Jun Wang1, Sundar A Christopher1, U. S. Nair1, Jeffrey S...
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Jun Wang 1 , Sundar A Christopher 1 , U. S. Nair 1 , Jeffrey S. Reid 2 , Elaine M. Prins 3 , Jim Szykman 4 1 Univ. of Alabama in Huntsville, Huntsville, AL; 2 Naval Research Laboratory, Monterey CA; 3 NOAA/NESDIS ORA, Madison, WI ; 4 EPA/ORD/NERL/Environmental Sciences Division, RTP, NC • GOES fire products from Wildfire Automated Biomass Burning Algorithm (WF_ABBA) algorithm (Prins et al., 1998). Smoke emission product based on the Fire Locating and Modeling of Burning Emissions (FLAMBE) algorithm (Reid et al., 2004) • RAMS-AROMA: online aerosol transport modeling (wet/dry deposition, advection, and emission), and δ-4-Stream radiative model for aerosol and cloud radiative effects. • Aerosols and their radiative effects are fully coupled and interact with meteorological fields at each time step and each model grid. • The simulations are run with and without consideration of smoke radiative effects. Comparison between the model results with observations made in Texas and DOE Atmospheric Radiation Measurements (ARM) in the Southern Great Plain (SGP) site in Oklahoma, provides a unique opportunity to study smoke radiative impacts during its transport in greater detail. Biomass burning is a major source of anthropogenic aerosols and trace gases in the troposphere. Due to increased deforestation and farming practices, biomass burning in Central America has expanded drastically in the last two decades. Under favorable meteorological conditions, smoke aerosols can be transported over the Gulf of Mexico and reach into the southeastern United States. Observation networks in the state of Texas show that the smoke event usually takes place 3~5 times per spring with maximum during late April and early May. These events can last from days to weeks, degrading the air quality to the unhealthy category, and impacting the regional climate. Because fire activities are usually unpredictable, traditional parameterization methods or pre-defined emission database are not applicable to define the smoke source function. Through the Assimilation and Radiation Online Modeling of Aerosols (AROMA) in the Regional Atmospheric System (RAMS), this study demonstrates that Fire Locating and Modeling of Burning Emissions (FLAMBE) derived from geostationary satellites can be used in the RAMS-AROMA to promptly forecast the transport of smoke and their impacts on air quality. With RAMS-AROMA, the smoke radiative forcing and their feedbacks on the surface energy budget, temperature, boundary layer height and mixing are investigated. Figure 2. 30-days (24 hour) averaged quantities form April 20 to May 20, 2003. (a) Averaged RAMS-AROMA AOT; (b) Averaged reduction of solar flux at the surface; (c) Difference of sensible heat due to smoke radiative effects (with smoke radiation – without smoke radiation); (d) Averaged MODIS AOT (both Aqua and Terra). Figure 3. 30-days (24 hour) averaged quantities form April 20 to May 20, 2003. (a) Averaged difference (with smoke radiative effects – without smoke radiative effects) of daily maximum temperature in the model first layer; (b) Averaged difference of PBL height (in meters); (c) Averaged change of smoke mass concentration near the surface; (d) Averaged change of smoke mass concentration at model layer 5 (850m above surface) MODIS 1. Through assimilating FLAMBE emission datasets into RAMS-AROMA, this study simulates the transport of smoke aerosols from Central American biomass burning activities during 20 April- 20 May 2003. Comparisons with surface PM 2.5 mass and aerosol optical thickness data sets show that the model can correctly simulate the spatial distribution of smoke pollutants as well as the timing and location of the smoke fronts. The simulated dry smoke mass near the surface highly correlates the measured PM 2.5 mass, with linear correlation coefficients (R) ranging from 0.7 to 0.85 with a mean (underestimated) bias of 12μgm -3 over 38 locations in Texas. This study implies that fire and emission products derived from geostationary satellites with high temporal resolutions have unique applications for the air quality modeling of long-range transported fire-related pollutants. 