Research: Graduate Student Profile

 

Snapshots...

Ousmane Ndiaye
Climate Studies, LDEO

Master in Physic of Atmosphere, University of Paris XII Val-de-Marnes, 1998.
Meteorological Engineering Degree, IHFR, 1996.

"Born in West Africa, where many aspects of society are dependent on climate outcomes, I couldn't find a better place than the International Research Institute for climate and society (IRI) to answer questions that my community has to deal with each year : malaria outbreaks and variability in yields in rain-fed agriculture. As I was growing up, I witnessed the degradation of the environment and natural resources (crop production and livestock), which generated my interest in climate prediction. DEES through its curriculum offers to me the theoretical fundation to understand these issues. IRI complements with a practical perspective by bringing climate information to users specific needs."

Predictability of Sahelian Climate Characteristics and its Impacts

My research focuses on understanding and predicting rainfall variability and its impacts (year to year and within the season) over the Sahelian region of West Africa. The Sahel region is a transition zone between desert to the North (Sahara desert) and tropical forest to the South. Rainfall in this region strongly influences many aspects of society, including agriculture (crop production and livestock), water resources, and vector born disease outbreaks (malaria is by far the first cause of mortality). Many studies, both statistical and dynamical, have related Sahelian rainfall variability to patterns of Sea Surface Temperature (SST), and a degree of predictability of the large-scale circulation patterns and associated seasonal rainfall totals in the region is well established.

A key issue concerns the changes in SST anomalies that take place in boreal spring and that currently limit the lead-time of seasonal predictions for the June-September rainfall season. One aim of the study is to provide a definitive analysis of this limitation, along with its reasons and possible ways to increase the lead-time of predictions beyond that currently achieved in models. A comparison of statistical and dynamical methods will be made.

Less is also known about how the large-scale predictability translates into ability to provide information on small spatial scales, and on the statistics of weather events through the season. These issues relate to how predictions can be made for variables like crop yield and vegetation greenness, and environmentally influenced diseases such as malaria. Through analysis of datasets and model outputs, key space and timescale prediction issues will be addressed, along with some key impact variables. The work will include more focused and detailed analyses on Senegal, comprising the westernmost zone of the Sahel, bordering the Atlantic Ocean.

My IRI website: http://iri.columbia.edu/cgi-bin/staff?ondiaye

Advisors: Adam Sobel, Neil Ward, and Doug Martinson.