Search NASA⌕ Search

SEARCH · Search NASA

Results for “Arctic radiation budget”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

The impacts of immersion ice nucleation parameterizations on Arctic mixed-phase stratiform cloud properties and the Arctic radiation budget in GEOS-5

The influence of four different immersion freezing parameterizations on Arctic clouds and the top-of-the atmosphere (TOA) and surface radiation fluxes is investigated in the fifth version of the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System (GEOS-5) with sea surface temperature, sea ice fraction and aerosol emissions held fixed. The different parameterizations were derived from a variety of sources, including classical nucleation theory, field and laboratory measurements. Despite the large spread in the ice-nucleating particle (INP) concentrations in the parameterizations, the cloud properties and radiative fluxes had a tendency to form two groups, with the lower INP concentration category producing larger water path and low-level cloud fraction during winter and early spring, whereas the opposite occurred during the summer season. The stability of the lower troposphere was found to strongly correlate with low-cloud fraction, and along with the effect of ice nucleation, ice sedimentation and melting rates, appears to explain the spring-to-summer reversal pattern in the relative magnitude of the cloud properties between the two categories of simulations. The strong modulation effect of the liquid phase on immersion freezing led to the successful simulation of the characteristic Arctic cloud structure, with a layer rich in supercooled water near cloud top and ice and snow at lower levels. Comparison with satellite retrievals and in situ data suggest that simulations with low INP concentrations more realistically represent Arctic clouds and radiation.

Arctic↗
NASA NTRS2017

The Regional Influence of the Arctic Oscillation and Arctic Dipole on the Wintertime Arctic Surface Radiation Budget and Sea Ice Growth

An analysis of 2000-2015 monthly Clouds and the Earth's Radiant Energy System-Energy Balanced and Filled (CERES-EBAF) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) data reveals statistically significant fall and wintertime relationships between Arctic surface longwave (LW) radiative flux anomalies and the Arctic Oscillation (AO) and Arctic Dipole (AD). Signifying a substantial regional imprint, a negative AD index corresponds with positive downwelling clear-sky LW flux anomalies (greater than10W m(exp -2)) north of western Eurasia (0 deg E-120 deg E) and reduced sea ice growth in the Barents and Kara Seas in November-February. Conversely, a positive AO index coincides with negative clear-sky LW flux anomalies and minimal sea ice growth change in October-November across the Arctic. Increased (decreased) atmospheric temperature and water vapor coincide with the largest positive (negative) clear-sky flux anomalies. Positive surface LW cloud radiative effect anomalies also accompany the negative AD index in December-February. The results highlight a potential pathway by which Arctic atmospheric variability influences the regional surface radiation budget over areas of Arctic sea ice growth.

Hegyi, Bradley M.↗
NASA NTRS1998

RADNET: A Neural Network-based Estimation of the Surface Radiation Budget in the Arctic from TOVS Brightness Temperatures

This report summarizes the main accomplishments of the project. Specifics are provided in three journal papers which are enclosed with this report. Two of the journal articles are currently in press, one has already been published. Our work focused on two main areas: (1) RadNet. The main objective of the project was the development of a neural network-based method to compute downwelling shortwave and longwave fluxes directly from TOVS HIRS and MSU brightness temperatures. (2) FlaxNet. A second objective of the project involved the development of neural network-based method for the calculation of surface fluxes based on radiative transfer physics.

