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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 measured net irradiances (median: -6.4 W m -2) for optically thin clouds, while the median value of only 0.1 W m -2 was determined for optically thicker clouds. [ABSTRACT FROM AUTHOR]

An accurate representation of the annual evolution of surface albedo, especially during the melting period, is crucial to obtain reliable climate model predictions. Therefore, the output of the surface albedo scheme of the coupled regional climate model HIRHAM–NAOSIM was evaluated against airborne and ground-based measurements. The observations were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2020 and during five aircraft campaigns in the European Arctic at different seasons between 2017 and 2022. We applied two approaches to the comparison, one relying on measured input parameters of surface type fraction and surface skin temperature (offline evaluation), the other using HIRHAM-NAOSIM simulations independently of our observational data (online evaluation). From the offline evaluation we found a seasonal-dependent bias between measured and modeled surface albedo for cloudless and cloudy situations. 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 showed that the overestimation of the modeled surface albedo may result from the overestimation of the modeled cloud cover. It was further shown that the surface type parametrization contributes significantly to the bias in albedo, especially in summer (drainage of melt ponds) and autumn (onset of refreezing). The difference of modeled and measured net irradiance for selected flights during the five flight campaigns was derived to estimate the impact of the model bias for the solar radiative energy budget. We revealed a negative bias between modeled and measured net irradiance (bias median: -6.4 W m−2) for optically thin clouds, while the median value of only 0.1 W m−2 was determined for optically thicker clouds. [ABSTRACT FROM AUTHOR]