Application of PRIM for understanding patterns in carbon dioxide model-observation differences

Abstract. Reducing uncertainties in regional carbon balances requires a better understanding of CO₂ transport in synoptic weather systems. Here, we apply the Patient Rule Induction Method (PRIM) to airborne observations of of potential temperature, wind speed, water vapor mixing ratio, and CO₂ dry mol fraction gathered during the Atmospheric Carbon and Transport (ACT)-America Summer 2016 and Winter 2017 campaigns. ACT observations were targeted at expert-designated cases of fair weather and near-frontal warm and cold sector air at atmospheric boundary-layer, lower-, and higher free tropospheric levels (ABL, LFT, and HFT, respectively).

We investigate atmospheric characteristics cases of these pre-defined cases and associated CO₂ model-observation-differences in the mesoscale WRF-Chem model. PRIM results separate winter- and summertime observations as well as observations from ABL, LFT, and HFT with enrichment factors of 4–20 inside the PRIM box compared to the entire dataset but cannot distinguish between near-frontal warm and cold sector observations in the higher free troposphere. Using the PRIM constrained atmospheric parameter space, we find that large magnitude model observation differences preferentially associated with times when atmospheric conditions are less typical. This association suggests that that PRIM could provide a useful tool for isolating atmospheric conditions with large-magnitude and non-Gaussian CO₂-residuals for targeted transport model evaluation and to potentially improve inversion results during synoptically active periods.