Atmospheric rivers (ARs) play a major role in transporting heat and moisture into the Arctic, yet their thermodynamic structure and regional impacts remain poorly understood. Here, we adopt a combined Eulerian-Lagrangian framework to investigate two intense ARs that penetrated into the central Arctic within one week in April 2020 during the MOSAiC field campaign. This study provides a comprehensive view of their large-scale dynamics, moisture sources, and thermodynamic evolution.The first AR entered the Arctic via the Siberian sector, driven by a highly anomalous quasi-stationary anticyclone over north-central Siberia. The second followed an Atlantic pathway and was associated with an unusually deep and persistent cyclone over Baffin Bay. Despite their distinct origins and pathways, both events produced extreme surface impacts, including widespread warming across Eurasia exceeding 9 degrees C over a 7 d period and intense precipitation along the Greenland coast and in the central Arctic. The events coincided with a notable decline in sea ice extent along eastern Greenland and in the Barents-Kara Sea, that is highly correlated with the AR-induced warming and rainfall.Backward trajectory analysis of parcels associated with extreme Arctic precipitation reveals distinct pathways and thermodynamic evolution. During both AR events, a subset of air parcels exhibiting classic AR characteristics is identified. These warm, moist, low-pressure airmasses ascend upon arrival and release intense precipitation. Moisture sources, however, differed by pathway: the Atlantic AR drew from the warm Gulf Stream region, while the Eurasian AR was fed by continental Eurasia. These findings highlight the diverse origins and mechanisms of ARs and their capacity to drive rapid Arctic climate and cryospheric changes.

Antarctica's weather and climate have global impacts, influencing weather patterns, ocean currents and sea levels worldwide. However, Antarctica is vast and complex, and the atmospheric processes that govern its climate are strongly influenced by its steep terrain, particularly around the coastal periphery. Our scientific understanding of this complex environment is hampered by the lack of reliable observations and gridded datasets at sufficiently high spatial and temporal resolution. High-resolution regional climate models, RCMs, can provide a solution to the sparsity of observational data and low resolution of reanalyses, facilitating more in-depth assessments of crucial climate variables like precipitation, wind and temperature that are strongly influenced by topography. Here we present and evaluate a comprehensive, high-quality, similar to 11 km resolution RCM dataset, the PolarRES ensemble, for the period 2000-2019. We show that the ensemble largely out-performs ERA5, especially with regard to variables like coastal winds and precipitation. There are no consistent seasonal differences in biases, but there are persistent regional biases. Victoria Land and the Trans-Antarctic Mountains are the regions the RCMs and ERA5 struggle the most with, which suggests that further investigation and model development is needed in this area. Each RCM has strengths and limitations, but overall the ensemble captures the observed weather and climate of Antarctica well. The PolarRES ensemble offers a novel and exciting way of evaluating climate processes and features, and we encourage researchers to use the data, which are freely available, to explore pertinent climate questions of local, regional and global significance.

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.

Abstract The complex terrain in mountainous regions makes it extremely difficult to accurately measure or model snowfall, which is a key component of the terrestrial water budget. This study addresses these challenges by using high-altitude frozen lakes as pressure-sensing surfaces to produce accurate observations of the water content of snowfall at a range of sites in the European Alps, west–central Himalayas, and central Rockies, which are subsequently used to test and constrain snowfall output from a 1.5-km resolution version of the atmosphere-only Met Office Unified Model (MetUM). The model resolution is on a similar scale to the size of the lakes, as well as sufficiently fine to represent the critical interactions between atmospheric flows and the complex orography that influences snowfall and especially extremes. The snowfall output from the MetUM is additionally fine tuned by adjusting the fall speed of snow particles so that it is best able to capture the observed snowfall amounts, especially for extreme events. The results presented here show that the MetUM is generally able to accurately simulate both the timing and amounts of the snowfall observations over mountainous regions. Moreover, the model is particularly good at representing extreme snowfall events, with our study also using model hydrometeor output to examine the microphysical conditions related to these conditions. Finally, we suggest that the model output can effectively be used as pseudo-observations and for the generation of high-resolution, long-term gridded snowfall products. Significance Statement Snowfall in mountainous regions is a vital source of freshwater, sustaining rivers that support both large populations and diverse ecosystems. However, the complex topography of these areas poses significant challenges for accurately measuring snowfall. This study tackles these difficulties by using high-altitude frozen lakes as natural pressure sensors to monitor snowfall water content across sites in the European Alps, the west–central Himalayas, and the central Rockies. These observations are used to constrain and refine snowfall simulations from a high-resolution version of the Met Office Unified Model. The results show that the model can accurately simulate both the timing and amounts of the snowfall observations and can effectively be used for the generation of long-term snowfall products.

