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Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers. Plain Language Summary: Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a difficult task. Being able to randomly simulate TCs and their features (such as wind speed) with mathematical models is therefore critical to build scenarios (and their corresponding probability) for land use planning and financial risk management. A common approach is to simulate TCs by tracking them directly in climate model outputs but this often underestimates the frequency of intense TCs while being computationally costly overall to generate a large number of events. For these reasons, many authors have looked into alternative approaches that replicate key physical features of TCs but rather using statistical models that are much less computationally demanding. This paper therefore presents a global TC model that leverages the strengths of both statistical and climate models to simulate a large number of TCs whose features are consistent with the physics and observations. As an important global phenomenon that affects TCs globally, we also integrate in our model the effects of El Niño. The paper focuses on the methodology and validation of each model component and concludes with global hazard maps for direct and indirect hits. Key Points: We present a global tropical cyclone (TC) wind model built upon a climate model large ensemble that can be used for risk analysisWe integrate ENSO into our model since it is a strong driver of storm annual frequency, cyclogenesis, trajectories, and intensityWe present global hazard maps consistent with statistical features of TC components and coherent with a global climate model [ABSTRACT FROM AUTHOR]

The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary: The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points: The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect techniqueThe Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate modelsThe median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback [ABSTRACT FROM AUTHOR]

A machine learning method is used to identify sources of long‐term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near‐surface zonal wind (U10)). Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has apredictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long‐lead signal originates from coupled wind‐SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO. Plain Language Summary: Many extreme events, such as floods or droughts, can be attributed to the El Niño Southern Oscillation, a mode of large‐scale ocean‐atmosphere coupled variability in the tropical Pacific Ocean occurring with a period of approximately 4 years. In this analysis, we use a machine learning methodology to disentangle the key atmospheric and oceanic ENSO components' relative contribution to its predictability, particularly the role of near‐surface 10‐m zonal wind. We quantify the potential for improved ENSO predictions for up to 2 years in advance and present a mechanistic understanding of the location of the sources of predictability. While equatorial sea surface temperature represents the primary source of ENSO predictability, the equatorial U10 plays a vital role from late spring to fall, from 1 to 2 years in advance. The enhanced predictability skill is shown to be linked to an SST anomaly originating in the Indian Ocean. The ML model used provides a new way to get new insights into the sources of predictability for ENSO and can be used as a simple but powerful tool to improve the underlying mechanistic understanding. Key Points: A deep learning‐based approach suggests near‐surface 10 m wind to play a significant role in providing skills for long‐term ENSO predictabilityThe skill of the 10‐m zonal wind is large from 12 months up to 23 months in advance (depending on the season)The signal is generated by coupled wind‐SST interactions in the Indian Ocean and later propagates across the Pacific [ABSTRACT FROM AUTHOR]

Subgrid‐scale processes, such as atmospheric gravity waves (GWs), play a pivotal role in shaping the Earth's climate but cannot be explicitly resolved in climate models due to limitations on resolution. Instead, subgrid‐scale parameterizations are used to capture their effects. Recently, machine learning (ML) has emerged as a promising approach to learn parameterizations. In this study, we explore uncertainties associated with a ML parameterization for atmospheric GWs. Focusing on the uncertainties in the training process (parametric uncertainty), we use an ensemble of neural networks to emulate an existing GW parameterization. We estimate both offline uncertainties in raw NN output and online uncertainties in climate model output, after the neural networks are coupled. We find that online parametric uncertainty contributes a significant source of uncertainty in climate model output that must be considered when introducing NN parameterizations. This uncertainty quantification provides valuable insights into the reliability and robustness of ML‐based GW parameterizations, thus advancing our understanding of their potential applications in climate modeling. Plain Language Summary: Climate models are unable to resolve processes that vary on length and time scales smaller than the model resolution and timestep. For example, atmospheric gravity waves (GWs), which are waves created when winds encounter disturbances to the flow, such as mountains, convection and fronts, can have wavelengths smaller than the spacing between grid cells. Climate models use "parameterizations" to capture the effect of these processes. Machine learning based parameterizations are becoming popular because they can learn relationships purely from data. However, we do not have a good understanding of the uncertainties introduced through machine learning parameterizations. This study estimates uncertainties associated with training a neural network (NN) GW parameterization. We explore uncertainties in the NN output, as well as the uncertainties in the climate model output, when the NN is used for the GW parameterization. Key Points: Using ensembles of neural networks, we learn parametric uncertainties associated with an emulator of a gravity wave parameterizationWhen coupled to the climate model, the ensemble of neural networks reveals increased climate variabilityParametric uncertainty dominates the Quasi‐Biennial Oscillation statistics, although polar vortex properties remain robust to parameters [ABSTRACT FROM AUTHOR]

