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Atmosphere, Volume 7, Issue 7 (July 2016) – 11 articles

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2970 KiB  
Article
Influence of Grid Resolution in Modeling of Air Pollution from Open Burning
by Duanpen Sirithian and Sarawut Thepanondh
Atmosphere 2016, 7(7), 93; https://doi.org/10.3390/atmos7070093 - 21 Jul 2016
Cited by 12 | Viewed by 5683
Abstract
Influences of different computational grid resolutions on modeled ambient benzene concentrations from open burning were assessed in this study. The CALPUFF (California Puff Mesoscale Dispersion Model) was applied to simulate maximum ground level concentration over the modeling domain of 100 × 100 km [...] Read more.
Influences of different computational grid resolutions on modeled ambient benzene concentrations from open burning were assessed in this study. The CALPUFF (California Puff Mesoscale Dispersion Model) was applied to simulate maximum ground level concentration over the modeling domain of 100 × 100 km2. Meteorological data of the year 2014 was simulated from the Weather Research and Forecasting (WRF) model. Four different grid resolutions were tested including 0.75 km, 1 km, 2 km and 3 km resolutions. Predicted values of the maximum 24-h average concentrations obtained from the finest grid resolution (0.75 km) were set as reference values. In total, there were 1089 receptors used as reference locations for comparison of the results from different computational grid resolutions. Comparative results revealed that the larger the grid resolution, the higher the over-prediction of the results. Nevertheless, it was found that increasing the grid resolution from the finest resolution (0.75 km) to coarser resolutions (1 km, 2 km and 3 km) resulted in reduction of computational time by approximately 66%, 97% and >99% as compared with the reference grid resolution, respectively. Results revealed that the grid resolution of 1 km is the most appropriate resolution with regard to both accuracy of predicted data and acceptable computational time for the model simulation of the open burning source. Full article
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<p>Study domain.</p> Full article ">Figure 2
<p>The maximum and the 95th percentile of 24-h average concentrations of benzene from 4 different grid resolutions.</p> Full article ">Figure 3
<p>Daily maximum values of benzene concentrations from different grid resolutions.</p> Full article ">Figure 4
<p>Spatial distributions of 24-h average concentrations of benzene (µg/m<sup>3</sup>) using different grids resolutions: (<b>a</b>) 0.75 km; (<b>b</b>) 1 km; (<b>c</b>) 2 km; and (<b>d</b>) 3 km.</p> Full article ">Figure 4 Cont.
<p>Spatial distributions of 24-h average concentrations of benzene (µg/m<sup>3</sup>) using different grids resolutions: (<b>a</b>) 0.75 km; (<b>b</b>) 1 km; (<b>c</b>) 2 km; and (<b>d</b>) 3 km.</p> Full article ">
16656 KiB  
Article
A WRF Simulation of an Episode of Contrails Covering the Entire Sky
by Jordi Mazon and David Pino
Atmosphere 2016, 7(7), 95; https://doi.org/10.3390/atmos7070095 - 20 Jul 2016
Cited by 2 | Viewed by 6404
Abstract
On 21 September 2012 the entire sky was covered by contrails over the Gulf of Lyon (NW of the Mediterranean basin). These clouds were well recorded by ground observers as well as by Meteosat imagery. The atmospheric characteristics at the levels where these [...] Read more.
On 21 September 2012 the entire sky was covered by contrails over the Gulf of Lyon (NW of the Mediterranean basin). These clouds were well recorded by ground observers as well as by Meteosat imagery. The atmospheric characteristics at the levels where these anthropic clouds formed are analyzed by performing a WRF simulation in the area where Meteosat recorded contrail clouds. According to the vertical profiles of temperature and the relative humidity respect to the ice (RHI), the environmental condition favors that the water vapor exhaust emitted by the aircraft engines reaches the deposition point and form crystal clouds, which spread out because the temperature remained below 230 K and the RHI was higher than 70% during the whole episode. Full article
(This article belongs to the Special Issue Advances in Clouds and Precipitation)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Meteosat images recorded in the IR channel at 11:00 (upper panel) and 14:00 UTC (lower panel) on 21 September 2011. The red square indicates the area where contrails are observed.</p> Full article ">Figure 1 Cont.
<p>Meteosat images recorded in the IR channel at 11:00 (upper panel) and 14:00 UTC (lower panel) on 21 September 2011. The red square indicates the area where contrails are observed.</p> Full article ">Figure 2
<p>Contrails covering the sky photographed over Barcelona on 21 September 2011 around 11 UTC.</p> Full article ">Figure 3
<p>ERA Interim reanalysis of geopotential height at 500 hPa (<b>upper panel</b>) and temperature at 850 hPa (<b>lower panel</b>) at 00:00 on 21 September 2011. The color bar in the upper panel shows the scale of the geopotential height, in decameters; in the lower panel the color bar shows the scale of air temperature, in <math display="inline"> <semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics> </math>C</p> Full article ">Figure 4
<p>Left panel, domains defined in WRF simulation. The simulated vertical profiles of temperature, dew point temperature and RHI are analyzed at the center point of the smallest domain (42.8 N, 4.7 E); Right panel shows the domain 3, where the unit tick mark is 1 km. The line AB indicates the direction of the vertical cross sections used for the vertical analysis of RHI.</p> Full article ">Figure 5
<p>Simulated vertical profiles of air temperature—solid line—and dew point temperature at 11:00 UTC—dashed line—(<b>upper panel</b>) and RHI (<b>lower panel</b>) at 02:00 (closed line), 11:00 (dashed line) and 21:00 UTC (short dashed line) in the middle of the small domain on 21 September 2011.</p> Full article ">Figure 6
<p>Horizontal cross section of RHI at 9.500 m height in domain 3 at (<b>a</b>) 02:00 (<b>b</b>) 11:00 and (<b>c</b>) 21:00 UTC on 21 September 2011. The black dot indicates the location of Barcelona city, where the picture shown in <a href="#atmosphere-07-00095-f002" class="html-fig">Figure 2</a> was taken.</p> Full article ">Figure 7
<p>Vertical cross section along the line AB shown in <a href="#atmosphere-07-00095-f004" class="html-fig">Figure 4</a> (lower panel), of RHI (in %, color contour) and temperature (in <math display="inline"> <semantics> <msup> <mrow/> <mo>°</mo> </msup> </semantics> </math>C, dashed lines) in domain 3 at (<b>a</b>) 02:00, (<b>b</b>) 11:00 and (<b>c</b>) 21:00 UTC on 21 September 2011. The scale of the horizontal axis is in km.</p> Full article ">
3779 KiB  
Article
Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations
by Young-Chan Noh, Byung-Ju Sohn, Yoonjae Kim, Sangwon Joo and William Bell
Atmosphere 2016, 7(7), 94; https://doi.org/10.3390/atmos7070094 - 18 Jul 2016
Cited by 22 | Viewed by 8071
Abstract
Temperature and water vapor profiles from the Korea Meteorological Administration (KMA) and the United Kingdom Met Office (UKMO) Unified Model (UM) data assimilation systems and from reanalysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) were assessed using collocated radiosonde observations [...] Read more.
