Spatially resolved evaluation of Earth system models with satellite column-averaged CO<sub>2</sub>

Abstract

Abstract. Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO2 concentrations. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission-driven CMIP5 and CMIP6 simulations with satellite data of column-average CO2 mole fractions (XCO2). XCO2 time series show a large spread among the model ensembles both in CMIP5 and CMIP6. Compared to the satellite observations, the models have a bias of +25 to −20 ppmv in CMIP5 and +20 to −15 ppmv in CMIP6, with the multi-model mean biases at +10 and +2 ppmv, respectively. The derived mean atmospheric XCO2 growth rate (GR) of 2.0 ppmv yr−1 is overestimated by 0.4 ppmv yr−1 in CMIP5 and 0.3 ppmv yr−1 in CMIP6 for the multi-model mean, with a good reproduction of the interannual variability. All models capture the expected increase of the seasonal cycle amplitude (SCA) with increasing latitude, but most models underestimate the SCA. Any SCA derived from data with missing values can only be considered an “effective” SCA, as the missing values could occur at the peaks or troughs. The satellite data are a combined data product covering the period 2003–2014 based on the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY)/Envisat (2003–2012) and Thermal And Near infrared Sensor for carbon Observation Fourier transform spectrometer/Greenhouse Gases Observing Satellite (TANSO-FTS/GOSAT) (2009–2014) instruments. While the combined satellite product shows a strong negative trend of decreasing effective SCA with increasing XCO2 in the northern midlatitudes, both CMIP ensembles instead show a non-significant positive trend in the multi-model mean. The negative trend is reproduced by the models when sampling them as the observations, attributing it to sampling characteristics. Applying a mask of the mean data coverage of each satellite to the models, the effective SCA is higher for the SCIAMACHY/Envisat mask than when using the TANSO-FTS/GOSAT mask. This induces an artificial negative trend when using observational sampling over the full period, as SCIAMACHY/Envisat covers the early period until 2012, with TANSO-FTS/GOSAT measurements starting in 2009. Overall, the CMIP6 ensemble shows better agreement with the satellite data than the CMIP5 ensemble in all considered quantities (XCO2, GR, SCA and trend in SCA). This study shows that the availability of column-integral CO2 from satellite provides a promising new way to evaluate the performance of Earth system models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations.

Can a regional-scale reduction of atmospheric CO<sub>2</sub> during the COVID-19 pandemic be detected from space? A case study for East China using satellite XCO<sub>2</sub> retrievals

“…For example, over extended anthropogenic source areas such as East China, the XCO 2 enhancement due to anthropogenic emissions is typically only approximately 1-2 ppm (0.25 %-0.5 % of 400 ppm) or even less (see e.g. Schneising et al, 2008Schneising et al, , 2013Hakkarainen et al, 2016Hakkarainen et al, , 2019Tohjima et al, 2020; and this study). A 10 % emission reduction would therefore only change the regional XCO 2 enhancement by 0.1 to 0.2 ppm.…”

Causal model evaluation of Arctic-midlatitude teleconnections in CMIP6

“…Following Gier et al (2020), we do not use the ensemble mean of each model since it would reduce the intrinsic variability of the single realisations.…”

Reconstructions and predictions of the global carbon budget with an emission-driven Earth system model

“…The CO 2 fluxes between the atmosphere and the underlying surface, and therefore the atmospheric carbon growth rate, vary substantially on interannual to decadal time scales (Peters et al, 2017;Friedlingstein et al, 2019;Landschützer et al, 2019;Friedlingstein et al, 2020). These variations reflect the combined effects of the internal variability of the global carbon cycle (Li and Ilyina, 2018;Séférian et al, 2018;Spring et al, 2020;Fransner et al, 2020) and its responses to external forcings (McKinley et al, 2020).…”

“…Moreover, the accumulated CO 2 fluxes from these standalone model simulations do not exactly match the observations. Therefore, the GCB is not closed but ends up with a budget imbalance term of up to 2 PgC yr −1 for some years, although the climatological mean value is small, 0.17 PgC yr −1 (Friedlingstein et al, 2020), which hinders the full attribution of the global carbon cycle variations. A large part of the budget imbalance could also be attributed to the mismatch of net biome production between the dynamic global vegetation models (DGVMs) used in the GCBs and inversions that match the atmospheric CO 2 growth rate (Bastos et al, 2020).…”