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DOE OSTI2020

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude–longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. Linear regressions of DMS against the environmental parameters show that on a global-scale mixed layer depth and solar radiation are the strongest predictors of DMS. These parameters capture ~9 % and ~7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (~39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures ~66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (20.12±0.43 Tg S yr –1 ) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

PSInet: a new global water potential network

Abstract Given the pressing challenges posed by climate change, it is crucial to develop a deeper understanding of the impacts of escalating drought and heat stress on terrestrial ecosystems and the vital services they offer. Soil and plant water potential play a pivotal role in governing the dynamics of water within ecosystems and exert direct control over plant function and mortality risk during periods of ecological stress. However, existing observations of water potential suffer from significant limitations, including their sporadic and discontinuous nature, inconsistent representation of relevant spatio-temporal scales and numerous methodological challenges. These limitations hinder the comprehensive and synthetic research needed to enhance our conceptual understanding and predictive models of plant function and survival under limited moisture availability. In this article, we present PSInet (PSI—for the Greek letter Ψ used to denote water potential), a novel collaborative network of researchers and data, designed to bridge the current critical information gap in water potential data. The primary objectives of PSInet are as follows. (i) Establishing the first openly accessible global database for time series of plant and soil water potential measurements, while providing important linkages with other relevant observation networks. (ii) Fostering an inclusive and diverse collaborative environment for all scientists studying water potential in various stages of their careers. (iii) Standardizing methodologies, processing and interpretation of water potential data through the engagement of a global community of scientists, facilitated by the dissemination of standardized protocols, best practices and early career training opportunities. (iv) Facilitating the use of the PSInet database for synthesizing knowledge and addressing prominent gaps in our understanding of plants’ physiological responses to various environmental stressors. The PSInet initiative is integral to meeting the fundamental research challenge of discerning which plant species will thrive and which will be vulnerable in a world undergoing rapid warming and increasing aridification.

Forestry↗

DOE OSTI2024

Land Use Change Alters Soil Organic Carbon: Constrained Global Patterns and Predictors

Abstract Land use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha −1 ) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha −1 ), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha −1 ) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha −1 ) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha −1 ), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha −1 ). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha −1 ) and 75.3% (7.0 ± 0.38 Mg ha −1 ) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies.

Environmental Sciences & Ecology↗

DOE OSTI2025

Data from TropiRoot 1.0 database: tropical root characteristics across environments

TropiRoot 1.0 is a new tropical root database with root characteristics across environment gradients. It has data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 includes root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology and root chemistry. This initiative represents an approximately 30% increase in the currently available data for tropical roots in the Fine Root Ecology Database (FRED). TropiRoot 1.0, contains root characteristics from 25 different countries where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data was available, including soil data, these data was either extracted and included in the database or their availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match the ones reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions, and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2008

The Impact of Soil Reflectance on the Quantification of the Green Vegetation Fraction from NDVI

The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing rnodeP between two NDVI endmembers: bare soil NDVI (NDVI(sub o)) and full vegetation NDVI (NDVI(sub infinity)). Usually it is assumed that NDVI(sub o), is close to zero (NDVI(sub o) approx.-0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soil NDVI computed from 2906 samples is much larger (NDVI=0.2) and is highly variable (standard deviation=O. 1). We show that the underestimation of NDVI(sub o) yields overestimations of Fg. The largest errors occur in grassland and shrubland areas. Using parameters for NDVI(sub o) and NDVI(sub infinity) derived from global scenes yields overestimations of Fg ((Delta) Fg*) that are larger than 0.2 for the majority of U.S. land cover types when pixel NDVI values are 0.2<NDVI(sub pixel)<0.4. Figure 1 shows how the Fg overestimation varies for the most common land cover types in the conterminous U.S. for typical seasonal NDVI values. When using conterminous U.S. scenes to derive NDV(sub o) and NDVI(sub infinity), the overestimation is less (0.10-0.17 for 0.2<NDVI(sub pixel)<0.4). As a result, parts of the conterminous U.S. are affected at different times of the year depending on the local seasonal NDVI cycle. We propose using global databases of NDVI(sub o) along with information on historical NDVI(sub pixel) values to compute a statistically most-likely estimate of Fg (Fg*). Using in situ measurements made at the Sevilleta LTER, we show that this approach yields better estimates of Fg than using global invariant NDVI(sub o) values estimated from whole scenes (Figure 2). At the two studied sites, the Fg estimate was adjusted by 52% at the grassland and 86% at the shrubland. More significant advances will require information on spatial distribution of soil reflectance.

