Showing papers by "Charles E. Miller published in 2022"

Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the United States

Abstract: Significance Large methane point sources exist across multiple source sectors (e.g., oil, gas, coal, livestock, waste). Lacking is a robust assessment of the relative contribution of strong methane point sources against total or regional budgets, which is needed for prioritizing mitigation. In this study, we flew airborne imaging spectrometers repeatedly over multiple basins in the United States to quantify large methane point sources across multiple sectors. We compared these point sources to satellite-based regional flux inversions and found that methane super-emitters consistently make up a sizable contribution to total the total flux in a basin. These results show that a significant climate benefit can be realized by specific isolation and remediation of relatively few sources.

Attribution of Space‐Time Variability in Global‐Ocean Dissolved Inorganic Carbon

Abstract: The inventory and variability of oceanic dissolved inorganic carbon (DIC) is driven by the interplay of physical, chemical, and biological processes. Quantifying the spatiotemporal variability of these drivers is crucial for a mechanistic understanding of the ocean carbon sink and its future trajectory. Here, we use the Estimating the Circulation and Climate of the Ocean‐Darwin ocean biogeochemistry state estimate to generate a global‐ocean, data‐constrained DIC budget and investigate how spatial and seasonal‐to‐interannual variability in three‐dimensional circulation, air‐sea CO2 flux, and biological processes have modulated the ocean sink for 1995–2018. Our results demonstrate substantial compensation between budget terms, resulting in distinct upper‐ocean carbon regimes. For example, boundary current regions have strong contributions from vertical diffusion while equatorial regions exhibit compensation between upwelling and biological processes. When integrated across the full ocean depth, the 24‐year DIC mass increase of 64 Pg C (2.7 Pg C year−1) primarily tracks the anthropogenic CO2 growth rate, with biological processes providing a small contribution of 2% (1.4 Pg C). In the upper 100 m, which stores roughly 13% (8.1 Pg C) of the global increase, we find that circulation provides the largest DIC gain (6.3 Pg C year−1) and biological processes are the largest loss (8.6 Pg C year−1). Interannual variability is dominated by vertical advection in equatorial regions, with the 1997–1998 El Niño‐Southern Oscillation causing the largest year‐to‐year change in upper‐ocean DIC (2.1 Pg C). Our results provide a novel, data‐constrained framework for an improved mechanistic understanding of natural and anthropogenic perturbations to the ocean sink.

Multi-year observations reveal a larger than expected autumn respiration signal across northeast Eurasia

Abstract: Abstract. Site-level observations have shown pervasive cold season CO2 release across Arctic and boreal ecosystems, impacting annual carbon budgets. Still, the seasonality of CO2 emissions are poorly quantified across much of the high latitudes due to the sparse coverage of site-level observations. Space-based observations provide the opportunity to fill some observational gaps for studying these high-latitude ecosystems, particularly across poorly sampled regions of Eurasia. Here, we show that data-driven net ecosystem exchange (NEE) from atmospheric CO2 observations implies strong summer uptake followed by strong autumn release of CO2 over the entire cold northeastern region of Eurasia during the 2015–2019 study period. Combining data-driven NEE with satellite-based estimates of gross primary production (GPP), we show that this seasonality implies less summer heterotrophic respiration (Rh) and greater autumn Rh than would be expected given an exponential relationship between respiration and surface temperature. Furthermore, we show that this seasonality of NEE and Rh over northeastern Eurasia is not captured by the TRENDY v8 ensemble of dynamic global vegetation models (DGVMs), which estimate that 47 %–57 % (interquartile range) of annual Rh occurs during August–April, while the data-driven estimates suggest 59 %–76 % of annual Rh occurs over this period. We explain this seasonal shift in Rh by respiration from soils at depth during the zero-curtain period, when sub-surface soils remain unfrozen up to several months after the surface has frozen. Additional impacts of physical processes related to freeze–thaw dynamics may contribute to the seasonality of Rh. This study confirms a significant and spatially extensive early cold season CO2 efflux in the permafrost-rich region of northeast Eurasia and suggests that autumn Rh from subsurface soils in the northern high latitudes is not well captured by current DGVMs.

