Abstract
Sea surface temperature is a key component of the climate record, with multiple independent records giving confidence in observed changes. As part of the European Space Agencies (ESA) Climate Change Initiative (CCI) the satellite archives have been reprocessed with the aim of creating a new dataset that is independent of the in situ observations, and stable with no artificial drift (<0.1 K decade−1 globally) or step changes. We present a method to assess the satellite sea surface temperature (SST) record for step changes using the Penalized Maximal t Test (PMT) applied to aggregate time series. We demonstrated the application of the method using data from version EXP1.8 of the ESA SST CCI dataset averaged on a 7 km grid and in situ observations from moored buoys, drifting buoys and Argo floats. The CCI dataset was shown to be stable after ~1994, with minimal divergence (~0.01 K decade−1) between the CCI data and in situ observations. Two steps were identified due to the failure of a gyroscope on the ERS-2 satellite, and subsequent correction mechanisms applied. These had minimal impact on the stability due to having equal magnitudes but opposite signs. The statistical power and false alarm rate of the method were assessed.
Highlights
Observations of the sea surface temperature (SST) forms one of the key components of the climate record (e.g., [1,2]), with in situ observations extending back over 150 years (e.g., [3,4,5,6,7]) and satellite-based estimates over 20 years (e.g., [8])
In recognition of the importance of independent estimates the (A)ATSR Reprocessing for Climate (ARC) project [13] aimed to produce a satellite sea surface temperature record based on measurements from the (Advanced) Along Track Scanning Radiometer ((A)ATSR) series of sensors
Within this paper we develop a method to assess the homogeneity of the European Space Agencies (ESA) SST Climate Change Initiative (CCI) dataset, using the Penalized Maximal t Test (PMT) [20] with satellite in situ differences aggregated over many different observations and platforms, and an ensemble approach to quantify the uncertainty in the timing and size of any change points
Summary
Observations of the sea surface temperature (SST) forms one of the key components of the climate record (e.g., [1,2]), with in situ observations extending back over 150 years (e.g., [3,4,5,6,7]) and satellite-based estimates over 20 years (e.g., [8]). Within this paper we develop a method to assess the homogeneity of the ESA SST CCI dataset, using the Penalized Maximal t Test (PMT) [20] with satellite in situ differences aggregated over many different observations and platforms, and an ensemble approach to quantify the uncertainty in the timing and size of any change points. Data from Experimental Version 1.8 of the ESA SST CCI project [24] have been used to develop and test the method presented in this paper These data have been extracted from a multi-sensor match-up database (MMD) [25] containing collocated swath or level 2 pre-processed (L2P) data from the (A)ATSR series of satellites and in situ observations from a variety of platforms. The in situ observations and satellite retrievals are briefly described
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