Search

The Geostationary Carbon Cycle Observatory (GeoCarb), to be launched in 2023, will be capable of measuring solar-induced chlorophyll fluorescence (SIF) and add to the current space-based record which started in 1995. GeoCarb will be unique as it will be the first geostationary satellite capable of sensing SIF over the American continents. Consequently, SIF measurements from GeoCarb can be performed much more flexibly compared to polar-orbiting platforms. With its scan mirror assembly, the instrument can point to any location on the Earth disc. This will allow measurements to be collected at various times of day, and measurement locations can be re-visited several times within a day. In expectation of the launch, we conduct an extensive, SIF-focused simulation study and explore the capability and limitations of the instrument and its particular sampling approach. Using cloud information from real measurements, as well as other observation- and model-based data, we produce over four million atmospheric simulations of GeoCarb measurements that the instrument would see throughout a full day. We then apply dedicated SIF retrieval algorithms on the simulated spectra and investigate the results along with cloud-screening performance and emergent retrieval biases and subsequent bias correction. Finally, we make comparisons with currently operating instruments where appropriate and show future science users of GeoCarb SIF what a typical day of measurements will yield.

The Geostationary Carbon Cycle Observatory (GeoCarb) will make measurements of greenhouse gases over the contiguous North and South American landmasses at daily temporal resolution. The extreme flexibility of observing from geostationary orbit induces an optimization problem, as operators must choose what to observe and when. The proposed scanning strategy for the GeoCarb mission tracks the sun's path from east to west and covers the entire area of interest in five coherent regions in the order of tropical South America east, tropical South America west, temperate South America, tropical North America, and temperate North America. We express this problem in terms of a geometric set cover problem, and use an incremental optimization (IO) algorithm to create a scanning strategy that minimizes expected error as a function of the signal-to-noise ratio (SNR). The IO algorithm used in this studied is a modified greedy algorithm that selects, incrementally at 5 min intervals, the scanning areas with the highest predicted SNR with respect to air mass factor (AF) and solar zenith angle (SZA) while also considering operational constraints to minimize overlapping scans and observations over water. As a proof of concept, two experiments are performed applying the IO algorithm offline to create an SNR-optimized strategy and compare it to the proposed strategy. The first experiment considers all landmasses with equal importance and the second experiment illustrates a temporary campaign mode that gives major urban areas greater importance weighting. Using a simple instrument model, we found that there is a significant performance increase with respect to overall predicted error when comparing the algorithm-selected scanning strategies to the proposed scanning strategy.

Enhanced CO2 mole fraction bands were often observed immediately ahead of cold front during the Atmospheric Carbon and Transport (ACT)-America mission and their formation mechanism is undetermined....

Spatial–temporal dynamics of aboveground biomass (AGB) and forest area affect the carbon cycle, climate and biodiversity in the Brazilian Amazon. Here we investigate interannual changes in AGB and forest area by analysing satellite-based annual AGB and forest area datasets. We found that the gross forest area loss was larger in 2019 than in 2015, possibly due to recent loosening of forest protection policies. However, the net AGB loss was three times smaller in 2019 than in 2015. During 2010–2019, the Brazilian Amazon had a cumulative gross loss of 4.45 Pg C against a gross gain of 3.78 Pg C, resulting in a net AGB loss of 0.67 Pg C. Forest degradation (73%) contributed three times more to the gross AGB loss than deforestation (27%), given that the areal extent of degradation exceeds that of deforestation. This indicates that forest degradation has become the largest process driving carbon loss and should become a higher policy priority. Carbon loss from forests occurs through deforestation or the degradation of existing forest. The loss of forest area in the Brazilian Amazon was higher in 2019 than following drought and an El Nino event in 2015, yet degradation drove three times more biomass loss than deforestation from 2010 to 2019.

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.

The Geostationary Carbon Observatory (GeoCarb) will make measurements of greenhouse gases over the land mass in the western hemisphere. The extreme flexibility of observing from geostationary orbit induces an optimization problem, as operators must choose what to observe and when. We express this problem in terms of an optimal subcovering problem, and use an Incremental Optimization (IO) algorithm to create a scanning strategy that minimizes expected error as a function of the signal-to-noise ratio (SNR), and show that this method outperforms the human selected strategy in terms of global error distributions.

The second NASA Earth Venture Mission, Geostationary Carbon Cycle Observatory (GeoCarb), will provide measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and solar-induced fluorescence (SIF) from Geostationary Orbit (GEO). The GeoCarb mission will deliver daily maps of column concentrations of CO2, CH4, and CO over the observed landmasses in the Americas at a spatial resolution of roughly 10 x 10 km. Persistent measurements of CO2, CH4, CO, and SIF will contribute significantly to resolving carbon emissions and illuminating biotic processes at urban to continental scales, which will allow the improvement of modeled biogeochemical processes in Earth System Models as well as monitor the response of the biosphere to disturbance. This is essential to improve understanding of the Carbon-Climate connection. In this paper, we introduce the instrument and the GeoCarb Mission, and we demonstrate the potential scientific contribution of the mission through a series of CO2 and CH4 simulation experiments. We find that GeoCarb will be able to constrain emissions at urban to continental spatial scales on weekly to annual time scales. The GeoCarb mission particularly builds upon the Orbiting Carbon Obserevatory-2 (OCO-2), which is flying in Low Earth Orbit.

Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty., A substantial portion of model uncertainty arises from model parameters and structures. Here, the authors show that alternative model structures with data-driven parameters project greater uncertainty in soil carbon responses to climate change than the conventional soil carbon model.