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The concept of solar geoengineering remains a topic of debate, yet it may be an effective way for cooling the Earth's temperature. Nevertheless, the impact of solar geoengineering on regional or local climate patterns is an active area of research. This study aims to evaluate the impact of solar geoengineering on precipitation and temperature extremes of the Muda River Basin (MRB), a very important agricultural basin situated in the northern Peninsular Malaysia. The analysis utilized the multi-model ensemble mean generated by four models that contributed to the Geoengineering Model Intercomparison Project (GeoMIP6). These models were configured to simulate the solar irradiance reduction (G6solar) and stratospheric sulfate aerosols (G6sulfur) strategies as well as the moderate (SSP245) and high emission (SSP585) experiments. Prior to the computation of extreme indices, a linear scaling approach was employed to bias correct the daily precipitation, maximum and minimum temperatures. The findings show that the G6solar and G6sulfur experiments, particularly the latter, could be effective in holding the increases in both annual and monthly mean precipitation totals and temperature extremes close to the increases projected under SSP245. For example, both G6solar and G6sulfur experiments project increases of temperature over the basin of 2 °C at the end of the 21st century as compared to 3.5 °C under SSP585. The G6solar and G6sulfur experiments also demonstrate some reliability in modulating the increases in precipitation extreme indices associated with flooding to match those under SSP245. However, the G6sulfur experiment may exacerbate dry conditions in the basin, as monthly precipitation is projected to decrease during the dry months from January to May and consecutives dry days are expected to increase, particularly during the 2045-2064 and 2065-2084 periods. Increases dry spells could indirectly affect agricultural and freshwater supplies, and pose considerable challenges to farmers.

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BACKGROUND: Recent climate changes have produced extreme climate events. This study focused on extreme snowfall and intended to discuss the vulnerability of temperate mammals against it through interspecies comparisons of spatial niches in northern Japan. We constructed niche models for seven non-hibernating species through wide-scaled snow tracking on skis, whose total survey length was 1144 km. RESULTS: We detected a low correlation (rs < 0.4) between most pairs of species niches, indicating that most species possessed different overwintering tactics. A morphological advantage in locomotion cost on snow did not always expand niche breadth. In contrast, a spatial niche could respond to (1) drastic landscape change by a diminishing understory due to snow, possibly leading to changes in predator-prey interactions, and (2) the mass of cold air, affecting thermoregulatory cost and food accessibility. When extraordinary snowfall occurred, the nonarboreal species with larger body sizes could niche shift, whereas the smaller-sized or semi-arboreal mammals did not. In addition, compared to omnivores, herbivores were prone to severe restriction of niche breadth due to a reduction in food accessibility under extreme climates. CONCLUSIONS: Dietary habits and body size could determine the redundancy of niche width, which may govern robustness/vulnerability to extreme snowfall events.

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Climate change is increasing the frequency and severity of short-term (~1 y) drought events-the most common duration of drought-globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function-aboveground net primary production (ANPP)-was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought.

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Anthropogenic climate change is increasingly threatening interpersonal violence, yet global evidence for related impacts and potential transmission mechanisms remains limited. We examine whether and how climate change, particularly climate extremes, affects interpersonal violence. Using the panel data of 140 countries and regions from 2000 to 2019, we find that hot and wet extremes precipitated increase in homicide rates globally. Economic level, inequality, and resources scarcity were important intermediaries through which climate extremes affected homicide, while the direct effects still dominated the total effects. We then reveal the heterogeneous effects of climate extremes, further suggesting that poor countries and regions with relatively small contributions to climate change were particularly sensitive to climate extremes. These findings elucidate a strong climate-violence link, helping explain implications of facilitating violence prevention and mitigating climate change.

