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This dataset contains the partitioning of the surface downwelling radiative fluxes by the Arctic sea ice by reflection, absorption, and transmission. The dataset covers the period from 1984 to 2022 and was provided on 25 km resolution on the NSIDC EASE2 grid. # Brighter Ocean: Arctic sea ice solar partitioning Dataset DOI: [10.5061/dryad.n02v6wxb5](https://doi.org/10.5061/dryad.n02v6wxb5) ## Description of the data and file structure Our focus is on the large-scale partitioning of the incident solar radiation between reflection to the atmosphere (albedo, *a*), absorption in the ice (absorptance, *A*) and transmission to the ocean (transmittance, *T*). These parameters were determined every day from 1984 to 2024 at every point on the Equal Area Scalable Earth 2.0 (EASE2) grid, within the sea ice extent boundary. The basis for the analysis relies on several satellite and model products. These include geophysical variables of shortwave radiation, surface melt and freeze onset dates, as well as sea ice concentration, age, and thickness. Sea ice albedo is estimated using a multiphase albedo evolution determined for first year ice (Perovich and Polashenski, 2012) and multiyear ice (Perovich et al., 2002; Light et al., 2022). Distinct albedo evolutions for first-year and multiyear sea ice are computed using Version 4 of the EASE-grid Sea Ice Age product by Tschudi et al. (2019). The dates of melt and freeze-up book-end the Arctic melt season and govern the seasonal evolution of surface albedo of sea ice. For the onset dates of continuous melt and continuous freeze, we use passive microwave retrievals at 25-km resolution produced by Markus et al. (2009). The ice concentration is a key parameter in determining the solar partitioning. We use passive microwave retrievals from the NASA Bootstrap algorithm (Comiso, 1986), which are available in the NOAA/NSIDC Climate Data Record (Meier et al., 2021). The product is provided at daily 25-km gridded resolutions. In some instances, temporal gaps in the data exist due to inconsistent satellite coverage. Transmittance through the ice is defined by an exponential decay law of the form and corrected for the near infrared light absorbed by the snow and ice and not transmitted. The correction ratio is estimated to vary from about 0.55 for clear skies to 0.62 for complete cloud cover. An average value of 0.58 is used in this study. The ice extinction coefficient for visible light. Based on field observations (Light et al., 2008, 2015), we use 1.0 m-1 for an ice cover dominated by bare ice (0.5 < *a~i~* < 0.7), 3.0 m-1 for a cover dominated by snow (*a~i~* > 0.7), and 0.7 m-1 for an ice cover dominated by melt ponds (*a~i~* < 0.5). *S*ea ice thickness and is determined using the simulated thickness from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) (Zhang and Rothrock, 2003). ### Files and variables #### File: BO_EASE2_gridded_25km_YYYY.nc **Description:** for year YYYY. **Variables:**  dimensions: X = 263, Y = 263, time=365 or 366. insol (time,Y,X): surface downwelling solar radiative flux sic (time,Y,X): sea ice concentration sith (time,Y,X): sea ice thickness iceage (time,Y,X): sea ice age emelt, efreeze, fmelt, ffreeze (Y,X): Early ('e')/full ('f') melt/freeze onset date, in day-of-year. alb_yyy (time,Y,X): ice albedo using albedo scheme yyy. The albedo scheme include MYI (using multi-year ice scheme), FYI (using first-year ice scheme), and AGE (combined multi-year and first-year albedo depending on ice age). h_xxx_yyy (time,Y,X): heat flux on xxx surface type using albedo scheme yyy, with unit of W/m^2^. The xxx surface types include nIC (assuming fully ice covered), ice (ice portion of the gridbox), ocn (open water portion of the gridbox). The yyy albedo scheme include MYI (using multi-year ice scheme), FYI (using first-year ice scheme), and AGE (combined multi-year and first-year albedo depending on ice age). An exemption is h_ocn_SIC, the heat input to the open water portion of the gridbox using the sea ice concentration. accu_h_xxx_yyy (time,Y,X): the accumulated heat input into the gridbox, with unit of MJ/m^2^. hthr_ice_xxx_KAPPA0 (time,Y,X): the heat through ice portion of the gridbox, using albedo scheme xxx and constant ice extinction coefficient kappa of 1, with unit of W/m^2^. hthr_ice_xxx_KAPPA_ALB (time,Y,X): the heat through ice portion of the gridbox, using albedo scheme xxx and ice extinction coefficient kappa depending on ice albedo value, with unit of W/m^2^. accu_hthr_ice_xxx_KAPPA0 (time,Y,X): the accumulated heat through ice portion of the gridbox, using albedo scheme xxx and constant ice extinction coefficient kappa of 1, with unit of MJ/m^2^. accu_hthr_ice_xxx_KAPPA_ALB (time,Y,X): the accumulated heat through ice portion of the gridbox, using albedo scheme xxx and ice extinction coefficient kappa depending on ice albedo value, with unit of MJ/m^2^. ## Code/software The software for processing and analyze this dataset is published on Zenodo: [10.5281/zenodo.17675721](https://doi.org/10.5281/zenodo.17675721) The developmental package is hosted o GitHub: [https://github.com/liuzheng-arctic/BrighterOcean](https://github.com/liuzheng-arctic/BrighterOcean) 

