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It is well established that El Niño–Southern Oscillation (ENSO) impacts the North Atlantic–European (NAE) climate, with the strongest influence in winter. In late winter, the ENSO signal travels via both tropospheric and stratospheric pathways to the NAE sector and often projects onto the North Atlantic Oscillation. However, this signal does not strengthen gradually during winter, and some studies have suggested that the ENSO signal is different between early and late winter and that the teleconnections involved in the early winter subperiod are not well understood. In this study, we investigate the ENSO teleconnection to NAE in early winter (November–December) and characterize the possible mechanisms involved in that teleconnection. To do so, observations, reanalysis data and the output of different types of model simulations have been used. We show that the intraseasonal winter shift of the NAE response to ENSO is detected for both El Niño and La Niña and is significant in both observations and initialized predictions, but it is not reproduced by free-running Coupled Model Intercomparison Project phase 5 (CMIP5) models. The teleconnection is established through the troposphere in early winter and is related to ENSO effects over the Gulf of Mexico and Caribbean Sea that appear in rainfall and reach the NAE region. CMIP5 model biases in equatorial Pacific ENSO sea surface temperature patterns and strength appear to explain the lack of signal in the Gulf of Mexico and Caribbean Sea and, hence, their inability to reproduce the intraseasonal shift of the ENSO signal over Europe. ; B.A. and M.P.B. were supported by the Natural Environment Research Council Grant NE/M006123/1, the European Project 398 603557- STRATOCLIM. B.A. was also funded by ''Ayudas para la contratación de personal posdoctoral de formación en docencia e investigación en los departamentos de la Universidad Complutense de Madrid.'' S.I. was supported by the PRIMAVERA project, funded by the European Union's Horizon 2020 Programme, Grant Agreement 641727. A.S. and N.D. were supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). ; Peer reviewed

Abstract. Windstorms are the most damaging natural hazard across western Europe. Risk modellers are limited by the observational data record to only ∼ 60 years of comprehensive reanalysis data that are dominated by considerable inter-annual variability. This makes estimating return periods of rare events difficult and sensitive to the choice of the historical period used. This study proposes a novel statistical method for estimating wind gusts across Europe based on observed windstorm footprints. A good description of extreme wind speeds is obtained by assuming that gust speed peaks over threshold are distributed exponentially, i.e. a generalised Pareto distribution having a zero shape parameter. The threshold and tail scale parameter are estimated at each location and used to calculate estimates of the 10- and 200-year return levels. The North Atlantic Oscillation (NAO) is particularly important for modulating lower return levels and modulating the threshold, with a less detectable influence on rarer extremes and the tail scale parameter. The length of historical data required to have the lowest error in estimating return levels is quantified using both observed and simulated time series of the historical NAO. For reducing errors in estimating 200-year return levels of an independent 10-year period, a data catalogue of at least 20 years is required. For lower return levels the NAO has a stronger influence on estimated return levels, and so there is more variability in estimates. Using theoretical estimates of future NAO states, return levels are largely outside the historical uncertainty, indicating significant increases in risk potential from windstorms in the next 100 years. Our method presents a framework for assessing high-return-period events across a range of hazards without the additional complexities of a full catastrophe model.

This study offers an overview of the low-frequency (i.e., monthly to seasonal) evolution, dynamics, predictability, and surface impacts of a rare Southern Hemisphere (SH) stratospheric warming that occurred in austral spring 2019. Between late August and mid-September 2019, the stratospheric circumpolar westerly jet weakened rapidly, and Antarctic stratospheric temperatures rose dramatically. The deceleration of the vortex at 10 hPa was as drastic as that of the first-ever-observed major sudden stratospheric warming in the SH during 2002, while the mean Antarctic warming over the course of spring 2019 broke the previous record of 2002 by similar to 50% in the midstratosphere. This event was preceded by a poleward shift of the SH polar night jet in the uppermost stratosphere in early winter, which was then followed by record-strong planetary wave-1 activity propagating upward from the troposphere in August that acted to dramatically weaken the polar vortex throughout the depth of the stratosphere. The weakened vortex winds and elevated temperatures moved downward to the surface from mid-October to December, promoting a record strong swing of the southern annular mode (SAM) to its negative phase. This record-negative SAM appeared to be a primary driver of the extreme hot and dry conditions over subtropical eastern Australia that accompanied the severe wildfires that occurred in late spring 2019. State-of-the-art dynamical seasonal forecast systems skillfully predicted the significant vortex weakening of spring 2019 and subsequent development of negative SAM from as early as late July. ; Australian Government Department of Agriculture, Rural R&D for Profit programme National Science Foundation (NSF) NSF - Directorate for Geosciences (GEO) Met Office Hadley Centre Climate Programme - BEIS Department for Environment, Food & Rural Affairs (DEFRA) NASA's Atmospheric Composition Modeling and Analysis Program Victorian Government (DELWP) Australian Government Department of Industry, Innovation and Science ...

