Spatiotemporal modeling of the potential impact of climate change on shifts in bioclimatic zones in Morocco.

This study aims to contribute to the understanding of the impact of climate change on bioclimatic zones in Morocco, providing insights into potential shifts and emphasizing the need for adaptation measures to protect vulnerable species and ecosystems. To achieve this, we utilized eight general circulation models (GCMs) to simulate climate conditions under two representative concentration scenarios (RCP4.5 and RCP8.5) for two future time points (2050 and 2070). The modeling of bioclimatic zone shifts was accomplished through the implementation of the random forest (RF) algorithm. Our findings indicate that the subhumid and humid areas are expected to experience the most significant shifts, particularly toward the semi-arid zone. Shifts from subhumid to semi-arid were the most pronounced, ranging from 17.91% (RCP8.5 in 2070) to 25.68% (RCP8.5 in 2050), while shifts from humid to semi-arid ranged from 10.16% (RCP4.5 in 2050) to 22.27% (RCP8.5 in 2070). The Saharan and arid zones are expected to be the least affected, with less than 1% and 11% of their original extent expected to change, respectively. Moreover, our results suggest that forest species such as Atlas cedar and oaks are among the most vulnerable to these shifts. Overall, this study highlights the inevitability of climate change's impact on Moroccan ecosystems and provides a basis for adaptation measures, especially considering the species adapted to the bioclimatic conditions that will dominate the respective affected regions.

Observation-constrained projections reveal longer-than-expected dry spells.

Climate models indicate that dry extremes will be exacerbated in many regions of the world1,2. However, confidence in the magnitude and timing of these projected changes remains low3,4, leaving societies largely unprepared5,6. Here we show that constraining model projections with observations using a newly proposed emergent constraint (EC) reduces the uncertainty in predictions of a core drought indicator, the longest annual dry spell (LAD), by 10-26% globally. Our EC-corrected projections reveal that the increase in LAD will be 42-44% greater, on average, than 'mid-range' or 'high-end' future forcing scenarios currently indicate. These results imply that by the end of this century, the global mean land-only LAD could be 10 days longer than currently expected. Using two generations of climate models, we further uncover global regions for which historical LAD biases affect the magnitude of projected LAD increases, and we explore the role of land-atmosphere feedbacks therein. Our findings reveal regions with potentially higher- and earlier-than-expected drought risks for societies and ecosystems, and they point to possible mechanisms underlying the biases in the current generation of climate models.

Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally.

Modeling how climate change may affect the potential distribution of species and communities typically utilizes bioclimatic variables. Distribution predictions rely on the values of the bioclimatic variable (e.g., precipitation of the wettest quarter). However, the ecological meaning of most of these variables depends strongly on the within-year position of a specific climate period (SCP), for example, the wettest quarter of the year, which is often overlooked. Our aim was to determine how the within-year position of the SCPs would shift (SCP shift) in reaction to climate change in a global context. We calculated the deviations of the future within-year position of the SCPs relative to the reference period. We used four future time periods, four scenarios, and four CMIP6 global climate models (GCMs) to provide an ensemble of expectations regarding SCP shifts and locate the spatial hotspots of the shifts. Also, the size and frequency of the SCP shifts were subjected to linear models to evaluate the importance of the impact modeler's decision on time period, scenario, and GCM. We found ample examples of SCP shifts exceeding 2 months, with 6-month shifts being predicted as well. Many areas in the tropics are expected to experience both temperature and precipitation-related shifts, but precipitation-related shifts are abundantly predicted for the temperate and arctic zones as well. The combined shifts at the Equator reinforce the likelihood of the emergence of no-analogue climates there. The shifts become more pronounced as time and scenario progress, while GCMs could not be ranked in a clear order in this respect. For most SCPs, the modeler's decision on the GCM was the least important, while the choice of time period was typically more important than the choice of scenario. Future predictive distribution models should account for SCP shifts and incorporate the phenomenon in the modeling efforts.

A novel statistical framework of drought projection by improving ensemble future climate model simulations under various climate change scenarios.

