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Earth Science Publications in 2019

2019 Publications:

  • Christopher Potter, NASA Ames, Olivia Alexander, Universities Space Research Association/Ames Research Center. Journal: European Journal of Remote Sensing (in press). Changes in Vegetation Cover and Snowmelt Timing in the Noatak National Preserve of Northwestern Alaska Estimated from MODIS and Landsat Satellite Image Analysis. 

Abstract: Trends and transitions in the growing season Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in ecosystems of the Noatak National Preserve in northwestern Alaska.

Abstract: ​Prior biosphere model impact on global terrestrial CO2 fluxes estimated from OCO-2 retrievals. This study led by multiple NASA Ames researchers (Sajeev Philip, Matthew Johnson, Chris Potter) in collaboration with members of the Orbiting Carbon Observatory-2 (OCO-2) Flux Team was recently published in Atmospheric Chemistry and Physics (ACP). This study assessed the impact of different state of the art global biospheric CO2 flux models, when applied as prior information, on inverse model “top-down” estimates of terrestrial CO2 fluxes obtained when assimilating OCO-2 observations. Results show that the assimilation of simulated XCO2 retrievals at OCO-2 observing modes over land results in posterior NEE estimates which generally reproduce “true” NEE globally. However, we find larger spread/uncertainty among posterior NEE estimates, when using different prior NEE fluxes, in regions and seasons that have limited OCO-2 observational coverage and a large range in “bottom-up” NEE fluxes. Overall, even with the availability of spatiotemporally dense OCO-2 data, noticeable residual differences (up to ∼20 %–30 % globally and 50 % regionally) in posterior NEE flux estimates remain that were caused by the choice of prior model flux values and the specification of prior flux uncertainties. This study was designed to assist the OCO-2 Flux Team and carbon cycle science community to assess one of the main variables causing inter-model variability and uncertainty in the posterior NEE flux estimates evident in the OCO-2 Multi-model inter-comparison project (MIP) (Crowell et al., 2019).

  • Michal Segal- Rozenhaimer, Alan Li, Kamalika Das, Ved Chirayath. Remote Sensing of Environment, 2019. Cloud Detection Algorithm for Multi-Modal Satellite Imagery using Convolutional Neural-Networks (CNN) 

Abstract: Cloud detection algorithms are crucial in many remote-sensing applications to allow an optimized processing of the acquired data, without the interference of the cloud fields above the surfaces of interest (e.g., land, coral reefs, etc.). While this is a well-established area of research, replete with a number of cloud detection methodologies, many issues persist for detecting clouds over areas of high albedo surfaces (snow and sand), detecting cloud shadows, and transferring a given algorithm between observational platforms. Particularly for the latter, algorithms are often platform-specific with corresponding rule-based tests and thresholds inherent to instruments and applied corrections. Here, we present a convolutional neural network (CNN) algorithm for the detection of cloud and cloud shadow fields in multi-channel satellite imagery from World-View-2 (WV-2) and Sentinel-2 (S-2), using their Red, Green, Blue, and Near-Infrared (RGB, NIR) channels. This algorithm is developed within the NASA NeMO-Net project, a multi-modal CNN for global coral reef classification which utilizes imagery from multiple remote sensing aircraft and satellites with heterogeneous spatial resolution and spectral coverage. Our cloud detection algorithm is novel in that it attempts to learn deep invariant features for cloud detection utilizing both the spectral and the spatial information inherent in satellite imagery. The first part of our work presents the CNN cloud and cloud shadow algorithm development (trained using WV-2 data) and its application to WV-2 (with a cloud detection accuracy of 89%) and to S-2 imagery (referred to as augmented CNN). The second part presents a new domain adaptation CNN-based approach (domain adversarial NN) that allows for better adaptation between the two satellite platforms during the prediction step, without the need to train for each platform separately. Our augmented CNN algorithm results in better cloud prediction rates as compared to the original S-2 cloud mask (81% versus 48%), but still, clear pixels prediction rate is lower than S-2 (81% versus 91%). Nevertheless, the application of the domain adaptation approach shows promise in better transferring the knowledge gained from one trained domain (WV-2) to another (S-2), increasing the prediction accuracy of both clear and cloudy pixels when compared to a network trained only by WV-2. As such, domain adaptation may offer a novel means of additional augmentation for our CNN-based cloud detection algorithm, increasing robustness towards predictions from multiple remote sensing platforms. The approach presented here may be further developed and optimized for global and multi-modal (multi-channel and multi-platform) satellite cloud detection capability by utilizing a more global dataset.

