The timing of sea ice melt onset (MO) strongly influences the length of the melt season and the subsequent summer ice evolution of Arctic sea ice. However, the atmospheric mechanisms governing the pronounced interannual and regional variability in MO remain incompletely understood. Here, we investigate the atmospheric contribution to the spatiotemporal variability of MO dates across the Arctic sea ice during spring (April–June) over 1980–2017. Using passive microwave satellite-derived MO dates and ERA5 reanalysis, we identify regions of extreme positive surface energy budget (SEB) anomalies (SEB events) as markers of episodic atmospheric influence and relate their occurrence and airmass origin to locally anomalous MO timings in a pixel-based framework. Very early MO occurs preferentially in the Beaufort, Kara, and Barents Seas and is associated with strongly enhanced frequency of SEB events during the 24 days preceding melt, with peak occurrence both near the melt date and about 3 weeks earlier. Earlier melt is further characterized by larger-than-usual anomalies in downward longwave and turbulent fluxes and predominantly associated with SEB events of Pacific and Atlantic airmass origin. In contrast, very late MO is located more widespread across the eastern Arctic Ocean and coincides with a significant absence of SEB events before melt, weaker downward longwave fluxes, positive downward shortwave anomalies under clear-sky conditions, and continental airmass inflows. These results demonstrate that synoptic-scale atmospheric processes are a key driver of regional MO variability and highlight the need for improved representation of springtime atmosphere–ice interactions in climate models to properly capture the seasonal transition in the Arctic.

The 5-yr (2024–28) World Meteorological Organization (WMO) World Weather Research Programme (WWRP) Polar Coupled Analysis and Prediction for Services (PCAPS) project aims to accelerate research on environmental forecasting and services in the Arctic and Antarctic regions. As climate change intensifies, rapid transformations in the polar regions have complex impacts on local and global socioecological systems, exacerbating existing challenges for residents and operators. PCAPS focuses on enhancing forecast actionability, impact, and fidelity through a multifaceted approach of user engagement, model and service improvements, and impact studies. Key outcomes will include support for improved predictions of small-scale processes, enhanced understanding of observing systems and the utility of artificial intelligence/machine learning (AI/ ML) applications, contributions toward the development of a tiered surface observing network, and strengthened cooperation between users, operational centers, and researchers. PCAPS explores approaches to improve forecasts for sea ice, surface wind, visibility, and other key variables, which are essential for developing salient forecasting services that can enhance operational and environmental safety in extreme polar environments. Through collaborations between social and physical scientists, PCAPS also assesses decision-making, risk perceptions, and interactions between forecasters, service providers, and users, including Indigenous communities, and considers political, legal, and economic implications of information delivery. PCAPS is expected to inform WMO’s contribution to the fifth International Polar Year (2032–33), support WMO Strategic Objectives, and build upon previous initiatives such as the Polar Prediction Project. SIGNIFICANCE STATEMENT: The purpose of the research is to further explore the opportunities and challenges to improve the accuracy, reliability, and usefulness of environmental forecasting services and products in the polar regions using a collaboration between social and physical researchers, operational centers, and users of these polar services.

In this study, we extend the Cloud Layers Unified by Binormals (CLUBB) turbulence scheme within the GFDL atmospheric model (AM4) by implementing direct momentum-flux prognosis and a multiscale turbulent lengthscale, to improve the simulation of nocturnal precipitation and associated Low-Level Jets (LLJs) over the Great Plains (GP). Toward this aim, we set up four AM4-CLUBB configurations: diagnosed momentum flux, prognosed momentum flux, diagnosed momentum flux with a multiscale turbulent lengthscale, and prognosed momentum flux with a multiscale turbulent lengthscale. Simulations are evaluated against the AM4 control, the Integrated Multi-satellitE Retrievals for GPM (IMERG), and the Doppler wind radar profiles from the Atmospheric Radiation Measurement program. Results show that all AM4-CLUBB configurations improve the precipitation timing from the unrealistic midday peak seen in the AM4 control simulation toward the satellite-observed nocturnal maximum. The configuration that prognoses momentum flux and uses a multi-scale turbulent lengthscale, best matches the timing and intensity of GP precipitation rate. This configuration is also that which more accurately simulates the ARM-observed nocturnal LLJ wind profiles, while increasing the frequency of counter-gradient momentum fluxes near the LLJ core compared to prognosing momentum fluxes with the original AM4-CLUBB turbulent lengthscale. Momentum budget analysis attributes this increase to a nearly fivefold enhancement in the buoyancy production term when using the multiscale formulation, and leads to stronger nocturnal convective activity, as diagnosed from the greater vertical velocity skewness and plume asymmetry.

