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At least 127 records · Page 7

Changes in Seasonal Cloud Cover over the Arctic Seas from Satellite and Surface Observations

Winter and spring changes in cloudiness are compared over the arctic seas (ocean areas north of 60 degrees N) from the TOVS (TIROS Operational Vertical Sounder) Polar Pathfinder retrievals and two separate datasets derived from the Advanced Very High Resolution Radiometer (AVHRR). All satellite products exhibit significant decreases in cloud fraction over the arctic seas during winter (December, January, February) on the order of 5 %/ decade. An equally striking increase in spring (March, April, May) cloudiness is evident from the TOVS Pathfinder (TPP) and the extended AVHRR Polar Pathfinder (APP-x) projects. In the Central Arctic these positive trends can be as large as 15 %/decade. Surface observations from the Russian drifting meteorological stations are consistent with satellite-observed changes during the 1980s. Negative trends in spring cloudiness reported by Comiso [2003] are in conflict with these findings. Spring changes in cloudiness are associated with changes in the atmospheric circulation. These dramatic, large-scale changes may have substantial impacts on the surface energy balance.

Schweiger, Axel J.↗

Increasing wintertime cloud opacity increases surface longwave radiation at a long-term Arctic observatory

As the Arctic warms, winter clouds are known and expected to change. Yet the extent to which these cloud changes amplify or dampen warming (cloud feedback) remains uncertain. This uncertainty results from systemic difficulties in modeling and observing Arctic low clouds. Surface-based observations avoid many of these difficulties. Here, we use two decades of surface-based observations (1998–2023) to constrain and explain longwave flux change during winter. We find that longwave flux into the surface is increasing and that this increase cannot be explained by direct impacts of temperature and greenhouse gases alone. Only when increasing cloud radiative effect (0.96 ± 0.64 W/m 2 /K) is considered can increasing longwave flux be explained. Cloud radiative effect increases due to increasing cloud opacity, which is driven equally by ice-only and mixed-phase clouds. The direct observational constraint from this work suggests that increasing cloud opacity drives increasing net surface radiation on Alaska’s North Slope during winter.

Bertrand, Leah [Univ. of Colorado, Boulder, CO (Un↗

The Vertical Distribution of Thin Features Over the Arctic Analysed from CALIPSO Observations: Optically Thin Clouds - Part 1

Clouds play a crucial role in the Arctic climate system. Therefore, it is essential to accurately and reliably quantify and understand cloud properties over the Arctic. It is also important to monitor and attribute changes in Arctic clouds. Here, we exploit the capability of the CALIPSO-CALIOP instrument and provide comprehensive statistics of tropospheric thin clouds, otherwise extremely difficult to monitor from passive satellite sensors.We use 4 yr of data (June 2006.May 2010) over the circumpolar Arctic, here defined as 67-82 deg. N, and characterize probability density functions of cloud base and top heights, geometrical thickness and zonal distribution of such cloud layers, separately for water and ice phases, and discuss seasonal variability of these properties. When computed for the entire study area, probability density functions of cloud base and top heights and geometrical thickness peak at 200-400, 1000-2000 and 400-800 m, respectively, for thin water clouds, while for ice clouds they peak at 6-8, 7-9 and 400-1000 m, respectively. In general, liquid clouds were often identified below 2 km during all seasons, whereas ice clouds were sensed throughout the majority of the upper troposphere and also, but to a smaller extent, below 2 km for all seasons.