Abstract: The use of satellite Aerosol Optical Thickness (AOT) from imaging spectrometers has been successful in quantifying and mapping high PM2.5 (particulate matter mass < 2.5 µm diameter) episodes for pollution abatement and health studies. However, some regions have high PM2.5 but poor estimation success. The challenges in using Aerosol Optical Thickness (AOT) from imaging spectrometers to characterize PM2.5 worldwide was especially evident in the wintertime San Joaquin Valley (SJV). The SJV’s attendant difficulties of high-albedo surfaces and very shallow, variable vertical mixing also occur in other significantly polluted regions around the world. We report on more accurate PM2.5 maps for the whole-winter period in the SJV, Nov 14, 2012–Dec 11, 2013. Intensive measurements by including NASA aircraft were made for several weeks in that winter, the DISCOVER-AQ California mission.​​

  • We found success with a relatively simple method based on calibration and checking with surface monitors and a characterization of vertical mixing, and incorporating specific understandings of the region’s climatology. We estimate PM2.5 to within ~ 7 µg m–3 RMSE and with R values of ~ 0.9, based on remotely sensed MAIAC (Multi-Angle Implementation of Atmospheric Correction) observations, and that certain further work will improve that accuracy. Mapping is at 1 km resolution. This allows a time sequence of mapped aerosols at 1 km for cloud-free days. We describe our technique as a “static estimation”. Estimation procedures like this one, not dependent on well-mapped source strengths or on transport error, should help full source-driven simulations by deconstructing processes. They also provide a rapid method to create a long-term climatology. Essential features of the technique are (a) daily calibration of the AOT to PM2.5 using available surface monitors, and (b) characterization of mixed-layer dilution using column water vapor (CWV, otherwise “precipitable water”). We noted that on multi-day timescales both water vapor and particles share near-surface sources and both fall to very low values with altitude; indeed, both are largely removed by precipitation. The existence of layers of H2O or aerosol not within the mixed layer adds complexity, but mixed-effects statistical regression captures essential proportionality of PM2.5 and the ratio variable (AOT/CWV). Accuracy is much higher than previous statistical models, and can be extended to the whole Aqua-satellite data record. The maps and time-series we show suggest a repeated pattern for large valleys like the SJV — progressive stabilization of the mixing height after frontal passages: PM2.5 is somewhat more determined by day-by-day changes in mixing than it is by the progressive accumulation of pollutants (revealed as increasing AOT). 

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  • Suntharalingam, P., et al. (2019), Anthropogenic nitrogen inputs and impacts on oceanic N2O fluxes in the northern Indian Ocean: The need for an integrated observation and modelling approach, Deep-Sea Research Part II, xxx, xxx, doi:10.1016/j.dsr2.2019.03.007.
    Abstract: Anthropogenically-derived nitrogen input to the northern Indian Ocean has increased significantly in recent decades, based on both observational and model-derived estimates. This external nutrient source is supplied by atmospheric deposition and riverine fluxes, and has the potential to affect the vulnerable biogeochemical systems of the Arabian Sea and Bay of Bengal, influencing productivity and oceanic production of the greenhouse gas nitrous-oxide (N2O). We summarize current estimates of this external nitrogen source to the northern Indian Ocean from observations and models, highlight implications for regional marine N2O emissions using model-based analyses, and make recommendations for measurement and model needs to improve current estimates and future predictions of this impact. Current observationally-derived estimates of deposition and riverine nitrogen inputs are limited by sparse measurements and uncertainties on an accurate characterization of nitrogen species composition. Ocean model assessments of the impact of external nitrogen sources on regional marine N2O production in the northern Indian Ocean estimate potentially significant changes but also have large associated uncertainties. We recommend an integrated program of basin-wide measurements combined with high-resolution modeling and a more detailed characterization of nitrogen-cycle process to address these uncertainties and improve current estimates and predictions.

