Seasonal Ice Research Articles

Features of the Behavior of Organic Compounds in Water and Bottom Sediments in the Kara Sea during Seasonal Ice Melting

Features of the Behavior of Organic Compounds in Water and Bottom Sediments in the Kara Sea during Seasonal Ice Melting

Numerical simulations of wave climate in the Baltic Sea: a review

Efforts towards the numerical simulation of the Baltic Sea wave properties, started in the 1950s, have reached maturity by the implementation of contemporary third generation spectral wave models, such as WAM and SWAN. The purpose of this paper is to give an overview of the relevant efforts since the beginning of numerical wave simulations. The Sverdrup-Munk-Bretschneider (SMB) type models are still valuable tools for rapid estimates of some properties of wave climate in single locations. The spatial resolution of spectral wave models for the entire sea has increased from about 20 km to 1 km, and to 100–200 m in specific areas. The number of directional bins has increased from 10–15 to 24–36 and the number of spectral frequency bins from about 15 to 35–42. The models replicate all main features of the wave climate of the Baltic Sea, such as an overall mild but intermittent wave climate, the predominance of short windseas and the scarcity of long swell, east-west asymmetry, the strong impact of seasonal ice, and the specific properties of wave growth in some areas. The wave climate changes involve variations in regional wave intensity, core properties of wave-driven sediment transport and wave set-up. Reconstruction of wave properties in the nearshore, archipelago areas, and in narrow subbasins remains a challenge. These areas require finer spatial resolution and possibly advancement of wave physics to account for changes in the spectral composition of wave fields and specific features of wave growth in narrow basins. Progress in these fields is a pillar for a number of applications, from the quantification of sediment transport to proper input into management issues of the coastal zone.

Ecosystems of the Siberian Arctic Seas–2021: Ecosystem of the Kara Sea in the Period of Seasonal Ice Melting (Cruise 83 of the R/V Akademik Mstislav Keldysh)

Ecosystems of the Siberian Arctic Seas–2021: Ecosystem of the Kara Sea in the Period of Seasonal Ice Melting (Cruise 83 of the R/V Akademik Mstislav Keldysh)

Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT–2 SAR Images and CNNs

With the increasing availability of SAR imagery in recent years, more research is being conducted using deep learning (DL) for the classification of ice and open water; however, ice and open water classification using conventional DL methods such as convolutional neural networks (CNNs) is not yet accurate enough to replace manual analysis for operational ice chart mapping. Understanding the uncertainties associated with CNN model predictions can help to quantify errors and, therefore, guide efforts on potential enhancements using more–advanced DL models and/or synergistic approaches. This paper evaluates an approach for estimating the aleatoric uncertainty [a measure used to identify the noise inherent in data] of CNN probabilities to map ice and open water with a custom loss function applied to RADARSAT–2 HH and HV observations. The images were acquired during the 2014 ice season of Lake Erie and Lake Ontario, two of the five Laurentian Great Lakes of North America. Operational image analysis charts from the Canadian Ice Service (CIS), which are based on visual interpretation of SAR imagery, are used to provide training and testing labels for the CNN model and to evaluate the accuracy of the model predictions. Bathymetry, as a variable that has an impact on the ice regime of lakes, was also incorporated during model training in supplementary experiments. Adding aleatoric loss and bathymetry information improved the accuracy of mapping water and ice. Results are evaluated quantitatively (accuracy metrics) and qualitatively (visual comparisons). Ice and open water scores were improved in some sections of the lakes by using aleatoric loss and including bathymetry. In Lake Erie, the ice score was improved by ∼2 on average in the shallow near–shore zone as a result of better mapping of dark ice (low backscatter) in the western basin. As for Lake Ontario, the open water score was improved by ∼6 on average in the deepest profundal off–shore zone.