Unlike other natural disasters, drought is one of the most severe threats to all living beings globally. Due to global climate change, the frequency and duration of droughts have increased in many parts of the world. Therefore, accurate prediction and forecasting of droughts are essential for effective mitigation policies and sustainable research. In recent research, the use of ensemble global climate models (GCMs) for simulating precipitation data is common. The objective of this research is to enhance the multi-model ensemble (MME) for improving future drought characterizations. In this research, we propose the use of relative importance metric (RIM) to address collinearity effects and point-wise discrepancy weights (PWDW) in GCMs. Consequently, this paper introduces a new statistical framework for weighted ensembles called the discrepancy-enhanced beta weighting ensemble (DEBWE). DEBWE enhances the weighted ensemble data of precipitation simulated by multiple GCMs. In DEBWE, we addressed uncertainties in GCMs arising from collinearity and outliers. To evaluate the effectiveness of the proposed weighting framework, we compared its performance with the simple average multi-model ensemble (SAMME), Taylor skill score ensemble (TSSE), and mutual information ensemble (MIE). Based on the Kling-Gupta efficiency (KGE) metric, DEBWE outperforms all competitors across all evaluation criteria. These inferences are based on the analysis of historical simulated data from 22 GCMs in the CMIP6 project. The quantitative performance indicators strongly support the superiority of DEBWE. The median and mean KGE values for DEBWE are 0.2650 and 0.2429, compared to SAMME (0.1000, 0.0991), TSSE (0.2600, 0.2397), and MIE (0.1550, 0.1511). For drought assessment, we computed the adaptive standardized precipitation index (SPI) for three future scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The steady-state probabilities suggest that normal drought (ND) is the most frequent condition, with extreme events (dry or wet) being less probable.

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950-2020 and 1850-2020), ERA5 reanalysis data (1950-2020), and climate model simulations (period 1970-2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies.

How changes projected by climate models can inform climate adaptation and marine sanctuary management: A collaborative prototype methodology.

Coral reefs are highly important ecosystems providing habitat for biodiverse marine life and numerous benefits for humans. However they face immense risks from climate change. To date, Representative Concentration Pathway (RCP) climate models have aided global discussions on possible policy responses to adapt to change, but tailored climate projections at a useful scale for environmental managers are often prohibitively expensive to produce. Our research addresses this problem by presenting a novel type of collaborative, participatory research that integrates 1) site specific climate metrics from the Community Earth System Model version 2 large ensemble (CESM2-LE), 2) ecosystem response models to determine Degree Heating Months and coral bleaching impacts, and 3) collaborative social science data from environmental manager engagement to see how managers in one of the most visited marine sanctuaries in the world are enacting adaptive governance, stewarding reefs through climate impacts of the future. Our research is valuable to decision-makers seeking opportunities for innovative policy responses to climate impacts focused on experimentation and dialogue.

Simulating the changes of the habitats suitability of chub mackerel (Scomber japonicus) in the high seas of the North Pacific Ocean using ensemble models under medium to long-term future climate scenarios.

Understanding and forecasting changes in marine habitats due to global climate warming is crucial for sustainable fisheries. Using future environmental data provided by Global Climate Models (GCMs) and occurrence records of Chub mackerel in the North Pacific Ocean (2014-2023), we built eight individual models and four ensemble models to simulate current habitat distribution and forecast changes under three future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) for the 2050s and 2100s. Ensemble models outperformed individual ones, with the weighted average algorithm model achieving the highest accuracy (AUC 0.994, TSS 0.929). Sea Surface Temperature (SST) and chlorophyll-a (Chla) significantly influenced habitat distribution. Predictions indicate current high suitability areas for Chub mackerel are concentrated beyond the 200-nautical-mile baseline. Under future climate scenarios, habitat suitability is expected to decline, with a shift towards higher latitudes and deeper waters. High suitability areas will be significantly reduced.

The ocean losing its breath under the heatwaves.

The world's oceans are under threat from the prevalence of heatwaves caused by climate change. Despite this, there is a lack of understanding regarding their impact on seawater oxygen levels - a crucial element in sustaining biological survival. Here, we find that heatwaves can trigger low-oxygen extreme events, thereby amplifying the signal of deoxygenation. By utilizing in situ observations and state-of-the-art climate model simulations, we provide a global assessment of the relationship between the two types of extreme events in the surface ocean (0-10 m). Our results show compelling evidence of a remarkable surge in the co-occurrence of marine heatwaves and low-oxygen extreme events. Hotspots of these concurrent stressors are identified in the study, indicating that this intensification is more pronounced in high-biomass regions than in those with relatively low biomass. The rise in the compound events is primarily attributable to long-term warming primarily induced by anthropogenic forcing, in tandem with natural internal variability modulating their spatial distribution. Our findings suggest the ocean is losing its breath under the influence of heatwaves, potentially experiencing more severe damage than previously anticipated.

