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Final 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 , Allen, David T. , Parmenter, Barbara
Institution: University of Texas at Austin
EPA Project Officer: Bloomer, Bryan
Project Period: November 1, 2003 through October 31, 2006 (Extended to October 31, 2007)
Project Amount: $750,000
RFA: Consequences of Global Change for Air Quality: Spatial Patterns in Air Pollution Emissions (2003)
Research Category: Global Climate Change , Air Quality and Air Toxics
Description:
Objective:The overall goal of the project is to couple climate models, biogenic emission estimation models, air quality models, and anthropogenic land use models to predict future air quality trends. We have successfully accomplished this goal by answering the following scientific questions through six peer-reviewed journal papers.
- Can land surface models used in climate modeling 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–generated biogenic emissions estimates can be directly attributed to the uncertainty in the input vegetation dataset?
- How much do biogenic emissions vary 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?
- How accurate is regional climate dynamic downscaling?
- What are the potential impacts of changing land use and land cover patterns, driven by urbanization and climate change, on air quality predictions?
- How do future climate change and urbanization, individually and together, affect regional air quality predictions?
- Can land surface models used in climate modeling do a reasonably accurate job of simulating the spatial variation and magnitude of biogenic emissions?
- How much of the uncertainty inherent in LSM-generated biogenic emissions estimates can be directly attributed to the uncertainty in the input vegetation dataset?
- How much do biogenic emissions vary 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?
- How accurate is regional climate dynamic downscaling?
- What are the potential impacts of changing land use and land cover patterns, driven by urbanization and climate change, on air quality predictions?
- How do future climate change and urbanization, individually and together, affect regional air quality predictions?
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 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 land-surface model (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 (in the Noah database). This work was published in Gulden and Yang (2006).
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 state-wide 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 top-down estimates based on the literature. When used in conjunction with detailed land-cover datasets, LSMs that are equipped with region-specific BVOC emission capacities produce reasonably accurate inherent BVOC fluxes.
What this means for the EPA: (1) Land surface models 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. (2) 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 that are in line with the mission of the EPA.
We evaluate the sensitivity of biogenic emissions simulated by an LSM to different representations of land-cover vegetation (Gulden et al., 2008). We drive the Community Land Model on a 0.1° grid over Texas, USA, from 1993–1998 using bilinearly interpolated North American Regional Reanalysis data. Two land-cover datasets provide the starting point for analysis: (1) a satellite derived vegetation and soil color database and (2) a vegetation-distribution dataset derived from ground surveys. These datasets help us to qualitatively characterize the uncertainty in land-cover representations. We systematically vary the datasets to examine the sensitivity of modeled emissions to variation in representation of bare-soil fraction, vegetation-type distribution, and phenology.
Different datasets’ representation of vegetation-type distribution leads to simulated mean statewide total biogenic emissions that vary by a factor of 3. Variation in specified bare-soil fraction causes simulated statewide average emissions that vary by a factor of 1.7. Scaling leaf area index values within reasonable bounds causes a near-linear change in simulated emissions. Differences in simulated values are largest for major metropolitan regions and for eastern and central Texas, where biogenic emissions are highest and where tropospheric ozone pollution is a significant concern. Changing bare-soil fraction alters simulated vegetation temperature and consequently indirectly affects modeled emissions (≤16% of inherent emissions capacity). Our estimates of model sensitivity to land-cover representation are consistent with those for other regions.
What this means for the EPA: 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 linked with 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.
Interannual variation in biogenic emissions is not well quantified, especially on regional scales. We use an LSM augmented with a short-term dynamic phenology scheme to estimate the interannual variation in the emission of biogenic volatile organic compounds (BVOCs) between 1982 and 2004 (Gulden et al., 2007). We use North American Regional Reanalysis data to drive two versions of the National Center for Atmospheric Research Community Land Model (CLM) on a 0.1° grid over eastern Texas. The first version is the standard CLM with prescribed leaf area index (LAI) (i.e., LAI varies seasonally but not interannually); the second version is the standard CLM augmented with a dynamic phenology scheme (CLM-DP) that allows LAI to respond to environmental variation. We calibrate CLM-DP using satellite-derived LAI as our visual constraint. When phenology is prescribed, the domain-mean (domain-maximum) average absolute departure from the monthly mean BVOC flux is 11.7% (70.6%); when phenology is allowed to vary with environmental conditions, it is 22.4% (137.7%). The domain-mean (domain-maximum) average absolute departure from the monthly mean flux is lower during summer: using CLM-DP, it is 15.7% (35.3%); using the standard CLM, it is 7.0% (23.0%). The domain-average, mean-normalized standard deviation of the June-July-August mean BVOC flux is 0.0619 when LAI is prescribed and 0.183 when LAI varies with environmental conditions. Our results imply that interannual variation of leaf biomass density, which is primarily driven by interannual variability of precipitation, is a significant contributor to year-to-year differences in BVOC flux on a regional scale, of at least equal importance to interannual variation of temperature and shortwave radiation. Phenology-driven biogenic emission variability is most pronounced in regions with relatively low emissions: as a grid cell’s mean BVOC flux decreases, the mean-normalized standard deviation of BVOC flux tends to increase. BVOC flux is most variable between years in subhumid, sparsely wooded regions where interannual variability of precipitation is relatively large.