Shelf-Sourced Methane in Surface Seawater at the Eurasian Continental Slope (Arctic Ocean)

This study traces the pathways of dissolved methane at the Eurasian continental slope (ECS) and the Siberian shelf break based on data collected during the NABOS-II expedition in August-September, 2013. We focus on the sea ice-ocean interface during seasonal strong ice melt. Our analysis reveals a patchy pattern of methane supersaturation related to the atmospheric equilibrium. We argue that sea ice transports methane from the shelf and that ice melt is the process that causes the heterogeneous pattern of methane saturation in the Polar Mixed Layer (PML). We calculate the solubility capacity and find that seasonal warming of the PML reduces the CH4 storage capacity and contributes to methane supersaturation and potential sea-air flux in summer. Cooling in autumn enhances the solubility capacity in the PML once again. The shifts in the solubility capacity indicate the buffering capacity for seasonal storage of atmospheric and marine methane in the PML. We discuss specific pathways for marine methane and the storage capacity of the PML on the ECS as a sink/source for atmospheric methane and methane sources from the Siberian shelf. The potential sea-air flux of methane is calculated and intrusions of methane plumes from the PML into the Cold Halocline Layer are described.

Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

Abstract. Two high-resolution model simulations are used to investigate the spatiotemporal variability of the Arctic Ocean sea level. The model simulations reveal barotropic sea level variability at periods of < 30 d, which is strongly captured by bottom pressure observations. The seasonal sea level variability is driven by volume exchanges with the Pacific and Atlantic oceans and the redistribution of the water by the wind. Halosteric effects due to river runoff and evaporation minus precipitation ice melting/formation also contribute in the marginal seas and seasonal sea ice extent regions. In the central Arctic Ocean, especially the Canadian Basin, the decadal halosteric effect dominates sea level variability. The study confirms that satellite altimetric observations and Gravity Recovery and Climate Experiment (GRACE) could infer the total freshwater content changes in the Canadian Basin at periods longer than 1 year, but they are unable to depict the seasonal and subseasonal freshwater content changes. The increasing number of profiles seems to capture freshwater content changes since 2007, encouraging further data synthesis work with a more complicated interpolation method. Further, in situ hydrographic observations should be enhanced to reveal the freshwater budget and close the gaps between satellite altimetry and GRACE, especially in the marginal seas.

Long-Term Ice Conditions in Yingkou, a Coastal Region Northeast of the Bohai Sea, between 1951/1952 and 2017/2018: Modeling and Observations

Bohai Sea ice creates obstacles for maritime navigation and offshore activities. A better understanding of ice conditions is valuable for sea-ice management. The evolution of 67 years of seasonal ice thickness in a coastal region (Yingkou) in the Northeast Bohai Sea was simulated by using a snow/ice thermodynamic model, using local weather-station data. The model was first validated by using seasonal ice observations from field campaigns and a coastal radar (the season of 2017/2018). The model simulated seasonal ice evolution well, particularly ice growth. We found that the winter seasonal mean air temperature in Yingkou increased by 0.33 °C/decade slightly higher than air temperature increase (0.27 °C/decade) around Bohai Sea. The decreasing wind-speed trend (0.05 m/s perdecade) was a lot weaker than that averaged (0.3 m/s per decade) between the early 1970s and 2010s around the entire Bohai Sea. The multi-decadal ice-mass balance revealed decreasing trends of the maximum and average ice thickness of 2.6 and 0.8 cm/decade, respectively. The length of the ice season was shortened by 3.7 days/decade, and ice breakup dates were advanced by 2.3 days/decade. All trends were statistically significant. The modeled seasonal maximum ice thickness is highly correlated (0.83, p < 0.001) with the Bohai Sea Ice Index (BoSI) used to quantify the severity of the Bohai Sea ice condition. The freezing-up date, however, showed a large interannual variation without a clear trend. The simulations indicated that Bohai ice thickness has grown continuously thinner since 1951/1952. The time to reach 0.15 m level ice was delayed from 3 January to 21 January, and the ending time advanced from 6 March to 19 February. There was a significant weakening of ice conditions in the 1990s, followed by some recovery in 2000s. The relationship between large-scale climate indices and ice condition suggested that the AO and NAO are strongly correlated with interannual changes in sea-ice thickness in the Yingkou region.

A regime shift in seasonal total Antarctic sea ice extent in the twentieth century

In stark contrast to the Arctic, there have been statistically significant positive trends in total Antarctic sea ice extent since 1979. However, the short and highly variable nature of observed Antarctic sea ice extent limits the ability to fully understand the historical context of these recent changes. To meet this challenge, we have created robust, observation-based reconstruction ensembles of seasonal Antarctic sea ice extent since 1905. Using these reconstructions, here we show that the observed period since 1979 is the only time all four seasons demonstrate significant increases in total Antarctic sea ice in the context of the twentieth century and that the observed increases are juxtaposed against statistically significant decreases throughout much of the early and middle twentieth century. These reconstructions provide reliable estimates of seasonally resolved total Antarctic sea ice extent and are skilful enough to better understand aspects of air–sea–ice interactions within the Antarctic climate system. Antarctic sea ice extent is thought to be stable or increasing, in contrast to Arctic declines. Estimates of seasonal Antarctic sea ice from reconstructions show that increases are confined to the satellite era, post-1979, with substantial decreases in the early and mid-twentieth century.