Montandon, L. M.↗

DOE OSTI2023

The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy

Abstract Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous ‘blue carbon’ studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon‐storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R‐shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision‐making.

Holmquist, James R.↗

DOE OSTI2013

Photosynthesis-dependent isoprene emission from leaf to planet in a global carbon-chemistry-climate model

We describe the implementation of a biochemical model of isoprene emission that depends on the electron requirement for isoprene synthesis into the Farquhar/Ball- Berry leaf model of photosynthesis and stomatal conductance that is embedded within a global chemistry-climate simulation framework. The isoprene production is calculated as a function of electron transport-limited photosynthesis, intercellular carbon dioxide concentration, and canopy temperature. The vegetation biophysics module computes the photosynthetic uptake of carbon dioxide coupled with the transpiration of water vapor and the isoprene emission rate at the 30 min physical integration time step of the global chemistry-climate model. In the model, the rate of carbon assimilation provides the dominant control on isoprene emission variability over canopy temperature. A control simulation representative of the present day climatic state that uses plant functional types (PFTs), prescribed phenology and generic PFT-specific isoprene emission potentials (fraction of electrons available for isoprene synthesis) reproduces 50% of the variability across different ecosystems and seasons in a global database of measured campaign-average fluxes. Compared to time-varying isoprene flux measurements at select sites, the model authentically captures the observed variability in the 30 min average diurnal cycle (R 2 = 64-96 %) and simulates the flux magnitude to within a factor of 2. The control run yields a global isoprene source strength of 451 TgC yr -1 that increases by 30% in the artificial absence of plant water stress and by 55% for potential natural vegetation.

Unger, N.↗

NASA NTRS2013

Photosynthesis-dependent Isoprene Emission from Leaf to Planet in a Global Carbon-chemistry-climate Model

We describe the implementation of a biochemical model of isoprene emission that depends on the electron requirement for isoprene synthesis into the FarquharBallBerry leaf model of photosynthesis and stomatal conductance that is embedded within a global chemistry-climate simulation framework. The isoprene production is calculated as a function of electron transport-limited photosynthesis, intercellular and atmospheric carbon dioxide concentration, and canopy temperature. The vegetation biophysics module computes the photosynthetic uptake of carbon dioxide coupled with the transpiration of water vapor and the isoprene emission rate at the 30 min physical integration time step of the global chemistry-climate model. In the model, the rate of carbon assimilation provides the dominant control on isoprene emission variability over canopy temperature. A control simulation representative of the present-day climatic state that uses 8 plant functional types (PFTs), prescribed phenology and generic PFT-specific isoprene emission potentials (fraction of electrons available for isoprene synthesis) reproduces 50 of the variability across different ecosystems and seasons in a global database of 28 measured campaign-average fluxes. Compared to time-varying isoprene flux measurements at 9 select sites, the model authentically captures the observed variability in the 30 min average diurnal cycle (R2 6496) and simulates the flux magnitude to within a factor of 2. The control run yields a global isoprene source strength of 451 TgC yr1 that increases by 30 in the artificial absence of plant water stress and by 55 for potential natural vegetation.

