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This paper aims to provide a new blocking definition with applicability to observations and model simulations. An updated review of previous blocking detection indices is provided and some of their implications and caveats discussed. A novel blocking index is proposed by reconciling two traditional approaches based on anomaly and absolute flows. Blocks are considered from a complementary perspective as a signature in the anomalous height field capable of reversing the meridional jet-based height gradient in the total flow. The method succeeds in identifying 2-D persistent anomalies associated to a weather regime in the total flow with blockage of the westerlies. The new index accounts for the duration, intensity, extension, propagation, and spatial structure of a blocking event. In spite of its increased complexity, the detection efficiency of the method is improved without hampering the computational time. Furthermore, some misleading identification problems and artificial assumptions resulting from previous single blocking indices are avoided with the new approach. The characteristics of blocking for 40 years of reanalysis (1950-1989) over the Northern Hemisphere are described from the perspective of the new definition and compared to those resulting from two standard blocking indices and different critical thresholds. As compared to single approaches, the novel index shows a better agreement with reported proxies of blocking activity, namely climatological regions of simultaneous wave amplification and maximum band-pass filtered height standard deviation. An additional asset of the method is its adaptability to different data sets. As critical thresholds are specific of the data set employed, the method is useful for observations and model simulations of different resolutions, temporal lengths and time variant basic states, optimizing its value as a tool for model validation. Special attention has been paid on the devise of an objective scheme easily applicable to General Circulation Models where observational thresholds may be unsuitable due to the presence of model bias. Part II of this study deals with a specific implementation of this novel method to simulations of the ECHO-G global climate model.

Air stagnation refers to an extended period of clear, stable conditions which can favour the accumulation of pollutants in the lower atmosphere. In Europe, weather conditions are strongly mediated by the North Atlantic eddy-driven jet stream. Descriptions of the jet stream typically focus on its latitudinal position or the strength of its wind speed, and its impacts are often studied under different latitudinal regimes of the jet. Herein, we evaluate the influence of the jet stream on European air stagnation using a new multiparametric jet diagnostic that provides a more complete description of jet stream characteristics. We report large influences of the jet stream on regional stagnation and uncover links with jet structure that go beyond knowledge of its latitude. Accordingly, air stagnation anomalies show different, and often opposite, responses to jets in a given latitudinal position but with different additional characteristics. Statistical modelling reveals that the monthly variability in air stagnation explained by the new jet diagnostic is substantially higher compared to one that only considers the jet’s latitude and intensity. Knowledge of the average location of the jet in a given month, as described by a latitude or longitude parameter, together with the variability in the jet’s shape, appear key for the statistical models of air stagnation. The relationship between air stagnation and the jet stream is often nonlinear, particularly for regions in southern Europe. For northern regions it is generally more linear, but the additional jet parameters are essential for describing stagnation variability. These results have implications for studying air stagnation and its pollution impacts in seasonal forecasts and climate change projections.

August 2018 saw the warmest Iberian heatwave since that of 2003. Recent climate change has exacerbated this event making it at least »1°C warmer than similar events since 1950–83.

The dynamical connection between Northern Hemisphere blocking events and the variability of the stratospheric polar vortex strength is studied. The analysis is based on the composite time evolution of the energy of baroclinic planetary waves during regional blocking occurrence. During Euro‐Atlantic blocking events, an in phase forcing of stationary zonal wavenumber 1 occurs. The enhanced wave amplitude is associated with a stratospheric polar vortex deceleration, which may result, at times, in Sudden Stratospheric Warming (SSW) events of displacement type. Pacific blocking composites reveal an in phase forcing of stationary zonal wavenumber 2. In most cases, the amplification of the wavenumber 2 does not reduce the vortex strength, being even accompanied by a mean vortex acceleration. However, if the amplification of wavenumber 2 is preceded by an amplification of wavenumber 1, the initial vortex deceleration forced by wavenumber 1 may be continued by wavenumber 2, and a SSW event of splitting type may occur.

Anthropogenic climate change (ACC) is driving an increase in the frequency, intensity, and duration of heatwaves (HWs), making the rapid attribution of these events essential for assessing climate‐related risks. Traditional attribution methods often suffer from selection bias, high computational costs, and delayed results, limiting their utility for real‐time decision‐making. In this study, we introduce a novel artificial intelligence (AI)‐driven attribution framework that integrates physics‐based ACC estimates from global climate models with state‐of‐the‐art AI weather prediction (AIWP) models. We apply this approach to four HWs across different climatic regions using two AIWP models (FourCastNet‐v2 and Pangu‐Weather) and one hybrid AI‐physics model (NeuralGCM). Our results show that AIWP models accurately predict HW intensity and spatial patterns, capturing key synoptic features such as persistent high‐pressure ridges. The attribution analysis reveals a robust ACC signal in all four events and a good agreement across models. Results from the hybrid model (NeuralGCM) suggest that the intensification of HWs due to ACC can largely be inferred from the atmospheric state a few days prior to the event, while sea surface temperature forcing becomes increasingly relevant at longer lead times and in specific regions. This study demonstrates that AI‐based attribution enables near real‐time and anticipatory assessment of HWs, offering a scalable and computationally efficient alternative to conventional methods. By providing timely and consistent attribution of extreme heat events, this approach enhances our ability to anticipate climate risks and inform adaptation strategies in a rapidly warming world. Plain Language Summary It is well accepted that climate change is increasing the occurrence and severity of heatwaves (HWs). However, when an event happens, it is important to understand how much climate change influenced that event. Traditional methods are often slow or require expensive resources, making them less useful for quick decision‐making. This study introduces a new approach using weather models powered by artificial intelligence (AI) to quickly estimate how climate change affects the intensity and characteristics of HWs. The method combines fast AI predictions with data from global climate models. We apply this approach to four major HWs in various climate regions and show that AI models can accurately predict HW conditions, including features like persistent high‐pressure systems. Our analysis finds that climate change made all four HWs more intense than they would have been in a preindustrial climate, with strong agreement across models. The results also show that while sea surface temperatures play a role, the main influence comes from changes in atmospheric conditions. This AI‐based method enables faster, near real‐time attribution of HWs, supporting quicker decisions and better planning in a warming world. Key Points Artificial intelligence (AI) weather prediction models provide reliable short‐term forecasts of major heatwaves (HWs) across diverse climate regions A hybrid method that combines AI weather forecasts with climate change estimates from global models enables rapid attribution of HWs Sea surface temperature forcing is secondary to atmospheric initial conditions in driving HW attribution signals

