The CWSLab workflow tool: an experiment in community code development

Dr Climate

Give anyone working in the climate sciences half a chance and they’ll chew your ear off about CMIP5. It’s the largest climate modelling project ever conducted and formed the basis for much of the IPCC Fifth Assessment Report, so everyone has an opinion on which are the best models, the level of confidence we should attach to projections derived from the models, etc, etc. What they probably won’t tell you about is the profound impact that CMIP5 has had on climate data processing and management. In the lead up to CMIP5 (2010/11), I was working at CSIRO in a support scientist role. When I think back on that time, I refer to it as The Great Data Duplication Panic. In analysing output from CMIP3 and earlier modelling projects, scientists simply downloaded data onto their local server (or even personal computer) and did their own analysis in isolation. At the CSIRO…

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About ecoquant

See Retired data scientist and statistician. Now working projects in quantitative ecology and, specifically, phenology of Bryophyta and technical methods for their study.
This entry was posted in climate, climate education, climate models, computation, differential equations, dynamical systems, environment, forecasting, geophysics, global warming, IPCC, mathematics, mathematics education, maths, meteorology, model comparison, NCAR, oceanography, open source scientific software, physics, Principles of Planetary Climate, Python 3, rationality, reasonableness, science, science education, state-space models, statistics, time series, transparency. Bookmark the permalink.

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