Observations and modeling of areal surface albedo and surface types in the Arctic

• Published in: The Cryosphere
• Main Authors: Jäkel, Evelyn, Becker, Sebastian, Sperzel, Tim R., Niehaus, Hannah, Spreen, Gunnar, Tao, Ran, Nicolaus, Marcel, Dorn, Wolfgang, Rinke, Annette, Brauchle, Jörg, Wendisch, Manfred
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2024
• Subjects: article; Verlagsveröffentlichung; Arctic; Arctic Ocean; albedo; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-18-1185-2024; https://noa.gwlb.de/receive/cop_mods_00072261; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00070483/tc-18-1185-2024.pdf; https://tc.copernicus.org/articles/18/1185/2024/tc-18-1185-2024.pdf

An accurate representation of the annual evolution of surface albedo of the Arctic Ocean, especially during the melting period, is crucial to obtain reliable climate model predictions in the Arctic. Therefore, the output of the surface albedo scheme of a coupled regional climate model (HIRHAM–NAOSIM) was evaluated against airborne and ground-based measurements. The observations were conducted during five aircraft campaigns in the European Arctic at different times of the year between 2017 and 2022; one of them was part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2020. We applied two approaches for the evaluation: (a) relying on measured input parameters of surface type fraction and surface skin temperature (offline) and (b) using HIRHAM–NAOSIM simulations independently of observational data (online). From the offline method we found a seasonally dependent bias between measured and modeled surface albedo. In spring, the cloud effect on surface broadband albedo was overestimated by the surface albedo parametrization (mean albedo bias of 0.06), while the surface albedo scheme for cloudless cases reproduced the measured surface albedo distributions for all seasons. The online evaluation revealed an overestimation of the modeled surface albedo resulting from an overestimation of the modeled cloud cover. Furthermore, it was shown that the surface type parametrization contributes significantly to the bias in albedo, especially in summer (after the drainage of melt ponds) and autumn (onset of refreezing). The lack of an adequate model representation of the surface scattering layer, which usually forms on bare ice in summer, contributed to the underestimation of surface albedo during that period. The difference between modeled and measured net irradiances for selected flights during the five airborne campaigns was derived to estimate the impact of the model bias for the solar radiative energy budget at the surface. We revealed a negative bias between modeled and ...