A variational data assimilation system for soil–atmosphere flux estimates for the Community Land Model (CLM3.5)

Abstract

Abstract. This paper presents the development and implementation of a spatio-temporal variational data assimilation system (4D-var) for the soil-vegetation-atmosphere transfer model "Community Land Model" (CLM3.5), along with the development of the adjoint code for the core soil-atmosphere transfer scheme of energy and soil moisture. The purpose of this work is to obtain an improved estimation technique for the energy fluxes (sensible and latent heat fluxes) between the soil and the atmosphere. Optimal assessments of these fluxes are neither available from model simulations nor measurements alone, while a 4D-var data assimilation has the potential to combine both information sources by a Best Linear Unbiased Estimate (BLUE). The 4D-var method requires the development of the adjoint model of the CLM which is established in this work. The new data assimilation algorithm is able to assimilate soil temperature and soil moisture measurements for one-dimensional columns of the model grid. Numerical experiments were first used to test the algorithm under idealised conditions. It was found that the analysis delivers improved results whenever there is a dependence between the initial values and the assimilated quantity. Furthermore, soil temperature and soil moisture from in situ field measurements were assimilated. These calculations demonstrate the improved performance of flux estimates, whenever soil property parameters are available of sufficient quality. Misspecifications could also be identified by the performance of the variational scheme.

ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation

“…The (re)implementation of Eq. 12within MITgcm provides a versatile environment for such projects, and for variational estimation purposes most generally (and is complementary to, e.g., Moore et al, 2011;Barth et al, 2014;Wilson et al, 2014;Hoppe et al, 2014).…”

“…Beyond the removal of unstable adjoint dependencies, other alterations of the adjoint are of practical value for optimization purposes. In particular, it is common practice to increase viscosity parameters to add stability to MITgcm adjoint simulations (Hoteit et al, 2005). Despite successful adjoint simulations with particular versions of the sea-ice model (Heimbach et al, 2010;Fenty and Heimbach, 2013), the sea-ice adjoint is omitted in ECCO v4 due to persisting issues.…”

ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation

“…The (re)implementation of Eq. ( 12) within MITgcm provides a versatile environment for such projects, and for variational estimation purposes most generally (and is complementary to, e.g., Moore et al, 2011;Barth et al, 2014;Wilson et al, 2014;Hoppe et al, 2014).…”