterize the dynamic physiological responses of tef to drought. Six selected tef genotypes were subjected to high-throughput whole-plant functional phenotyping to assess multiple physiological responses to contrasting water regimes. Drought stress led to a substantial reduction in total, shoot and root dry weights, by 59%, 62% and 44%, respectively (averaged across genotypes), and an increase of 50% in the root-to- shoot ratio, relative to control treatment. Drought treatment induced also significant reductions in stomatal conductance, transpiration, osmotic potential and water-use efficiency, increased chlorophyll content and delayed heading. Tef genotypes exhibited diverse water-use strategies under drought: water-conserving (isohydric) or non-conserving (anisohydric), or an intermediate strategy, as well as variation in drought-recovery rate. Genotype RTC-290b exhibited outstanding multifaceted drought-adaptive performance, including high water-use efficiency coupled with high productivity under drought and control treatments, high chlorophyll and transpiration under drought, and faster drought recovery rate. This study provides a first insight into the dynamic functional physiological responses of tef to water deficiency and the variation between genotypes in drought-adaptive strategies. These results may serve as a baseline for further studies and for the development of drought-resistant tef varieties. [ABSTRACT FROM AUTHOR]

Heat extremes frequently hit different regions synchronously during boreal summer over the Eurasian continent. A remarkable coupling is first revealed between Eurasian heat extreme occurrence and individual extratropical intraseasonal oscillation (EISO). Further, the combined EISOs facilitate and largely increase the occurrence probabilities of synchronous Eurasian heat extremes. These dominant combined EISOs together contribute 20–45% to the total heat extreme days over the five Eurasian regions where the climatological heat extremes occur most frequently. A multi-model hindcast further shows that the subseasonal prediction exhibits higher skills for synchronous heat extremes over the combined-EISO hotspot regions when the associated combined EISOs are active, supporting the notion that the monitoring and prediction of EISOs are crucial for heat extremes' early warning. Skillful prediction of EISOs opens a pathway for heat extremes' prediction by extending it from the weather to the subseasonal timescales. [ABSTRACT FROM AUTHOR]

The food crisis has increased demand for agricultural resources due to various factors such as extreme weather, energy crises, and conflicts. A solar greenhouse enables counter-seasonal winter cultivation due to its thermal insulation, thus alleviating the food crisis. The root temperature is of critical importance, although the mechanism of soil thermal environment change remains uncertain. This paper presents a comprehensive study of the soil thermal environment of a solar greenhouse in Jinzhong City, Shanxi Province, employing a variety of analytical techniques, including theoretical, experimental, and numerical simulation, and deep learning modelling. The results of this study demonstrate the following: During the overwintering period, the thermal environment of the solar greenhouse floor was divided into a low-temperature zone, a constant-temperature zone, and a high-temperature zone; the distance between the low-temperature boundary and the southern foot was 2.6 m. The lowest temperature in the low-temperature zone was 11.06 °C and the highest was 19.05 °C. The floor in the low-temperature zone had to be heated; the lowest value of the constant-temperature zone was 18.29 °C, without heating. The minimum distance between the area of high temperature and the southern foot of the solar greenhouse was 8 m and the lowest temperature reading was 19.29 °C. The indoor soil temperature tended to stabilise at a depth of 45 cm, and the lowest temperature reading at a horizontal distance of 1400 mm from the south foot was 19.5 °C. The Fluent and LSTM models fitted well and the models can be used to help control soil temperature during overwintering in extreme climates. The research can provide theoretical and data support for the crop areas and the heating of pipelines in the solar greenhouse. [ABSTRACT FROM AUTHOR]

Over the last decade, a large effort has been made to understand how extreme climate events disrupt species interactions. Yet, it is unclear how these events affect plants and herbivores directly, via metabolic changes, and indirectly, via their subsequent altered interaction. We exposed common milkweed (Asclepias syriaca) and monarch caterpillars (Danaus plexippus) to control (26:14°C, day:night) or heat wave (HW) conditions (36:24°C, day:night) for 4 days and then moved each organism to a new control or HW partner to disentangle the direct and indirect effects of heat exposure on each organism. We found that the HW directly benefited plants in terms of growth and defence expression (increased latex exudation and total cardenolides) and insect her'bivores through faster larval development. Conversely, indirect HW effects caused both plant latex and total cardenolides to decrease after subsequent herbivory. Nonetheless, increasing trends of more toxic cardenolides and lower leaf nutritional quality after herbivory by HW caterpillars likely led to reduced plant damage compared to controls. Our findings reveal that indirect impacts of HWs may play a greater role in shaping plant‐herbivore interactions via changes in key physiological traits, providing valuable understanding of how ecological interactions may proceed in a changing world. [ABSTRACT FROM AUTHOR]