Landfast Ice Controls on Turbulence in Antarctic Coastal Seas

AbstractKnowledge of the ocean surface layer beneath Antarctic landfast ice is sparse. In this article surface layer turbulent and fine structure are quantified with and without landfast ice at the same West Antarctic Peninsula location. Landfast ice reduced turbulence levels locally to an order of magnitude less than ice‐free values, and near‐inertial energy and sub‐inertial tidal energy levels to less than half their ice‐free values. Vertical turbulent heat and nutrient fluxes were, respectively, 6 and 10 times greater than previously estimated. Under‐ice tidal energy dissipation over the entire Antarctic continental shelf due to seasonal landfast ice cover is estimated to be between 788 MW to ∼6 GW. The total rate of wind‐generated turbulence in the surface ocean is greatly reduced by the presence of seasonal landfast ice to an average of 14% of the ice‐free value, but with large sectoral variations. Counter‐intuitively, however, tides and wind contribute approximately equally to the turbulent kinetic energy budget of the upper ocean between the Antarctic coastline and the maximal landfast ice extent, with large sectoral variations, attributed to geographic variations in the strength of the barotropic tide.

Turbulent Mixing in a Changing Arctic Ocean

Historically, the Arctic Ocean has been considered an ocean of low variability and weak turbulent mixing. However, the decline in seasonal sea ice cover over the past couple of decades has led to increased coupling between the atmosphere and the ocean, with potential enhancement of turbulent mixing. Here, we review studies that allow identifying energy sources and pathways that lead to turbulent mixing in an increasingly ice-free Arctic Ocean. We find that the evolution of wind-generated, near-inertial oscillations is highly sensitive to the seasonal sea ice cycle, but the response varies greatly between the continental shelves and the abyssal ocean and between the eastern and western ocean basins. There is growing interest in the role of tides and continental shelf waves in driving mixing over sloping topography. Both dissipate through the development of unsteady lee waves. The role eddies play in transporting shelf water into the basins and in supporting mixing has become more apparent as technological advances have permitted higher resolution observations of sea ice retreat. The importance of the dissipation of unsteady lee waves and of eddies in driving mixing highlights the need for parameterizations of these phenomena in regional ocean models and climate simulations.

A New Approach for the Estimation of Lake Ice Thickness From Conventional Radar Altimetry

Lake ice thickness (LIT), a thematic product of Lakes as an Essential Climate Variable (ECV), is sensitive to changes in air temperature and on-ice snow mass. Here, a novel and efficient analytic method (retracking approach) is presented for the estimation of LIT from Ku-band (13.6 GHz) radar altimetry data. The new retracker, referred to as LRM_LIT, is based on the physical modeling of the conventional radar echoes (also called Low Resolution Mode or LRM) over ice-covered lakes that show a characteristic step-like feature in their leading edge attributed to the reflection of radar waves at the snow-ice and ice-water interfaces. The method is applied to Jason-2 and Jason-3 data acquired over Great Slave Lake, Canada, over three ice seasons (2013-2016). As expected, the agreement between the Jason-2 and Jason-3 LIT estimates over their overlapping period (2016 ice season) is excellent with a mean bias error of 0.013 m and root mean square error (RMSE) of 0.024 m. LIT estimates from LRM_LIT are in good agreement with simulations from a thermodynamic lake ice model and in situ measurements with RMSE values of the order of few centimeters for the three winter seasons. The retracker also provides a robust way to assess the accuracy of LIT estimates which is in the order of 0.10 m when the ice cover is well established and prior to melt onset. In addition, LRM_LIT captures the seasonal transitions during the freeze-up and breakup periods and ice growth over different winter seasons, making it a promising method for monitoring inter-annual variability and trends in LIT from past and current conventional radar altimetry missions.

