Search

Governments restricted mobility and effectively shuttered much of the global economy in response to the COVID‐19 pandemic. Six San Francisco Bay Area counties were the first region in the United States to issue a "shelter‐in‐place" order asking non‐essential workers to stay home. Here we use CO2 observations from 35 Berkeley Environment, Air‐quality and CO2 Network (BEACO2N) nodes and an atmospheric transport model to quantify changes in urban CO2 emissions due to the order. We infer hourly emissions at 900‐m spatial resolution for 6 weeks before and 6 weeks during the order. We observe a 30% decrease in anthropogenic CO2 emissions during the order and show that this decrease is primarily due to changes in traffic (–48%) with pronounced changes to daily and weekly cycles; non‐traffic emissions show small changes (–8%). These findings provide a glimpse into a future with reduced CO2 emissions through electrification of vehicles. Plain Language Summary: This work uses atmospheric observations to quantify the changes in urban CO2 emissions from different sectors in response to COVID‐19 mobility regulations. Key Points: A 30% decrease in urban CO2 emissions was observed from the San Francisco Bay Area in response to COVID‐19 mobility restrictionsChanges are primarily driven by a decrease in CO2 emissions from traffic (–48%)There is a large change to the weekly and diurnal cycle of emissions with reductions in morning rush hour emissions [ABSTRACT FROM AUTHOR]

Where does the carbon released by burning fossil fuels go? Currently, ocean and land systems remove about half of the CO2 emitted by human activities; the remainder stays in the atmosphere. These removal processes are sensitive to feedbacks in the energy, carbon, and water cycles that will change in the future. Observing how much carbon is taken up on land through photosynthesis is complicated because carbon is simultaneously respired by plants, animals, and microbes. Global observations from satellites and air samples suggest that natural ecosystems take up about as much CO2 as they emit. To match the data, our land models generate imaginary Earths where carbon uptake and respiration are roughly balanced, but the absolute quantities of carbon being exchanged vary widely. Getting the magnitude of the flux is essential to make sure our models are capturing the right pattern for the right reasons. Combining two cutting-edge tools, carbonyl sulfide (OCS) and solar-induced fluorescence (SIF), will help develop an independent answer of how much carbon is being taken up by global ecosystems. Photosynthesis requires CO2, light, and water. OCS provides a spatially and temporally integrated picture of the "front door" of photosynthesis, proportional to CO2 uptake and water loss through plant stomata. SIF provides a high-resolution snapshot of the "side door," scaling with the light captured by leaves. These two independent pieces of information help us understand plant water and carbon exchange. A coordinated effort to generate SIF and OCS data through satellite, airborne, and ground observations will improve our process-based models to predict how these cycles will change in the future. [ABSTRACT FROM AUTHOR]

Methane is the second most important anthropogenic greenhouse gas in the Earth's atmosphere. Reducing methane emissions is consequently an important element in limiting the global temperature increase below 2°C compared to preindustrial times. Therefore, a good knowledge of source strengths and source locations is required. Anthropogenic methane emissions often originate from point sources or small areal sources, such as fugitive emissions at oil and gas production sites or landfills. Airborne remote sensing instruments such as the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) with meter scale imaging capabilities are able to yield information about the locations and magnitudes of methane sources, especially in areas with many potential emission sources. To extract methane column enhancement information from spectra recorded with the AVIRIS-NG instrument, different retrieval algorithms have been used, e.g. the matched filter (MF) or the Iterative Maximum A Posteriori DOAS (IMAP-DOAS) retrieval. The WFM-DOAS algorithm, successfully applied to AVIRIS-NG data in this study, fills a gap between those retrieval approaches by being a fast, non-iterative algorithm based on a first order approximation of the Lambert-Beer law, which calculates the change in gas concentrations from deviations from one background radiative transfer calculation using precalculated weighting functions specific to the state of the atmosphere during the overflight. This allows the fast quantitative processing of large data sets. Although developed for high spectral resolution measurements from satellite instruments such as SCIAMACHY, TROPOMI and the MAMAP airborne sensor, the algorithm can be applied well to lower spectral resolution AVIRIS-NG measurements. The data set examined here was recorded in Canada over different gas and coal extraction sites as part of the larger Arctic Boreal Vulnerability Experiment (ABoVE) Airborne Campaign in 2017. The noise of the retrieved CH4 imagery over bright surfaces (1μWcm-2nm-1sr-1 at 2140nm) was typically ±2.3% of the background total column of CH4 when fitting strong absorption lines around 2300nm, but could reach over ±5% for darker surfaces (<0.3μWcm-2nm-1sr -1 at 2140nm). Additionally, a worst case large scale bias due to the assumptions made in the WFM-DOAS retrieval was estimated to be ±5.4%. Radiance and fit quality filters were implemented to exclude the most uncertain results from further analysis, mostly due to either dark surfaces or surfaces, where the surface spectral reflection structures are similar to CH4 absorption features at the spectral resolution of the AVIRIS-NG instrument. We detected several methane plumes in the AVIRIS-NG images recorded during the ABoVE Airborne Campaign. For four of those plumes, the emissions were estimated using a simple cross sectional flux method. The retrieved fluxes originated from well pads and cold vents and ranged between (89±46)kg (CH4)h-1 and (141±87)kg (CH4) h-1. The wind uncertainty was a significant source of uncertainty for all plumes, followed by the single pixel retrieval noise and the uncertainty due to atmospheric variability. For one plume the wind was too low to estimate a trustworthy emission rate, although a plume was visible. [ABSTRACT FROM AUTHOR]