Temperature and water vapor profiles from the Korea Meteorological Administration (KMA) and the United Kingdom Met Office (UKMO) Unified Model (UM) data assimilation systems and from reanalysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) were assessed using collocated radiosonde observations from the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) for January–December 2012. The motivation was to examine the overall performance of data assimilation outputs. The difference statistics of the collocated model outputs versus the radiosonde observations indicated a good agreement for the temperature, amongst datasets, while less agreement was found for the relative humidity. A comparison of the UM outputs from the UKMO and KMA revealed that they are similar to each other. The introduction of the new version of UM into the KMA in May 2012 resulted in an improved analysis performance, particularly for the moisture field. On the other hand, ECMWF reanalysis data showed slightly reduced performance for relative humidity compared with the UM, with a significant humid bias in the upper troposphere. ECMWF reanalysis temperature fields showed nearly the same performance as the two UM analyses. The root mean square differences (RMSDs) of the relative humidity for the three models were larger for more humid conditions, suggesting that humidity forecasts are less reliable under these conditions. Full article
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<p>(<b>a</b>) Number of collocated data from the radiosonde observations and numerical weather prediction (NWP) model outputs from the Korea Meteorological Administration (UM-KMA), United Kingdom Met Office (UM-UKMO), and European Centre for Medium-Range Weather Forecasts Interim reanalysis data (ERA-I), and (<b>b</b>) mean temperature (T) and relative humidity (RH) (solid lines with dots) and associated mean uncertainties (ε<sub>T</sub> for temperature and ε<sub>RH</sub> for relative humidity; dashed lines with triangles) profiles from GRUAN observations.</p> Full article ">Figure 2
<p>Vertical distributions of the mean biases (<b>a</b>) of the temperature, relative humidity, and normalized relative humidity for UM-KMA, UM-UKMO, and ERA-I. Their related root mean square differences (RMSDs) (<b>b</b>) are shown in the three panels.</p> Full article ">Figure 3
<p>Scatter plots of the relative humidity from the Korea Meteorological Administration model (UM-KMA) versus the United Kingdom Met Office model (UM-UKMO) for (<b>a</b>) 200, (<b>b</b>) 250, (<b>c</b>) 300, and (<b>d</b>) 350 hPa in January–May. The dashed lines represent the perfect matches.</p> Full article ">Figure 4
<p>Scatter plots of the relative humidity from the Korea Meteorological Administration model (UM-KMA) versus the United Kingdom Met Office model (UM-UKMO) for (<b>a</b>) 200, (<b>b</b>) 250, (<b>c</b>) 300, and (<b>d</b>) 350 hPa in June–December 2012. The dashed lines represent the perfect matches.</p> Full article ">Figure 5
<p>Error statistics of the Korea Meteorological Administration model (UM-KMA) temperature and relative humidity profiles classified according to the total precipitable water between the surface and 100 hPa (TPW, in kg·m<sup>−2</sup>). (<b>a</b>) Bias and (<b>b</b>) RMSD.</p> Full article ">Figure 6
<p>Vertical distribution of the standard deviation of the radiosonde relative humidity profiles classified according to the total precipitable water (TPW in kg·m<sup>−2</sup>) between the surface and 100 hPa.</p> Full article ">Figure 7
<p>Error statistics of the United Kingdom Met Office model (UM-UKMO) temperature and relative humidity profiles classified according to the total precipitable water between the surface and 100 hPa (TPW, in kg·m<sup>−2</sup>). (<b>a</b>) Bias and (<b>b</b>) RMSD.</p> Full article ">Figure 8
<p>Error statistics of the European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim) temperature and relative humidity profiles classified according to the total precipitable water between the surface and 100 hPa (TPW, in kg·m<sup>−2</sup>). (<b>a</b>) Bias and (<b>b</b>) RMSD.</p> Full article ">Figure 8 Cont.
<p>Error statistics of the European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim) temperature and relative humidity profiles classified according to the total precipitable water between the surface and 100 hPa (TPW, in kg·m<sup>−2</sup>). (<b>a</b>) Bias and (<b>b</b>) RMSD.</p> Full article ">
3042 KiB  
Article
Comparison of Ozone Production Regimes between Two Mexican Cities: Guadalajara and Mexico City
by Isao Kanda, Roberto Basaldud, Miguel Magaña, Armando Retama, Ryushi Kubo and Shinji Wakamatsu
Atmosphere 2016, 7(7), 91; https://doi.org/10.3390/atmos7070091 - 18 Jul 2016
Cited by 13 | Viewed by 6179
Abstract
Ozone concentrations have been increasing in the Guadalajara Metropolitan Area (GMA) in Mexico. To help devise efficient mitigation measures, we investigated the ozone formation regime by a chemical transport model (CTM) system WRF-CMAQ. The CTM system was validated by field measurement data of [...] Read more.
Ozone concentrations have been increasing in the Guadalajara Metropolitan Area (GMA) in Mexico. To help devise efficient mitigation measures, we investigated the ozone formation regime by a chemical transport model (CTM) system WRF-CMAQ. The CTM system was validated by field measurement data of ground-level volatile organic compounds (VOC) and vertical profiles of ozone in GMA as well as in the Mexico City Metropolitan Area (MCMA). By conducting CTM simulations with modified emission rates of VOC and nitrogen oxides (NOx), the ozone formation regime in GMA was found to lie between VOC-sensitive and NOx-sensitive regimes. The result is consistent with the relatively large VOC/NOx emission ratio in GMA compared to that in MCMA where the ozone formation regime is in the VOC-sensitive regime. Full article
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<p>Central parts of GMA (<b>a</b>) and MCMA (<b>b</b>). Gray shade indicates the urban landuse area. Circles indicate the air-monitoring stations. Filled circles are selected stations listed in <a href="#atmosphere-07-00091-t003" class="html-table">Table 3</a>. Stars indicate the locations of the meteorological observatories where ozonesonde was launched. Elevation contours are drawn at 200 m intervals.</p> Full article ">Figure 2
<p>Activity coefficients <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>T</mi> <mo stretchy="false">^</mo> </mover> <mi>s</mi> </msub> <mrow> <mo>(</mo> <mi>d</mi> <mo>,</mo> <mspace width="0.166667em"/> <mi>h</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> of “human” and “household” activity derived from a Japanese survey [<a href="#B23-atmosphere-07-00091" class="html-bibr">23</a>]. <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>T</mi> <mo stretchy="false">^</mo> </mover> <mi>s</mi> </msub> <mrow> <mo>(</mo> <mi>d</mi> <mo>,</mo> <mspace width="0.166667em"/> <mi>h</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> does not depend on <span class="html-italic">d</span>.</p> Full article ">Figure 3
<p>Activity coefficients <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>T</mi> <mo stretchy="false">^</mo> </mover> <mi>s</mi> </msub> <mrow> <mo>(</mo> <mi>d</mi> <mo>,</mo> <mspace width="0.166667em"/> <mi>h</mi> <mo>)</mo> </mrow> </mrow> </semantics> </math> of passenger cars (A) and trucks (C) defined as average traffic flow rates in the traffic census conducted in 2003 in MCMA. The day index <span class="html-italic">d</span> ranges from 0 (Monday) to 6 (Sunday), and the hour index <span class="html-italic">h</span> ranges from 0 to 23. Note that letter B is assigned to public transportation, which is not plotted because of the large fluctuations due to the small traffic volume, in the original traffic census database.</p> Full article ">Figure 4
