Developing global maps of carbon dioxide (CO₂) mole fraction (in units of parts per million) near the Earth’s surface can help identify locations where major amounts of CO₂ are entering and exiting the atmosphere, thus providing valuable insights into the carbon cycle and mitigating the greenhouse effect of atmospheric CO₂. Existing satellite remote sensing data do not provide measurements of the CO₂ mole fraction near the surface. Japan’s Greenhouse gases Observing SATellite (GOSAT) is sensitive to average CO₂ over the entire column, and NASA’s Atmospheric InfraRed Sounder (AIRS) is sensitive to CO₂ in the middle troposphere. One might expect that lower-atmospheric CO₂ could be inferred by differencing GOSAT column-average and AIRS mid-tropospheric data. However, the two instruments have different footprints, measurement-error characteristics, and data coverages. In addition, the spatio-temporal domains are large, and the AIRS dataset is massive. In this article, we describe a spatio-temporal data-fusion (STDF) methodology based on reduced-dimensional Kalman smoothing. Our STDF is able to combine the complementary GOSAT and AIRS datasets to optimally estimate lower-atmospheric CO₂ mole fraction over the whole globe. Further, it is designed for massive remote sensing datasets and accounts for differences in instrument footprint, measurement-error characteristics, and data coverages. This article has supplementary material online.

Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements. Previous research in comparing MCMC and Optimal Estimation (OE) for the OCO-2 retrieval has highlighted the issues of slow convergence of MCMC, and furthermore OE and MCMC not necessarily agreeing with the simulated ground truth. In this work, we exploit the inherent low information content of the OCO-2 measurement and use the Likelihood-Informed Subspace (LIS) dimension reduction to significantly speed up the convergence of MCMC. We demonstrate the strength of this analysis method by assessing the non-Gaussian shape of the retrieval’s posterior distribution, and the effect of operational OCO-2 prior covariance’s aerosol parameters on the retrieval. We further show that in our test cases we can use this analysis to improve the retrieval to retrieve the simulated true state significantly more accurately and to characterize the non-Gaussian form of the posterior distribution of the retrieval problem.

Climate models produce output over decades or longer at high spatial and temporal resolution. Starting values, boundary conditions, greenhouse gas emissions, and so forth make the climate model an uncertain representation of the climate system. A standard paradigm for assessing the quality of climate model simulations is to compare what these models produce for past and present time periods, to observations of the past and present. Many of these comparisons are based on simple summary statistics called metrics. In this article, we propose an alternative: evaluation of competing climate models through probabilities derived from tests of the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals. The probabilities are based on the behavior of summary statistics of climate model output and observational data over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences. The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients. Here, we compare monthly sequences of CMIP5 model output of average global near-surface temperature anomalies to similar sequences obtained from the well-known HadCRUT4 data set as an illustration.