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To understand the role of the Antarctic Circumpolar Current (ACC) in the polar seasonality and its remote effect on the Arctic climate, we use the Community Earth System Model to perform Drake Passage (DP) open and closed experiments. Model results illustrate that in the opened DP, the ACC and Atlantic Meridional Overturning Circulation (AMOC) strengthen, leading to a colder Antarctic and a warmer Arctic. Notably, the temperature changes in both the Antarctic and the Arctic show significant seasonal differences, with the largest polar response during the cold seasons. Around the Antarctic, both the ACC and overturning circulation exhibit stronger acceleration in winter than in summer, causing more pronounced cooling in winter. Furthermore, negative seasonal energy transfer mechanism amplifies this cooling. In contrast, around the Arctic, the AMOC and ocean heat transport show relatively insignificant seasonal variation. Instead, it is the downward latent and sensible fluxes that induce amplified winter warming.

Bulk aerodynamic methods have been shown to perform poorly in computing turbulent heat fluxes at glacier surfaces during shallow katabatic winds. Katabatic surface layers have different wind shear and flux profiles to the surface layers for which the bulk methods were developed, potentially invalidating their use in these conditions. In addition, eddy covariance-derived turbulent heat fluxes are unlikely to be representative of surface conditions when eddy covariance data are collected close to the wind speed maximum (WSM). Here we utilize two months of eddy covariance and meteorological data measured at three different heights (1 m, 2 m, and 3 m) at Kaskawulsh Glacier in the Yukon, Canada, to re-examine the performance of bulk methods relative to eddy covariance-derived fluxes under different near-surface flow regimes. We propose a new set of processing methods for one-level eddy covariance data to ensure the validity of calculated fluxes during highly variable flows and low-level wind speed maxima, which leads to improved agreement between eddy covariance-derived and modelled fluxes across all flow regimes, with the best agreement (correlation >0.9) 1 m above the surface. Contrary to previous studies, these results show that adequately processed eddy covariance data collected at or above the WSM can provide valid estimates of surface heat fluxes.

Climate models agree that a reduction of day‐to‐day temperature variability at mid‐to‐high latitudes during the cold season is a robust forced response to anthropogenic global warming. Although recent observations show a similar reduction, how much the observed change is forced and how much is internal variability is uncertain. Here, using large‐ensemble simulations and a Ridge Regression detection tool, we decompose the observed day‐to‐day temperature variability changes since 1950 into contributions of forced and internal components. Our findings show that the observed reduction since the mid‐1970s is dominated by a forced response (about 90%). Observations and models show consistent mechanisms responsible for this reduction in a warming world: the reduction is manifested as cold days warming faster than hot days, driven by Arctic sea‐ice loss and associated reduction in the latitudinal temperature gradient, but not by large‐scale atmospheric circulation changes. Overall, our study detects a robust influence of the Arctic changes on lower latitude day‐to‐day temperature variability, and suggests that this impact will continue in the coming decades. Over recent decades, day‐to‐day temperature variability at Northern Hemisphere mid and high latitudes has decreased, especially in the autumn and winter seasons. It is unclear how much the observed reduction is driven by human‐caused global warming. Our study utilizes a novel machine‐learning detection tool to demonstrate that the reduction since the mid‐1970s is primarily driven by human‐caused global warming rather than internal variability within the climate system.

