Accurately exchanging energy between the atmosphere and the ocean-ice systems through surface heat fluxes is crucial for climate modelling. We present an improved version of Coupled Arctic Prediction System (CAPS) by revising flux coupling for conservation of heat. Our results show that the model with improved flux coupling can better simulate sea ice conditions during the period of Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC).

This study presents continuous ship-borne measurements of methane (CH₄) concentration and isotopic composition monitored during an ice drift expedition in 2020. Using trajectory analysis, we linked atmospheric CH₄ variabilities to air mass pathways transported over open water or sea-ice. The study highlights the potential of ship-borne observations to fill significant data gaps in the high Arctic.

We study snow density from the MOSAiC expedition. Several snow density schemes were tested and compared with observation. A thermodynamic ice model was employed to assess the impact of snow density and precipitation on the thermal regime of sea ice. The parameterized mean snow densities are consistent with observations. Increased snow density reduces snow and ice temperatures, promoting ice growth, while increased precipitation leads to warmer snow and ice temperatures and reduced ice thickness.

The Arctic is warming faster than the global average and an investigation of aerosol–cloud–sea ice interactions is crucial for studying its climate system. During the ARTofMELT Expedition 2023, particle and sensible heat fluxes were measured over different surfaces. Wide lead surfaces acted as particle sources, with the strongest sensible heat fluxes, while closed ice surfaces acted as particle sinks. In this study, methods to measure these interactions are improved, enhancing our understanding of Arctic climate processes.

We use three sophisticated climate models to examine extreme precipitation in a critical region of West Antarctica. We found that rainfall probably occurred during the two cases we examined and that it was generated by the interaction of air with steep topography. Our results show that kilometre-scale models are useful tools for exploring extreme precipitation in this region and that more observations of rainfall are needed.

It is demonstrated that the explicit consideration of the cloud dependence of the snow surface albedo in a climate model results in a more realistic simulation of the surface albedo during the snowmelt period in late May and June. Although this improvement appears to be relatively insubstantial, it has significant impact on the simulated sea-ice volume and extent in the model due to an amplification of the snow/sea-ice albedo feedback, one of the main contributors to Arctic amplification.

Cyclones strongly influence weather predictability but still cannot be fully characterised in the Arctic because of the sparse coverage of meteorological measurements. A potential approach to compensate for this is the use of satellite measurements of ozone, because cyclones impact the tropopause and therefore also ozone. In this study we used this connection to investigate the correlation between ozone and the tropopause in the Arctic and to identify cyclones with satellite ozone observations.

We investigated a melt event over the Ross Ice Shelf. We use regional climate models and a firn model to simulate the melt and compare the results with satellite data. We find that the firn model aligned well with observed melt days in certain parts of the ice shelf. The firn model had challenges accurately simulating the melt extent in the western sector. We identified potential reasons for these discrepancies, pointing to limitations in the models related to representing the cloud properties.

We use a simple experiment to explore the non-CO₂ impacts of aviation on climate, which are considerably larger than the impact of the sector’s carbon emissions alone. We show that the main effect of our experiments – which intend to mimic the effect of aircraft soot emissions reaching existing high-altitude cirrus clouds – is to extend cloud lifetime, thereby enhancing their effect on climate.

The results of the surface albedo scheme of a coupled regional climate model were evaluated against airborne and ground-based measurements conducted in the European Arctic in different seasons between 2017 and 2022. We found a seasonally dependent bias between measured and modeled surface albedo for cloudless and cloudy situations. The strongest effects of the albedo model bias on the net irradiance were most apparent in the presence of optically thin clouds.

We directly measured methane and carbon dioxide exchange between ocean or sea ice and the atmosphere during an icebreaker-based expedition to the central Arctic Ocean (CAO) in summer 2021. These measurements can help constrain climate models and carbon budgets. The methane measurements, the first such made in the CAO, are lower than previous estimates and imply that the CAO is an insignificant contributor to Arctic methane emission. Gas exchange rates are slower than previous estimates.

We present a new method to analyse the influence of atmospheric rivers (ARs), cyclones, and fronts on the precipitation in the Arctic, based on two campaigns: ACLOUD (early summer 2017) and AFLUX (early spring 2019). There are differences between both campaign periods: in early summer, the precipitation is mostly related to ARs and fronts, especially when they are co-located, while in early spring, cyclones isolated from ARs and fronts contributed most to the precipitation.

Model projections suggest large differences in future Antarctic surface melting even for similar greenhouse gas scenarios and warming rates. We show that clouds containing a larger amount of liquid water lead to stronger melt. As surface melt can trigger the collapse of the ice shelves (the safety band of the Antarctic Ice Sheet), clouds could be a major source of uncertainties in projections of sea level rise.

Snowfall is an important climate indicator. However, microphysical snowfall processes are challenging for atmospheric models. In this study, the performance of a regional climate model is evaluated in modeling the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations. Excellent agreement in averaged annual snowfall rates is found, and the shown methodology offers a promising diagnostic tool to investigate the shown differences further.

Sea ice thickness anomalies during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) winter in January, February and March 2020 were simulated with the coupled Regional Arctic climate System Model (RASM) and compared with CryoSat-2/SMOS satellite data. Hindcast and ensemble simulations indicate that the sea ice anomalies are driven by nonlinear interactions between ice growth processes and wind-driven sea-ice transports, with dynamics playing a dominant role.

We focus on anomalous moisture transport events known as atmospheric rivers (ARs). During ACLOUD and PASCAL, three AR events were identified: 30 May, 6 June, and 9 June 2017. We explore their spatio-temporal evolution and precipitation patterns using measurements, reanalyses, and a model. We show the importance of the following: Atlantic and Siberian pathways during spring–summer in the Arctic, AR-associated heat/moisture increase, precipitation phase transition, and high-resolution datasets.

This study uses reanalysis data to investigate the role of atmospheric blocking, prevailing high-pressure systems and mid-latitude cyclones in driving high-Arctic wintertime warm extreme events. These events are mainly preceded by Ural and Scandinavian blocks, which are shown to be significantly influenced and amplified by cyclones in the North Atlantic. It also highlights processes that need to be well captured in climate models for improving their representation of Arctic wintertime climate.

Arctic warming is pronounced, and one factor in this is the poleward atmospheric transport of heat and moisture. This study assesses the 4D structure of an Arctic moisture intrusion event which occurred in June 2017. For the first time, high-resolution pan-Arctic ICON simulations are performed and compared with global models, reanalysis, and observations. Results show the added value of high resolution in the event representation and the impact of the intrusion on the surface energy fluxes.