An official website of the United States government
Melt ponds on sea ice play an important role in the Arctic climate system. Their presence alters the partitioning of solar radiation: decreasing reflection, increasing absorption and transmission to the ice and ocean, and enhancing melt. The spatiotemporal properties of melt ponds thus modify ice albedo feedbacks and the mass balance of Arctic sea ice. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition presented a valuable opportunity to investigate the seasonal evolution of melt ponds through a rich array of atmosphere-ice-ocean measurements across spatial and temporal scales. In this study, we characterize the seasonal behavior and variability in the snow, surface scattering layer, and melt ponds from spring melt to autumn freeze-up using in situ surveys and auxiliary observations. We compare the results to satellite retrievals and output from two models: the Community Earth System Model (CESM2) and the Marginal Ice Zone Modeling and Assimilation System (MIZMAS). During the melt season, the maximum pond coverage and depth were 21% and 22 ± 13 cm, respectively, with distribution and depth corresponding to surface roughness and ice thickness. Compared to observations, both models overestimate melt pond coverage in summer, with maximum values of approximately 41% (MIZMAS) and 51% (CESM2). This overestimation has important implications for accurately simulating albedo feedbacks. During the observed freeze-up, weather events, including rain on snow, caused high-frequency variability in snow depth, while pond coverage and depth remained relatively constant until continuous freezing ensued. Both models accurately simulate the abrupt cessation of melt ponds during freeze-up, but the dates of freeze-up differ. MIZMAS accurately simulates the observed date of freeze-up, while CESM2 simulates freeze-up one-to-two weeks earlier. This work demonstrates areas that warrant future observation-model synthesis for improving the representation of sea-ice processes and properties, which can aid accurate simulations of albedo feedbacks in a warming climate.
more »
« less
The Effects of Summer Snowfall on Arctic Sea Ice Radiative Forcing
Abstract Snow is the most reflective natural surface on Earth. Since fresh snow on bare sea ice increases the surface albedo, the impact of summer snow accumulation can have a negative radiative forcing effect, which would inhibit sea ice surface melt and potentially slow sea‐ice loss. However, it is not well known how often, where, and when summer snowfall events occur on Arctic sea ice. In this study, we used in situ and model snow depth data paired with surface albedo and atmospheric conditions from satellite retrievals to characterize summer snow accumulation on Arctic sea ice from 2003 to 2017. We found that, across the Arctic, ∼2 snow accumulation events occurred on initially snow‐free conditions each year. The average snow depth and albedo increases were ∼2 cm and 0.08, respectively. 16.5% of the snow accumulation events were optically thick (>3 cm deep) and lasted 2.9 days longer than the average snow accumulation event (3.4 days). Based on a simple, multiple scattering radiative transfer model, we estimated a −0.086 ± 0.020 W m−2change in the annual average top‐of‐the‐atmosphere radiative forcing for summer snowfall events in 2003–2017. The following work provides new information on the frequency, distribution, and duration of observed snow accumulation events over Arctic sea ice in summer. Such results may be particularly useful in understanding the impacts of ephemeral summer weather on surface albedo and their propagating effects on the radiative forcing over Arctic sea ice, as well as assessing climate model simulations of summer atmosphere‐ice processes.