photosynthesis↗

DOE OSTI2014

A calibration procedure to improve global rice yield simulations with EPIC

Crop models are increasingly used to assess impacts of climate change/variability and management practices on productivity and environmental performance of alternative cropping systems. Calibration is an important procedure to improve reliability of model simulations, especially for large area applications. However, global-scale crop model calibration has rarely been exercised due to limited data availability and expensive computing cost. Here we present a simple approach to calibrate Environmental Policy Integrated Climate (EPIC) model for a global implementation of rice. We identify four parameters (potential heat unit – PHU, planting density – PD, harvest index – HI, and biomass energy ratio – BER) and calibrate them regionally to capture the spatial pattern of reported rice yield in 2000. Model performance is assessed by comparing simulated outputs with independent FAO national data. The comparison demonstrates that the global calibration scheme performs satisfactorily in reproducing the spatial pattern of rice yield, particularly in main rice production areas. Spatial agreement increases substantially when more parameters are selected and calibrated, but with varying efficiencies. Among the parameters, PHU and HI exhibit the highest efficiencies in increasing the spatial agreement. Simulations with different calibration strategies generate a pronounced discrepancy of 5–35% in mean yields across latitude bands, and a small to moderate difference in estimated yield variability and yield changing trend for the period of 1981–2000. Present calibration has little effects in improving simulated yield variability and trends at both regional and global levels, suggesting further works are needed to reproduce temporal variability of reported yields. This study highlights the importance of crop models’ calibration, and presents the possibility of a transparent and consistent up scaling approach for global crop simulations given current availability of global databases of weather, soil, crop calendar, fertilizer and irrigation management information, and reported yield.

Crop model↗

NASA NTRS2004

Global Autocorrelation Scales of the Partial Pressure of Oceanic CO2

A global database of approximately 1.7 million observations of the partial pressure of carbon dioxide in surface ocean waters (pCO2) collected between 1970 and 2003 is used to estimate its spatial autocorrelation structure. The patterns of the lag distance where the autocorrelation exceeds 0.8 is similar to patterns in the spatial distribution of the first baroclinic Rossby radius of deformation indicating that ocean circulation processes play a significant role in determining the spatial variability of pCO2. For example, the global maximum of the distance at which autocorrelations exceed 0.8 averages about 140 km in the equatorial Pacific. Also, the lag distance at which the autocorrelation exceed 0.8 is greater in the vicinity of the Gulf Stream than it is near the Kuroshio, approximately 50 km near the Gulf Stream as opposed to 20 km near the Kuroshio. Separate calculations for times when the sun is north and south of the equator revealed no obvious seasonal dependence of the spatial autocorrelation scales. The pCO2 measurements at Ocean Weather Station (OWS) 'P', in the eastern subarctic Pacific (50 N, 145 W) is the only fixed location where an uninterrupted time series of sufficient length exists to calculate a meaningful temporal autocorrelation function for lags greater than a few days. The estimated temporal autocorrelation function at OWS 'P', is highly variable. A spectral analysis of the longest four pCO2 time series indicates a high level of variability occurring over periods from the atmospheric synoptic to the maximum length of the time series, in this case 42 days. It is likely that a relative peak in variability with a period of 3-6 days is related to atmospheric synoptic period variability and ocean mixing events due to wind stirring. However, the short length of available time series makes identifying temporal relationships between pCO2 and atmospheric or ocean processes problematic.