What this means for the EPA: On a regional scale, natural interannual variation in BVOC flux is considerable and likely overwhelms any differences in BVOC flux due to longer-term climate change. The assumption that BVOC flux is constant year-to-year should be re-examined. Understanding the mechanisms that drive biogenic emission change will help EPA researchers to develop better predictive tools.
Regional climate models (RCMs) have often been used to dynamically downscale coarse-resolution global climate simulations because in so doing they provide policy-makers with high-resolution climate information necessary for assessment studies. One objective of this project is to nest an RCM within a coarse-resolution global climate model to investigate the impacts of climate change and land user/land cover change on biogenic emissions and air quality in Texas. Before this framework is developed, we must assess how accurate the RCM is in modeling regional climate.
The common methodology in dynamical regional climate downscaling employs a continuous integration of a limited-area model with a single initialization of the atmospheric fields and frequent updates of lateral boundary conditions based on general circulation model outputs or reanalysis datasets. Our recent study (Lo et al., 2008) suggests alternative methods that can be more skillful than the traditional one in obtaining high-resolution climate information. We use the Weather Research and Forecasting (WRF) model with a grid spacing at 36 km over the conterminous U.S. to dynamically downscale the 1-degree NCEP Global Final Analysis (FNL). We perform three types of experiments for the entire year of 2000: 1) continuous integrations with a single initialization as usually done, 2) consecutive integrations with frequent re-initializations, and 3) as 1) but with a 3-dimensional nudging being applied. The simulations are evaluated in a high temporal scale (6-hourly) by comparison with the 32-km NCEP North American Regional Reanalysis (NARR). Compared to NARR, the downscaling simulation using the full 3-D nudging shows the highest skill, while the continuous run produces the lowest skill. While the re-initialization runs give an intermediate skill, a run with a more frequent (e.g. weekly) re-initialization outperforms that with the less frequent re-initialization (e.g. monthly). Dynamical downscaling outperforms bi-linear interpolation, especially for meteorological fields near the surface over the mountainous regions. The 3-D nudging generates realistic regional-scale patterns that are not resolved by simply updating the lateral boundary conditions as done traditionally, therefore significantly improving the accuracy of generating regional climate information.
What this means for the EPA: unlike global reanalysis datasets, which are considered “perfect” boundary conditions in dynamical downscaling, global climate simulations are “imperfect” boundary conditions. These biases must be corrected before they are used in downscaling for impacts studies. Importantly, an RCM with the 3-D nudging will likely produce better skill with the bias-corrected global climate simulations when used as boundary conditions.
The impact of future air quality controls is often assessed using photochemical models with projected emissions. In these model projections, land covers are generally presumed to remain constant. The goal of this work is to assess the impact of this assumption of constant land covers on model predicted ozone concentrations, using projected land cover changes in the area in and around Austin, Texas as a case study.
Land covers impact predicted air pollutant concentrations by influencing deposition velocities, biogenic air pollutant emissions, albedo, soil moisture, and other physical parameters. The focus in this work will be on the impacts of changes in land cover on biogenic emissions and deposition velocities.
Projections of future land covers for Austin, Texas were developed through the Envision Central Texas program. Envision Central Texas (ECT) is community-driven regional visioning which began in 2001. ECT has developed four possible growth scenarios (referred to as Scenarios A-D) which represent particular patterns of growth in Central Texas. All of the Scenarios are based on a doubling of population, but the Scenarios assume very different types of growth. For example, ECT Scenario A concentrates development in new communities following conventional subdivision patterns of low-density development, while ECT Scenario D concentrates population growth in existing communities at much higher density.
