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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) 

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. 

Let $E/\mathbf{Q}$be an elliptic curve and let $p$be an odd prime of good reduction for $E$. Let $K$be an imaginary quadratic field satisfying the classical Heegner hypothesis and in which $p$splits. The goal of this paper is two-fold: (1) we formulate a $p$-adic BSD conjecture for the $p$-adic $L$-function $L_{\mathfrak{p}}^{\mathrm{B}\mathrm{D}\mathrm{P}}$introduced by Bertolini–Darmon–Prasanna [Duke Math. J. 162 (2013), pp. 1033–1148]; and (2) for an algebraic analogue $F_{\stackrel{¯<\#comment/>}{\mathfrak{p}}}^{\mathrm{B}\mathrm{D}\mathrm{P}}$of $L_{\mathfrak{p}}^{\mathrm{B}\mathrm{D}\mathrm{P}}$, we show that the “leading coefficient” part of our conjecture holds, and that the “order of vanishing” part follows from the expected “maximal non-degeneracy” of an anticyclotomic $p$-adic height. In particular, when the Iwasawa–Greenberg Main Conjecture $\left(F_{\stackrel{¯<\#comment/>}{\mathfrak{p}}}^{\mathrm{B}\mathrm{D}\mathrm{P}}\right)=\left(L_{\mathfrak{p}}^{\mathrm{B}\mathrm{D}\mathrm{P}}\right)$is known, our results determine the leading coefficient of $L_{\mathfrak{p}}^{\mathrm{B}\mathrm{D}\mathrm{P}}$at $T=0$up to a $p$-adic unit. Moreover, by adapting the approach of Burungale–Castella–Kim [Algebra Number Theory 15 (2021), pp. 1627–1653], we prove the main conjecture for supersingular primes $p$under mild hypotheses. In the $p$-ordinary case, and under some additional hypotheses, similar results were obtained by Agboola–Castella [J. Théor. Nombres Bordeaux 33 (2021), pp 629–658], but our method is new and completely independent from theirs, and apply to all good primes. 

ABSTRACT As modern agriculture faces increasing demands for efficiency and automation, this study presents a novel, untethered soft gripper system designed for autonomous and efficient harvesting. At the core of this innovation is a piston‐driven, pneumatically actuated gripper embedded with flexible tactile sensors, enabling operation without an external air source. The system integrates a compact motorized syringe, forming a closed‐loop fluid circuit that provides precise pressure control for adaptive grasping. The pneumatic actuation mechanism regulates air pressure from −30 to 180 kPa, allowing the gripper to perform delicate and adaptive handling, particularly suited for tree fruits and other fragile crops. A key feature of the system is its intelligent control mechanism, which seamlessly combines pneumatic and electrical systems to enhance autonomy and versatility in agricultural applications. The integration of size recognition and adaptive grasping, enabled by force feedback from embedded tactile sensors, ensures safe, efficient, and damage‐free harvesting. Demonstrating exceptional potential for autonomous agricultural operations, the untethered soft gripper system offers enhanced independence, maneuverability, and adaptability across diverse harvesting environments. Its ability to optimize crop handling while minimizing damage highlights its significance as a pioneering solution for the future of automated agriculture.