A Review of Arctic Sea Ice Climate Predictability in Large-Scale Earth System Models

We provide a high-level review of sea ice models used for climate studies and of the recent advances made with these models to understand sea ice predictability. Models currently in use for the Coupled Model Intercomparison Project and new developments coming online that will enable enhanced predictions are discussed. Previous work indicates that seasonal sea ice can be predicted based on mechanisms associated with long-lived ice thickness or ocean heat anomalies. On longer timescales, internal climate variability is an important source of uncertainty, although anthropogenic forcing is sizable, and studies suggest that anthropogenic signals have already emerged from internal climate noise. Using new analysis from the Multi-Model Large Ensemble, we show that while models differ in the magnitude and timing of predictable signals, many ice predictability characteristics are robust across multiple models. This includes the reemergence of predictable seasonal signals in ice area and the sizable uncertainty in predictions of ice-free Arctic timing associated with internal variability.

Winter thermal and ice regimes of small Karelian lakes against the background of regional climatic variability

This article investigates regularities of thermal and ice regimes of three small lakes of Karelia under current climatic conditions. Data of measurements of water temperature at autonomous stations and results of numerical calculations of the dates of ice-on and ice-off and the thickness of ice in these lakes using the one-dimensional parameterized model FLake in the anomalously warm winter season of 2019-2020 are analyzed. Data obtained are compared with long-term values over 1994-2019. The dates of the ice-on and ice-off on the lakes were quite close; however, on two larger lakes, intermediate ice destruction was observed at the beginning of winter, due to which the duration of the ice season differed between lakes by two weeks. The winter months of 2019-2020 were 6.4-9.4 ° C warmer than the baseline, which was reflected in a noticeably smaller ice thickness on the lakes compared to previous years of measurements (40-48 cm at the end of March 2020 compared to the values ​​in mid-April in 1994-2004 - 65-85 cm and in 2005-2018 - 50-65 cm). The decrease in ice thickness contributed to an early onset (mid-March) and long duration (more than five weeks) of spring subglacial convection. The model calculation, taking into account the atmospheric impact based on the ERA-5 re-analysis, reproduced the main features of the ice regime of the lakes, including the intermediate destruction of ice at the beginning of winter on two larger lakes. Significant regression relationships have been obtained between the dates of ice-on and ice-off on Lake Vendyurskoe, the dates of the onset and duration of spring under-ice convection, and the characteristics of the regional climate of southern Karelia (air temperature and the number of days with thaw in winter and spring months) for 1994-2020. The relationship between the dates of ice-on and the water temperature in the lake in winter is shown.

Гидрометеорологические опасные природные явления Северного Каспия в зимний период на фоне климатических изменений

Hydrometeorological hazards of the Northern Caspian during the winter periods 1950–2020—due to climatic changes were investigated. Hydrometeorological hazards for the period 1950–2020 are considered: severe winter periods, early ice freeze-up, storm waves, wind surge, as well as cumulative hydrometeorological hazards—combinations of storm waves and surge. Geographic information system (GIS) “Ice regime of the southern seas of Russia” is the information basis for the study of the ice regime of the Caspian Sea. Storm activity in the Caspian Sea were reconstructed using SWAN spectral wave model. Based on the cumulative freezing-degree days winters were divided by severity—mild, moderate and severe. During the study period, moderate types of winters prevail (59.4 %), and the number of severe and mild winters is the same and amounts to 14 pcs. (20.3 %) of each type. Due to climate change, the number of mild winters is increasing, and the number of severe ones is decreasing. Since 1985, 3 severe winters (2002/03, 2007/08, 2011/12) have been recorded. As a result, the ice cover area decreased by ∼7–10 %, and the duration of the ice season at the observation point Peshnoy was reduced by 5 days. 157 situations of combinations of storm surge and storm waves are identified: 140 are cases of potential situations of cumulative phenomena of a combination of storm and surge phenomena with a speed of 15 m/s or more of winds of effective directions. The largest number of cases of cumulative dangerous hydrometeorological hazards with a wind speed of more than 15 m/s is observed in March. After the 2000s there was an increase in the number of cases and duration of dangerous hydrometeorological hazards in November and March. A direct relationship between the number of cases of cumulative dangerous hydrometeorological hazards and the severity of winters has not been found.

