Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns

Jäkel, Evelyn; Stapf, Johannes; Wendisch, Manfred; Nicolaus, Marcel; Dorn, Wolfgang; Rinke, Annette

doi:10.5194/tc-13-1695-2019

Cited by 27 publications

(37 citation statements)

References 46 publications

(75 reference statements)

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“…The overwhelming majority of the observed and modeled total (solar plus terrestrial) surface CRE values are positive over sea ice, which indicates that clouds have a warming effect on the surface (Figure 4a). This is consistent with the relatively high surface albedo values at the onset of the melting period during ACLOUD (Jäkel et al, 2019;, which decreases the cooling effect of clouds in the solar spectral range. Similar to the net surface irradiance, ICON overestimates the total surface CRE (Figure 4a), which is mainly caused by less cooling due to solar CRE (Figure 4b), while the modeled terrestrial CRE again matches the observed surface terrestrial CRE (Figure 4c).…”

Section: Surface Net Irradiances and Cloud Radiative Effect Over Sea supporting

confidence: 87%

Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic

Kretzschmar

Stapf

Klocke

et al. 2020

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Abstract. Clouds play a potentially important role in Arctic climate change, but are poorly represented in current atmospheric models across scales. To improve the representation of Arctic clouds in models, it is necessary to compare models to observations to consequently reduce this uncertainty. This study compares aircraft observations from the Arctic Cloud Observations Using Airborne Measurements during Polar Day (ACLOUD) campaign in May/June 2017 around Svalbard, Norway – to simulations using the ICON (ICOsahedral Non-hydrostatic) model in its numerical weather prediction (NWP) set-up at 1.2 km resolution. By comparing measurements of solar and terrestrial irradiances during ACLOUD flights to the respective properties in ICON, we showed that the model systematically overestimates the transmissivity of the mostly liquid clouds during the campaign. This model bias is traced back to the way cloud condensation nuclei (CCN) get activated into cloud droplets in the two-moment, bulk microphysical scheme used in this study. This process is parameterized as function of grid-scale vertical velocity in the microphysical scheme used, but in-cloud turbulence cannot sufficiently be resolved at 1.2 km horizontal resolution in Arctic clouds. By parameterizing subgrid-scale vertical motion as a function of turbulent kinetic energy, we are able to achieve a more realistic CCN activation into cloud droplets. Additionally, we showed that by scaling the presently used CCN activation profile, the hydrometeor number concentration could be modified to be in better agreement with ACLOUD observations in our revised CCN activation parameterization. This consequently results in an improved representation of cloud optical properties in our ICON simulations.

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Section: Surface Net Irradiances and Cloud Radiative Effect Over Sea supporting

confidence: 87%

Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic

Kretzschmar

Stapf

Klocke

et al. 2020

Preprint

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“…Interdisciplinary research conducted within the last decades has led to a broader, but not yet complete, understanding of the rapid and, compared to midlatitudes, enhanced warming in the Arctic (so-called Arctic amplification) (Gillett et al, 2008;Overland et al, 2011;Serreze and Barry, 2011;Stroeve et al, 2012;Jeffries et al, 2013;Cohen et al, 2014;Wendisch et al, 2017). Since the numerous interactions of physical processes, responsible for Arctic amplification, are intertwined and difficult to observe, climate models are needed to quantify the individual contributions of feedback processes to Arctic climate change (Screens and Simmonds, 2010;Pithan and Mauritsen, 2014).…”

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confidence: 99%

Reassessment of shortwave surface cloud radiative forcing in the Arctic: consideration of surface-albedo–cloud interactions

et al. 2020

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Abstract. The concept of cloud radiative forcing (CRF) is commonly applied to quantify the impact of clouds on the surface radiative energy budget (REB). In the Arctic, specific radiative interactions between microphysical and macrophysical properties of clouds and the surface strongly modify the warming or cooling effect of clouds, complicating the estimate of CRF obtained from observations or models. Clouds tend to increase the broadband surface albedo over snow or sea ice surfaces compared to cloud-free conditions. However, this effect is not adequately considered in the derivation of CRF in the Arctic so far. Therefore, we have quantified the effects caused by surface-albedo–cloud interactions over highly reflective snow or sea ice surfaces on the CRF using radiative transfer simulations and below-cloud airborne observations above the heterogeneous springtime marginal sea ice zone (MIZ) during the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign. The impact of a modified surface albedo in the presence of clouds, as compared to cloud-free conditions, and its dependence on cloud optical thickness is found to be relevant for the estimation of the shortwave CRF. A method is proposed to consider this surface albedo effect on CRF estimates by continuously retrieving the cloud-free surface albedo from observations under cloudy conditions, using an available snow and ice albedo parameterization. Using ACLOUD data reveals that the estimated average shortwave cooling by clouds almost doubles over snow- and ice-covered surfaces (−62 W m−2 instead of −32 W m−2), if surface-albedo–cloud interactions are considered. As a result, the observed total (shortwave plus longwave) CRF shifted from a warming effect to an almost neutral one. Concerning the seasonal cycle of the surface albedo, it is demonstrated that this effect enhances shortwave cooling in periods when snow dominates the surface and potentially weakens the cooling by optically thin clouds during the summertime melting season. These findings suggest that the surface-albedo–cloud interaction should be considered in global climate models and in long-term studies to obtain a realistic estimate of the shortwave CRF to quantify the role of clouds in Arctic amplification.