Stationarity, a cornerstone in hydraulic design, is now under scrutiny due to anthropogenic activities and climate change. Numerous studies have sought to identify non‐stationarity (NS); however, a comprehensive assessment of time invariance in all statistical properties of a time series is less explored. This study presents a non‐overlapping block‐stratified random sampling (NBRS) framework leveraging the strengths of several nonparametric tests to assess NS. The NBRS approach exclusively detects NS and distinguishes between various forms of stationarity, including weak and strict. A variant of NBRS is proposed in this study to identify the underlying stochastic process(es) influencing NS in hydroclimatic extremes. Furthermore, a nonparametric clustering approach is used to unveil spatial clusters showcasing NS due to shifts in mean, variance, distribution of time series or a combination of these factors. A comparative assessment of the modified NBRS approach with traditional trend and change point methods is also performed. The proposed methodology is applied to assess the presence of NS in 28 hydroclimatic indices derived for the west‐central river basins of India, exhibiting diverse physio‐climatic settings, for the study period 1973–2021. The modified NBRS approach rigorously explores NS within extreme hydroclimatic indices, conclusively pinpointing its root causes and profound implications for hydrologic design. The applicability of the modified NBRS approach to gridded and point datasets is also demonstrated. The findings highlight the limitations of conventional trend and change point tests in capturing time‐invariant characteristics in heteroscedastic variables (such as streamflow and rainfall extremes) compared to the NBRS approach. The research reveals that NS in rainfall and streamflow extremes primarily results from distributional shifts, whilst temperature extremes are influenced by changes in mean and distribution properties. This research deepens our understanding of the evolving patterns in hydroclimatic extremes in a changing climate. [ABSTRACT FROM AUTHOR]

A novel sub‐sampling method has been used to isolate the dynamic effects of the response of the North Atlantic Oscillation (NAO) and the Siberian High (SH) from the total response to projected Arctic sea‐ice loss under 2°C global warming above preindustrial levels in very large initial‐condition ensemble climate simulations. Thermodynamic effects of Arctic warming are more prominent in Europe while dynamic effects are more prominent in Asia/East Asia. This explains less‐severe cold extremes in Europe but more‐severe cold extremes in Asia/East Asia. For Northern Eurasia, dynamic effects overwhelm the effect of increased moisture from a warming Arctic, leading to an overall decrease in precipitation. We show that the response scales linearly with the dynamic response. However, caution is needed when interpreting inter‐model differences in the response because of internal variability, which can largely explain the inter‐model spread in the NAO and SH response in the Polar Amplification Model Intercomparison Project. Plain Language Summary: The projected loss of Arctic sea‐ice under 2°C global warming will cause large warming in the Arctic region and climate and weather anomalies outside the Arctic. The warming in the Arctic will mean warmer airmasses coming from the Arctic and also more moisture from the open Arctic Ocean. Furthermore, it will also change atmospheric circulation. These effects together will determine the impacts of Arctic warming. In this study, we introduce a novel sub‐sampling method to isolate atmospheric circulation change in response to the Arctic warming. The method involves selecting members of simulations from the experiment with future Arctic sea‐ice conditions, the average of which is equal to the average of the members of simulations in the experiment with present‐day Arctic sea‐ice conditions. We found that atmospheric circulation change in European regions is relatively weak so that warming effects will dominate the climate and weather response there. On the other hand, atmospheric circulation change will dominate the climate and weather response in East Eurasia. We also found that stronger atmospheric circulation changes will generally increase the response to the Arctic warming. We suggest caution when assessing whether different responses in different models can be interpreted as true differences in model physics. Key Points: A novel sub‐sampling method is introduced to isolate the role of dynamics in the response to projected Arctic sea‐ice lossA dynamical Siberian High response dominates the temperature response over East Eurasia while that of the North Atlantic Oscillation is weakInter‐model differences in Polar Amplification Model Intercomparison Project likely contain a large fraction of internal variability due to the unconstrained dynamic effects [ABSTRACT FROM AUTHOR]