DeVasthale, Abhay↗

Observational Evidence Against Mountain-Wave Generation of Ice Nuclei as a Prerequisite for the Formation of Three Solid Nitric Acid Polar Stratospheric Clouds Observed in the Arctic in Early December 1999

A number of recently published papers suggest that mountain-wave activity in the stratosphere, producing ice particles when temperatures drop below the ice frost point, may be the primary source of large NAT particles. In this paper we use measurements from the Advanced Very High Resolution Radiometer (AVHRR) instruments on board the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites to map out regions of ice clouds produced by stratospheric mountain-wave activity inside the Arctic vortex. Lidar observations from three DC-8 flights in early December 1999 show the presence of solid nitric acid (Type Ia or NAT) polar stratospheric clouds (PSCs). By using back trajectories and superimposing the position maps on the AVHRR cloud imagery products, we show that these observed NAT clouds could not have originated at locations of high-amplitude mountain-wave activity. We also show that mountain-wave PSC climatology data and Mountain Wave Forecast Model 2.0 (MWFM-2) raw hemispheric ray and grid box averaged hemispheric wave temperature amplitude hindcast data from the same time period are in agreement with the AVHRR data. Our results show that ice cloud formation in mountain waves cannot explain how at least three large scale NAT clouds were formed in the stratosphere in early December 1999.

MOUNTAIN WAVES↗

Seasonal Clear-Sky Flux and Cloud Radiative Effect Anomalies in the Arctic Atmospheric Column Associated with the Arctic Oscillation and Arctic Dipole

The impact of the Arctic Oscillation (AO) and Arctic Dipole (AD) on the radiative flux into the Arctic mean atmospheric column is quantified. 3-month-averaged AO and AD indices are regressed with corresponding surface and top-of-atmosphere (TOA) fluxes from the CERES-SFC and CERES-TOA EBAF datasets over the period 2000-2014. An increase in clear-sky fluxes into the Arctic mean atmospheric column during fall is the largest net flux anomaly associated with AO, primarily driven by a positive net longwave flux anomaly (i.e. increase of net flux into the atmospheric column) at the surface. A decrease in the Arctic mean atmospheric column cloud radiative effect during winter and spring is the largest flux anomaly associated with AD, primarily driven by a change in the longwave cloud radiative effect at the surface. These prominent responses to AO and AD are widely distributed across the ice-covered Arctic, suggesting that the physical process or processes that bring about the flux change associated with AO and AD are distributed throughout the Arctic.

Hegyi, Bradley M.↗

Cloud-atmosphere impacts on the central Arctic surface energy budget (Final Report)

This project investigated how clouds and atmosphere-sea ice interactions shape the central Arctic surface energy budget using observations from the MOSAiC expedition and coordinated model experiments. Analyses focused on three themes. First, a new method applied to thermistor string data revealed that snow thermal conductivity is systematically higher than assumed in most models, strongly influencing conductive heat flux and sea ice growth, especially under clear skies. Second, a full annual cycle of surface energy budget observations, combined with detailed cloud microphysical retrievals, demonstrated how cloud regimes regulate radiative, turbulent, and conductive fluxes, driving seasonal contrasts in sea ice energy balance: in winter, radiative forcing elicits strong turbulent and conductive responses, while in summer excess energy is primarily partitioned into surface melt. Third, these observations were used to evaluate and improve regional and global forecast models. While some models captured key Arctic cloud-radiation states, most exhibited persistent deficiencies in representing liquid-containing clouds, snow-on-sea-ice processes, and flux responses to radiative forcing. Together, these results provide unprecedented benchmarks for understanding coupled Arctic processes, improve parameterizations of snow, sea ice, and cloud interactions, and highlight critical pathways by which atmospheric variability drives sea ice change in a rapidly evolving Arctic system. Lastly, the project was a resounding success, resulting in 38 peer-reviewed publications and dozens of presentations, while also supporting an early career scientist.

54 ENVIRONMENTAL SCIENCES↗

Daytime Cloud Property Retrievals Over the Arctic from Multispectral MODIS Data

Improving climate model predictions over Earth's polar regions requires a complete understanding of polar clouds properties. Passive satellite remote sensing techniques can be used to retrieve macro and microphysical properties of polar cloud systems. However, over the Arctic, there is minimal contrast between clouds and the background snow surface observed in satellite data, especially for visible wavelengths. This makes it difficult to identify clouds and retrieve their properties from space. Variable snow and ice cover, temperature inversions, and the predominance of mixed-phase clouds further complicate cloud property identification. For this study, the operational Clouds and the Earth s Radiant Energy System (CERES) cloud mask is first used to discriminate clouds from the background surface in Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data. A solar-infrared infrared nearinfrared technique (SINT) first used by Platnick et al. (2001) is used here to retrieve cloud properties over snow and ice covered regions.

