Evaluation of the accuracy of an offline seasonally-varying matrix transport model for simulating ideal age. (September 2016)

• Record Type: Journal Article
• Authors: Bardin, Ann; Primeau, François; Lindsay, Keith; Bradley, Andrew
• Is Part Of: Ocean modelling. Volume 105(2016:Sep.)
• Page Start: 25
• Page End: 33
• Publication Date: 2016-09
• Subjects: Implicit solver -- Ideal age -- Newton–Krylov -- Offline -- Transport matrix -- Global ocean modeling; Oceanography -- Periodicals; Océanographie -- Périodiques; Oceanography; Periodicals; 551.46
• DOI: 10.1016/j.ocemod.2016.07.003
• Languages: English
• ISSNs: 1463-5003

Abstract

Highlights: Compares equilibrium ideal age using a dynamical OGCM with using transport matrices. Bias of 12% younger for annually-averaged transport matrices versus dynamical OGCM. Bias of 4% younger for monthly-averaged transport matrices. Bias of less than 2% younger for 5-day-averaged transport matrices. Abstract: Newton–Krylov solvers for ocean tracers have the potential to greatly decrease the computational costs of spinning up deep-ocean tracers, which can take several thousand model years to reach equilibrium with surface processes. One version of the algorithm uses offline tracer transport matrices to simulate an annual cycle of tracer concentrations and applies Newton's method to find concentrations that are periodic in time. Here we present the impact of time-averaging the transport matrices on the equilibrium values of an ideal-age tracer. We compared annually-averaged, monthly-averaged, and 5-day-averaged transport matrices to an online simulation using the ocean component of the Community Earth System Model (CESM) with a nominal horizontal resolution of 1° × 1° and 60 vertical levels. We found that increasing the time resolution of the offline transport model reduced a low age bias from 12% for the annually-averaged transport matrices, to 4% for the monthly-averaged transport matrices, and to less than 2% for the transport matrices constructed from 5-day averages. The largest differences were in areas with strong seasonal changes in the circulation, such as the Northern Indian Ocean. For many applications the relatively small bias obtained using the offline model makes the offline approach attractive because it uses significantly less computer resources and is simpler to set up and run.