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Primary productivity response to climatic drivers varies temporally, indicating state-dependent interactions between climate and productivity. Previous studies primarily employed equation-based approaches to clarify this relationship, ignoring the state-dependent nature of ecological dynamics. Here, using 40 y of climate and productivity data from 48 grassland sites across Mongolia, we applied an equation-free, nonlinear time-series analysis to reveal sensitivity patterns of productivity to climate change and variability and clarify underlying mechanisms. We showed that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions, with the opposite pattern observed for annual mean temperature. Furthermore, productivity responded negatively to decreasing annual aridity that integrated precipitation and temperature across Mongolia. Productivity responded negatively to interannual variability in precipitation and aridity in mesic regions but positively in arid regions. Overall, interannual temperature variability enhanced productivity. These response patterns are largely unrecognized; however, two mechanisms are inferable. First, time-delayed climate effects modify annual productivity responses to annual climate conditions. Notably, our results suggest that the sensitivity of annual productivity to increasing annual precipitation and decreasing annual aridity can even be negative when the negative time-delayed effects of annual precipitation and aridity on productivity prevail across time. Second, the proportion of plant species resistant to water and temperature stresses at a site determines the sensitivity of productivity to climate variability. Thus, we highlight the importance of nonlinear, state-dependent sensitivity of productivity to climate change and variability, accurately forecasting potential biosphere feedback to the climate system.

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The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low latency to determine biosphere responses and carbon-climate feedbacks. Here, we develop a satellite-based rapid attribution workflow and demonstrate its use at a 1-2-month latency to attribute drivers of the carbon cycle feedbacks during the 2020-2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil-plant-atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture-deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions.

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In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used to advance the estimation because it allowed for statistical analysis and comparison of climatological elements such time series, means, extremes, and trends. During these 69 years, a more erratic DTR trend was seen in the research area. This study investigates the suitability of three deep neural networks for one-step-ahead DTR time series (DTRTS) forecasting, including recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and auto-regressive integrated moving average exogenous (ARIMAX). To evaluate the effectiveness of models in the testing set, six statistical error indicators, including root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), percent bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd), were chosen. The Wilson score approach was used to do a quantitative uncertainty analysis on the prediction error to forecast the outcome DTR. The findings show that the LSTM outperforms the other models in terms of its capacity to forget, remember, and update information. It is more accurate on datasets with longer sequences and displays noticeably more volatility throughout its gradient descent. The results of a sensitivity analysis on the LSTM model, which used RMSE values as an output and took into account different look-back periods, showed that the amount of history used to fit a time series forecast model had a direct impact on the model's performance. As a result, this model can be applied as a fresh, trustworthy deep learning method for DTRTS forecasting.

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SignificanceThe western Pacific subtropical high (WPSH) channels moisture from the tropics that underpins the East Asian summer climate. Interannual variability of the WPSH dominates climate extremes in the densely populated countries of East Asia. In 2020, an anomalously strong WPSH led to catastrophic floods with hundreds of deaths, 28,000 homes destroyed, and tens of billions in economic damage in China alone. How the frequency of such strong WPSH events will change is of great societal concern. Our finding of an increase in future WPSH variability, translating into an increased frequency of climate extreme as seen in the 2020 episode, highlights the increased risks for the billions of people in the densely populated East Asia with profound socioeconomic consequences.

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The longleaf pine (LLP) savanna ecosystem once covered ~ 92 million acres of the Southeast USA, but due to anthropogenic activities such as logging and fire suppression, only 3% of its once widespread historic range remains. While many restoration efforts are underway to conserve this biodiverse ecosystem, restoration must be done in the context of climate change. In the last few decades, heatwaves have increased in frequency and intensity across the Southeastern USA with further increases predicted. To expand our understanding of LLP savanna restoration in light of these changes, we ran a series of three simulated heatwave greenhouse experiments through a Course-based Undergraduate Research Experience (CURE) incorporating ~ 150 undergraduate researchers per experiment. We measured plant growth metrics for four understory grasses commonly used in LLP savanna restoration efforts. We found that while most grass plug individuals survived heatwave conditions, aboveground production was reduced due to heatwaves. This productivity decrease could result in less biomass available for the essential vegetation fire feedback loop, where fire increases grass biomass, and in turn, more grass provides more fuel for fire. These results imply that land managers can proactively compensate for biomass loss due to heatwaves by planting more grass plugs during initial restoration. Supplementary Information: The online version contains supplementary material available at 10.1007/s11258-021-01212-7.

