Abstract. Representation of clouds in climate models is challenging, not the least due to their heterogeneous spatial structures and dynamic behavior. In this study, the potential of advanced machine learning (ML) techniques to identify and categorize mesoscale low-level cloud structures in satellite imagery is explored, with particular emphasis on those patterns that are frequently observed over the trade wind regions of the south Atlantic Ocean. Rectified Level 1.5 satellite images from the spinning enhanced visible and infrared imager (SEVIRI) for the year 2021 are used for the analysis. To assess the potential gains in classification accuracy under limited labeled datasets, several deep learning approaches are evaluated. The analysis considers a custom-built convolutional neural network, a pre-trained 50-layer residual neural network adapted through transfer learning using EuroSat, and a self-supervised vision transformer framework known as DINOv2 (self-distillation with no labels version 2). The embeddings, i.e. the feature representations yielded by DINOv2 are used in two separate approaches, one based on manually-labeled data and the other using the k-means clustering algorithm. The results show that combining the DINOv2 model with a multilayer perceptron and training on labeled data achieves the highest cloud pattern classification accuracy among the evaluated ML approaches.

Transpiration drives up to 40 % of terrestrial precipitation, with forests playing a critical role. This study combines long-term sap flow measurements and surface-based cloud observations to examine how different clouds affect tree transpiration across boreal and temperate European forests. Under specific cloudy conditions, sap flow can exceed clear-sky levels, reflecting distinct radiation effects of various cloud types. However, overall cloudiness reduces maximum sap flow by up to 40 %. A key finding is the contrasting influence of low- and highaltitude clouds: low-altitude clouds suppress transpiration by limiting incoming radiation, while high-altitude clouds have negligible impact even when overcast. Structural equation modelling further indicates a pathway linking transpiration to cloudiness when other meteorological and site factors are accounted for. Satellite data show a decline in low-altitude cloud fraction over boreal forests, and a highly simplified model-based order-of-magnitude estimate suggests a potential associated increase in transpiration equivalent to -0.6-1.2 mm of precipitation annually. These results emphasize how climate change driven changes in cloud cover and type may alter the moisture flux to the atmosphere, impacting regional and global water cycles.

Given the high rate of sea ice loss and the Arctic amplification, the dynamical processes responsible for airmass transport into or out of the Arctic, thus affecting the seasonal melt and recovery of sea ice, need to be understood and scrutinized from different observational perspectives. In a classical, rather binary view of transport “into or out of the Arctic”, a lot of attention in the recent years has rightfully been given on understanding the role of heat and moisture transport into the Arctic in regulating the sea ice melt. However, the cold and dry Arctic airmasses with long residence times are more than occasionally transported out of the Arctic over the open ocean waters, creating one of the most spectacular air mass transformations: the marine cold air outbreaks (MCAOs). The most tangible manifestation of MCAOs are the convectively rolled, narrow cloud streets formed over open water off the edges of the Arctic sea ice in the Nordic and Barents Seas, seen vividly in visible satellite imageries. MCAOs can also locally influence the onset of sea ice melt as they often happen in spring. By combining nearly 20 years of remotely sensed data from the hyperspectral Atmospheric Infrared Sounder (AIRS), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Clouds and the Earth’s Radiant Energy System (CERES) instruments onboard NASA’s Aqua satellite, this study presents a climatological view of the vertical structure of atmosphere and the cloud radiative effects during MCAOs in the northeast Atlantic.