The Southern Ocean is the dominant marine sink for anthropogenic carbon, absorbing around 40% of carbon emitted since industrialisation, but it is a remote and challenging region to measure. Sparsity of observational data is the main cause of uncertainty in air-sea carbon flux in the Southern Ocean. Year-round observations of CO2 mixing ratios can aid understanding of air-sea flux in this critical region and provide valuable insight into how the carbon sink is changing over time as well as its seasonal and interannual variability. This work presents ten years of high frequency in situ carbon dioxide mixing ratios measured from two coastal Antarctic research stations; Halley, operated by the British Antarctic Survey, and the German research station, Neumayer. This data set provides a rare long-term measurement of CO2 in the Southern Ocean region, allowing annual growth rates, seasonal changes and interannual variability to be studied. The mean annual growth rate was calculated to be ~2.4 ppm year-1 between 2013 and 2022. The coastal location of these stations mean they are ideally placed to explore air-sea CO2 exchange in the Southern Ocean. Both the Halley and Neumayer records show short-term fluctuations in CO2 mixing ratios during the summer, with up to ~0.5 ppm decreases in CO2 over the course of a day, about one fifth of the average annual growth rate. Air mass trajectory analysis carried out using Hysplit with ERA5 meteorological data, suggests that these decreases in CO2 correspond to periods where the air sampled has spent time over the Southern Ocean, suggesting CO2 uptake has occurred. This work explores the possible drivers for the short-term variability in CO2 mixing ratios, focusing on the role of ocean uptake in the summer.

In recent decades, the Arctic has warmed nearly four times faster than the global average, undergoing profound changes as a result. A key factor in this accelerated warming is the meridional transport of atmospheric water vapour. Particularly, intense intrusions of moisture and heat, so-called atmospheric rivers (ARs), are rare phenomena to reach the high latitudes, but can have severe impacts on the Arctic environment.In this study, we examine an AR pair in April 2020 using a combination of Eulerian and Lagrangian methods alongside observational data from Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The event consisted of two distinct ARs that followed separate pathways - one across Siberia and the other across the Atlantic - before converging in the central Arctic within the span of one week. Large-scale atmospheric circulation patterns associated with these ARs show a combination of low and high pressure systems on the flanks of the ARs, channelling moisture and heat northward. Notably, our results show that the Siberian AR was linked to extreme heat anomalies, whereas the Atlantic AR primarily transported abundant moisture.Backward air parcel trajectories calculated using LAGRANTO provide new insights into the complex dynamics of Arctic ARs, revealing details of their distinct pathways and moisture source regions. Analysis of these trajectories also uncovers a strong connection between the observed sea ice melt in the Barents-Kara Sea and the interaction of an AR with the ice edge, underscoring the significant influence of ARs on the Arctic climate system.

This study introduces a novel approach to post-processing (i.e. downscaling and bias-correcting) reanalysis-driven regional climate model daily precipitation outputs that can be generalised to ungauged mountain locations by leveraging sparse in situ observations and a probabilistic regression framework. We call this post-processing approach generalised probabilistic regression (GPR) and implement it using both generalised linear models and artificial neural networks (i.e. multi-layer perceptrons). By testing the GPR post-processing approach across three Hindu Kush Himalaya (HKH) basins with varying hydro-meteorological characteristics and four experiments, which are representative of real-world scenarios, we find it performs consistently much better than both raw regional climate model output and deterministic bias correction methods for generalising daily precipitation post-processing to ungauged locations. We also find that GPR models are flexible and can be trained using data from a single region or multiple regions combined together, without major impacts on model performance. Additionally, we show that the GPR approach results in superior skill for post-processing entirely ungauged regions, by leveraging data from other regions as well as ungauged high-elevation ranges. This suggests that GPR models have potential for extending post-processing of daily precipitation to ungauged areas of HKH. Whilst multi-layer perceptrons yield marginally improved results overall, generalised linear models are a robust choice, particularly for data-scarce scenarios, i.e. post-processing extreme precipitation events and generalising to completely ungauged regions.

