The climate emulator development has rapidly advanced in recent years with the innovative statistical and machine learning approaches. The computationally efficient climate emulators are a good tool for modelling the forced response and internal variability of the part of the climate system. Although the output of typical emulators has a limited number of variables; its use for evaluating scenario uncertainty, geoengineering applications, impact assessment, policy making, and high-resolution regional projections is quite prominent in the recent times.
This session welcomes a broad range of contributions focused on the analysis of large ensemble datasets and climate emulators spanning all components of the climate system. It specifically covers topics: (a) the detection and attribution of climate change, (b) new statistical and machine learning methodologies to identify the forced change and internal variability, (c) assessment of model uncertainty and climate projection, (d) geoengineering studies, (e) development and applications of the climate emulators, and (f) tipping point and climate hysteresis.
Orals: Thu, 7 May, 16:15–18:00 | Room 0.14
Discernible differences in global climate responses under varying greenhouse gas emission scenarios are commonly assumed to emerge only after 20 to 30 years. Here we show that mitigation benefits are detectable within a decade (9±6 years) over the global land area when high-resolution gridded climate data are analysed with a machine learning approach. By retaining spatial information, we uncover regional warming signals that remain hidden when relying on global averages and identify the regions in which these signals first emerge using an explainability framework. Even when restricting our analysis to subregions, we find a detectable signal to emerge over the land area of the four highest emitting countries in 13 (±6) years. These results demonstrate that detectable climate benefits of greenhouse gas mitigation appear much earlier than previously recognised and suggest that high emitting countries would also experience near-term benefits from bending the emissions curve.
How to cite: Shmuel, A., Schwind, N., Kornhuber, K., Milo, R., and Schleussner, C.-F.: Climate mitigation benefits emerge within a decade, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7914, https://doi.org/10.5194/egusphere-egu26-7914, 2026.
Droughts are prolonged periods of water shortages driven by meteorological conditions or human behaviour, affecting social, economic, financial, and eco-systems. These hydroclimatic phenomena can lead to crop failures, diminished water supplies, and negatively impact the environment, posing challenges to food security and economic stability. Meteorological drivers of drought (e.g. precipitation, temperature, radiation patterns) fluctuate due to natural climate variability, but can also be impacted by long-term climatic changes.
We exploit the concept of climate model analogs to single out specific observed drought events based on the standardized precipitation index, in order to attribute and explain the dynamic drivers that lead to these events months before they occur. In particular the sea surface temperature drivers which remotely modulate hydroclimatic variability over continents in seasonal to annual timescales via teleconnections. This is done by subsampling the most similar events in a large multi-model ensemble to the observed target event and extracting their common signals of prior sea surface temperature conditions and their temporal evolution. In a complementary manner, and as a counterfactual experiment, we predict the standardized precipitation index based on analogs of observed sea surface temperature conditions months before the target drought event and evaluate its skill by comparing predicted values against observed SPI data.
We have extended the use of an established method of selecting climate model analogs to attribute drought events to internal climate variability. The method can still be further extended to quantify the impact of externally forced climate change versus internal climate variability.
How to cite: Acosta Navarro, J. C., Toreti, A., and Volpi, D.: Using climate model analogs form large ensembles to attribute drought events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20747, https://doi.org/10.5194/egusphere-egu26-20747, 2026.
Reliable projections of surface climate over Europe depend on the trustworthiness of simulations of the North Atlantic atmospheric circulation from climate models. Recent examples of discrepancies between models and observations have raised the possibility that models cannot capture the long-term observed trends in the North Atlantic circulation. Here, we examine the ability of models contributed to the Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP) to simulate historical changes in the atmospheric and oceanic circulation in the North Atlantic sector. From 1951 to 2014, the wintertime North Atlantic jet has strengthened, and the NAO has trended towards its positive phase, like the Northern Annular Mode in the stratosphere. All-forcing historical simulations show only a very weak trend that is missing from nearly all individual models. Nonetheless, the models skilfully predict the observed multi-decadal variability of the NAO, and the sum of the individual forcing simulations simulates trends closer to that observed. Specifically, the hist-GHG ensemble captures the observed signal better than historical; however, there is substantial non-additivity between the sum of each of the individual forcings and historical. Some of this nonadditivity appears to be linked to sea surface temperature warming near the ice-edge in the Barents-Kara Sea and near Greenland. The divergence between models and observations is much less pronounced over land or when the period since 1979 is considered. In this period, the models vs. observations discrepancy is mainly in boreal summer and appears to be due to aerosols. Our results suggest that projections of atmospheric circulation in the Euro-Atlantic sector may be unreliable because they underestimate the response to human emissions or the magnitude of multidecadal-to-centennial time scale internal variability; we cannot rule out observational uncertainty as a potential cause.