2. The spatial distribution of RAMS-AROMA smoke AOT is consistent with the MODIS AOT, both showing a clear transport path of smoke aerosols from Central America to the Gulf of Mexico and Southeastern United States. Along this path, because of smoke radiative effects, the downward solar radiation at the surface is decreased by 40~70Wm -2 and the radiative heating rate is increased by 0.8k/day in the lower boundary layer (not shown). Due to heat capacity difference between ocean and land, the decrease of solar energy at the surface results in a decrease of the sensible heat and latent heat over land by 10~20 Wm -2 and 5-10Wm -2 respectively, and minimal changes over the ocean. As a consequence, the maximum air temperature in the lowest model layer (within 100m near the surface) is decreased by about 0.1~0.25 °C over land. However, over the ocean, it increases about 0.1~0.2°C, because the increase of heating rate outweighs the temperature changes caused by the PBL mixing and sensible heat processes. 3. The decrease of air temperature over the land results in a decrease of PBL height by 30~70meters over the regions where the path of smoke transport is located. This leads to the increase of smoke concentration in the lower PBL, and the decrease of smoke concentration in the upper PBL. 4. This study shows that the atmospheric response to the smoke radiative effects is complex and important. Its complexity implies that traditional forcing concept (∆Ts/∆F ≈ constant) that applies to the greenhouse gases can not be simply applied to the smoke aerosol that involves surface and PBL feedbacks. Its importance indicated that consideration of smoke radiative effects in PBL is imperative during air quality modeling of the smoke episode. This research was supported by NASA’s Radiation Sciences, Interdisciplinary sciences and ACMAP programs. J. Wang was supported by the NASA Earth System Science Fellowship program. (b) (a) (c) (d) Smoke AOT Reduction of Solar Flux (Wm -2 ) Sensible Heat Difference (Wm -2 ) MODIS AOT Max Temperature Difference (°C) PBL difference (m) (a) (b) (c) (d) Difference of Smoke mass in lower PBL Difference of Smoke mass in upper PBL Smoke Mass Concentration at Surface Model vs. Obs. Email: [email protected] ; URL: http://www.nsstc.uah.edu/~wangjun Figure 1. (a) Modeled dry smoke concentration near the surface on 9 May 2003. Solid dots show the locations of different EPA PM2.5 observation sites and are color-coded based on air quality categories. The upper limits of PM2.5 mass (in μgm -3 ), for the categories of good, moderate, unhealthy for special groups (e.g., elderly and children), unhealthy, very unhealthy and hazardous are 15.4, 40.4, 65.4, 150.4, 250.4, 500.4, respectively. Pink contour lines are the geopotential heights at 700hpa at 1200 CDT (Central Daylight Time, UTC-5), 9 May 2003. Letters H and L in the color pink locate the major high and low synoptic systems. The boundary of Texas State is highlighted with thick solid black lines. The black square in the state of Oklahoma represents the location of the DOE ARM SGP site. The dot-dashed line shows the spatial domain of fine model grids. Figure 1 (b) (c) and (d) are same as figure 1 (a) but for May 10th, May 11th and May 12th respectively. Superimposed on (a) (b) (c) and (d) are the best estimate of smoke coverage (arch shape in red color) in the state of Texas made by the Texas air quality monitoring agency (http://www.tnrcc.state.tx.us/updated/air/monops /airpollevents/2003/030429ani-coverage.html). (e) Measured column AOT (blue dots) and modeled smoke AOT (line in pink) at the ARM SGP site. Bars in red and green colors show the daily-averaged AOT and Angstrom exponents derived from the ground-based AOT measurements. (f) Time series of measured PM2.5 mass and modeled smoke concentration over Brownsville, Texas. (g) Comparison of daily mean PM2.5 mass with the modeled smoke mass concentration near the surface at several locations representing Southern TX (STX), East TX (ETX), Central TX (CTX), Northern TX (NTX) and Western TX (WTX) respectively (these regions are outlined by the black-dashed line in Texas in figure 1a). The best linear fit equation, the linear correlation coefficients (R) and the number of comparison pairs (N) are also shown.
