1. The Role of the West African Monsoon on Atlantic Tropical Cyclone Activity
 THIS TALK
Introduction into the WAM
Atlantic Tropical Cyclone Variability
A Climate Perspective
A Weather Perspective
Final Comments
Acknowledgements
Anantha Aiyyer (North Carolina State University)
Gareth Berry (SUNY at Albany)
Susanna Hopsch (SUNY at Albany)
George Kiladis (ESRL, Boulder)
Nick Hall (Universite Paul Sabatier, Toulouse)
NOAA and NSF

2. 1. Introduction to the West African Monsoon
NDVI image for August 2000, from Pathfinder AVHRR, highlighting the marked meridional gradients in surface conditions over tropical North Africa and zonal symmetry.

3. 1. Introduction to the West African Monsoon
Key features of the West African Monsoon Climate System during Boreal summer
Heat Low
SAL
AEJ
ITCZ
Cold Tongue

4. 1. Introduction to the West African Monsoon
Key features of the West African Monsoon Climate System during Boreal summer
Heat Low
SAL
AEJ
Sahel
ITCZ
Cold Tongue

5. 1. Introduction to the West African Monsoon
Motivation: Societal need for improved climate prediction over West Africa
Time series ( ) of average normalized April-October rainfall departure for 20 stations in the West African Soudano-Sahel zone (11-18N and West of 10E); following methodology of Lamb and Peppler, 1992).

6. 1. Introduction to the West African Monsoon
Motivation: Societal need for improved climate prediction over West Africa
What will happen next?
Time series ( ) of average normalized April-October rainfall departure for 20 stations in the West African Soudano-Sahel zone (11-18N and West of 10E); following methodology of Lamb and Peppler, 1992).

7. 1. Introduction to the West African Monsoon

8. 1. Introduction to the West African Monsoon?

9. 1. Introduction to the West African Monsoon
Key weather systems in the West African and Tropical Atlantic regions
An ideal region to study scale interactions
SAL
AEWs TC
MCSs

10. 1. Introduction to the West African Monsoon

11. 2. Atlantic Tropical Cyclone Variability

12. 2. Atlantic Tropical Cyclone Variability
There exists marked Atlantic Tropical Cyclone Variability (ATCV).
What are the causes of this variability?

13. 2. Atlantic Tropical Cyclone Variability
Known factors:
Atlantic Sea Surface Temperatures
ENSO
West African rainfall
Phase of QBO!!!

14. 2. Atlantic Tropical Cyclone Variability
Known factors:
Atlantic Sea Surface Temperatures
ENSO
West African rainfall
Phase of QBO!!!

15. 2. Atlantic Tropical Cyclone Variability
Goldenberg and Shapiro (1996)
Linear correlation coefficients:
ENSO – ATCV
Sahel Rainfall – ATCV +0.70
This is usually interpreted in terms of the impact ENSO and Sahel rainfall have on the large-scale environment where tropical cyclones form e.g. vertical shear.

16. 3. A Climate Perspective
Aiyyer and Thorncroft (2008)

17. 3. A Climate Perspective
Understanding the processes that influence the MDR shear and its variability is very important
West Africa and East Pacific both provide important anomalous heat sources that can impact the MDR shear through tropical teleconnections
Thorncroft and Pytharoulis (2001)

18. 3. A Climate Perspective: Approach
Data: NCEP-NCAR Reanalyses ,
Here we take a shear-centered view focusing on May-October vertical wind shear between 850mb and 200mb
We separate the wind data into Low Frequency (LF, >8yrs) and High Frequency (HF, yrs) components
Dominant modes of vertical shear are extracted using EOF analysis of LF and HF shear.
Aiyyer and Thorncroft (2008)

19. 3. A Climate Perspective
Shading shows percentage variance in shear explained

20. 3. A Climate Perspective

21. 3. A Climate Perspective

22. 3. A Climate Perspective

23. 3. A Climate Perspective

24. 3. A Climate Perspective

25. 3. A Climate Perspective

26. 3. A Climate Perspective

27. 3. A Climate Perspective

28. 3. A Climate Perspective
Some Conclusions:
Variability in the large-scale environment in the tropical Atlantic strongly influences ATCV.
ATCV is influenced by ENSO and West African rainfall:
associated with changes in shear patterns in the MDR
(consistent with Bell and Chelliah, 2006)
ENSO is more important on interannual timescales
West African rainfall is more important on multidecadal timescales

29. 4. A Weather Perspective
Most Atlantic tropical cyclones form from African weather systems – but which one?