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are a group of temperature-sensitive membrane lipids found in bacteria that have been widely used in palaeo-temperature reconstruction. Despite recent advances in analytical methods, calibration datasets and statistical modelling approaches, one of the current challenges in Quaternary science remains in determining the most appropriate calibration model for reconstructing past changes in climate. We address this challenge by expanding existing calibration datasets, and by evaluating calibration models constructed using a range of statistical modelling approaches. We further evaluate model performance by applying the calibrations to published downcore records from contrasting environments and across different Quaternary timescales. Our study expands existing calibrations and includes new data from Antarctic lakes, providing greater confidence and applicability across a wider range of global environments. Results show robust brGDGT-temperature relationships on a global scale within the temperature range of approximately -2 degrees C to +31 degrees C covered in this study, with the random forest (RF) models performing the best (highest R2cv and lowest RMSEP) to estimate mean temperature of Months Above Freezing (MAF) and Mean Summer (air) Temperature (MST). Examination of uncertainties suggests the best models are accurately modelling all the features of the brGDGT-temperature relationships. To evaluate model performance downcore we apply and recommend a suite of exploratory statistical analyses to help identify core-samples that have unusual, no-analogue compositions, and use measures of correlation and concordance to summarise the similarity in trends and absolute values among reconstructions as a tool to suggest which reconstructions may be more reliable and where to use caution. Our results demonstrate that, although cross-validated calibration R2 cv and RMSEP may indicate good model performance for the calibration data, a thorough assessment is required to assess reconstruction reliability when a model is applied downcore at a specific site. Our findings highlight the complexities and caveats of different methods for global temperature calibrations. The implications of our work are also relevant to other calibration studies in Quaternary science.

The Southern Ocean plays a critical role in modulating excess atmospheric carbon dioxide, accounting for roughly 40% of global ocean anthropogenic CO2 uptake since industrialisation. Given its significance in the global carbon cycle, understanding the Southern Ocean carbon sink is important but studies show high uncertainties in the magnitude and evolution of this carbon sink. The Southern Ocean is a remote and challenging region to measure, and the resulting sparsity of observational data is the main cause of uncertainty in air-sea carbon flux in the region. Long term, high-temporal-frequency data sets especially are rare for the Southern Ocean, but these can give valuable insights into the carbon cycle processes occurring in the region.This work presents ten years of high-temporal-frequency in situ atmospheric carbon dioxide mixing ratios measured from two coastal Antarctic research stations; Halley, operated by the British Antarctic Survey, and the German research station, Neumayer. The coastal location of these stations means they are ideally placed to explore air-sea CO2 exchange over the Southern Ocean. Both the Halley and Neumayer records show short-term fluctuations in CO2 mixing ratios during austral summer, with over ~0.5 ppm decreases in CO2 sometimes observed over the course of a day - about one fifth of the average annual growth rate (~2.4 ppm per year-1 for this 10-year record). Analysis of air mass trajectories reveal that these fluctuations in CO2 occur when the sampled air has spent considerable time in contact with the Southern Ocean, suggesting CO2 uptake has occurred, leading to the reduced CO2 mixing ratios observed.We present an in-depth analysis of the drivers of the short-term variability observed during austral summer, including the role of mixing height, sea-ice coverage, wind speed and biology. Observational data represent an important tool with which to tease out key factors determining Southern Ocean CO2 uptake, and thus in assessing how uptake may evolve in the future.

In this study, available large ensemble datasets in the Coupled Model Intercomparison Phase 6 (CMIP6) archive were used to provide the first multi-variate overview of the evolution of extreme seasons over Antarctica and the Southern Ocean during the 20th and 21st centuries following medium-to-high radiative forcing scenarios. The results show significant differences between simulated changes in background mean climate and changes in low (10th percentile) and high (90th percentile) extreme seasons. Regional winter warming is most pronounced for cold extremes. In summer, there are more pronounced increases in high extremes in precipitation and westerly wind during the ozone hole formation period (late 20th century), affecting coastal regions and, in particular, the Antarctic Peninsula. At midlatitudes, there is a reduction in the range of summer season wind extremes. Suggested mechanisms for these differences are provided relating to sea ice retreat and westerly jet position.

