Huang, Boyin and Thorne, Peter and Smith, Thomas M. and Liu, Wei and Lawrimore, Jay and Banzon, Viva F. and Zhang, Huai-Min and Peterson, Thomas C. and Menne, Matthew (2016) Further Exploring and Quantifying Uncertainties for Extended ReconstructedSea Surface Temperature (ERSST) Version 4 (v4). Journal of Climate, 29. pp. 3119-3142. ISSN 0894-8755

Preview

Download (3MB) | Preview

Abstract

The uncertainty in Extended Reconstructed SST (ERSST) version 4 (v4) is reassessed based upon 1) reconstruction uncertainties and 2) an extended exploration of parametric uncertainties. The reconstruction uncertainty (Ur) results from using a truncated (130) set of empirical orthogonal teleconnection functions (EOTs), which yields an inevitable loss of information content, primarily at a local level. The Ur is assessed based upon 32 ensemble ERSST.v4 analyses with the spatially complete monthly Optimum Interpolation SST product. The parametric uncertainty (Up) results from using different parameter values in quality control, bias adjustments, and EOT definition etc. The Up is assessed using a 1000-member ensemble ERSST.v4 analysis with different combinations of plausible settings of 24 identified internal parameter values. At the scale of an individual grid box, the SST uncertainty varies between 0.3° and 0.7°C and arises from both Ur and Up. On the global scale, the SST uncertainty is substantially smaller (0.03°–0.14°C) and predominantly arises from Up. The SST uncertainties are greatest in periods and locales of data sparseness in the nineteenth century and relatively small after the 1950s. The global uncertainty estimates in ERSST.v4 are broadly consistent with independent estimates arising from the Hadley Centre SST dataset version 3 (HadSST3) and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2). The uncertainty in the internal parameter values in quality control and bias adjustments can impact the SST trends in both the long-term (1901–2014) and “hiatus” (2000–14) periods.

Item Type:	Article
Keywords:	Atm/Ocean Structure/ Phenomena; Sea surface temperature; Observational techniques and algorithms; Data processing; In situ oceanic observations; Sampling; Mathematical and statistical techniques; Error analysis; Models and modeling; Ensembles;
Academic Unit:	Faculty of Social Sciences > Geography Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
Item ID:	10804
Identification Number:	https://doi.org/10.1175/JCLI-D-15-0430.1
Depositing User:	Peter Thorne
Date Deposited:	21 May 2019 16:53
Journal or Publication Title:	Journal of Climate
Publisher:	American Meteorological Society
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

Repository Staff Only(login required)

Item control page

Downloads

Origin of downloads