Development of the WRF-CO2 4D-Var assimilation system v1.0

Tao Zheng,Nancy H. F. French,Martin Baxter

doi:10.5194/gmd-11-1725-2018

Tao Zheng, Nancy H. F. French + Show 1 more

Open Access

https://doi.org/10.5194/gmd-11-1725-2018

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Abstract

Abstract. Regional atmospheric CO2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function, which quantifies the sensitivity of a receptor to flux sources. In this paper, an adjoint-based four-dimensional variational (4D-Var) assimilation system, WRF-CO2 4D-Var, is developed to provide an alternative approach. This system is developed based on the Weather Research and Forecasting (WRF) modeling system, including the system coupled to chemistry (WRF-Chem), with tangent linear and adjoint codes (WRFPLUS), and with data assimilation (WRFDA), all in version 3.6. In WRF-CO2 4D-Var, CO2 is modeled as a tracer and its feedback to meteorology is ignored. This configuration allows most WRF physical parameterizations to be used in the assimilation system without incurring a large amount of code development. WRF-CO2 4D-Var solves for the optimized CO2 flux scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the limited memory Broyden–Fletcher–Goldfarb–Shanno (BFGS) minimization algorithm (L-BFGS-B) and the second uses the Lanczos conjugate gradient (CG) in an incremental approach. WRFPLUS forward, tangent linear, and adjoint models are modified to include the physical and dynamical processes involved in the atmospheric transport of CO2. The system is tested by simulations over a domain covering the continental United States at 48 km × 48 km grid spacing. The accuracy of the tangent linear and adjoint models is assessed by comparing against finite difference sensitivity. The system's effectiveness for CO2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4D-Var for regional CO2 inversions.

Highlights

While rising atmospheric CO2 has been well documented by observations, major uncertainties still exist in attributing it to specific processes (Gurney et al, 2002; Peylin et al, 2013)
Weather Research and Forecasting (WRF)-CO2 4D-Var was developed as a data assimilation system designed to constrain surface CO2 fluxes by combining an online atmospheric chemistry transport model and observation data in a Bayesian framework
The cost function and its gradient required by the optimization schemes are calculated by WRF-CO2 4D-Var’s three component models: forward, tangent linear, and adjoint models, all developed on top of the WRFPLUS system

Summary

Introduction

While rising atmospheric CO2 has been well documented by observations, major uncertainties still exist in attributing it to specific processes (Gurney et al, 2002; Peylin et al, 2013). Since the early study by Enting et al (1995), a large amount of effort has been devoted to developing and applying atmospheric CO2 inversion methods Most of these inversions are based on a Bayesian framework, and a wide range of different approaches has been used, including synthesis inversion (Rayner et al, 1999; Bousquet et al, 1999; Peylin et al, 2002; Gurney et al, 2002), geostatistical estimation (Michalak et al, 2004; Gourdji et al, 2012), Kalman smoother (Bruhwiler et al, 2005), ensemble Kalman smoother (Peters et al, 2005), and 4-D variational inversion (Chevallier et al, 2005; Baker et al, 2010). Because all of the inversion approaches use a chemistry– transport model (CTM) to relate CO2 fluxes to atmospheric

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