How to cite: Kuchar, A., Garfinkel, C., Avisar, D., and Simpson, I. and the Large Ensembles for Attribution of Dynamically-driven ExtRemes (LEADER): North Atlantic working group: Challenges faced by the Large Ensemble Single Forcing Model Intercomparison Project models in representing the response of the North Atlantic atmospheric and oceanic circulation to external forcings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18284, https://doi.org/10.5194/egusphere-egu26-18284, 2026.
Initial-condition ensembles of chemistry-climate models are useful tools for separating anthropogenic signals in atmospheric composition from the noise of internally-generated climate variability. The noise generated by these ensembles can also be leveraged for risk assessment by quantifying the likelihood of extreme pollution outcomes under the same emissions scenario. However, the high computational cost of chemistry-climate models, especially when they include fully interactive chemistry schemes, limits their use for large-ensemble experiments. Here, we propose an efficient surrogate modeling framework for generating synthetic realizations (i.e. ensemble members) of surface ozone projections that can reproduce the statistics of an initial-condition chemistry-climate model ensemble (the 13-member CESM2-WACCM6 ensemble). For a given emissions scenario, our approach infers internal variability in surface ozone by applying a surrogate trained on a single interactive-chemistry realization to monthly meteorological fields from a large initial-condition ensemble run without interactive chemistry. This avoids the need for simulating multiple interactive-chemistry members and is most appropriate when chemistry feedbacks on meteorology are weak (i.e., when meteorological variability is not substantially altered by interactive chemistry). We compare multiple regression-based surrogate modeling approaches including linear, tree-based, and Gaussian process models and assess trade-offs between local training (separate surrogate fits at each grid cell) and global training (a single surrogate fit to all grid cells).
Among our evaluation metrics, we use the area-weighted root mean square error (RMSE) between the synthetic ensemble and the full-complexity model ensemble statistics, evaluated over populated grid cells, to summarize surrogate skill. We compute the RMSE for the externally forced component (ensemble-mean climatology and linear trend) and for the statistics of the internal variability component computed after removing the ensemble-mean (e.g., standard deviation (SD), 90th percentile (q90), exceedance probability above the 90th percentile (P(>q90)). Locally-trained surrogates reproduce the ensemble-mean climatology and trend with very low error (RMSE ~= 0.14–0.17 ppbv and 0.56–0.77 ppbv per 40 years, respectively), whereas global training exhibits substantially larger errors (RMSE ~= 2.1–7.2 ppbv and 2.4–7.4 ppbv per 40 years), indicating that global training struggles to represent the spatially varying forced response set by the spatial pattern of emissions. For internal variability, the local Gaussian process surrogate best reproduces the spread and tail behavior, achieving the lowest errors in SD (RMSE = 0.61 ppbv), q90 (RMSE = 0.51 ppbv), and P(>q90) (RMSE = 0.025, unitless). Overall, our framework enables the efficient generation of a synthetic initial-condition ensemble for surface ozone that can reproduce both the ensemble-mean response and the statistics of the internal variability at a fraction of the computational cost.
How to cite: Le Roy, E., Balamurugan, V., Chen, J., Fiore, A., and Selin, N.: A surrogate modeling framework for inferring internal variability and forced change in surface ozone, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15550, https://doi.org/10.5194/egusphere-egu26-15550, 2026.
State-of-the-art climate models simulate a wide range of future warming for the 21st century, even when run with identical anthropogenic and natural forcings. Constraining this uncertainty in future warming is a key challenge in climate science: it is the foundation for the calculation of carbon budgets and resulting climate policy, and it is indispensable for the design of appropriate climate adaptation measures on global, regional and local levels.