Schweiger, Axel↗
NASA NTRSDate not supplied

Quantifying Changes in the Arctic Shortwave Cloud Radiative Effects

The shortwave cloud radiative effect (SWCRE) is important on the Arctic surface radiation budget and the major source of inter-model spread in predictions of Arctic climate. To better understand the individual contributions of various radiative processes to changes in SWCRE, the paper presents the use of the extended APRP (Atmospheric Radiative Perturbation Potential) method. This involves adding the absorptivity for the upward beam and considering differences in reflectivity between upward and downward beams, as well as analyzing the cloud masking effect resulting from changes in surface albedo in more detail. Using data from the CMIP5 and CMIP6 climate models, the study decomposes the SWCRE over the Arctic surface and analyzes inter-model differences in quadrupled CO2 simulations. The study takes into account the fact that the response of SWCRE to Arctic warming is influenced by changes in surface albedo, cloud amount, and cloud microphysics. Results show that in the sunlight season, the reduction in surface albedo associated with sea ice loss is directly linked to strong negative SWCRE, which explains the considerable model discrepancy. Arctic clouds can hinder the positive surface albedo feedback by changing the albedo in two ways: (1) decreasing incoming shortwave radiation due to cloud reflection and (2) by decreasing the shortwave reaching the surface after being reflected by clouds. In addition, increased (decreased) cloud amount and cloud liquid water are shown to be less (more) incoming shortwave fluxes at the surface, but not dominating factors to the Arctic surface radiation budget and its inter-model variation. Overall, the extended APRP method offers a useful tool for analyzing the complex interactions between clouds and radiative process, reasonably decomposes the individual SWCRE responses at the Arctic surface, and emphasizes that considering not only the cloud amount or its properties, but also surface albedo change is critical for the prediction of SWCRE on the Arctic surface.

Arctic cloud↗
NASA NTRS2001

Arctic Stratus Cloud Properties and Their Effect on the Surface Radiation Budget: Selected Cases from FIRE ACE

To study Arctic stratus cloud properties and their effect on the surface radiation balance during the spring transition season, analyses are performed using data taken during three cloudy and two clear days in May 1998 as part of the First ISCCP Regional Experiment (FIRE) Arctic Cloud Experiment (ACE). Radiative transfer models are used in conjunction with surface- and satellite-based measurements to retrieve the layer-averaged microphysical and shortwave radiative properties. The surface-retrieved cloud properties in Cases 1 and 2 agree well with the in situ and satellite retrievals. Discrepancies in Case 3 are due to spatial mismatches between the aircraft and the surface measurements in a highly variable cloud field. Also, the vertical structure in the cloud layer is not fully characterized by the aircraft measurements. Satellite data are critical for understanding some of the observed discrepancies. The satellite-derived particle sizes agree well with the coincident surface retrievals and with the aircraft data when they were collocated. Optical depths derived from visible-channel data over snow backgrounds were overestimated in all three cases, suggesting that methods currently used in satellite cloud climatologies derive optical depths that are too large. Use of a near-infrared channel with a solar infrared channel to simultaneously derive optical depth and particle size appears to alleviate this overestimation problem. Further study of the optical depth retrieval is needed. The surface-based radiometer data reveal that the Arctic stratus clouds produce a net warming of 20 W m(exp -2) in the surface layer during the transition season suggesting that these clouds may accelerate the spring time melting of the ice pack. This surface warming contrasts with the net cooling at the top of the atmosphere (TOA) during the same period. All analysis of the complete FIRE ACE data sets will be valuable for understanding the role of clouds during the entire melting and refreezing process that occurs annually in the Arctic.

Doug, Xiquan↗
NASA NTRS2001

Improvements in AVHRR Daytime Cloud Detection Over the ARM NSA Site

Clouds play an important role in the radiation budget over Arctic and Antarctic. Because of limited surface observing capabilities, it is necessary to detect clouds over large areas using satellite imagery. At low and mid-latitudes, satellite-observed visible (VIS; 0.65 micrometers) and infrared (IR; 11 micrometers) radiance data are used to derive cloud fraction, temperature, and optical depth. However, the extreme variability in the VIS surface albedo makes the detection of clouds from satellite a difficult process in polar regions. The IR data often show that the surface is nearly the same temperature or even colder than clouds, further complicating cloud detection. Also, the boundary layer can have large areas of haze, thin fog, or diamond dust that are not seen in standard satellite imagery. Other spectral radiances measured by satellite imagers provide additional information that can be used to more accurately discriminate clouds from snow and ice. Most techniques currently use a fixed reflectance or temperature threshold to decide between clouds and clear snow. Using a subjective approach, Minnis et al. (2001) found that the clear snow radiance signatures vary as a function of viewing and illumination conditions as well as snow condition. To routinely process satellite imagery over polar regions with an automated algorithm, it is necessary to account for this angular variability and the change in the background reflectance as snow melts, vegetation grows over land, and melt ponds form on pack ice. This paper documents the initial satellite-based cloud product over the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site at Barrow for use by the modeling community. Cloud amount and height are determined subjectively using an adaptation of the methodology of Minnis et al. (2001) and the radiation fields arc determined following the methods of Doelling et al. (2001) as applied to data taken during the Surface Heat and Energy Budget of the Arctic (SHEBA). The procedures and data produced in this empirically based analysis will also facilitate the development of the automated algorithm for future processing of satellite data over the ARM NSA domain. Results are presented for May, June, and July 1998. ARM surface data are use to partially validate the results taken directly over the ARM site.