Aerosols from biomass burning significantly impact human health, climate and society. These particles can be both natural and human-induced sources. While wildfires are considered a natural source, seasonal burning of agricultural fields before planting is an example for anthropogenic sources. With increasing global temperatures, the frequency and intensity of wildfires are escalating. Despite their importance, our understanding of these aerosols and their accurate representation in global emission inventories remain inadequate. The current estimates of the global direct radiative forcing of these aerosols range from net cooling to net warming in climate models. This shows how little we know about these aerosols and the chemical transformation they undergo as they age when they are advected farther away from the source regions. The optical properties of these biomass burning aerosols depends on the type of vegetation that is burnt, the type of burning and the prevailing meteorological conditions. Hence, in this study, we attempt to evaluate their optical properties at the source and also, as they are transported away from their source and age.Here, we use MODIS data to locate the fires in Africa and we chose those fires based on the percentage of area burnt and the fire intensity. A trajectory model, HYSPLIT, is run to get the trajectory of the fire plumes. These trajectories are then collocated with the CALIPSO tracks to estimate the optical properties of these aerosols as they age away from the source regions.

Ongoing deforestation and global warming are driving critical changes in the Amazon rainforest, a region essential to the global climate system. Model and observational studies have found drier conditions and changes in rainfall patterns in the Amazon; however, fewer studies have examined the role of clouds. Using the state-of-the-art CLARA-A3 satellite climate data record and ERA5 reanalysis data between 1982 and 2020, this study analyzes spatiotemporal trends in cloud cover and surface properties. Our results show a slight but statistically insignificant increase in total cloud cover across the Amazon, but regional and seasonal variations are pronounced. The northern Amazon exhibits a significant increase in high-level cloud fractional cover during the dry-wet transition, while the southern Amazon shows significant declining trends during the dry season across all cloud types. A significant correlation is observed between El Ni & ntilde;o-Southern Oscillation and high-level cloud cover variability, especially in the northeastern Amazon, while mid-and low-level clouds show a weak relationship with sea surface temperature variability. Consistent trends in surface heat fluxes and humidity, likely tied to land use and surface changes, may influence these lower cloud layers. However, the application of multiple linear regression reveals the analyzed variables poorly predict medium-and low-level cloud cover, leaving the exact drivers of the observed cloud cover trends uncertain. These findings are consistent with prior research on hydroclimatic trends, implying a drier dry season and a wetter wet season. Further research is recommended to clarify causality and implications for the Amazon climate system.

Efficient transitioning to renewable energy requires a fundamental understanding of the past and future climate change. This is particularly true in the case of solar energy, since the surface incoming solar radiation (SIS) is heavily regulated by atmospheric essential climate variables (ECVs) such as aerosols and clouds. Given the complexity of the interactions and feedbacks in the Earth system, even small changes in ECVs could have large direct and indirect effects on SIS. The net efficacy of the solar energy systems designed therefore depends on how well we account for the role of ECVs in modulating SIS. In this study, by leveraging the satellite-based climate data record (CDR) CLARA-A3, we investigate the recent trends in SIS and cloud properties over Europe during the 1982–2020 period. Furthermore, we derive emerging climatic trend regimes that are relevant for solar energy applications. Results show a large-scale increase in SIS in spring and early summer over Europe, particularly noticeable in April and June. The corresponding trends in cloud fraction and cloud optical thickness and their correlation with SIS suggest an increasingly important role of clouds in defining the favourable and unfavourable conditions for solar energy applications. We also note a strong spatiotemporal variability in trends and correlations. The results provide valuable metrics for the evaluation of climate models that have a dynamically integrated solar energy component.

