Projections of temperature and precipitation changes in Xinjiang from 2021 to 2050 based on the CMIP6 model.

Xinjiang is one of the most sensitive regions in China in terms of its response to climate change. Against the background of global warming, analyses and predictions using different scenarios for Xinjiang should be conducted. The spatial and temporal distribution characteristics and trends of future temperature and precipitation trends should be considered to provide a scientific basis for the government to respond to future climate change. In this paper, using the CN05.1 dataset and seven models from the sixth phase of the Coupled Model Intercomparison Project, the delta downscaling method is used to predict the temperature and precipitation changes in Xinjiang Province from 2021 to 2050 under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The results show that (1) most models of CMIP6 have a good effect on temperature simulation in Xinjiang, and the mean values as well as the trends of the temperatures expressed by the multi-model ensemble averaging are in good agreement with the observed data and have a high degree of confidence. The observed precipitation increase rate is significantly higher than that predicted by the model, and the simulation results of each model overestimate the precipitation. (2) The mean annual temperatures in the Xinjiang region increase at rates of 0.32°C/10 a, 0.46°C/10 a, 0.47°C/10 a and 0.67°C/10 a, respectively, under the four scenarios. The rates of temperature increase in the four seasons exhibit the following pattern: autumn > summer > spring > winter. (3) From 2021 to 2050, the average annual precipitation in Xinjiang will change at rates of 3.95 mm/10 a, 1.90 mm/10 a, 2.50 mm/10 a, and 8.67 mm/10 a, respectively, under the four scenarios. The precipitation amounts predicted under the different scenarios increase at the slowest rates in winter and at faster rates in spring. Spatially, the precipitation in the whole Xinjiang region under the four scenarios shows an increasing trend. Overall, except for the SSP1-2.6 scenario, the rates of increase in precipitation increase gradually in all seasons during the future period as the emission scenarios increase. Overall, the climate of the Xinjiang region will be characterized by warming and humidification from 2021 to 2050.

Overconfidence in climate overshoot.

Global emission reduction efforts continue to be insufficient to meet the temperature goal of the Paris Agreement1. This makes the systematic exploration of so-called overshoot pathways that temporarily exceed a targeted global warming limit before drawing temperatures back down to safer levels a priority for science and policy2-5. Here we show that global and regional climate change and associated risks after an overshoot are different from a world that avoids it. We find that achieving declining global temperatures can limit long-term climate risks compared with a mere stabilization of global warming, including for sea-level rise and cryosphere changes. However, the possibility that global warming could be reversed many decades into the future might be of limited relevance for adaptation planning today. Temperature reversal could be undercut by strong Earth-system feedbacks resulting in high near-term and continuous long-term warming6,7. To hedge and protect against high-risk outcomes, we identify the geophysical need for a preventive carbon dioxide removal capacity of several hundred gigatonnes. Yet, technical, economic and sustainability considerations may limit the realization of carbon dioxide removal deployment at such scales8,9. Therefore, we cannot be confident that temperature decline after overshoot is achievable within the timescales expected today. Only rapid near-term emission reductions are effective in reducing climate risks.

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.

Future increase in extreme El Niño supported by past glacial changes.

El Niño events, the warm phase of the El Niño-Southern Oscillation (ENSO) phenomenon, amplify climate variability throughout the world1. Uncertain climate model predictions limit our ability to assess whether these climatic events could become more extreme under anthropogenic greenhouse warming2. Palaeoclimate records provide estimates of past changes, but it is unclear if they can constrain mechanisms underlying future predictions3-5. Here we uncover a mechanism using numerical simulations that drives consistent changes in response to past and future forcings, allowing model validation against palaeoclimate data. The simulated mechanism is consistent with the dynamics of observed extreme El Niño events, which develop when western Pacific warm pool waters expand rapidly eastwards because of strongly coupled ocean currents and winds6,7. These coupled interactions weaken under glacial conditions because of a deeper mixed layer driven by a stronger Walker circulation. The resulting decrease in ENSO variability and extreme El Niño occurrence is supported by a series of tropical Pacific palaeoceanographic records showing reduced glacial temperature variability within key ENSO-sensitive oceanic regions, including new data from the central equatorial Pacific. The model-data agreement on past variability, together with the consistent mechanism across climatic states, supports the prediction of a shallower mixed layer and weaker Walker circulation driving more frequent extreme El Niño genesis under greenhouse warming.

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.

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.

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.

Carbon emissions from the 2023 Canadian wildfires.

The 2023 Canadian forest fires have been extreme in scale and intensity with more than seven times the average annual area burned compared to the previous four decades1. Here, we quantify the carbon emissions from these fires from May to September 2023 on the basis of inverse modelling of satellite carbon monoxide observations. We find that the magnitude of the carbon emissions is 647 TgC (570-727 TgC), comparable to the annual fossil fuel emissions of large nations, with only India, China and the USA releasing more carbon per year2. We find that widespread hot-dry weather was a principal driver of fire spread, with 2023 being the warmest and driest year since at least 19803. Although temperatures were extreme relative to the historical record, climate projections indicate that these temperatures are likely to be typical during the 2050s, even under a moderate climate mitigation scenario (shared socioeconomic pathway, SSP 2-4.5)4. Such conditions are likely to drive increased fire activity and suppress carbon uptake by Canadian forests, adding to concerns about the long-term durability of these forests as a carbon sink5-8.

NPCC4: Tail risk, climate drivers of extreme heat, and new methods for extreme event projections.

We summarize historic New York City (NYC) climate change trends and provide the latest scientific analyses on projected future changes based on a range of global greenhouse gas emissions scenarios. Building on previous NPCC assessment reports, we describe new methods used to develop the projections of record for sea level rise, temperature, and precipitation for NYC, across multiple emissions pathways and analyze the issue of the "hot models" associated with the 6th phase of the Coupled Model Intercomparison Project (CMIP6) and their potential impact on NYC's climate projections. We describe the state of the science on temperature variability within NYC and explain both the large-scale and regional dynamics that lead to extreme heat events, as well as the local physical drivers that lead to inequitable distributions of exposure to extreme heat. We identify three areas of tail risk and potential for its mischaracterization, including the physical processes of extreme events and the effects of a changing climate. Finally, we review opportunities for future research, with a focus on the hot model problem and the intersection of spatial resolution of projections with gaps in knowledge in the impacts of the climate signal on intraurban heat and heat exposure.

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.

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.

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.

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.

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.

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.

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.

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.

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.