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Despite global warming, a region in the North Atlantic ocean has been observed to cool, a phenomenon known as the warming hole. Its emergence has been linked to a slowdown of the Atlantic meridional overturning circulation, which leads to a reduced ocean heat transport into the warming hole region. Here we show that, in addition to the reduced low-latitude heat import, increased ocean heat transport out of the region into higher latitudes and a shortwave cloud feedback dominate the formation and temporal evolution of the warming hole under greenhouse gas forcing. In climate model simulations of the historical period, the low-latitude Atlantic meridional overturning circulation decline does not emerge from natural variability, whereas the accelerating heat transport to higher latitudes is clearly attributable to anthropogenic forcing. Both the overturning and the gyre circulation contribute to the increased high-latitude ocean heat transport, and therefore are critical to understand the past and future evolutions of the warming hole.The North Atlantic ocean warming hole has been linked to reduced tropical heat import. Model simulations show an anthropogenically forced increased heat export poleward from the region, by overturning and gyre circulation, and shortwave cloud feedback control the warming hole formation and growth.

Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. We aimed to develop and validate a set of deep learning algorithms for automated detection of the following key findings from these scans: intracranial haemorrhage and its types (ie, intraparenchymal, intraventricular, subdural, extradural, and subarachnoid); calvarial fractures; midline shift; and mass effect. We retrospectively collected a dataset containing 313 318 head CT scans together with their clinical reports from around 20 centres in India between Jan 1, 2011, and June 1, 2017. A randomly selected part of this dataset (Qure25k dataset) was used for validation and the rest was used to develop algorithms. An additional validation dataset (CQ500 dataset) was collected in two batches from centres that were different from those used for the development and Qure25k datasets. We excluded postoperative scans and scans of patients younger than 7 years. The original clinical radiology report and consensus of three independent radiologists were considered as gold standard for the Qure25k and CQ500 datasets, respectively. Areas under the receiver operating characteristic curves (AUCs) were primarily used to assess the algorithms. The Qure25k dataset contained 21 095 scans (mean age 43 years; 9030 [43%] female patients), and the CQ500 dataset consisted of 214 scans in the first batch (mean age 43 years; 94 [44%] female patients) and 277 scans in the second batch (mean age 52 years; 84 [30%] female patients). On the Qure25k dataset, the algorithms achieved an AUC of 0·92 (95% CI 0·91–0·93) for detecting intracranial haemorrhage (0·90 [0·89–0·91] for intraparenchymal, 0·96 [0·94–0·97] for intraventricular, 0·92 [0·90–0·93] for subdural, 0·93 [0·91–0·95] for extradural, and 0·90 [0·89–0·92] for subarachnoid). On the CQ500 dataset, AUC was 0·94 (0·92–0·97) for intracranial haemorrhage (0·95 [0·93–0·98], 0·93 [0·87–1·00], 0·95 [0·91–0·99], 0·97 [0·91–1·00], and 0·96 [0·92–0·99], respectively). AUCs on the Qure25k dataset were 0·92 (0·91–0·94) for calvarial fractures, 0·93 (0·91–0·94) for midline shift, and 0·86 (0·85–0·87) for mass effect, while AUCs on the CQ500 dataset were 0·96 (0·92–1·00), 0·97 (0·94–1·00), and 0·92 (0·89–0·95), respectively. Our results show that deep learning algorithms can accurately identify head CT scan abnormalities requiring urgent attention, opening up the possibility to use these algorithms to automate the triage process. Qure.ai.

The dry half of the year from May to October over the Maritime Continent (MC) has experienced unprecedented damages from forest fires in recent decades. The observed interannual rainfall variability during this period is closely tied to sea surface temperature (SST) variability over the equatorial Pacific (EP). Therefore, the future evolution of EP SST can be expected to influence the climatological precipitation over the MC. Whilst multi-model means (MMMs) suggest a future drying trend over the south-western part of the MC, there is considerable model uncertainty. Here, using a storyline approach with the 38 climate models from Coupled Model Intercomparison Project Phase 6, we distinguish the model uncertainty associated with changes in the zonal EP SST gradient from that associated with the basin-wide EP (BEP) warming. We find that an increase in east-to-west EP SST gradient would bring more rainfall over the north-eastern regions including northern Borneo, Sulawesi and New Guinea. In contrast, the intensity of the basin-wide warming of EP SST is directly linked with the drying response seen over the south-western MC in the MMM. This drying affects the highly vulnerable regions of Sumatra and Kalimantan for forest fires. Our results suggest that a storyline under higher BEP warming accompanied by an El-Niño like change in zonal SST gradient would lead to even drier climatic conditions over these key regions. However, the observed record of more than one hundred years favours a storyline of lower BEP warming accompanied by a La-Niña like change in zonal SST gradient, which would lead to minimal drying over the south-western MC and wetter conditions over the north-eastern parts of the MC.

