CMIP5 Variability Studies; spectral analyses that characterize the frequency dependence of changes in climate variability.
Data in this collection are from an Intercomparison fast-track project run in coordination with the the 7 GGCMs and focused on updating the state of knowledge on climate change vulnerabilities, impacts, and adaptations using the most current library of climate model outputs (CMIP5).
The GGCMI Initiative brings together a diverse international community of crop modelers for climate impact assessment, model intercomparison and improvement at the global scale.
Models and Frameworks
Framework for easily building CGE models in AMPL using GTAP data. Iincludes CE-Trade, CE-Bio, CE-Enery models.
pSIMS is a suite of tools, data, and models developed to facilitate access to high-resolution climate impact modeling.
Codes for GGCMI Project, including aggregation, detrending and bias-correction, multi-model ensembles, cCalculating metrics and rescaling.
Impacts Research Support Scripts
Work with Cropland Data Layer dataset
Resample our GDD and total precipitation data to match the T106 grid used in Iizumi's yield estimates.
Download data from the Penn State Soil Info web site for analysis
Generate geographic grid cell lists for regions defined by collections of GADM polygons
Convert NARR .grb files to DSSAT .WTH files using Swift
Retrieve the ISI-MIP input data from PIK
Download the National Biomass and Carbon Dataset from the Woods Hole Research Center
Convert African agriculture data from FAO CountrySTAT to GIS formats
Various utilities and codes developed for pSIMS applications around the 1896 project
A Python version of the FUND model [work-in-progress]
The RPS calculator allows the user to explore the conditions for RPS success or failure in different states.
Land Use / Land Cover
Climate and Weather
Fortran code for generating lots of weather files for crop models from NetCDF files.
Download data from the CFS (Climate Forecast System) archive and CFS2 rolling updates.
Summer Scholars Research Projects
Collaboration with Knowledge Lab (knowledgelab.org) based on Bookworm (https://github.com/bookworm)
This repo contains Python code written by Amanda Zhang and Carah Alexander for a RDCEP project analyzing residential energy use patterns. There is code showing how we compiled CSV data into an SQL database, how we wrote extracted commonly used SQL data in to more user-friendly CSVs, and the various ways we plotted the data to find patterns in energy consumption.