Li, Zhen↗

DOE OSTI2018

Enhanced peak growth of global vegetation and its key mechanisms

The annual peak growth of vegetation is critical in characterizing the capacity of terrestrial ecosystem productivity and shaping the seasonality of atmospheric CO 2 concentrations. The recent greening of global lands suggests an increasing trend of terrestrial vegetation growth, but whether or not the peak growth has been globally enhanced still remains unclear. Here, we use two global datasets of gross primary productivity (GPP) and a satellite-derived Normalized Difference Vegetation Index (NDVI) to characterize recent changes in annual peak vegetation growth (that is, GPP max and NDVI max ). We demonstrate that the peak in the growth of global vegetation has been linearly increasing during the past three decades. About 65% of the NDVI max variation is evenly explained by expanding croplands (21%), rising CO 2 (22%) and intensifying nitrogen deposition (22%). The contribution of expanding croplands to the peak growth trend is substantiated by measurements from eddy-flux towers, sun-induced chlorophyll fluorescence and a global database of plant traits, all of which show that croplands have a higher photosynthetic capacity than other vegetation types. The large contribution of CO 2 is also supported by a meta-analysis of 466 manipulative experiments and 15 terrestrial biosphere models. Moreover, we show that the contribution of GPP max to the change in annual GPP is less in the tropics than in other regions. These multiple lines of evidence reveal an increasing trend in the peak growth of global vegetation. These results draw attention to the vital roles of agricultural intensification and atmospheric changes in reshaping the seasonality of global vegetation growth.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Drought timing influences the legacy of tree growth recovery

Whether and how the timing of extreme events affects the direction and magnitude of legacy effects on tree growth is poorly understood. In this study, we use a global database of Ring-Width Index (RWI) from 2,500 sites to examine the impact and legacy effects (the departure of observed RWI from expected RWI) of extreme drought events during 1948–2008, with a particular focus on the influence of drought timing. We assessed the recovery of stem radial growth in the years following severe drought events with separate groupings designed to characterize the timing of the drought. We found that legacies from extreme droughts during the dry season (DS droughts) lasted longer and had larger impacts in each of the 3 years post drought than those from extreme droughts during the wet season (WS droughts). At the global scale, the average integrated legacy from DS droughts (0.18) was about nine times that from WS droughts (0.02). Site-level comparisons also suggest stronger negative impacts or weaker positive impacts of DS droughts on tree growth than WS droughts. Our results, therefore, highlight that the timing of drought is a crucial factor determining drought impacts on tree recovery. Further increases in baseline aridity could therefore exacerbate the impact of punctuated droughts on terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2008

The Time Series Technique for Aerosol Retrievals over Land from MODIS: Algorithm MAIAC

Atmospheric aerosols interact with sun light by scattering and absorbing radiation. By changing irradiance of the Earth surface, modifying cloud fractional cover and microphysical properties and a number of other mechanisms, they affect the energy balance, hydrological cycle, and planetary climate [IPCC, 2007]. In many world regions there is a growing impact of aerosols on air quality and human health. The Earth Observing System [NASA, 1999] initiated high quality global Earth observations and operational aerosol retrievals over land. With the wide swath (2300 km) of MODIS instrument, the MODIS Dark Target algorithm [Kaufman et al., 1997; Remer et al., 2005; Levy et al., 2007] currently complemented with the Deep Blue method [Hsu et al., 2004] provides daily global view of planetary atmospheric aerosol. The MISR algorithm [Martonchik et al., 1998; Diner et al., 2005] makes high quality aerosol retrievals in 300 km swaths covering the globe in 8 days. With MODIS aerosol program being very successful, there are still several unresolved issues in the retrieval algorithms. The current processing is pixel-based and relies on a single-orbit data. Such an approach produces a single measurement for every pixel characterized by two main unknowns, aerosol optical thickness (AOT) and surface reflectance (SR). This lack of information constitutes a fundamental problem of the remote sensing which cannot be resolved without a priori information. For example, MODIS Dark Target algorithm makes spectral assumptions about surface reflectance, whereas the Deep Blue method uses ancillary global database of surface reflectance composed from minimal monthly measurements with Rayleigh correction. Both algorithms use Lambertian surface model. The surface-related assumptions in the aerosol retrievals may affect subsequent atmospheric correction in unintended way. For example, the Dark Target algorithm uses an empirical relationship to predict SR in the Blue (B3) and Red (B1) bands from the 2.1 m channel (B7) for the purpose of aerosol retrieval. Obviously, the subsequent atmospheric correction will produce the same SR in the red and blue bands as predicted, i.e. an empirical function of 2.1. In other words, the spectral, spatial and temporal variability of surface reflectance in the Blue and Red bands appears borrowed from band B7. This may have certain implications for the vegetation and global carbon analysis because the chlorophyll-sensing bands B1, B3 are effectively substituted in terms of variability by band B7, which is sensitive to the plant liquid water. This chapter describes a new recently developed generic aerosol-surface retrieval algorithm for MODIS. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm simultaneously retrieves AOT and surface bi-directional reflection factor (BRF) using the time series of MODIS measurements.