Transcript of Jun Wang1, Sundar A Christopher1, U. S. Nair1, Jeffrey S...
Jun Wang1, Sundar A Christopher1, U. S. Nair1, Jeffrey S. Reid2, Elaine M. Prins3, Jim Szykman4
1Univ. of Alabama in Huntsville, Huntsville, AL; 2Naval Research Laboratory, Monterey CA; 3NOAA/NESDIS ORA, Madison, WI ; 4EPA/ORD/NERL/Environmental Sciences Division, RTP, NC
• GOES fire products from Wildfire Automated Biomass Burning Algorithm (WF_ABBA) algorithm (Prins et al., 1998). Smoke emission product based on the Fire Locating and Modeling of Burning Emissions (FLAMBE) algorithm (Reid et al., 2004)
• RAMS-AROMA: online aerosol transport modeling (wet/dry deposition, advection, and emission), and δ-4-Stream radiative model for aerosol and cloud radiative effects.
• Aerosols and their radiative effects are fully coupled and interact with meteorological fields at each time step and each model grid.
• The simulations are run with and without consideration of smoke radiative effects. Comparison between the model results with observations made in Texas and DOE Atmospheric Radiation Measurements (ARM) in the Southern Great Plain (SGP) site in Oklahoma, provides a unique opportunity to study smoke radiative impacts during its transport in greater detail.
Biomass burning is a major source of anthropogenic aerosols and trace gases in the troposphere. Due to increased deforestation and farming practices, biomass burning in Central America has expanded drastically in the last two decades. Under favorable meteorological conditions, smoke aerosols can be transported over the Gulf of Mexico and reach into the southeastern United States.
Observation networks in the state of Texas show that the smoke event usually takes place 3~5 times per spring with maximum during late April and early May. These events can last from days to weeks, degrading the air quality to the unhealthy category, and impacting the regional climate.
Because fire activities are usually unpredictable, traditional parameterization methods or pre-defined emission database are not applicable to define the smoke source function. Through the Assimilation and Radiation Online Modeling of Aerosols (AROMA) in the Regional Atmospheric System (RAMS), this study demonstrates that Fire Locating and Modeling of Burning Emissions (FLAMBE) derived from geostationary satellites can be used in the RAMS-AROMA to promptly forecast the transport of smoke and their impacts on air quality. With RAMS-AROMA, the smoke radiative forcing and their feedbacks on the surface energy budget, temperature, boundary layer height and mixing are investigated.
Figure 2. 30-days (24 hour) averaged quantities form April 20 to May 20, 2003. (a) Averaged RAMS-AROMA AOT; (b) Averaged reduction of solar flux at the surface; (c) Difference of sensible heat due to smoke radiative effects (with smoke radiation – without smoke radiation); (d) Averaged MODIS AOT (both Aqua and Terra).
Figure 3. 30-days (24 hour) averaged quantities form April 20 to May 20, 2003. (a) Averaged difference (with smoke radiative effects – without smoke radiative effects) of daily maximum temperature in the model first layer; (b) Averaged difference of PBL height (in meters); (c) Averaged change of smoke mass concentration near the surface; (d) Averaged change of smoke mass concentration at model layer 5 (850m above surface)
MODIS
1. Through assimilating FLAMBE emission datasets into RAMS-AROMA, this study simulates the transport of smoke aerosols from Central American biomass burning activities during 20 April- 20 May 2003. Comparisons with surface PM2.5 mass and aerosol optical thickness data sets show that the model can correctly simulate the spatial distribution of smoke pollutants as well as the timing and location of the smoke fronts. The simulated dry smoke mass near the surface highly correlates the measured PM2.5 mass, with linear correlation coefficients (R) ranging from 0.7 to 0.85 with a mean (underestimated) bias of 12µgm-3 over 38 locations in Texas. This study implies that fire and emission products derived from geostationary satellites with high temporal resolutions have unique applications for the air quality modeling of long-range transported fire-related pollutants.
