Characterization of OCO-2 and ACOS-GOSAT biases and errors for CO₂ flux estimates

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

STRUCTURAL THRESHOLD REGRESSION

This paper introduces the structural threshold regression (STR) model that allows for an endogenous threshold variable as well as for endogenous regressors. This model provides a parsimonious way of modeling nonlinearities and has many potential applications in economics and finance. Our framework can be viewed as a generalization of the simple threshold regression framework of Hansen (2000, Econometrica 68, 575–603) and Caner and Hansen (2004, Econometric Theory 20, 813–843) to allow for the endogeneity of the threshold variable and regime-specific heteroskedasticity. Our estimation of the threshold parameter is based on a two-stage concentrated least squares method that involves an inverse Mills ratio bias correction term in each regime. We derive its asymptotic distribution and propose a method to construct confidence intervals. We also provide inference for the slope parameters based on a generalized method of moments. Finally, we investigate the performance of the asymptotic approximations using a Monte Carlo simulation, which shows the applicability of the method in finite samples.

Correcting for Surface Pressure Background Bias in Ensemble-Based Analyses

Model error is the component of the forecast error that is due to the difference between the dynamics of the atmosphere and the dynamics of the numerical prediction model. The systematic, slowly varying part of the model error is called model bias. This paper evaluates three different ensemble-based strategies to account for the surface pressure model bias in the analysis scheme. These strategies are based on modifying the observation operator for the surface pressure observations by the addition of a bias-correction term. One estimates the correction term adaptively, while another uses the hydrostatic balance equation to obtain the correction term. The third strategy combines an adaptively estimated correction term and the hydrostatic-balance-based correction term. Numerical experiments are carried out in an idealized setting, where the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model is integrated at resolution T62L28 to simulate the evolution of the atmosphere and the T30L7 resolution Simplified Parameterization Primitive Equation Dynamics (SPEEDY) model is used for data assimilation. The results suggest that the adaptive bias-correction term is effective in correcting the bias in the data-rich regions, while the hydrostatic-balance-based approach is effective in data-sparse regions. The adaptive bias-correction approach also has the benefit that it leads to a significant improvement of the temperature and wind analysis at the higher model levels. The best results are obtained when the two bias-correction approaches are combined.