<p>Gridded distribution of time-averaged emission intensities of NO and VOC (equivalent to OLT in RADM2 in terms of OH-reactivity) summed over the vertical layers. SMN (an acronym for the Spanish name Servicio Meteorológico Nacional) denotes meteorological observatory, and AQ station denotes air-quality monitoring station. Elevation contours are drawn at 500 m intervals beginning with 500 m.</p> Full article ">Figure 5
<p>Proportions of fixed, area, and mobile sources in the anthropogenic emissions of NO<span class="html-italic"><sub>x</sub></span> and VOC (equivalent to OLT in RADM2 in terms of OH-reactivity). Only emissions in urban grid cells were taken into account. The radius of a pie chart is proportional to the total emission rate relative to that in MCMA. (<b>a-1</b>) MCMA, NO<span class="html-italic"><sub>x</sub></span>, (<b>a-2</b>) MCMA, VOC, (<b>b-1</b>) GMA, NO<span class="html-italic"><sub>x</sub></span>, (<b>b-2</b>) GMA, VOC.</p> Full article ">Figure 6
<p>Emissions of NO<span class="html-italic"><sub>x</sub></span> and VOC (equivalent to OLT in RADM2 in terms of OH-reactivity) in the urban grid cells of MCMA and GMA. (<b>a</b>) Emission intensity per area; (<b>b</b>) VOC/NO<span class="html-italic"><sub>x</sub></span> ratio.</p> Full article ">Figure 7
<p>WRF simulation domains for GMA (<b>a</b>) and MCMA (<b>b</b>). The panel frame corresponds to the outermost domain d01. The circle indicates the meteorological observatory where ozonesonde observation was conducted.</p> Full article ">Figure 8
<p>Comparison between field data (blue) and CTM simulations (red) at Pedregal (PED) station in MCMA.</p> Full article ">Figure 9
<p>Comparison between field data (blue) and CTM simulations (red) in GMA. The location is the Centro (CEN) station except for CO that was evaluated at the Oblatos (OBL) station because the data at the Centro station was missing.</p> Full article ">Figure 10
<p>Comparison of vertical profiles of O<sub>3</sub> concentration between ozonesonde observations (<b>blue</b>) and CTM simulations (<b>red</b>). (<b>a</b>) MCMA; (<b>b</b>) GMA. Solid line with large circle markers indicates the CTM result at the target time indicated above each panel while long-dashed and short-dashed lines with small circle markers indicate the CTM results of one hour before and after the target time, respectively. The CTM results represent averages in 9 grid cells around the observation site. The local standard time is the indicated UTC minus 6 h. Note that the daylight-saving time was in effect during the GMA campaign.</p> Full article ">Figure 11
<p>Meridional-vertical section of O<sub>3</sub> concentration (ppb) averaged through May 2014 at the Guadalajara longitude calculated by the MOZART-4/GEOS-5 global chemical modeling system [<a href="#B31-atmosphere-07-00091" class="html-bibr">31</a>].</p> Full article ">Figure 12
<p>Contour plots of daily maximum O<sub>3</sub> concentration in <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>α</mi> <mo>,</mo> <mspace width="0.166667em"/> <mi>β</mi> <mo>)</mo> </mrow> </semantics> </math> space on 8 March 2012 in MCMA, where <span class="html-italic">α</span> and <span class="html-italic">β</span> denote the multiplication factors on the emission rates of VOC and NO<span class="html-italic"><sub>x</sub></span>, respectively. The plot panels are shown at every other grid points, i.e., at 6 km spacing. The star marker indicates the meteorological observatory where ozonesonde was launched. The circles indicate the air monitoring stations denoted by abbreviations, some of which are listed in <a href="#atmosphere-07-00091-t003" class="html-table">Table 3</a>.</p> Full article ">Figure 13
<p>Contour plots of daily maximum O<sub>3</sub> concentration in <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>α</mi> <mo>,</mo> <mspace width="0.166667em"/> <mi>β</mi> <mo>)</mo> </mrow> </semantics> </math> space on 6 May 2014 in GMA, where <span class="html-italic">α</span> and <span class="html-italic">β</span> denote the multiplication factors on the emission rates of VOC and NO<span class="html-italic"><sub>x</sub></span>, respectively. The plot panels are shown at all the grid points, i.e., at 3 km spacing. The star marker indicates the meteorological observatory where ozonesonde was launched. The circles indicate the air monitoring stations denoted by abbreviations, some of which are listed in <a href="#atmosphere-07-00091-t003" class="html-table">Table 3</a>.</p> Full article ">Figure 14
<p>Contour plots of daily maximum O<sub>3</sub> concentration on <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>α</mi> <mo>,</mo> <mspace width="0.166667em"/> <mi>β</mi> <mo>)</mo> </mrow> </semantics> </math> space on 4 May 2014 in GMA, where <span class="html-italic">α</span> and <span class="html-italic">β</span> denote the multiplication factor on the emission rates of VOC and NO<span class="html-italic"><sub>x</sub></span>, respectively. Only the results in the four grid cells near the Centro (CEN) station are shown.</p> Full article ">
5132 KiB  
Article
An FDTD Study of Errors in Magnetic Direction Finding of Lightning Due to the Presence of Conducting Structure Near the Field Measuring Station
by Yosuke Suzuki, Shohei Araki, Yoshihiro Baba, Toshihiro Tsuboi, Shigemitsu Okabe and Vladimir A. Rakov
Atmosphere 2016, 7(7), 92; https://doi.org/10.3390/atmos7070092 - 15 Jul 2016
Cited by 5 | Viewed by 4903
Abstract
Lightning electromagnetic fields in the presence of conducting (grounded) structure having a height of 60 m and a square cross-section of 40 m × 40 m within about 100 m of the observation point are analyzed using the 3D finite-difference time-domain (FDTD) method. [...] Read more.
Lightning electromagnetic fields in the presence of conducting (grounded) structure having a height of 60 m and a square cross-section of 40 m × 40 m within about 100 m of the observation point are analyzed using the 3D finite-difference time-domain (FDTD) method. Influence of the conducting structure on the two orthogonal components of magnetic field is analyzed, and resultant errors in the estimated lightning azimuth are evaluated. Influences of ground conductivity and lightning current waveshape parameters are also examined. When the azimuth vector passes through the center of conducting structure diagonally (e.g., azimuth angle is 45°) or parallel to its walls (e.g., azimuth angle is 0°), the presence of conducting structure equally influences Hx and Hy, so that Hx/Hy is the same as in the absence of structure. Therefore, no azimuth error occurs in those configurations. When the conducting structure is not located on the azimuth vector, the structure influences Hx and Hy differently, with the resultant direction finding error being greater when the structure is located closer to the observation point. Full article
(This article belongs to the Special Issue Electromagnetic Fields and Waves with Special Attention to Lightning Flashes)
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Figure 1

Figure 1
<p>FDTD simulation model of a vertical lightning channel above flat, perfectly-conducting ground: (<b>a</b>) a single quadrant model with two magnetic walls; and (<b>b</b>) its equivalent four-quadrant model.</p> Full article ">Figure 2
<p>Plan view of the model for studying the influence of conducting structure (not to scale) on azimuth measured with respect to the west direction for the case of the true lightning azimuth <span class="html-italic">φ</span> = 45°.</p> Full article ">Figure 3
<p>Illustration of lightning direction finding from <span class="html-italic">H<sub>x</sub></span> and <span class="html-italic">H<sub>y</sub></span> for the case of the true lightning azimuth <span class="html-italic">φ</span> = 45<sup>○</sup>: (<b>a</b>) Ideal case of no azimuth error (direction found from <span class="html-italic">H<sub>x</sub></span> and <span class="html-italic">H<sub>y</sub></span> is the same as the true direction to lightning); (<b>b</b>) the case of the presence of 60-m tall conducting structure at (7080, 7040) (see <a href="#atmosphere-07-00092-t001" class="html-table">Table 1</a>), which causes an azimuth error <span class="html-italic">Δφ</span> = 16.</p> Full article ">Figure 4