The last glacial inception (LGI) marks the transition from the interglacial warm climate to the glacial period with extensive Northern Hemisphere ice sheets and colder climate. This transition is initiated by decreasing boreal summer insolation but requires positive feedbacks to stimulate the appearance of perennial snow. We perform simulations of LGI with climate model AWI‐ESM‐2.1, forced by the astronomical and greenhouse gas forcing of 115,000 years before present. To compare with the preindustrial (PI) simulation, we use a consistent definition of the seasons during the LGI and the PI and evaluate model output on an angular astronomical calendar. Our study reveals a prominent role of the sea‐ice albedo feedback to amplify the delayed climate signal at polar latitudes. Through a radiative budget analysis, we examine that the ice‐albedo feedback exceeds the shortwave radiative forcing, contributing to the cooling and high latitude snow built‐up during LGI. Plain Language Summary: The onset of the last ice age marks the transition from the interglacial warm climate to the ice age with extensive Northern Hemisphere (NH) ice sheets and colder climate. This transition is initiated by decreasing boreal summer irradiance and reinforced by positive feedbacks. We perform climate simulations under the astronomical and greenhouse gas forcing, and use a consistent definition of seasons. Our study shows that a delayed ice‐albedo feedback plays an important role in generating a cold NH climate. Key Points: Climate simulations reveal year‐round cooling in the Northern Hemisphere high latitudes during the last glacial inceptionA refined angular calendar provides a tool to analyze the seasonal dynamicsWe identify the crucial role of the albedo feedback on the high‐latitude radiative budget [ABSTRACT FROM AUTHOR]

Global climate change is often thought of as a steady and approximately predictable physical response to increasing forcings, which then requires commensurate adaptation. But adaptation has practical, cultural and biological limits, and climate change may pose unanticipated global hazards, sudden changes or other surprises–as may societal adaptation and mitigation responses. These poorly known factors could substantially affect the urgency of mitigation as well as adaptation decisions. We outline a strategy for better accommodating these challenges by making climate science more integrative, in order to identify and quantify known and novel physical risks including those arising from interactions with ecosystems and society. We need to do this even–or especially–when they are highly uncertain, and to explore risks and opportunities associated with mitigation and adaptation responses by engaging across disciplines. We argue that upcoming climate assessments need to be more risk‐aware, and suggest ways of achieving this. These strategies improve the chances of anticipating potential surprises and identifying and communicating "safe landing" pathways that meet UN Sustainable Development Goals and guide humanity toward a better future. Plain Language Summary: Global risks may arise from many places, not all of which are being considered at the moment in the usual projections of future climate. Some risks are being overlooked, sometimes by most of the physical science community itself, or else by others who need information about future climate. We think physical climate sciences can do a lot to remedy this situation by looking more systematically at a broader array of risks and possible futures, narrowing them down, and collaboratively seeking better ways to express them. In this perspective we argue why this is important and explain a few ways it can be done. Key Points: Climate science needs to adjust to account systematically for low‐likelihood, high‐impact risks and comprehensive pathway interactionsAchieving this requires new strategies; several are outlined here, including increased model coupling and scenario explorationSuccess will require collaboration between physical sciences and other disciplines [ABSTRACT FROM AUTHOR]

In 2023, Antarctic sea‐ice extent (SIE) reached record lows, with winter SIE falling to 2.5Mkm2 below the satellite era average. With this multi‐model study, we investigate the occurrence of anomalies of this magnitude in latest‐generation global climate models. When these anomalies occur, SIE takes decades to recover: this indicates that SIE may transition to a new, lower, state over the next few decades. Under internal variability alone, models are extremely unlikely to simulate these anomalies, with return period >1000 years for most models. The only models with return period <1000 years for these anomalies have likely unrealistically large interannual variability. Based on extreme value theory, the return period is reduced from 2650 years under internal variability to 580 years under a strong climate change forcing scenario. Plain Language Summary: In 2023, the area of winter Antarctic sea ice fell to the lowest measured since satellite records began in late 1978. It is still under debate how far this low can be explained by natural variations, and how much can be explained by climate change. Global climate models are tools used to study past and predict future global change. We show that, without climate change, the latest generation of these models are extremely unlikely to simulate a sea‐ice reduction from the mean as large as observed in winter 2023. Including strong climate change quadruples the chance of such a reduction, but the chance is still very low. When these rare reductions are simulated, sea ice takes around 10 years to recover to a new, lower, area: this indicates that Antarctic sea ice may transition to a new, lower, state over the next few decades. Key Points: The latest generation of global climate models rarely simulate an Antarctic sea‐ice extent anomaly as negative as observed in winter 2023The return period for such an anomaly is 2650 years under internal variability, decreasing to 580 years under strong climate change forcingAfter the anomaly occurs, sea‐ice extent recovers within a decade to a new, lower state [ABSTRACT FROM AUTHOR]