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The influences of climate change on the features of extreme rainfall events have become unprecedented that needs improved understanding at all levels for planning effective management strategies of the potential risks. This study aims to assess the potential influences of climate change on extreme rainfall characteristics in flood-vulnerable city of Adama. Daily precipitation records of 1967-2016 and projection of global circulation models (GCMs): CanESM2 and HadCM3 for 2021-2070 were disaggregated into shorter time resolutions using the Hyetos model. Gumbel type I probability distribution and power-regression model ([Formula: see text] were used for deducing intensity-duration-frequency (IDF) curves and for describing their functions, respectively. The extreme rainfall intensity of the historical and future periods for a range of storm durations and return periods were compared and contrasted. A close agreement is obtained between the observed and the modeled rainfall intensity with high values of coefficient of determination (> 0.996) and Nash-Sutcliffe efficiency (> 0.850). Besides, statistically significant (p < 0.05) direct linear relationship is found between the return periods and the coefficient parameter of the IDF models. Moreover, the intensity of extreme precipitation over 2021-2070 in Adama city would increase up to 49.5%, depending on storm duration and return period considered. This could have consequences of the way the city's drainage infrastructures are designed, operated, and sustained. Hence, flood-prone areas should be recognized in order to formulate effective strategies for mitigation and adaption of potential impacts. The standards for designing future drainage infrastructures should also be updated aiming to reflect the effects of climatic change.

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The analysis of multi-temporal and spatial trends of rainfall in a river basin is an essential approach for water resource planning and management approach. In this study, a combination of trend analysis and spatial-temporal variability of the rainfall for 1970-2017 was applied to examine rainfall distribution patterns in a coastal watershed, Santa Maria da Vitória River Basin (southeastern Brazil). Data from 42 meteorological stations were analyzed using kriging as a geostatistical tool for point data interpolation. Trends in rainfall were computed using the RClimDex package with eleven extreme climate indices. The results have shown spatial and temporal rainfall variability, with drought events becoming more persistent in recent years in the upper sector of the basin, where agricultural land use prevails. Water shortage may impact crops and threatening the water supply and hydropower production. Trend analysis suggests that the annual total wet-day precipitation (PRCPTOT) increases in the coastal section and decreases in the upper basin sector. Consecutive dry days (CDD) and consecutive wet days (CWD) show a strong positive tendency in the lower basin section, where the metropolitan area is located, flooding risks increase in response to positive trends of intensive short-term rainfall events. These results support managers developing and planning sustainability strategies to assure water security and subsidize adaptative responses to extreme hydrological events.

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Temperature and precipitation are the two most critical climate variables and their extreme states have more severe impacts than average states on both human society and natural ecosystem. In this study, an integrated multivariate trend-frequency analysis (IMTFA) approach is developed for the risk assessment of climate extremes under the global warming. Through incorporating multiple time series analysis techniques (i.e., M-K test, Sen's slope estimator and Pettitt test) and copula function into a general framework, IMTFA is capable not only of analyzing the temporal trends and change points of extreme temperatures and precipitations, but also of quantifying their univariate and multivariate risks. IMTFA is applied to the Central Asia with considering a long-term (1881-2018) observation data. Our findings are: (i) significant wetting and warming trends were occurred in the Central Asia over past one hundred years, where 42.5%, 59.4% and 79.2% stations have change points for extreme precipitations, maximum and minimum temperatures, respectively; (ii) the occurrences of extreme climate events show obviously spatial heterogeneity, where the highest risks of meteorological drought, flood and frost events are occurred in the southwest, southeast and northeast regions, respectively; (iii) global warming significantly affects the intensities and frequencies of extreme precipitations and temperatures, and their univariate and multivariate risks are intensified in the most regions of Central Asia. The above findings can provide more valuable information for risk assessment and disaster adaptation of climate extremes in Central Asia.

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Tropicalization is a term used to describe the transformation of temperate ecosystems by poleward-moving tropical organisms in response to warming temperatures. In North America, decreases in the frequency and intensity of extreme winter cold events are expected to allow the poleward range expansion of many cold-sensitive tropical organisms, sometimes at the expense of temperate organisms. Although ecologists have long noted the critical ecological role of winter cold temperature extremes in tropical-temperate transition zones, the ecological effects of extreme cold events have been understudied, and the influence of warming winter temperatures has too often been left out of climate change vulnerability assessments. Here, we examine the influence of extreme cold events on the northward range limits of a diverse group of tropical organisms, including terrestrial plants, coastal wetland plants, coastal fishes, sea turtles, terrestrial reptiles, amphibians, manatees, and insects. For these organisms, extreme cold events can lead to major physiological damage or landscape-scale mass mortality. Conversely, the absence of extreme cold events can foster population growth, range expansion, and ecological regime shifts. We discuss the effects of warming winters on species and ecosystems in tropical-temperate transition zones. In the 21st century, climate change-induced decreases in the frequency and intensity of extreme cold events are expected to facilitate the poleward range expansion of many tropical species. Our review highlights critical knowledge gaps for advancing understanding of the ecological implications of the tropicalization of temperate ecosystems in North America.