Сравнительный анализ данных ледовитости Берингова моря по данным Sea Ice Index и Masie

This paper presents a comparative analysis of the ice cover of the Bering Sea. The analysis was performed according to the National Snow & Ice Data Center (NSIDC) using NASA algorithms Team (Sea Ice Index) and Near-Real-Time Passive Microwave/Visible Data Sharing DMSP SSMIS Daily Polar gridded Sea Ice Concentrations and Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data (MASIE-NH). The absolute and relative difference of the ice cover values were calculated using the algorithms Sea Ice Index and MASIE-NH with daily discreteness for 14 ice seasons from 2006 to 2020. Despite the fact that the spatial resolutions of the data of the algorithms under consideration and the quantitative criteria for the condition for classifying a pixel as pure water or ice extent differed (the side of the pixel was 25 and 4 km, identification was 15 and 40 %, respectively), the curves of the average seasonal variation of the ice cover were in phase and that was confirmed by the high value of the correlation coefficient (0.92). It was determined that the difference in ice cover values were not critical and were within the calculation limits, which allowed using data from both sources without calculating correction factors. Sea Data Ice Index data should be appropriate for long-term analysis of inter-seasonal variability, since data series of observations with daily discreteness have been available since 1978. It was concluded that the use of both sources would be quite acceptable in the analysis of intra-seasonal fluctuations. A characteristic feature of Sea Ice Index was noted—the presence of ice throughout the warm season. Although according to literary sources, such a phenomenon in the Bering Sea was typical only for severe types of winters. That was probably due to the technical difficulties in identifying the ice extent using passive microwave sounding methods.

Seasonal ice dynamics in the lower ablation zone of Dagongba Glacier, southeastern Tibetan Plateau, from multitemporal UAV images

AbstractMost glaciers on the Tibetan Plateau have experienced continuous mass losses in response to global warming. However, the seasonal dynamics of glaciers on the southeastern Tibetan Plateau have rarely been reported in terms of glacier surface elevation and velocity. This paper presents a first attempt to explore the seasonal dynamics of the debris-covered Dagongba Glacier within the southeastern Tibetan Plateau. We use the multitemporal unoccupied aerial vehicle images collected over the lower ablation zone on 8 June and 17 October 2018, and 13 May 2019, and then perform an analysis concerning climatic fluctuations. The results reveal that the mean surface elevation decrease of the Dagongba Glacier during the warm season ($2.81\pm 0.44$ m) was remarkably higher than the cold season ($0.72\pm 0.45$ m). Particularly notable glacier surface elevation changes were found around supraglacial lakes and ice cliffs where ice ablation rates were $\sim$3 times higher than the average. In addition, a larger longitudinal decline of glacier surface velocity was observed in the warm season than that in the cold season. In terms of further comparative analysis, the Dagongba Glacier experienced a decrease in surface velocity between 1982–83 and 2018–19, with a decrease in the warm season possibly twice as large as that in the cold season.

Seasonal Variations and Drivers of Surface Ocean pCO2 in the Seasonal Ice Zone of the Eastern Indian Sector, Southern Ocean

AbstractTo quantitatively assess the inorganic carbon cycle in the eastern Indian sector of the Southern Ocean (80–150°E, south of 60°S), we measured ocean surface temperature, salinity, total alkalinity (TA), the partial pressure of carbon dioxide (pCO2), and concentrations of chlorophyll‐a (chl a), dissolved inorganic carbon (DIC), and nutrients during the KY18 survey (December 2018–January 2019). The sea–air CO2 flux in this region was −8.3 ± 12.7 mmol m−2 day−1 (−92.1 to +10.6 mmol m−2 day−1). The ocean was therefore a weak CO2 sink. Based on the DIC and TA in the temperature minimum layer, we estimated the change of pCO2 from winter to summer (δpCO2) due to changes in water temperature, salinity, and biological activity (photosynthesis). The spatial distribution of pCO2 in the western part (80–110°E) of the study area was mainly driven by biological activity, which decreased pCO2 from December to early January, and in the eastern part (110–150°E) by temperature, which increased pCO2 from January to February. We also examined the changes in the CO2 concentrations (xCO2) over time by comparing data from 1996 with our data (2018–2019). The oceanic and atmospheric xCO2 increased by 23 and 45 ppm in 23 years, respectively. These changes of ocean xCO2 were mainly driven by an increase in CO2 uptake from the atmosphere as a result of the rise in atmospheric xCO2 and increase in biological activity associated with the change in the water‐mass distribution.

