Abstract. The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO 2 ) with the accuracy, resolution, and coverage needed to quantify CO 2 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 CO 2 dry air mole fraction, X CO 2 , 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 X CO 2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes X CO 2 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 X CO 2 gradient is small. Enhanced X CO 2 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 X CO 2 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 X CO 2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO 2 . As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart X CO 2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

Persistent above average precipitation and runoff and associated increased sediment transfers from cultivated ecosystems to rivers and oceans are due to changes in climate and human action. The US Upper Midwest has experienced a 37% increase in precipitation , leading to increased crop damage from excess water and off-farm loss of soil and nutrients. Farmer adaptive management responses to changing weather patterns have potential to reduce crop losses and address degrading soil and water resources. This research used farmer survey (n = 4778) and climate data to model influences of geophysical context, past weather, on-farm flood and saturated soils experiences, and risk and vulnerability perceptions on management practices. Seasonal precipitation varied across six Upper Midwest subregions and was significantly associated with variations in management. Increased warm-season precipitation (2007)(2008)(2009)(2010)(2011) relative to the past 40 yr was positively associated with no-till, drainage, and increased planting on highly erodible land (HEL). Experience with saturated soils was significantly associated with increased use of drainage and less use of notill, cover crops, and planting on HEL. Farmers in counties with a higher percentage of soils considered marginal for row crops were more likely to use no-till, cover crops, and plant on HEL. Respondents who sell corn through multiple markets were more likely to have planted cover crops and planted on HEL in 2011. This suggests that regional climate conditions may not well represent individual farmers' actual and perceived experiences with changing climate conditions. Accurate climate information downscaled to localized conditions has potential to influence specific adaptation strategies.

Abstract. We present an analysis of uncertainties in global measurements of the column averaged dry-air mole fraction of CO 2 (XCO 2 ) 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 XCO 2 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 XCO 2 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 XCO 2 . 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.

Remote sensing of the atmosphere has provided a wealth of data for analyses and inferences in earth science. Satellite observations can provide information on the atmospheric state at fine spatial and temporal resolution while providing substantial coverage across the globe. For example, this capability can greatly enhance the understanding of the space-time variation of the greenhouse gas, carbon dioxide (CO2), since ground-based measurements are limited. NASA's Orbiting Carbon Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight daily, and the mission's retrieval algorithm processes these indirect measurements into estimates of atmospheric CO2. The retrieval is an inverse problem and consists of a physical forward model for the transfer of radiation through the atmosphere that includes absorption and scattering by gases, aerosols, and the surface. The model and other algorithm inputs introduce key sources of uncertainty into the retrieval problem. This article develops a computationally efficient surrogate model that is embedded in a simulation experiment for studying the impact of uncertain inputs on the distribution of the retrieval error. Abstract. Remote sensing of the atmosphere has provided a wealth of data for analyses and 5 inferences in Earth science. Satellite observations can provide information on the atmospheric state 6 at fine spatial and temporal resolution while providing substantial coverage across the globe. For 7 example, this capability can greatly enhance the understanding of the space-time variation of the 8 greenhouse gas, carbon dioxide (CO 2 ), since ground-based measurements are limited. NASA's Or-9 biting Carbon Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight 10 daily, and the mission's retrieval algorithm processes these indirect measurements into estimates of 11 atmospheric CO 2 . The retrieval is an inverse problem and consists of a physical forward model for 12 the transfer of radiation through the atmosphere that includes absorption and scattering by gases, 13 aerosols, and the surface. The model and other algorithm inputs introduce key sources of uncertainty 14 into the retrieval problem. This article develops a computationally efficient surrogate model that is 15 embedded in a simulation experiment for studying the impact of uncertain inputs on the distribution 16 of the retrieval error. 17

Abstract. 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 joined the 705 km Afternoon Constellation on 3 August 2014. On monthly time scales, 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 data set.