<p>FDTD-computed waveforms of <span class="html-italic">H<sub>x</sub></span> and <span class="html-italic">H<sub>y</sub></span> at an observation point at (7080 m, 7080 m) for a lightning strike to a flat, perfectly-conducting ground (TL model, <span class="html-italic">v</span> = 150 m/μs).</p> Full article ">Figure 5
<p>FDTD-computed waveform of total azimuthal magnetic field at an observation point located at (7080 m, 7080 m) and the corresponding waveform computed using Equation (3) (TL model, <span class="html-italic">v</span> = 150 m/μs).</p> Full article ">Figure 6
<p>Color-coded azimuth error (in degrees) due to the presence of a nearby conducting structure having a height of 60 m and a square cross-section of 40 m × 40 m for the case of true azimuth <span class="html-italic">φ</span> equal to 45°. Each small square, except for the one in the center, represents the position of conducting structure, with the total number of such positions being 24. The large plus sign in the center ‘+’ indicates the location of observation point, and the arrow indicates the true direction to lightning.</p> Full article ">Figure 7
<p>Color-coded azimuth error (in degrees) due to the presence of a nearby conducting structure having a height of 60 m and a square cross-section of 40 m × 40 m for the case of true azimuth <span class="html-italic">φ</span> equal to 45° and current risetimes equal to (<b>a</b>) 0.5 μs and (<b>b</b>) 3 μs.</p> Full article ">Figure 8
<p>Color-coded azimuth error due to the presence of a nearby conducting structure having a height of 60 m and a square cross-section of 40 m × 40 m for the case of true azimuth <span class="html-italic">φ</span> equal to 45° and ground conductivity values equal to (<b>a</b>) 0.1 mS/m; (<b>b</b>) 5 mS/m; and (<b>c</b>) 1000 mS/m.</p> Full article ">Figure 9
<p>Azimuth errors as a function of ground conductivity when the conducting structure is located at (7000 m, 7000 m), (7000 m, 7080 m), (7040 m, 7080 m), (7120 m, 7080 m), and (7160 m, 7080 m).</p> Full article ">Figure 10
<p>Color-coded azimuth error due to the presence of a nearby conducting structure having a height of 60 m and a square cross-section of 40 m × 40 m for true azimuth <span class="html-italic">φ</span> equal to 45° and different values of structure conductivity equall to (<b>a</b>) 0.1 mS/m; (<b>b</b>) 1 mS/m; (<b>c</b>) 10 mS/m; and (<b>d</b>) 1000 mS/m.</p> Full article ">Figure 10 Cont.
<p>Color-coded azimuth error due to the presence of a nearby conducting structure having a height of 60 m and a square cross-section of 40 m × 40 m for true azimuth <span class="html-italic">φ</span> equal to 45° and different values of structure conductivity equall to (<b>a</b>) 0.1 mS/m; (<b>b</b>) 1 mS/m; (<b>c</b>) 10 mS/m; and (<b>d</b>) 1000 mS/m.</p> Full article ">Figure 11
<p>Color-coded azimuth error due to the presence of a nearby conducting structure having a height of 60 m and a square cross-section of 40 m × 40 m for the case of true azimuth <span class="html-italic">φ</span> equal to 26.6° (tan<sup>−1</sup> <span class="html-italic">φ</span> = 0.5).</p> Full article ">Figure 12
<p>Illustration of locations of lightning channel, conducting structure, and observation point when the center of the structure is located on the straight line that connects the lightning channel with the observation point: (<b>a</b>) true azimuth <span class="html-italic">φ</span> is 26.6°; (<b>b</b>) true azimuth <span class="html-italic">φ</span> is 45°; (<b>c</b>) true azimuth <span class="html-italic">φ</span> is 0°.</p> Full article ">
2998 KiB  
Article
Modeling CH4 Emissions from Natural Wetlands on the Tibetan Plateau over the Past 60 Years: Influence of Climate Change and Wetland Loss
by Tingting Li, Qing Zhang, Zhigang Cheng, Zhenfeng Ma, Jia Liu, Yu Luo, Jingjing Xu, Guocheng Wang and Wen Zhang
Atmosphere 2016, 7(7), 90; https://doi.org/10.3390/atmos7070090 - 8 Jul 2016
Cited by 10 | Viewed by 5075
Abstract
The natural wetlands of the Tibetan Plateau (TP) are considered to be an important natural source of methane (CH4) to the atmosphere. The long-term variation in CH4 associated with climate change and wetland loss is still largely unknown. From 1950 [...] Read more.
The natural wetlands of the Tibetan Plateau (TP) are considered to be an important natural source of methane (CH4) to the atmosphere. The long-term variation in CH4 associated with climate change and wetland loss is still largely unknown. From 1950 to 2010, CH4 emissions over the TP were analyzed using a model framework that integrates CH4MODwetland, TOPMODEL, and TEM models. Our simulation revealed a total increase of 15% in CH4 fluxes, from 6.1 g m−2 year−1 to 7.0 g m−2 year−1. This change was primarily induced by increases in temperature and precipitation. Although climate change has accelerated CH4 fluxes, the total amount of regional CH4 emissions decreased by approximately 20% (0.06 Tg—i.e., from 0.28 Tg in the 1950s to 0.22 Tg in the 2000s), due to the loss of 1.41 million ha of wetland. Spatially, both CH4 fluxes and regional CH4 emissions showed a decreasing trend from the southeast to the northwest of the study area. Lower CH4 emissions occurred in the northwestern Plateau, while the highest emissions occurred in the eastern edge. Overall, our results highlighted the fact that wetland loss decreased the CH4 emissions by approximately 20%, even though climate change has accelerated the overall CH4 emission rates over the last six decades. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The distribution of natural wetlands across the Tibetan Plateau (data from Niu et al., 2012 [<a href="#B15-atmosphere-07-00090" class="html-bibr">15</a>]) and location of the study sites.</p> Full article ">Figure 2
<p>Simulated and observed seasonal variations of CH<sub>4</sub> emissions and the observed air temperatures and water table depths. (<b>a</b>), (<b>b</b>), (<b>c</b>) and and (<b>d</b>) are the CH<sub>4</sub> emissions from the Zoige CME site, the Zoige CMU site, the Haibei CAL site and the Haibei HVU site; (<b>e</b>), (<b>f</b>), (<b>g</b>) and (<b>h</b>) are the air temperatures and water table depths from the Zoige CME site, the Zoige CMU site, the Haibei CAL site and the Haibei HVU site.</p> Full article ">Figure 3
<p>Comparison of observed and simulated CH<sub>4</sub> fluxes.</p> Full article ">Figure 4
<p>Temporal variations of (<b>a</b>) simulated decadal mean CH<sub>4</sub> fluxes, (<b>b</b>) decadal mean wetland area and regional CH<sub>4</sub>, (<b>c</b>) decadal mean air temperature, (<b>d</b>) decadal mean precipitation. Boxplots show the median, average values, and interquartile range, with whiskers extending to the most extreme data point within 1.5 × (75% – 25%) data range. Triangles represent the decadal mean wetland area.</p> Full article ">Figure 5
<p>Regression between annual mean CH<sub>4</sub> fluxes and (<b>a</b>) air temperature and (<b>b</b>) annual precipitation.</p> Full article ">Figure 6
<p>Spatial variations of CH<sub>4</sub> fluxes and regional CH<sub>4</sub> emissions. (<b>a</b>) CH<sub>4</sub> fluxes in 1950s; (<b>b</b>) CH<sub>4</sub> fluxes in 2000s; (<b>c</b>) CH<sub>4</sub> fluxes of 2000s minus 1950s; (<b>d</b>) regional CH<sub>4</sub> emissions in 1950s; (<b>e</b>) regional CH<sub>4</sub> emissions in 2000s; (<b>f</b>) regional CH<sub>4</sub> emissions of 2000s minus 1950s.</p> Full article ">
2487 KiB  
Article
Feasibility Study of Multi-Wavelength Differential Absorption LIDAR for CO2 Monitoring
by Chengzhi Xiang, Xin Ma, Ailin Liang, Ge Han, Wei Gong and Fa Yan
Atmosphere 2016, 7(7), 89; https://doi.org/10.3390/atmos7070089 - 30 Jun 2016
Cited by 11 | Viewed by 5846
Abstract
To obtain a better understanding of carbon cycle and accurate climate prediction models, highly accurate and temporal resolution observation of atmospheric CO2 is necessary. Differential absorption LIDAR (DIAL) remote sensing is a promising technology to detect atmospheric CO2. However, the [...] Read more.