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Elucidating the variation in grassland belowground biomass (BGB) and its response to changes in climatic variables are key issues in plant ecology research. In this study, BGB data for five ecoregions (cold steppe, temperate dry steppe, savanna, humid savanna, and humid temperate) were used to examine the effects of climatic variability and extremes on the BGB of C3- and C4-dominated grasslands. Results showed that BGB varied significantly across the ecoregions, with the highest levels in cold steppe and the lowest in savanna. The results indicated that growing-season temperature, maximum and minimum temperatures and their interactions had significantly positive effects on the single-harvest BGB of C3 plants in colder ecoregions (i.e., humid temperate and cold steppe) and of C4 plants in arid ecoregions (i.e., temperate dry steppe and savanna). The single-harvest BGB of C3 plants in arid ecoregions and C4 plants in humid savanna ecoregion declined with increasing temperature during the growing season. Growing-season precipitation exerted significant positive effects on the single-harvest BGB of C4 plants in arid ecoregions. Annual temperature variables negatively impacted the annual BGB of humid temperate ecoregion, because of the dominance of C3 plants. Increasing cumulative growing-season precipitation elevated and the mean annual temperature reduced the annual BGB of both categories of plants in arid ecoregions. Compared with normal climates, extreme dry events during the growing season enhanced single-harvest BGB in colder ecoregions. The single-harvest BGB of C4 plants in savanna tended to increase during extreme wet and decrease during moderate dry events compared to normal climates. This study suggests that the differential effects of climatic variability and extremes on BGB can be explained by differences in plant types, and ecoregions. These findings on the responses of the BGB to climatic variability and extremes constitute important scientific evidence emphasizing the need to maintain ecosystem stability across ecoregions.

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As a consequence of ongoing climate change, the frequency of extreme heat events is expected to increase. Recurring heat pulses may disrupt functions supported by soil microorganisms, thus affecting the entire ecosystem. However, most perturbation experiments only test effects of single heat events, and therefore it remains largely unknown how soil microorganisms react to repeated pulse events. Here we present data from a lab experiment exposing 32 filamentous fungi, originally isolated from the same soil, to sequential heat perturbations. Soil saprobic fungi isolates were exposed to one or two heat pulses: mild (35°C/2 h), strong (45°C/1 h), or both in sequence (35°C/2 h+45°C/1 h), and we assessed growth rate. Out of the 32 isolates 13 isolates showed an antagonistic response, 3 isolates a synergistic response and 16 isolates responded in an additive manner. Thus the 32 filamentous fungal isolates used here showed the full range of possible responses to an identical heat perturbation sequence. This diversity of responses could have consequences for soil-borne ecosystem services, highlighting the potential importance of fungal biodiversity in maintaining such services, particularly in the context of climate change.

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Ocean temperature extreme events such as marine heatwaves are expected to intensify in coming decades due to anthropogenic global warming. Reported ecological and economic impacts of marine heatwaves include coral bleaching, local extinction of mangrove and kelp forests and elevated mortalities of invertebrates, fishes, seabirds and marine mammals. In contrast, little is known about the impacts of marine heatwaves on microbes that regulate biogeochemical processes in the ocean. Here we analyse the daily output of a near-global ocean physical-biogeochemical model simulation to characterize the impacts of marine heatwaves on phytoplankton blooms in 23 tropical and temperate oceanographic regions from 1992 to 2014. The results reveal regionally coherent anomalies of shallower surface mixing layers and lower surface nitrate concentrations during marine heatwaves. These anomalies exert counteracting effects on phytoplankton growth through light and nutrient limitation. Consequently, the responses of phytoplankton blooms are mixed, but can be related to the background nutrient conditions of the study regions. The blooms are weaker during marine heatwaves in nutrient-poor waters, whereas in nutrient-rich waters, the heatwave blooms are stronger. The corresponding analyses of sea-surface temperature, chlorophyll a and nitrate based on satellite observations and in situ climatology support this relationship between phytoplankton bloom anomalies and background nitrate concentration. Given that nutrient-poor waters are projected to expand globally in the 21st century, this study suggests increased occurrence of weaker blooms during marine heatwaves in coming decades, with implications for higher trophic levels and biogeochemical cycling of key elements.

