Francesca M. Hopkins

A framework for representing microbial decomposition in coupled climate models

Accurate prediction of future atmospheric CO2 concentrations is essential for evaluating climate change impacts on ecosystems and human societies. One major source of uncertainty in model predictions is the extent to which global warming will increase atmospheric CO2 concentrations through enhanced microbial decomposition of soil organic carbon. Recent advances in microbial ecology could help reduce this uncertainty, but current global models do not represent direct microbial control over decomposition. Instead, all of the coupled climate models reviewed in the most recent Intergovernmental Panel on Climate Change (IPCC) report assume that decomposition is a first-order decay process, proportional to the size of the soil carbon pool. Here we argue for the development of a new generation of models that link decomposition directly to the size and activity of microbial communities in coupled global models. This process begins with the formulation and validation of fine-scale models that capture fundamental microbial mechanisms without excessive mathematical complexity. These mechanistic models must then be scaled up through an aggregation process and validated at ecosystem to global scales prior to incorporation into global climate models (GCMs). Parameterizing microbial models at the global scale is challenging because some microbial properties such as in situ extracellular enzyme activities are very difficult to measure directly. New parameter fitting procedures may therefore be needed to infer the values of important microbial variables. Validating decomposition models at the global scale is also a challenge, and has not yet been accomplished with the land carbon models embedded in current GCMs. Fortunately new global datasets on soil carbon stocks and fluxes offer promising opportunities to validate both existing land carbon models and new microbial models. If challenges in scaling, parameterization, and validation can be overcome, a new generation of microbially-based decomposition models could substantially improve predictions of carbon–climate feedbacks in the Earth system. Development of new models structures would also reduce any bias due to the assumption of first-order decomposition across all of the models currently referenced in reports of the IPCC.

Toward more realistic projections of soil carbon dynamics by Earth system models

Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

Explicitly representing soil microbial processes in Earth system models

©2015. American Geophysical Union. All Rights Reserved. Microbes influence soil organic matter decomposition and the long-term stabilization of carbon (C) in soils. We contend that by revising the representation of microbial processes and their interactions with the physicochemical soil environment, Earth system models (ESMs) will make more realistic global C cycle projections. Explicit representation of microbial processes presents considerable challenges due to the scale at which these processes occur. Thus, applying microbial theory in ESMs requires a framework to link micro-scale process-level understanding and measurements to macro-scale models used to make decadal- to century-long projections. Here we review the diversity, advantages, and pitfalls of simulating soil biogeochemical cycles using microbial-explicit modeling approaches. We present a roadmap for how to begin building, applying, and evaluating reliable microbial-explicit model formulations that can be applied in ESMs. Drawing from experience with traditional decomposition models, we suggest the following: (1) guidelines for common model parameters and output that can facilitate future model intercomparisons; (2) development of benchmarking and model-data integration frameworks that can be used to effectively guide, inform, and evaluate model parameterizations with data from well-curated repositories; and (3) the application of scaling methods to integrate microbial-explicit soil biogeochemistry modules within ESMs. With contributions across scientific disciplines, we feel this roadmap can advance our fundamental understanding of soil biogeochemical dynamics and more realistically project likely soil C response to environmental change at global scales.

Warming accelerates decomposition of decades-old carbon in forest soils

Global climate carbon-cycle models predict acceleration of soil organic carbon losses to the atmosphere with warming, but the size of this feedback is poorly known. The temperature sensitivity of soil carbon decomposition is commonly determined by measuring changes in the rate of carbon dioxide (CO2) production under controlled laboratory conditions. We added measurements of carbon isotopes in respired CO2 to constrain the age of carbon substrates contributing to the temperature response of decomposition for surface soils from two temperate forest sites with very different overall rates of carbon cycling. Roughly one-third of the carbon respired at any temperature was fixed from the atmosphere more than 10 y ago, and the mean age of respired carbon reflected a mixture of substrates of varying ages. Consistent with global ecosystem model predictions, the temperature sensitivity of the carbon fixed more than a decade ago was the same as the temperature sensitivity for carbon fixed less than 10 y ago. However, we also observed an overall increase in the mean age of carbon respired at higher temperatures, even correcting for potential substrate limitation effects. The combination of several age constraints from carbon isotopes showed that warming had a similar effect on respiration of decades-old and younger (<10 y) carbon but a greater effect on decomposition of substrates of intermediate (between 7 and 13 y) age. Our results highlight the vulnerability of soil carbon to warming that is years-to-decades old, which makes up a large fraction of total soil carbon in forest soils globally.

Spatial patterns and source attribution of urban methane in the Los Angeles Basin

Urban areas are increasingly recognized as a globally important source of methane to the atmosphere; however, the location of methane sources and relative contributions of source sectors are not well known. Recent atmospheric measurements in Los Angeles, California, USA, show that more than a third of the citys methane emissions are unaccounted for in inventories and suggest that fugitive fossil emissions are the unknown source. We made on-road measurements to quantify fine-scale structure of methane and a suite of complementary trace gases across the Los Angeles Basin in June 2013. Enhanced methane levels were observed across the basin but were unevenly distributed in space. We identified 213 methane hot spots from unknown emission sources. We made direct measurements of ethane to methane (C_2H_6/CH_4) ratios of known methane emission sources in the region, including cattle, geologic seeps, landfills, and compressed natural gas fueling stations, and used these ratios to determine the contribution of biogenic and fossil methane sources to unknown hot spots and to local urban background air. We found that 75% of hot spots were of fossil origin, 20% were biogenic, and 5% of indeterminate source. In regionally integrated air, we observed a wider range of C_2H_6/CH_4 values than observed previously. Fossil fuel sources accounted for 58–65% of methane emissions, with the range depending on the assumed C_2H_6/CH_4 ratio of source end-members and model structure. These surveys demonstrated the prevalence of fugitive methane emissions across the Los Angeles urban landscape and suggested that uninventoried methane sources were widely distributed and primarily of fossil origin.

Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project – Part 1: calibration, urban enhancements, and uncertainty estimates

We report continuous surface observations of carbon dioxide (CO2) and methane (CH4) from the Los Angeles (LA) Megacity Carbon Project during 2015. We devised a calibration strategy, methods for selection of background air masses, calculation of urban enhancements, and a detailed algorithm for estimating uncertainties in urban-scale CO2 and CH4 measurements. These methods are essential for understanding carbon fluxes from the LA megacity and other complex urban environments globally. We estimate background mole fractions entering LA using observations from four “extra-urban” sites including two “marine” sites located south of LA in La Jolla (LJO) and offshore on San Clemente Island (SCI), one “continental” site located in Victorville (VIC), in the high desert northeast of LA, and one “continental/mid-troposphere” site located on Mount Wilson (MWO) in the San Gabriel Mountains. We find that a local marine background can be established to within ~1 ppm CO2 and ~10 ppb CH4 using these local measurement sites. Overall, atmospheric carbon dioxide and methane levels are highly variable across Los Angeles. “Urban” and “suburban” sites show moderate to large CO2 and CH4 enhancements relative to a marine background estimate. The USC (University of Southern California) site near downtown LA exhibits median hourly enhancements of ~20 ppm CO2 and ~150 ppb CH4 during 2015 as well as ~15 ppm CO2 and ~80 ppb CH4 during mid-afternoon hours (12:00–16:00 LT, local time), which is the typical period of focus for flux inversions. The estimated measurement uncertainty is typically better than 0.1 ppm CO2 and 1 ppb CH4 based on the repeated standard gas measurements from the LA sites during the last 2 years, similar to Andrews et al. (2014). The largest component of the measurement uncertainty is due to the single-point calibration method; however, the uncertainty in the background mole fraction is much larger than the measurement uncertainty. The background uncertainty for the marine background estimate is ~10 and ~15 % of the median mid-afternoon enhancement near downtown LA for CO2 and CH4, respectively. Overall, analytical and background uncertainties are small relative to the local CO2 and CH4 enhancements; however, our results suggest that reducing the uncertainty to less than 5 % of the median mid-afternoon enhancement will require detailed assessment of the impact of meteorology on background conditions.

Mitigation of methane emissions in cities: How new measurements and partnerships can contribute to emissions reduction strategies

Cities generate 70% of anthropogenic greenhouse gas emissions, a fraction that is growing with global urbanization. While cities play an important role in climate change mitigation, there has been little focus on reducing urban methane emissions. Here we develop a conceptual framework for methane mitigation in cities by describing emission processes, the role of measurements, and a need for new institutional partnerships. Urban methane emissions are likely to grow with expanding use of natural gas and organic waste disposal systems in growing population centers; however, we currently lack the ability quantify this increase. We also lack systematic knowledge of the relative contribution of these distinct source sectors on emissions. We present new observations from 4 North American cities to demonstrate that methane emissions vary in magnitude and sector from city to city, and hence require different mitigation strategies. Detections of fugitive emissions from these systems suggest that current mitigation approaches are absent or ineffective. These findings illustrate that tackling urban methane emissions will require research efforts to identify mitigation targets, develop and implement new mitigation strategies, and monitor atmospheric methane levels to ensure the success of mitigation efforts. This research will require a variety of techniques to achieve these objectives, and should be deployed in cities globally. We suggest that metropolitan-scale partnerships may effectively coordinate systematic measurements and actions focused on emission reduction goals.

Vista-LA: Mapping methane emitting infrastructure in the Los Angeles megacity

Methane (CH4) is a potent greenhouse gas (GHG) and a critical target of climate mitigation efforts. However, actionable emission reduction efforts are complicated by large uncertainties in the methane budget on relevant scales. Here, we present Vista, a Geographic Information System (GIS)-based approach to map potential methane emissions sources in the South Coast Air Basin (SoCAB) that encompasses Los Angeles, an area with a dense, complex mixture of methane sources. The goal of this work is to provide a database that, together with atmospheric observations, improves methane emissions estimates in urban areas with complex infrastructure. We aggregated methane source location information into three sectors (energy, agriculture, and waste) following the frameworks used by the State of California GHG Inventory and the Intergovernmental Panel on Climate Change (IPCC) Guidelines for GHG Reporting. Geospatial modeling was applied to publicly available datasets to precisely geolocate facilities and infrastructure comprising major anthropogenic methane source sectors. The final database, Vista-Los Angeles (Vista-LA), is presented as maps of infrastructure known or expected to emit CH4. Vista-LA contains over 33 000 features concentrated on < 1 % of land area in the region. Currently, Vista-LA is used as a planning and analysis tool for atmospheric measurement surveys of methane sources, particularly for airborne remote sensing, and methane “hotspot” detection using regional observations. This study represents a first step towards developing an accurate, spatially resolved methane flux estimate for point sources in SoCAB, with the potential to address discrepancies between bottom–up and top–down methane emissions accounting in this region. The Vista-LA datasets and associated metadata are available from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC; https://doi.org/10.3334/ORNLDAAC/1525).