Highest ocean heat in four centuries places Great Barrier Reef in danger.

Mass coral bleaching on the Great Barrier Reef (GBR) in Australia between 2016 and 2024 was driven by high sea surface temperatures (SST)1. The likelihood of temperature-induced bleaching is a key determinant for the future threat status of the GBR2, but the long-term context of recent temperatures in the region is unclear. Here we show that the January-March Coral Sea heat extremes in 2024, 2017 and 2020 (in order of descending mean SST anomalies) were the warmest in 400 years, exceeding the 95th-percentile uncertainty limit of our reconstructed pre-1900 maximum. The 2016, 2004 and 2022 events were the next warmest, exceeding the 90th-percentile limit. Climate model analysis confirms that human influence on the climate system is responsible for the rapid warming in recent decades. This attribution, together with the recent ocean temperature extremes, post-1900 warming trend and observed mass coral bleaching, shows that the existential threat to the GBR ecosystem from anthropogenic climate change is now realized. Without urgent intervention, the iconic GBR is at risk of experiencing temperatures conducive to near-annual coral bleaching3, with negative consequences for biodiversity and ecosystems services. A continuation on the current trajectory would further threaten the ecological function4 and outstanding universal value5 of one of Earth's greatest natural wonders.

Carbon system state determines warming potential of emissions.

Current strategies to hold surface warming below a certain level, e. g., 1.5 or 2°C, advocate limiting total anthropogenic cumulative carbon emissions to ∼0.9 or ∼1.25 Eg C (1018 grams carbon), respectively. These allowable emission budgets are based on a near-linear relationship between cumulative emissions and warming identified in various modeling efforts. The IPCC assesses this near-linear relationship with high confidence in its Summary for Policymakers (§D1.1 and Figure SPM.10). Here we test this proportionality in specially designed simulations with a latest-generation Earth system model (ESM) that includes an interactive carbon cycle with updated terrestrial ecosystem processes, and a suite of CMIP simulations (ZecMIP, ScenarioMIP). We find that atmospheric CO2 concentrations can differ by ∼100 ppmv and surface warming by ∼0.31°C (0.46°C over land) for the same cumulated emissions (≈1.2 Eg C, approximate carbon budget for 2°C target). CO2 concentration and warming per 1 Eg of emitted carbon (Transient Climate Response to Cumulative Carbon Emissions; TCRE) depend not just on total emissions, but also on the timing of emissions, which heretofore have been mainly overlooked. A decomposition of TCRE reveals that oceanic heat uptake is compensating for some, but not all, of the pathway dependence induced by the carbon cycle response. The time dependency clearly arises due to lagged carbon sequestration processes in the oceans and specifically on land, viz., ecological succession, land-cover, and demographic changes, etc., which are still poorly represented in most ESMs. This implies a temporally evolving state of the carbon system, but one which surprisingly apportions carbon into land and ocean sinks in a manner that is independent of the emission pathway. Therefore, even though TCRE differs for different pathways with the same total emissions, it is roughly constant when related to the state of the carbon system, i. e., the amount of carbon stored in surface sinks. While this study does not fundamentally invalidate the established TCRE concept, it does uncover additional uncertainties tied to the carbon system state. Thus, efforts to better understand this state dependency with observations and refined models are needed to accurately project the impact of future emissions.

Impacts of future climate change on rice yield based on crop model simulation-A meta-analysis.

