Featured Tools
Featured Datasets
The Global Gridded Crop Model Intercomparison Phase II effort evaluates 13 process-based crop models over a structured parameter sweep across CO2, temperature, water, and applied nitrogen (‘CTWN’).
Parameters for simple statistical models that allow impacts modelers to reproduce the output of crop models over arbitrary climate and management scenarios, for the four main staple crops (maize, wheat, soy, and rice).
The GGCMI Phase 1 effort provides global 0.5 degree crop model output over historical scenarios from 14 different modeling groups simulating up to 15 different crops.
LongRunMIP is an ongoing model intercomparison effort that collects millennial-scale climate simulations from fully coupled general circulation models.
CMIP5 Variability Studies; spectral analyses that characterize the frequency dependence of changes in climate variability.
Code Repositories
Web Tools Code
Models, Frameworks, and Analysis Tools
Optimized collection of functions to study variability in global climate models, weather databases, etc. by Kevin Schwarzwald
Project weather data by using distributional changes in climate ensembles; an optimized implementation of the 'quantile mapping' procedure by Haugen et al., 2018
The model copyright belongs to Yongyang Cai (cai.619@osu.edu), Alla Golub (golub@purdue.edu) and Thomas Hertel (hertel@purdue.edu).
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
Resample our GDD and total precipitation data to match the T106 grid used in Iizumi's yield estimates.
Other Studies
Land Use / Land Cover
Climate and Weather
Summer Scholars Research Projects
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.