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Online via Microsoft Teams
07 June 2021 to
11 June 2021
Climate Services, Mandira Singh Shrestha & Santosh Nepal
Organizers: ICIMOD and Met Office
In October 2020, ICIMOD and the Met Office, UK’s national meteorological agency, partnered with the World Climate Research Programme, the Swedish Meteorological and Hydrological Institute, and the Indian Institute of Tropical Meteorology (IITM) – Pune, to organize a training on Regional climate change projections: Climate change analysis using CORDEX regional climate models over South Asia. Organized under the UK Aid supported Asia Regional Resilience to a Changing Climate (ARRCC) programme, the training is part of ARRCC’s institutional capacity-building approach to strengthen the knowledge and capabilities of staff working at key national and regional institutions, delivering climate services to provide information needs for informed climate change responses in South Asia.
As a follow-on activity to the 2020 training, country focused trainings are being organized for professionals from different national meteorological and hydrological services (NMHS) in ARRCC focal countries– Afghanistan, Bangladesh, Nepal, and Pakistan – under ICIMOD’s Climate Services Initiative. The 2021 edition will focus on using CORDEX regional climate models to carry out spatial and temporal analysis of climate change over South Asia.
This training will introduce climate change science, and will focus on bias calculation and selection of few representative models for a defined area of interest. The training will delve deeper into how R based tools can be used to analyse and visualize climate change projections spatially at different time scales.
The training aims to build underpinning knowledge and skills for analysing climate change projections using CORDEX regional climate model simulations.
This training will help participants better understand climate model projections using 17 regional climate models, selecting the representative models which replicate the historical climate cycles, and assessing spatial and temporal variability of present and future climate change over a defined area of interest.
In addition to gaining an overall understanding of climate modelling and downscaling, the participants will be able to:
In its current iteration, this training is being offered to professionals from the Department of Hydrology and Meteorology (DHM), and relevant institutions in Nepal.
Due to the current COVID19 pandemic, the training will be organized virtually.
Kabi Raj Khatiwada
Arun Bhakta Shrestha
Mandira Singh Shrestha
Timings are in Nepal Standard Time (NPT, UTC+05:45).
Troubleshooting with the installation of the required software
Session chair: Ghulam Rasul, ICIMOD
Welcome remarks: Ghulam Rasul, ICIMOD
Opening remarks– Joseph Daron, Met Office
Objectives of the training –Santosh Nepal, ICIMOD
Institutional capacity-building approach for regional climate projections – Mandira Shrestha, ICIMOD
Introductions and expectations from participants
Climate modelling and approaches to regional and local downscaling – Joseph Daron and Cathryn Fox, Met Office
Climate change and its impact on the HKH region – Arun Shrestha and Santosh Nepal, ICIMOD
Application of future climate projections in Nepal – TBD, DHM
Extracting CORDEX datasets (precipitation and temperatures)
Clipping datasets for an area of interest (Koshi)
Preparing datasets for visualization
Annual climate patterns of all 17 CORDEX models
Discussions about climatic patterns
Understanding the present climatology (precipitation and temperature) using APHRODITE data (1976–2005)
Comparison of CORDEX datasets with reference datasets – APHRODITE
Calculate seasonal bias (step 1)
Calculate annual bias (step 2)
Narrow down the model to 4 RCMs (RCP4.5) and 4 RCMs (RCP8.5)
Ensemble mean of selected 4 models
Calculation of future scenarios of precipitation and temperature at both scenarios
Q & A followed by discussion
Calculation of the annual and seasonal change for the season
Preparation of the maps
Q&A followed by discussion
Calculation of the Scatter plot with min-max error bars
Understanding of the uncertainty among the models
Discussion on the results