Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere
The distribution of methane (CH 4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH 4 mixing ratio (XCH 4), which is being measured increasingly for the assessment of CH 4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH 4 accurately for estimating surface emissions from XCH 4. Simulations from three CTMs are tested against XCH 4 observations from the Total Carbon Column Network (TCCON). We analyze how the model–TCCON agreement in XCH 4 depends on the model representation of stratospheric CH 4 distributions. Model equivalents of TCCON XCH 4 are computed with stratospheric CH 4 fields from both the model simulations and from satellite-based CH 4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH 4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Using MIPAS-based stratospheric CH 4 fields in place of model simulations improves the model–TCCON XCH 4 agreement for all models. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH 4 bias is significantly reduced from 38.1 to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 to 4.3 ppb) and LMDz (Laboratoire de Météorologie Dynamique model with zooming capability; from 6.8 to 4.3 ppb). Replacing model simulations with MIPAS stratospheric CH 4 fields adjusted to ACE-FTS reduces the average XCH 4 bias for ACTM (3.3 ppb), but increases the average XCH 4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that model errors in simulating stratospheric CH 4 contribute to model biases. Current satellite instruments cannot definitively measure stratospheric CH 4 to sufficient accuracy to eliminate these biases. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH 4 and, hence, its contribution to XCH 4. Therefore, it would be worthwhile to analyze how individual model components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) impact the simulation of stratospheric CH 4 and XCH 4.