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Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2measurements for joint flux inversion and establishing a long-term atmospheric CO2 data record. Wevalidate recent satellite observation of OCO-2 v7 and ACOS-GOSAT v7.3 using similar analysis as previouswork (Kulawik et al. (2016) and Frankenberg et al. (2106)) through comparisons to the HIAPER Pole-to-Pole Observations (HIPPO) and the Total Carbon Column Observing Network (TCCON) to estimate biasesand errors affecting the understanding of carbon cycle science. CarbonTracker RT is also compared tothe validation data, and additionally used to evaluate the mismatch between the HIPPO observationtimeframe and the OCO-2 record, which are offset by 3-7 years. Some key metrics that are validatedinclude the seasonal cycle phase and amplitude, latitudinal gradient by season, regional biases, anderrors with respect to averaging.

Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (X(sub CO2)) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 parts per million vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to (error(sup 2) equals a(sup 2) plus b(sup 2) divided by n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of 0.3 parts per million, with biases larger than the TCCON predicted bias uncertainty of 0.4 parts per million at many stations. We find that GOSAT and CT2013b under-predict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53 degrees North latitude, MACC over-predicts between 26 and 37 degrees North latitude, and CT2013b under-predicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in June-July-August; there is correlation between 0.2 and 0.8 in the NH, with models showing 10-50 percent the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45-67 degrees North latitude for GOSAT and CT2013b.

The Orbiting Carbon Observatory-2 (OCO-2) Team has continued working closely with the Greenhouse Gases Observing Satellite (GOSAT) team to (i) conduct vicarious calibration experiments in Railroad Valley, NV, U.S.A., (ii) retrieve estimates of X CO2 from OCO-2 and GOSAT TANSO-FTS data, and (iii) validate these products using internationally recognized standards. The OCO-/GOSAT relationship was reinforced in March 2015 with the signing a new Memorandum of Understanding between NASA and the GOSAT and GOSAT-2 partners. Over the past year, while the GOSAT team has been finalizing the V201 product, the OCO-2 team has focused primarily on the delivery of the OCO-2 product, providing new opportunities for cross calibration of the OCO-2 and GOSAT instruments and cross validation of their products. These efforts are just now producing results. Members of the OCO-2 team have also continued the analysis of the GOSAT V161.161 data and the ACOS B3.5 products. Those efforts have produced 2 new refereed publications and several others in review. The OCO-2 now preparing to reprocess the entire GOSAT TANSO-FTS data record using the GOSAT V201 product and OCO-2 B7 algorithm to produce a 7-year, multi-satellite climate data record for X(sub CO2).

The industrial period and modern age is characterized by combustion of coal, oil, and natural gas for primary energy and transportation leading to rising levels of atmospheric of CO2. This increase, which is being carefully measured, has ramifications throughout the biological world. Through remote sensing, it is possible to measure how many molecules of CO2 lie in a defined column of air. However, other gases and particles are present in the atmosphere, such as aerosols and water, which make such measurements more complicated1. Understanding the detailed geometry and path length of the observation is vital to computing the concentration of CO2. Comparing these satellite readings with ground-truth data (TCCON) the systematic errors arising from these sources can be assessed. Once the error is understood, it can be scaled for in the retrieval algorithms to create a set of data, which is closer to the TCCON measurements1. Using this process, the algorithms are being developed to reduce bias, within.1% worldwide of the true value. At this stage, the accuracy is within 1%, but through correcting small errors contained in the algorithms, such as accounting for the scattering of sunlight, the desired accuracy can be achieved.

The Aura Validation Experiment (AVE) focuses on validating Aura satellite measurements of important atmospheric trace gases using ground-based, aircraft, and balloon-borne instruments. Global satellite observations of CO from the Tropospheric Emission Spectrometer (TES) on the EOS Aura satellite have been ongoing since September 2004. This paper discusses CO validation experiments during the Oct-AVE (2004 Houston, Texas) and CR-AVE (2006 San Jose, Costa Rica) campaigns. The coincidences in location and time between the satellite observations and the available in situ profiles for some cases are not ideal. However, the CO distribution patterns in the two validation flight areas are shown to have very little variability in the aircraft and satellite . observations, thereby making them suitable for validation comparisons. TES CO profiles, which typically have a retrieval uncertainty of 10-20%, are compared with in situ CO measurements from NASA Ames Research Center's Argus instrument taken on board the WB-57F aircraft during Oct-AVE. TES CO retrievals during CR-AVE are compared with in situ measurements from Jet Propulsion Laboratory's Aircraft Laser Infrared Absorption Spectrometer (ALIAS) instrument as well as with the Argus instrument, both taken on board the WB-57F aircraft. During CR-AVE, the average overall difference between ALIAS and Argus CO was 4%, with the ALIAS measurement higher. During individual flights, 2-min time-averaged differences between the two in situ instruments had standard deviation of 14%. The TES averaging kernels and a priori constraint profiles for CO are applied to the in situ data for proper comparisons to account for the reduced vertical resolution and the influence of the a priori in the satellite-derived profile. In the TES sensitive pressure range, approx.700-200 hPa, the in situ profiles and TES profiles agree within 5-10%, less than the variability in CO distributions obtained by both TES and the aircraft instruments in the two regions. TES CO is slightly lower than in situ measurements in the Houston area (midlatitudes) and slightly higher than in situ CO measurements in the Costa Rica region (tropical).