Pan‐Arctic soil moisture control on tundra carbon sequestration and plant productivity

Abstract: Long‐term atmospheric CO2 concentration records have suggested a reduction in the positive effect of warming on high‐latitude carbon uptake since the 1990s. A variety of mechanisms have been proposed to explain the reduced net carbon sink of northern ecosystems with increased air temperature, including water stress on vegetation and increased respiration over recent decades. However, the lack of consistent long‐term carbon flux and in situ soil moisture data has severely limited our ability to identify the mechanisms responsible for the recent reduced carbon sink strength. In this study, we used a record of nearly 100 site‐years of eddy covariance data from 11 continuous permafrost tundra sites distributed across the circumpolar Arctic to test the temperature (expressed as growing degree days, GDD) responses of gross primary production (GPP), net ecosystem exchange (NEE), and ecosystem respiration (ER) at different periods of the summer (early, peak, and late summer) including dominant tundra vegetation classes (graminoids and mosses, and shrubs). We further tested GPP, NEE, and ER relationships with soil moisture and vapor pressure deficit to identify potential moisture limitations on plant productivity and net carbon exchange. Our results show a decrease in GPP with rising GDD during the peak summer (July) for both vegetation classes, and a significant relationship between the peak summer GPP and soil moisture after statistically controlling for GDD in a partial correlation analysis. These results suggest that tundra ecosystems might not benefit from increased temperature as much as suggested by several terrestrial biosphere models, if decreased soil moisture limits the peak summer plant productivity, reducing the ability of these ecosystems to sequester carbon during the summer.

Using atmospheric trace gas vertical profiles to evaluate model fluxes: a case study of Arctic-CAP observations and GEOS simulations for the ABoVE domain

Abstract: Abstract. Accurate estimates of carbon–climate feedbacks require an independent means for evaluating surface flux models at regional scales. The altitude-integrated enhancement (AIE) derived from the Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of this bulk quantity for surface flux model evaluation. This bulk quantity leverages background mole fraction values from the middle free troposphere, is agnostic to uncertainties in boundary layer height, and can be derived from model estimates of mole fractions and vertical gradients. To demonstrate the utility of the bulk quantity, six airborne profiling surveys of atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) throughout Alaska and northwestern Canada between April and November 2017 were completed as part of NASA's Arctic–Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4, and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons of the AIE bulk quantity from aircraft observations and GEOS simulations of atmospheric CO2, CH4, and CO highlight the fidelity of the modeled surface fluxes. The model–data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broad-scale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are captured in CO observations.

Potential Satellite Monitoring of Surface Organic Soil Properties in Arctic Tundra From SMAP

Abstract: Surface organic carbon content and soil moisture (SM) represent first‐order controls on permafrost thaw and vulnerability, yet remain challenging to map accurately. Here we explored the links between surface organic soil properties and SM dynamics in the Alaska North Slope through data analysis and process‐based modeling. Our analysis, based on in situ SM and brightness temperature data from the Soil Moisture Active Passive (SMAP) mission, indicated that the SM drydown process in Arctic tundra is closely related to surface soil organic carbon (SOC) properties. More rapid drydown was generally observed in areas with high SOC concentration (SOCC) or low bulk density. The drydown timescale derived from the SMAP polarization ratio (PR) was significantly correlated with SoilGrids surface (0–5 cm) SOCC data (R = −0.54 ∼ −0.68, p < 0.01) at regional scale. To understand the process, we used a coupled permafrost hydrology and microwave emission model to simulate changes in the L‐band PR during the thaw season. The model accounts for the variations in organic soil hydraulic and dielectric properties with SOC content and decomposition state. Model sensitivity runs showed larger L‐band PR decreases during the early thaw season in soils with higher SOCC consistent with the above analysis, whereby highly organic soils (SOCC > 34.8%) drain water more easily with a larger amount of water discharged or lost (through evapotranspiration) relative to soils with less carbon concentration (SOCC < 17.4%). Our findings indicate that satellite L‐band observations are sensitive to tundra SM and carbon properties, and may provide critical constraints on predictions of Arctic permafrost thaw and vulnerability.

A multi-city urban atmospheric greenhouse gas measurement data synthesis

Abstract: Urban regions emit a large fraction of anthropogenic emissions of greenhouse gases (GHG) such as carbon dioxide (CO2) and methane (CH4) that contribute to modern-day climate change. As such, a growing number of urban policymakers and stakeholders are adopting emission reduction targets and implementing policies to reach those targets. Over the past two decades research teams have established urban GHG monitoring networks to determine how much, where, and why a particular city emits GHGs, and to track changes in emissions over time. Coordination among these efforts has been limited, restricting the scope of analyses and insights. Here we present a harmonized data set synthesizing urban GHG observations from cities with monitoring networks across North America that will facilitate cross-city analyses and address scientific questions that are difficult to address in isolation.