Chakrapani, V.↗
NASA NTRSDate not supplied

Towards a More Realistic Representation of Surface Albedo in NASA CERES Satellite Products: A Comparison with MOSAiC Field Campaign

Observing the Arctic from space is one of the most challenging tasks in climate science. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other scene type and comes from several sources. The one-year long MOSAiC expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes has been conducted using in-situ measurements from MOSAiC flux stations. The CERES SYN1deg product overestimates the SW_down flux by 11.40 Wm-2 and underestimates the SW_up flux by −15.70 Wm-2 and LW_down fluxes by −13.30 Wm-2 at the surface during summertime. In addition, large differences are found in the LW_up flux (~320 Wm-2) when the surface reaches melting point (~0℃). The large negative bias in upwelling shortwave flux can be attributed to the underestimation of surface albedo (−0.15) in SYN1deg. In addition to direct comparison, a series of perturbation experiments with a radiative transfer model are performed to estimate the contributions to the differences. By correcting both cloud and albedo inputs, the biases in SW_net flux and LW_net flux can be reduced to less than half of the control run biases to +19.90% and −10.53%, respectively. Furthermore, a compensating effect between underestimation of broadband albedo and overestimation of spectral albedo in visible and mid-infrared bands in SYN1deg datasets is found and contributes to the shortwave flux differences. The difference in CERES broadband albedo (~20 Wm 2) contributes to larger uncertainty in SW_up flux than spectral albedo shape (~3 Wm 2). The results of this study inform the future development of CERES products and will ultimately reduce uncertainties in Arctic surface radiation budget derived from satellite measurements.

Yiyi Huang↗
NASA NTRSDate not supplied

Isolating the Surface Type Influence on Arctic Cloud Properties

In response to anthropogenic climate change, substantial declines in sea ice have been observed. An urgent question is whether and how clouds respond to the changing Arctic surface. Due to the important radiative influence of clouds, the response of clouds to sea ice cover change constitutes a potentially important climate feedback within the Arctic. The sign of this feedback is strongly influenced by the seasonality of the cloud response to sea ice loss. Given the importance of this phenomenon, previous research focused on using observations of inter-annual variability to quantify the cloud-sea ice relationship. However, the reliance on inter-annual variability to assess the covariance between clouds and sea ice make it challenging to control for the influence of large-scale meteorology on the clouds. We have devised an approach that applies an in situ, airborne observational strategy to quantify the influence of surface type on Arctic clouds to satellite data. Our event-based method composites cloud property differences for ocean, marginal ice zone, and sea ice surface types for >5000 MIZ crossing events. We find that cloud properties in non-summer months below ~1.5 km are influenced by surface type such that greater cloud fraction and water content are found over ocean relative to sea ice regions. Results are statistically significantly at the 95% confidence level. During summer, the surface type does not influence the cloud properties. The primary cause of the surface type dependent cloud property differences is the thermodynamic profiles differences that occur between the two surface types: namely, ocean footprints are warmer, moister, have more positive surface turbulent fluxes and are less stable than their sea ice counterparts. We find that the differences in thermodynamic profiles by surface type that correspond to the cloud property differences also explains a significant portion of the variability across events. Our analysis provides evidence that the influence of surface type on cloud properties fundamentally occurs due to the inherent surface temperature differences between the surface types. Significant surface type influences on cloud properties are only found in the presence of surface temperature differences between the surface types and only non-summer months meet this criterion. A conceptual model of this process is as follows, fundamental properties of the ocean and sea ice surface types (e.g., surface albedo, heat capacity/thermal inertia, surface roughness, and thermal conductance) lead to systematic differences in the surface temperature by surface type. In response to this surface temperature difference, atmosphere boundary layer processes (including turbulent mixing and radiation) communicate the surface temperature differences to the lower atmosphere driving the greater stability over sea ice than ocean. These differences in the background thermodynamic state profiles between the surface types yield atmospheric conditions that are more conducive to cloud development over ocean than sea ice. The details of these boundary layer processes are sensitive to the surface type but may not be fundamental to the resulting thermodynamic structure and cloud differences. Thus, our results indicate that to accurately model that cloud response to sea ice loss in climate models the surface type properties that yield the systematic differences in ocean and sea ice temperature (e.g., surface albedo, surface turbulent fluxes parameters, and thermal conductance) must be accurately represented.