The four possible land use development scenarios were combined with Texas vegetation maps to arrive at projected changes in land use at a spatial scale of 4 kilometers. The Global Biogenic Emissions and Interactions System version 3.1 (GloBEIS v3.1) was used to develop the emission inventory (Song et al., 2008). The land use/land cover (LULC) input data required by GloBEIS 3.1 were derived from two different databases. The first database, referred to as the Basecase, is a ground-survey dataset that was previously developed and available at a 1-km resolution for a domain encompassing most of Texas; The LULC database contains emission factor data for 156 different vegetation types, including 41 species (e.g., Quercus alba), 80 genera (e.g., Quercus), and 35 land cover types (e.g., Pecan Elm forest). Each classification is assigned a vegetation species, leaf biomass, and density distribution. The second LULC database was derived from the ECT Scenarios. These scenarios used only 10-16 land cover classes (10 land cover types for Scenario A and 16 for other scenarios); the land cover classifications included information on pervious ground cover but relatively little information on vegetation types. The ECT Scenario LULCs, therefore, were overlaid on Basecase land covers to understand how development would impact LULC at the species and genus level. When ECT Scenarios are overlaid on the Basecase land covers, a mapping can be made between the original vegetation types and the new land cover classes, for the regions where future development is expected. For these areas, ECT planners have estimated the fraction of pervious cover for each development type. This fraction of pervious cover is then used to calculate the fraction of the original trees remaining in that area.
Hourly ambient surface temperature, wind speed and humidity estimates were derived from simulations using the MM5 meteorological model. MM5 is the fifth generation NCAR/Penn State Mesoscale Model. Estimates of PAR flux were based on calculations done by the University of Maryland and the National Oceanic and Atmospheric Administration (NOAA) for the Global Energy and Water Cycle Experiment (GEWEX) Continent Scale International Project (GCIP). NOAA uses a modified version of the GEWEX surface radiation budget (SRB) algorithm (version 1.1) to calculate radiation flux fields from Geostationary Operational Environmental Satellite (GOES-8) data. A gridded photochemical model (Comprehensive Air Quality Model with extensions, CAMx, www.camx.com 
) was then used to predict the spatial and temporal patterns of ozone concentrations, based on the revised emissions and deposition velocities.
Predictions of biogenic emissions, deposition rates and 1-hour averaged ozone concentrations using the new ECT Scenarios were compared to predictions based on the LULC dataset which are currently used for Texas air quality studies. The differences in LULC led to 1-6% reductions in daily biogenic emissions in the 5-county area that includes Austin. The regions with reductions in isoprene emissions were located in newly developed areas. The reductions in biogenic emissions led to reductions in maximum ozone concentrations. Reductions in daily maximum ozone concentrations, due to decreases in biogenic emissions associated with increased urbanization, ranged from 0.05 to 1.2 ppb, with typical values of 0.1 ppb for the Austin area. The corresponding differences in deposition velocities due to development led to decreases in ozone concentrations of up to 1.4 ppb. Maximum decreases occurred during early morning and late night hours. In contrast, changes in deposition velocities led to increases in daytime ozone concentrations. Maximum increases in daytime ozone concentrations due to changes in deposition velocities were 0.6 ppb.
Combined, the effects of land cover changes on biogenic emissions and deposition velocities result in afternoon increases in ozone concentrations of up to 0.3 ppb, but decreases in ozone concentrations of approximately 0.7 ppb at the locations where peak ozone concentrations are predicted to occur in the afternoon. Figure 5.3 shows the spatial distribution of estimated ozone concentrations for the Basecase, and the differences in ozone concentrations between Basecase and ECT Scenario A with changes in emission, dry deposition, and both.
Precursor response studies show that a 15% reduction of anthropogenic VOC emissions led to reductions of 0.25 to 0.6 ppb in daily maximum 1-hour averaged ozone concentrations for the Austin area.
What this means for the EPA: These results are comparable to many commonly employed control strategies, suggesting large errors may result if land covers are assumed to be constant.
