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Dust: Data and Sources Dr Richard Washington University of Oxford Dr Martin Todd University College London Prof Andrew Goudie University of Oxford Dr Mike.

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Presentation on theme: "Dust: Data and Sources Dr Richard Washington University of Oxford Dr Martin Todd University College London Prof Andrew Goudie University of Oxford Dr Mike."— Presentation transcript:

1 Dust: Data and Sources Dr Richard Washington University of Oxford Dr Martin Todd University College London Prof Andrew Goudie University of Oxford Dr Mike Bithell University of Cambridge

2 Outline Dust Data –Surface based synops (from GCOS, 117 stations, 3hrly 1980-2000) –Surface based synops (from local Met Agencies) –TOMS AI data –IDDI –Shuttle Photographs, NOAA images etc Climate Data and Trajectory modelling –Reanalysis Data (NCEP and ERA-15) 3D winds –25 point forward trajectory clusters –every 6 hours for 10days, 1979-1998 –Determine regional transport corridors –Re-circulation –Interannual variability

3 Nr Missing Months 1980-2000

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7 GCOS Data Compared with locally Obtained Data

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12 In Salah visibility 80-00

13 Outline Dust Data –Surface based synops (from GCOS, 117 stations) –Surface based synops (from local Met Agencies) Niger, Chad, Algeria, Mali, Mari –TOMS AI data –IDDI –Shuttle Photographs, NOAA images etc Climate Data and Trajectory modelling –Reanalysis Data (NCEP and ERA-15) 3D winds –25 point forward trajectory clusters –every 6 hours for 10days, 1979-1998 –Determine regional transport corridors –Re-circulation

14 Niger visibility 70-00

15 N’Djamena, Chad: Haze Vs Obs Rainfall By season

16 Bilma, Niger: sandstorms Vs TOMS by season

17 North African Surface Dust storms Correlation TOMS 1980-2000

18 Bilma surface visibility Vs TOMS (march 1980-2000)

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20 Outline Dust Data –Surface based synops (from GCOS, 117 stations) –Surface based synops (from local Met Agencies) Niger, Chad, Algeria, Mali, Mari –TOMS AI data –IDDI –Shuttle Photographs, NOAA images etc Climate Data and Trajectory modelling –Reanalysis Data (NCEP and ERA-15) 3D winds –25 point forward trajectory clusters –every 6 hours for 10days, 1979-1998 –Determine regional transport corridors –Re-circulation

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23 REOF2 of TOMS AI monthly anomalies for the period 1980-05/93 and 1997-2000

24 REOF3 of TOMS AI monthly anomalies for the period 1980-05/93 and 1997-2000

25 REOF5 of TOMS AI monthly anomalies for the period 1980-05/93 and 1997-2000

26 REOF6 of TOMS AI monthly anomalies for the period 1980-05/93 and 1997-2000

27 REOF7 of TOMS AI monthly anomalies for the period 1980-05/93 and 1997-2000

28 REOF8 of TOMS AI monthly anomalies for the period 1980-05/93 and 1997-2000

29 Apr Potential Sand Flux overlay TOMS q = 2.61 U 3 * pg –1 (1-U * /U * )(1+U * /U * ) 2

30 Sahel TOMS (white contours), potential sand flux (black contours), with DEM (colour). LTM for JFM 80-00.

31 Central Asia TOMS (white contours), potential sand flux (black contours), with DEM (colour). Long term mean for 80-00.

32 Correlation of monthly visibility anomalies at Nouakchott (Mauritania) with global NCEP derived potential sand flux for 1980-2000

33 Correlation of monthly visibility anomalies at Bobo-Dioulasso (Burkina Faso) with global NCEP derived potential sand flux for 1980-2000

34 Outline Dust Data –Surface based synops (from GCOS, 117 stations) –Surface based synops (from local Met Agencies) Niger, Chad, Algeria, Mali, Mari –TOMS AI data –IDDI –Shuttle Photographs, NOAA images etc Climate Data and Trajectory modelling –Reanalysis Data (NCEP and ERA-15) 3D winds –25 point forward trajectory clusters –every 6 hours for 10days, 1979-1998 –Determine regional transport corridors –Re-circulation

35 Bodele Djouf region

36 Parcel trajectory climatology May (1979-98) Bodele depression (17.5N, 17E) 54% of trajectories cross the 15W meridian 27% of trajectories remain over N. Africa 21% of trajectories remain over Sahara (> 15N)

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39 Parcel trajectory climatology June (1979-98) Bodele depression (17.5N, 17E) 79% of trajectories cross the 15W meridian 13% of trajectories remain over N Africa 9% of trajectories remain over Sahara (> 15N)

40 Parcel trajectory climatology Dec (1979-98) Bodele depression (17.5N, 17E) 66% of trajectories cross the 15W meridian 23% of trajectories remain over N. Africa 6% of trajectories remain over Sahara (> 15N)

41 Dust Recycling potential May June July Dec

42 Parcel trajectory climatology May (1979-98) Bodele depression (17.5N, 17E) Mean 0-15N

43 Parcel trajectory climatology Dec (1979-98) Bodele depression (17.5N, 17E) Mean 0-15N

44 TOMS AI over Bodele region (15-17N, 16-18E) mean May 1991 May 1989

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47 May 1991 Long. Lat. Height (hPa)

48 May 1989 Height (hPa) Lat. Long.

49 Djouf region

50 TOMS AI over Djouf region (17.5-22.5N, 2.5-7.5W)

51 Parcel trajectory density climatology July (1979-98) Djouf region (17.5-22.5N, 2.5-7.5W) % trajectories remain over Sahara (> 15N < 30N) 69 33 6 % trajectories crossing the 15W meridian 33 66 83 After days 3 5 10

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53 Annual CMAP precipitation over Djouf region Annual TOMS AI over Djouf region

54 Conclusions Surface Synops GCOS data: expensive and patchy Surface Synops from Met Agencies Synops-TOMS AI: sensible but weak Major mineral dust source regions identified from TOMS AI data: Bodele and Djouf Trajectory modelling allows definition of dust transport corridors –Pronounced seasonal cycle and interannual variability

55 500hPa 80W 40E 10S 40N Parcel trajectory climatology May (1979-98) Bodele depression (17.5N, 17E)

56 Parcel trajectory density climatology July (1979-98) Djouf region (17.5-22.5N, 2.5-7.5W)

57 West of 15W Over Sahara >15N <30N Over Atlantic

58 Parcel trajectory climatology May (1979-93) Bodele depression (17.5N, 17E)

59 TOMS mean AI May 1980-93

60 Parcel trajectory climatology July (1979-98) Bodele depression (17.5N, 17E)

61 Parcel trajectory density climatology July (1979-98) Djouf region (17.5-22.5N, 2.5-7.5W) Mean 10-25N

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