Arctic radiation budget↗
NASA NTRSDate not supplied

CERES Top-of-atmosphere and Surface fluxes in the Arctic: A comparison with ARISE and MOSAiC measurements

Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. This presentation summarizes results from comparisons of CERES data with the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic and the Arctic Radiation and Icebridge Sea ice Experiment (ARISE). The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by 11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0C). The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). The ARISE results indicate the some of these differences in the SW are due to the scene identification and representation of anisotropy in partly cloud scenes. Switching from imager-based to passive microwave-based sea ice data in the CERES inversion process reduces the differences in the grid box average fluxes and in the sea ice partly cloudy scene anisotropy in the instantaneously-matched footprints. Our analysis indicates that calibration and sampling uncertainty limit the ability to place strong constraints (<7%) on CERES TOA fluxes with aircraft measurements. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.

Patrick C Taylor↗
NASA NTRSDate not supplied

Arctic Radiation-Cloud-Aerosol-Surface Interaction eXperiment (ARCSIX)

The Arctic climate system is amidst a transition. Over the last 40 years, the Arctic sea ice pack has transformed from a predominantly thick, multi-year sea ice to a predominantly thin, seasonal sea ice, termed the “New Arctic”. The observed rapid changes in the Arctic sea ice pack are an integral part of the Arctic Amplification phenomenon and represent a response to and a feedback on global climate change. As a result, the role of the Arctic within the global climate system is changing. Substantial uncertainty exists in our understanding of the atmosphere-surface interactions within the Arctic system, limiting our knowledge of the Arctic’s role in the future climate. Advancing our understanding of the Arctic climate system requires (1) measurements of the coupling between radiative processes and sea ice surface properties during summer sea ice melt; (2) measurements of the processes controlling the predominant Arctic cloud regimes and their properties (Fig. 1); and (3) improvements in the ability to monitor Arctic cloud, radiation, and sea ice processes from space. A key challenge is that thin, low clouds that are radiatively important to the Arctic surface energy budget can go undetected (Fig. 1). The Arctic Radiation-Cloud-Aerosol-Surface-Interaction eXperiment (ARCSIX) is an airborne campaign based at the Pituffik Space Base in Greenland from May-August 2024 sponsored by the National Aeronautics and Space Administration (NASA) to address these needs. ARCSIX consists of two airborne measurement campaigns taking place in two 3-week intervals during the early and late sea ice melt season: late May through early June and late July through early August, respectively. ARCSIX science is guided by three broad science questions that encapsulate the key influences of radiation-cloud-aerosol-sea ice coupling and a remote sensing and modeling objective: Science Question 1 (Radiation): What is the impact of the predominant summer Arctic cloud types on the radiative surface energy budget? Science Question 2 (Cloud Life Cycle): What processes control the evolution and maintenance of the predominant cloud regimes in the summertime Arctic? Science Question 3 (Sea Ice): How do the two-way interactions between surface properties and atmospheric forcings affect the sea ice evolution? Remote Sensing and Modeling Objective: Enhance our long-term space-based monitoring and predictive capabilities of Arctic sea ice, clouds, and aerosols.

