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Inclusion of biomass burning in WRFChem: impact of wildfires on transboundary haze over South East Asia Xing YU Tropical Marine Science InsHtute NaHonal University of Singapore 9 th June 2014 The 2nd Workshop of Atmospheric ComposiHon and the Asian Monsoon 810 June 2015, Bangkok, Thailand

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Page 1: Inclusion)of)biomass)burning)in)WRF3Chem: impactof ... · Inclusion)of)biomass)burning)in)WRF3Chem: impactof)wildfires)on) transboundary)haze) over)South)EastAsia)) Xing)YU) Tropical)Marine)Science)InsHtute)

Inclusion  of  biomass  burning  in  WRF-­‐Chem:  impact  of  wildfires  on  transboundary  haze  

over  South  East  Asia      

Xing  YU  Tropical  Marine  Science  InsHtute  NaHonal  University  of  Singapore  

 9th  June  2014  

 

The  2nd  Workshop  of  Atmospheric  ComposiHon  and  the  Asian  Monsoon  8-­‐10  June  2015,  Bangkok,  Thailand    

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Contents  

•  Background  of  hotspots  in  South  East  Asia  •  Haze  case  in  Jun  2013  •  Model  configuraHon  •  SimulaHon  results  •  Summary  

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Background  

•  Over  the  past  two  decades,  fires  and  haze  in  the  ASEAN  region  have  been  influenced  by  rapid  demographic  changes,  increased  human  acHvity,  and  climatological  factors.    

•  Singapore  has  been  affected  by  severe  smoke  haze  due  to  forest  fires  in  the  region  periodically.  

•  Usually,  smoke  haze  affected  Singapore  in  Sep  and  Oct.  

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Satellite  image  on  19  June  NOAA-­‐18  20130619  0752  UTC  Hotspot  count  173  

Haze  in  June  2013  

Regional  Haze  Map  20  Jun  2013    

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ObservaHons    

NOTE:  Some  hotspots  may  not  be  detected  by  satellite  when  there  is  presence  of  thick  cloud  cover/smoke  haze  or  when  the  hotspots  are  not  covered  by  the  satellite  scan.  

39   32   27  38   30   29   24   32   33   30   27   36  

63   65  

128  

102  116  

196  220  

320  

368  

325  

140  127  

82  68   73   67  

85  73  

85   82  

36  

7   13   5   3  22  

46  

101  

138  117  

187  173  

85  

21  

118  

227  

437  

174  

59  42  

15   7   7  0  

50  

100  

150  

200  

250  

300  

350  

400  

450  

500  

1   2   3   4   5   6   7   8   9   10   11   12   13   14   15   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30  

SG  PM10  

hotspots  in  Sumatra  

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PM2.5  in  Singapore    

North  South  East  West  Central  

Maximum:  8pm  20  Jun  310  at  South  Area  4  Hmes  per  day  before  21  June  (00,  08  ,12  16  SGT)  

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Low  level  prevail  wind  

•  Two  tropical  storms  in  the  SCS  on  17-­‐23  June,  strengthened  the  westerly  winds.  

•  A  tropical  storm  in  the  SCS  landed  over  Hanoi,  winds  over  SG  shided  from  SSW  to  SSE  on  24  June.  This  direcHon  shid  miHgates  polluHon  in  Singapore,  but  exacerbates  in  South  P  Malaysia.  

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Large  scale  circulaHon  

1.  Indian  Monsoon.  2.  Tropical  depression  in  SCS.  3.  Tropical  depression  in  South  East  Indian  Ocean.  

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TRMM  24-­‐27  June  

•  Drier  atmospheric  condiHons  prevailed  at  Sumatra,  P  Malaysia,  and  Singapore.    •  Ader  26  June,  more  rain  clouds  appearing  in  Sumatra,  subdues  the  hotspots.  

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Experiments  (Control  Run)  1. Model: WRF/CHEM V3.5 2.  Grids: 150×120; resolution 18 km, 3.  Input data: 1°× 1° (FNL) 4. Model initial time: 00 UTC from 10 June 5.  Integration length: 20 days 6.  Biomass burning source: Hotspots from MODIS 7.  Biomass updates at every 00 UTC. 8.  Anthropogenic source: GOddard Chemistry Aerosol

Radiation and Transport (GOCART) dust model, 1 degree resolution.