Spangenberg, Douglas A.↗

Arctic cloudiness - Comparison of ISCCP-C2 and Nimbus-7 satellite-derived cloud products with a surface-based cloud climatology

One surface-based and two satellite arctic cloud climatologies are compared in terms of the annual cycle and spatial patterns of total monthly cloud amounts. Additionally, amounts and spatial patterns of low, middle, and high cloud type are compared. The surface-based dataset is for the years 1951-81, while the satellite-based data are for 1979-85 and 1983-86. The satellite cloud amounts are generally 5-35 percent less than the surface observations over the entire Arctic. However, regional differences may be as high as 45 percent. During July the surface-based cloud amounts for the central Arctic are about 40 percent greater than the satellite-based, but only 10 percent greater in the Norwegian Sea area. Surprisingly, (ISCCP) cloud climatology and surface observations agree better during winter than during summer. Possible reasons for these differences are discussed.

Schweiger, Axel J.↗

Remote Sensing of Liquid Water and Ice Cloud Optical Thickness and Effective Radius in the Arctic: Application of Airborne Multispectral MAS Data

A multispectral scanning spectrometer was used to obtain measurements of the reflection function and brightness temperature of clouds, sea ice, snow, and tundra surfaces at 50 discrete wavelengths between 0.47 and 14.0 microns. These observations were obtained from the NASA ER-2 aircraft as part of the FIRE Arctic Clouds Experiment, conducted over a 1600 x 500 km region of the north slope of Alaska and surrounding Beaufort and Chukchi Seas between 18 May and 6 June 1998. Multispectral images of the reflection function and brightness temperature in 11 distinct bands of the MODIS Airborne Simulator (MAS) were used to derive a confidence in clear sky (or alternatively the probability of cloud), shadow, and heavy aerosol over five different ecosystems. Based on the results of individual tests run as part of the cloud mask, an algorithm was developed to estimate the phase of the clouds (water, ice, or undetermined phase). Finally, the cloud optical thickness and effective radius were derived for both water and ice clouds that were detected during one flight line on 4 June. This analysis shows that the cloud mask developed for operational use on MODIS, and tested using MAS data in Alaska, is quite capable of distinguishing clouds from bright sea ice surfaces during daytime conditions in the high Arctic. Results of individual tests, however, make it difficult to distinguish ice clouds over snow and sea ice surfaces, so additional tests were added to enhance the confidence in the thermodynamic phase of clouds over the Beaufort Sea. The cloud optical thickness and effective radius retrievals used 3 distinct bands of the MAS, with the newly developed 1.62 and 2.13 micron bands being used quite successfully over snow and sea ice surfaces. These results are contrasted with a MODIS-based algorithm that relies on spectral reflectance at 0.87 and 2.13 micron.

