Arctic Cloud Cover Research Articles

Seasonal variations of Arctic cloud in recent 14 years using CALIPSO-GOCCP

This study documents the three-dimensional structure and long-term trend of Arctic cloud cover with 14-year Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations from a seasonal perspective. Results show that Arctic Ocean presents larger low-level cloudiness and more evident vertical contrast between low and high-level cloud cover than Arctic land. Furthermore, Arctic Atlantic experiences the largest total and low-level cloud cover all year long, with relatively weak seasonal variation. In contrast, the low-level cloud cover over Arctic Pacific displays a pronounced bimodal annual cycle, as well as the low-level cloud cover over Arctic land. Arctic Atlantic with the maximum cloud fraction has the highest altitude of about 1.2 km above open sea in winter, which is near the surface in summer. The cloudiness in the lower troposphere over open water is significantly larger than that over ice except in winter, whereas, the liquid-containing cloud over open water is significantly larger than that over ice in the lower troposphere in four seasons. Moreover, though the liquid-containing cloud dominates the low-level cloud cover, its increasing trend is driven by liquid-containing and ice-containing cloud together. Instead, the ice-containing cloud dominates the high-level cloud cover and its increasing trend in all seasons over most Arctic regions, except a negative trend over Greenland in winter. Our work presents a more comprehensive picture of the Arctic cloud cover, which provides an insight into understanding of Arctic cloud and Arctic climate system.

Cloud cover and cloud types in the Eurasian Arctic in 1936–2012

AbstractThe Arctic is a cloudy place. It has been recognized that the Arctic cloud cover is sensitive to different climatic factors such as sea ice extent and atmospheric circulation indices. Moreover, several influential climate feedbacks, for example, the summertime cloud‐radiation feedback, have been recognized. Yet, the cloud cover studies were limited in time to the satellite era observations and fragmentary data sets from meteorological stations. Here, we present the complete long‐term cloud records from 86 meteorological stations in the Eurasian Arctic. The stations are located on the coast and islands of the region from the Barents to Chukchi Seas. Thus, this study is complementing and extending the study by Chernokulsky et al. (2017) where the cloud data from the Norwegian through Kara Seas were presented. Our data set comprises the entire period of observations at each station. However, we present the area‐wide analysis only over the historical period of 1936–2012 when there were sufficient density of stations and cloud records for the coherent analysis. The total cloud cover, which on multiannual average constitutes 69–74% in different areas, increases in the warmer periods. The strongest increase is found in the convective cloud cover, particularly in the Chukchi Sea. We observe statistical evidence of transition between stratiform and convective cloud types. The cloud characteristics reveal the strongest correlations with the Atlantic circulation indices and the sea ice concentration in all Eurasian Arctic areas. The correlations with the Pacific circulation indices are much less significant. The obtained cloud data sets disclose much smaller scale features and variability, which deserve further research.

Evaluation of Six Atmospheric Reanalyses over Arctic Sea Ice from Winter to Early Summer

AbstractThis study evaluates the performance of six atmospheric reanalyses (ERA-Interim, ERA5, JRA-55, CFSv2, MERRA-2, and ASRv2) over Arctic sea ice from winter to early summer. The reanalyses are evaluated using observations from the Norwegian Young Sea Ice campaign (N-ICE2015), a 5-month ice drift in pack ice north of Svalbard. N-ICE2015 observations include surface meteorology, vertical profiles from radiosondes, as well as radiative and turbulent heat fluxes. The reanalyses simulate surface analysis variables well throughout the campaign, but have difficulties with most forecast variables. Wintertime (January–March) correlation coefficients between the reanalyses and observations are above 0.90 for the surface pressure, 2-m temperature, total column water vapor, and downward longwave flux. However, all reanalyses have a positive wintertime 2-m temperature bias, ranging from 1° to 4°C, and negative (i.e., upward) net longwave bias of 3–19 W m−2. These biases are associated with poorly represented surface inversions and are largest during cold-stable periods. Notably, the recent ERA5 and ASRv2 datasets have some of the largest temperature and net longwave biases, respectively. During spring (April–May), reanalyses fail to simulate observed persistent cloud layers. Therefore they overestimate the net shortwave flux (5–79 W m−2) and underestimate the net longwave flux (8–38 W m−2). Promisingly, ERA5 provides the best estimates of downward radiative fluxes in spring and summer, suggesting improved forecasting of Arctic cloud cover. All reanalyses exhibit large negative (upward) residual heat flux biases during winter, and positive (downward) biases during summer. Turbulent heat fluxes over sea ice are simulated poorly in all seasons.