The Ross Ice Shelf, West Antarctica, experienced an extensive melt event in January 2016. We examine the representation of this event by the HIRHAM5 and MetUM high-resolution regional atmospheric models, as well as a sophisticated offline-coupled firn model forced with their outputs. The model results are compared with satellite-based estimates of melt days. The firn model estimates of the number of melt days are in good agreement with the observations over the eastern and central sectors of the ice shelf, while the HIRHAM5 and MetUM estimates based on their own surface schemes are considerably underestimated, possibly due to deficiencies in these schemes and an absence of spin-up. However, the firn model simulates sustained melting over the western sector of the ice shelf, in disagreement with the observations that show this region as being a melt-free area. This is attributed to deficiencies in the HIRHAM5 and MetUM output and particularly a likely overestimation of night-time net surface radiative flux. This occurs in response to an increase in night-time downwelling longwave flux from around 180–200 to 280 W m−2 over the course of a few days, leading to an excessive amount of energy at the surface available for melt. Satellite-based observations show that this change coincides with a transition from clear-sky to cloudy conditions, with clouds containing both liquid water and ice water. The models capture the initial clear-sky conditions but seemingly struggle to correctly represent cloud properties associated with the cloudy conditions, which we suggest is responsible for the radiative flux errors.

We investigate an unusual extensive ice-free feature (EIF) within the pack ice that developed in the central Weddell Sea in December 1980 on the edge of the multi-year sea ice off the east coast of the Antarctic Peninsula. The EIF was first apparent on satellite imagery on 8 December 1980 and expanded until it reached its largest areal extent of ~5.4 × 105 km2 on 26 December. The combined influences of near-record strength ( ~ 15 ms−1) cold winds from the Antarctic continent (transporting sea ice northward and creating an area of thin ice), increased shortwave radiation and net heat flux into the ocean, passage of deep polar storms, and the upwelling of high saline warm water led to the opening of this unique EIF. It is still the largest ice-free feature within the pack ice resembling a polynya observed in the central Weddell Sea during the satellite era, contributing significantly to the 1981 Weddell Sea sea ice extent minimum of 0.793 × 106 km2, the lowest on record. The development mechanism of this EIF was different from the 1970’s Weddell open ocean polynya which occurred within the winter sea ice cover through enhanced ocean convection.

Polar ecosystems are experiencing amongst the most rapid rates of regional warming on Earth. Here, we discuss ‘omics’ approaches to investigate polar biodiversity, including the current state of the art, future perspectives and recommendations. We propose a community road map to generate and more fully exploit multi-omics data from polar organisms. These data are needed for the comprehensive evaluation of polar biodiversity and to reveal how life evolved and adapted to permanently cold environments with extreme seasonality. We argue that concerted action is required to mitigate the impact of warming on polar ecosystems via conservation efforts, to sustainably manage these unique habitats and their ecosystem services, and for the sustainable bioprospecting of novel genes and compounds for societal gain.

This is the data used in the paper "The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica"

Meteorological records at Signy Station in the South Orkney Islands (SOIs) have recently been digitized to cover the period of 1947–1995. This study compares the newly available near‐surface air temperatures at Signy with those from a nearby station, Orcadas and with reanalysis datasets to provide a more comprehensive picture of the weather and climate variability in the SOIs. Temperatures from both stations show a higher degree of variability in winter than summer, but the variability differs in terms of its relationship to the dominant wind directions and sea ice influences. The two stations differ markedly in terms of their respective warm temperature events, largely due to orography‐induced föhn winds at Signy Station as northwesterlies flow over Coronation Island. ERA5 reproduces the monthly to annual averages exceedingly well but underestimates both cold and warm tails of station temperatures. Temperature trends in the SOIs are also considered in terms of changes in large‐scale circulation and the sea surface temperature over the Brazil‐Falkland Confluence. However, caution is required in interpreting the long‐term temperature trends estimated from reanalysis data as most of the reanalyses show a cold bias before 1979, which is most likely caused by misrepresentation of the sea ice.