Ongoing Progress Toward NASA's Surface Biology and Geology Mission

Abstract: Pursuant to recommendations by the National Academies of Science, Engineering and Medicine's Earth Science Decadal Survey [1], the National Aeronautics and Space Administration (NASA) has announced the development of an Earth System Observatory (ESO), a series of missions designed to observe processes across the Earth's interior, surface and atmosphere. A key component of this system is the Surface Biology and Geology (SBG) investigation. SBG will measure the composition and properties of Earth's land, inland waters, and coastal oceans. The notional architecture consists of multiple spacecraft slated for launch in the 2027–2028 timeframe (Figure 1). Target science questions and geophysical variables span diverse disciplines including terrestrial and aquatic ecology, geology, vulcanology, hydrology and cryospheric sciences (Figure 2). Beyond simply measuring geophysical variables for each discipline, SBG will provide information about the links between the different domains, enabling a more comprehensive understanding of the Earth as a connected system. SBG measurements will also benefit a wide range of societal applications including agriculture, terrestrial and aquatic biodiversity, natural hazards, public health, and management of water and other natural resources [2]. SBG will also coordinate measurements, data products, and analyses with other ESO elements to deliver an integrated Earth System perspective of Earth and its changing climate.

Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters

Abstract: High‐resolution space‐based spectral imaging of the Earth's surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal‐to‐noise ratio. Different applications drive divergent instrument designs, so optimization for wide‐reaching missions is complex. The Surface Biology and Geology component of NASA's Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications‐agnostic, data‐driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high‐dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the ID decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal‐to‐noise levels. This decrease in information content has implications for all derived products. ID is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher‐level algorithms, products, applications, or disciplines.

Comparing airborne algorithms for greenhouse gas flux measurements over the Alberta oil sands

Abstract: Abstract. To combat global warming, Canada has committed to reducing greenhouse gases to be (GHGs) 40 %–45 % below 2005 emission levels by 2025. Monitoring emissions and deriving accurate inventories are essential to reaching these goals. Airborne methods can provide regional and area source measurements with small error if ideal conditions for sampling are met. In this study, two airborne mass-balance box-flight algorithms were compared to assess the extent of their agreement and their performance under various conditions. The Scientific Aviation's (SciAv) Gaussian algorithm and the Environment and Climate Change Canada's top-down emission rate retrieval algorithm (TERRA) were applied to data from five samples. Estimates were compared using standard procedures, by systematically testing other method fits, and by investigating the effects on the estimates when method assumptions were not met. Results indicate that in standard scenarios the SciAv and TERRA mass-balance box-flight methods produce similar estimates that agree (3 %–25 %) within algorithm uncertainties (4 %–34 %). Implementing a sample-specific surface extrapolation procedure for the SciAv algorithm may improve emission estimation. Algorithms disagreed when non-ideal conditions occurred (i.e., under non-stationary atmospheric conditions). Overall, the results provide confidence in the box-flight methods and indicate that emissions estimates are not overly sensitive to the choice of algorithm but demonstrate that fundamental algorithm assumptions should be assessed for each flight. Using a different method, the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) independently mapped individual plumes with emissions 5 times larger than the source SciAv sampled three days later. The range in estimates highlights the utility of increased sampling to get a more complete understanding of the temporal variability of emissions and to identify emission sources within facilities. In addition, hourly on-site activity data would provide insight to the observed temporal variability in emissions and make a comparison to reported emissions more straightforward.

Error and Uncertainty Degrade Topographic Corrections of Remotely Sensed Data

Abstract: Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth's land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth's mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo‐photogrammetry at ∼30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo‐photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root‐mean‐square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine‐scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth's mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth's surface.