To obtain a better understanding of carbon cycle and accurate climate prediction models, highly accurate and temporal resolution observation of atmospheric CO2 is necessary. Differential absorption LIDAR (DIAL) remote sensing is a promising technology to detect atmospheric CO2. However, the traditional DIAL system is the dual-wavelength DIAL (DW-DIAL), which has strict requirements for wavelength accuracy and stability. Moreover, for on-line and off-line wavelengths, the system’s optical efficiency and the change of atmospheric parameters are assumed to be the same in the DW-DIAL system. This assumption inevitably produces measurement errors, especially under rapid aerosol changes. In this study, a multi-wavelength DIAL (MW-DIAL) is proposed to map atmospheric CO2 concentration. The MW-DIAL conducts inversion with one on-line and multiple off-line wavelengths. Multiple concentrations of CO2 are then obtained through difference processing between the single on-line and each of the off-line wavelengths. In addition, the least square method is adopted to optimize inversion results. Consequently, the inversion concentration of CO2 in the MW-DIAL system is found to be the weighted average of the multiple concentrations. Simulation analysis and laboratory experiments were conducted to evaluate the inversion precision of MW-DIAL. For comparison, traditional DW-DIAL simulations were also conducted. Simulation analysis demonstrated that, given the drifting wavelengths of the laser, the detection accuracy of CO2 when using MW-DIAL is higher than that when using DW-DIAL, especially when the drift is large. A laboratory experiment was also performed to verify the simulation analysis. Full article
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<p>Block of the multi-wavelength differential absorption LIDAR (MW-DIAL) system. M1 is a semi-transmitting reflecting mirror; M2 and M4 are one-sided antireflection-coated glass devices; M3, M5, and M6 are reflecting mirrors.</p> Full article ">Figure 2
<p>Calculated absorption cross sections of CO<sub>2</sub> and H<sub>2</sub>O based on HITRAN 2012 and HITEMP 2010.</p> Full article ">Figure 3
<p>Selections of the on-line and off-line wavelengths in the region of R16 for the dual-wavelength DIAL (DW-DIAL). The black dotted and red lines are the absorption lines of CO<sub>2</sub> and H<sub>2</sub>O, respectively.</p> Full article ">Figure 4
<p>Selections of the multiple wavelengths in the region of R16 for the MW-DIAL. The black dotted and red lines are the absorption lines of CO<sub>2</sub> and H<sub>2</sub>O, respectively.</p> Full article ">Figure 5
<p>Simulated echo signal of DW-DIAL.</p> Full article ">Figure 6
<p>Simulated echo signal of DW-DIAL with signal to noise ratio (SNR) of 80 and 50.</p> Full article ">Figure 7
<p>Simulated echo signal of DW-DIAL with SNR of 30 and 10.</p> Full article ">Figure 8
<p>Differential absorption optical depth (DAOD) of DW-DIAL with SNR of 80 and 50.</p> Full article ">Figure 9
<p>DAOD of DW-DIAL with SNRs of 30 and 10.</p> Full article ">Figure 10
<p>Simulated echo signal of MW-DIAL.</p> Full article ">Figure 11
<p>System configuration of the wavelength control unit.</p> Full article ">
6692 KiB  
Article
Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model
by Tianhao Zhang, Wei Gong, Zhongmin Zhu, Kun Sun, Yusi Huang and Yuxi Ji
Atmosphere 2016, 7(7), 88; https://doi.org/10.3390/atmos7070088 - 27 Jun 2016
Cited by 29 | Viewed by 5620
Abstract
The estimation of ambient particulate matter with diameter less than 10 µm (PM10) at high spatial resolution is currently quite limited in China. In order to make the distribution of PM10 more accessible to relevant departments and scientific research institutions, [...] Read more.
The estimation of ambient particulate matter with diameter less than 10 µm (PM10) at high spatial resolution is currently quite limited in China. In order to make the distribution of PM10 more accessible to relevant departments and scientific research institutions, a semi-physical geographically weighted regression (GWR) model was established in this study to estimate nationwide mass concentrations of PM10 using easily available MODIS AOD and NCEP Reanalysis meteorological parameters. The results demonstrated that applying physics-based corrections could remarkably improve the quality of the dataset for better model performance with the adjusted R2 between PM10 and AOD increasing from 0.08 to 0.43, and the fitted results explained approximately 81% of the variability in the corresponding PM10 mass concentrations. Annual average PM10 concentrations estimated by the semi-physical GWR model indicated that many residential regions suffer from severe particle pollution. Moreover, the deviation in estimation, which primarily results from the frequent changes in elevation, the spatially heterogeneous distribution of monitoring sites, and the limitations of AOD retrieval algorithm, was acceptable. Therefore, the semi-physical GWR model provides us with an effective and efficient method to estimate PM10 at large scale. The results could offer reasonable estimations of health impacts and provide guidance on emission control strategies in China. Full article
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<p>Spatial distribution of the 1344 PM<sub>10</sub> monitoring sites (solid yellow triangles) used for sample collection in this study, which displays little coverage on plateau and desert areas.</p> Full article ">Figure 2
<p>Histograms and descriptive statistics of the variables in the whole model fitting data set.</p> Full article ">Figure 3
<p>Scatter plot of original PM<sub>10</sub> against original AOD (<b>a</b>); and revised PM<sub>10</sub> against revised AOD (<b>b</b>) with linear regression and confidence ellipse of 95%.</p> Full article ">Figure 4
<p>Scatter plot of cross validation for the original GWR model, using conventional linear regression (<b>a</b>); and linear regression with intercept fixed at zero (<b>b</b>).</p> Full article ">Figure 5
<p>Scatter plot of cross validation for the physics-based GWR model, with conventional linear regression (<b>a</b>); and linear regression with intercept fixed at zero (<b>b</b>).</p> Full article ">Figure 6
<p>Comparisons of seasonal mean AOD-derived PM<sub>10</sub> and ground-measured PM<sub>10</sub> mass concentrations.</p> Full article ">Figure 7
<p>Annual average ground measured PM<sub>10</sub> mass concentrations depicted using color-classified symbols (See legend at right).</p> Full article ">Figure 8
<p>Spatial distribution of annual average PM<sub>10</sub> mass concentrations for the GWR model.</p> Full article ">
8633 KiB  
Article
Sensitivity Study on High-Resolution Numerical Modeling of Static Topographic Data
by Joon-Bum Jee and Sangil Kim
Atmosphere 2016, 7(7), 86; https://doi.org/10.3390/atmos7070086 - 27 Jun 2016
Cited by 8 | Viewed by 5433
Abstract
Both research-grade and operational numerical weather prediction models perform simulations with horizontal grid spacings as fine as 1 km, and their multi-scale terrain data have become increasingly important for high-resolution model forecasting. This study focused on the influence of multi-scale surface databases of [...] Read more.