Modes of climate variability affect global and regional climates on different spatio-temporal scales, and they have important impacts on human activities and ecosystems. As these modes are a useful tool for simplifying the understanding of the climate system, it is crucial that we gain improved knowledge of their long-term past evolution and interactions over time to contextualise their present and future behaviour. We review the literature focused on proxy-based reconstructions of modes of climate variability during the Holocene (i.e., the last 11.7 thousand years) with a special emphasis on i) proxy-based reconstruction methods; ii) available proxy-based reconstructions of the main modes of variability, i.e., El Niño Southern Oscillation, Pacific Decadal Variability, Atlantic Multidecadal Variability, the North Atlantic Oscillation, the Southern Annular Mode and the Indian Ocean Dipole; iii) major interactions between these modes; and iv) external forcing mechanisms related to the evolution of these modes. This review shows that modes of variability can be reconstructed using proxy-based records from a wide range of natural archives, but these reconstructions are scarce beyond the last millennium, partly due to the lack of robust chronologies with reduced dating uncertainties, technical issues related to proxy calibration, and difficulty elucidating their stationary impact (or not) on regional climates over time. While for each mode the available reconstructions tend to agree at mutidecadal timescales, they show notable disagreement on shorter timescales beyond the instrumental period. The reviewed evidence suggests that the intrinsic variability of modes can be modulated by external forcing, such as orbital, solar, volcanic, and anthropogenic forcing. The review also highlights some modes experience higher variability over the instrumental period, which is partly ascribed to anthropogenic forcing. These features stress the paramount importance of further studying their past variations using long climate-proxy records for the progress of climate science. ; A.H. is supported by a Beatriu de Pinós –Marie Curie Cofund programme fellowship (2016 BP 00023) and HOLMODRIVE - North Atlantic Atmospheric Patterns influence on Western Iberia Climate: From the Lateglacial to the Present (PTDC/CTA-GEO/29029/2017) project funded by the Fundação para a Ciência e a Tecnologia, Portugal (FCT). A.H., S.G., and S.P-R. thank the Spanish research project PaleoModes (CGL2016-75281-C2-1-R) which provided some of their financial support. E.M.-C. contribution was funded by the project PARAMOUR (30454083) from the EOS program by the F.R.SFNRS. CM-P is supported by the Royal Society (ref: DH150185). P.O. contribution has been supported by the Ramon y Cajal senior tenure programme from the Spanish Ministry of Economy and Competitiveness. L.C-B. acknowledges support from the ERC-funded project GC2.0 (Global Change 2.0: Unlocking the past for a clearer future, grant number 694481). The Spanish PTI PolarCSIC ( https://www.polarcsic.es ) is also acknowledged for providing partial financial support to S.G. R.M. was partially supported by the Swedish Research Council (grant DNR2013-8421). A. P.'s work was supported by a Science Foundation Ireland Career Development Award (17/CDA/4695), a research centre award (12/RC/2289_P2), an investigator award (16/IA/4520), and a Marine Research Programme funded by the Irish Government, cofinanced by the European Regional Development Fund (Grant-Aid Agreement No. PBA/CC/18/01). A.A.S. was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. J.S. is supported by the strategic research program of Modelling the Regional and Global Earth system (MERGE) hosted by the Faculty of Science at Lund University. D.S. is supported by Blue-Action (European Union's Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union's Horizon 2020 research and innovation programme under grant agreement no 776613) projects as well as by the French national program LEFE/INSU with VADEMECUM project. G. X. thanks the funding from the Chinese Scholarship Council (201704910171). This study is partly based on discussions held during the joint workshop of the CLIMOVAR group and IBCC-lo2k project, Barcelona, 25–27 September 2013. PAGES and IBCC-lo2k are thanked for supporting this workshop. Finally, we also thank three anonymous reviewers for their helpful comments on an earlier version of the manuscript ; Peer Reviewed ; Postprint (author's final draft)

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. ; DMS, AAS, NJD, LH and RE were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Econom´ıa y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Sk´lodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE.WAM, HP, KMand KP were supported by the German FederalMinistry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). NK, IB, FC and YW were supported by the Norwegian Research Council projects SFE (grant 270733) the Nordic Center of excellent ARCPATH (grant 76654) and the Trond Mohn Foundation, under the project number : BFS2018TMT01 and received grants for computer time from the Norwegian Program for supercomputing (NOTUR2, NN9039K) and storage grants (NORSTORE, NS9039K). JM, LFB and DS are supported by Blue-Action (European Union Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no 776613) projects. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 Grant OCE-1243015. ; Peer Reviewed ; Postprint (author's final draft)