9.  Data assimilation is applied. 10.  Convection scheme: Grell 3D ensemble scheme

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Hotspot  definiHon  •  Data  source:  hfps://firms.modaps.eosdis.nasa.gov/  •  Brightness  >  320  k.  (1  km  resoluHon)  

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Results  Simulated  pm2.5    (maximum  value  over  (103.5  104.5E,  1-­‐1.5N)    

Observed  pm2.5  (maximum  value  over  5  areas  of  Singapore)  

Maximum  140,  less  than  half  of  observed  310  

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Error  of  wind    

0.0  

1.0  

2.0  

3.0  

4.0  

5.0  

6.0  

7.0  

8.0  

10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  

m/s  

Day  of  June  

RMSE  of  speed  at  850  hpa  fnl-­‐ana-­‐spd  fnl-­‐fcst-­‐spd  

0.0  

20.0  

40.0  

60.0  

80.0  

100.0  

120.0  

10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  

Degree  

Day  of  June  

Mean  Error  of  direc=or  at  850  hpa  

fnl-­‐ana-­‐dir  

fnl-­‐fcst-­‐dir  

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SensiHve  Experiments  •   replace  FNL  by  0.5°× 0.5° GFS    •  Increasing  emission  rate  by  factor  of  5.  

Sumatra   Amazon  

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Burning  materials  •  A  new  mapping  tool  based  on  NASA  satellite  data  confirms  

that  the  majority  of  fires  that  drove  the  haze  over  Sumatra  and  Malaysia  were  concentrated  in  deforested  peatlands  and  scrub,  rather  than  natural  forest  areas.    

•  World  Resources  InsHtute  (WRI)  and  Eyes  on  the  Forest,  a  Sumatra-­‐based  coaliHon  of  environmental  groups.  WRI  data  indicates  that  roughly  half  of  hotspots  recorded  during  the  peak  of  the  haze  case  were  located  in  concessions  belonging  to  palm  oil  and  pulp/paper  companies.  

•  Unlike  the  major  fires  in  the  region  in  1982-­‐1983,  1997-­‐1998  and  2006,  few  fires  were  located  in  natural  forest  areas.    

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SensiHve  Experiments  

0.0  1.0  2.0  3.0  4.0  5.0  6.0  7.0  8.0  

10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  Day  of  June  

RMSE  of  speed  at  850  hpa  fnl-­‐ana-­‐spd  gfs-­‐ana-­‐spd  

0.0  1.0  2.0  3.0  4.0  5.0  6.0  7.0  8.0  

10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  Day  of  June  

RMSE  of  speed  at  850  hpa  

fnl-­‐fcst-­‐spd  gfs-­‐fcst-­‐spd  

0.0  

20.0  

40.0  

60.0  

80.0  

100.0  

120.0  

10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  

Degree  

Day  of  June  

Mean  Error  of  direc=on  at  850  hpa  fnl-­‐ana-­‐dir  gfs-­‐ana-­‐dir  

0.0  

20.0  

40.0  

60.0  

80.0  

100.0  

120.0  

10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  

Degree  

Day  of  June  

Mean  Error  of  direc=on  at  850  hpa  fnl-­‐fcst-­‐dir  gfs-­‐fcst-­‐dir  

Analysis  

Forecast  

Wind  speed   Wind  direcHon  

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Comparison  of  PM2.5  Original  emission  rate   Modified  emission  rate  

ObservaHon   FNL   GFS  

Not  sensiHve  to  meteorological  fields.  Maximum  reaches  300  ug/m3  ,  second  peak  240  ug/m3    

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Summary  and  quesHons  Consider  3  direcHons  to  improve  haze  simulaHon.  1)  Large  scale  meteorological  circulaHon.  Not  

sensiHve  to  FNL/GFS,  but  may  be  improved  by  cycling,  rather  than  a  long  Hme  integraHon.  

2)  How  to  esHmate  the  emissions  of  burning  peatland  and  scrub-­‐field  experiments?  

3)  how  to  uHlize  available  satellite  observaHons,  not  only  fire  locaHon,  but  also,  fire  size,  Fire  RadiaHve  Power,  materials…  

4)  When  cloud  covers  the  target  area,  how  to  monitor  the  hotspot  locaHon?  


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