Quantifying Northern High Latitude Gross Primary Productivity (GPP) Using Carbonyl Sulfide (OCS)

Abstract: The northern high latitude (NHL, 40°N to 90°N) is where the second peak region of gross primary productivity (GPP) other than the tropics. The summer NHL GPP is about 80% of the tropical peak, but both regions are still highly uncertain (Norton et al. 2019, https://doi.org/10.5194/bg-16-3069-2019). Carbonyl sulfide (OCS) provides an important proxy for photosynthetic carbon uptake. Here we optimize the OCS plant uptake fluxes across the NHL by fitting atmospheric concentration simulation with the GEOS‐CHEM global transport model to the aircraft profiles acquired over Alaska during NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (2012–2015). We use the empirical biome‐specific linear relationship between OCS plant uptake flux and GPP to derive the six plant uptake OCS fluxes from different GPP data. Such GPP‐based fluxes are used to drive the concentration simulations. We evaluate the simulations against the independent observations at two ground sites of Alaska. The optimized OCS fluxes suggest the NHL plant uptake OCS flux of −247 Gg S year−1, about 25% stronger than the ensemble mean of the six GPP‐based OCS fluxes. GPP‐based OCS fluxes systematically underestimate the peak growing season across the NHL, while a subset of models predict early start of season in Alaska, consistent with previous studies of net ecosystem exchange. The OCS optimized GPP of 34 PgC yr−1 for NHL is also about 25% more than the ensembles mean from six GPP data. Further work is needed to fully understand the environmental and biotic drivers and quantify their rate of photosynthetic carbon uptake in Arctic ecosystems.

Metrics for assessing Linear Inverse Problems: a case study of a Trace Gas Inversion

Abstract: . Multiple metrics have been proposed and utilized to assess the performance of linear Bayesian and geostatistical in- 1 verse problems. These metrics are mostly related to assessing reduction in prior uncertainties, comparing modeled observations 2 to true observations, and checking distributional assumptions. These metrics though important should be augmented with sen- 3 sitivity analysis to obtain a comprehensive understanding of the performance of inversions and critically improve confidence in 4 the estimated fluxes. With this motivation, we derive analytical forms of the local sensitivities with respect to the number of in- 5 puts such as measurements, covariance parameters, covariates, and forward operator or jacobian. In addition to local sensitivity, 6 we develop a framework for global sensitivity analysis that shows the apportionment of the uncertainty of different inputs to 7 an inverse problem. The proposed framework is applicable to any other domain that employs linear Bayesian and geostatistical 8 inverse methods. We show the application of our methodology in the context of an atmospheric inverse problem for estimating 9 urban GHG emissions in Los Angeles. Within its context, we also propose a mathematical framework to construct correlation 10 functions and components of uncertainty matrices from a pre-computed jacobian that encompasses non-stationary structures. by row-based normalization and rank influence of observations in governing gridscale estimates of ˆ s Qualitatively, and row-based assessment the spatio-temporal estimates of ˆ s when point the dominant of it an insight into temporal aggregation error as the information encoded in instantaneous measurement can get lost over the coarser time-period of inversion. This aggregation error also manifests and is determined by the resolution at which fluxes are obtained. in many situations these aggregation are as the choice of the spatio-temporal resolution of inversions is governed by the density of observations in space time.

A multi-city urban atmospheric greenhouse gas measurement data synthesis

Abstract: Urban regions emit a large fraction of anthropogenic emissions of greenhouse gases (GHG) such as carbon dioxide (CO2) and methane (CH4) that contribute to modern-day climate change. As such, a growing number of urban policymakers and stakeholders are adopting emission reduction targets and implementing policies to reach those targets. Over the past two decades research teams have established urban GHG monitoring networks to determine how much, where, and why a particular city emits GHGs, and to track changes in emissions over time. Coordination among these efforts has been limited, restricting the scope of analyses and insights. Here we present a harmonized data set synthesizing urban GHG observations from cities with monitoring networks across North America that will facilitate cross-city analyses and address scientific questions that are difficult to address in isolation.

Gold Rush: Money-Image in Deleuze and Chaplin

Abstract: Abstract:Tracking similarities between film and money, specifically focusing on connections between the financial economy and the films of Charlie Chaplin, this essay examines the dynamics between the movement of the Tramp and the movements of financial markets. It explores the power of lifeless money to activate and command people, driving them to labor, starvation, and even death, yet also providing them a gold rush of stimulation. What does the power of money and what Deleuze calls its obverse, film, mean for democratic politics, especially in terms of the challenges that it presents to conceptions of political action centered on human sovereignty? Cinematic conceptions—such as movement, emotion, and time—help us think of monetary activity in a new light, as an activity shared between organic and inorganic objects. Moreover, thinking becomes less about isolating objects and more about categories that cannot be nailed down as singularities, like movement or time—realities that escape our ability to conceptualize them as objects. Money is a rush—of emotion and time—and we should begin to use it that way.