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“…High spatial resolution surface albedo (30 m) at the expense of reduced temporal resolution (16 d) are generated by Operational Land Imager (OLI), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) onboard the Landsat satellites (He et al, 2018b). A 34-year time series of black-sky surface albedo (SAL), as part of the second edition of the CLoud, Albedo, and surface RAdiation data set (CLARA-A2), has been derived from AVHRR measurements onboard NOAA and Metop (Meteorological Operational) satellites (Riihelä et al, 2013;Karlsson et al, 2017). Furthermore, surface albedo can be retrieved from collaborative observations of multiple platforms, e.g., GlobAlbedo (Muller et al, 2012;Lewis et al, 2013) and Global LAnd Surface Satellite (GLASS, Liu et al, 2013;Liang et al, 2013), or from radiation budget data sets, e.g., Clouds and the Earth's Radiant Energy System -Energy Balanced And Filled (CERES-EBAF, 2014;Loeb et al, 2018) and the International Satellite Cloud Climatology Project (ISCCP, Zhang et al, 1995.…”

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Broadband albedo of Arctic sea ice from MERIS optical data

et al. 2020

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Abstract. Arctic summer sea ice experiences rapid changes in its sea-ice concentration, surface albedo, and the melt pond fraction. This affects the energy balance of the region and demands an accurate knowledge of those surface characteristics in climate models. In this paper, the broadband albedo (300–3000 nm) of Arctic sea ice is derived from MEdium Resolution Imaging Spectrometer (MERIS) optical swath data by transforming the spectral albedo as an output from the Melt Pond Detector (MPD) algorithm with a newly developed spectral-to-broadband conversion (STBC). The new STBC replaces the previously applied spectral averaging method to provide a more accurate broadband albedo product, which approaches the accuracy of 0.02–0.05 required in climate simulations and allows a direct comparison to broadband albedo values from climate models. The STBC is derived empirically from spectral and broadband albedo measurements over landfast ice. It is validated on a variety of simultaneous spectral and broadband field measurements over Arctic sea ice, is compared to existing conversion techniques, and performs better than the currently published algorithms. The root-mean-square deviation (RMSD) between broadband albedo that was measured and converted by the STBC is 0.02. Other conversion techniques, the spectral averaging method and the linear combination of albedo values from four MERIS channels, result in higher RMSDs of 0.09 and 0.05, respectively. The improved MERIS-derived broadband albedo values are validated with airborne measurements. Results show a smaller RMSD of 0.04 for landfast ice than the RMSD of 0.07 for drifting ice. The MERIS-derived broadband albedo is compared to broadband albedo from ERA5 reanalysis to examine the albedo parameterization used in ERA5. Both albedo products agree over large scales and in temporal patterns. However, consistency in point-to-point comparison is rather poor, with differences up to 0.20, correlations between 0.69 and 0.79, and RMSDs in excess of 0.10. Differences in sea-ice concentration and cloud-masking uncertainties play a role, but most discrepancies can be attributed to climatological sea-ice albedo values used in ERA5. They are not adequate and need revising, in order to better simulate surface heat fluxes in the Arctic. The advantage of the resulting broadband albedo data set from MERIS over other published data sets is the accompanied data set of available melt pond fraction. Melt ponds are the main reason for the sea-ice albedo change in summer but are currently not represented in climate models and atmospheric reanalysis. Additional information about melt evolution, together with accurate albedo retrievals, can aid the challenging representation of sea-ice optical properties in those models in summer.

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Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns

Cited by 27 publications

References 46 publications

Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic

Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic

Reassessment of shortwave surface cloud radiative forcing in the Arctic: consideration of surface-albedo–cloud interactions

Broadband albedo of Arctic sea ice from MERIS optical data

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