Thicker Clouds and Accelerated Arctic Sea Ice Decline: The Atmosphere‐Sea Ice Interactions in Spring

AbstractObservations show that increased Arctic cloud cover in the spring is linked with sea ice decline. As the atmosphere and sea ice can influence each other, which one plays the leading role in spring remains unclear. Here we demonstrate, through observational data diagnosis and numerical modeling, that there is active coupling between the atmosphere and sea ice in early spring. Sea ice melting and thus the presence of more open water lead to stronger evaporation and promote cloud formation that increases downward longwave flux, leading to even more ice melt. Spring clouds are a driving force in the disappearance of sea ice and displacing the mechanism of atmosphere‐sea ice coupling from April to June. These results suggest the need to accurately model interactions of Arctic clouds and radiation in Earth System Models in order to improve projections of the future of the Arctic.

Isolating the Liquid Cloud Response to Recent Arctic Sea Ice Variability Using Spaceborne Lidar Observations

AbstractWhile the radiative influence of clouds on Arctic sea ice is known, the influence of sea ice cover on Arctic clouds is challenging to detect, separate from atmospheric circulation, and attribute to human activities. Providing observational constraints on the two‐way relationship between sea ice cover and Arctic clouds is important for predicting the rate of future sea ice loss. Here we use 8 years of CALIPSO (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations) spaceborne lidar observations from 2008 to 2015 to analyze Arctic cloud profiles over sea ice and over open water. Using a novel surface mask to restrict our analysis to where sea ice concentration varies, we isolate the influence of sea ice cover on Arctic Ocean clouds. The study focuses on clouds containing liquid water because liquid‐containing clouds are the most important cloud type for radiative fluxes and therefore for sea ice melt and growth. Summer is the only season with no observed cloud response to sea ice cover variability: liquid cloud profiles are nearly identical over sea ice and over open water. These results suggest that shortwave summer cloud feedbacks do not slow long‐term summer sea ice loss. In contrast, more liquid clouds are observed over open water than over sea ice in the winter, spring, and fall in the 8 year mean and in each individual year. Observed fall sea ice loss cannot be explained by natural variability alone, which suggests that observed increases in fall Arctic cloud cover over newly open water are linked to human activities.

Effect of retreating sea ice on Arctic cloud cover in simulated recent global warming

Abstract. This study investigates the effect of sea ice reduction on Arctic cloud cover in historical simulations with the coupled atmosphere–ocean general circulation model MIROC5. Arctic sea ice has been substantially retreating since the 1980s, particularly in September, under simulated global warming conditions. The simulated sea ice reduction is consistent with satellite observations. On the other hand, Arctic cloud cover has been increasing in October, with about a 1-month lag behind the sea ice reduction. The delayed response leads to extensive sea ice reductions because the heat and moisture fluxes from the underlying open ocean into the atmosphere are enhanced. Sensitivity experiments with the atmospheric part of MIROC5 clearly show that sea ice reduction causes increases in cloud cover. Arctic cloud cover increases primarily in the lower troposphere, but it decreases in the near-surface layers just above the ocean; predominant temperature rises in these near-surface layers cause drying (i.e., decreases in relative humidity), despite increasing moisture flux. Cloud radiative forcing due to increases in cloud cover in autumn brings an increase in the surface downward longwave radiation (DLR) by approximately 40–60 % compared to changes in clear-sky surface DLR in fall. These results suggest that an increase in Arctic cloud cover as a result of reduced sea ice coverage may bring further sea ice retreat and enhance the feedback processes of Arctic warming.

Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

Effects of explicit atmospheric convection at high CO2.

The effect of clouds on climate remains the largest uncertainty in climate change predictions, due to the inability of global climate models (GCMs) to resolve essential small-scale cloud and convection processes. We compare preindustrial and quadrupled CO2 simulations between a conventional GCM in which convection is parameterized and a "superparameterized" model in which convection is explicitly simulated with a cloud-permitting model in each grid cell. We find that the global responses of the two models to increased CO2 are broadly similar: both simulate ice-free Arctic summers, wintertime Arctic convection, and enhanced Madden-Julian oscillation (MJO) activity. Superparameterization produces significant differences at both CO2 levels, including greater Arctic cloud cover, further reduced sea ice area at high CO2, and a stronger increase with CO2 of the MJO.