Given the importance of aerosols and clouds and their interactions in the climate system, it is imperative that the global Earth system models accurately represent processes associated with them. This is an important prerequisite if we are to narrow the uncertainties in future climate projections. In practice, this means that continuous model evaluations and improvements grounded in observations are necessary. Numerous studies in the past few decades have shown both the usability and the limitations of utilizing satellite-based observations in understanding and evaluating aerosol-cloud interactions, particularly under varying meteorological and satellite sensor sensitivity paradigms. Furthermore, the vast range of spatio-temporal scales at which aerosol and cloud processes occur adds another dimension to the challenges faced when evaluating climate models.In this context, the aim of this study is two-fold. (1) We evaluate the most recent, significant changes in the representation of aerosol and cloud processes implemented in the EC-Earth3-AerChem model in the framework of the EU project FORCeS compared with its previous CMIP6 version (Coupled Model Intercomparison Project Phase 6; https://pcmdi.llnl.gov/CMIP6/, last access: 13 February 2019). We focus particularly on evaluating cloud physical properties and radiative effects, wherever possible, using a satellite simulator. We report on the overall improvements in the EC-Earth3-AerChem model. In particular, the strong warm bias chronically seen over the Southern Ocean is reduced significantly. (2) A statistical, maximum covariance analysis is carried out between aerosol optical depth (AOD) and cloud droplet (CD) effective radius based on the recent EC-Earth3-AerChem/FORCeS simulation to understand to what extent the Twomey effect can manifest itself in the larger spatio-temporal scales. We focus on the three oceanic low-level cloud regimes that are important due to their strong net cooling effect and where pollution outflow from the nearby continent is simultaneously pervasive. We report that the statistical covariability between AOD and CD effective radius is indeed dominantly visible even at the climate scale when the aerosol amount and composition are favourably preconditioned to allow for aerosol-cloud interactions. Despite this strong covariability, our analysis shows a strong cooling/warming in shortwave cloud radiative effects at the top of the atmosphere in our study regions associated with an increase/decrease in CD effective radius. This cooling/warming can be attributed to the increase/decrease in low cloud fraction, in line with previous observational studies.

Biomass burning plumes are frequently transported over the southeast Atlantic (SEA) stratocumulus deck during the southern African fire season (June–October). The plumes bring large amounts of absorbing aerosols and enhanced moisture, which can trigger a rich set of aerosol–cloud–radiation interactions with climatic consequences that are still poorly understood. We use large-eddy simulation (LES) to explore and disentangle the individual impacts of aerosols and moisture on the underlying stratocumulus clouds, the marine boundary layer (MBL) evolution, and the stratocumulus-to-cumulus transition (SCT) for three different meteorological situations over the southeast Atlantic during August 2017. For all three cases, our LES shows that the SCT is driven by increased sea surface temperatures and cloud-top entrainment as the air is advected towards the Equator. In the LES model, aerosol indirect effects, including impacts on drizzle production, have a small influence on the modeled cloud evolution and SCT, even when aerosol concentrations are lowered to background concentrations. In contrast, local semi-direct effects, i.e., aerosol absorption of solar radiation in the MBL, cause a reduction in cloud cover that can lead to a speed-up of the SCT, in particular during the daytime and during broken cloud conditions, especially in highly polluted situations. The largest impact on the radiative budget comes from aerosol impacts on cloud albedo: the plume with absorbing aerosols produces a total average 3 d of simulations. We find that the moisture accompanying the aerosol plume produces an additional cooling effect that is about as large as the total aerosol radiative effect. Overall, there is still a large uncertainty associated with the radiative and cloud evolution effects of biomass burning aerosols. A comparison between different models in a common framework, combined with constraints from in situ observations, could help to reduce the uncertainty.

Four decades of satellite-based observations of clouds are now available and there are currently four different global cloud climate data records (CDRs) available providing 35+ years of data from the passive imagers, namely CLARA-A3, ESA Cloud CCI, ISCCP and PATMOS-X. This contribution presents a comprehensive assessment of the latest versions of these four long-term cloud CDRs. Given the fact that clouds cover nearly 70% of our planet and exert strong control on the radiation budget through their effects on radiation, precipitation, circulation and through their susceptibility to aerosols, such a periodic observational assessment of their global state is necessary from the climate perspective. CLARA-A3, PATMOS-X and ISCCP have been improved considerably in the recent years, thanks mainly to improved calibration, better training, algorithm developments, and rigorous validations.This contribution will focus on three main areas:1) Presenting the state-of-the-art global climatologies of cloud properties from the said CDRs, while highlighting the agreements and disagreements among them.2) Evaluating the decadal stability of cloudiness in these CDRs using CALIPSO and MODIS as the references in light of the stringent requirements set by the WMO Global Climate Observing Systems (GCOS).3) Investigating the robust trends in global cloudiness and their commonalities across the CDRs.

