Grantee Research Project Results
2005 Progress Report: Impacts of Climate Change and Land Cover Change on Biogenic Volatile Organic Compounds (BVOCs) Emissions in Texas
EPA Grant Number: R831452Title: Impacts of Climate Change and Land Cover Change on Biogenic Volatile Organic Compounds (BVOCs) Emissions in Texas
Investigators: Yang, Zong-Liang , Parmenter, Barbara , Allen, David T.
Institution: The University of Texas at Austin
EPA Project Officer: Chung, Serena
Project Period: November 1, 2003 through October 31, 2006 (Extended to October 31, 2007)
Project Period Covered by this Report: November 1, 2004 through October 31, 2005
Project Amount: $750,000
RFA: Consequences of Global Change for Air Quality: Spatial Patterns in Air Pollution Emissions (2003) RFA Text | Recipients Lists
Research Category: Air , Air Quality and Air Toxics , Climate Change
Objective:
The objective of this research project is to couple climate models, biogenic emission estimation models, air quality models, and anthropogenic land use models to predict future air quality trends. During 2005, the work focused on answering the following scientific questions:
- Can land surface models used in climate modeling can do a reasonably accurate job of simulating the spatial variation and magnitude of biogenic emissions?
- How much of the uncertainty inherent in land-surface-model (LSM)-generated biogenic emissions estimates can be attributed directly to the uncertainty in the input vegetation dataset?
- How much do biogenic emissions vary naturally from year to year?
- What are the relative contributions of direct climate variation (changes in photosynthetically active radiation, changes in temperature) and indirect climate variation (changes in leaf biomass in response to short-term environmental change) to interannual variability of biogenic emissions?
- What are the potential impacts of changing land use and land cover patterns, driven by urbanization and climate change, on air quality predictions?
Progress Summary:
Can Land Surface Models Used in Climate Modeling Do A Reasonably Accurate Job of Simulating the Spatial Variation and Magnitude of Biogenic Emissions?
Building on initial work done in Year 1 of the project, we developed a method to incorporate species-based variation of the emission of biogenic volatile organic compounds (BVOCs) into regional climate and weather models. We previously had converted a species-based land cover database for Texas into a database compatible with the Community Land Model (CLM) and a database compatible with the Noah LSM. We linked the LSM-compatible land cover databases to the original species-based dataset as a means to derive region-specific BVOC emission capacities for each plant functional type (in the CLM database) and for each land cover type (PFT; in the Noah database).
We showed that the spatial distribution of inherent BVOC flux (defined as the product of the BVOC emission capacity and the leaf biomass density) derived using the Texas-specific BVOC emission capacities is well correlated with the spatial distribution of inherent BVOC flux calculated using the original species data (r = 0.89). The mean absolute error for the emission-capacity-derived inherent flux distribution is an order of magnitude lower than the statewide range of inherent fluxes.
The inherent BVOC flux distributions derived using region-specific BVOC emission capacities are more consistent with observations than is the BVOC flux distribution derived using the CLM3-standard BVOC emission capacities, which are topdown estimates based on the literature. When used in conjunction with detailed land cover datasets, land-surface models that are equipped with region-specific BVOC emission capacities produce reasonably accurate inherent BVOC fluxes.
What This Means. LSMs employed in climate modeling can be used as a surrogate for purpose-specific biogenic emission modules (e.g., GLOBEIS) without a significant compromise in accuracy of flux fields generated. The ground-referenced land cover databases derived here are likely more accurate than their satellite-derived counterparts; they can be used for a variety of regional model simulations in Texas for a wide range of ecosystem-information-dependent applications.
How Much of the Uncertainty Inherent in LSM-Generated Biogenic Emissions Estimates Can Be Directly Attributed to the Uncertainty in the Input Vegetation Dataset?
We looked at how uncertainty in vegetation dataset affects the magnitude of biogenic emissions simulated by LSMs. We drove the CLM3 offline at 0.1º resolution from 1993 to 1998 using bilinearly interpolated North American Regional Reanalysis (NARR) meteorological forcing data. Two land cover datasets provide the starting points for uncertainty analysis: (1) a Moderate Resolution Imaging Spectroradiometer-derived vegetation and soil color database; and (2) a vegetation distribution dataset derived from ground-survey data. These datasets serve as rough bounds on the uncertainty inherent to vegetation datasets used in LSMs. We systematically varied both datasets to examine how distinct specifications of percent vegetated area, vegetation-type distribution, and phenological parameters directly and indirectly alter modeled biogenic emission flux. We used each pair of datasets to initialize the CLM and then examined differences in modeled BVOC over the analysis period.
