Abstract

Sea Surface Temperature (SST) observations from space have been made by the Along Track Scanning Radiometers (ATSRs) providing 20 years (August 1991–April 2012) of high quality data. As part of the ESA Climate Change Initiative (CCI) project, SSTs have been retrieved from the ATSRs. Here, the quality of CCI SST (Phase 1) from ATSRs is validated against drifting buoys. Only CCI ATSR SSTs (Version 1.1) are considered, to facilitate the comparison with the precursor dataset ATSR Reprocessing for Climate (ARC). The CCI retrievals compared with drifting buoys have a median difference slightly larger than 0.1 K. The median SST difference is larger in the tropics (∼0.3 K) during the day, with the night time showing a spatially homogeneous pattern. ATSR-2 and AATSR show similar performance in terms of Robust Standard Deviation (RSD) being 0.2–0.3 K during night and about 0.1 K higher during day. On the other hand, ATSR-1 shows increasing RSD with time from 0.3 K to over 0.6 K. Triple collocation analysis has been applied for the first time on TMI/ATSR-2 observations and for daytime conditions when the wind speed is greater than 10 m/s. Both day and night results indicate that since 2004, the random uncertainty of drifting buoys and CCI AATSR is rather stable at about 0.22 K. Before 2004, drifting buoys have larger values (∼0.3 K), while ATSR-2 shows slightly lower values (∼0.2 K). The random uncertainty for AMSR-E is about 0.47 K, also rather stable with time, while as expected, the TMI has higher values of ∼0.55 K. It is shown for the first time that the AMSR-E random uncertainty changes with latitude, being ∼0.3 K in the tropics and about double this value at mid-latitudes. The SST uncertainties provided with the CCI data are slightly overestimated above 0.45 K and underestimated below 0.3 K during the day. The uncertainty model does not capture correctly the periods with instrument problems after the ATSR-1 3.7 μ m channel failed and the gyro failure of ERS-2. During the night, the uncertainties are slightly underestimated. The CCI SSTs (Phase 1) do not yet match the quality of the ARC dataset when comparing to drifting buoys. The value of the ARC median bias is closer to zero than for CCI, while the RSD is about 0.05 K lower for ARC. ARC also shows a more homogeneous geographical distribution of median bias and RSD, although the differences between the two datasets are small. The observed discrepancies between CCI and ARC during the period of ATSR-1 are unexplained given that both datasets use the same retrieval method.

Highlights

  • Sea Surface Temperature (SST) is an Essential Climate Variable (ECV) for which there are available observations continuously since 1850 [1,2] made mainly by ships, and from drifting and moored buoys during the recent decades

  • Summary and Conclusions The Climate Change Initiative (CCI) SSTs (Phase 1, Version 1.1) from the Along Track Scanning Radiometers (ATSRs) covering the period 1991–2012 have been assessed against collocated observations from drifting buoys and compared with the corresponding comparisons for the ATSR Reprocessing for Climate (ARC)

  • The ARC dataset is provided at a spatial resolution of 0.1◦, while CCI has a finer resolution of 0.05◦

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Summary

Introduction

Sea Surface Temperature (SST) is an Essential Climate Variable (ECV) for which there are available observations continuously since 1850 [1,2] made mainly by ships, and from drifting and moored buoys during the recent decades. SST is directly related to and often dictates the exchanges of heat, momentum and gases between the ocean and the atmosphere [3], making it an important geophysical parameter for climate variability monitoring and prediction, operational weather and ocean forecasting, ecosystem assessment and military operations [4]. SST observations have been made operationally from space using the AVHRR instruments since 1981, as they offer the advantage of global coverage in contrast to in situ measurements [5,6]. The measurements from ATSRs have been used to assess the quality of in situ SST observations [10], to bias correct other satellite SST retrievals either directly [11,12] or through an SST analysis system [13], and to estimate the background error covariance parameters in SST analysis systems [14]

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