This paper presents the third edition of The Satellite Application Facility on Climate Monitoring's (CM SAF) cloud, albedo, and surface radiation dataset from advanced very-high-resolution radiometer (AVHRR) data, CLARA-A3. The content of earlier CLARA editions, namely cloud, surface albedo, and surface radiation products, has been extended with two additional surface albedo products (blue- and white-sky albedo), three additional surface radiation products (net shortwave and longwave radiation, and surface radiation budget), and two top of atmosphere radiation budget products (reflected solar flux and outgoing longwave radiation). The record length is extended to 42 years (1979–2020) by also incorporating results from the first version of the advanced very high resolution radiometer imager (AVHRR/1). A continuous extension of the climate data record (CDR) has also been implemented by processing an interim climate data record (ICDR) based on the same set of algorithms but with slightly changed ancillary input data. All products are briefly described together with validation results and intercomparisons with currently existing similar CDRs. The extension of the product portfolio and the temporal coverage of the data record, together with product improvements, is expected to enlarge the potential of using CLARA-A3 for climate change studies and, in particular, studies of potential feedback effects between clouds, surface albedo, and radiation. The CLARA-A3 data record is hosted by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) CM SAF and is freely available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

The World Meteorological Organization (WMO) recommends that the most recent 30-year period, i.e., 1991–2020, be used to compute the climate normals of geophysical variables. A unique aspect of this recent 30-year period is that the satellite-based observations of many different essential climate variables are available during this period, thus opening up new possibilities to provide a robust, global basis for the 30-year reference period in order to allow climate-monitoring and climate change studies. Here, using the satellite-based climate data record of cloud and radiation properties, CLARA-A3, for the month of January between 1981 and 2020, we illustrate the difference between the climate normal, as defined by guidelines from WMO on calculations of 30 yr climate normals, and climatology. It is shown that this difference is strongly dependent on the climate variable in question. We discuss the impacts of the nature and availability of satellite observations, variable definition, retrieval algorithm and programmatic configuration. It is shown that the satellite-based climate data records show enormous promise in providing a climate normal for the recent 30-year period (1991–2020) globally. We finally argue that the holistic perspectives from the global satellite community should be increasingly considered while formulating the future WMO guidelines on computing climate normals.

This paper investigates the quality of global cloud fraction and cloud-top height products provided by the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from the AVHRR data (CLARA-A3) climate data record (CDR) produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). Compared with with CALIPSO–CALIOP cloud lidar data and six other cloud CDRs, including the predecessor CLARA-A2, CLARA-A3 has improved cloud detection, especially over ocean surfaces, and improved geographical variation and cloud detection efficiency. In addition, CLARA-A3 exhibits remarkable improvements in the accuracy of its global cloud-top height measurements. For example, in tropical regions, previous underestimations for high-level clouds are reduced by more than 2 km. By taking advantage of more realistic descriptions of global cloudiness, this study attempted to estimate trends in the observable fraction of low-level clouds, acknowledging their importance in producing a net climate cooling effect. The results were generally inconclusive in the tropics, mainly due to the interference of El Nino modes during the period under study. However, the analysis found small negative trends over oceanic surfaces outside the core tropical region. Further studies are needed to verify the significance of these results.