The Max Planck Institute Grand Ensemble (MPI‐GE) is the largest ensemble of a single comprehensive climate model currently available, with 100 members for the historical simulations (1850–2005) and four forcing scenarios. It is currently the only large ensemble available that includes scenario representative concentration pathway (RCP) 2.6 and a 1% CO2 scenario. These advantages make MPI‐GE a powerful tool. We present an overview of MPI‐GE, its components, and detail the experiments completed. We demonstrate how to separate the forced response from internal variability in a large ensemble. This separation allows the quantification of both the forced signal under climate change and the internal variability to unprecedented precision. We then demonstrate multiple ways to evaluate MPI‐GE and put observations in the context of a large ensemble, including a novel approach for comparing model internal variability with estimated observed variability. Finally, we present four novel analyses, which can only be completed using a large ensemble. First, we address whether temperature and precipitation have a pathway dependence using the forcing scenarios. Second, the forced signal of the highly noisy atmospheric circulation is computed, and different drivers are identified to be important for the North Pacific and North Atlantic regions. Third, we use the ensemble dimension to investigate the time dependency of Atlantic Meridional Overturning Circulation variability changes under global warming. Last, sea level pressure is used as an example to demonstrate how MPI‐GE can be utilized to estimate the ensemble size needed for a given scientific problem and provide insights for future ensemble projects. Key Points The 100‐member MPI‐GE is currently the largest publicly available ensemble of a comprehensive climate model MPI‐GE currently has the most forcing scenarios of all large ensemble projects: RCP2.6, RCP4.5, RCP8.5, and 1% CO2 The power of MPI‐GE is to estimate the forced response and internal variability, including changing variability, to unprecedented precision

Recent advances in numerical modeling have enabled km‐scale climate simulations, improving global climate representation and local‐scale projections, critical to climate adaptation strategies. In this context, the present study assesses the performance of such models over coastal shelf seas—key climate‐sensitive regions—in their ability to represent the sea surface temperature (SST) and air temperature. Compared to satellite and reanalysis data, the models exhibit systematic warm biases (∼${\\sim} $ 3°C in SST, ∼${\\sim} $ 1.5°C in air temperature) in summer across several shelf seas: the European shelf, the Gulf of Maine, the Yellow sea, the Arctic and Patagonian shelves. These biases strongly correlate with tidal mixing fronts, driven by the dissipation of the barotropic tide and identified by the Simpson‐Hunter parameter. These findings suggest that missing tidal mixing is a significant error source on coastal shelves, highlighting the need for improved ocean mixing representations to enhance model accuracy.

The El Niño-Southern Oscillation (ENSO) influences climate variability globally, encompassing various other modes of variability, and thus represents a key predictable climate signal on seasonal timescales. Yet, its response to greenhouse warming remains uncertain, with models projecting a range of outcomes. Here, we demonstrate that in response to warming, a state-of-the-art high-resolution climate model simulates a rapid transition from a moderate-amplitude irregular regime, as observed in the current climate, to a highly regular oscillation with intensifying amplitude. This behaviour can be attributed to increasing air-sea feedbacks, which approach criticality in the second half of this century, and growing atmospheric noise. As ENSO intensifies in this model, it synchronizes with other prominent climate modes, such as the North Atlantic Oscillation and the Indian Ocean Dipole, thereby imprinting its regular, predictable variability on them. If realized, this global climate mode resonance would have wide-ranging whiplash impacts on regional hydroclimates. The authors of this study perform simulations with a high-resolution climate model and show that global warming may trigger an abrupt shift in the tropical climate system towards stronger and more predictable ENSO cycles, intensifying climate impacts across the globe.

