The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO₂) with the accuracy, resolution, and coverage needed to quantify CO₂ fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO₂ dry air mole fraction, XCO₂, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO₂ derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO₂ enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south XCO₂ gradient is small. Enhanced XCO₂ coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south XCO₂ gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO₂ across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO₂. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO₂ changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

We present an analysis of uncertainties in global measurements of the column averaged dry-air mole fraction of CO₂ (XCO₂) by the NASA Orbiting Carbon Observatory-2 (OCO-2). The analysis is based on our best estimates for uncertainties in the OCO-2 operational algorithm and its inputs, and uses simulated spectra calculated for the actual flight and sounding geometry, with measured atmospheric analyses. The simulations are calculated for land nadir and ocean glint observations. We include errors in measurement, smoothing, interference, and forward model parameters. All types of error are combined to estimate the uncertainty in XCO₂ from single soundings, before any attempt at bias correction has been made. From these results we also estimate the "variable error" which differs between soundings, to infer the error in the difference of XCO₂ between any two soundings. The most important error sources are aerosol interference, spectroscopy, and instrument calibration. Aerosol is the largest source of variable error. Spectroscopy and calibration, although they are themselves fixed error sources, also produce important variable errors in XCO₂. Net variable errors are usually < 1 ppm over ocean and ∼ 0.5–2.0 ppm over land. The total error due to all sources is ∼ 1.5–3.5 ppm over land and ∼ 1.5–2.5 ppm over ocean.

The transport of freshwater from continents to oceans through rivers has traditionally been estimated by routing runoff from land surface models within river models to obtain discharge. This paradigm imposes that errors are transferred from runoff to discharge, yet the analytical propagation of uncertainty from runoff to discharge has never been derived. Here we apply statistics to the continuity equation within a river network to derive two equations that propagate the mean and variance/covariance of runoff errors independently. We validate these equations in a case study of the rivers in the western United States and, for the first time, invert observed discharge errors for spatially distributed runoff errors. Our results suggest that the largest discharge error source is the joint variability of runoff errors across space, not the mean or amplitude of individual errors. Our findings significantly advance the science of error quantification in model‐based estimates of river discharge.

We review the singular value decomposition (SVD) framework and use it for quantifying and discerning vertical information in greenhouse gas retrievals from column integrated absorption measurements. While the commonly used traditional Bayesian optimal estimation (OE) assumes a prior distribution in order to regularize the inversion problem, the SVD approach identifies principal components that can be retrieved from the measurement without explicitly specifying a prior mean and prior covariance matrix. We review the SVD method, explicitly recognize the use of an uninformative prior and show it to incur no bias from the choice of the prior. We also make the connection between the SVD method and the pseudo-inverse, which makes it more intuitive and easy to understand. We illustrate the use of the SVD method on an integrated path differential absorption CO₂ lidar measurement model and verify our derivations and bias-free properties versus optimal estimation using numerical simulations. In contrast, traditional OE retrievals exhibit bias when the prior mean used in the retrieval differs from the true mean. Hence, the SVD method is particularly useful for situations in which knowledge of the prior mean and prior covariance of the true state (e.g., greenhouse gas profiles) is inadequate.

As drought occurs in a region it can have cascading effects through the water cycle. In this study, we explore the temporal co-evolution of various components of the hydrologic cycle in California from 2002 to 2018. We combine information from the Gravity Recovery and Climate Experiment (GRACE) satellites, the North American Land Data Assimilation System (NLDAS) suite of models, and the California Department of Water Resources (DWR) reservoir levels to analyze dynamics of Total Water Storage (TWS), soil moisture, snow pack, large reservoir storage, and ultimately, groundwater. For TWS, a trend of −2 cm/yr is observed during the entire time period of our analysis; however, this rate increases to about −5 cm/yr during drought periods (2006−2010 and 2012−2016). Results indicate that the majority of the loss in TWS is caused by groundwater depletion. Using proper error accounting, we are able to identify the start, the peak, and the ending of the drought periods for each individual water state variable in the study domain. We show that snow and soil moisture are impacted earlier and recover faster than surface water and groundwater. The annual and year-to-year dynamics shown in our results portray a clear cascading effect of the hydrologic cycle on the scale of 8−16 months.