Our recent study (Jiang et al., 2008) quantifies the effects of climate change under future A1B scenario and land-use change on surface ozone (O3) in the greater Houston area. We applied the Weather Research and Forecasting model with Chemistry (WRF/Chem) to the Houston area for August of current (2001–2003) and future (2051–2053) years. The model was forced by downscaled 6-hourly Community Climate System Model (CCSM) version 3 outputs. High-resolution current-year land-use data from National Land Cover Database (NLCD) and future-year land-use distribution based on projected population density for the Houston area were used in the WRF/Chem model coupled with an Urban Canopy Model (UCM). Our simulations show that there is generally a 2°C increase in near-surface temperature over much of the modeling domain due to future changes in climate and land-use. In the urban area, the effect of climate change alone accounts for an increase of 2.6 ppb in daily maximum 8-hr O3 concentrations and land-use change exerts more influence than climate change. The combined effect of climate and land-use change on surface O3 concentrations can be up to 6.2 ppb. We also found that impacts of climate change and land-use change on O3 concentrations differ across the various areas of the domain. The increase in extreme O3 days can be up to 4–5 days in August, in which land-use contributes to 2–3 days increase. Additional sensitivity experiments show that the effect of future change in anthropogenic emissions is on the same order of those induced by climate and land-use change on extreme O3 days.
What this means for the EPA: These results suggest that future urban air quality studies must consider the effects of climate change, urbanization, and emissions.
In addition to answering the above six questions through six peer-reviewed papers, this grant has graduated one Ph.D. (Jihee Song), trained three Ph.D. students (Lindsey Gulden, Jin-oh Kim, Xiaoyan Jiang), and two postdocs (Yiwen Xu and Jeff Chunfung Lo). The website of this grant is http://www.geo.utexas.edu/climate/climate_change.html .
Journal Articles on this Report: 4 Displayed | Download in RIS Format
| Other project views: | All 21 publications | 4 publications in selected types | All 4 journal articles |
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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 (2006) R831452 (Final) |
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Gulden LE, Yang Z-L, Niu G-Y. Interannual variation in biogenic emissions on a regional scale. Journal of Geophysical Research 2007;112(D14103), doi:10.1029/2006JD008231. |
R831452 (Final) |
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Gulden LE, Yang Z-L, Niu G-Y. Sensitivity of biogenic emissions simulated by a land-surface model to land-cover representations. Atmospheric Environment 2008;42(18):4185-4197. |
R831452 (Final) |
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Lo JC-F, Yang Z-L, Pielke Sr RA. Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. Journal of Geophysical Research 2008;113(D09112), doi:10.1029/2007JD009216. |
R831452 (Final) |
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volatile organic compounds (VOCs), nitrogen oxides, general circulation models, precipitation, scaling, tropospheric ozone, south central, Texas, air, Ecosystem Protection/Environmental Exposure & Risk, RFA, Scientific Discipline, Atmospheric Sciences, Chemistry, Environmental Engineering, Monitoring/Modeling, 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. Air, Ecosystem Protection/Environmental Exposure & Risk, Geographic Area, RFA, Scientific Discipline, Atmospheric Sciences, Chemistry, Ecology and Ecosystems, Environmental Engineering, Environmental Monitoring, Monitoring/Modeling, State, climate change, particulate matter, Global Climate Change, Texas (TX), 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, ecosystem models, emissions monitoring, environmental measurement, environmental stress, global change, greenhouse gas, greenhouse gases, meteorology, modeling,
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Ecosystem Protection/Environmental Exposure & Risk, Air, Geographic Area, Scientific Discipline, RFA, climate change, Ecological Risk Assessment, Chemistry, Atmospheric Sciences, Environmental Engineering, particulate matter, Monitoring/Modeling, Environmental Monitoring, State, aerosols, meteorology, climate model, Global Climate Change, atmospheric models, airborne aerosols, air quality, ozone, atmospheric dispersion models, greenhouse gas, monitoring organics, climatic influence, air quality models, climate models, Texas (TX), aerosol formation, atmospheric chemistry, climate variability, environmental measurement, environmental stress, global change, atmospheric particulate matter, emissions monitoring, modeling, ambient air pollution, anthropogenic stress, atmospheric aerosol particles, ecological models, ambient aerosol, atmospheric transport, ecosystem models, greenhouse gases
Relevant Websites:
http://www.geo.utexas.edu/climate/climate_change.html
http://www.geo.utexas.edu/climate/
http://www.engr.utexas.edu/che/directories/faculty/dallen.cfm
Progress and Final Reports:
2004 Progress Report
2005 Progress Report
2006 Progress Report
Original Abstract