As sea ice disappears, the emergence of open ocean deep convection in the Arctic, which would enhance ice loss, has been suggested. Here, using 36 state‐of‐the‐art climate models and up to 50 ensemble members per model, we show that Arctic deep convection is rare under the strongest warming scenario. Only five models have convection by 2100, while 11 have had convection by the middle of the run. For all, the deepest mixed layers are in the eastern Eurasian basin. When that region undergoes a salinification and increasing wind speeds, the models convect; yet most models are freshening. The models that do not convect have the strongest halocline and most stable sea ice, but those that lose their ice earliest ‐because of their strongly warming Atlantic Water‐ do not have a persistent deep convection: it shuts down mid‐century. Halocline and Atlantic Water changes urgently need to be better constrained in models. Plain Language Summary: Both observations and modeling simulations suggest that deep vertical mixing (or deep convection) in winter may become the new normal in the Arctic as sea ice disappears, which further accelerates that disappearance. These simulations are often done using only one model, so here we used all models available that participated in the Climate Model Intercomparison Project Phase 6 (CMIP6), for the strongest warming scenario. We show that after removing those that are already inaccurate in the present, and even with a restrictive threshold, most models have no deep convection in the Arctic, or extremely rarely. Only five still had deep convection by the time the run finishes in 2100. We found that deep convection regions and periods are associated with a saltier, windier surface, while the rest of the Arctic and/or run freshens. The subsurface properties were crucial too: a deep halocline, that is, strongly stratified model, leads to less ice loss and no deep convection. In contrast, models with a shallow halocline and strongly warming Atlantic Water lose their ice and convect earliest, although it does not persist. As water mass properties are poorly represented in CMIP6 models, these need improving to better constrain Arctic deep convection and sea ice projections. Key Points: Oceanic deep convection does not emerge and persist in the Arctic in the majority of Climate Model Intercomparison Project Phase 6 models, despite a drop in the Nordic SeasArctic deep convection occurs only when both surface salinity and winds are increasing, year round, yet most models are fresheningA deep halocline and stable ice cover hinder deep convection, while early ice loss due to warmer Atlantific water can also stop it rapidly [ABSTRACT FROM AUTHOR]

This study examines the impact of ocean advection and surface freshwater flux on the non‐seasonal, upper‐ocean salinity variability in two climate model simulations with eddy‐resolving and eddy‐parameterized ocean components (HR and LR, respectively). We assess the realism of each simulation by comparing their sea surface salinity (SSS) variance with satellite and Argo float estimates. In the extratropics, the HR variance is about five times larger than that in LR and agrees with Argo. In turn, the extratropical satellite SSS variance is smaller than that from HR and Argo by about a factor of two, potentially caused by the insufficient resolution of radiometers to capture mesoscale features and their low sensitivity to SSS in cold waters. Using a simplified salinity conservation equation for the upper‐50‐m ocean, we find that the advection‐driven variance in HR is, on average, 10 times larger than the surface flux‐driven variance, reflecting the action of mesoscale processes. Plain Language Summary: This study explores the importance of ocean currents, evaporation, and rainfall for driving changes in the salt concentration in the upper ocean (known as salinity) in two climate model simulations with differing ocean resolutions. The high‐resolution model (HR) simulates ocean currents with dimensions of tens of km, while the low‐resolution model (LR) can only simulate currents with hundreds of km in size. When comparing their simulated sea surface salinity variations with those captured by satellites and autonomous floats from the Argo array, the salinity variability in the high‐resolution model is similar to the Argo data at mid to high latitudes and about five times stronger than that in the low‐resolution model. The satellite data show a variability two times smaller than HR and Argo in the same regions, potentially due to their insufficient spatial resolution at higher latitudes and their low sensitivity to the surface salinity in cold waters. Using a simple equation describing the conservation of salinity in the upper ocean, we have shown that small‐scale ocean currents drive most of the salinity variability in HR, while in LR, ocean currents play a much smaller role. Key Points: We investigate how advection and surface flux affect upper‐50‐m salinity variance in eddy‐resolving and eddy‐parameterized climate modelsThe extratropical variance in the eddy‐resolving run matches Argo and is much larger than in the eddy‐parameterized run and satellite dataThe larger upper‐ocean salinity variance in the eddy‐resolving run is predominantly driven by mesoscale ocean processes [ABSTRACT FROM AUTHOR]