Lyapustin, Alexei↗

DOE OSTI2017

A global trait-based approach to estimate leaf nitrogen functional allocation from observations

Nitrogen is one of the most important nutrients for plant growth and a major constituent of proteins that regulate photosynthetic and respiratory processes. However, a comprehensive global analysis of nitrogen allocation in leaves for major processes with respect to different plant functional types is currently lacking. This study integrated observations from global databases with photosynthesis and respiration models to determine plant-functional-type-specific allocation patterns of leaf nitrogen for photosynthesis (Rubisco, electron transport, light absorption) and respiration (growth and maintenance), and by difference from observed total leaf nitrogen, an unexplained “residual” nitrogen pool. Based on our analysis, crops partition the largest fraction of nitrogen to photosynthesis (57%) and respiration (5%) followed by herbaceous plants (44% and 4%). Tropical broadleaf evergreen trees partition the least to photosynthesis (25%) and respiration (2%) followed by needle-leaved evergreen trees (28% and 3%). In trees (especially needle-leaved evergreen and tropical broadleaf evergreen trees) a large fraction (70% and 73% respectively) of nitrogen was not explained by photosynthetic or respiratory functions. Compared to crops and herbaceous plants, this large residual pool is hypothesized to emerge from larger investments in cell wall proteins, lipids, amino acids, nucleic acid, CO2 fixation proteins (other than Rubisco), secondary compounds, and other proteins. Our estimates are different from previous studies due to differences in methodology and assumptions used in deriving nitrogen allocation estimates. Unlike previous studies, we integrate and infer nitrogen allocation estimates across multiple plant functional types, and report substantial differences in nitrogen allocation across different plant functional types. Furthermore, the resulting pattern of nitrogen allocation provides insights on mechanisms that operate at a cellular scale within leaves, and can be integrated with ecosystem models to derive emergent properties of ecosystem productivity at local, regional, and global scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Russian anthropogenic black carbon: Emission reconstruction and Arctic black carbon simulation

Development of reliable source emission inventories is needed to advance the understanding of the origin of Arctic haze using chemical transport modeling. This paper develops a regional anthropogenic black carbon (BC) emission inventory for the Russian Federation, the largest country by land area in the Arctic Council. Activity data from combination of local Russia information and international resources, emission factors based on either Russian documents or adjusted values for local conditions, and other emission source data are used to approximate the BC emissions. Emissions are gridded at a resolution of 0.1° × 0.1° and developed into a monthly temporal profile. Total anthropogenic BC emission of Russia in 2010 is estimated to be around 224 Gg. Gas flaring, a commonly ignored black carbon source, contributes a significant fraction of 36.2% to Russia's total anthropogenic BC emissions. Other sectors, i.e., residential, transportation, industry, and power plants, contribute 25.0%, 20.3%, 13.1%, and 5.4%, respectively. Three major BC hot spot regions are identified: the European part of Russia, the southern central part of Russia where human population densities are relatively high, and the Urals Federal District where Russia's major oil and gas fields are located but with sparse human population. BC simulations are conducted using the hemispheric version of Community Multi-scale Air Quality Model with emission inputs from a global emission database EDGAR (Emissions Database for Global Atmospheric Research)-HTAPv2 (Hemispheric Transport of Air Pollution) and EDGAR-HTAPv2 with its Russian part replaced by the newly developed Russian BC emissions, respectively. The simulation using the new Russian BC emission inventory could improve 30–65% of absorption aerosol optical depth measured at the AERONET sites in Russia throughout the whole year as compared to that using the default HTAPv2 emissions. At the four ground monitoring sites (Zeppelin, Barrow, Alert, and Tiksi) in the Arctic Circle, surface BC simulations are improved the most during the Arctic haze periods (October–March). The poor performance of Arctic BC simulations in previous studies may be partly ascribed to the Russian BC emissions built on out-of-date and/or missing information, which could result in biases to both emission rates and the spatial distribution of emissions. Finally, this study highlights that the impact of Russian emissions on the Arctic haze has likely been underestimated, and its role in the Arctic climate system needs to be reassessed. The Russian black carbon emission source data generated in this study can be obtained via http://abci.ornl.gov/download.shtml or http://acs.engr.utk.edu/Data.php.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