Understanding natural climate variability and its driving factors is crucial to assessing future climate change. Therefore, comparing proxy-based climate reconstructions with forcing factors as well as comparing these with paleoclimate model simulations is key to gaining insights into the relative roles of internal versus forced variability. A review of the state of modelling of the climate of the last millennium prior to the CMIP5–PMIP3 (Coupled Model Intercomparison Project Phase 5–Paleoclimate Modelling Intercomparison Project Phase 3) coordinated effort is presented and compared to the available temperature reconstructions. Simulations and reconstructions broadly agree on reproducing the major temperature changes and suggest an overall linear response to external forcing on multidecadal or longer timescales. Internal variability is found to have an important influence at hemispheric and global scales. The spatial distribution of simulated temperature changes during the transition from the Medieval Climate Anomaly to the Little Ice Age disagrees with that found in the reconstructions. Thus, either internal variability is a possible major player in shaping temperature changes through the millennium or the model simulations have problems realistically representing the response pattern to external forcing. A last millennium transient climate response (LMTCR) is defined to provide a quantitative framework for analysing the consistency between simulated and reconstructed climate. Beyond an overall agreement between simulated and reconstructed LMTCR ranges, this analysis is able to single out specific discrepancies between some reconstructions and the ensemble of simulations. The disagreement is found in the cases where the reconstructions show reduced covariability with external forcings or when they present high rates of temperature change.

Solar variability represents a source of uncertainty in the future forcings used in climate model simulations. Current knowledge indicates that a descent of solar activity into an extended minimum state is a possible scenario. With aid of experiments from a state-of-the-art Earth system model,we investigate the impact of a future solar minimum on Northern Hemisphere climate change projections. This scenario is constructed from recent 11 year solar-cycle minima of the solar spectral irradiance, and is therefore more conservative than the 'grand' minima employed in some previous modeling studies. Despite the small reduction in total solar irradiance (0.36 W m−2), relatively large responses emerge in the winter Northern Hemisphere, with a reduction in regional-scale projected warming by up to 40%. To identify the origin of the enhanced regional signals, we assess the role of the different mechanisms by performing additional experiments forced only by irradiance changes at different wavelengths of the solar spectrum. We find that a reduction in visible irradiance drives changes in the stationary wave pattern of the North Pacific and sea-ice cover. A decrease in UV irradiance leads to smaller surface signals, although its regional effects are not negligible. These results point to a distinct but additive role of UV and visible irradiance in the Earth's climate, and stress the need to account for solar forcing as a source of uncertainty in regional scale projections.

Since 1974, there has been a significant increasing trend in land and sea surface temperatures of 0.5 and 0.24°C decade⁻¹, respectively, in the NW Iberian Peninsula. Over the same period, annual precipitation does not show any trend, although some tendencies have been detected at seasonal scales. A significant positive trend, on average of 2 cm decade⁻¹, was also observed in sea level rise from 1943 onwards. Ekman transport perpendicular to the coast (upwelling index) showed a decrease from 1975 to 2008 at both annual and seasonal scales. In addition, the flow of the Miño River (the main river in the area) has also decreased at a mean rate of 18 m³ s⁻¹ decade⁻¹ since 1970. At a synoptic scale, winter cyclone frequency and winter and spring blocking activity have decreased since the 1950s, which may partially explain the winter precipitation decline and the winter and spring temperature increases. These changes in synoptic systems are also in agreement with reported trends in the dominant variability modes of atmospheric circulation affecting NW Iberia, particularly a pronounced positive trend in the North Atlantic Oscillation from the 1970s to the 1990s.

Stratospheric Sudden Warmings (SSWs) strongly affect the polar stratosphere during winter months mainly in the Northern Hemisphere. The intraseasonal distribution and type of SSWs for the 1958–1979 and 1979–2002 periods in ERA‐40 and NCEP‐NCAR reanalyses reveal differences. In the pre‐satellite era, most events occur in January and are vortex splits. In the post‐satellite era, the distribution is bimodal (peaking in December and February), and shows more displacement events. The difference in the seasonal distribution of SSWs leads to changes in the climatological state of stratospheric temperatures, with differences up to 5.9 K at 10 hPa and 3.6 K at 20 hPa in February between pre‐ and post‐1979 periods. We find that the temperature evolution at 20 hPa is in better qualitative agreement with theoretical expectations than at 10 hPa. Hence, 10 hPa may be affected more strongly by artifacts related with satellite data assimilation, which have, however, limited impact on identification of SSWs. Key Points SSWs have an imprint in climatology according to its intraseasonal distribution There is a decadal change in the occurrence of SSWs Problems in temperature at the 10 hPa level in ERA40 and NCEP‐NCAR reanalyses