Coupled Numerical Weather Prediction (NWP) models have only recently been implemented for short‐term environmental prediction and both challenges and benefits are evident in polar regions. Their simulation of surface exchange over sea ice depends on the model's sea‐ice characteristics, however these are hard to constrain due to a lack of in situ and accurate remotely sensed observations. We focus on the Fram Strait region during peak melt conditions and during the passage of an Arctic cyclone: very challenging conditions for coupled NWP. We use in situ aircraft observations from the Arctic Summertime Cyclones field campaign in July‐August 2022, plus satellite products, to evaluate a set of 5‐day forecasts from the Met Office Unified Model. Our model set ups are based on operational GC4 (Global Coupled 4) and developmental GC5 (Global Coupled 5) configurations, which use the CICE5.1 and SI3 sea‐ice models respectively. We find a combination of deficiencies in the simulated sea‐ice field, due to initialization and modeling problems. An initially low concentration of sea ice results in excessive absorption of shortwave radiation by the ocean, leading to excessive basal melting of the sea ice, and further sea‐ice loss; leading to relatively poorly simulated sea‐ice fields in general. In contrast, the passage of an Arctic cyclone and its impact on sea‐ice velocities are captured well. Although we demonstrate several deficiencies in the short‐term forecasts of two state‐of‐the‐art coupled NWP models, we also find promising aspects of model performance and some clear benefits from a fully coupled atmosphere‐ice‐ocean system. Plain Language Summary Weather prediction in the Arctic requires an accurate representation of sea ice as it plays a key role in the exchange of momentum, heat and moisture between the surface and the atmosphere. We investigate a challenging set of conditions for weather forecasting: the passage of an Arctic cyclone over Fram Strait in the European Arctic during peak summertime sea ice melting. We use observations made during the Arctic Summertime Cyclones field campaign in July‐August 2022 to evaluate forecasts from the Met Office Unified Model that feature ocean and sea ice model components that interact (are “coupled”) with the atmosphere. We find discrepancies in the simulated sea ice field that result from issues in the satellite observations fed into the models and model biases. A lack of sea ice results in increased heat absorption by the ocean, after which the warmer water melts the sea ice faster, forming a feedback loop known as the ice‐albedo feedback. The passing cyclone also drives changes in the sea ice cover, but we find that these effects are generally simulated well in the forecasts. Overall, despite the issues discussed, we find that using such coupled models are advancing weather prediction in the Arctic. Key Points Sea ice melts too fast in 5‐day coupled forecasts from August 2022, verified using in situ aircraft observations and satellite‐based products The assimilated sea‐ice fraction product is biased low, leading to accelerated sea ice melt through the ice‐albedo feedback mechanism The passage of an Arctic cyclone provides a short‐term dynamical forcing of the sea ice that is reasonably well represented in the forecasts

Arctic rain-on-snow (ROS) events can have significant impacts on Arctic wildlife and socio-economic systems. This study addresses the meteorology of two different Arctic ROS events. The first, occurring near Nuuk, Greenland, generated significant impacts, including slush avalanches. The second, less severe, event occurred within the community of Iqaluit, Nunavut, Canada. This research utilizes atmospheric reanalysis, automated surface observation station data and atmospheric soundings to determine the meteorological conditions driving these events and the differences between each case. In both cases, atmospheric blocking played a leading role in ROS initiation, with atmospheric rivers – narrow bands of high water vapor transport, typically originating from the tropics and subtropics – having both direct and indirect effects. Cyclone-induced low-level jets and resultant ‘warm noses’ of higher air temperatures and moisture transport were other key features in ROS generation. To our knowledge, our study is the first to visualize how the varying strength and manifestation of these coupled features contribute to differences in the severity of Arctic ROS events. The meteorological drivers identified here find support from other studies on Arctic ROS events and are similar to weather features associated with Arctic precipitation events of extreme magnitude.

The natural cosmogenic radionuclide 7Be (T1/2 = 53.4 d) is supplied to the surface ocean from the atmosphere and, in the Arctic Ocean, can be used as a tracer of the efficiency with which sea ice intercepts the atmospheric fluxes of chemical species and of the importance of ice as a transport mechanism for particulate matter and chemical species. Analyses of 7Be in samples of surface water, surface sea ice, water beneath the ice, sea ice sediments, and precipitation from the Eurasian Basin of the Arctic Ocean show that the fraction of sea ice coverage determines the amount of 7Be in the surface water. When sea ice coverage is <40%, the 7Be inventory in the upper ocean (130 ± 19 Bq m−2) is in good agreement with that expected from the inventory from 7Be atmospheric flux (128 ± 21 Bq m−2). In contrast, when ice coverage is >80%, the water column inventory drops to 58 ± 20 Bq m−2. The 7Be inventory in sea ice is 39 ± 23 Bq m−2, and mass balance calculations show that sea ice can intercept 30 ± 18% of the atmospheric flux of 7Be during the studied period. We suggest that other atmospherically transported contaminants should be similarly intercepted. 7Be in the ice also can be used to estimate that the annual transport and release of sediment to the ablation area of the Fram Strait is ∼500 g m−2, a value comparable to previously measured fluxes in sediment traps deployed in the area. Key Points Sea ice intercepts 7Be atmospheric fluxes efficiently Sea ice coverage determines the amount of 7Be in the surface water 7Be is a useful tracer to study atmosphere‐sea ice‐ocean interaction