After stagnating in the early 2000s, the atmospheric methane growth rate has been positive since 2007 with a significant acceleration starting in 2014. While causes for previous growth rate variations are still not well determined, this recent increase can be studied with dense surface and satellite observations. Here, we use an ensemble of six multi-tracer atmospheric inversions that have the capacity to assimilate the major tracers in the methane oxidation chain - namely methane, formaldehyde, and carbon monoxide - to simultaneously optimize both the methane sources and sinks at each model grid. We show that the recent surge of the atmospheric growth rate between 2010-2013 and 2014-2017 is most likely explained by an increase of global CH4 emissions by 17.5 ± 1.5 Tg yr-1 (mean ± 1σ), while variations in CH4 sinks remained small. The inferred emission increase is consistently supported by both surface and satellite observations, with leading contributions from the tropics wetlands (~ 35 %) and anthropogenic emissions in China (~ 20 %). Such a high consecutive atmospheric growth rate has not been observed since the 1980s and corresponds to unprecedented global total CH4 emissions. [ABSTRACT FROM AUTHOR]

The terrestrial carbon sink has significantly increased in the past decades, but the underlying mechanisms are still unclear. The current synthesis of process-based estimates of land and ocean sinks requires an additional sink of 0.6 PgC yr−1 in the last decade to explain the observed airborne fraction. A concurrent global fire decline was observed in association with tropical agriculture expansion and landscape fragmentation. Here we show that a decline of 0.2 ± 0.1 PgC yr−1 in fire emissions during 2008–2014 relative to 2001–2007 also induced an additional carbon sink enhancement of 0.4 ± 0.2 PgC yr−1 attributable to carbon cycle feedbacks, amounting to a combined sink increase comparable to the 0.6 PgC yr−1 budget imbalance. Our results suggest that the indirect effects of fire, in addition to the direct emissions, is an overlooked mechanism for explaining decadal-scale changes in the land carbon sink and highlight the importance of fire management in climate mitigation. In recent history the amount of carbon captured by terrestrial systems has increased, but the processes driving this process has remained poorly constrained. Here the authors use a global carbon model to show that a decrease in wildfires has caused the land carbon sink to increase in the past few decades. [ABSTRACT FROM AUTHOR]

Photosynthesis by terrestrial plants represents the majority of CO2 uptake on Earth, yet it is difficult to measure directly from space. Estimating Gross Primary Production (GPP) from remote sensing indices is a primary source of uncertainty, in particular for observing seasonal variations in evergreen forests. Recent vegetation remote sensing techniques have highlighted spectral regions sensitive to dynamic changes in leaf/needle carotenoid composition, showing promise for tracking seasonal changes in photosynthesis of evergreen forests. However, continuous daily measurements of spectrally resolved canopy reflectance are limited in these ecosystems. To investigate this potential, we continuously measured vegetation reflectance (400-900nm) using a canopy spectrometer system, PhotoSpec, mounted on top of an eddy-covariance flux tower in a subalpine evergreen forest at Niwot Ridge, Colorado, USA. We analyzed driving spectral components in the measured canopy reflectance using both statistical and process-based approaches. The decomposed spectral components relate directly to carotenoid pigments and co-vary seasonally with GPP, supporting the interpretation of the Photochemical Reflectance Index (PRI) and the Chlorophyll/Carotenoid Index (CCI). We show that using features from the entire 400-900nm range show additional spectral changes near the red-egde but do not outperform the PRI or CCI indices for GPP predictions. In addition, we can quantitatively determine needle-scale chlorophyll to carotenoid ratios as well as anthocyanin contents using full spectrum inversions, both of which tightly correlated with seasonal GPP changes. Reconstructing GPP from vegetation reflectance using Partial Least Squares Regression (PLSR) explained approximately 87% of the variability in observed GPP. Our results link the seasonal variation of reflectance to the pool size of photoprotective pigments, highlighting all spectral locations within 400-900nm associated with GPP seasonality in evergreen forests. [ABSTRACT FROM AUTHOR]