Two three-dimensional reanalysis datasets of atmospheric composition, the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), are analyzed for the years 2003-2018 with respect to dust transport into the Arctic. The reanalyses agree on that the largest mass transport of dust into the Arctic occurs across western Russia during spring and early summer, but substantial transport events occasionally also occur across other geographical areas during all seasons. In many aspects, however, the reanalyses show considerable differences: the mass transport in MERRA-2 is substantially larger, more spread out, and occurs at higher altitudes than in CAMSRA, while the transport in CAMSRA is to a higher degree focused to well-defined events in space and time; the integrated mass transport of the 10 most intense 36-hour dust events in CAMSRA constitutes 6 % of the total integrated dust transport 2003-2018, whereas the corresponding value for MERRA-2 is only 1 %. Furthermore, we compare the reanalyses with surface measurements of dust in the Arctic and dust extinction retrievals from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite data. This comparison indicates that CAMSRA underestimates the dust transport into the Arctic and that MERRA-2 likely overestimates it. The discrepancy between CAMSRA and MERRA-2 can partially be explained by the assimilation process where too little dust is assimilated in CAMSRA while in MERRA-2, the assimilation process increases the dust concentration in remote areas. Despite the profound differences between the reanalyses regarding dust transport into the Arctic, this study still brings new insights into the spatio-temporal distribution of the transport. We estimate the annual dust transport into the Arctic to be within the range 1.5-31 Tg, where the comparison with observations indicates that the lower end of the interval is less likely.

Forty years of cloud observations are available globally from satellites, allowing derivation of climate data records (CDRs) for climate change studies. The aim of this study is to investigate how stable these cloud CDRs are and whether they qualify stability requirements recommended by the WMO’s Global Climate Observing System (GCOS). We also investigate robust trends in global total cloud amount (CA) and cloud top temperature (CTT) that are significant and common across all CDRs. The latest versions of four global cloud CDRs, namely CLARA-A3, ESA Cloud CCI, PATMOS-x, and ISCCP-HGM are analysed. This assessment finds that all three AVHRR-based cloud CDRs (i.e., CLARA-A3, ESA Cloud CCI and PATMOS-x) satisfy even the strictest GCOS stability requirements for CA and CTT when averaged globally. While CLARA-A3 is most stable in global averages when tested against MODIS-Aqua, PATMOS-x offers the most stable CDR spatially. While we find these results highly encouraging, there remain, however, large spatial differences in the stability of and across the CDRs. All four CDRs continue to agree on the statistically significant decrease in global cloud amount over the last four decades, although this decrease is now weaker compared to the previous assessments. This decreasing trend has been stabilizing or even reversing in the last two decades; the latter is seen also in MODIS-Aqua and CALIPSO GEWEX datasets. Statistically significant trends in CTT are observed in global averages in the AVHRR-based CDRs, but the spatial agreement in the sign and the magnitude of the trends is weaker compared to those in CA. We also present maps of Common Stability Coverage and Common Trend Coverage that could provide a valuable metric to carry out an ensemble-based analysis of the CDRs.

On 13 April 2022, the Russian warship Moskva was hit by two Ukrainian Neptune anti-ship missiles in the Black Sea, leading to its demise. Before launching an anti-ship missile, a target must first be detected and positioned, for example, by an accompanying radar system. However, when the missiles hit the Moskva she was well beyond the normal radar horizon of any ground-based radar system, making the ship undetectable under normal circumstances. Using meteorological reanalysis data, we show that at the time of the missile launch the prevailing weather conditions allowed a ground-based radar to detect targets far beyond the normal radar horizon through anomalous propagation conditions. During such conditions, the atmospheric index of refraction decreases rapidly with height, making electromagnetic radiation bend downward to, partly or fully, compensate the curvature of the Earth. The results show that atmospheric conditions must be considered carefully, even during warfare, as their impact on radar wave propagation can be considerable.