We can conclude the following:
- Uncertainty in the representation of land-surface vegetation contributes a full order of magnitude to the uncertainty in biogenic emissions simulated by LSMs.
- The largest contributor to this uncertainty is the wide range of PFT distributions that are considered reasonable by the scientific community. The ground-survey-derived PFT distribution resulted in monthly mean BVOC fluxes that were an average of 2.98 times as large as the estimates produced by a run using a satellite-derived PFT distribution.
- Divergent representations of bare soil fraction also contribute significantly to uncertainty in simulated BVOC flux: a run using satellite-derived bare soil fraction produced BVOC estimates 1.66 times as great as a run using the bare-soil fraction from the less-densely-vegetated ground-survey-derived dataset.
- Scaling leaf area index within reasonable bounds (50–150% of the satellite-derived estimates) causes a linear decrease and increase, respectively, of simulated biogenic emissions.
- Different specifications of bare soil fraction can have a significant indirect effect on modeled actual BVOC flux (up to 16% of inherent BVOC flux) through modification of state variables that control vegetation temperature, although it is not known whether the modeled indirect effects are model artifacts or representations of reality.
What This Means. Urban planners and air quality managers who make use of LSM-based model predictions of BVOC emissions should be aware of the significant uncertainty (~ 1 order of magnitude) in the magnitude BVOC flux estimates that result from uncertainty in the land cover dataset used. When LSMs are used as lower bounds for climate models, the uncertainty in BVOC flux derived from uncertainty in the land cover dataset will increase the uncertainty of all BVOC-related radiative, carbon-cycle, and atmospheric-chemistry feedbacks within the model.
1. How Much Do Biogenic Emissions Vary Naturally From Year to Year?
We used a regional land-surface model equipped with a short-term dynamic vegetation module to characterize the interannual variability in BVOC emissions in Texas. We added a short-term dynamic phenology module to the CLM3. We drove the augmented model (CLM3-STDP) at 0.1º resolution from 1979 to 2002 using bilinearly interpolated NARR meteorological forcing data. To initialize the model, we used the ground-survey-derived land cover dataset derived as part of the scientific research to answer Question 1 above. The model runs used Texas-specific BVOC emissions capacities previously described.
CLM3-STDP allows the leaf area index of each PFT to respond at each time step to variations in meteorological input (e.g., incident shortwave radiation, atmospheric temperature) and model state variables (e.g., soil moisture). Simulated interannual variability in leaf area index, which in large part controls interannual variability in biogenic emissions, is considerable. Leaf area index in large part controls interannual variation in biogenic emissions. When the dynamic phenology module was employed, the average absolute departure from the simulated mean total BVOC flux was 18.4 percent.
The current generation of the model does a reasonably accurate job of simulating interannual variation in leaf area index; however, interannual variability modeled in the initial model runs is higher than that observed by Advanced Very High-Resolution Radiometer data. We currently are calibrating the model to obtain better agreement with observations. The calibrated model will be used in conjunction with the historical meteorological data to provide the U.S. Environmental Protection Agency (EPA) with an estimate of natural interannual variability in biogenic emissions in Texas. This will be evaluated in the context of current EPA policy.
What This Means. On a regional scale, interannual variation in BVOC flux is considerable and likely overwhelms any differences in BVOC flux as a result of longer-term climate change. The assumption that BVOC flux is constant year-to-year should be reexamined. Additional investigation is warranted.
2. What Are the Relative Contributions of Direct Climate Variation (Changes in Photosynthetically Active Radiation, Changes in Temperature) and Indirect Climate Variation (Changes in Leaf Biomass in Response to Short-Term Environmental Change) to Interannual Variability of Biogenic Emissions?