Chapter 3 in Sea Ice: Its Physics, Chemistry, Biology, Geology and Societal Importance 

Abstract Solar radiation is the key energy input to the ocean. In the Arctic Ocean and its peripheral seas, the distribution of solar radiation is strongly modulated by the presence of sea ice. In this study, we combined satellite and model products to investigate solar radiation partitioning between reflection to the atmosphere, absorption in the ice, and transmission to the ocean over 1984–2024. We present total annual solar heat partitioning, relative contributions to energy deposition from ice and open water, and trends in large‐scale partitioning. The Arctic exhibited a decreasing trend in albedo (0.019 decade⁻¹) due to decreasing sea ice areal coverage and thickness. Consequently, solar transmittance into the ocean increased by 0.031 decade⁻¹, resulting in an additional ∼300 MJ m⁻²of heat input over 1984–2024. A brighter, warmer ocean contributes to Arctic Amplification and may alter the functioning of the Arctic marine ecosystem. 

Abstract. Identifying the processes that drive the rapid climatological retreat phase of Antarctica’s annual sea-ice cycle is crucial to understanding, modelling and attributing observed trends and recent high variability in sea-ice extent, and to projecting future sea-ice conditions and impacts accurately. To date, the rapid annual retreat of Antarctic sea ice each spring–summer has been largely attributed to lateral and basal melting of ice floes, enhanced by wave-induced breakup of large floes. Here, based on observations and modelling, we propose that waves play important additional roles in generating previously-neglected surface and interior melting, by removing snow from small floes, flooding them, and pulverising them into slush. Results here show a resultant estimated reduction in albedo by 0.38–0.54, that increases melting by 0.9–5.2 cm day-1 at 60–70o S compared to a snow-covered floe of first-year ice, and depending on surface type, wave-flooding coverage, latitude and ice density. Rapid proliferation of algae within and on the high-light and high-nutrient environment of the wave-modified ice reduces the albedo by a further 0.1 to increase the melt-rate enhancement to 1.1–6.1 cm day-1. Melting is further accelerated by a wave-induced ice–albedo feedback mechanism, similar to that associated with Arctic melt ponds but involving seawater rather than freshwater. This positive feedback is strengthened by ice-algal greening. Floe thinning and weakening by wave-melting initiate additional dynamic–thermodynamic feedbacks by increasing the likelihood of both wave-flooding and flexural breakup, leading to further floe melting. Wave melting and the associated physical–biological feedbacks will likely increase in importance in a predicted stormier and warmer Southern Ocean, and will also become more prevalent in a changing Arctic. There are implications for global weather and climate, the health of the ocean and its ecosystems, fisheries, ice-shelf stability and sea-level rise, atmospheric and oceanic biogeochemistry, and human activities. 

