Abstract

Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.

Highlights

  • Sustained high-quality observations are essential for improving our understanding of the ocean and its interactions with the atmosphere and the overall Earth system

  • Coupled model integrations with prescribed radiative forcing have been the backbone of the coordinated experiments for the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP) that were designed for contributing to the Intergovernmental Panel on Climate Change (IPCC)

  • Such efforts are underway in the United States as detailed in National Oceanographic and Atmospheric Administration (NOAA)’s strategic implementation plan (SIPv4, 2017), which is a partnership among NASA, NOAA, the Department of Defense (DoD), and the Joint Center for Satellite Data Assimilation (JCSDA) and contributing external and international agencies

Read more

Summary

INTRODUCTION

Sustained high-quality observations are essential for improving our understanding of the ocean and its interactions with the atmosphere and the overall Earth system. Mathematical methods from the field of Data Assimilation (DA) allow information provided from observations to be propagated in time and space to unobserved areas using the dynamical and physical constraints imposed by numerical models. When these methods are applied to form the aforementioned historical reconstructions, this procedure is called a retrospective analysis, or “reanalysis” (Kalnay et al, 1996; Dee et al, 2014). We review the current state-ofthe-art of DA applied to the ocean and collectively look forward over the decade to make our own predictions about what kind of complementary in situ and satellite observations will be required to advance reanalysis and prediction, address end-user engagement, identify opportunities for integration, and connect to many of the themes of OceanObs’

METHODOLOGICAL DEVELOPMENTS IN OCEAN DATA ASSIMILATION
CONNECTING OCEAN DATA ASSIMILATION WITH OCEAN OBSERVING EFFORTS
THE ADVENT OF COUPLED DATA ASSIMILATION
OCEAN AND COUPLED EARTH SYSTEM REANALYSIS
ADVANCES AND UNSOLVED CHALLENGES IN PRODUCING OCEAN REANALYSES
ADVANCES AND UNSOLVED CHALLENGES IN PRODUCING COUPLED REANALYSES
USING OCEAN OBSERVATIONS TO IMPROVE PREDICTION
PREDICTION AT SUBSEASONAL TO SEASONAL TIMESCALES
PREDICTION AT DECADAL CLIMATE TIMESCALES
CONCLUSION
Findings
AUTHOR CONTRIBUTIONS

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.