As described in the description of research addressing Question 3 above, our initial research has shown that the contribution of interannual variation in leaf area index to variation in total BVOC flux is likely to be significant. As part of our current ongoing research, we seek to estimate the relative contribution of short-term environmental variation (i.e., changes in canopy temperature, photosynthetically active radiation) and seasonal environmental variation (i.e., variation in leaf-area index in response to climate conditions) to simulated BVOC flux. When leaf-area index is kept interannually constant, the average absolute departure from the mean BVOC flux is 11.7 percent. When dynamic phenology is employed, the average absolute departure from the mean BVOC flux is 18.4 percent when the short-term dynamic vegetation is employed. These values may be used to estimate roughly the relative contribution of short-term environmental variation and seasonal environmental variation to simulated BVOC flux. It is important to note, however, that changes in leaf-area index change canopy temperature (and thus, the model’s emission scale factor that modulates emissions in response to leaf-temperature change). Changes in leaf-area index also slightly modify the scale factor modulating biogenic emissions in response to changes in photosynthetically active radiation hitting the leaf surface.
What This Means. Understanding the mechanisms that drive biogenic emission change will help researchers to more finely tune future research efforts.
3. What are the Potential Impacts of Changing Land Use and Land Cover Patterns, Driven by Urbanization and Climate Change, on Air Quality Predictions?
We have completed an evaluation of the impacts of urbanization on land covers, using Austin, Texas as a case study. For Austin, a community-based planning team, Envision Central Texas, developed four possible land use scenarios that could result from a doubling of population in Central Texas. These scenarios were combined with Texas vegetation maps to arrive at projected changes in land use, biogenic emissions, and air pollutant deposition velocities at a spatial scale of 4 kilometers. A gridded photochemical model (Comprehensive Air Quality Model with extensions, CAMx) was then used to predict the spatial and temporal patterns of ozone concentrations, based on the revised emissions and deposition velocities. The differences in land cover led to 1-5 percent reductions in daily biogenic emissions in the five-county area that includes Austin; these reductions in biogenic emissions, and the corresponding differences in deposition velocities, led to reductions in maximum ozone concentrations. Reductions in daily maximum ozone concentrations, as a result of increased urbanization, ranged from 0.05 to 1.4 ppb, with typically values of 0.1 ppb for the Austin area.
What This Means. These results are comparable to many commonly employed control strategies.
Future Activities:
The focus during Year 3 of the project will be on coupling the climate and biogenic emission estimation models. We will calibrate the dynamic phenology module using satellite-derived leaf-area index dataset (see Question 3 above), and we will perform parameter substitution experiments to more precisely quantify the contribution of short-term and seasonal environmental variation to estimated BVOC flux. The land planning team will expand their work to Houston and Dallas, focusing on data acquisition and scenario development.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
Other project views: | All 22 publications | 5 publications in selected types | All 5 journal articles |
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Type | Citation | ||
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Gulden LE, Yang Z-L. Development of species-based, regional emission capacities for simulation of biogenic volatile organic compound emissions in land-surface models: an example from Texas, USA. Atmospheric Environment 2006;40(8):1464-1479. |
R831452 (2005) R831452 (2006) R831452 (Final) |
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Supplemental Keywords:
volatile organic compounds, VOCs, nitrogen oxides, general circulation models, precipitation, scaling, tropospheric ozone, climate change, particulate matter, Global Climate Change, aerosol formation, aerosols, air quality, air quality models, airborne aerosols, ambient aerosol, ambient air pollution, anthropogenic stress, atmospheric aerosol particles, atmospheric chemistry, atmospheric dispersion models, atmospheric models, atmospheric particulate matter, atmospheric transport, climate, climate model, climate models, climate variability, climatic influence, ecological models, environmental measurement, environmental stress, global change, greenhouse gas, greenhouse gases, meteorology,, RFA, Scientific Discipline, Air, Geographic Area, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, Chemistry, climate change, State, Monitoring/Modeling, Atmospheric Sciences, Ecological Risk Assessment, Environmental Engineering, anthropogenic stress, aerosol formation, ambient aerosol, atmospheric particulate matter, atmospheric dispersion models, ecosystem models, environmental monitoring, environmental measurement, meteorology, climatic influence, emissions monitoring, global change, ozone, air quality models, climate, modeling, climate models, greenhouse gases, airborne aerosols, atmospheric aerosol particles, atmospheric transport, Texas (TX), environmental stress, ecological models, climate model, greenhouse gas, monitoring organics, aerosols, atmospheric models, Global Climate Change, atmospheric chemistry, air quality, ambient air pollutionRelevant Websites:
http://www.geo.utexas.edu/climate/AQ/index.htm Exit
http://www.geo.utexas.edu/climate/ Exit
http://www.engr.utexas.edu/che/directories/faculty/dallen.cfm Exit
http://wnt.utexas.edu/architecture/people/faculty/parmenterf.html Exit
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.