The advanced scatterometer (ASCAT) is a radar system carried on board the ESA/EUMETSAT METOP series of satellites. It is designed for the purpose of r…The advanced scatterometer (ASCAT) is a radar system carried on board the ESA/EUMETSAT METOP series of satellites. It is designed for the purpose of retrieving wind field over oceans. It also provides information on surface soil moisture content and sea ice. Although ASCAT uses a linear frequency modulated pulse with a center frequency of 5.255 GHz (C-band), it is subject to radio frequency interference (RFI). This paper analyses seven years of ASCAT data and shows an increase of the number of noise outliers and an increase of the noise background level over specific land areas. This suggests that the outliers are not a natural occurrence, but are due to RFI from ground-based equipments. As regards the observed increase of the noise background level, it is not straightforward to associate possible RFI sources which could have caused it. However, since the ASCAT has a dynamic range of about 30 dB, the worse measured increase of 1 dB in the noise floor has almost no impact on performance, in particular, on soil moisture retrieval. In addition, the effect of the noise outliers on the estimate of the ASCAT receiver filter shape function used in the processing is also examined and is found to introduce errors of up to 0.4 dB. However, the occurrence of the noise outliers is generally very low, typically two out of 60 000 noise measurements per day, so the impact on the operational use of ASCAT data for wind vector retrieval is limited.more
Satellite-based cloud, radiation flux, and sea ice records covering 34 years are used 1) to investigate autumn cloud cover trends over the Arctic, 2) …Satellite-based cloud, radiation flux, and sea ice records covering 34 years are used 1) to investigate autumn cloud cover trends over the Arctic, 2) to assess its relation with declining sea ice using Granger causality (GC) analysis, and 3) to discuss the contribution of the cloud–sea ice (CSI) feedback to Arctic amplification. This paper provides strong evidence for a positive CSI feedback with the capability to contribute to autumnal Arctic amplification. Positive low-level cloud fractional cover (CFClow) trends over the Arctic ice pack are found in October and November (ON) with magnitudes of up to about 19.6% per decade locally. Statistically significant anticorrelations between sea ice concentration (SIC) and CFClow are observed in ON over melting zones, suggesting an association. The GC analysis indicated a causal two-way interaction between SIC and CFClow. Interpreting the resulting F statistic and its spatial distribution as a relation strength proxy, the influence of SIC on CFClow is likely stronger than the reverse. ERA-Interim reanalysis data suggest that ON CFClow is impacted by sea ice melt through surface–atmosphere coupling via turbulent heat and moisture fluxes. Due to weak solar insolation in ON, net cloud radiative forcing (CRF) exerts a warming effect on the Arctic surface. Increasing CFClow induces a large-scale surface warming trend reaching magnitudes of up to about 18.3 W m22 per decade locally. Sensitivities of total CRF to CFClow ranges between 10.22 and 10.66 W m22 per percent CFClow. Increasing surface warming can cause a melt season lengthening and hinders formation of perennial ice. Ó 2020 American Meteorological Society.more
With the accelerating impact of global warming, the changes of Arctic sea ice has become a focal point of research. Due to the spatial heterogeneity a…With the accelerating impact of global warming, the changes of Arctic sea ice has become a focal point of research. Due to the spatial heterogeneity and the complexity of its evolution, long-term prediction of Arctic sea ice remains a challenge. In this article, a spatial attention U-Net (SAU-Net) method integrated with a gated spatial attention mechanism is proposed. Extracting and enhancing the spatial features from the historical atmospheric and SIC data, this method improves the accuracy of Arctic sea ice prediction. During the test periods (2018-2020), our method can skillfully predict the Arctic sea ice up to 12 months, outperforming the naive U-Net, linear trend models, and dynamical models, especially in extreme sea ice scenarios. The importance of different atmospheric factors affecting sea ice prediction are also analyzed for further exploration.more
Within Chapter 2, changes are assessed from in situ and remotely sensed data and products and from indirect evidence of longer-term changes based upon…Within Chapter 2, changes are assessed from in situ and remotely sensed data and products and from indirect evidence of longer-term changes based upon a diverse range of climate proxies. The time-evolving availability of observations and proxy information dictate the periods that can be assessed. Wherever possible, recent changes are assessed for their significance in a longer-term context, including target proxy periods, both in terms of mean state and rates of changemore