Greenland atmospheric blocking, a persistent anticyclonic pattern, strongly influences local and regional weather and climate. It is known to significantly exacerbate Greenland ice sheet melt and mass loss in summer as well as influence atmospheric conditions over the North Atlantic. Greenland blocking has been observed to increase in intensity since the summer of 2000s, but this trend has partly reversed after 2012. This decadal variability is highly correlated with the negative phase of the North Atlantic Oscillation (NAO), the dominant pattern of climate variability in the North Atlantic. However, summer NAO shows different temporal variation in June in comparison with later summer months, i.e. July and August. In this study, we analyse the individual summer months in turn to evaluate differences between their respective spatial patterns of Greenland blocking events. We use different approaches including a self-organising map to evaluate individual blocking days, and an event-based analysis to assess the development of blocking events over the course of 7 d. The results show that spatial patterns of Greenland blocking are similar between July and August but are distinctly different in June. In particular, Greenland blocking in June is strongly related to cyclonic wave breaking over the eastern Atlantic. Our analysis using wave activity flux of the zonally varying mean flow reveals a distinct pattern of wave energy and pseudo-momentum associated with cyclonic wave breaking prior to Greenland blocking high anomalies in June, in contrast to the other two summer months. This might partially explain the difference in the spatial patterns and evolution of blocking in June compared with July and August.

Abstract. The climate of Antarctica is showing increasing signs of being impacted by the warming trend in global temperatures, which has potential to result in accelerated break up of key ice shelves, which would contribute to global sea level rise. Here, we present a novel database of Antarctic extreme weather events over a selection of key ice shelves (Larsen, George VI, Wilkins, Abbot, Thwaites, Totten, Amery, Lazarev), using simulations from four regional climate models (RCMs: RACMO2, HCLIM, MetUM and MAR), driven by the ERA5 reanalysis, examining surface air temperature, precipitation, wind and surface pressure. In addition, we examine trends in the frequency of extreme events above or below specified thresholds (5th, 10th, 50th, 90th and 95th percentiles) and spatial atmospheric circulation and temperature anomaly patterns over Antarctica that are commonly associated with extreme events over key ice shelves. The RCM simulations have been compared with station observations close to the ice shelves, and we developed regressions to estimate simulated values during periods when only one or two of the RCMs were available.

Cold extremes continue to have considerable impacts on a wide range of sectors including health, energy, agriculture, and infrastructure. On a global scale, the frequency and intensity of cold extremes are declining due to anthropogenic climate change. This general decreasing trend in cold extremes is well captured by models for the historical period. However, there are strong regional and seasonal differences in cold extreme occurrence that can be attributed to the variability of large-scale dynamical drivers of cold extremes such as sea ice, the polar stratosphere, and ENSO. The uncertain future evolution of these large-scale drivers, as well as shortcomings in our understanding of the links between these drivers and cold extremes, make it difficult to constrain the magnitude and year-to-year variability of the projected decrease in cold extremes. This review reveals a range of unresolved questions pertaining to the dynamical forcing of cold extremes and their evolution in a changing climate.

There is growing evidence of the climate of Antarctica being impacted by global heating. For example, the Antarctic sea ice extent has sharply declined since around 2015 and has been at record or near record lows in most years since. This has potential to result in accelerated break up of key ice shelves, which would contribute to global sea level rise and have further impacts on local and regional climate in and around Antarctica. There have recently been some notable extreme events such as the atmospheric river event in March 2022, and exceptional melt events over the Antarctic Peninsula in the summer of 2019/20. Here, we present a new database of Antarctic meteorological extremes over a selection of key ice shelves, using simulations from four regional climate models (RCMs: RACMO2, HCLIM, MetUM and MAR), driven by the ERA5 reanalysis, examining surface temperature, precipitation, wind and surface pressure. In addition, we examine trends in the frequency of extreme events above specified thresholds, and spatial anomaly patterns over Antarctica that are commonly associated with extreme events over key ice shelves. The RCM simulations have been compared with station observations close to the ice shelves, and we developed regressions to estimate simulated values during periods when not all of the RCMs were available. The results of the database are compared with weather system drivers such as atmospheric rivers, storm tracks and blocking, as well as teleconnections such as the Southern Annular Mode, to determine links between these drivers and the occurrence of meteorological extremes over the ice shelves. Over most of the ice shelves covered, the frequency of extreme events has not shown a strong trend over the period, but there is evidence of some extremes, especially high temperature extremes, having increased in frequency since the mid-2010s over some of the ice shelves, which could be related to the sharp decline in Antarctic sea ice extent.