Light transmission through a sea ice cover has strong implications for the heat content of the upper ocean, the magnitude of bottom and lateral ice melt, and primary productivity in the ocean. Light transmittance in the vicinity of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) Central Observatory was estimated by driving a two-stream radiative transfer model with physical property observations. Data include point and transect observations of snow depth, surface scattering layer thickness, ice thickness, and pond depth. The temporal evolution of light transmittance at specific sites and the spatial variability along transect lines were computed. Ponds transmitted 4–6 times as much solar energy per unit area as bare ice. On July 25, ponds covered about 18% of the area and contributed roughly 50% of the sunlight transmitted through the ice cover. Approximating the transmittance along a transect line using average values for the physical properties will always result in lower light transmittance than finding the average light transmittance using the full distribution of points. Transmitted solar energy calculated using the standard five ice thickness categories and three surface types used in the Los Alamos sea ice model CICE, the sea ice component of many weather and climate models, was only about 1 W m−2 less than using all the points along the transect. This minor difference suggests that the important processes and resulting feedbacks relating to solar transmittance can be represented in models that use five or more categories of ice thickness distributions. 

Abstract. The melting of sea ice floes from the edges (lateral melting) results in open-water formation and subsequently increases absorption of solar shortwave energy. However, lateral melt plays a small role in the sea ice mass budget in both hemispheres in most climate models. This is likely influenced by the simple parameterization of lateral melting in sea ice models that are constrained by limited observations. Here we use a coupled climate model (CESM2.0) to assess the sensitivity of modeled sea ice state to the lateral melt parameterization in preindustrial and 2×CO2 runs. The runs explore the implications of how lateral melting is parameterized and structural changes in how it is applied. The results show that sea ice is sensitive both to the parameters determining the effective lateral melt rate and the nuances in how lateral melting is applied to the ice pack. Increasing the lateral melt rate is largely compensated for by decreases in the basal melt rate but still results in a significant decrease in sea ice concentration and thickness, particularly in the marginal ice zone. Our analysis suggests that this is tied to the increased efficiency of lateral melting at forming open water during the summer melt season, which drives the majority of the ice–albedo feedback. The more seasonal Southern Hemisphere ice cover undergoes larger relative reductions in sea ice concentration and thickness for the same relative increase in lateral melt rate, likely due to the hemispheric differences in the role of the sea-ice–upper-ocean coupling.Additionally, increasing the lateral melt rate under a 2×CO2 forcing, where sea ice is thinner, results in a smaller relative change in sea ice mean state but suggests that open-water-formation feedbacks are likely to steepen the decline to ice-free summer conditions.Overall, melt processes are more efficient at forming open water in thinner ice scenarios (as we are likely to see in the future), suggesting the importance of accurately representing thermodynamic evolution. Revisiting model parameterizations of lateral melting with observations will require finding new ways to represent salient physical processes. 

The microstructure of the uppermost portions of a melting Arctic sea ice cover has a disproportionately large influence on how incident sunlight is reflected and absorbed in the ice/ocean system. The surface scattering layer (SSL) effectively backscatters solar radiation and keeps the surface albedo of melting ice relatively high compared to ice with the SSL manually removed. Measurements of albedo provide information on how incoming shortwave radiation is partitioned by the SSL and have been pivotal to improving climate model parameterizations. However, the relationship between the physical and optical properties of the SSL is still poorly constrained. Until now, radiative transfer models have been the only way to infer the microstructure of the SSL. During the MOSAiC expedition of 2019–2020, we took samples and, for the first time, directly measured the microstructure of the SSL on bare sea ice using X-ray micro-computed tomography. We show that the SSL has a highly anisotropic, coarse, and porous structure, with a small optical diameter and density at the surface, increasing with depth. As the melting surface ablates, the SSL regenerates, maintaining some aspects of its microstructure throughout the melt season. We used the microstructure measurements with a radiative transfer model to improve our understanding of the relationship between physical properties and optical properties at 850 nm wavelength. When the microstructure is used as model input, we see a 10–15% overestimation of the reflectance at 850 nm. This comparison suggests that either a) spatial variability at the meter scale is important for the two in situ optical measurements and therefore a larger sample size is needed to represent the microstructure or b) future work should investigate either i) using a ray-tracing approach instead of explicitly solving the radiative transfer equation or ii) using a more appropriate radiative transfer model.