Patrick Taylor↗
NASA NTRS2000

Principal Component Analysis of Arctic Solar Irradiance Spectra

During the FIRE (First ISCPP Regional Experiment) Arctic Cloud Experiment and coincident SHEBA (Surface Heat Budget of the Arctic Ocean) campaign, detailed moderate resolution solar spectral measurements were made to study the radiative energy budget of the coupled Arctic Ocean - Atmosphere system. The NASA Ames Solar Spectral Flux Radiometers (SSFRs) were deployed on the NASA ER-2 and at the SHEBA ice camp. Using the SSFRs we acquired continuous solar spectral irradiance (380-2200 nm) throughout the atmospheric column. Principal Component Analysis (PCA) was used to characterize the several tens of thousands of retrieved SSFR spectra and to determine the number of independent pieces of information that exist in the visible to near-infrared solar irradiance spectra. It was found in both the upwelling and downwelling cases that almost 100% of the spectral information (irradiance retrieved from 1820 wavelength channels) was contained in the first six extracted principal components. The majority of the variability in the Arctic downwelling solar irradiance spectra was explained by a few fundamental components including infrared absorption, scattering, water vapor and ozone. PCA analysis of the SSFR upwelling Arctic irradiance spectra successfully separated surface ice and snow reflection from overlying cloud into distinct components.

Rabbette, Maura↗
NASA NTRS2002

Evaluation of Methods to Estimate the Surface Downwelling Longwave Flux during Arctic Winter

Surface longwave radiation fluxes dominate the energy budget of nighttime polar regions, yet little is known about the relative accuracy of existing satellite-based techniques to estimate this parameter. We compare eight methods to estimate the downwelling longwave radiation flux and to validate their performance with measurements from two field programs in thc Arctic: the Coordinated Eastern Arctic Experiment (CEAREX ) conducted in the Barents Sea during the autumn and winter of 1988, and the Lead Experiment performed in the Beaufort Sea in the spring of 1992. Five of the eight methods were developed for satellite-derived quantities, and three are simple parameterizations based on surface observations. All of the algorithms require information about cloud fraction, which is provided from the NASA-NOAA Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) polar pathfinder dataset (Path-P): some techniques ingest temperature and moisture profiles (also from Path-P): one-half of the methods assume that clouds are opaque and have a constant geometric thickness of 50 hPa, and three include no thickness information whatsoever. With a somewhat limited validation dataset, the following primary conclusions result: (1) all methods exhibit approximately the same correlations with measurements and rms differences, but the biases range from -34 W sq m (16% of the mean) to nearly 0; (2) the error analysis described here indicates that the assumption of a 50-hPa cloud thickness is too thin by a factor of 2 on average in polar nighttime conditions; (3) cloud-overlap techniques. which effectively increase mean cloud thickness, significantly improve the results; (4) simple Arctic-specific parameterizations performed poorly, probably because they were developed with surface-observed cloud fractions; and (5) the single algorithm that includes an estimate of cloud thickness exhibits the smallest differences from observations.

Chiacchio, Marc↗
NASA NTRS1983

Aerosol, radiation, and climate

Airborne, spaceborne, and ground-based measurements are used to study the radiative and climatic effects of aerosols. The data, which are modelled with a hierarchy of radiation and climate models, and their implications are summarized. Consideration is given to volcanic aerosols, polar stratospheric clouds, and the Arctic haze. It is shown that several types of aerosols (volcanic particles and the Arctic haze) cause significant alterations to the radiation budget of the regions where they are located.