more »
« less
- Award ID(s):
- 2325430
- PAR ID:
- 10558018
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Volume:
- 129
- Issue:
- 14
- ISSN:
- 2169-897X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract The ongoing Arctic warming has been pronounced in winter and has been associated with an increase in downward longwave radiation. While previous studies have demonstrated that poleward moisture flux into the Arctic strengthens downward longwave radiation, less attention has been given to the impact of the accompanying increase in snowfall. Here, utilizing state-of-the-art sea ice models, we show that typical winter snowfall (snow water equivalent) anomalies of around 1.0 cm, accompanied by positive downward longwave radiation anomalies of ∼5 W m −2 , can cause basinwide sea ice thinning by around 5 cm in the following spring over the Arctic seas in the Eurasian–Pacific seas. In extreme cases, this is followed by a shrinking of summer ice extent. In the winter of 2016/17, anomalously strong warm, moist air transport combined with ∼2.5-cm increase in snowfall (snow water equivalent) decreased spring ice thickness by ∼10 cm and decreased the following summer sea ice extent by 5%–30%. This study suggests that small changes in the pattern and volume of winter snowfall can strongly impact the sea ice thickness and extent in the following seasons.more » « less
-
Abstract. We assess the influence of snow on sea ice in experimentsusing the Community Earth System Model version 2 for a preindustrial and a2xCO2 climate state. In the preindustrial climate, we find that increasingsimulated snow accumulation on sea ice results in thicker sea ice and acooler climate in both hemispheres. The sea ice mass budget response differsfundamentally between the two hemispheres. In the Arctic, increasing snowresults in a decrease in both congelation sea ice growth and surface sea icemelt due to the snow's impact on conductive heat transfer and albedo,respectively. These factors dominate in regions of perennial ice but have asmaller influence in seasonal ice areas. Overall, the mass budget changeslead to a reduced amplitude in the annual cycle of ice thickness. In theAntarctic, with increasing snow, ice growth increases due to snow–iceformation and is balanced by larger basal ice melt, which primarily occursin regions of seasonal ice. In a warmer 2xCO2 climate, the Arctic sea icesensitivity to snow depth is small and reduced relative to that of thepreindustrial climate. In contrast, in the Antarctic, the sensitivity tosnow on sea ice in the 2xCO2 climate is qualitatively similar to thesensitivity in the preindustrial climate. These results underscore theimportance of accurately representing snow accumulation on sea ice incoupled Earth system models due to its impact on a number of competingprocesses and feedbacks that affect the melt and growth of sea ice.more » « less
-
null (Ed.)Abstract The sea ice-albedo feedback (SIAF) is the product of the ice sensitivity (IS), that is, how much the surface albedo in sea ice regions changes as the planet warms, and the radiative sensitivity (RS), that is, how much the top-of-atmosphere radiation changes as the surface albedo changes. We demonstrate that the RS calculated from radiative kernels in climate models is reproduced from calculations using the “approximate partial radiative perturbation” method that uses the climatological radiative fluxes at the top of the atmosphere and the assumption that the atmosphere is isotropic to shortwave radiation. This method facilitates the comparison of RS from satellite-based estimates of climatological radiative fluxes with RS estimates across a full suite of coupled climate models and, thus, allows model evaluation of a quantity important in characterizing the climate impact of sea ice concentration changes. The satellite-based RS is within the model range of RS that differs by a factor of 2 across climate models in both the Arctic and Southern Ocean. Observed trends in Arctic sea ice are used to estimate IS, which, in conjunction with the satellite-based RS, yields an SIAF of 0.16 ± 0.04 W m −2 K −1 . This Arctic SIAF estimate suggests a modest amplification of future global surface temperature change by approximately 14% relative to a climate system with no SIAF. We calculate the global albedo feedback in climate models using model-specific RS and IS and find a model mean feedback parameter of 0.37 W m −2 K −1 , which is 40% larger than the IPCC AR5 estimate based on using RS calculated from radiative kernel calculations in a single climate model.more » « less
-
This dataset contains the corresponding photos of the albedo data recorded on the sea ice surface June-September, 2020, during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition expedition in the Central Arctic Ocean. The corresponding measurements were made in three modes: (i) along ‘albedo lines’, between 60-200 meters (m) in length, with measurements every 5 meters (or 10 meters on leg 3), (ii) at specific ‘library sites,’ or (iii) ‘experiments’. Albedo lines were chosen with the aim of crossing representative surface conditions during the summer sea ice evolution, including snow-covered ridges, bare ice, and ponded ice. Included in the dataset are classification of the surface cover and depth for most measurements. This dataset is collocated with the spectral albedo dataset (doi.org/10.18739/A2FT8DK8Z) and broadband albedo dataset (doi.org/10.18739/A2KK94D36).more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2023JD040667
