A New Global Ocean Climatology

Kanwal Shahzadi,Alexander Barth,Simona Simoncelli,Charles Troupin,Nadia Pinardi,Vladyslav Lyubartsev

doi:10.3389/fenvs.2021.711363

Abstract

A new global ocean temperature and salinity climatology is proposed for two time periods: a long time mean using multiple sensor data for the 1900–2017 period and a shorter time mean using only profiling float data for the 2003–2017 period. We use the historical database of World Ocean Database 2018. The estimation approach is novel as an additional quality control procedure is implemented, along with a new mapping algorithm based on Data Interpolating Variational Analysis. The new procedure, in addition to the traditional quality control approach, resulted in low sensitivity in terms of the first guess field choice. The roughness index and the root mean square of residuals are new indices applied to the selection of the free mapping parameters along with sensitivity experiments. Overall, the new estimates were consistent with previous climatologies, but several differences were found. The cause of these discrepancies is difficult to identify due to several differences in the procedures. To minimise these uncertainties, a multi-model ensemble mean is proposed as the least uncertain estimate of the global ocean temperature and salinity climatology.

Highlights

Defining the climatological state of the ocean is a formidable task
Two versions of a global ocean climatology for temperature and salinity were estimated using a new interpolation scheme, DIVAnd, which enables a better assessment of coastal constraints
We demonstrated that an additional quality control is required to produce a good quality climatology

Summary

Introduction

Defining the climatological state of the ocean is a formidable task. Climatology can be defined as the study of the statistics of environmental variables that characterise the ocean’s physical and biochemical state. As defined by Daley (1993): “Spatial analysis is the estimation by numerical algorithm of state variables on a three-dimensional regular grid from observations available at irregularly distributed locations.”. These numerical algorithms are based on theoretical and statistical assumptions that have significantly evolved over the past 20 years. Averaging water masses across the deep portions of different ocean basins that are completely disconnected on the timescales of 100 years give rise to high standard deviations in deep waters Notwithstanding these limitations and the simplicity of the first guess, the use of DIVAnd and AQC makes the analysis quite insensitive to the background as shown below. We select the background according to the computed climatology residuals, calculated as: ri(xoα, yoβ, zc) H(θic(xk, yj, zp)) − yo(xoα, yoβ, zc) (6)

Objectives

Findings

Discussion

Conclusion