Despite continued global warming and progressive thinning of Arctic sea ice, the rate of September sea ice decline has slowed markedly since the 2012 record low. This apparent stabilization challenges expectations of monotonic ice loss and remains poorly understood. Here we show that the slowdown reflects a regime shift in atmosphere–sea ice coupling, rather than a weakening of atmospheric influence. Around 2004, the relationship between the June Greenland High (GH) and September Arctic sea ice extent intensified dramatically, with the correlation strengthening from r = −0.17 (1979–2003) to r = −0.80 (2004–2025). This shift coincides with the transition to a thinner, younger, and more mobile ice cover that is more responsive to atmospheric forcing, alongside enhanced intra-seasonal persistence of summer Arctic circulation. Within this strengthened coupling regime, post-2012 GH weakening accounts for approximately 60–70% of the observed slowdown in September sea ice decline. Crucially, this apparent stabilization entails pronounced downstream impacts, including intensified marine heatwaves in the Norwegian–Barents Seas, accelerated rainfall transition in the Atlantic Arctic, and amplified summer heat extremes over northern Europe. Our findings demonstrate that atmosphere–sea ice coupling is non-stationary and has fundamentally strengthened in the New Arctic, amplifying the influence of circulation variability on both sea ice trajectories and midlatitude climate extremes, with important implications for prediction and climate projections.

The Greenland Ice Sheet is a major contributor to global sea-level rise, having lost ~4,900 Gt of ice since 1992 and already added ~13 mm to global mean sea-level. Even without further warming, it is committed to at least ~274 mm of additional sea-level rise, and complete melting would ultimately raise sea level by ~7 m. In this Review, we synthesise changes in Greenland Ice Sheet surface melt from 1500 to 2200 CE. Surface melt has increased rapidly by ~1% per year since the 1990s, driven by regional warming and changes in atmospheric circulation, particularly enhanced blocking. Several unprecedented extreme melt events lasting several days have occurred since 2007, with record cases such as in July 2012 affecting nearly the entire ice sheet surface. Absorbed shortwave radiation is the dominant driver of seasonal melt, but turbulent heat fluxes, cloud processes and albedo feedbacks strongly modulate melt variability across space and time. Climate models diverge in their representation of these processes, with projected melt and surface mass loss differing by up to a factor of two between three different state-of-the-art regional climate models even under identical forcing. Despite advances in regional climate and Greenland melt modelling, key uncertainties remain in quantifying extreme melt events and their drivers, meltwater retention, firn processes and the coupling between atmospheric forcing and surface energy balance, limiting confidence in projections. Addressing these gaps requires expanded observations, improved process representation in models and integrated use of emerging data-driven approaches to better constrain future melt and its contribution to sea-level rise. Greenland Ice Sheet melt has intensified since the 1990s with implications for sea-level rise. This Review synthesises paleoclimate records, observations and regional climate melt model simulations to explore the patterns and drivers of melt variability and extremes, and assess uncertainties in future projections.

Weather and climate extremes are increasingly occurring in the Arctic. In this Review, we evaluate historical and projected changes in rare Arctic extremes across the atmosphere, cryosphere and ocean and elucidate their driving mechanisms. Clear shifts occur in mean and extreme distributions after ~2000. For instance, pre-2000 to post-2000 observational probabilities of 1.5 standard deviation events increase by 20% for atmospheric heat waves, 76.7% for Atlantic layer warm events, 83.5% for Arctic sea ice loss and 62.9% for Greenland Ice Sheet melt extent — in many cases, low probability, rare extreme events in the early period become the norm in the latter period. These observed changes can be explained using a ‘pushing and triggering’ concept, representing interplay between external forcing and internal variability: long-term warming destabilizes the climate system and ‘pushes’ it to a new state, allowing subsequent variability associated with large-scale atmosphere–ocean–ice interactions and synoptic systems to ‘trigger’ extreme events over different timescales. Ongoing anthropogenic warming is expected to further increase the frequency and magnitude of extremes, such that simulated probabilities of 1.5 standard deviation events increase by 72.6% for atmospheric heat waves, 68.7% for Atlantic layer warm events and 93.3% for Greenland Ice Sheet melt rate between historic (1984–2014) and future (2069–2099) periods under a very high emission scenario. Future research should prioritize the development of physically based metrics, enhance high-resolution observation and modelling capabilities and improve understanding of multiscale Arctic climate drivers. Rare and extreme climate events have increasingly occurred in the Arctic since ~2000. This Review outlines the observed and projected changes in atmospheric, oceanic and cryospheric extremes and explains their increasing occurrence through a ‘pushing and triggering’ framework.

