Report on Africa Adaptation Programme (AAP) - Capacity Building on setting up and use of climate data server for climate change adaptation studies and modeling. University of Mauritius, Reduit 30 July -03 August 2012
Introduction The Africa Adaptation Programme (AAP) was established under the Japan-UNDP Joint Framework for building partnership among African countries in order to address Climate Change in Africa. It was established at the Fourth Tokyo International Conference for Africa Development organized in Yokohama, Japan in May 2008. Twenty countries across Africa (including Mauritius) are presently working with the AAP to develop the e-infrastructure and capabilities needed to access, analyse and apply data to planning thus promoting a climate resilient sustainable development by building resilience of their national development strategies.
The AAP supports the long-term efforts of targeted African countries to further develop their capability to successfully identify, design and implement holistic adaptation and disaster risk reduction programmes that are aligned with national development priorities. In this regard, AAP aimed at creating an environment for more informed and capable adaptation decisions and practice in each country. The cost of delivering the e-infrastructure in each AAP country is roughly US$50,000 over a period of 3 months. It includes the procurement of an HPC climate data server, installation assistance and training associated with the management of climate data servers and analysis of climate data
Objectives of the training
• Provide support for AAP countries by building an e-¬‐infrastructure for climate data and climate simulations. • Provide a computational environment to satisfy the different requirements posed by users • Team up with organization/institutions that share the similar common requirement to share effort and ideas .( Do not re-¬‐invent the wheel approach )
Desired outcomes: • The data infrastructure should be distributed and easily available to all users. • The data infrastructure which would house the datasets would also have the capability of providing computationally-¬‐intensive services for data analysis and visualization. • The data analysis and visualization component would be provided through a web portal with a collection of open source tools, data and methods (IDV, Google Earth, TRENDS, NSFM, OpenGIS, R, RClimdex, netCDF, etc).