Impacts of Temperature Measurements From Sea Turtles on Seasonal Prediction Around the Arafura Sea

Takeshi Doi,Hiroyuki Suganuma,Katsufumi Sato,Takuya Fukuoka,Andrea Storto

doi:10.3389/fmars.2019.00719

Abstract

In this work, we added the assimilation of subsurface temperature measurements obtained from sea turtles around the Arafura Sea from June to October 2017 into an operational seasonal prediction system. The impact of these measurements was explored by conducting the so-called ocean observing system experiments. It was found that the prediction of regional sea surface temperatures around the Arafura Sea is significantly improved at 3-4 months lead-time. The results also showed that the addition of temperature measurements from sea turtles into the existing Global Ocean Observing System (including satellites, mooring buoys, ships, and profiling floats) may open a new door to improving regional seasonal prediction through better representation of the initial state of the upper ocean.

Highlights

Observations of ocean temperature and salinity are essential for the initialization of seasonal prediction with a dynamical ocean–atmosphere coupled model (Vidard et al, 2007) because a potential source of seasonal predictability arises from the long memory associated with the high heat capacity of the ocean relative to the atmosphere
The quality control procedures were embedded in the variational data assimilation scheme and relied on background quality checks, FIGURE 2 | Horizontal map of the subsurface ocean observational points in 50–150 m-depth that are assimilated to the SINTEX-F2 seasonal prediction system during June–August 2017
The maximum difference was about 10 m in the region of 133◦–138◦E, 10◦–5◦S, which is consistent with the location of the temperature measurements from the sea turtles (Figures 1, 2)

Summary

INTRODUCTION

Observations of ocean temperature and salinity are essential for the initialization of seasonal prediction with a dynamical ocean–atmosphere coupled model (Vidard et al, 2007) because a potential source of seasonal predictability arises from the long memory associated with the high heat capacity of the ocean relative to the atmosphere. As an additional data source, ocean observational data from marine animal-borne instruments via satellites in near real time are emerging (Harcourt et al, 2019). Some marine animal-borne instruments can provide in situ subsurface temperature profile data from parts of the oceans wherein little or no other data are currently available. If some of the animal-borne instruments were capable of real-time subsurface temperature data beyond the data coverage already achieved by the present Global Ocean Observing System, the information could be proven for all ranges of prediction: weather, subseasonal, seasonal, multiyear, decadal, and longer-scales climate prediction.

MATERIALS AND METHODS

RESULTS

DISCUSSION

ETHICS STATEMENT