Both research-grade and operational numerical weather prediction models perform simulations with horizontal grid spacings as fine as 1 km, and their multi-scale terrain data have become increasingly important for high-resolution model forecasting. This study focused on the influence of multi-scale surface databases of topographical height and land use on the modeling of atmospheric circulation in a megacity. The default data were the global 30S United States Geographic Survey terrain data set and Moderate Resolution Imaging Spectroradiometer land-use data. The capacity for topographical expression under the combined scale effect was evaluated against observational data. The experiments showed that surface input data using finer resolutions for the Weather Research and Forecasting model with 1-km resolution gave better topographical expression and meteorological reproduction in a megacity and agreed with observational data in the fields of temperature and relative humidity, but precipitation values were not sensitive to the surface input data when verified against a suite of observational data including, but not limited to, ground-based instruments. The results indicated that the use of high-resolution databases improved the local atmospheric circulation in a megacity and that a fine-scale model was sensitive to the resolution of the surface input data whereas a coarse-scale model was less sensitive to it. Full article
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<p>Model domains for (<b>a</b>) the outer domain of 5 km; (<b>b</b>) the inner domain of 1km (rectangular box in <a href="#atmosphere-07-00086-f001" class="html-fig">Figure 1</a>a); (<b>c</b>) the digital elevation model (DEM); and (<b>d</b>) the land use in Seoul and its metropolitan areas. Red dots and green dots in (<b>c</b>,<b>d</b>) represent AWS sites in urban and rural areas, respectively. The black dot in (<b>d</b>) indicates the Seoul meteorological station (37.57°N latitude, 126.97°E longitude).</p> Full article ">Figure 2
<p>Distribution of surface height in the inner domain for (<b>a</b>) Control; (<b>b</b>) 75S; (<b>c</b>) 03S; and (<b>d</b>) 01S. The colorbar represents elevation in meters.</p> Full article ">Figure 3
<p>Distribution of surface land use in the inner domain for (<b>a</b>) Control; (<b>b</b>) 75S; (<b>c</b>) 03S; and (<b>d</b>) 01S. The colorbar represents land use type according to the Korea Land Cover 33 type system. The magenta/purple colors represent urban areas and other colors are for rural areas.</p> Full article ">Figure 4
<p>Topographical database for east–west and south–north cross sections shown in <a href="#atmosphere-07-00086-f001" class="html-fig">Figure 1</a>d. The x-axis is (<b>a</b>) longitude and (<b>b</b>) latitude. The y-axis is surface height in meter. The colorbar indicates the type of land use. Vertical blue boxes represent locations of the Han River.</p> Full article ">Figure 5
<p>Histogram of frequency (%) versus (<b>a</b>) land use and (<b>b</b>) topographic altitude for the inner domain.</p> Full article ">Figure 6
<p>Hourly and daily accumulated rainfall retrieved from (<b>a</b>) radar; (<b>b</b>) AWS; and (<b>c</b>) rainfall analysis on 27 July 2011 by Korea Meteorology Association (KMA).</p> Full article ">Figure 7
<p>Accumulated daily rainfall on 27 July 2011 for (<b>a</b>) Control; (<b>b</b>) 75S; (<b>c</b>) 03S and (<b>d</b>) 01S.</p> Full article ">Figure 8
<p>Time series for (<b>a</b>) rainfall, (<b>b</b>) temperature, and (<b>c</b>) wind speed from 0000UTC 26 July 2011 to 1200UTC 26 July 2011 for AWS (red), Control (green), 75 S (light blue), 03S (yellow), and 01S (red).</p> Full article ">Figure 9
<p>Time series of differences of <b>(a)</b> rainfall, <b>(b)</b> temperature at 2 m, and <b>(c)</b> wind speed at 10 m from 0000UTC 26 July 2011 to 1200UTC 26 July 2011 between 75S, 03S, and 01S and Control.</p> Full article ">Figure 10
<p>Vertical profiles of temperature, wind speed, and relative humidity on the Han River grid (37.521°N and 126.954°E) for (<b>a</b>) one-hour forecasting and (<b>b</b>) 7-h forecasting results. Note that 0000UCT is the same 0900 Local Sidereal Time (LST).</p> Full article ">Figure 11
<p>Relative difference of simulations relative to Control for 75S (red solid line), 03S (blue dashed line), and 01S (yellow dotted line). (<b>a</b>) South–north and (<b>b</b>) East–west cross sections on 37.55°N for (1st row) global shortwave radiation, (2nd row) sensible heat flux, (3rd row) latent heat flux, and (4th row) planetary boundary layer height at 1600LST. The light gray line represents topographical height along the sections shown in <a href="#atmosphere-07-00086-f001" class="html-fig">Figure 1</a>d. In the left window of each panel, the red lines represent the variables in the control run.</p> Full article ">
4465 KiB  
Review
A Review of ENSO Influence on the North Atlantic. A Non-Stationary Signal
by Belén Rodríguez-Fonseca, Roberto Suárez-Moreno, Blanca Ayarzagüena, Jorge López-Parages, Iñigo Gómara, Julián Villamayor, Elsa Mohino, Teresa Losada and Antonio Castaño-Tierno
Atmosphere 2016, 7(7), 87; https://doi.org/10.3390/atmos7070087 - 25 Jun 2016
Cited by 68 | Viewed by 12721
Abstract
The atmospheric seasonal cycle of the North Atlantic region is dominated by meridional movements of the circulation systems: from the tropics, where the West African Monsoon and extreme tropical weather events take place, to the extratropics, where the circulation is dominated by seasonal [...] Read more.