The Transient Climate Response (TCR) is an idealized metric commonly used to quantify future warming in climate models. Climate models simulate a wide range of 1-3K for TCR (Forster et al. 2021). Existing emergent constraints on TCR based on historical temperature trends had led to a consistent downward revision of this range at the time when CMIP6 simulations were first published, consistently excluding models with very high TCR values. However, recent evidence based on Earth's energy imbalance (EEI) shows the opposite, i.e., that models with higher TCR values reproduce observed EEI trends better (Myhre et al. 2025). In this work, we address and reconcile this apparent discrepancy. We first improve existing temperature-based constraints by statistically removing internal variability in global temperature and EEI from spatial surface temperature anomalies (Gyuleva et al. 2025). We show that earlier temperature-based TCR constraints were biased low due to cooling variability contributions in recent decades. We then show that recent trends in both variability-adjusted temperature and energy imbalance point to higher TCR values, yet constraints based on shorter and more recent trends are much more uncertain. Our results highlight that constraints based on Earth’s energy imbalance are a valuable source of observational evidence to constrain future warming in addition to temperature-based constraints. Our results further suggest that previous constraints on TCR have to be revised upward due to the combined effects of variability and the inclusion of evidence from Earth’s energy imbalance.
References
Forster, P., T. Storelvmo, K. Armour, W. Collins, J.-L. Dufresne, D. Frame, D. Lunt, T. Mauritsen, M. Palmer, M. Watanabe, M. Wild, and H. Zhang (2021). “The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity”. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Ed. by V. Masson-Delmotte, P. Zhai, A. Pirani, S. L. Connors, C. P´ean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. B. R. Matthews, T. K. Maycock, T. Waterfield, O. Yelek¸ci, R. Yu, and B. Zhou. Cambridge, United Kingdom; New York, NY, USA: Cambridge University Press, pp. 923–1054. url: https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-7/.
Gyuleva, G., R. Knutti, and S. Sippel (2025). “Combination of Internal Variability and Forced Response Reconciles Observed 2023–2024 Warming”. en. In:Geophysical Research Letters 52.14. eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2025Ge2025GL115270. Issn: 1944-8007. url: https://onlinelibrary.wiley.com/doi/abs/10.1029/2025GL115270 (visited on 08/18/2025).
Myhre, G., Ø. Hodnebrog, N. Loeb, and P. M. Forster (June 2025). “Observed trend in Earth energy imbalance may provide a constraint for low climate sensitivity models”. In: Science 388.6752. Publisher: American Association for the Advancement of Science, pp. 1210–1213. url: https://www.science.org/doi/10.1126/science.adt0647 (visited on 07/04/2025).
How to cite: Gyuleva, G., Fischer, E., Knutti, R., and Sippel, S.: Recent Temperature and Energy Imbalance Trends Point to Higher Estimates of Future Warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17120, https://doi.org/10.5194/egusphere-egu26-17120, 2026.
The impact of interbasin linkage on the weather/climate and ecosystems is significantly broader and profounder than that of only appearing in an individual basin. Here, we reveal that a decadal linkage of sea surface temperature (SST) has emerged between western Australian coast and western–central tropical Pacific since 1985, associated with continuous intensification of decadal variabilities (8–16 years). The rapid SST changes in both tropical Indian Ocean and Indo-Pacific warm pool in association to greenhouse gases and volcanoes are emerging factors resulting in enhanced decadal co-variabilities between these two regions since 1985. These SST changes induce enhanced convection variability over the Maritime Continent, leading to stronger easterlies in the western–central tropical Pacific during the warm phase off western Australian coast. The above changes bring about cooling in the western–central tropical Pacific and strengthened Leeuwin Current and anomalous cyclonic wind off western Australian coast, and ultimately resulting in enhanced coupling between these two regions. Our results suggest that enhanced decadal interbasin connections can offer further understanding of decadal changes under future warmer conditions.
How to cite: Ding, Y., Lin, P., Liu, H., Wu, B., Li, Y., Chen, L., Zhang, L., Hu, A., and Han, W.: Emergence of decadal linkage between Western Australian coast and Western–central tropical Pacific, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4672, https://doi.org/10.5194/egusphere-egu26-4672, 2026.