Pollack, J. B.↗
NASA NTRSDate not supplied

The Surface Albedo of Sea Ice in CMIP6 and the Implications for the Surface Albedo Feedback

The Arctic has experienced rapid sea ice loss and a substantial decline in surface albedo, significantly impacting its radiation budget. Climate models from the Coupled Model Intercomparison Project (CMIP6) reproduce these changes. However, inconsistencies remain among models regarding the magnitude, spatial distribution, and seasonal patterns of Arctic surface albedo evolution. This study investigates these discrepancies by comparing model outputs with observation from the Clouds and the Earth's Radiant Energy System (CERES). We develop a decomposition method to assess the contributions of sea ice albedo, sea ice concentration, and sea ice extent to Arctic surface albedo. Over land, differences in snow cover account for the substantial inter-model spread in surface albedo, while over the ocean, sea ice albedo, concentration, and extent all contribute. Comparisons between CMIP6 and the Atmospheric Model Intercomparison Project (AMIP) simulations, which use identically prescribed sea ice concentrations, reveals considerable inter-model spread in Arctic Ocean surface albedo due to differences in sea ice albedo. Applying the decomposition method to projections shows that models predicting larger decreases in sea ice concentration and extent, especially in the Central Arctic, exhibit lower surface albedo and stronger sea ice albedo feedback. Beyond 2050, Arctic Ocean surface albedo decline is mainly influenced by sea ice extent indicating that the retreat of the ice edge is the most important process to constrain the surface albedo feedback. This study provides insights into factors contributing to the spread and changes in Arctic surface albedo and the associated sea ice albedo feedback.

Patrick C Taylor↗
NASA NTRS2002

Application of LES (Large Eddy Simulation) to Understanding and Parameterizing the Arctic Cloudy Boundary Layer

The GEWEX (Global Energy and Water Cycle Experiment) Cloud Systems Studies Working Group 5 Polar Clouds was formed largely to exploit the enormous potential of the FIRE.ACE (FIRE Arctic Cloud Experiment) and SHEBA (Surface Heat Budget of the Arctic Ocean) data sets to improve parameterizations of clouds, radiation, and atmospheric boundary layer processes in the polar regions. The web page for this project is a clearinghouse of information for polar clouds in general and specifically for FIRE.ACE and SHEBA modelling activities. Note that the GCSS Working Group and Polar Clouds leverages heavily the FIRE.ACE investment, since it involves a large group of international scientists and has adopted the modelling goals and other issues related to cloud and radiation processes that are articulated by the FIRE.ACE Science Plan. The supplemental funds in this proposal are targeted at providing the continued coordination for the GCSS Working Group on Polar Clouds, which will insure that final FIRE.ACE project objectives regarding modelling and cloud and radiative processes are achieved.

Curry, Judith↗
NASA NTRSDate not supplied

Role of Arctic Low Clouds in the Arctic Climate System: Isolating the Surface Type Influence on Arctic Low-Clouds

Interactions between sea ice and clouds represent a mechanism through which sea ice influences climate. Understanding the cloud response to the rapidly changing Arctic surface properties is necessary for improving climate simulations and projection of future change inside and outside the Arctic. Towards the goal of understanding the cloud response to sea ice, we composite cloud properties for ice-free, marginal ice zone (MIZ), and ice-covered surfaces during MIZ crossing events to analyze the cloud property differences between surface types. Restricting the analysis to MIZ crossing events enables the isolation of the sea ice effect on clouds from meteorological factors. We find larger cloud fraction and total water concentration below ~1.5 km over ice-free relative to ice-covered surfaces during non-summer months. During summer, the results suggest larger cloud fraction and total water concentration over ice-free surfaces, however differences do not exceed observational uncertainty. Cloud property differences are linked to atmospheric thermodynamic profile differences, namely ice-free surfaces are warmer, moister, less stable, and have more positive surface turbulent fluxes than ice-covered surfaces. Ice-free minus ice-covered cloud property differences scale with surface temperature differences and are only found in the presence of a surface temperature difference. Our results suggest a 0.02 cloud fraction and 0.005 g m 3 total water concentration increase (~5%) at the level of maximum cloud fraction between 2000-2021 due to the observed Arctic sea ice decline in fall, corresponding to ~2 Wm 2 increase in the net surface radiative flux supporting a positive sea ice-cloud radiative feedback in fall and winter and a negative sea ice-cloud radiative feedback in spring. We propose an updated conceptual model where the average surface type influence on cloud properties is mediated by surface temperature differences between ice-free and ice-covered surfaces.

Arctic climate↗