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In tropics, especially Southeast Asia (SEA), heat wave (HW) research is seriously scarce although several global studies have projected this region to be greatly susceptible to increasing HW events under climate change scenarios. Using the recently released ERA5 reanalysis data, we find that in most parts of SEA, HWs are becoming more frequent, longer-lasting and stronger, no matter using dry-bulb or wet-bulb temperatures to define HW. The increasing trends of HW characteristics based on minimum temperatures are larger than those based on maximum temperatures, suggesting an alarming situation of anomalously warm night. HW characteristics based on wet-bulb temperatures show higher increasing rates in the IndoChina Peninsula and Malay Peninsula than those based on dry-bulb temperatures. Nearly all HW characteristics are significantly correlated with El Niño index, but Indian Ocean Dipole only significantly impacts HW characteristics based on wet-bulb temperature in Java. Results derived from other reanalysis products exhibit general agreement with those from ERA5, lending support to the findings reported herein. This study highlights the different role of humidity in changing HW trends in different regions of SEA, and calls for attention to the associated risk of increasing nighttime temperatures during HWs.

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Climate-related disasters cause substantial disruptions to human societies. With climate change, many extreme weather and climate events are expected to become more severe and more frequent. The International Disaster Database (EM-DAT) records climate-related disasters associated with observed impacts such as affected people and economic damage on a country basis. Although disasters are classified into different meteorological categories, they are usually not linked to observed climate anomalies. Here, we investigate countrywide climate features associated with disasters that have occurred between 1950 and 2015 and have been classified as droughts, floods, heat waves, and cold waves using superposed epoch analysis. We find that disasters classified as heat waves are associated with significant countrywide increases in annual mean temperature of on average 0.13 ∘C and a significant decrease in annual precipitation of 3.2%. Drought disasters show positive temperature anomalies of 0.08 ∘C and a 4.8 % precipitation decrease. Disasters classified as droughts and heat waves are thus associated with significant annual countrywide anomalies in both temperature and precipitation. During years of flood disasters, precipitation is increased by 2.8 %. Cold wave disasters show no significant signal for either temperature or precipitation. We further find that climate anomalies tend to be larger in smaller countries, an expected behavior when computing countrywide averages. In addition, our results suggest that extreme weather disasters in developed countries are typically associated with larger climate anomalies compared to developing countries. This effect could be due to different levels of vulnerability, as a climate anomaly needs to be larger in a developed country to cause a societal disruption. Our analysis provides a first link between recorded climate-related disasters and observed climate data, which is an important step towards linking climate and impact communities and ultimately better constraining future disaster risk.

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Higher biodiversity can stabilize the productivity and functioning of grassland communities when subjected to extreme climatic events. The positive biodiversity-stability relationship emerges via increased resistance and/or recovery to these events. However, invader presence might disrupt this diversity-stability relationship by altering biotic interactions. Investigating such disruptions is important given that invasion by non-native species and extreme climatic events are expected to increase in the future due to anthropogenic pressure. Here we present one of the first multisite invader × biodiversity × drought manipulation experiment to examine combined effects of biodiversity and invasion on drought resistance and recovery at three semi-natural grassland sites across Europe. The stability of biomass production to an extreme drought manipulation (100% rainfall reduction; BE: 88 days, BG: 85 days, DE: 76 days) was quantified in field mesocosms with a richness gradient of 1, 3, and 6 species and three invasion treatments (no invader, Lupinus polyphyllus, Senecio inaequidens). Our results suggest that biodiversity stabilized community productivity by increasing the ability of native species to recover from extreme drought events. However, invader presence turned the positive and stabilizing effects of diversity on native species recovery into a neutral relationship. This effect was independent of the two invader's own capacity to recover from an extreme drought event. In summary, we found that invader presence may disrupt how native community interactions lead to stability of ecosystems in response to extreme climatic events. Consequently, the interaction of three global change drivers, climate extremes, diversity decline, and invasive species, may exacerbate their effects on ecosystem functioning.

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Climatic extremes, such as severe drought, are expected to increase in frequency and magnitude with climate change. Thus, identifying mechanisms of resilience is critical to predicting the vulnerability of ecosystems. An exceptional drought (

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