Over the past four decades, zonal contrast in the tropical Pacific sea surface temperature (SST) has strengthened in observations but weakened in majority of climate model simulations. This model–observation discrepancy cannot be explained by internal mode of interdecadal climate variability in the Pacific alone, and the source of possible model errors remains unclear. Here, using observations and a large ensemble of historical simulations by a climate model, we identified that the simulated SST pattern associated with the Atlantic Multidecadal Variability (AMV) is biased in the tropical Pacific despite the time evolution of the AMV being reproduced well. Observations suggest that the positive AMV acts to increase the Pacific zonal SST contrast whereas this teleconnection process falsely weakens it in the model, which is a common feature in other climate models, and correcting the AMV-related SST pattern, which is likely an externally forced response, partly reconciles the model-observation discrepancy.
How to cite: Lin, Y.-C. and Watanabe, M.: North Atlantic influence reconciling model-observation discrepancy in the tropical Pacific warming pattern, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2908, https://doi.org/10.5194/egusphere-egu26-2908, 2026.
Simulation ensembles of the Earth system response to different emission scenarios are a crucial element of climate science. The ability to generate such ensembles using physics-based Earth System Models (ESMs) is limited given their enormous computational and data storage requirements. Lightweight statistical or ML-based emulators calibrated on ESM data successfully capture ESM output for a large range of scenarios. The novel SCALES-MESH emulator framework is designed in a modular way, separately generating the forced response and variability on the regional level (SCALES), then downscaling to gridded emulations using a conditional score-based generative model (MESH).
We here introduce the SCALES variability module, that utilises causal discovery and inference methods to construct natural variability for the emulations. Aggregates of selected variables on the level of IPCC regions are used to derive the interactions between regions and multi-time-lag autocorrelation behavior from ESM data, which are then translated into vector-autoregressive causal network models. With these we can generate ensembles of monthly variability that are able to address key challenges of variability emulation: (i) reproduce the spatiotemporal behavior and relationships of these variables, (ii) in particular also long-range interactions across regions for different time lags, (iii) introduce additional multivariate dependencies, and (iv) superimpose variability on multiple timescales, including multi-annual modes of oceanic variability, which, for instance, (v) enables us to emulate time series of temperature in the Pacific ocean regions that capture ENSO-like patterns.
How to cite: Högner, A., Schwind, N., Kain, V., Nauels, A., Nicholls, Z., Zecchetto, M., and Schleussner, C.-F.: Modelling multi-scale regional variability with causal networks, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9769, https://doi.org/10.5194/egusphere-egu26-9769, 2026.
Extreme weather events such as heavy rainfall and heat waves are crucial issues. However, seasonal-to-interannual prediction of such local extremes remains challenging, particularly in the mid-latitudes, due to a small signal-to-noise ratio and the limited resolution of climate models in representing mesoscale circulation and complex topography.
In this study, we assess the potential predictability of both the mean state (monthly mean temperature) and extremes (monthly maximum temperature and the number of extremely hot days) over Japan. We employ 100-member large-ensemble AGCM simulations and high-resolution regional climate model downscaling. Two types of simulations are analyzed: historical simulations, forced by historical SST, sea ice, and atmospheric forcings (ozone, greenhouse gases, and aerosols); and non-warming simulations, forced by detrended SST and preindustrial levels of these forcings.
Our results indicate that the potential predictability of temperature extremes is generally lower than that of the mean state. Notably, we detected a significant and spatially localized difference in predictability of extremes between historical and non-warming conditions, whereas no such difference was found for the mean state.
To elucidate the mechanisms underlying this difference in predictability, we further examine variability in several ocean basins and their teleconnections to local atmospheric circulation.
How to cite: Obara, K. and Imada, Y.: Potential Predictability of Regional Temperature Extremes Estimated from High-Resolution Large Ensemble Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15707, https://doi.org/10.5194/egusphere-egu26-15707, 2026.
In the past the fields of initialised decadal climate predictions and uninitialised climate projections have emerged as separated areas of research. They are used to provide important information on the future development for stakeholders on time scales from a few years to centuries. Furthermore, with so-called SMILEs (single model initial-condition large ensembles) the community provides ensembles in the hundreds of members allowing a better estimation of developments of extremes and uncertainties.