A multitower measurement network estimate of California's methane emissions

In this paper, we present an analysis of methane (CH 4 ) emissions using atmospheric observations from five sites in California's Central Valley across different seasons (September 2010 to June 2011). CH 4 emissions for spatial regions and source sectors are estimated by comparing measured CH 4 mixing ratios with transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) predictions based on two 0.1° CH 4 (seasonally varying “California-specific” (California Greenhouse Gas Emission Measurements, CALGEM) and a static global (Emission Database for Global Atmospheric Research, release version 42, EDGAR42)) prior emission models. Region-specific Bayesian analyses indicate that for California's Central Valley, the CALGEM- and EDGAR42-based inversions provide consistent annual total CH 4 emissions (32.87 ± 2.09 versus 31.60 ± 2.17 Tg CO 2 eq yr -1 ; 68% confidence interval (CI), assuming uncorrelated errors between regions). Summing across all regions of California, optimized CH 4 emissions are only marginally consistent between CALGEM- and EDGAR42-based inversions (48.35 ± 6.47 versus 64.97 ± 11.85 Tg CO 2 eq), because emissions from coastal urban regions (where landfill and natural gas emissions are much higher in EDGAR than CALGEM) are not strongly constrained by the measurements. Combining our results with those from a recent study of the South Coast Air Basin narrows the range of estimates to 43–57 Tg CO2eq yr -1 (1.3–1.8 times higher than the current state inventory). Finally, these results suggest that the combination of rural and urban measurements will be necessary to verify future changes in California's total CH 4 emissions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Using greenhouse gas fluxes to define soil functional types

Soils provide key ecosystem services and directly control ecosystem functions; thus, there is a need to define the reference state of soil functionality. Most common functional classifications of ecosystems are vegetation-centered and neglect soil characteristics and processes. We propose Soil Functional Types (SFTs) as a conceptual approach to represent and describe the functionality of soils based on characteristics of their greenhouse gas (GHG) flux dynamics. We used automated measurements of CO2, CH4 and N2O in a forested area to define SFTs following a simple statistical framework. This study supports the hypothesis that SFTs provide additional insights on the spatial variability of soil functionality beyond information represented by commonly measured soil parameters (e.g., soil moisture, soil temperature, litter biomass). We discuss the implications of this framework at the plot-scale and the potential of this approach at larger scales. This approach is a first step to provide a framework to define SFTs, but a community effort is necessary to harmonize any global classification for soil functionality. A global application of the proposed SFT framework will only be possible if there is a community-wide effort to share data and create a global database of GHG emissions from soils.

Petrakis, Sandra↗

DOE OSTI2024

Responses of Marginal and Intrinsic Water-Use Efficiency to Changing Aridity Using FLUXNET Observations

According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water-use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non-linear mWUE-VPD relationship at a sub-daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid-range of VPD. Despite the highly non-linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem-level inherent water-use efficiency (IWUE) and mWUE using the slope, m *. We further identify site-specific m * and its variability with changing site-level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites.

54 ENVIRONMENTAL SCIENCES↗