The Greenland Ice Sheet (GrIS), a major driver of global sea-level rise, holds approximately 7 meters of sea-level equivalent. Despite its critical role, significant uncertainties remain about its mass balance and response to climate forcing over the past few centuries, particularly before the satellite era. This study aims to address these gaps by reconstructing a high-resolution (1x1 km) monthly surface mass balance (SMB) dataset spanning AD 1421–2024 and quantifying its contributions to historical and contemporary sea-level changes using the Positive Degree Day (PDD) modelling approach. The novel SMB dataset integrates long-term climate reanalysis inputs (ERA5 and ModE-RA). They are then validated and corrected against available ice-core records and weather station observations using a Bayesian approach to formally constrain the uncertainties. Preliminary analysis indicates signidficant SMB-driven mass loss due to climatic forcing during recent past, potentially offering new insights into the relative contributions of SMB and ice dynamics to GrIS total mass changes during latter half of the last millennium. These results represent a significant advancement in understanding the GrIS’s historical behaviour and links with climate change and can form a valuable baseline for improving the accuracy of future SMB and sea-level rise projections. By addressing critical knowledge gaps, this work enhances our ability to predict the long-term impacts of climate change on the GrIS and global sea levels.

The future state of the global water cycle and the prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation Experiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing, and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling, and experimental research and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here, we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2-3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multiscale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

During mid-January through February 2025 the low-level stratospheric polar vortex (LSPV) at 100 mb extended from North America across the pole to east-central Asia, a stretched pattern, coinciding with a period of extended cold weather on both continents. The LSPV, and its associated tropospheric blocking projections, were a main organizing feature. Weather events were mostly contained in the tropospheric west-east wave guide. For the United States (US), cold conditions occurred in a corridor that includes Illinois, through Mississippi and Virginia. During the second half of January, a 500 mb ridge-trough was set up off the west coast of California, driving cold temperatures into the southeastern US, with record snow in New Orleans. During early February there were cold events in Korea and Eastern Asia; an intermediate timing between cold air outbreaks in the US. The end of February saw a second US cold air outbreak. Although the stretched LSPV at 100 mb had a one and a half month duration extending over the subarctic, mid-tropospheric weather events were of shorter duration in both the US and Eastern Asia. The troughing at 100 mb in the stretched LSPV pattern coincided with event-based barotropic atmospheric troughing response at lower levels. Tropospheric weather events can coexist with the LSPV and thus contribute to sub-seasonal forecasting.

Today's Arctic is characterized by a lengthening of the sea ice melt season, as well as by fast and at times unseasonal melt events. Such anomalous melt cases have been identified in Pacific and Atlantic Arctic sector sea ice studies. Through observational analyses, we document an unprecedented, concurrent marginal ice zone melt event in the Bering Sea and Labrador Sea in March of 2023. Taken independently, variability in the cold-season ice edge at synoptic timescales is common. However, such anomalous, short-term ice loss over either region during the climatological sea ice maxima is uncommon, and the tandem ice loss that occurred qualifies this as a rare event. The atmospheric setting that supported the unseasonal melt events was preceded by a sudden stratospheric warming event amidst background La Ni & ntilde;a conditions that led to positive tropospheric height anomalies across much of the Arctic and the development of anomalous mid-troposphere ridges over the ice loss regions. These large-scale anticyclonic centers funneled extremely warm and moist airstreams onto the ice causing melt. Further analysis identified the presence of atmospheric rivers within these warm airstreams whose characteristics likely contributed to this bi-regional ice melt event. Whether such a confluence of anomalous wintertime events associated with troposphere-stratosphere coupling may occur more often in a warming Arctic remains a research area ripe for further exploration.