With our new experiment we combine all these research fields to learn more about the future and about our model systems. In this study we have extended all members of the decadal prediction system with the MPI-ESM-LR, initialised between 1960 and 2024, to the end of 2100. Besides using it as a prediction system for up to 40 lead years, we can view this system as a large ensemble. The 1040 ensemble members does not only allow us to better estimate the trends of extremes, but show us also some extremes, which are up to now hardly seen with large ensembles.
In our analysis we focus on summer temperature extremes in Central Europe at the end of the century. We show how extremes are changing over time and what advantages a large model with more than thousand members has compared to a smaller uninitialised large ensemble created with the same model. Furthermore, we show the effect of initialisation within the model on variables like surface temperature and AMOC and investigate how long we can detect the initialisation compared to an uninitialised model. Finally, we will in light of these findings discuss the consequences we can draw for how we do and interpret initialised predictions and uninitialised projections in our community.
How to cite: Düsterhus, A. and Brune, S.: From predictions to projections: A large ensemble of initialised predictions for the end of the century, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8053, https://doi.org/10.5194/egusphere-egu26-8053, 2026.
Numerous detection and attribution (DA) studies have identified long-term trends in various characteristics of extreme events using general circulation models (GCMs) and attributed them to anthropogenic external forcing; however, many aspects remain poorly understood, with respect to regional extreme events that cannot be captured by GCMs and the long-term modulation of extreme events arising from non-anthropogenic factors such as multidecadal internal ocean variability. To address this challenge, we performed atmospheric GCM (AGCM)-based large-ensemble simulations and regional downscaling simulations for each ensemble member based on a regional climate model over more than 70 years to investigate the mechanisms by which large-scale and long-term climate variations modulate the frequency and intensity of topographically influenced local-scale extreme events. Through analysis of this dataset, we identified inherent multidecadal-scale signals in local, orographic precipitation over East Asia, and the results suggest that their modulation is linked to multidecadal variability in multiple ocean basins.
This approach can be extended to event attribution studies of regional-scale extreme events by adding large-ensemble simulations under non-warming conditions, enabling a quantitative assessment of the impacts of anthropogenic global warming on fine-scale orographic rainfall and heatwaves that have previously been difficult to detect. Such event attribution studies have attracted growing attention not only from scientists but also from society; however, a major bottleneck lies in their high computational cost. Recently, we have developed a new statistical framework that integrates our large-ensemble experiments with extreme-value statistics, thereby enhancing our capacity to communicate scientific information effectively to society. In this presentation, we will also introduce the newly established Weather Attribution Center Japan, which plays a key role in translating these research outcomes into societal applications.
How to cite: Imada, Y., Takahashi, C., Watanabe, M., and Mori, N.: Utilizing high-resolution large-ensemble simulations to understand long-term variability and forced changes in local extreme events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17847, https://doi.org/10.5194/egusphere-egu26-17847, 2026.
Changes in temperature variability affect the frequency and intensity of extreme events, as well as the regional range of temperatures that ecosystems and society need to adapt to. While accurate projections of temperature variability are vital for understanding climate change and its impacts, they remain highly uncertain. We use rank-frequency analysis to evaluate the performance of eleven single model initial-condition large ensembles (SMILEs) against observations in the historical period, and use those that best represent historical regional variability to constrain projections of future temperature variability. Constrained projections from the best-performing SMILEs still show large uncertainties in the intensity and the sign of the variability change for large areas of the globe. Our results highlight poorly modelled regions where observed variability is not well represented such as large parts of Australia, South America, and Africa, particularly in their local summer season, underscoring the need for further modelling improvements over crucial regions. In these regions, the constrained projected change is typically larger than in the unconstrained ensemble, suggesting that in these regions, multi-model mean projections may underestimate future variability change.
How to cite: Suarez-Gutierrez, L. and Maher, N.: Temperature variability projections remain uncertain after constraining them to best performing Large Ensembles of individual Climate Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3907, https://doi.org/10.5194/egusphere-egu26-3907, 2026.
