Characterization of transport errors in chemical forecasts from a global tropospheric chemical transport model

Abstract

We propose a new methodology to characterize errors in the representation of transport processes in chemical transport models. We constrain the evaluation of a global three-dimensional chemical transport model (GEOS-CHEM) with an extended dataset of carbon monoxide (CO) concentrations obtained during the Transport and Chemical Evolution over the Pacic (TRACE-P) aircraft campaign. The TRACE-P mission took place over the western Pacic, a region frequently impacted by continental outflow associated with different synoptic-scale weather systems (such as cold fronts) and deep convection, and thus provides a valuable dataset for our analysis. Model simulations using both forecast and assimilated meteorology are examined. Background CO concentrations are computed as a function of latitude and altitude and subsequently subtracted from both the observed and the model datasets to focus on the ability of the model to simulate variability on a synoptic scale. Dierent sampling strategies (i.e., spatial displacement and smoothing) are applied along the ight tracks to search for systematic model biases. Statistical quantities such as correlation coefficient and centered root-mean-square dierence are computed between the simulated and the observed elds and are further intercompared using Taylor diagrams. We find no systematic bias in the model for the TRACE-P region when we consider the entire dataset (i.e., from the surface to 12 km ). This result indicates that the transport error in our model is globally unbiased, which has important implications for using the model to conduct inverse modeling studies. Using the First-Look assimilated meteorology only provides little improvement of the correlation, in comparison with the forecast meteorology. These general statements can be rened when the entire dataset is divided into different vertical domains, i.e., the lower troposphere (<2 km), the middle troposphere (2-6 km), and the upper troposphere (>6 km). The best agreementbetween the observations and the model is found in the lower and middle troposphere. Downward displacements in the lower troposphere provide a better fit with the observed value, which could indicate a problem in the representation of boundary layer height in the model. Signicant improvement is also found for downward and southward displacements in the upper troposphere. There are several potential sources of errors in our simulation ofthe continental outow in the upper troposphere which could lead to suchbiases, including the location and/or the strength of deep convective cells as well as that of wildfires in Southeast Asia.