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    Research Program:     Atmospheric Composition Modeling and Analysis Program (ACMAP) 

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Research Program: Applied Sciences Program (ASP)

Abstract: 
Earth Radiation Budget Experiment (ERBE) Wide-Field-of-View (WFOV) nonscanner instrument onboard Earth Radiation Budget Satellite (ERBS) provided critical 15-year outgoing broadband irradiances at the top of atmosphere (TOA) from 1985 to 1999 for studying Earth’s climate. However, earlier studies show that the uncertainty in this radiation data set (Ed3) is significantly higher after the Mt. Pinatubo eruption in 1991 and satellite battery issue in 1993. Furthermore, Lee et al. showed that the transmission of ERBS WFOV shortwave dome degraded due to exposure to direct sunlight. To account for this degradation, a simple time-dependent but spectral-independent correction model was implemented in the past. This simple spectral-independent model did not completely remove the shortwave sensor artifact as seen in the temporal growth of the tropical mean day-minus-night longwave irradiance. A new temporal–spectral-dependent correction model of shortwave dome transmissivity loss similar to that used in the Clouds and the Earth’s Radiant Energy System (CERES) project is developed and applied to the 15-year ERBS WFOV data. This model is constrained by the solar transmission obtained from ERBS WFOV shortwave nonscanner instrument observations of the Sun during biweekly in-flight solar calibration events. This new model is able to reduce the reported tropical day-minusnight longwave irradiance trend by ≈34%. In addition, the slope of this new trend is observed to be consistent over different regions. The remaining trend is accounted using a postprocess Ed3Rev1 correction. Furthermore, the time series analysis of these data over the Libya-4 desert site showed that the shortwave data are stable to within 0.7%.
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Abstract: 
Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.

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Research Program: Radiation Science Program (RSP)

Abstract: 
The radiative flux data and other meteorological data in the BSRN archive start in 1992, but the RadFlux data, the clear-sky radiative fluxes at the BSRN sites empirically inferred through regression analyses of actually observed clear-sky fluxes, did not come into existence until the early 20 0 0s, and at first, they were limited to the 7 NOAA SURFRAD and 4 DOE ARM sites, a subset of the BSRN sites. Recently, the RadFlux algorithm was applied more extensively to the BSRN sites for the production of clear-sky ground-based fluxes. At the time of this writing, there are 7119 site-months of clear-sky fluxes at 42 BSRN sites spanning from 1992 to late 2017. These data provide an unprecedented opportunity to validate the satellite-based clear-sky fluxes. In this paper, the GEWEX SRB GSW(V3.0) clear-sky shortwave downward fluxes spanning 24.5 years from July 1983 to December 2007, the CERES SYN1deg(Ed4A) and EBAF(Ed4.0) clear-sky shortwave fluxes spanning March 20 0 0 to mid-2017 are compared with their RadFlux counterparts on the hourly, 3-hourly, daily and monthly time scales. All the three datasets show reasonable agreement with their ground-based counterparts. Comparison of the satellite-based surface shortwave clear-sky radiative fluxes to the BSRN RadFlux analysis shows negative biases (satellite-based minus RadFlux). Further analysis shows that the satellite-based atmosphere contains greater aerosol loading as well as more precipitable water than RadFlux analysis estimates.

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Research Program: Radiation Science Program (RSP)

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Mission: OCO-2

Abstract: 
Photosynthetic phenology has large effects on the land–atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO2) concentrations, which reflect expected future environmental conditions. Using more than 880 urban–rural gradients across the Northern Hemisphere (≥30° N), combined with concurrent satellite retrievals of Sun-induced chlorophyll fluorescence (SIF) and atmospheric CO2, we investigated the combined impacts of elevated CO2 and temperature on photosynthetic phenology at the large scale. The results showed that, under urban conditions of elevated CO2 and temperature, vegetation photosynthetic activity began earlier (−5.6 ± 0.7 d), peaked earlier (−4.9  ± 0.9 d) and ended later (4.6 ± 0.8 d) than in neighbouring rural areas, with a striking two- to fourfold higher climate sensitivity than greenness phenology. The earlier start and peak of season were sensitive to both the enhancements of CO2 and temperature, whereas the delayed end of season was mainly attributed to CO2 enrichments. We used these sensitivities to project phenology shifts under four Representative Concentration Pathway climate scenarios, predicting that vegetation will have prolonged photosynthetic seasons in the coming two decades. This observationdriven study indicates that realistic urban environments, together with SIF observations, provide a promising method for studying vegetation physiology under future climate change.

