Abstract
Abstract. Satellite and airborne remote sensing provide complementary capabilities for the observation of the sea ice cover. However, due to the differences in footprint sizes and noise levels of the measurement techniques, as well as sea ice's variability across scales, it is challenging to carry out inter-comparison or consistently study these observations. In this study we focus on the remote sensing of sea ice thickness parameters and carry out the following: (1) the analysis of variability and its statistical scaling for typical parameters and (2) the consistency study between airborne and satellite measurements. By using collocating data between Operation IceBridge and CryoSat-2 (CS-2) in the Arctic, we show that consistency exists between the variability in radar freeboard estimations, although CryoSat-2 has higher noise levels. Specifically, we notice that the noise levels vary among different CryoSat-2 products, and for the European Space Agency (ESA) CryoSat-2 freeboard product the noise levels are at about 14 and 20 cm for first-year ice (FYI) and multi-year ice (MYI), respectively. On the other hand, for Operation IceBridge and NASA's Ice, Cloud, and land Elevation Satellite (ICESat), it is shown that the variability in snow (or total) freeboard is quantitatively comparable despite more than a 5-year time difference between the two datasets. Furthermore, by using Operation IceBridge data, we also find widespread negative covariance between ice freeboard and snow depth, which only manifests on small spatial scales (40 m for first-year ice and about 80 to 120 m for multi-year ice). This statistical relationship highlights that the snow cover reduces the overall topography of the ice cover. Besides this, there is prevalent positive covariability between snow depth and snow freeboard across a wide range of spatial scales. The variability and consistency analysis calls for more process-oriented observations and modeling activities to elucidate key processes governing snow–ice interaction and sea ice variability on various spatial scales. The statistical results can also be utilized in improving both radar and laser altimetry as well as the validation of sea ice and snow prognostic models.
Highlights
Sea ice and its snow cover are an integral component of the earth’s climate system
CS-2 shows overall much larger variability than Operation IceBridge (OIB): (1) in the original resolution for both OIB and CS-2, standard deviation (STDEV) of Fr is already larger in CS-2 than OIB, and (2) on the scale of 400 m (CS2 footprint size in the along-track direction), STDEV of Fr of OIB is lower than 1/3 of that of CS-2
By aligning OIB with the along-track footprint size of CS-2 at 400 m, we show that the difference of Fr variability (STDEV) between CS-2 and OIB is in the range of 20 to 40 cm
Summary
Sea ice and its snow cover are an integral component of the earth’s climate system. Basin-scale Arctic sea ice concentration observations have been available since 1978 with passive microwave satellite remote sensing (Cavalieri et al, 1999). During this period, the Arctic sea ice cover has undergone drastic changes, with record lows of September extent minimums as the most prominent feature. The thermodynamics of sea ice and the polar air–sea interaction are greatly modulated by the snow over the sea ice (Webster et al, 2018).
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