Intercomparison of Salinity Products in the Beaufort Gyre and Arctic Ocean

Salinity is the primary determinant of the Arctic Ocean’s density structure. Freshwater accumulation and distribution in the Arctic Ocean have varied significantly in recent decades and certainly in the Beaufort Gyre (BG). In this study, we analyze salinity variations in the BG region between 2012 and 2017. We use in situ salinity observations from the Seasonal Ice Zone Reconnaissance Surveys (SIZRS), CTD casts from the Beaufort Gyre Exploration Project (BGP), and the EN4 data to validate and compare with satellite observations from Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Aquarius Optimally Interpolated Sea Surface Salinity (OISSS), and Arctic Ocean models: ECCO, MIZMAS, HYCOM, ORAS5, and GLORYS12. Overall, satellite observations are restricted to ice-free regions in the BG area, and models tend to overestimate sea surface salinity (SSS). Freshwater Content (FWC), an important component of the BG, is computed for EN4 and most models. ORAS5 provides the strongest positive SSS correlation coefficient (0.612) and lowest bias to in situ observations compared to the other products. ORAS5 subsurface salinity and FWC compare well with the EN4 data. Discrepancies between models and SIZRS data are highest in GLORYS12 and ECCO. These comparisons identify dissimilarities between salinity products and extend challenges to observations applicable to other areas of the Arctic Ocean.

Autonomous System for Lake Ice Monitoring.

Continuous monitoring of ice cover belongs to the key tasks of modern climate research, providing up-to-date information on climate change in cold regions. While a strong advance in ice monitoring worldwide has been provided by the recent development of remote sensing methods, quantification of seasonal ice cover is impossible without on-site autonomous measurements of the mass and heat budget. In the present study, we propose an autonomous monitoring system for continuous in situ measuring of vertical temperature distribution in the near-ice air, the ice strata and the under-ice water layer for several months with simultaneous records of solar radiation incoming at the lake surface and passing through the snow and ice covers as well as snow and ice thicknesses. The use of modern miniature analog and digital sensors made it possible to make a compact, energy efficient measurement system with high precision and spatial resolution and characterized by easy deployment and transportation. In particular, the high resolution of the ice thickness probe of 0.05 mm allows to resolve the fine-scale processes occurring in low-flow environments, such as freshwater lakes. Several systems were tested in numerous studies in Lake Baikal and demonstrated a high reliability in deriving the ice heat balance components during ice-covered periods.

A Cloudier Picture of Ice-Albedo Feedback in CMIP6 Models

Increased solar absorption is an important driver of Arctic Amplification, the interconnected set of processes and feedbacks by which Arctic temperatures respond more rapidly than global temperatures to climate forcing. The amount of sunlight absorbed in the Arctic is strongly modulated by seasonal ice and snow cover. Sea ice declines and shorter periods of seasonal snow cover in recent decades have increased solar absorption, amplifying local warming relative to the planet as a whole. However, this Arctic albedo feedback would be substantially larger in the absence of the ubiquitous cloud cover that exists throughout the region. Clouds have been observed to mask the effects of reduced surface albedo and slow the emergence of secular trends in net solar absorption. Applying analogous metrics to several models from the 6thClimate Model Intercomparison Project (CMIP6), we find that ambiguity in the influence of clouds on predicted Arctic solar absorption trends has increased relative to the previous generation of climate models despite better agreement with the observed albedo sensitivity to sea ice variations. Arctic albedo responses to sea ice loss are stronger in CMIP6 than in CMIP5 in all summer months. This agrees better with observations, but models still slightly underestimate albedo sensitivity to sea ice changes relative to observations. Never-the-less, nearly all CMIP6 models predict that the Arctic is now absorbing more solar radiation than at the start of the century, consistent with recent observations. In fact, many CMIP6 models simulate trends that are too strong relative to internal variability, and spread in predicted Arctic albedo changes has increased since CMIP5. This increased uncertainty can be traced to increased ambiguity in how clouds influence natural and forced variations in Arctic solar absorption. While nearly all CMIP5 models agreed with observations that clouds delay the emergence of forced trends, about half of CMIP6 models suggest that clouds accelerate their emergence from natural variability. Isolating atmospheric contributions to total Arctic reflection suggests that this diverging behavior may be linked to stronger Arctic cloud feedbacks in the latest generation of climate models.