Abstract
Abstract. In this study, we present the development of a new coupled weather and carbon monoxide (CO) data assimilation system based on the Environment and Climate Change Canada (ECCC) operational ensemble Kalman filter (EnKF). The estimated meteorological state is augmented to include CO. Variable localization is used to prevent the direct update of meteorology by the observations of the constituents and vice versa. Physical localization is used to damp spurious analysis increments far from a given observation. Perturbed surface flux fields are used to account for the uncertainty in CO due to errors in the surface fluxes. The system is demonstrated for the estimation of three-dimensional CO states using simulated observations from a variety of networks. First, a hypothetically dense, uniformly distributed observation network is used to demonstrate that the system is working. More realistic observation networks, based on surface hourly observations, and space-based observations provide a demonstration of the complementarity of the different networks and further confirm the reasonable behavior of the coupled assimilation system. Having demonstrated the ability to estimate CO distributions, this system will be extended to estimate surface fluxes in the future.
Highlights
Environment and Climate Change Canada (ECCC) operates a greenhouse gas (GHG) measurement network that has seen rapid expansion during the past decade
This section discusses the improvement in the carbon monoxide (CO) state due to the assimilation of hypothetically dense in situ network (HYPNET), surface observations and MOPITT-like retrievals, in four separate experiments
A new atmospheric composition data assimilation system based on an operational weather forecast model (EC-CAS v1.0) was developed and validated for the estimation of the three-dimensional state of CO using simulated observations from the HYPNET, ECCC, GAW and MOPITT networks
Summary
Environment and Climate Change Canada (ECCC) operates a greenhouse gas (GHG) measurement network that has seen rapid expansion during the past decade. Systems focused on the influence of CO on climate are typically “inversion systems” wherein observations of CO concentrations are used to estimate CO surface fluxes Again, both ensemble (Miyazaki et al, 2015, 2012) and variational (Jiang et al, 2017, 2015a, b, 2013, 2011; Fortems-Cheiney et al, 2011) approaches have been used. EC-CAS v1.0 adapts the operational ensemble Kalman filter (EnKF) (Houtekamer et al, 2014) to perform a coupled meteorology and CO state estimation as in Barré et al (2015) and Gaubert et al (2016) With this choice, EC-CAS v1.0 can directly simulate and account for all components of transport error (i.e., errors arising from model formulation, meteorological state and constituent initial conditions) as well as observation and surface flux errors; see Polavarapu et al (2016) for a detailed discussion of transport errors. The outline of the paper is as follows: Sect. 2 describes the various components of EC-CAS system; Sect. 3 presents the experimental design; Sect. 4 describes the data assimilation (DA) experiments and their results; and Sect. 5 presents the conclusions of this work and delineates planned future developments of EC-CAS
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