Satellite-based evidence of regional and seasonal Arctic cooling by brighter and wetter clouds

Lelli, Luca; Vountas, Marco; Khosravi, Narges; Burrows, John P.

doi:10.5194/acp-2022-28

Cited by 1 publication

(1 citation statement)

References 74 publications

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“…Cloud properties, measured at the ground-based sites, which are sparse, can be complemented by satellite retrievals to improve the coverage of observations. Improved knowledge of the spatial and temporal variation of clouds micro-physical (e.g., Cloud Optical Depth (COD), Liquid Water Path (LWP)) and macro-physical properties (e.g., Cloud Fraction (CF), Cloud Top height (CTH)) in the Arctic improve our understanding of cloud processes and radiation balance (e.g., He et al (2019); Devasthale et al (2020); Lelli et al (2022)). Cloud data products from satellite remote sensing provide daily global coverage and high spatial resolution over the Arctic.…”

Section: Introductionmentioning

confidence: 99%

Validation of the Cloud_CCI cloud products in the Arctic

Vinjamuri

Vountas

Lelli

et al. 2023

Preprint

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Abstract. The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions, and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency’s (ESA) Cloud Climate Change Initiative (Cloud_CCI) project, which uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84° N, 24.29° E), North Slope of Alaska (NSA, 71.32° N, 156.61° W), Ny-Ålesund (Ny-Å, 78.92° N, 11.93° E), and Summit (72.59° N, 38.42° W). The Liquid Water Path (LWP) ground-based data are retrieved from microwave radiometers, while the Cloud Top Height (CTH) has been determined from the integrated lidar-radar measurements. The quality of the satellite products, Cloud Mask and Cloud Optical Depth (COD), have been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit. The Cloud_CCI COD results for liquid water clouds are in better agreement with the NSA radiometer data than those for ice clouds. For liquid water clouds, the Cloud_CCI COD is underestimated roughly by 2.8 Optical Depth (OD) units. When ice clouds are included, the underestimation increases to about 4.6 OD units. The Cloud_CCI LWP is overestimated over Hyytiälä by ≈ 7 gm−2, over NSA by ≈ 16 gm−2, and over Ny-Å by ≈ 24 gm−2. Over Summit, CCI LWP is overestimated for values ≤ 20 gm−2 and underestimated for values > 20 gm−2. Overall the results of the CCI LWP retrievals are within the ground-based instrument uncertainties. For CTH retrievals, the Cloud_CCI product overestimates single-layer clouds. To understand the effects of multi layer clouds on the CTH retrievals, the statistics are compared between the single layer clouds and all types (single + multi layer). When the multi layer clouds are included (i.e., all types), the observed CTH overestimation become underestimations of about 360–420 m. The CTH results over Summit station showed the highest biases compared to the other three sites. To understand the scale-dependent differences between the satellite and ground-based data, the Bland-Altman method is applied. This method does not identify any scale-dependent differences for all the selected cloud parameters except for the retrievals over the Summit station. In summary, the Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements, made at the four high-latitude sites.

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Section: Introductionmentioning

confidence: 99%