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
This paper introduces the OCO2CLD-LIDARAUX product, which uses the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and the Orbiting Carbon Observatory-2 (OCO-2) hyperspectral A-band spectrometer. CALIPSO provides a prior cloud top pressure (Ptop ) for an OCO-2-based retrieval of cloud optical depth, Ptop and cloud geometric thickness expressed in hPa. Measurements are of single-layer liquid clouds over oceans from September 2014 to December 2016 when collocated data are available. Retrieval performance is best for solar zenith angles <45◦ and when the cloud phase classification, which also uses OCO-2’s weak CO2 band, is more confident. The highest quality optical depth retrievals agree with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) with discrepancies smaller than the MODIS-reported uncertainty. Retrieved thicknesses are consistent with a substantially subadiabatic structure over marine stratocumulus regions, in which extinction is weighted towards the cloud top. Cloud top pressure in these clouds shows a 4 hPa bias compared with CALIPSO which we attribute mainly to the assumed vertical structure of cloud extinction after showing little sensitivity to the presence of CALIPSO-identified aerosol layers or assumed cloud droplet effective radius. This is the first case of success in obtaining internal cloud structure from hyperspectral A-band measurements and exploits otherwise unused OCO-2 data. This retrieval approach should provide additional constraints on satellite-based estimates of cloud droplet number concentration from visible imagery, which rely on parameterization of the cloud thickness.
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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
Optimal Estimation (OE) is a popular algorithm for remote sensing retrievals, partly due to its explicit parameterization of the sources of error and the ability to propagate them into estimates of retrieval uncertainty. These properties require specification of the prior distribution of the state vector. In many remote sensing applications, the true priors are multivariate and hard to characterize properly. Instead, priors are often constructed based on subject-matter expertise, existing empirical knowledge, and a need for computational expediency, resulting in a \working prior.” This paper explores the retrieval bias and the inaccuracy in retrieval uncertainty by explicitly separating the true prior (the probability distribution of the underlying state) from the working prior (the probability distribution used within the OE algorithm). We and that, in general, misspecifying the mean in the working prior will lead to biased retrievals, and misspecifying the covariance in the working prior will lead to inaccurate estimates of the retrieval uncertainty, though their effects vary depending on the state-space signal-to-noise ratio of the observing instrument. Our results point towards some attractive properties of a class of uninformative priors that is implicit for least-squares retrievals. Further, our derivations provide a theoretical basis, and an understanding of the trade-offs involved, for the popular OE practice of inflating a working prior covariance in order to reduce the prior’s impact on a retrieval. Finally, our results also lead to practical recommendations for specifying the prior mean and covariance in OE.

Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
Spectroscopic information on atmospheric gases, such as absorption line positions, intensities and widths, is important for radiative transfer calculations in climate and weather models. The widely used HIgh resolution TRANsmission (HITRAN) spectroscopic database is updated regularly. A line-by-line radiative transfer model has been used to investigate the impacts of the updates of HITRAN from 2008 to 2016 on clear-sky calculations of absorption and solar heating rates in the near-infrared region from 4,000 to 10,000 cm−1 (1.0–2.5 μm). These calculations were carried out for the tropical and sub-arctic atmospheres at solar zenith angles of 30◦ and 75◦ using HITRAN absorption line parameters for H2O, CO2, O3, N2O, CO, CH4 and O2 as well as the MT_CKD 2.5 (Mlawer-Tobin-Clough-Kneizys-Davies) continua for H2O, CO2, O3 and O2. It was found that the improvements in HITRAN absorption line parameters led to modest increases in the spectrally integrated absorbed downward solar fluxes and heating rates. The differences in absorbed fluxes are larger between HITRAN2016 and HITRAN2008 (maximum difference ∼1.3%) than between HITRAN2016 and HITRAN2012 (largest difference ∼0.3%). The corresponding maximum differences in heating rates are less than 2%. In order to improve accuracy, HITRAN2016 should be used in preference to earlier line-lists for calculating near-infrared absorbed solar fluxes and heating rates; but for calculations of clear-sky heating rates in the near-infrared region that do not require better than 2% accuracy, line parameters from HITRAN2012 and HITRAN2008 may still be used. Although the revisions of HITRAN led to relatively small differences in the spectrally integrated quantities, the improved quality of near-infrared line parameters will have a significant impact on other atmospheric applications such as the measurements of CO2 concentrations at a precision of less than 1% in Greenhouse Gases Observatory Satellite (GOSAT) and Orbiting Carbon Observatory-2 (OCO-2) missions.