2. The spatial distribution of RAMS-AROMA smoke AOT is consistent with the MODIS AOT, both showing a clear transport path of smoke aerosols from Central America to the Gulf of Mexico and Southeastern United States. Along this path, because of smoke radiative effects, the downward solar radiation at the surface is decreased by 40~70Wm-2 and the radiative heating rate is increased by 0.8k/day in the lower boundary layer (not shown). Due to heat capacity difference between ocean and land, the decrease of solar energy at the surface results in a decrease of the sensible heat and latent heat over land by 10~20 Wm-2 and 5-10Wm-2 respectively, and minimal changes over the ocean. As a consequence, the maximum air temperature in the lowest model layer (within 100m near the surface) is decreased by about 0.1~0.25 °C over land. However, over the ocean, it increases about 0.1~0.2°C, because the increase of heating rate outweighs the temperature changes caused by the PBL mixing and sensible heat processes.
3. The decrease of air temperature over the land results in a decrease of PBL height by 30~70meters over the regions where the path of smoke transport is located. This leads to the increase of smoke concentration in the lower PBL, and the decrease of smoke concentration in the upper PBL.
4. This study shows that the atmospheric response to the smoke radiative effects is complex and important. Its complexity implies that traditional forcing concept (∆Ts/∆F ≈ constant) that applies to the greenhouse gases can not be simply applied to the smoke aerosol that involves surface and PBL feedbacks. Its importance indicated that consideration of smoke radiative effects in PBL is imperative during air quality modeling of the smoke episode.
This research was supported by NASA’s Radiation Sciences, Interdisciplinary sciences and ACMAP programs. J. Wang was supported by the NASA Earth System Science Fellowship program.
(b)(a)
(c) (d)
Smoke AOT Reduction of Solar Flux (Wm-2)
Sensible Heat Difference (Wm-2) MODIS AOT
Max Temperature Difference (°C) PBL difference (m)
(a) (b)
(c) (d)Difference of Smoke mass in lower PBL Difference of Smoke mass in upper PBL
Smoke Mass Concentration at SurfaceModel vs. Obs.
Email: [email protected]; URL: http://www.nsstc.uah.edu/~wangjun
Figure 1. (a) Modeled dry smoke concentration near the surface on 9 May 2003. Solid dots show the locations of different EPA PM2.5 observation sites and are color-coded based on air quality categories. The upper limits of PM2.5 mass (in µgm-3), for the categories of good, moderate, unhealthy for special groups (e.g., elderly and children), unhealthy, very unhealthy and hazardous are 15.4, 40.4, 65.4, 150.4, 250.4, 500.4, respectively. Pink contour lines are the geopotential heights at 700hpa at 1200 CDT (Central Daylight Time, UTC-5), 9 May 2003. Letters H and L in the color pink locate the major high and low synoptic systems. The boundary of Texas State is highlighted with thick solid black lines. The black square in the state of Oklahoma represents the location of the DOE ARM SGP site. The dot-dashed line shows the spatial domain of fine model grids. Figure 1 (b) (c) and (d) are same as figure 1 (a) but for May 10th, May 11th and May 12th respectively. Superimposed on (a) (b) (c) and (d) are the best estimate of smoke coverage (arch shape in red color) in the state of Texas made by the Texas air quality monitoring agency (http://www.tnrcc.state.tx.us/updated/air/monops/airpollevents/2003/030429ani-coverage.html). (e) Measured column AOT (blue dots) and modeled smoke AOT (line in pink) at the ARM SGP site. Bars in red and green colors show the daily-averaged AOT and Angstrom exponents derived from the ground-based AOT measurements. (f) Time series of measured PM2.5 mass and modeled smoke concentration over Brownsville, Texas. (g) Comparison of daily mean PM2.5 mass with the modeled smoke mass concentration near the surface at several locations representing Southern TX (STX), East TX (ETX), Central TX (CTX), Northern TX (NTX) and Western TX (WTX) respectively (these regions are outlined by the black-dashed line in Texas in figure 1a). The best linear fit equation, the linear correlation coefficients (R) and the number of comparison pairs (N) are also shown.
