A Call for New Approaches to Quantifying Biases in Observations of Sea Surface Temperature

Elizabeth C Kent,Christopher J Merchant,Giulia Carella,David I Berry,John J Kennedy,Souichiro Yasui,Shoji Hirahara,Victor Venema,Alexey Kaplan,Thomas M Smith,Boyin Huang,Masayoshi Ishii,Yoshihiro Fukuda ,Phil Jones ,C P Atkinson ,Nick Rayner ,D E Parker ,Simone Morak‐Bozzo ,Finban Lindgren ,Huai‐Min Zhang

doi:10.1175/bams-d-15-00251.1

Abstract

Abstract Global surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications. Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method. New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and high-quality observations for validation and bias model development are likely to remain major challenges.

Highlights

Comparisons with validation data should cover a range of diagnostics, including mean bias and variance relative to validation data evaluated across a range of locations and throughout the annual and diurnal cycles
Some satellite datasets covering the 1990s to the present are of the desired accuracy and are largely independent of the in situ record (Merchant et al 2012, 2014); they are suited to validation or independent assessment of sea surface temperature (SST) bias adjustments applied to ship observations
Ocean weather ships and underway observations from research vessels are potential sources of validation data. They may be affected by biases, there is a greater chance of obtaining a full set of high-quality marine meteorological variables and metadata

Summary

Introduction

Bias estimation for sea surface temperature is discussed and recommendations for improving data, observational metadata, and uncertainty modeling are given. SR02 originally used the bias model rationale is that biases in NMAT are more straight- only in the pre–World War II (WWII) period domiforward to adjust (Kent et al 2013; section S1 of nated by bucket measurements (Fig. 3).

Results

Conclusion