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 transcript:

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

Outline Dust Data –Surface based synops (from GCOS, 117 stations, 3hrly ) –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, –Determine regional transport corridors –Re-circulation –Interannual variability

Nr Missing Months

GCOS Data Compared with locally Obtained Data

In Salah visibility 80-00

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, –Determine regional transport corridors –Re-circulation

Niger visibility 70-00

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

Bilma, Niger: sandstorms Vs TOMS by season

North African Surface Dust storms Correlation TOMS

Bilma surface visibility Vs TOMS (march )

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, –Determine regional transport corridors –Re-circulation

REOF2 of TOMS AI monthly anomalies for the period /93 and

REOF3 of TOMS AI monthly anomalies for the period /93 and

REOF5 of TOMS AI monthly anomalies for the period /93 and

REOF6 of TOMS AI monthly anomalies for the period /93 and

REOF7 of TOMS AI monthly anomalies for the period /93 and

REOF8 of TOMS AI monthly anomalies for the period /93 and

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

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

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

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

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

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, –Determine regional transport corridors –Re-circulation

Bodele Djouf region

Parcel trajectory climatology May ( ) 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)

Parcel trajectory climatology June ( ) 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)

Parcel trajectory climatology Dec ( ) 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)

Dust Recycling potential May June July Dec

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

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

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

May 1991 Long. Lat. Height (hPa)

May 1989 Height (hPa) Lat. Long.

Djouf region

TOMS AI over Djouf region ( N, W)

Parcel trajectory density climatology July ( ) Djouf region ( N, W) % trajectories remain over Sahara (> 15N < 30N) % trajectories crossing the 15W meridian After days

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

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

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

Parcel trajectory density climatology July ( ) Djouf region ( N, W)

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

Parcel trajectory climatology May ( ) Bodele depression (17.5N, 17E)

TOMS mean AI May

Parcel trajectory climatology July ( ) Bodele depression (17.5N, 17E)

Parcel trajectory density climatology July ( ) Djouf region ( N, W) Mean 10-25N