King, Michael D.↗

Preliminary design for Arctic atmospheric radiative transfer experiments

If current plans are realized, within the next few years, an extraordinary set of coordinated research efforts focusing on energy flows in the Arctic will be implemented. All are motivated by the prospect of global climate change. SHEBA (Surface Energy Budget of the Arctic Ocean), led by the National Science Foundation (NSF) and the Office of Naval Research (ONR), involves instrumenting an ice camp in the perennial Arctic ice pack, and taking data for 12-18 months. The ARM (Atmospheric Radiation Measurement) North Slope of Alaska and Adjacent Arctic Ocean (NSA/AAO) Cloud and Radiation Testbed (CART) focuses on atmospheric radiative transport, especially in the presence of clouds. The NSA/AAO CART involves instrumenting a sizeable area on the North Slope of Alaska and adjacent waters in the vicinity of Barrow, and acquiring data over a period of about 10 years. FIRE (First ISCCP (International Satellite Cloud Climatology Program) Regional Experiment) Phase 3 is a program led by the National Aeronautics and Space Administration (NASA) which focuses on Arctic clouds, and which is coordinated with SHEBA and ARM. FIRE has historically emphasized data from airborne and satellite platforms. All three program anticipate initiating Arctic data acquisition during spring, 1997. In light of his historic opportunity, the authors discuss a strawman atmospheric radiative transfer experimental plan that identifies which features of the radiative transport models they think should be tested, what experimental data are required for each type of test, the platforms and instrumentation necessary to acquire those data, and in general terms, how the experiments could be conducted. Aspects of the plan are applicable to all three programs.

Zak, B. D.↗

Chlorine chemistry on polar stratospheric cloud particles in the Arctic winter

Simultaneous in situ measurements of hydrochloric acid (HCl) and chlorine monoxide (ClO) in the Arctic winter vortex showed large HCl losses of up to 1 ppbv, which were correlated with high ClO levels of up to 1.4 ppbv. Air parcel trajectory analysis identified that this conversion of inorganic chlorine occurred at air temperatures of less than 196 -/+ 4 kelvin. High ClO was always accompanied by loss of HCl mixing ratios equal to 1/2(ClO+ 2Cl2O2). These data indicate that the heterogeneous reaction HCl + ClONO2 - Cl2 + HNO3 on particles of polar stratospheric clouds establishes the chlorine partitioning, which, contrary to earlier notions, begins with an excess of ClONO2, not HCl.

Webster, C. R.↗

Cloud-Scale Numerical Modeling of the Arctic Boundary Layer

The research objective of this NASA grant-funded project was to determine in detail how large-scale processes. in combination with cloud-scale radiative, microphysical, and dynamical processes, govern the formation and multi-layered structure of Arctic stratus clouds. This information will be useful for developing and improving 1D (one dimensional) boundary layer models for the Arctic. Also, to quantitatively determine the effects of leads on the large-scale budgets of sensible heat, water vapor, and condensate in a variety of Arctic winter conditions. This information will be used to identify the most important lead-flux processes that require parameterization in climate models. Our approach was to use a high-resolution numerical model, the 2D (two dimensional) University of Utah Cloud Resolving Model (UU CRM), and its 1D version, the University of Utah Turbulence Closure Model (UU TCM), a boundary layer model based on third-moment turbulence closure, as well as a large-eddy simulation (LES) model originally developed by C.H. Moeng.

Kruegen, Steven K.↗

A Satellite-Based Estimate of Combustion Aerosol Cloud Microphysical Effects over the Arctic Ocean

Climate predictions for the rapidly changing Arctic are highly uncertain, largely due to a poor understanding of the processes driving cloud properties. In particular, cloud fraction (CF) and cloud phase (CP) have major impacts on energy budgets, but are poorly represented in most models, often because of uncertainties in aerosol–cloud interactions. Here, we use over 10 million satellite observations coupled with aerosol transport model simulations to quantify large scale microphysical effects of aerosols on CF and CP over the Arctic Ocean during polar night, when direct and semi-direct aerosol effects are minimal. Combustion aerosols over sea ice are associated with very large (~10W/sq.m) differences in longwave cloud radiative effects at the sea ice surface. However, co-varying meteorological changes on factors such as CF likely explain the majority of this signal. For example, combustion aerosols explain at most 40% of the CF differences between the full dataset and the clean-condition subset, compared to between 57% and 91% of the differences that can be predicted by co-varying meteorology. After normalizing for meteorological regime, aerosol microphysical effects have small but significant impacts on CF, CP, and precipitation frequency on an Arctic-wide scale. These effects indicate that dominant aerosol–cloud microphysical mechanisms are related to the relative fraction of liquid-containing clouds, with implications for a warming Arctic.

Zamora, Lauren M.↗