Rice is one of the world's major food crops. Changes in major climatic factors such as temperature, rainfall, solar radiation and carbon dioxide (CO2) concentration have an important impact on rice growth and yield. However, many of the current studies that predict the impact of future climate change on rice yield are affected by uncertainties such as climate models, climate scenarios, model parameters and structure, and showing great differences. This study was based on the assessment results of the impact of climate change on rice in the future of 111 published literature, and comprehensively analyzed the impact and uncertainty of climate change on rice yield. This study utilized local polynomial (Loess) regression analysis to investigate the impact of changes in mean temperature, minimum temperature, maximum temperature, solar radiation, and precipitation on relative rice yield variations within a complete dataset. A linear mixed-effects model was used to quantitatively analyze the relationships between the restricted datasets. The qualitative analysis based on the entire dataset revealed that rice yields decreased with increasing average temperature. The precipitation changed between 0 and 25 %, it was conducive to the stable production of rice, and when the precipitation changed >25 %, it would cause rice yield reduction. The change of solar radiation was less than -1.15 %, the rice yield increases with the increase of solar radiation, and when the change of solar radiation exceeds -1.15 %, the rice yield decreases. Elevated CO2 concentrations and management practices could mitigate the negative effects of climate change. The results of a quantitative analysis utilizing the mixed effects model revealed that average temperature, precipitation, CO2 concentration, and adaptation methods all had a substantial impact on rice production, and elevated CO2 concentrations and management practices could exert positive influences on rice production. For every 1 °C and 1 % increase in average temperature and precipitation, rice yield decreased by 3.85 % and 0.56 %, respectively. For every 100 ppm increase in CO2 concentration, rice yield increased by 7.1 %. The variation of rice yield under different climate models, study sites and climate scenarios had significant variability. Elevated CO2 concentrations and management practices could compensate for the negative effects of climate change, benefiting rice production. This study comprehensively collected and analyzed a wide range of literature and research, which provides an in-depth understanding of the impacts of climate change on rice production and informs future research and policy development.

Increased crossing of thermal stress thresholds of vegetation under global warming.

Temperature extremes exert a significant influence on terrestrial ecosystems, but the precise levels at which these extremes trigger adverse shifts in vegetation productivity have remained elusive. In this study, we have derived two critical thresholds, using standard deviations (SDs) of growing-season temperature and satellite-based vegetation productivity as key indicators. Our findings reveal that, on average, vegetation productivity experiences rapid suppression when confronted with temperature anomalies exceeding 1.45 SD above the mean temperature during 2001-2018. Furthermore, at temperatures exceeding 2.98 SD above the mean, we observe the maximum level of suppression, particularly in response to the most extreme high-temperature events. When Earth System Models are driven by a future medium emission scenario, they project that mean temperatures will routinely surpass both of these critical thresholds by approximately the years 2050 and 2070, respectively. However, it is important to note that the timing of these threshold crossings exhibits spatial variation and will appear much earlier in tropical regions. Our finding highlights that restricting global warming to just 1.5°C can increase safe areas for vegetation growth by 13% compared to allowing warming to reach 2°C above preindustrial levels. This mitigation strategy helps avoid exposure to detrimental extreme temperatures that breach these thresholds. Our study underscores the pivotal role of climate mitigation policies in fostering the sustainable development of terrestrial ecosystems in a warming world.

Wind power density in areas of Northeastern Brazil from Regional Climate Models for a recent past.

Investments in renewable energy sources are increasing in several countries, especially in wind energy, as a response to global climate change caused by the burning of fossil fuels for electricity generation. Thus, it is important to evaluate the Regional Climate Models that simulate wind speed and wind power density in promising areas for this type of energy generation with the least uncertainty in recent past, which is essential for the implementation of wind farms. Therefore, this research aims to calculate the wind power density from Regional Climate Models in areas at Northeast of Brazil from 1986 to 2005. Initially, the ECMWF-ERA5 reanalysis data was validated against observed data obtained from Xavier. The results were satisfactory, showing a strong correlation in areas of Ceará and Rio Grande do Norte (except during the SON season), and some differences in relation to the wind intensity registered by observed data, particularly during the JJA season. Then, the Regional Climate Models RegCM4.7, RCA4 and Remo2009 were validated against the ECMWF-ERA5 reanalysis data, with all models successfully representing the wind speed pattern, especially from December to May. Four specific areas in Northeast of Brazil were selected for further study. In these areas, the RCMs simulations were evaluated to identify the RCM with the best statistical indices and consequently the lowest associated uncertainty for each area. The selected RCMs were: RegCM4.7_HadGEM2 (northern coastal of Ceará and northern coastal of Rio Grande do Norte) and RCA4_Miroc (Borborema and Central Bahia). Finally, the wind power density was calculated from the selected RCM for each area. The northern regions of Rio Grande do Norte and Ceará exhibited the highest wind power density.

Land use modulates resistance of grasslands against future climate and inter-annual climate variability in a large field experiment.

