Abstract
Abstract. Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.
Highlights
From a perspective of climate change, it is important to know how fast the total volume of sea ice is changing
We have focused in particular on achieving low sensitivity to the error sources over ice and open water, performance in areas covered by melt ponds in summer and thin ice in autumn
In order to perform a fair comparison of the algorithms, we developed a special set of tie points (Appendix A) based on the round robin data package (RRDP) for both hemispheres and for each of the three radiometers: AMSR-E, Sensor Microwave Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR)
Summary
From a perspective of climate change, it is important to know how fast the total volume of sea ice is changing. Consistency in sea ice climate records is crucial for understanding of internal variability and external forcing Accuracy (expressed by bias) is the difference between the mean retrieval and the true value. Precision (expressed by standard deviation, SD) is the range within which repeated retrievals of the same quantity scatter around the mean value (see Brucker et al, 2014, where precision is addressed in detail). The average accuracy of commonly known algorithms, such as NASA Team (Cavalieri et al, 1984) and Bootstrap (Comiso, 1986), is reported to be within ± 5 % in winter in a compact (high concentration) ice pack.
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