The impacts of global climate change in response to increasing greenhouse gasses are spatio-temporally heterogeneous and are observed in a number of essential climate variables (ECVs). Among the ECVs that are highly relevant for the agriculture and forestry applications are clouds, precipitation and the incoming surface solar radiation (SIS). The past trends in these three agrometeorological ECVs and, more importantly, the co-variability among them can impact future agriculture and forestry policies and practices, their resilience and conservation. Therefore, using 37-year long climate data records spanning from 1982 to 2018 from the satellite-and surface based observing systems, we investigate the co-variability of trends in cloudiness, precipitation and SIS over Scandinavia during the summer months (April through September).The results reveal a complex nature of such co-variability among the trends in these three climate variables over Scandinavia. We report that the total cloudiness has decreased over much of Scandinavia. The decrease is most pronounced and statistically significant over southern Scandinavia in April, over the western coast in July and over much of northern Scandinavia in August. These decreasing trends are mainly due to reductions in the low and middle level clouds. The trends in all-sky incoming surface radiation are opposite in nature and broadly follow the spatio-temporal patterns of the trends in total cloudiness. The precipitation trends are heterogeneous, both spatially and temporally. The analysis of co-variability of trends reveals three distinct area-regimes that are relevant for assessing the changes in the land use and land cover.

Given the vast expanse of oceans on our planet, marine aerosols (and sea salt in particular) play an important role in the climate system via multitude of direct and indirect effects. The efficacy of their net impact, however, depends strongly on the local meteorological conditions that influence their physical, optical and chemical properties. Understanding the coupling between aerosol properties and meteorological conditions is therefore important. It has been historically difficult to statistically quantify this coupling over larger oceanic areas due to the lack of suitable observations, leading to large uncertainties in the representation of aerosol processes in climate models. Perhaps no other region shows higher uncertainties in the representation of marine aerosols and their effects than the Southern Ocean. During winter the Southern Ocean boundary layer is dominated by sea salt emissions. Here, using 10 years of austral winter period (June, July and August, 2007–2016) space-based aerosol profiling by CALIOP-CALIPSO in combination with meteorological reanalysis data, we investigated the sensitivity of marine aerosol properties over the Southern Ocean (40–65∘ S) to various meteorological parameters, such as vertical relative humidity (RH), surface wind speed and sea surface temperature (SST) in terms of joint histograms. The sensitivity study is done for the climatological conditions and for the enhanced cyclonic and anticyclonic conditions in order to understand the impact of large-scale atmospheric circulation on the aerosol properties. We find a clear demarcation in the 532 nm aerosol backscatter and extinction at RH around 60 %, irrespective of the state of the atmosphere. The backscatter and extinction increase at higher relative humidity as a function of surface wind speed. This is mainly because of the water uptake by the wind-driven sea salt aerosols at high RH near the ocean surface resulting in an increase in size, which is confirmed by the decreased depolarization for the wet aerosols. An increase in aerosol backscatter and extinction is observed during the anticyclonic conditions compared to cyclonic conditions for the higher wind speeds and relative humidity, mainly due to aerosols being confined to the boundary layer, and their proximity to the ocean surface facilitates the growth of the particles. We further find a very weak dependency of aerosol backscatter on SSTs at lower wind speeds. However, when the winds are stronger than about 12 m s−1, the backscattering coefficient generally increases with SST. When aerosol properties are investigated in terms of aerosol verticality and in relation to meteorological parameters, it is seen that the aerosol backscatter values in the free troposphere (pressure <850 hPa) are much lower than in the boundary layer, irrespective of the RH and the three weather states. This indicates that the local emissions from the ocean surface make the dominant contribution to aerosol loads over the Southern Ocean. A clear separation of particulate depolarization is observed in the free and lower troposphere, more prominent in the climatological mean and the cyclonic states. For RH > 60 %, low depolarization values are noticeable in the lower troposphere, which is an indication of the dominance of water-coated and mostly spherical sea salt particles. For RH < 60 %, there are instances when the aerosol depolarization increases in the boundary layer; this is more prominent in the mean and anticyclonic cases, which can be associated with the presence of drier aerosol particles. Based on the joint histograms investigated here, we provide third-degree polynomials to obtain aerosol extinction and backscatter as a function of wind speed and relative humidity. Additionally, backscattering coefficient is also expressed jointly in terms of wind speed and sea surface temperature. Furthermore, depolarization is expressed as a function of relative humidity. These fitting functions would be useful to test and improve the parameterizations of sea salt aerosols in the climate models. We also note some limitations of our study. For example, interpreting the verticality of aerosol properties (especially depolarization) in relation to the meteorological conditions in the free and upper troposphere (pressure <850 hPa) was challenging. Furthermore, we do not see any direct evidence of sudden crystallization (efflorescence), deliquescence or hysteresis effects of the aerosols. Observing such effects will likely require a targeted investigation of individual cases considering tracer transport, rather than the statistical sensitivity study that entails temporally and geographically averaged large data sets.