General Circulation Model (GCMs) are essential tools for projecting future precipitation trends; however, structural biases and shared errors across models raise concerns about whether the ensemble consensus genuinely reflects physical climate signals. While most bias correction (BC) studies focus on improving the statistical accuracy of individual models, the implications of BC on the structural uncertainty and collective consistency of multi-model ensembles remain underexplored. This study investigates how the Robust Multivariate Bias Correction (RoMBC) method, beyond reducing model-level errors, reconfigures the interpretation of precipitation trends and inter-model consensus within CMIP6 ensembles. We applied RoMBC and the conventional univariate quantile mapping (QM) to monthly precipitation outputs from ten CMIP6 GCMs and evaluated their performance and trend fidelity against the ERA5 reanalysis. RoMBC consistently outperformed QM across all statistical metrics—including Kling–Gupta Efficiency, root mean square error, and Pearson correlation—and better captured the spatial patterns and directions of long-term trends, as assessed via seasonal Mann–Kendall tests. More importantly, the Data Concurrence Index (DCI) revealed that RoMBC strengthened inter-model agreement in Europe while weakening it in Asia, suggesting that it removes spurious consensus caused by common biases and exposes underlying structural uncertainty. Additionally, ensemble agreement remained consistently low in Australia and Africa, regardless of the BC method, indicating inherently high uncertainty in those regions. These findings suggest that RoMBC does not simply reduce uncertainty but rather reshapes the ensemble structure to more faithfully represent the inter-model spread of projected signals. This work highlights the importance of expanding BC evaluation beyond individual model performance, offering a novel perspective on interpreting ensemble-based future precipitation projections.(This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2025-23523230))
How to cite: Park, H. and Kim, S.: An Assessment of Multivariate Bias Correction Effects and Model Consistency in CMIP6 Monthly Precipitation Trends, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15774, https://doi.org/10.5194/egusphere-egu26-15774, 2026.
Oceanic meridional heat transport (OHT) reaches its maximum near 20°–30°N, with values around 2 petawatts (PW; 1 PW = 1015 W), accounting for approximately 30% of the total heat transport in the Earth system. Within this latitude band, strong poleward-flowing subtropical western boundary currents (WBCs) play a dominant role in OHT due to their high potential temperature and swift flow velocities. This study uses an ensemble of 10 high resolution climate models and focuses on the projected changes in the two major Northern Hemisphere WBCs—the Gulf Stream and the Kuroshio Current—and their impacts on heat transport under global warming.
For the Gulf Stream, high-resolution models successfully capture the eastern branch of the current near the Bahamas, known as the Antilles Current. As a subsurface current centered around 500 m depth, the Antilles Current exhibits relatively weak mean volume transport (5 Sverdrups; 1 Sv = 106m³/s) and heat transport (0.3 PW), an order of magnitude lower than the Florida Current, the primary branch of the Gulf Stream. However, under global warming, the projected reduction in the Antilles Current (3.8 Sv) is comparable to that of the Florida Current, resulting in a 0.17 PW decline in heat transport. This accounts for the majority of the total decrease in meridional heat transport across 26.5°N in the North Atlantic.
Similarly, we examine changes in the Kuroshio Current and its subsurface branch, the Ryukyu Current. Ensemble-mean results from four eddy-resolving climate models indicate that between 1950 and 2050, the Kuroshio Current in the East China Sea strengthens by 1.2 ± 0.6 Sv, while the Ryukyu Current weakens rapidly by 6.2 ± 2.5 Sv. This leads to a net reduction of 5.0 ± 2.6 Sv in total transport for the Kuroshio system, accompanied by a 0.3 PW decrease in heat transport. This trend is consistent with observational estimates over the period 1958–2022. The underlying mechanisms include a weakening of the subtropical wind field, which reduces total transport in both the Kuroshio and Ryukyu Currents. In addition, enhanced ocean stratification under global warming causes the current system to shoal and weakens flow topography interactions, contributing to the observed strengthening of the Kuroshio Current and the concurrent rapid weakening of the Ryukyu Current.
How to cite: Cai, J.: The Disappearing Subsurface Western Boundary Current in the Northern Hemisphere Under a Warming Climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16058, https://doi.org/10.5194/egusphere-egu26-16058, 2026.