Climate and land-use change are key drivers of global change. Full-factorial field experiments in which both drivers are manipulated are essential to understand and predict their potentially interactive effects on the structure and functioning of grassland ecosystems. Here, we present 8 years of data on grassland dynamics from the Global Change Experimental Facility in Central Germany. On large experimental plots, temperature and seasonal patterns of precipitation are manipulated by superimposing regional climate model projections onto background climate variability. Climate manipulation is factorially crossed with agricultural land-use scenarios, including intensively used meadows and extensively used (i.e., low-intensity) meadows and pastures. Inter-annual variation of background climate during our study years was high, including three of the driest years on record for our region. The effects of this temporal variability far exceeded the effects of the experimentally imposed climate change on plant species diversity and productivity, especially in the intensively used grasslands sown with only a few grass cultivars. These changes in productivity and diversity in response to alterations in climate were due to immigrant species replacing the target forage cultivars. This shift from forage cultivars to immigrant species may impose additional economic costs in terms of a decreasing forage value and the need for more frequent management measures. In contrast, the extensively used grasslands showed weaker responses to both experimentally manipulated future climate and inter-annual climate variability, suggesting that these diverse grasslands are more resistant to climate change than intensively used, species-poor grasslands. We therefore conclude that a lower management intensity of agricultural grasslands, associated with a higher plant diversity, can stabilize primary productivity under climate change.

Future changes in coastal upwelling and biological production in eastern boundary upwelling systems.

Upwelling along oceanic eastern boundaries has attracted significant attention due to its profound effects on ocean productivity and associated biological and socioeconomic implications. However, uncertainty persists regarding the evolution of coastal upwelling with climate change, particularly its impact on future biological production. Here, using a series of state-of-the-art climate models, we identify a significant seasonal advancement and prolonged duration of upwelling in major upwelling systems. Nevertheless, the upwelling intensity (total volume of upwelled water) exhibits complex changes in the future. In the North Pacific, the upwelling is expected to attenuate, albeit with a minor magnitude. Conversely, in other basins, coastal upwelling diminishes significantly in equatorward regions but displays a slight decline or even an enhancement at higher latitudes. The climate simulations also reveal a robust connection between changes in upwelling intensity and net primary production, highlighting the crucial impact of future coastal upwelling alterations on marine ecosystems.

Deeper and stronger North Atlantic Gyre during the Last Glacial Maximum.

Subtropical gyre (STG) depth and strength are controlled by wind stress curl and surface buoyancy forcing1,2. Modern hydrographic data reveal that the STG extends to a depth of about 1 km in the Northwest Atlantic, with its maximum depth defined by the base of the subtropical thermocline. Despite the likelihood of greater wind stress curl and surface buoyancy loss during the Last Glacial Maximum (LGM)3, previous work suggests minimal change in the depth of the glacial STG4. Here we show a sharp glacial water mass boundary between 33° N and 36° N extending down to between 2.0 and 2.5 km-approximately 1 km deeper than today. Our findings arise from benthic foraminiferal δ18O profiles from sediment cores in two depth transects at Cape Hatteras (36-39° N) and Blake Outer Ridge (29-34° N) in the Northwest Atlantic. This result suggests that the STG, including the Gulf Stream, was deeper and stronger during the LGM than at present, which we attribute to increased glacial wind stress curl, as supported by climate model simulations, as well as greater glacial production of denser subtropical mode waters (STMWs). Our data suggest (1) that subtropical waters probably contributed to the geochemical signature of what is conventionally identified as Glacial North Atlantic Intermediate Water (GNAIW)5-7 and (2) the STG helped sustain continued buoyancy loss, water mass conversion and northwards meridional heat transport (MHT) in the glacial North Atlantic.

Tree rings reveal the transient risk of extinction hidden inside climate envelope forecasts.

Given the importance of climate in shaping species' geographic distributions, climate change poses an existential threat to biodiversity. Climate envelope modeling, the predominant approach used to quantify this threat, presumes that individuals in populations respond to climate variability and change according to species-level responses inferred from spatial occurrence data-such that individuals at the cool edge of a species' distribution should benefit from warming (the "leading edge"), whereas individuals at the warm edge should suffer (the "trailing edge"). Using 1,558 tree-ring time series of an aridland pine (Pinus edulis) collected at 977 locations across the species' distribution, we found that trees everywhere grow less in warmer-than-average and drier-than-average years. Ubiquitous negative temperature sensitivity indicates that individuals across the entire distribution should suffer with warming-the entire distribution is a trailing edge. Species-level responses to spatial climate variation are opposite in sign to individual-scale responses to time-varying climate for approximately half the species' distribution with respect to temperature and the majority of the species' distribution with respect to precipitation. These findings, added to evidence from the literature for scale-dependent climate responses in hundreds of species, suggest that correlative, equilibrium-based range forecasts may fail to accurately represent how individuals in populations will be impacted by changing climate. A scale-dependent view of the impact of climate change on biodiversity highlights the transient risk of extinction hidden inside climate envelope forecasts and the importance of evolution in rescuing species from extinction whenever local climate variability and change exceeds individual-scale climate tolerances.