The boundary layer is the lowest layer of the atmosphere that connects the free troposphere with the surface through exchange of momentum, heat, gases and particles. Transport is achieved by turbulent motions and the thickness of the boundary layer depends on the turbulence intensity. Its depth ranges from a few meters in stably stratified weak-wind clear-sky winter cases, to several hundred meters in the cloud-capped well-mixed boundary layer prevailing in summer, but also common in winter. Turbulence over sea ice, as elsewhere, is generated through mechanical production by vertical wind shear and buoyancy effects. Key differences include the presence of sea ice and a weak diurnal but pronounced seasonal cycle.

Representing the glaciation of mixed-phase clouds in terms of the Wegener-Bergeron- Findeisen process is a challenge for many weather and climate models, which tend to overestimate this process because cloud dynamics and microphysics are not accurately represented. As turbulence is essential for the transport of water vapor from evaporating liquid droplets to ice crystals, we developed a statistical model using established closures to assess the role of small-scale turbulence. The model successfully captures results of direct numerical simulations and we use it to assess the role of small-scale turbulence. We find that small-scale turbulence broadens the droplet-size distribution somewhat, but it does not significantly affect the glaciation time on submeter scales. However, our analysis indicates that turbulence on larger spatial scales is likely to affect ice growth. While the model must be amended to describe larger scales, the present work facilitates a path forward to understanding the role of turbulence in the Wegener-Bergeron-Findeisen process.

In Arctic warm-air intrusions, air masses undergo a series of radiative, turbulent, cloud, and precipitation processes, the sum of which constitutes the air mass transformation. During the Arctic air mass transformation, heat and moisture are transferred from the air mass to the Arctic environment, melting the sea ice and potentially reinforcing feedback mechanisms responsible for the amplified Arctic warming. We tackle this complex, poorly understood phenomenon from a Lagrangian perspective using the warm-air intrusion event on 12–14 March captured by the 2022 HALO-(𝒜𝒞)³ campaign. Our trajectory analysis of the event suggests that the intruding air mass can be treated as a cohesive air column, therefore justifying the use of a single-column model. In this study, we test this hypothesis using the Atmosphere–Ocean Single-Column Model (AOSCM). The rates of heat and moisture depletion vary along the advection path due to the changing surface properties and large-scale vertical motion. Cloud radiative cooling and turbulent mixing in the stably stratified boundary layer are constant sinks of heat throughout the air mass transformation. Boundary layer cooling intensifies over the marginal ice zone and forces the development of a low-level cloud underneath the advected one. As the air mass flows past the marginal ice zone, large-scale updrafts dominate the temperature and moisture changes through adiabatic cooling and condensation. The ability of the Lagrangian AOSCM framework to simulate elements of the air mass transformation seen in aircraft observations, reanalysis, and operational forecast data makes it an attractive tool for future model analysis and diagnostics development. Our findings can benefit the understanding of the timescales and driving mechanisms of Arctic air mass transformation and help determine the contribution of warm-air intrusions in Arctic amplification.