Recent successes in passive remote sensing of far-red solar induced chlorophyll fluorescence (SIF) have spurred development and integration of canopy-level fluorescence models in global terrestrial biosphere models (TBMs) for climate and carbon cycle research. The interaction of fluorescence with photochemistry at the leaf- and canopy- scale provides opportunities to diagnose and constrain model simulations of photosynthesis and related processes, through direct comparison to and assimilation of tower, airborne, and satellite data. TBMs describe key processes relating to absorption of sunlight, leaf-level fluorescence emission, scattering and reabsorption throughout the canopy. Here, we analyze simulations from an ensemble of process-based TBM-SIF models (SiB3, SiB4, CLM4.5, CLM5.0, BETHY, ORCHIDEE, BEPS) at a subalpine evergreen needleleaf forest near Niwot Ridge, Colorado. These models are forced with tower observed meteorological data, and analyzed against continuous far-red SIF and gross primary productivity (GPP) partitioned eddy covariance data at diurnal and synoptic scales during the growing season (July-August 2017). Our primary objective is to summarize the site-level state of the art in TBM-SIF modeling over a relatively short time period (summer) when light, structure, and pigments are similar, setting the stage for regional- to global-scale analyses. We find that these models are generally well constrained in simulating photosynthetic yield, but show strongly divergent patterns in the simulation of absorbed photosynthetic active radiation (PAR), absolute GPP and fluorescence, quantum yields, and light response at leaf and canopy scale. This study highlights the need for mechanistic modeling of non-photochemical quenching in stressed and unstressed environments, and improved representation of light absorption (APAR), distribution of sunlit and shaded light, and radiative transfer from leaf to canopy scale. [ABSTRACT FROM AUTHOR]

Raman scattering in the Earth's atmosphere is caused predominantly by its most abundant molecular components, N 2 and O 2. After the computation of the optical properties that govern the spectral and angular redistribution of light due to various inelastic scattering events, viz. rotational Raman scattering (RRS), vibrational Raman scattering (VRS), and rovibrational Raman scattering (RVRS), covered in Part I of this series, the next challenge in the simulation of inelastic scattering in the Earth's atmosphere is to carry out radiative transfer (RT) computations across several wavelengths simultaneously. In this part of our work, we provide the RT formulation for fully polarized simulations of inelastic scattering using the matrix-operator-method-based RT model vSmartMOM. The formalism is optimized for easy use with GPUs, allowing an unprecedented speedup of accurate multi-wavelength RT computations of inelastic scattering using the full Stokes-vector, thus allowing its operational use without coarse spectral binning (Rozanov and Vountas, 2014), or single scattering approximations (Sioris and Evans, 1999) at longer wavelengths. After comparing our model against the current state-of-the-art, we demonstrate the use of vSmartMOM to simulate Raman lidar measurements, the Ring effect, the ghosting of Fraunhofer lines due to vibrational Raman scattering and spectral corrections due to inelastic scattering in the O 2 A-band in the Earth's atmosphere. We use our model (1.) to validate the convention of neglecting the contribution of VRS and RVRS, and (2.) to quantify the speed and accuracy of the single scattering approximation in the O 2 A-band. • First exact polarized simulations of rotational, vibrational and rovibrational Raman scattering. • GPU acceleration achieves unprecedented speed-ups, allowing operational use. • Applications include Raman Lidar, Ring effect, ghosting of Fraunhofer lines, and the infilling of telluric lines of absorption lke the O 2 A-band. • Exciting implications for trace gas retrievals in the UV/Vis, greenhouse gas retrievals, and fluorescence studies. [ABSTRACT FROM AUTHOR]

Solar-Induced chlorophyll Fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here we present an oversampling and downscaling method to obtain 500-m spatial resolution SIF over California. We report daily values based on a 14-day window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODIS vegetation indices (NDVI, EVI, and NIRv) at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas SIF has a low sensitivity to clouds and can detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatio-temporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatio-temporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) retrieved from satellite spectrometers can be a highly valuable proxy for photosynthesis. The SIF signal is very small and notoriously difficult to measure, requiring sub-nanometer spectral resolution measurements, which to-date are only available from atmospheric spectrometers sampling at coarse spatial resolution. For example, the widely used SIF dataset derived from the GOME-2 mission is typically provided in 0.5 decimal degree composites. This paper presents a new SIF dataset based on GOME-2 satellite observations with an enhanced spatial resolution of 0.05 decimal degrees and an 8-day time step covering the period 2007–2018. It leverages on a proven methodology that relies on using a light use efficiency (LUE) modelling approach to establishing a semi-empirical relationship between SIF and various explanatory variables derived from remote sensing at finer spatial resolution. An optimal set of explanatory variables is selected based on an independent validation with OCO-2 SIF observations, which are only sparsely available but have a high accuracy and spatial resolution. After a bias-correction, the resulting downscaled SIF data shows high spatio-temporal agreement with the first SIF retrievals from the new TROPOMI mission, opening the path towards establishing a surrogate archive for this promising new dataset. We foresee that this new SIF dataset should be a valuable asset for Earth System Science in general, and for monitoring vegetation productivity in particular. [ABSTRACT FROM AUTHOR]