The atmospheric seasonal cycle of the North Atlantic region is dominated by meridional movements of the circulation systems: from the tropics, where the West African Monsoon and extreme tropical weather events take place, to the extratropics, where the circulation is dominated by seasonal changes in the jetstream and extratropical cyclones. Climate variability over the North Atlantic is controlled by various mechanisms. Atmospheric internal variability plays a crucial role in the mid-latitudes. However, El Niño-Southern Oscillation (ENSO) is still the main source of predictability in this region situated far away from the Pacific. Although the ENSO influence over tropical and extra-tropical areas is related to different physical mechanisms, in both regions this teleconnection seems to be non-stationary in time and modulated by multidecadal changes of the mean flow. Nowadays, long observational records (greater than 100 years) and modeling projects (e.g., CMIP) permit detecting non-stationarities in the influence of ENSO over the Atlantic basin, and further analyzing its potential mechanisms. The present article reviews the ENSO influence over the Atlantic region, paying special attention to the stability of this teleconnection over time and the possible modulators. Evidence is given that the ENSO–Atlantic teleconnection is weak over the North Atlantic. In this regard, the multidecadal ocean variability seems to modulate the presence of teleconnections, which can lead to important impacts of ENSO and to open windows of opportunity for seasonal predictability. Full article
(This article belongs to the Special Issue El Niño Southern Oscillation)
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<p>(<b>Top Panel</b>) Expansion coefficients of the leading MCA mode calculated between the anomalies of tropical Pacific SST (blue bars, left axis) and the anomalous Sahelian rainfall (red line, left axis) in JAS. Superimposed, the 21-year centered moving correlation windows (green line, right axis) and significant correlation (black filled circles) between both expansion coefficients; (<b>Bottom Panels</b>): (<b>First</b>) Regression maps of the SST expansion coefficient of the leading MCA mode onto the SST (left) and rainfall (center). Correlation maps (right) between the cross validated hindcast of rainfall performed only with the leading MCA mode. The whole time period is used for the analysis. (<b>Second</b>) As (first) but using the years corresponding to the center of the significant correlation windows (green dots in the green curve). (<b>Third</b>) As (first) and (second) but using the years corresponding to the center of the non-significant correlation windows. The percentage of squared covariance fraction is indicated in the left bottom corner of the figure.</p> Full article ">Figure 2
<p>(<b>a</b>) Previous August-October SSTs (red/blue shadings in K·std<sup>−1</sup>) and vertical 850–250 hPa wind shear (black/purple contour values at ±0.5 m·s<sup>−1</sup>·std<sup>−1</sup>) regressed onto Niño 3.4 index (December-February). Statistically significant areas of enhanced/reduced wind shear marked with crosses/dots (95% confidence interval). The Atlantic Hurricane Main Development Region is shown in a black rectangle; (<b>b</b>) Anomaly from long term mean: Number of Tropical Storms and Hurricanes over the North Atlantic (June to November, blue and magenta lines—11 years smoothing). The Atlantic Multidecadal Oscillation index (red dashed line). Twenty-one-year running correlations (centered) between Tropical Storm activity (hurricanes included) and Niño 3.4 index (black line). There is a 95% confidence interval (<span class="html-italic">t</span> test) in thicker segments. Data sources: <span class="html-italic">HURDAT2</span>, <span class="html-italic">HadISST</span> and <span class="html-italic">NCE</span>P.</p> Full article ">Figure 3
<p>Analysis of the stratospheric pathway of ENSO-European weather teleconnection. (<b>a</b>–<b>d</b>) Regression maps of different anomalous 7-year high-pass filtered fields on the SST expansion coefficient of the first co-variability mode of the tropical SSTs and the European rainfall in FMA for the P period: (<b>a</b>) Area-averaged geopotential height over the polar cap (90°–60° N) in different months (from OND to FMA) (contour interval: 10 m); (<b>b</b>–<b>d</b>) Geopotential height at 500 hPa in OND, DJF and FMA, respectively (contour interval: 10 m); (<b>e</b>–<b>h</b>) Same as (<b>a</b>–<b>d</b>) but for the N period. Shadings correspond to statistically significant correlations between the SST index and the Z anomalies at a 95% confidence level (Monte-Carlo test with 400 permutations); (<b>i</b>) 20-year centered moving correlation windows between the 7-year high-pass filtered geopotential anomalies at 10 hPa and averaged over the polar cap (90°N–60°N) in DJF and the 7-year high-pass filtered SST anomalies over the El Niño 3.4 area in the same months. Thick line indicates statistically significant values at a 90% confidence level (Monte-Carlo test with 400 permutations).</p> Full article ">Figure 4
<p>(<b>Top Panel</b>) Expansion coefficients of the leading MCA mode calculated between the anomalies of tropical Pacific SST (blue bars, left axis) and the anomalous Euro-Mediterranean rainfall (red line, left axis) in FMA. Superimposed, the 21-year centered moving correlation windows (green line, right axis) and significant correlation (black filled circles, right axis) between both expansion coefficients. The black curve corresponds to the AMO index; (<b>Bottom Panels</b>) (<b>First</b>) Regression maps of the SST expansion coefficient of the leading MCA mode onto the SST (left) and rainfall (center); Correlation maps (right) between the cross validated hindcast of rainfall performed only with the leading MCA mode. The whole time period is used for the analysis. (<b>Second</b>) As (first) but using the years corresponding to the center of the significant correlation windows (green dots in the green curve). (<b>Third</b>) As (first) and (second) but using the years corresponding to the center of the non-significant correlation windows. The percentage of squared covariance fraction is indicated in the left bottom corner of the figure.</p> Full article ">
8495 KiB  
Article
Sulfate Aerosols from Non-Explosive Volcanoes: Chemical-Radiative Effects in the Troposphere and Lower Stratosphere
by Giovanni Pitari, Daniele Visioni, Eva Mancini, Irene Cionni, Glauco Di Genova and Ilaria Gandolfi
Atmosphere 2016, 7(7), 85; https://doi.org/10.3390/atmos7070085 - 23 Jun 2016
Cited by 17 | Viewed by 8486
Abstract
SO2 and H2S are the two most important gas-phase sulfur species emitted by volcanoes, with a global amount from non-explosive emissions of the order 10 Tg-S/yr. These gases are readily oxidized forming SO42− aerosols, which effectively scatter the [...] Read more.
SO2 and H2S are the two most important gas-phase sulfur species emitted by volcanoes, with a global amount from non-explosive emissions of the order 10 Tg-S/yr. These gases are readily oxidized forming SO42− aerosols, which effectively scatter the incoming solar radiation and cool the surface. They also perturb atmospheric chemistry by enhancing the NOx to HNO3 heterogeneous conversion via hydrolysis on the aerosol surface of N2O5 and Br-Cl nitrates. This reduces formation of tropospheric O3 and the OH to HO2 ratio, thus limiting the oxidation of CH4 and increasing its lifetime. In addition to this tropospheric chemistry perturbation, there is also an impact on the NOx heterogeneous chemistry in the lower stratosphere, due to vertical transport of volcanic SO2 up to the tropical tropopause layer. Furthermore, the stratospheric O3 formation and loss, as well as the NOx budget, may be slightly affected by the additional amount of upward diffused solar radiation and consequent increase of photolysis rates. Two multi-decadal time-slice runs of a climate-chemistry-aerosol model have been designed for studying these chemical-radiative effects. A tropopause mean global net radiative flux change (RF) of −0.23 W·m−2 is calculated (including direct and indirect aerosol effects) with a 14% increase of the global mean sulfate aerosol optical depth. A 5–15 ppt NOx decrease is found in the mid-troposphere subtropics and mid-latitudes and also from pole to pole in the lower stratosphere. The tropospheric NOx perturbation triggers a column O3 decrease of 0.5–1.5 DU and a 1.1% increase of the CH4 lifetime. The surface cooling induced by solar radiation scattering by the volcanic aerosols induces a tropospheric stabilization with reduced updraft velocities that produce ice supersaturation conditions in the upper troposphere. A global mean 0.9% decrease of the cirrus ice optical depth is calculated with an indirect RF of −0.08 W·m−2. Full article
