We present a comparison of atmospheric transport models that simulate carbonyl sulfide (COS). This is part II of the ongoing Atmospheric Transport Model (ATM) Inter-comparison Project (TransCom–COS). Differently from part I, we focus on seven model intercomparison by transporting two recent COS inversions of NOAA surface data within TM5-4DVAR and LMDz models. The main goals of TransCom-COS part II are (a) to compare the COS simulations using the two sets of optimized fluxes with simulations that use a control scenario (part I) and (b) to evaluate the simulated tropospheric COS abundance with aircraft-based observations from various sources. The output of the seven transport models are grouped in terms of their vertical mixing strength: strong and weak mixing. The results indicate that all transport models capture the meridional distribution of COS at the surface well. Model simulations generally match the aircraft campaigns HIPPO and ATom. Comparisons to HIPPO and ATom demonstrate a gap between observed and modelled COS over the Pacific Ocean at 0–40N, indicating a potential missing source in the free troposphere. The effects of seasonal continental COS uptake by the biosphere, observed on HIPPO and ATom over oceans, is well reproduced by the simulations. We found that the strength of the vertical mixing within the column as represented in the various atmospheric transport models explains much of the model to model differences. We also found that weak-mixing models transporting the optimized flux derived from the strong-mixing TM5 model show a too strong seasonal cycle at high latitudes.

The coastal ocean contributes to regulating atmospheric greenhouse gas concentrations by taking up carbon dioxide (CO2) and releasing nitrous oxide (N2O) and methane (CH4). Major advances have improved our understanding of the coastal air-sea exchanges of these three gasses since the first phase of the Regional Carbon Cycle Assessment and Processes (RECCAP in 2013), but a comprehensive view that integrates the three gasses at the global scale is still lacking. In this second phase (RECCAP2), we quantify global coastal ocean fluxes of CO2, N2O and CH4 using an ensemble of global gap-filled observation-based products and ocean biogeochemical models. The global coastal ocean is a net sink of CO2 in both observational products and models, but the magnitude of the median net global coastal uptake is ~60% larger in models (-0.72 vs. -0.44 PgC/yr, 1998-2018, coastal ocean area of 77 million km2). We attribute most of this model-product difference to the seasonality in sea surface CO2 partial pressure at mid- and high-latitudes, where models simulate stronger winter CO2 uptake. The global coastal ocean is a major source of N2O (+0.70 PgCO2-e /yr in observational product and +0.54 PgCO2-e /yr in model median) and of CH4 (+0.21 PgCO2-e /yr in observational product), which offsets a substantial proportion of the net radiative effect of coastal \co uptake (35-58% in CO2-equivalents). Data products and models need improvement to better resolve the spatio-temporal variability and long term trends in CO2, N2O and CH4 in the global coastal ocean.

Reducing uncertainty in the global carbon budget requires better quantification of ocean CO2 uptake and its temporal variability. Several methodologies for reconstructing air-sea CO2 exchange from sparse pCO2 observations indicate larger decadal variability than estimated using ocean models. We develop a new application of multiple Large Ensemble Earth system models to assess these reconstructions’ ability to estimate spatiotemporal variability. With our Large Ensemble Testbed, pCO2 fields from 25 ensemble members each of four independent Earth system models are subsampled as the observations and the reconstruction is performed as it would be with real- world observations. The power of a testbed is that the perfect reconstruction is known for each of the 100 original model fields; thus, reconstruction skill can be comprehensively assessed. We find that a commonly used neural-network approach can skillfully reconstruct air-sea CO2 fluxes when and where it is trained with sufficient data. Flux bias is low for the global mean and Northern Hemisphere, but can be regionally high in the Southern Hemisphere. The phase and amplitude of the seasonal cycle are accurately reconstructed outside of the tropics, but longer-term variations are reconstructed with only moderate skill. For Southern Ocean decadal variability, insufficient sampling leads to a 39% [15%:58%, interquartile range] overestimation of amplitude, and phasing is only moderately correlated with known truth (r=0.54 [0.46:0.63]). Globally, the amplitude of decadal variability is overestimated by 21% [3%:34%]. Machine learning, when supplied with sufficient data, can skillfully reconstruct ocean properties. However, data sparsity remains a fundamental limitation to quantification of decadal variability in the ocean carbon sink.

We present a comparison of atmospheric transport model simulations for carbonyl sulfide (COS), within the framework of the ongoing atmospheric tracer transport model intercomparison project “TransCom”. Seven atmospheric transport models participated in the inter-comparison experiment and provided simulations of COS mixing ratios in the troposphere over a 9-year period (2010–2018), using prescribed state-of-the-art surface fluxes for various components of the atmospheric COS budget: biospheric sink, oceanic source, sources from fire and industry. Since the biosphere is the largest sink of COS, we tested sink estimates produced by two different biosphere models. The main goals of TransCom-COS are (a) to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio-temporal variability of COS mixing ratios in the troposphere, and (b) to assess the sensitivity of simulated tropospheric COS mixing ratios to the seasonal and diurnal variability of the COS biosphere fluxes. To this end, a control case with state-of-the-art seasonal fluxes of COS was constructed. Models were run with the same fluxes and without chemistry to isolate transport differences. Further, two COS flux scenarios were compared: one using a biosphere flux with a monthly time resolution and the other using a biosphere flux with a three-hourly time resolution. In addition, we investigated the sensitivity of the simulated concentrations to different biosphere fluxes and to indirect oceanic emissions through dimethylsulfide (DMS) and carbon disulfide (CS2). The modelled COS mixing ratios were assessed against in-situ observations from surface stations and aircraft.