It is widely accepted that Arctic amplification (AA)—enhanced Arctic warming relative to global warming—will increasingly moderate cold-air outbreaks (CAOs) to the midlatitudes. Yet, some recent studies also argue that AA over the last three decades to the rest of the present century may contribute to more frequent severe winter weather including disruptive cold spells. To prepare society for future extremes, it is necessary to resolve whether AA and severe midlatitude winter weather are coincidental or physically linked. Severe winter weather events in the northern continents are often related to a range of stratospheric polar vortex (SPV) configurations and atmospheric blocking, but these dynamical drivers are complex and still not fully understood. Here we review recent research advances and paradigms including a nonlinear theory of atmospheric blocking that helps to explain the location, timing and duration of AA/midlatitude weather connections, studies of the polar vortex’s zonal asymmetric and intra-seasonal variations, its southward migration over continents, and its surface impacts. We highlight novel understanding of SPV variability—polar vortex stretching and a stratosphere–troposphere oscillation—that have remained mostly hidden in the predominant research focus on sudden stratospheric warmings. A physical explanation of the two-way vertical coupling process between the polar vortex and blocking highs, taking into account local surface conditions, remains elusive. We conclude that evidence exists for tropical preconditioning of Arctic-midlatitude climate linkages. Recent research using very large-ensemble climate modelling provides an emerging opportunity to robustly quantify internal atmospheric variability when studying the potential response of midlatitude CAOs to AA and sea-ice loss.

Atmospheric blocking is a phenomenon that can lead to extreme weather events over a large region, yet its causes are not fully understood. Global climate models show limitations in representing Northern Hemisphere blocking, especially its frequency, and decadal variability in Greenland blocking in summer in the recent decades. In this study we evaluate the ability of high-resolution (HighResMIP) Earth System Models (ESMs) to simulate summer blocking over the Greenland area, using different but complementary methods to describe the characteristics of blocking. We find that the HighResMIP ensemble can reproduce the spatial pattern of Greenland blocking events, albeit with systematic biases, and capture the relative frequencies of the main blocking patterns: namely the wave breaking structure, North Atlantic ridge, and omega-type blocking. However, the HighResMIP ensemble fails to simulate the observed temporal variations of Greenland blocking index (GB2) and the extremely high values of daily GB2 observed in recent decades. In addition, we do not find clearly superior representation of blocking features from higher-resolution in HighResMIP models compared with lower-resolution models. We also find large sea surface temperature (SST) biases over the North Atlantic and seas surrounding Greenland, and biases in moisture transport over the North Atlantic toward Greenland, especially over the western flank of blocking areas, which might together contribute to model biases in the representation of blocking magnitude.

The variability of the Antarctic and Greenland ice sheets occurs on various timescales and is important for projections of sea level rise; however, there are substantial uncertainties concerning future ice-sheet mass changes. In this Review, we explore the degree to which short-term fluctuations and extreme glaciological events reflect the ice sheets’ long-term evolution and response to ongoing climate change. Short-term (decadal or shorter) variations in atmospheric or oceanic conditions can trigger amplifying feedbacks that increase the sensitivity of ice sheets to climate change. For example, variability in ocean-induced and atmosphere-induced melting can trigger ice thinning, retreat and/or collapse of ice shelves, grounding-line retreat, and ice flow acceleration. The Antarctic Ice Sheet is especially prone to increased melting and ice sheet collapse from warm ocean currents, which could be accentuated with increased climate variability. In Greenland both high and low melt anomalies have been observed since 2012, highlighting the influence of increased interannual climate variability on extreme glaciological events and ice sheet evolution. Failing to adequately account for such variability can result in biased projections of multi-decadal ice mass loss. Therefore, future research should aim to improve climate and ocean observations and models, and develop sophisticated ice sheet models that are directly constrained by observational records and can capture ice dynamical changes across various timescales. The different contributions of long-term and short-term variability to the evolution of ice sheets lead to substantial uncertainties in ice sheet models. This Review describes the response of ice sheets to oceanic, atmospheric and hydrological processes across a range of timescales.

