Search

The impact of a slightly modified broadband snow surface albedo parameterization, which explicitly considers the cloud dependence of the snow albedo, is evaluated in simulations of a coupled regional climate model of the Arctic. The cloud dependence of the snow albedo leads to a more realistic simulation of the variability of the surface albedo during the snowmelt period in late May and June. In particular, the reproduction of lower albedo values under cloud-free/broken-cloud conditions during the snowmelt period represents an improvement and results in an earlier disappearance of the snow cover and an earlier onset of sea-ice melt. In this way, the consideration of the cloud dependence of the snow albedo results in an amplification of the two-stage snow/ice albedo feedback in the model. This finds expression in considerably increased sea-ice melt during the summer months and ends up in a new quasi-stationary equilibrium in sea ice with statistically significant lower sea-ice volume and statistically significant lower summer sea-ice area. [ABSTRACT FROM AUTHOR]

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]

The impact of a revised snow surface albedo parameterization, which explicitly considers the cloud dependence of the snow albedo, is evaluated in simulations of a coupled regional climate model of the Arctic. The revised snow surface albedo parameterization leads to a more realistic simulation of the variability of the surface albedo during the snow melt period in late May and June. In particular, the reproduction of lower albedo values under cloud-free/broken-cloud conditions during the snow melt period represents a major improvement and results in an earlier disappearance of the snow cover and an earlier onset of sea-ice melt. In this way, the consideration of the cloud dependence of the snow albedo results in an amplification of the two-stage snow-albedo/ice-albedo feedback in the model. This finds expression in additional loss of sea-ice volume of more than 1000 km3 and additional reduction of summer sea-ice extent of around 250,000 km2 during one melting period, with accumulating magnitude of the overall changes in subsequent years. [ABSTRACT FROM AUTHOR]

The modeling of the atmospheric boundary layer over sea ice is still challenging because of the complex interaction between clouds, radiation and turbulence over the often inhomogeneous sea ice cover. There is still much uncertainty concerning sea ice roughness, near‐surface thermal stability and related processes, and their accurate parameterization. Here, a regional Arctic climate model forced by ERA‐Interim data was used to test the sensitivity of climate simulations to a modified surface flux parameterization for wintertime conditions over the Arctic. The reference parameterization as well as the modified one is based on Monin–Obukhov similarity theory, but different roughness lengths were prescribed and the stability dependence of the transfer coefficients for momentum, heat and moisture differed from each other. The modified parameterization accounts for the most comprehensive observations that are presently available over sea ice in the inner Arctic. Independent of the parameterization used, the model was able to reproduce the two observed dominant winter states with respect to cloud cover and longwave radiation. A stepwise use of the different parameterization assumptions showed that modifications of both surface roughness and stability dependence had a considerable impact on quantities such as air pressure, wind and near‐surface turbulent fluxes. However, the reduction of surface roughness to values agreeing with those observed during the Surface Heat Budget of the Arctic Ocean campaign led to an improvement in the western Arctic, while the modified stability parameterization had only a minor impact. The latter could be traced back to the model's underestimation of the strength of stability over sea ice. Future work should concentrate on possible reasons for this underestimation and on the question of generality of the results for other climate models. [ABSTRACT FROM AUTHOR]