Less winter cloud aids summer 2013 Arctic sea ice return from 2012 minimum

In September 2012, Arctic sea ice cover reached a record minimum for the satellite era. The following winter the sea ice quickly returned, carrying through to the summer when ice extent was 48% greater than the same time in 2012. Most of this rebound in the ice cover was in the Chukchi and Beaufort Seas, areas experiencing the greatest decline in sea ice over the last three decades. A variety of factors, including ice dynamics, oceanic and atmospheric heat transport, wind, and solar insolation anomalies, may have contributed to the rebound. Here we show that another factor, below-average Arctic cloud cover in January–February 2013, resulted in a more strongly negative surface radiation budget, cooling the surface and allowing for greater ice growth. More thick ice was observed in March 2013 relative to March 2012 in the western Arctic Ocean, and the areas of ice growth estimated from the negative cloud cover anomaly and advected from winter to summer with ice drift data, correspond well with the September ice concentration anomaly pattern. Therefore, decreased wintertime cloud cover appears to have played an important role in the return of the sea ice cover the following summer, providing a partial explanation for large year-to-year variations in an otherwise decreasing Arctic sea ice cover.

Cloud influence on and response to seasonal Arctic sea ice loss

Recent declines in Arctic sea ice extent provide new opportunities to assess cloud influence on and response to seasonal sea ice loss. This study combines unique satellite observations with complementary data sets to document Arctic cloud and atmospheric structure during summer and early fall. The analysis focuses on 2006–2008, a period over which ice extent plummeted to record levels, substantial variability in atmospheric circulation patterns occurred, and spaceborne radar and lidar observations of vertical cloud structure became available. The observations show that large‐scale atmospheric circulation patterns, near‐surface static stability, and surface conditions control Arctic cloud cover during the melt season. While no summer cloud response to sea ice loss was found, low clouds did form over newly open water during early fall. This seasonal variation in the cloud response to sea ice loss can be explained by near‐surface static stability and air‐sea temperature gradients. During summer, temperature inversions and weak air‐sea temperature gradients limit atmosphere‐ocean coupling. In contrast, relatively low static stability and strong air‐sea gradients during early fall permit upward turbulent fluxes of moisture and heat and increased low cloud formation over newly open water. Because of their seasonal timing, cloud changes resulting from sea ice loss play a minor role in regulating ice‐albedo feedbacks during summer, but may contribute to a cloud‐ice feedback during early fall.

The Influence of Changes in Cloud Cover on Recent Surface Temperature Trends in the Arctic

Abstract A method is presented to assess the influence of changes in Arctic cloud cover on the surface temperature trend, allowing for a more robust diagnosis of causes for surface warming or cooling. Seasonal trends in satellite-derived Arctic surface temperature under clear-, cloudy-, and all-sky conditions are examined for the period 1982–2004. The satellite-derived trends are in good agreement with trends in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product and surface-based weather station measurements in the Arctic. Surface temperature trends under clear and cloudy conditions have patterns similar to the all-sky trends, though the magnitude of the trends under cloudy conditions is smaller than those under clear-sky conditions, illustrating the negative feedback of clouds on the surface temperature trends. The all-sky surface temperature trend is divided into two parts: the first part is a linear combination of the surface temperature trends under clear and cloudy conditions; the second part is caused by changes in cloud cover as a function of the clear–cloudy surface temperature difference. The relative importance of these two components is different in the four seasons, with the first part more important in spring, summer, and autumn, but with both parts being equally important in winter. The contribution of biases in satellite retrievals is also evaluated.

On the reliability of simulated Arctic sea ice in global climate models

While most of the global climate models (GCMs) currently being evaluated for the IPCC Fourth Assessment Report simulate present‐day Arctic sea ice in reasonably good agreement with observations, the intermodel differences in simulated Arctic cloud cover are large and produce significant differences in downwelling longwave radiation. Using the standard thermodynamic models of sea ice, we find that the GCM‐generated spread in longwave radiation produces equilibrium ice thicknesses that range from 1 to more than 10 meters. However, equilibrium ice thickness is an extremely sensitive function of the ice albedo, allowing errors in simulated cloud cover to be compensated by tuning of the ice albedo. This analysis suggests that the results of current GCMs cannot be relied upon at face value for credible predictions of future Arctic sea ice.

The Impact of Cloud Feedbacks on Arctic Climate under Greenhouse Forcing*

Abstract The simulation of Arctic cloud cover and the sensitivity of Arctic climate to cloud changes are investigated using an atmosphere–mixed-layer ocean GCM (GENESIS2). The model is run with and without changes in three-dimensional cloud fraction under 2 × CO2 radiative forcing. This model was chosen in part because of its relatively successful representation of modern Arctic cloud cover, a trait attributable to the parameterized treatment of mixed-phase microphysics. Simulated modern Arctic cloud fraction is insensitive to model biases in surface boundary conditions (SSTs and sea ice distribution), but the modeled Arctic climate is sensitive to high-frequency cloud variability. When forced with increased CO2 the model generally simulates more (less) vertically integrated cloudiness in high (low) latitudes. In the simulation without cloud feedbacks, cloud fraction is fixed at its modern control value at all grid points and all levels while CO2 is doubled. Compared with this fixed-cloud experiment, the ...