Simulation of turbulence in the atmospheric boundary layer (ABL) is challenging due to the wide range of turbulent scales in the flow. To leverage the currently available computational power for high-resolution simulation of atmospheric turbulence, fluid solvers that scale well on large compute clusters are required. We present a new large eddy simulation (LES) framework based on the open-source solver Nek5000, which uses the highly parallelizable spectral element method (SEM) for spatial discretization. We document the Nek5000 framework for LES of thermally stratified atmospheric boundary layers and present results from the solver for neutral, convective, and stably stratified boundary layers. To verify that the solver is capable of accurately representing important features of the ABL, we compare our results to an established LES solver and find very good agreement in statistics as well as coherent structures. We also compare results with two different subgrid-scale models and conclude that one based on the subgrid-scale turbulent kinetic energy performs better together with the SEM.

The current generation of state-of-the-art global climate models are being run at increasingly higher horizontal resolutions, with the goal of resolving organised deep convection explicitly. How a kilometre-scale resolution impacts the representation of the atmospheric boundary layer is, however, not well known. Using statistical analysis on global fields as well as high-frequency data at selected locations, produced with the Integrated Forecasting System (IFS) model for the Next Generation Earth-system Models (nextGEMS) project, we investigate the horizontal resolution dependence of some boundary-layer processes. We find that a change in resolution from 9 to 2.8 km causes no substantial changes to boundary-layer properties and processes at most of the locations studied, although some global changes are detected that indicate circulation changes. Small changes to the boundary-layer depth and structure are found in the Tropics. The short simulation length and lack of data for optimal boundary-layer analysis limits the conclusions, especially in relation to the connection between the boundary layer and the atmosphere general circulation.

The Diurnal Land–Atmosphere Coupling Experiment (DICE) aims to explore the complex interactions between the land surface and atmospheric boundary layer, which are generally not well understood and difficult to isolate in models. The project involves over 10 different models, combining expertise from both land-surface and atmospheric boundary-layer modelling groups. A simple three-stage methodology is designed to assess land–atmosphere feedbacks. Stage 1: the individual components are assessed in isolation, driven and evaluated against observational data; stage 2: the impact of coupling is investigated; stage 3: the sensitivity of the stand-alone models to variations in driving data is explored. For this initial study, a 3-day clear-sky period in the mid-west United States over, an assumed simple, predominantly grass surface was simulated using data from the CASES-99 field campaign. Key conclusions from the study include: (1) the memory of vegetation state within land-surface models needs attention; (2) the height of atmospheric forcing for land-surface models is important, particularly for the nocturnal boundary layer, and this has implications for both observations and vertical resolution for atmospheric models; (3) land–atmosphere feedbacks reduce errors in simulated surface fluxes at the expense of the accuracy of the variables that the models are designed to simulate (e.g., temperature, humidity, and wind speed); (4) problems remain in representing the stable boundary layer in atmospheric models; (5) the mixing of temperature and humidity within the boundary layer may need to be represented separately; (6) differences in daytime profiles of heat, moisture, and momentum between models are mainly due to the way the models erode the inversion at the top of the boundary layer, rather than differences in the surface fluxes. Resultant variations in modelled boundary-layer heights have a substantial impact on relative humidity and could partially explain variations in coupling strength between models in the Global Land–Atmosphere Coupling Experiment.

The Year of Polar Prediction (YOPP), an international research initiative organized by the World Meteorological Organization’s (WMO) World Weather Research Program from 2013 to 2022, aimed to markedly enhance environmental prediction capabilities in the polar regions and beyond, particularly in the context of a rapidly changing climate. YOPP achieved this through a concerted effort in observation, modeling, verification, user engagement, and educational activities. This article offers a comprehensive overview of YOPP’s key outcomes and impacts, using a dual approach that merges qualitative success stories with quantitative metrics. Scientifically, the focus is on the role of polar observations in improving prediction accuracy, enhanced understanding of processes to support model development, advancements in forecast verification, particularly in sea ice prediction, an improved understanding of the interconnections between polar and midlatitude regions, and effective user engagement. This paper also discusses how these scientific discoveries have been converted into practical applications, emphasizing the route from science to services. Additionally, it summarizes the education, communication, outreach, and coordination efforts employed to maximize YOPP’s impact. Finally, the article provides a series of recommendations for future research, informed by the insights gained from YOPP’s experiences and recent radical developments in technology.