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remoteFurther, the complex terrain of high-elevation evergreen systems is generally not amenable to eddy covariance carbon flux measurements. As a result, accurately determining the timing of seasonal GPP onset and cessation in evergreen forests remains a major challenge in terrestrial biosphere models and satellite remote sensing (7, 16, 17).

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
High-resolution atmospheric transport simulations were used to investigate the potential for detecting carbon dioxide (CO2) plumes of the city of Berlin and neighboring power stations with the Copernicus Anthropogenic Carbon Dioxide Monitoring (CO2M) mission, which is a proposed constellation of CO2 satellites with imaging capabilities. The potential for detecting plumes was studied for satellite images of CO2 alone or in combination with images of nitrogen dioxide (NO2) and 5 carbon monoxide (CO) to investigate the added value of measurements of other gases co-emitted with CO2 that have better signal-to-noise ratios. The additional NO2 and CO images were either generated for instruments on the same CO2M satellites (2×2 km2 resolution) or for the Sentinel-5 instrument (7×7 km2 ) assumed to fly two hours earlier than CO2M. Realistic CO2, CO and NO2 fields were simulated at 1×1 km2 horizontal resolution with COSMO-GHG model for the year 2015, and used as input for an orbit simulator to generate synthetic observations of columns of CO2, CO and NO2 for constellations of up 10 to six satellites. A new plume detection algorithm was applied to detect coherent structures in the images of CO2, NO2 or CO against instrument noise and variability in background levels. Although six satellites with an assumed swath of 250 km were sufficient to overpass Berlin on a daily basis, only about 50 out of 365 plumes per year could be observed in conditions suitable for emission estimation due to frequent cloud cover. With the CO2 instrument only 6 and 16 of these 50 plumes could be detected assuming a high (σVEG50 = 1.0 ppm) and low noise (σVEG50 = 0.5 ppm) scenario, respectively, because the CO2 15 signals were often too weak. A CO instrument with specifications similar to the Sentinel-5 mission performed worse than the CO2 instrument, while the number of detectable plumes could be significantly increased to about 35 plumes with an NO2 instrument. Using NO2 observations from the Sentinel-5 platform instead resulted in a significant spatial mismatch between NO2 and CO2 plumes due to the two hours time difference between Sentinel-5 and CO2M. The plumes of the coal-fired power plant Jänschwalde were easier to detect with the CO2 instrument (about 40-45 plumes per year), but again, an NO2 instrument 20 performed significantly better (about 70 plumes). Auxiliary measurements of NO2 were thus found to greatly enhance the capability of detecting the location of CO2 plumes, which will be invaluable for the quantification of CO2 emissions from large point sources.

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 

While heterotrophic respiration (Rh ) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least-observed carbon fluxes, particularly outside the midlatitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single datadriven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a topdown fashion at monthly temporal resolution and 4◦ × 5◦ spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System-Flux (CMS-Flux) with an optimally scaled gross primary productivity dataset based on satelliteobserved solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially varying carbon use efficiency retrieved in a model–data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6 ± 19.3 Pg C yr−1 . Sensitivity and uncertainty analyses showed that the top-down Rh are more sensitive to the choice of input gross primary productivity (GPP) and net ecosystem productivity (NEP) datasets than to the assumption of a static carbon use efficiency (CUE) value, with the possible exception of the wet tropics. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, using new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes and are 30 % greater overall (43.6 Pg C yr−1 vs. 33.4 Pg C yr−1 ). The uncertainty ranges of both methods are comparable, except poleward of 45◦ N, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 