The Atlantic Meridional Overturning Circulation (AMOC) plays an important role in the coupled ocean-climate system and in global climate change. The analysis of its own behaviour and the understanding its links to other climate dynamics is of paramount importance today as we encounter an increasing pressure to adapt to climate change. Due to the enormous complexity, it is almost impossible to establish accurate models, purely based on first-principle modelling approaches, that can perfectly represent the relationships between the AMOC and other dynamic climate parameters. Data-based or data-driven modelling methods, can therefore provide an attractive alternative solution. Systematic regular and continuous measurement of the AMOC time series began in April 2004. The main objective of the paper is to use the monthly data of the AMOC measured during April 2004-Febuary 2017, together with the North Atlantic Oscillation (NAO) index, and density anomalies of the Gulf of Mexico, Labrador Sea and Norwegian Sea, measured during the same period, to investigate and understand the quantitative relationship between the AMOC and four drivers (NAO and the three density anomaly variables). In doing so, nonlinear system identification methods and the Nonlinear AutoRegressive Moving Average with Exogenous input (NARMAX) method are employed to develop a quantitative model that relates the AMOC to the four drivers. Experimental results show that the derived nonlinear model skillfully captures and represents the dynamics of the AMOC based on the other four variables. One of the findings from this study is that the use of autoregressive variables can help improve the prediction of the AMOC.

Abstract Dynamical seasonal forecast models are improving with time but tend to underestimate the amplitude of atmospheric circulation variability and to have lower skill in predicting summer variability than in winter. Here, we construct Nonlinear AutoRegressive Moving Average models with eXogenous inputs (NARMAX) to develop the analysis of drivers of North Atlantic atmospheric circulation and jet‐stream variability, focusing on the East Atlantic (EA) and Scandinavian (SCA) patterns as well as the North Atlantic Oscillation (NAO) index. New time series of these indices are developed from empirical orthogonal function (EOF) analysis. Geopotential height data from the ERA5 reanalysis are used to generate the EOFs. Sets of predictors with known associations with these drivers are developed and used to formulate a sliding‐window NARMAX model. This model demonstrates a high degree of predictive accuracy, as indicated by its average correlation coefficients over the testing period (2006–2021): 0.78 for NAO, 0.83 for EA and 0.68 for SCA. In comparison, the SEAS5 and GloSea5 dynamical forecast models exhibit lower correlations with observed circulation changes: for NAO, the correlation coefficients are 0.51 for SEAS5 and 0.34 for GloSea5, for EA they are 0.15 and 0.09, respectively, and for SCA, they are 0.28 and 0.24, respectively. Comparison of NARMAX predictions with forecasts and hindcasts from the SEAS5 and GloSea5 models highlights areas where NARMAX can be used to help improve seasonal forecast skill and inform the development of dynamical models, especially in the case of summer.

The Atlantic Meridional Overturning Circulation (AMOC) plays an important role in the coupled ocean-climate system and in global climate change. The analysis of its own behaviour and the understanding its links to other climate dynamics is of paramount importance today as we encounter an increasing pressure to adapt to climate change. Due to the enormous complexity, it is almost impossible to establish accurate models, purely based on first- principle modelling approaches, that can perfectly represent the relationships between the AMOC and other dynamic climate parameters. Data-based or data-driven modelling methods, can therefore provide an attractive alternative solution. Systematic regular and continuous measurement of the AMOC time series began in April 2004. The main objective of the paper is to use the monthly data of the AMOC measured during April 2004-Febuary 2017, together with the North Atlantic Oscillation (NAO) index, and density anomalies of the Gulf of Mexico, Labrador Sea and Norwegian Sea, measured during the same period, to investigate and understand the quantitative relationship between the AMOC and four drivers (NAO and the three density anomaly variables). In doing so, nonlinear system identification methods and the Nonlinear AutoRegressive Moving Average with Exogenous input (NARMAX) method are employed to develop a quantitative model that relates the AMOC to the four drivers. Experimental results show that the derived nonlinear model skillfully captures and represents the dynamics of the AMOC based on the other four variables. One of the findings from this study is that the use of autoregressive variables can help improve the prediction of the AMOC.