Arctic sea-ice cover has declined drastically in recent decades, notably in the Barents-Kara Seas (BKS). Previous research has linked low autumn BKS-ice cover to subsequent cold Eurasian winters. This lagged relationship was observed in 2020/2021. Using a causal network framework grounded in known physical mechanisms, we assess how strongly one factor directly influences another (i.e. the causal relationship) and apply this analysis to the anomalies from 2020/2021. We show that although year-to-year BKS-ice variations have only a minor impact on Central Asia temperatures, that causal effect becomes important for attribution and prediction when we consider the large long-term trend in BKS-ice cover. In particular, we find that Central Asia’s 2021 negative winter surface air temperature anomaly (relative to 1980–2020) can be fully explained by the BKS-ice anomaly. We further estimate that BKS-ice loss has more than halved the winter warming over Central Asia over the past 40 years. Hence rather than debating a cooling trend in Central Asia during winter, we propose shifting the focus to the influence of Arctic Amplification on anticipated warming trends. This study illustrates the efficacy of causal network analysis, which has implications for seasonal prediction and attribution of midlatitude winter anomalies.

Wildfire events are driven by complex interactions of climate and anthropogenic interventions. Predictions of future wildfire events, their extremity, and their impact on the environment and economy must account for the interactions between these drivers. Economic policy and land use decisions influence the susceptibility of an area to climate extremes, the probability of burning, and future decision making. To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia’s 2015 fire events, which saw significant (>5%) production losses of palm oil. We explored analogous events under three warming conditions and two storylines (multi-model ensemble mean climate change and high impact). We employed a model chain consisting of CMIP6 climate modeling to quantify climate change impacts, a wildfire climate impacts and adaptation model (FLAM) to predict burned areas, and the Global biosphere management model (GLOBIOM) to predict the resultant production losses and socio-economic consequences in the oil palm sector in Indonesia and, by extension, the EU. FLAM is a mechanistic, modular fire model used to reproduce and project wildfires based on various scenario criteria and input variables, whereas GLOBIOM is a global economic land use model, which assesses competition for land use and provides economic impacts based on scenario data. We found that total burned area and production loss can increase by up to 25% and lead to local price increases up to 70%, with only minor differences beyond 2.5 degrees of warming. Our results highlight the importance of considering the interactions of future warming, drought conditions, and extreme weather events when predicting their impacts on oil palm losses and burned area. This study sets the stage for further exploration on the impacts of land management policies on local and international environments and economies in the context of global warming.

The observed winter Barents-Kara Sea (BKS) sea ice concentration (SIC) has shown a close association with the second empirical orthogonal function (EOF) mode of Eurasian winter surface air temperature (SAT) variability, known as Warm Arctic Cold Eurasia (WACE) pattern. However, the potential role of BKS SIC on this WACE pattern of variability and on its long-term trend remains elusive. Here, we show that from 1979 to 2022, the winter BKS SIC and WACE association is most prominent and statistically significant for the variability at the sub-decadal time scale for 5–6 years. We also show the critical role of the multi-decadal trend in the principal component of the WACE mode of variability for explaining the overall Eurasian winter temperature trend over the same period. Furthermore, a large multi-model ensemble of atmosphere-only experiments from 1979 to 2014, with and without the observed Arctic SIC forcing, suggests that the BKS SIC variations induce this observed sub-decadal variability and the multi-decadal trend in the WACE. Additionally, we analyse the model simulated first or the leading EOF mode of Eurasian winter SAT variability, which in observations, closely relates to the Arctic Oscillation (AO). We find a weaker association of this mode to AO and a statistically significant positive trend in our ensemble simulation, opposite to that found in observation. This contrasting nature reflects excessive hemispheric warming in the models, partly contributed by the modelled Arctic Sea ice loss.

Large ensemble simulations with six atmospheric general circulation models involved are utilized to verify the interdecadal Pacific oscillation (IPO) impacts on the trend of Eurasian winter surface air temperatures (SAT) during 1998–2013, a period characterized by the prominent Eurasia cooling (EC). In our simulations, IPO brings a cooling trend over west-central Eurasia in 1998–2013, about a quarter of the observed EC in that area. The cooling is associated with the phase transition of the IPO to a strong negative. However, the standard deviation of the area-averaged SAT trends in the west EC region among ensembles, driven by internal variability intrinsic due to the atmosphere and land, is more than three times the isolated IPO impacts, which can shadow the modulation of the IPO on the west Eurasia winter climate.