30. Hurricane Bonnie
A reminder of the importance of West African weather systems on hurricanes
Hurricane Charlie
Hurricane Frances
Hurricane Ivan
Provided by A. Aiyyer

31. 4. A Weather Perspective
OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September
Day 0
Streamfunction (contours 1 X 105 m2 s-1)
Wind (vectors, largest around 2 m s-1)
OLR (shading starts at +/- 6 W s-2), negative blue
Kiladis, Thorncroft, Hall (2006)

32. OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September
Day-4
Streamfunction (contours 1 X 105 m2 s-1)
Wind (vectors, largest around 2 m s-1)
OLR (shading starts at +/- 6 W s-2), negative blue

33. OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September
Day-3
Streamfunction (contours 1 X 105 m2 s-1)
Wind (vectors, largest around 2 m s-1)
OLR (shading starts at +/- 6 W s-2), negative blue

34. OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September
Day-2
Streamfunction (contours 1 X 105 m2 s-1)
Wind (vectors, largest around 2 m s-1)
OLR (shading starts at +/- 6 W s-2), negative blue

35. OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September
Day-1
Streamfunction (contours 1 X 105 m2 s-1)
Wind (vectors, largest around 2 m s-1)
OLR (shading starts at +/- 6 W s-2), negative blue

36. OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September
Day 0
Streamfunction (contours 1 X 105 m2 s-1)
Wind (vectors, largest around 2 m s-1)
OLR (shading starts at +/- 6 W s-2), negative blue

37. OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September
Day+1
Streamfunction (contours 1 X 105 m2 s-1)
Wind (vectors, largest around 2 m s-1)
OLR (shading starts at +/- 6 W s-2), negative blue

38. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.
See Berry, Thorncroft and Hewson (2007)

39. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

40. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

41. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

42. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

43. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

44. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

45. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

46. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

47. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

48. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

49. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

50. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

51. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

52. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

53. 4. A Weather Perspective: Multi-scale Diagnostics
315K Potential Vorticity (Coloured contours every 0.1PVU greater than 0.1 PVU) with 700hPa trough lines and easterly jet axes from the GFS analysis (1 degree resolution), overlaid on METEOSAT-7 IR imagery.

54. 4. A Weather Perspective: Importance of the Guinea Highlands
AEWs often get a “boost” before they leave Africa; associated with mergers of PV from upstream and in situ generation.
The Guinea Highlands region is one of the wettest regions of tropical North Africa.
GPCP rainfall (mm/day) for Aug-Sep, )

55. 4. A Weather Perspective: Importance of the Guinea Highlands
Does the nature of the AEWs and embedded MCSs impact the probability of tropical cyclogenesis downstream?
Hopsch and Thorncroft, 2008

56. 4. A Weather Perspective
What about variability in the weather systems?

57. 4. A Weather Perspective
There is a hint that the number of strong vortices leaving the West African coast impacts ATCV but this is far from being a sure case.
Recent analysis in the ERA40 datset (Hopsch et al, 2006) suggests this relationship to be weak on interannual timescales - but not on interdecadal timescales!

58. 5. Final Comments: Multi-decadal timescales
West Africa is currently in a wet phase – linked to naturally varying AMO :
or an anthropogenic AMO –type SST pattern (Mann and Emanuel, 2006)?
or the Atlantic Meridional Mode (Vimont and Kossin, 2007)?
The associated low-shear is associated with enhanced ATCV.
There are more favorable “seedlings” leaving West Africa in the wet phase.
Vecchi and Soden (2007) suggest that shear in the Atlantic will increase during the next century.

59. 5. Final Comments: Interannual timescales
ENSO is the most important influence on Atlantic shear and related ATCV
Its influence has increased in recent decades (not shown)
Intraseasonal?
Needs more work - See Maloney (2008) paper on MJO
Daily timescales
The nature of the AEWs leaving West Africa can influence the probability of tropical cyclogenesis (especially in the East Atlantic)
Convection and associated PV generation over the Guinea Highlands appears to be particularly important – and yet there are no observations there!