# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 1 Introduction  
The first routine measurements of atmospheric carbon dioxide (CO\({}_{2}\)) were started at Mauna Loa Observatory in 1958 by [PERSON]. These measurements now form the iconic Keeling curve that shows, in addition to seasonal variations, rapidly increasing levels of CO\({}_{2}\) in the atmosphere due to the burning of fossil fuels. Now, 60 years later, atmospheric CO\({}_{2}\) concentrations are measuredall over the world and show levels that are about 100 ppm higher than they were in 1958, when they were about 315 ppm. However, the measurement networks are still denser in the Western world. One of the most important applications of CO\({}_{2}\) measurements is to infer the surface CO\({}_{2}\) fluxes, i.e., sources and sinks (or emissions into and removal from the atmosphere). A variety of methods are commonly used to derive CO\({}_{2}\) fluxes from spatially resolved estimates of column-averaged CO\({}_{2}\) dry air mole fraction, XCO\({}_{2}\). These methods can be broadly classified as analytic mass balance, plume dispersion, or atmospheric inversion methods. All three approaches predict or optimize surface fluxes needed to reproduce the observed XCO\({}_{2}\) distribution in the presence of the time-varying wind field. Ground-based in situ measurements provide the best available constraints on CO\({}_{2}\) concentrations, but the existing ground based network is unevenly distributed and includes few stations in the tropics, high latitudes, or over the ocean. One way to extend this network is to use space-based measurements of the XCO\({}_{2}\)[1; 2]. The main advantage of space-based observations is that they can provide high spatial and temporal resolution on global scale with the same measurement device. The principal challenge is the need for unprecedented levels of precision and accuracy to resolve and quantify CO\({}_{2}\) fluxes. High precision measurements are needed to clearly resolve CO\({}_{2}\) concentration anomalies in the context of their large background concentrations. For example, source pixel mass balance methods [3] indicate that a single-sounding precision of \(\sim\)0.25% (\(\sim\)1 part per million (ppm) out of the 400 ppm background) is needed within a 4 square km satellite footprint to detect a \(\sim\)10 kiloton/day CO\({}_{2}\) source in the presence of a \(\sim\)5 km/h wind. Spatially and temporally coherent biases in the measurements must also be minimized because, regional-scale biases in XCO\({}_{2}\) no larger than a few tenths of a part per million can introduce carbon flux errors exceeding 0.7 Gigatons of carbon per year [4; 5]. Ideally, the inverse modeling systems that estimate CO\({}_{2}\) fluxes from atmospheric measurements, can assimilate information from all available measurement types. The first space-based instrument designed to retrieve estimates of XCO\({}_{2}\) throughout the atmospheric column from high resolution spectra of reflected sunlight was SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chatography), launched by the European Space Agency onboard the Envisat spacecraft in 2002 [6]. The first mission that provided XCO\({}_{2}\) measurements at a precision better than 0.6% is the Japanese GOSAT (Greenhouse Gases Observing Satellite), which has produced CO\({}_{2}\) and methane (CH\({}_{4}\)) measurements since April 2009 [7]. The follow-on mission GOSAT-2 was launched in 2018. In 2014, NASA launched the Orbiting Carbon Observatory-2 (OCO-2), which has been providing CO\({}_{2}\) measurements since September 2014 [1; 2; 8]. An increasing number of CO\({}_{2}\) measuring satellites, including the Chinese TanSat [9; 10], Fen-Yun-3D, and Gaofen 5 have been launched since 2016 and others are being planned (see, e.g., [[https://en.wikipedia.org/wiki/Space-based_measurements_of_carbon_dioxide](https://en.wikipedia.org/wiki/Space-based_measurements_of_carbon_dioxide)]([https://en.wikipedia.org/wiki/Space-based_measurements_of_carbon_dioxide](https://en.wikipedia.org/wiki/Space-based_measurements_of_carbon_dioxide))).