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Our understanding of sea ice and its role within Earth's climate system is underpinned by observation. Observations come in many forms, from qualitative records to quantitative data, and they have one key thing in common: they are made in situ. Direct measurements comprise most in situ observations; however, remote sensing technologies are also regularly used in situ to measure sea-ice physical properties. In this chapter, we provide an overview of in situ observations (including remote sensing) of sea ice from expeditions, drifting ice stations, autonomous platforms, and ongoing observation programs. We give a chronological account of sea-ice observations, highlighting the technological breakthroughs in sea-ice measurement techniques that have expanded observational capabilities. The chapter concludes with an outlook of future sea-ice observations and ways to bring observational and modeling efforts together to accelerate knowledge of the polar regions and Earth's climate. 

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) 

Snow on sea ice plays a critical role in the global climate system, affecting the growth and decay of the ice, polar ecosystems, human activities, and the retrieval of sea ice characteristics from remote sensing data. Its complex interactions with sea ice and the atmosphere pose challenges for accurately modeling these processes, especially as episodic warming and rainfall increase in polar regions. Coordinated field observations and advancements in remote sensing are essential to improving the representation of snow processes in climate models. Strengthening collaboration between observational, remote sensing, and modeling communities will enhance climate predictions and our understanding of snow–sea ice feedbacks in the Earth system. 

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 Atmospheric rivers (ARs) in winter can induce significant melting of sea ice as they approach the ice cover. However, due to the complex physical properties of sea ice, the specific processes within the ice pack that are responsible for its response to ARs remain poorly understood. This study aims to shed light on this question using a stand‐alone sea ice model forced by observed atmospheric boundary conditions. The findings reveal that the AR induced ice melt and hindered ice growth in the marginal seas are attributed to a combination of thermodynamic and dynamic processes. The AR‐wind transports ice floes from the marginal seas back to the central Arctic dynamically, resulting in a thickening of the ice cover in that region. Among the thermodynamic processes, reduced congelation growth (54%–56%), enhanced basal melting (17%–26%), and inhibited snow‐ice formation (11%–21%) play major roles in the sea ice loss in the marginal seas. 

Abstract SnowModel-LG reconstructs snow depth and density over sea ice, explicitly resolving important snow sinks like blowing snow sublimation, static surface sublimation and melt, but not snow-ice formation. To examine snow sinks on level sea ice, we coupled SnowModel-LG with HIGHTSI, a 1-D thermodynamic sea-ice model, to create SMLG_HS. SMLG_HS simulations of snow depth and level ice thickness were evaluated against high-resolution airborne observations from the western Arctic, highlighting the importance of snow mass redistribution processes, i.e. snow’s tendency to leave level ice and accumulate over deformed ice due to wind-induced redistribution. Not accounting for snow mass redistribution, SMLG_HS overestimates snow depth on level ice, resulting in underestimation of level ice thickness and overestimation of snow-ice thickness. Our case study shows that snow depth on level ice needs to be reduced by 40% to simulate both snow depth and level ice thickness realistically in the western Arctic in April 2017. An independent analysis of snow volume distribution between level and deformed sea ice using airborne radar observations supported the model results and revealed a linear relationship that enables estimating the amount of snow remaining on level ice at the end of winter based on the amount of ice deformation. 

Abstract. The melt of snow and sea ice during the Arctic summer is a significant source of relatively fresh meltwater. The fate of this freshwater, whether in surface melt ponds or thin layers underneath the ice and in leads, impacts atmosphere–ice–ocean interactions and their subsequent coupled evolution. Here, we combine analyses of datasets from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (June–July 2020) for a process study on the formation and fate of sea ice freshwater on ice floes in the Central Arctic. Our freshwater budget analyses suggest that a relatively high fraction (58 %) is derived from surface melt. Additionally, the contribution from stored precipitation (snowmelt) outweighs by 5 times the input from in situ summer precipitation (rain). The magnitude and rate of local meltwater production are remarkably similar to those observed on the prior Surface Heat Budget of the Arctic Ocean (SHEBA) campaign, where the cumulative summer freshwater production totaled around 1 m during both. A relatively small fraction (10 %) of freshwater from melt remains in ponds, which is higher on more deformed second-year ice (SYI) compared to first-year ice (FYI) later in the summer. Most meltwater drains laterally and vertically, with vertical drainage enabling storage of freshwater internally in the ice by freshening brine channels. In the upper ocean, freshwater can accumulate in transient meltwater layers on the order of 0.1 to 1 m thick in leads and under the ice. The presence of such layers substantially impacts the coupled system by reducing bottom melt and allowing false bottom growth; reducing heat, nutrient, and gas exchange; and influencing ecosystem productivity. Regardless, the majority fraction of freshwater from melt is inferred to be ultimately incorporated into the upper ocean (75 %) or stored internally in the ice (14 %). Terms such as the annual sea ice freshwater production and meltwater storage in ponds could be used in future work as diagnostics for global climate and process models. For example, the range of values from the CESM2 climate model roughly encapsulate the observed total freshwater production, while storage in melt ponds is underestimated by about 50 %, suggesting pond drainage terms as a key process for investigation. 

Abstract The conductive heat flux through the snow and ice is a critical component of the mass and energy budgets in the Arctic sea ice system. We use high horizontal resolution (3–15 cm) measurements of snow topography to explore the impacts of sub-meter-scale snow surface roughness on heat flux as simulated by the Finite Element method. Simulating horizontal heat flux in a variable snow cover modestly increases the total simulated heat flux. With horizontal heat flux, as opposed to simple 1D-vertical heat flux modeling, the simulated heat flux is 10% greater than that for uniform snow with the same mean snow thickness for a 31.5 × 21 m region of sea ice (the largest region we studied). Vertical-only (1D) heat flux simulates just a 6% increase for the same region. However, this is highly dependent on observation resolution. Had we measured the snow cover at 1 m horizontal spacing or greater, simulating horizontal heat flux would not have changed the net heat flux from that simulated with vertical-only heat flux. These findings suggest that measuring and modeling snow roughness at sub-meter horizontal scales may be necessary to accurately represent horizontal heat flux on level Arctic sea ice.