The Role of the West African Monsoon on Atlantic Tropical Cyclone Activity THIS TALK Introduction into the WAM Atlantic Tropical Cyclone Variability 3. A Climate Perspective 4. A Weather Perspective 5. 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
1. Introduction to the West African Monsoon NDVI image for 21-31 August 2000, from Pathfinder AVHRR, highlighting the marked meridional gradients in surface conditions over tropical North Africa and zonal symmetry.
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
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
1. Introduction to the West African Monsoon Motivation: Societal need for improved climate prediction over West Africa Time series (1941-2001) 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).
1. Introduction to the West African Monsoon Motivation: Societal need for improved climate prediction over West Africa What will happen next? Time series (1941-2001) 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).
1. Introduction to the West African Monsoon
1. Introduction to the West African Monsoon ?
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
1. Introduction to the West African Monsoon
2. Atlantic Tropical Cyclone Variability
2. Atlantic Tropical Cyclone Variability There exists marked Atlantic Tropical Cyclone Variability (ATCV). What are the causes of this variability?
2. Atlantic Tropical Cyclone Variability Known factors: Atlantic Sea Surface Temperatures ENSO West African rainfall Phase of QBO!!!
2. Atlantic Tropical Cyclone Variability Known factors: Atlantic Sea Surface Temperatures ENSO West African rainfall Phase of QBO!!!
2. Atlantic Tropical Cyclone Variability Goldenberg and Shapiro (1996) Linear correlation coefficients: ENSO – ATCV -0.41 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.
3. A Climate Perspective Aiyyer and Thorncroft (2008)
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)
3. A Climate Perspective: Approach Data: NCEP-NCAR Reanalyses , 1948-2004 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, 1.5-8 yrs) components Dominant modes of vertical shear are extracted using EOF analysis of LF and HF shear. Aiyyer and Thorncroft (2008)
3. A Climate Perspective Shading shows percentage variance in shear explained
3. A Climate Perspective
3. A Climate Perspective
3. A Climate Perspective
3. A Climate Perspective
3. A Climate Perspective
3. A Climate Perspective
3. A Climate Perspective
3. A Climate Perspective
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
4. A Weather Perspective Most Atlantic tropical cyclones form from African weather systems – but which one?
Hurricane Bonnie A reminder of the importance of West African weather systems on hurricanes Hurricane Charlie Hurricane Frances Hurricane Ivan Provided by A. Aiyyer
4. A Weather Perspective OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September 1979-1993 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)
OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September 1979-1993 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
OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September 1979-1993 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
OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September 1979-1993 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
OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September 1979-1993 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
OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September 1979-1993 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
OLR and 850 hPa Flow Regressed against TD-filtered OLR (scaled -20 W m2) at 10N, 10W for June-September 1979-1993 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
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)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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, 1997-2007)
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
4. A Weather Perspective What about variability in the weather systems?
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!
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.
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!