Parameterizing the Impact of Unresolved Temperature Variability on the Large‐Scale Density Field: Part 1. Theory. (25th November 2020)

• Record Type: Journal Article
• Authors: Stanley, Z.; Grooms, I.; Kleiber, W.; Bachman, S. D.; Castruccio, F.; Adcroft, A.
• Is Part Of: Journal of advances in modeling earth systems. Volume 12:Number 12(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-25
• Subjects: equation of state -- stochastic parameterization -- subgrid‐scale variability; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2020MS002185
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Unresolved temperature and salinity fluctuations interact with a nonlinear seawater equation of state to produce significant errors in the ocean model evaluation of the large‐scale density field. It is shown that the impact of temperature fluctuations dominates the impact of salinity fluctuations and that the error in density is, to leading order, proportional to the product of a subgrid‐scale temperature variance and a second derivative of the equation of state. Two parameterizations are proposed to correct the large‐scale density field: one deterministic and one stochastic. Free parameters in both parameterizations are fit using fine‐resolution model data. Both parameterizations are computationally efficient as they require only one additional evaluation of a nonlinear equation at each grid cell. A companion paper will discuss the climate impacts of the parameterizations proposed here. Plain Language Summary: The density of seawater is an important quantity in ocean models. There is an error in the way ocean models calculate density, which is especially pronounced in the ocean models used for climate forecasting. We use data from the output of a high‐resolution ocean model to quantify this error in density and propose a correction. Key Points: Unresolved temperature fluctuations interact with nonlinear seawater equation of state to produce significant errors in large‐scale density We propose two data informed, computationally efficient parameterizations to correct this error in density The first proposed parameterization is deterministic while the second is stochastic