Parallel session B4 : Observations for Monitoring , Understanding and Predicting Climate.

IMG_4983AN ASSESSMENT OF AFRICA’S CLIMATE OBSERVING NETWORKS AND DATA INCLUDING STRATEGIES FOR RESCUING FOF CLIMATIC DATA-    Dr.Buruhani Salum

Dr. Buruhani said “under the WMO Regional Basic Climatological Network (RBCN) and Regional Basic Synoptic Network (RBSN), Africa is comparably better-covered than other continents. However, the spatial coverage of the network varies widely from country to country.This disparity and unevenness in national network coverage introduce bias to the data especially when using in studies, research and development activities.”

RAINFALL VARIABILITY AND THE RECENT CLIMATE EXTREMES IN NIGERIA-Cyprian Okoloye, Nigerian Meteorological Agency

Mr. Cyprian “The type of climate hazards and their spatial variations, the expected frequency and intensity of impacts of climate change on urban areas can no longer be predicted by solely relying on historical data, local experiences and institutional memory.”

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Side events at Africa Climate Conference 2013.

Prof.Yanda introduces  the missions and functions  of UDSM- Centre  for  Climate  Change( CCC)  at Africa Climate Conference 2013    in  one  of  the  side  events hosted  by  Norwegian   Embassy.

 

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Parallel session B3 : Climate Variability and Predictability.

IMG_4978B3: Climate Variability and Predictability

Chair: Laban Ogallo, ICPAC : Moderator: Alessandra Giannini, IRI

The Chair reminded the participants that the goal of the conference is to find out what are  our users, what are their needs, the current state of knowledge and knowledge gaps.

  1. Changes in East Africa  in observations and the ECHAM-5 Model Run with observed Sea Surface Temperatures: Brant Liebmann, University of Colorado
  • Attempt to improve seasonal forecast in precipitation in East Africa
  • Using topography of East Africa
  • Simulation model
  • Period of the study was from 1979 to 2012
  • Broken into three regions
  • Exclude the Ethiopian highland region and Victoria plateau because of the high amount of rainfall there.
  • Change =trend X length of record
  • The model observed changes in horn precipitation from 1979 to 2012 in March and May, and in October and December.

The inter-annual anomaly of October-December Horn precipitation is well-simulated by the model ‘ensemble-average’, although knowing SSTs in the east Pacific gives almost good results. The ensemble-average correctly predicts the sign of precipitation anomaly in March-May in two-thirds of years (mostly from precipitation over Indonesia).

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Dr Bilal, Vice President – URT, on climate change at ACC 2013.

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Prof. Mukandala, VC- UDSM, on climate change at ACC 2013

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