Biomass-burning aerosol layers overlaying stratocumulus clouds are frequent over the Southeast Atlantic during the southern African fire season (June-October). This scenario can trigger a rich set of aerosol-cloud-radiation interactions with climatic consequences that are still poorly quantified. Although satellites and in-situ measurements provide useful information on these situations, the covariance between aerosols and meteorology makes it difficult to disentangle any causal aerosol impacts on stratocumulus clouds, a problem that can be avoided when using models. In this work, we have incorporated aerosol-radiation interactions into the large-eddy simulation code MIMICAV5 to study how a biomass burning aerosol layer (composed of black carbon and organic carbon) affects an underlying stratocumulus cloud over the Southeast Atlantic. More specifically, we explore how the arrival time (time of the day during the simulation) of the absorbing aerosol layer cloud affects the underlying stratocumulus cloud properties. In addition, we explore the susceptibility of cloud droplet number concentration in the stratocumulus cloud to the absorbing aerosol number concentration above the cloud.

In this study, warm and moist air intrusions (WaMAIs) over the Arctic Ocean sectors of Barents Sea, Kara Sea, Laptev Sea, East Siberian Sea, Chukchi Sea, and Beaufort Sea in 40 recent winters (from 1979 to 2018) are identified from the ERA5 reanalysis using both Eulerian and Lagrangian views. The analysis shows that WaMAIs, fueled by Arctic blocking, cause a relative surface warming and hence a sea-ice reduction by exerting positive anomalies of net thermal irradiances and turbulent fluxes on the surface. Over Arctic Ocean sectors with land-locked sea ice in winter, such as Laptev Sea, East Siberian Sea, Chukchi Sea, and Beaufort Sea, the total surface energy-budget is dominated by net thermal irradiance. From a Lagrangian perspective, total water path (TWP) increases linearly with the downstream distance from the sea-ice edge over the completely ice-covered sectors, inducing almost linearly increasing net thermal irradiance and total surface energy-budget. However, over the Barents Sea, with an open ocean to the south, total net surface energy-budget is dominated by the surface turbulent flux. With the energy in the warm-and-moist air continuously transported to the surface, net surface turbulent flux gradually decreases with distance, especially within the first 2∘ north of the ice edge, inducing a decreasing but still positive total surface energy-budget. The boundary-layer energy-budget patterns over the Barents Sea can be categorized into three classes: radiation-dominated, turbulence-dominated, and turbulence-dominated with cold dome, comprising about 52 %, 40 %, and 8 % of all WaMAIs, respectively. Statistically, turbulence-dominated cases with or without cold dome occur along with 1 order of magnitude larger large-scale subsidence than the radiation-dominated cases. For the turbulence-dominated category, larger turbulent fluxes are exerted to the surface, probably because of stronger wind shear. In radiation-dominated WaMAIs, stratocumulus develops more strongly and triggers intensive cloud-top radiative cooling and related buoyant mixing that extends from cloud top to the surface, inducing a thicker well-mixed layer under the cloud. With the existence of cold dome, fewer liquid water clouds were formed, and less or even negative turbulent fluxes could reach the surface.