All measurements of XCO2 from space have systematic errors. To reduce a large fraction of these errors, a bias correction is applied to XCO2 retrieved from GOSAT and OCO-2 spectra using the ACOS retrieval algorithm. The bias correction uses, among other parameters, the surface pressure difference between the retrieval and the meteorological reanalysis. Relative errors in the surface pressure estimates, however, propagate nearly 1: 1 into relative errors in biascorrected XCO2. For OCO-2, small errors in the knowledge of the pointing of the observatory (up to ∼ 130 arcsec) introduce a bias in XCO2 in regions with rough topography. Erroneous surface pressure estimates are also caused by a coding error in ACOS version 8, sampling meteorological analyses at wrong times (up to 3 h after the overpass time). Here, we derive new geolocations for OCO-2’s eight footprints and show how using improved knowledge of surface pressure estimates in the bias correction reduces errors in OCO-2’s v9 XCO2 data.

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 

Advanced satellite technology has been providing unique observations of global carbon dioxide (CO2) concentrations. These observations have revealed important CO2 variability at different timescales and over regional and planetary scales. Satellite CO2 retrievals have revealed that stratospheric sudden warming and the Madden-Julian Oscillation can modulate atmospheric CO2 concentrations in the mid-troposphere. Atmospheric CO2 also demonstrates variability at interannual timescales. In the tropical region, the El Niño–Southern Oscillation and the Tropospheric Biennial Oscillation can change atmospheric CO2 concentrations. At high latitudes, mid-tropospheric CO2 concentrations can be influenced by the Northern Hemispheric annular mode. In addition to modulations by the largescale circulations, sporadic events such as wildfires, volcanic eruptions, and droughts, which change CO2 surface emissions, can cause atmospheric CO2 concentrations to increase significantly. The natural variability of CO2 summarized in this review can help us better understand its sources and sinks and its redistribution by atmospheric motion.

Global satellite CO2 data offer a unique opportunity to explore CO2 variability in different regions.

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 

The observational requirements for space-based quantification of anthropogenic CO2 emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based monitoring of CO2 emissions: pixel size and revisit rate, and we introduce a new method utilizing multiple images simultaneously to significantly improve emission estimates. The impact of pixel size ranging from 2–10 km for space-based imaging spectrometers is investigated using plume model simulations, accounting for biases in the observations. Performance of rectangular pixels is compared to square pixels of equal area. The findings confirm the advantage of the smallest pixels in this range and the advantage of square pixels over rectangular pixels. A method of averaging multiple images is introduced and demonstrated to be able to estimate emissions from small sources when the individual images are unable to distinguish the plume. Due to variability in power plant emissions, results from a single overpass cannot be directly extrapolated to annual emissions, the most desired timescale for regulatory purposes. We investigate the number of overpasses required to quantify annual emissions with a given accuracy, based on the mean variability from the 50 highest emitting US power plants. Although the results of this work alone are not sufficient to define the full architecture of a future CO2 monitoring constellation, when considered along with other studies, they may assist in informing the design of a space-based system to support anthropogenic CO2 emission monitoring.

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
The Orbiting Carbon Observatory-3 (OCO-3) is NASA’s next instrument dedicated to extending the record of the dry-air mole fraction of column carbon dioxide (XCO2) and solar-induced fluorescence (SIF) measurements from space. The current schedule calls for a launch from the Kennedy Space Center no earlier than April 2019 via a Space-X Falcon 9 and Dragon capsule. The instrument will be installed as an external payload on the Japanese Experimental Module Exposed Facility (JEM-EF) of the International Space Station (ISS) with a nominal mission lifetime of 3 years. The precessing orbit of the ISS will allow for viewing of the Earth at all latitudes less than approximately 52◦ , with a ground repeat cycle that is much more complicated than the polar-orbiting satellites that so far have carried all of the instruments capable of measuring carbon dioxide from space.

The grating spectrometer at the core of OCO-3 is a direct copy of the OCO-2 spectrometer, which was launched into a polar orbit in July 2014. As such, OCO-3 is expected to have similar instrument sensitivity and performance characteristics to OCO-2, which provides measurements of XCO2 with precision better than 1 ppm at 3 Hz, with each viewing frame containing eight footprints approximately 1.6 km by 2.2 km in size. However, the physical configuration of the instrument aboard the ISS, as well as the use of a new pointing mirror assembly (PMA), will alter some of the characteristics of the OCO-3 data compared to OCO-2. Specifically, there will be significant differences from day to day in the sampling locations and time of day. In addition, the flexible PMA system allows for a much more dynamic observation-mode schedule.