Novel satellite measurements of solar‐induced chlorophyll fluorescence (SIF) can improve our understanding of global photosynthesis; however, little is known about how to interpret the controls on its spectral variability. To address this, we disentangle simultaneous drivers of fluorescence spectra by coupling active and passive fluorescence measurements with photosynthesis. We show empirical and mechanistic evidence for where, why, and to what extent leaf fluorescence spectra change. Three distinct components explain more than 95% of the variance in leaf fluorescence spectra under both steady‐state and changing illumination conditions. A single spectral shape of fluorescence explains 84% of the variance across a wide range of species. The magnitude of this shape responds to absorbed light and photosynthetic up/down regulation; meanwhile, chlorophyll concentration and nonphotochemical quenching control 9% and 3% of the remaining spectral variance, respectively. The spectral shape of fluorescence is remarkably stable where most current satellite retrievals occur ("far‐red," >740nm), and dynamic downregulation of photosynthesis reduces fluorescence magnitude similarly across the 670‐ to 850‐nm range. We conduct an exploratory analysis of hourly red and far‐red canopy SIF in soybean, which shows a subtle change in red:far‐red fluorescence coincident with photosynthetic downregulation but is overshadowed by longer‐term changes in canopy chlorophyll and structure. Based on our leaf and canopy analysis, caution should be taken when attributing large changes in the spectral shape of remotely sensed SIF to plant stress, particularly if data acquisition is temporally sparse. Ultimately, changes in SIF magnitude at wavelengths greater than 740 nm alone may prove sufficient for tracking photosynthetic dynamics. Plain Language Summary: Satellite remote sensing provides a global picture of photosynthetic activity—allowing us to see when, where, and how much CO2 plants are assimilating. To do this, satellites measure a small emission of energy from the plants called chlorophyll fluorescence. However, this measurement is typically made across a narrow wavelength range, while the emission spectrum (650–850 nm) is quite dynamic. We show where, why, and to what extent leaf fluorescence spectra change across a diverse range of species and conditions, ultimately informing canopy remote sensing measurements. Results suggest that wavelengths currently used by satellites are stable enough to track the downregulation of photosynthesis resulting from stress, while spectral shape changes respond more strongly to dynamics in canopy structure and chlorophyll concentration. Key Points: We disentangle chlorophyll fluorescence spectra into three distinct components explaining >95% of the variance in the spectral shapeThe spectral shape of chlorophyll fluorescence is stable >740 nm, modulated by distinct chlorophyll and nonphotochemical quenching featuresUsing spectral shape changes to infer plant stress via remote sensing will be challenging—mostly dominated by chlorophyll and structure [ABSTRACT FROM AUTHOR]

Methane is the second most important anthropogenic greenhouse gas in the Earth climate system but emission quantification of localized point sources has been proven challenging, resulting in ambiguous regional budgets and source categories distributions. Although recent advancements in airborne remote sensing instruments enable retrievals of methane enhancements at unprecedented resolution of 1-5 m at regional scales, emission quantification of individual sources can be limited by the lack of knowledge of local wind speed. Here, we developed an algorithm that can estimate flux rates solely from mapped methane plumes, avoiding the need for ancillary information on wind speed. The algorithm was trained on synthetic measurements using Large Eddy Simulation under a range of background wind speeds of 1-10 m/s and source emission rates ranging from 10 to 1000 kg/hr. The surrogate measurements mimic plume mapping performed by the next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and provide an ensemble of 2-D snapshots of column methane enhancements at 5m spatial resolution. We make use of the integrated total methane enhancement in each plume, denoted as Integrated Methane Enhancement (IME), and investigate how this IME relates to the actual methane flux rate. Our analysis shows that the IME corresponds to the flux rate non-linearly and is strongly dependent on the background wind speed over the plume. We demonstrate that the plume width, defined based on the plume angular distribution around its main axis, provides information on the associated background wind speed. This allows us to invert source flux rate based solely on the IME and the plume-shape itself. On average, the error estimate based on randomly generated plumes is approximately 30 % for an individual estimates and less than 10 % for an aggregation of 30 plumes. A validation against a natural gas controlled release experiment agree to within 32 %, supporting the basis for the applicability of this technique to quantifying point sources over large geographical area in airborne field campaigns and future space-based observations. [ABSTRACT FROM AUTHOR]