A critical gap remains in identifying physical drivers of decadal Diurnal Temperature Range (DTR) changes over Eurasia. Here, we detect a distinct summertime DTR reversal over Eurasia around the 1990s by a novel energy diagnosis methodology based on observations and MPI-ESM large-ensemble simulations. This reversal is primarily driven by decadal changes in net shortwave radiation and surface turbulence fluxes, linked to an accelerated decline in cloud cover and intensified cloud-radiative effects. Furthermore, this reversal pattern emerges from the superposition of spatially coherent and heterogeneous components. The former is largely attributable to external forcings, while the latter is amplified by internal dynamics. This heterogeneous structure exhibits pronounced strong–weak–strong anomalies stretching from Europe to mid‑latitude East Asia. It is driven by high‑altitude wave activities with a circumglobal teleconnection triggered by a tripolar North Atlantic sea surface temperature pattern.
How to cite: Yang, L., Lin, P., Zhang, L., Zhou, Y., and Liu, H.: Internal Climate Dynamics Underpin the Recent Decadal Reversal of Eurasian Summer Diurnal Temperature Range, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9105, https://doi.org/10.5194/egusphere-egu26-9105, 2026.
Characterising climate variability and evaluating the effects of global warming require an understanding of the spatiotemporal evolution of sea surface temperature (SST) variability. The ability of many current methods to capture changes in variability under a changing climate is limited because they assume stationary covariance structures. The covariance regression framework of Hoff and Niu (2012) is used in this study to enable the smooth evolution of SST covariance structures over time.
We analyse equatorial SST anomalies from CMIP6 climate model simulations from 1850 to 2100. The analysis is carried out in a reduced-dimensional space using principal components, and the results' sensitivity to the number of retained modes is systematically assessed. Model evaluation is based on variance explained and diagnostics based on likelihood.
The proposed framework provides insight into changes in the structure of equatorial SST variability by visualising evolving SST variance, covariance, and correlation patterns using Hovmöller representations. The results demonstrate how time-varying covariance models can be used to identify coherent large-scale patterns of variability, particularly over the tropical Pacific, and diagnose climate-driven changes in SST structure.
The results demonstrate that equatorial SST variability is consistently increased by both the CESM2 and MRI-ESM2 models. The largest increases are seen in the eastern and central Pacific regions. Variance is steadily increasing in the twenty-first century, according to kernel-based estimates. However, this event is better described by the Hoff covariance regression, which incorporates a coherent large-scale temporal structure. Because the signals are strong and consistent throughout the entire Pacific basin and weaker and less regular in the Indian and Atlantic sectors, the covariance and association patterns remain constant over time. While both models exhibit similar geographic patterns, CESM2 exhibits a larger and more consistent increase in variance than MRI-ESM2, which exhibits smaller and more erratic changes. The study's primary finding is that SST variation increases with global mean temperature, despite correlation patterns remaining largely consistent across models and measurement techniques.Work is still ongoing on other CMIP6 models.
How to cite: Alotaibi, I., Collins, M., and Stephenson, D.: Change in Equatorial SST Variability in CMIP6Climate Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10143, https://doi.org/10.5194/egusphere-egu26-10143, 2026.
Extreme weather events, including heatwaves and heavy rainfall, have become increasingly frequent over Japan in recent decades. Quantifying the relative contributions of anthropogenic climate change and natural internal variability to individual events through event attribution (EA) is therefore an important scientific challenge. Here, we develop a statistical method for rapid EA based on existing long-term large-ensemble climate simulations and observational datasets. The method constructs regression models linking large-scale sea surface temperature patterns and associated atmospheric variability to the probability distributions of surface air temperature and precipitation. This approach enables attribution analyses without performing event-specific numerical simulations. We have already applied this method to recent heatwave events over Japan, and the results of the rapid EA analyses have been made publicly available through the “Weather Attribution Center (WAC Japan)”. Furthermore, the method can also be applied to heavy rainfall events, yielding reliable estimates. In this presentation, we present an overview of the newly developed statistical approach, its applications, and our ongoing efforts.
How to cite: Takahashi, C., Imada, Y., and Kawase, H.: A New Statistical Approach for Rapid Attribution of Extreme Weather Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18229, https://doi.org/10.5194/egusphere-egu26-18229, 2026.
How to cite: Undorf, S., deVera, T., Brouillet, A., Tett, S., Ribeiro, A., Quesada-Chacón, D., Iyakaremye, V., and Gornott, C.: Large-ensemble climate model data for impact attribution and projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19985, https://doi.org/10.5194/egusphere-egu26-19985, 2026.
