Precipitation and its changes with warming in CMIP6
Scientific Motivation
Precipitation is a key variable driving and responding to atmosphere, ocean, and land surface climate. What are some key aspects of precipitation, it’s variability and change, in CMIP6 simulations? How do they differ from CMIP5 simulations?
We can get more specific if people want to look at something in particular.
Proposed Hacking
There are many aspects of precipitation change to explore. We could do a deep dive on a specific aspect of precipitation change, if people have particular desires. Barring other proposals, we will update Figure 3 from Pendergrass et al 2017 with CMIP6 data in a shareable notebook (or binder?).
Anticipated Data Needs
Monthly pr, huss, and tas from historical and at least one ssp. Likely targets if the mission expands are daily (or higher temporal resolution) pr, and tas from piControl and abrupt4xCO2. We may want analogous data from CMIP5 if we’re going to do specific comparisons too.
Anticipated Software Tools
To accomplish something, we won’t need much more than standard tools like xarray and matplotlib. It can grow though too. We can even build on @rabernat’s binder based on some code I’d written. Cloud Example: 3hr Precip Frequency Distribution
Desired Collaborators
Anyone interested in precipitation, and/or extremes. It’s worth noting that at least one of us will be participating remotely, so we’ll experiment with asynchronous collaboration (though if we don’t want decentralized computing, it could focus on cheyenne at NCAR).
this is Chiara Lepore, I am a Research Scientist at LDEO.
We met at some AGU long time ago.
I used to work on extreme precip before switching gear to severe weather in general, but I am very interested in helping and participating in this project (I miss extreme precip!). I have usually worked on reanalysis and gauge data. (I have a couple of papers on somewhat related analysis, but on higher resolution scales, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014GL062247, https://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-15-0331.1 , that were painstakingly done before xarray/xhistogram era!). I am very much looking forward to apply these new tools.
I am an experienced Python user, and I will be participating from LDEO.
I am Di Chen, I am a new postdoc at UCLA.
We have met at AGU, and I was at your talk in SUNY Albany back in 2014.
I am interested in extreme precipitation and the future changes. Currently at UCLA, I am working on a project about narrowing the uncertainties in model projections, leveraging observation and CMIP6 models, with a focus on developing emergent constraints.
Your work on precipitation has inspired my research considerably. I look forward to working with you on the Hackathon project!
I will be participating from NCAR. I am half-new to Python, hopefully I can catch up with the team