Out of the frying pan into the fire: Predicted warming in alpine streams suggests hidden consequences for aquatic ectotherms.

Thermal regimes of aquatic ecosystems are predicted to change as climate warming progresses over the next century, with high-latitude and high-elevation regions predicted to be particularly impacted. Here, we have modelled alpine stream water temperatures from air temperature data and used future predicted air temperature trajectories (representative concentration pathway [rcp] 4.5 and 8.5) to predict future water temperatures. Modelled stream water temperatures have been used to calculate cumulative degree days (CDDs) under current and future climate conditions. These calculations show that degree days will accumulate more rapidly under the future climate scenarios, and with a stronger effect for higher CDD values (e.g., rcp 4.5: 18-28 days earlier [CDD = 500]; 42-55 days earlier [CDD = 2000]). Changes to the time to achieve specific CDDs may have profound and unexpected consequences for alpine ecosystems. Our calculations show that while the effect of increased CDDs may be relatively small for organisms that emerge in spring-summer, the effects for organisms emerging in late summer-autumn may be substantial. For these organisms, the air temperatures experienced upon emergence could reach 9°C (rcp 4.5) or 12°C (rcp 8.5) higher than under current climate conditions, likely impacting on the metabolism of adults, the availability of resources, including food and suitable oviposition habitat, and reproductive success. Given that the movement of aquatic fauna to the terrestrial environment represents an important flux of energy and nutrients, differential changes in the time periods to achieve CDDs for aquatic and terrestrial fauna may de-couple existing predator-prey interactions.

CMIP6 ESMs overestimate greening and the photosynthesis trends in Dryland East Asia.

The Dryland East Asia (DEA) is one of the largest inland arid regions, and vegetation is very sensitive to climate change. The complex environment in DEA with defects of modeling construction make it difficult to simulate and predict changes in vegetation structure and productivity. Here, we use the emergent constraint (EC) method to constrain the future interannual leaf area index (LAI) and gross primary productivity (GPP) trends in DEA, under four scenarios of the latest Sixth Coupled Model Intercomparison Project (CMIP6) model ensemble. LAI and GPP increase in all scenarios in the near term (2015-2050), with continued growth in SSP370 and SSP585 and stasis in SSP126 and SSP245 in the far term (2051-2100). However, after building effective EC relationships, the constrained increasing trends of LAI (GPP) are reduced by 43.5 %-53.9 % (30.5 %-50.0 %) compared with the uncertainties of the original ensemble, which are reduced by 10.0 %-45.7 % (4.6 %-34.3 %). We also extend the EC in moving windows and grid cells, further strengthening the robustness of the constraints, especially by illustrating spatial sources of these emergent relationships. Overestimations of LAI and GPP trends suggest that current CMIP6 models may be insufficient to capture the complex relationships between climate change and vegetation dynamics in DEA; however, these models can be adjusted based on established emergent relationships.

Amplified seasonality in western Europe in a warmer world.

Documenting the seasonal temperature cycle constitutes an essential step toward mitigating risks associated with extreme weather events in a future warmer world. The mid-Piacenzian Warm Period (mPWP), 3.3 to 3.0 million years ago, featured global temperatures approximately 3°C above preindustrial levels. It represents an ideal period for directed paleoclimate reconstructions equivalent to model projections for 2100 under moderate Shared Socioeconomic Pathway SSP2-4.5. Here, seasonal clumped isotope analyses of fossil mollusk shells from the North Sea are presented to test Pliocene Model Intercomparison Project 2 outcomes. Joint data and model evidence reveals enhanced summer warming (+4.3° ± 1.0°C) compared to winter (+2.5° ± 1.5°C) during the mPWP, equivalent to SSP2-4.5 outcomes for future climate. We show that Arctic amplification of global warming weakens mid-latitude summer circulation while intensifying seasonal contrast in temperature and precipitation, leading to an increased risk of summer heat waves and other extreme weather events in Europe's future.