The response of the atmospheric boundary layer (ABL) to subgrid-scale variations of sea ice properties and fracturing is poorly understood and not taken into account in mesoscale Numerical Weather Prediction (NWP) model parametrizations. In this paper we analyze three-dimensional air circulation within the ABL over fragmented sea ice. A series of idealized high-resolution simulations with the Weather Research and Forecasting (WRF) model is performed for several spatial distributions of ice floes and leads for two values of sea ice concentration (0.5 and 0.9) and several ambient wind speed profiles. The results show that the convective circulation within the ABL is sensitive to the subgrid-scale spatial distribution of sea ice. Considerable variability of several domain-averaged quantities – cloud liquid water content, surface turbulent heat flux (THF) – is found for different arrangements of floes. Moreover, the organized structure of air circulation leads to spatial covariance of variables characterizing the ABL. Based on the example of THF, it is demonstrated that this covariance may lead to substantial errors when THF values are estimated from area-averaged quantities, as it is done in mesoscale NWP models. This suggests the need for developing suitable parametrizations of ABL effects related to subgrid-scale sea ice features for these models.

The presence of leads with open water or thin ice is an important feature of the Arctic sea ice cover. Leads regulate the heat, gas and moisture fluxes between the ocean and atmosphere and are areas of high ice growth rates during periods of freezing conditions. Here, an algorithm providing an automatic lead detection based on synthetic aperture radar images is described that can be applied to a wide range of Sentinel-1 scenes. By using both the HH and the HV channels instead of single co-polarised observations the algorithm is able to classify more leads correctly. The lead classification algorithm is based on polarimetric features and textural features derived from the grey-level co-occurrence matrix. The Random Forest classifier is used to investigate the importance of the individual features for lead detection. The precision–recall curve representing the quality of the classification is used to define threshold for a binary lead/sea ice classification. The algorithm is able to produce a lead classification with more that 90% precision with 60% of all leads classified. The precision can be increased by the cost of the amount of leads detected. Results are evaluated based on comparisons with Sentinel-2 optical satellite data.

The complex topography and size of High Mountain Asia (HMA) result in large differences in glacier mass-balance variability and climate sensitivity. Current understanding of these sensitivities is limited by simplifications in past studies’ model structure. This study overcomes this limitation by using a mass-balance model to investigate the climatic mass-balance variability and climate sensitivity of 16 glaciers covering major mountain ranges in HMA. Generally, glaciers in the southeast have higher mass turnover while glaciers at the margins of HMA show higher interannual mass-balance variability. All glaciers are most sensitive to temperature perturbations in summer. The climatic mass balance of 15 glaciers is most sensitive to precipitation perturbations in summer or spring and summer, even if the seasonal accumulation peak is not in summer. Only one glacier's mass balance (Chhota Shigri Glacier) is most sensitive to precipitation perturbations in winter. Glaciers with high mass turnover and high summer-precipitation ratio are more sensitive to temperature perturbations. Sensitivity experiments reveal that besides the non-linearity of mass-balance temperature sensitivity, mass-balance precipitation sensitivity is non-linear as well. Furthermore, resolving the diurnal cycle of albedo, (re)freezing and the differentiation between liquid and solid precipitation are important to assess climate sensitivity of glaciers in HMA.