Sea ice Concentration as an Essential Climate Variable
Pedersen, Leif Toudal1; Ivanova, Natalia2; Saldo, Roberto3; Heygster, Georg4; Tonboe, Rasmus1; Lavergne, Thomas5; Mäkynen, Marko6; Kern, Stefan7
1Danish Meteorological Institute, DENMARK; 2Nansen Environmental and Remote Sensing Center, NORWAY; 3DTU-Space, DENMARK; 4Institute of Environmental Physics, University of Bremen, GERMANY; 5Norwegian Meteorological Institute, NORWAY; 6Finnish Meteorological Institute, FINLAND; 7University of Hamburg Institute of Oceanography, GERMANY
Sea ice concentration has been derived globally from satellite observations since the 1970s. A multitude of algorithms have been developed and applied, and a number of data-sets exist. Sea ice concentration data-sets are used to monitor the climatic decline in sea ice extent in the Arctic as well as the seasonal cycle in the Southern Ocean where so far, no significant climatic trend has been observed over the almost 40 years of observations. Since the launch of the Electronically Scanning Microwave Radiometer (ESMR) in late 1972, data are available almost continuously (with a few gaps), and space-borne microwave radiometers will continue to provide these data for many years through the continuation of SSMIS and AMSR type of instruments as well as others.
Sea ice concentration is thus an excellent climate variable providing us essential information about the state of our planet.
There is an increasing interest in using the sea ice concentration data-sets for validation of models and for assimilation into models. These users require more elaborate information about uncertainties in the sea ice concentration data than hitherto available, and also express some curiosity about the many different data-sets and algorithms. The purpose of the ESA Climate Change Initiative's sea ice concentration effort is thus to perform an elaborate algorithm intercomparison and validation, select an appropriate (set of) algorithm(s), and produce a data-set with detailed uncertainty estimates. Data-sets for validation has been compiled into a so-called round-robin data package that contains satellite observations of microwave brightness temperatures as well as high quality reference sea ice concentration from independent sources. Here we will report on the generation of this reference data-set and the algorithm intercomparison efforts we have carried out.