This paper outlines the science objectives of the OCO-3 mission and, using a simulation of 1 year of global observations, characterizes the spatial sampling, time-of-day coverage, and anticipated data quality of the simulated L1b. After application of cloud and aerosol prescreening, the L1b radiances are run through the operational L2 full physics retrieval algorithm, as well as post-retrieval filtering and bias correction, to examine the expected coverage and quality of the retrieved XCO2 and to show how the measurement objectives are met. In addition, results of the SIF from the IMAP– DOAS algorithm are analyzed. This paper focuses only on the nominal nadir–land and glint–water observation modes, although on-orbit measurements will also be made in transition and target modes, similar to OCO-2, as well as the new snapshot area mapping (SAM) mode.

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
NASA’s carbon dioxide mission, Orbiting Carbon Observatory-2, began operating in September 2014. In this paper, we analyze four years (2015–2018) of global (60◦ S–60◦ N) XCO2 anomalies and their annual variations and seasonal patterns. We show that the anomaly patterns in the column-averaged CO2 dry air mole fraction, XCO2, are robust and consistent from year-to-year. We evaluate the method by comparing the anomalies to fluxes from anthropogenic, biospheric, and biomass burning and to model-simulated local concentration enhancements. We find that, despite the simplicity of the method, the anomalies describe the spatio-temporal variability of XCO2 (including anthropogenic emissions and seasonal variability related to vegetation and biomass burning) consistently with more complex model-based approaches. We see, for example, that positive anomalies correspond to fossil fuel combustion over the major industrial areas (e.g., China, eastern USA, central Europe, India, and the Highveld region in South Africa), shown as large positive XCO2 enhancements in the model simulations. We also find corresponding positive anomalies and fluxes over biomass burning areas during different fire seasons. On the other hand, the largest negative anomalies correspond to the growing season in the northern middle latitudes, characterized by negative XCO2 enhancements from simulations and high solar-induced chlorophyll fluorescence (SIF) values (indicating the occurrence of photosynthesis). The largest discrepancies between the anomaly patterns and the model-based results are observed in the tropical regions, where OCO-2 shows persistent positive anomalies over every season of every year included in this study. Finally, we demonstrate how XCO2 anomalies enable the detection of anthropogenic signatures for several local scale case studies, both in the Northern and Southern Hemisphere. In particular, we analyze the XCO2 anomalies collocated with the recent TROPOspheric Monitoring Instrument NO2 observations (used as indicator of anthropogenic fossil fuel combustion) over the Highveld region in South Africa. The results highlight the capability of satellite-based observations to monitor natural and man-made CO2 signatures on global scale.
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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
Vicarious calibration methods use well- changes in their response as they age or accumulate contacharacterized surface sites to complement other on-orbit minants. To meet these requirements, many instruments have radiometric calibration techniques. Since 2009, NASA’s Orbiting on-board calibrator (OBC) systems that employ lamps and calCarbon Observatory-2 (OCO-2) and Japan’s Greenhouse gasses Observing SATellite teams have conducted annual campaigns ibration targets. Some also use observations of radiometrically at Railroad Valley, NV, USA, for this purpose. These sensors stable astronomical targets such as the sun, moon, or standard pose special challenges due to their large footprint sizes and stars. These capabilities can be augmented by observations view angles. OCO-2 sweeps the playa surface during a targeted of well-characterized earth targets including pseudoinvariant overpass of the test site, and records data at a number of viewing calibration sites (PICS) and vicarious calibration (VicCal) angles. The smallest of these is selected for processing, thereby minimizing the off-nadir correction. Surface reflectances at nadir targets, which are routinely characterized by SI-traceable are recorded by the field team, and the Moderate Resolution surface and atmospheric measurements [1], [2]. Each of these Imaging Spectroradiometer (MODIS) surface reflectance calibration methodologies has its strengths and weaknesses. product is used to provide the small, off-nadir correction. 1) OBC: OBCs can be observed frequently to track rapid


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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of kilometers to a few kilometers. Changes in the model horizontal resolution affect not only atmospheric transport but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds.