Extreme warm events in the South Orkney Islands (SOIs) are investigated using synoptic observations from Signy and Orcadas stations for 1947–1994 and 1956–2019 respectively. Defining the extremes as temperatures exceeding the 95th percentile of the temperature distribution, we reveal the characteristics and associated drivers of the warm events, especially the top 10 events in both summer and winter. At both stations, extreme warm events often involve a combined effect of atmospheric rivers (ARs) and localised föhn warming, with distinct characteristics due to the station locations relative to Coronation Island, the largest and highest island of the SOIs. For example, warm events at Signy are warmer (by an average of around 3°C) than the corresponding concurrent temperatures at Orcadas. The number of warm events per year has significantly increased over the record periods at both stations, which could potentially impact ecosystems by increasing melting of snow and ice. Extreme warm events at Signy are dominated by föhn warming in combination with ARs originating from the Southern Atlantic Ocean, where warm, moisture‐rich air is rapidly advected towards the islands by enhanced northerly winds. By contrast, the Orcadas warm extremes involve both warm‐air advection and föhn warming associated with enhanced northwesterlies/westerlies with ARs originating in the Pacific Ocean that travel across the Drake Passage. Simulation of one of the top 10 warm events for Signy station using a 1‐km grid spacing configuration of the atmosphere‐only UK Met Office Unified Model is used to disentangle the role of local versus large‐scale forcing. We find that the majority of the warming can be attributed to föhn effects for the case study. These results demonstrate the complexity of Antarctic temperature extremes.

We calculate a regional surface “melt potential” index (MPI) over Antarctic ice shelves that describes the frequency (MPI-freq, %) and intensity (MPI-int, K) of daily maximum summer temperatures exceeding a melt threshold of 273.15 K. This is used to determine which ice shelves are vulnerable to melt-induced hydrofracture and is calculated using near-surface temperature output for each summer from 1979/80 to 2018/19 from two high-resolution regional atmospheric model hindcasts (using the MetUM and HIRHAM5). MPI is highest for Antarctic Peninsula ice shelves (MPI-freq 23-35%, MPI-int 1.2-2.1 K), lowest (2-3%, < 0 K) for Ronne-Filchner and Ross ice shelves, and around 10-24% and 0.6-1.7 K for the other West and East Antarctic ice shelves. Hotspots of MPI are apparent over many ice shelves, and they also show a decreasing trend in MPI-freq. The regional circulation patterns associated with high MPI values over West and East Antarctic ice shelves are remarkably consistent for their respective region but tied to different large-scale climate forcings. The West Antarctic circulation resembles the central Pacific El Niño pattern with a stationary Rossby wave and a strong anticyclone over the high-latitude South Pacific. By contrast, the East Antarctic circulation comprises a zonally symmetric negative Southern Annular Mode pattern with a strong regional anticyclone on the plateau and enhanced coastal easterlies/weakened Southern Ocean westerlies. Values of MPI are 3-4 times larger for a lower temperature/melt threshold of 271.15 K used in a sensitivity test, as melting can occur at temperatures lower than 273.15 K depending on snowpack properties.

We use meteorological measurements from three drifting buoys to evaluate the performance of the ERA-Interim and ERA5 atmospheric reanalyses from the European Center for Medium-Range Weather Forecasts over the Weddell Sea ice zone. The temporal variability in surface pressure and near-surface air temperature is captured well by the two reanalyses but both reanalyses exhibit a warm bias relative to the buoy measurements. This bias is small at temperatures close to 0 degrees C but reaches 5-10 degrees C at -40 degrees C. For two of the buoys the mean temperature bias in ERA5 is significantly smaller than that in ERA-Interim while for the third buoy the biases in the two products are comparable. 10 m wind speed biases in both reanalyses are small and may largely result from measurement errors associated with icing of the buoy anemometers. The biases in downwelling shortwave and longwave radiation are significant in both reanalyses but we caution that the pattern of bias is consistent with potential errors in the buoy measurements, caused by accumulation of snow and ice on the radiometers. Overall, our study suggests that, with the exception of near-surface temperature, both reanalyses reproduce the buoy measurements to within the limits of measurement uncertainty. We suggest that the significant biases in near-surface air temperature may result from the simplified representation of sea ice used in the reanalysis models, and we recommend the use of a more sophisticated representation of sea ice, including variable ice and snow thicknesses, in future reanalyses.

Abstract On 25 February 2022 Antarctic sea ice extent dropped to a satellite‐era record low level of 1.92 × 106 km2, 0.92 × 106 km2 below the long‐term mean. The area of sea ice was also at a record low level of 1.24 × 106 km2. Although no individual sector was at a record low, at the minimum there were negative sea ice anomalies in all sectors of the Southern Ocean, with the largest in the Ross (contributing 46%) and Weddell Seas (26%). The Amundsen Sea Low had a record low depth in October/November 2021, with a series of very deep depressions giving strong offshore winds. These accelerated ice loss during the melt season, creating a 1.00 × 106 km2 coastal polynya in the Ross Sea. In the northern Weddell Sea, westerly winds of record strength led to ice export from the region.