(This article belongs to the Special Issue Atmospheric Aerosols and Their Radiative Effects)
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Graphical abstract

Graphical abstract
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<p>Calculated zonally and annually averaged total optical depth of sulfuric acid aerosols from non-explosive volcanoes at λ = 0.55 µm (red line, right axis scale, ×10<sup>3</sup>) and related SO<sub>2</sub> emissions (black line, left axis scale, units kg-S·km<sup>−2</sup>·yr<sup>−1</sup>).</p> Full article ">Figure 2
<p>ULAQ model vertical profiles of aerosol extinction at selected latitude bands (λ = 0.55 µm), in the stratosphere and mid-upper troposphere for VE and REF cases (annual averages), evaluated with SAGE-II data (1999–2002) [<a href="#B39-atmosphere-07-00085" class="html-bibr">39</a>].</p> Full article ">Figure 3
<p>(<b>a</b>) ULAQ model zonally and annually averaged UTLS aerosol optical thickness (5.5–25.5 km) for VE and REF cases, evaluated with SAGE-II data (1999–2002 average); (<b>b</b>), as in (<b>a</b>), but for the total aerosol optical thickness, evaluated with zonally averaged AVHRR data over the oceans: AVHRR-1 [<a href="#B42-atmosphere-07-00085" class="html-bibr">42</a>]; AVHRR-2 [<a href="#B43-atmosphere-07-00085" class="html-bibr">43</a>], and AERONET values at tropical and Northern Hemisphere stations (years 2007–2009) [<a href="#B44-atmosphere-07-00085" class="html-bibr">44</a>]. The AVHRR retrieval of Zhao et al. [<a href="#B42-atmosphere-07-00085" class="html-bibr">42</a>] refers to years 1985–1988, the one of Mishchenko et al. [<a href="#B43-atmosphere-07-00085" class="html-bibr">43</a>] to years 1985–1988, with a different retrieval algorithm.</p> Full article ">Figure 4
<p>As in <a href="#atmosphere-07-00085-f002" class="html-fig">Figure 2</a>, but for the aerosol SAD. The model calculated SAD includes aerosols with <span class="html-italic">r</span> &gt; 0.05 µm.</p> Full article ">Figure 5
<p>Zonally and annually averaged changes of aerosol extinction at λ = 0.55 µm due to non-explosive volcanoes (VE-REF): absolute changes (units 10<sup>−4</sup>·km<sup>−1</sup>; contour line increment 2.5 × 10<sup>−4</sup>·km<sup>−1</sup>) (<b>a</b>); and percent changes (contour line increment 6%) (<b>b</b>).</p> Full article ">Figure 6
<p>As in <a href="#atmosphere-07-00085-f005" class="html-fig">Figure 5</a>, but for the aerosol SAD: units in panel (<b>a</b>) are µm<sup>2</sup>·cm<sup>−3</sup>. The contour line increment is 1 µm<sup>2</sup>·cm<sup>−3</sup> in panel (<b>a</b>) and 6% in panel (<b>b</b>).</p> Full article ">Figure 7
<p>Cirrus ice extinction changes (VE-REF) (units 10<sup>−4</sup>·km<sup>−1</sup>; contour line increment 2 × 10<sup>−4</sup>·km<sup>−1</sup>); (<b>a</b>) and total ice extinction (VE case) (contour line increment 50 × 10<sup>−4</sup>·km<sup>−1</sup>) (<b>b</b>). Dashed/dotted lines are the tropopause height and the 238 K contour, respectively, both taken as the annual average + 1σ of the monthly variation.</p> Full article ">Figure 8
<p>(<b>a</b>) Tropospheric NO<sub>x</sub> changes (VE-REF) (pptv); (<b>b</b>) as in (<b>a</b>), but for OH (pptv); and (<b>c</b>) as in (<b>a</b>), but for O<sub>3</sub> (ppbv). The contour line increments are: 1.5 pptv (<b>a</b>); 0.0015 pptv; (<b>b</b>) and 0.3 ppbv (<b>c</b>).</p> Full article ">Figure 9
<p>Tropospheric NO<sub>x</sub> evaluation of ULAQ model results for experiments VE and REF, using data from a collection of aircraft campaigns [<a href="#B54-atmosphere-07-00085" class="html-bibr">54</a>]: (<b>a</b>) PEM-Tropics-A-DC8 (Christmas Island, August–October 1996); (<b>b</b>) as in (<b>a</b>), but for Fiji; (<b>c</b>) PEM-West-A-DC8 (Hawaii, September–October 1991); (<b>d</b>) TRACE-A-DC8 (East Brazil Coast, September–October 1991); (<b>e</b>) as in (<b>c</b>), but for China Coast; and (<b>f</b>) as in (<b>c</b>), but for Japan. Units are pptv (NO<sub>x</sub> volume mixing ratio). The thick-black solid lines show the observations mean values; the uncertainty intervals are shown with solid whiskers (±1σ) and dotted whiskers (minimum and maximum). Blue solid and red dashed lines are for VE and REF results of the ULAQ model, respectively.</p> Full article ">Figure 10
<p>Evaluation of ULAQ model calculated O<sub>3</sub> (VE case), using observations from HALOE [<a href="#B56-atmosphere-07-00085" class="html-bibr">56</a>] and TES/Aura Level 3 ozone monthly data [<a href="#B57-atmosphere-07-00085" class="html-bibr">57</a>]. (<b>a</b>–<b>c</b>) Annual mean latitudinal sections in ppmv, at pressure layers: (<b>a</b>) 50–100 hPa; (<b>b</b>) 100–200 hPa; and (<b>c</b>) 200–500 hPa. The grey areas show ±1σ of the climatological zonal mean values (averaged over years 1991–2005 for HALOE, and 2005–2012 for TES/Aura).</p> Full article ">Figure 11
<p>(<b>a</b>) Evaluation of the ULAQ model calculated O<sub>3</sub> column (zonally and annually averaged values for the VE simulation), using NIWA combined total column ozone data [<a href="#B55-atmosphere-07-00085" class="html-bibr">55</a>]; and (<b>b</b>) calculated O<sub>3</sub> column changes (VE-REF) in DU and percent.</p> Full article ">Figure 12
<p>(<b>a</b>) Zonally and annually averaged UTLS changes of SO<sub>4</sub> (pptv, the contour line increment is 3 pptv); (<b>b</b>) aerosol SAD change averaged between the tropopause and 25 km altitude (zonal annual mean), in absolute units (µm<sup>2</sup>·cm<sup>−3</sup>, left scale, black line) and in percent (right scale, red line); and (<b>c</b>) zonally and annually averaged heterogeneous loss frequency changes of NO<sub>x</sub> (percent, with contour line increment 2.5%). Black dashed lines in panels (<b>a</b>) and (<b>c</b>) show the pressure altitude of the mean thermal tropopause ±1σ, where the standard deviation is relative to the monthly variability.</p> Full article ">Figure 13
<p>(<b>a</b>) Zonally and annually averaged UTLS NO<sub>x</sub> mixing ratio changes (VE-REF) (pptv; contour line spacing 0.15 pptv); and (<b>b</b>) zonally and annually averaged stratospheric O<sub>3</sub> mixing ratio changes (VE-REF) (ppbv; contour line spacing is 5 ppbv between −30 and −5 ppbv; it is 1 ppbv between −5 and 10 ppbv).</p> Full article ">Figure 14
<p>(<b>a</b>) VE-REF percent changes of J-NO<sub>2</sub> and J-O<sub>3</sub> (tropics, diurnal average). Dotted lines include only the aerosol perturbation. Dashed lines also include tropospheric O<sub>3</sub> changes. Solid lines include stratospheric and tropospheric O<sub>3</sub> changes in addition to the aerosol perturbation. (<b>b</b>) Same as the solid lines in panel (<b>a</b>), but for the O<sub>2</sub> photolysis.</p> Full article ">Figure 15
<p>Annually and zonally averaged radiative flux changes (VE-REF) at the tropopause (W·m<sup>−2</sup>), as a function of latitude, including the aerosol direct and indirect effects, cirrus ice changes and indirect sulfate aerosol effects on O<sub>3</sub> via NO<sub>x</sub>.</p> Full article ">Figure 16
<p>Volcanic emissions as used in the model calculations (relative units, global mean 9.6 Tg-S/yr). The size of the red circles denotes the source strength.</p> Full article ">Figure 17
<p>Average vertical profile of SO<sub>2</sub> volcanic emissions as used in the model calculations (red bars, upper scale, units kg-S·km<sup>−3</sup>·yr<sup>−1</sup>, global mean 9.6 Tg-S/yr) and calculated VE-REF anomalies of mean tropical and global SO<sub>2</sub> vertical profiles, with solid and dashed black lines, respectively (lower scale, pptv). The tropical mean is between 20S and 20N latitude.</p> Full article ">
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