The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high-resolution fluxes that are more diffuse at lower resolutions.

We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO2 and wind modeling error.

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Mission: Orbiting Carbon Observatory-2 (OCO-2)

Abstract: 
This study presents and evaluates an updated algorithm for quantification of absorbing aerosols above clouds (AACs) from passive satellite measurements. The focus is biomass burning in the south-eastern Atlantic Ocean during the 2016 and 2017 ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign deployments. The algorithm retrieves the above-cloud aerosol optical depth (AOD) and underlying liquid cloud optical depth and is applied to measurements from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) from 1997 to 2017. Airborne NASA Ames Spectrometers for Sky-Scanning, SunTracking Atmospheric Research (4STAR) and NASA Langley High Spectral Resolution Lidar 2 (HSRL2) data collected during ORACLES provide important validation for spectral AOD for MODIS and VIIRS; as the SeaWiFS mission ended in 2010, it cannot be evaluated directly. The 4STAR and HSRL2 comparisons are complementary and reveal performance generally in line with uncertainty estimates provided by the optimal estimation retrieval framework used. At present the two MODIS-based data records seem the most reliable, although there are differences between the deployments, which may indicate that the available data are not yet sufficient to provide a robust regional validation. Spatiotemporal patterns in the data sets are similar, and the time series are very strongly correlated with each other (correlation coefficients from 0.95 to 0.99). Offsets between the satellite data sets are thought to be chiefly due to differences in absolute calibration between the sensors. The available validation data for this type of algorithm are limited to a small number of field campaigns, and it is strongly recommended that such airborne measurements continue to be made, both over the southern Atlantic Ocean and elsewhere.

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Research Program: Atmospheric Composition

Mission: ORACLES

Abstract: 
The total effect of aerosols, both directly and on cloud properties, remains the biggest source of uncertainty in anthropogenic radiative forcing on the climate. Correct characterization of intensive aerosol optical properties, particularly in conditions where absorbing aerosol is present, is a crucial factor in quantifying these effects. The Southeast Atlantic Ocean (SEA), with seasonal biomass burning smoke plumes overlying and mixing with a persistent stratocumulus cloud deck, offers an excellent natural laboratory to make the observations necessary to understand the complexities of aerosol-cloud-radiation interactions. The first field deployment of the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign was conducted in September of 2016 out of Walvis Bay, Namibia.

Data collected during ORACLES-2016 are used to derive aerosol properties from an unprecedented number of simultaneous measurement techniques over this region. Here we present results from six of the eight independent instruments or instrument combinations, all applied to measure or retrieve aerosol absorption and single scattering albedo. Most but not all of the biomassburning aerosol was located in the free troposphere, in relative humidities typically ranging up to 60%. We present the single scattering albedo (SSA), absorbing and total aerosol optical depth (AOD and AAOD), and absorption, scattering, and extinction Ångström exponents (AAE, SAE, EAE) for specific case studies looking at near-coincident and -colocated measurements from multiple instruments, and SSAs for the broader campaign average over the monthlong deployment. For the case studies, we find that SSA agrees within the measurement uncertainties between multiple instruments, though, overall cases, there is no strong correlation between values reported by one instrument and another. We also find that agreement between the instruments is more robust at higher aerosol loading (AOD400 > 0.4).

The campaign-wide average and range shows differences in the values measured by each instrument. We find the ORACLES-2016 campaign-average SSA at 500nm (SSA500) to be between 0.85 and 0.88, depending on the instrument considered (4STAR, AirMSPI, or in situ measurements), with the inter-quartile ranges for all instruments between 0.83 and 0.89. This is consistent with previous September values reported over the region (between 0.84 and 0.90 for SSA at 550nm). The results
suggest that the differences observed in the campaign-average values may be dominated by instrument-specific spatial sampling differences and the natural physical variability in aerosol conditions over the SEA, rather than fundamental methodological differences.

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Research Program: Atmospheric Composition

Mission: ORACLES

Abstract: 
Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO2 but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remotesensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated (R2 = 0.62–0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.


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Research Program: Carbon Cycle & Ecosystems Program (CCEP)

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Research Program: Carbon Cycle & Ecosystems Program (CCEP)