Observing System Simulation Experiments and Adjoint Sensitivity Analysis: Methods for Observational Programs in the Arctic Ocean

Oceana Francis,Max Yaremchuk,Vladimir Luchin,Jacob Stroh,David Hebert,Pamela Posey,Gleb Panteleev

doi:10.14430/arctic4603

Abstract

Over recent decades, the Arctic Ocean (AO) has experienced profound climate changes. To study these climate changes, several regional observational programs have been started. Because of complicated logistics and remoteness, in situ observations in the AO are extremely expensive. Therefore, an efficient ocean observational system in the AO is critical to understand environmental changes in the Arctic. Observing System Simulation Experiments (OSSEs) and Adjoint Sensitivity Analysis (ASA) are powerful tools that could be used in the optimization of existing and incoming observational programs in the AO. These optimal planning tools recommended by the Study of Environmental Arctic Change (SEARCH) implementation plan, and widely used in atmospheric research, are still rarely implemented in physical and biological oceanography. We provide several examples of how the OSSE and ASA can be used to optimize the locations of high frequency radars and biological tracer surveys and leveraged toward creating an inexpensive drifter observational program capable of providing sufficient information to reconstruct the circulation in the northern Bering, Chukchi, and southern Beaufort Seas.

Highlights

With the diminishing of sea ice during the past decades, we have been observing significant changes in the hydrophysical conditions in the Arctic Ocean
We describe the basic ideas behind the Adjoint Sensitivity Analysis (ASA) and Observing System Simulation Experiments (OSSEs) techniques, and show how the application of these tools may help to optimize the location of the high frequency radar (HFR), identify the gaps in the existing observational programs, justify a drifter observational program, and increase the information content of various passive-tracer observations collected during ship surveys
We present the results of a sensitivity analysis under the assumption that prior error variances (σ of c) are smaller than the combined observational and representation error variances, and we estimate the values of σ from the statistics of the model/data misfits of the U.S Navy’s Arctic Cap Nowcast/Forecast System (ACNFS) data assimilative model

Summary

Introduction

With the diminishing of sea ice during the past decades, we have been observing significant changes in the hydrophysical conditions in the Arctic Ocean. By applying simple algorithms that take into account the geographical location of different HFRs on the adjoint sensitivity map (see Fig. 2b), we can estimate the reduction of the Bering Strait transport errors using observations from any HFR pair and the efficiency of those pairs.

Results

Conclusion