RDCEP
  • Research Areas Publications
  • Graduate & Postdoctoral Undergraduate K-12 Education
  • Events
  • Data & Tools
  • Our Mission People Job Opportunities Contact Us
  • search
RDCEP
  • Research/
    • Research Areas
    • Publications
  • Education/
    • Graduate & Postdoctoral
    • Undergraduate
    • K-12 Education
  • Events/
  • Data & Tools/
  • About Us/
    • Our Mission
    • People
    • Job Opportunities
    • Contact Us
  • search/
Ultra-high-resolution Models Improve Representation of Rainfall
RDCEP

Center for Robust Decision making on Climate and Energy Policy

Homepage Banner Images

RDCEP
  • Research/
    • Research Areas
    • Publications
  • Education/
    • Graduate & Postdoctoral
    • Undergraduate
    • K-12 Education
  • Events/
  • Data & Tools/
  • About Us/
    • Our Mission
    • People
    • Job Opportunities
    • Contact Us
  • search/
Ultra-high-resolution Models Improve Representation of Rainfall

Ultra-high-resolution Models Improve Representation of Rainfall

Climate simulations generally produce rainfall that is too diffuse and weak. New regional simulations at 4 km create more realistic events. (Chang et al 2018)

Cloud Classification with Deep Learning

Cloud Classification with Deep Learning

Potential changes in clouds are the largest uncertainty in projections of future climate. New approaches for unsupervised classification let satellite observations better inform climate science. (Kurihana et al., 2022)

The Global Gridded Crop Model Intercomparison Project (GGCMIP) Phase II Experiment

The Global Gridded Crop Model Intercomparison Project (GGCMIP) Phase II Experiment

A new collaborative effort by 12 modeling groups disentangles agricultural yield responses to four different factors: temperature, rainfall, nitrogen application, and CO2. (Franke et al., 2019)

Innovative Training in Data Science for Energy and Environmental Research

Innovative Training in Data Science for Energy and Environmental Research

A new program at UChicago supports PhD and MS students in data-driven environmental research. September boot camps provide skills training in programming and statistical analysis.

New Statistical Methods for Studying Changes in Climate

New Statistical Methods for Studying Changes in Climate

New methods based on ensembles of climate model output allow examining changes in entire temperature distributions, including extremes. In the N. midlatitudes, wintertime distributions narrow by 2100. (Haugen et al 2018)

Coding and Data Exploration with Smart Lamps

Coding and Data Exploration with Smart Lamps

Hands-on Interactive Curriculum for Middle through High School Students and Teachers

Reduced Future Wintertime Variability Affects Mortality

Reduced Future Wintertime Variability Affects Mortality

Chicago winters warm strongly in the 40-member LENS ensemble, seen in estimated changes in daily temperature distributions. Wintertime lows (blue lines) rise by up to 12 degrees by 2100. (Black dashed line = no change). (Schwarzwald et al. 2019)

1 2 3 4 5 6 7
Previous Next
Ultra-high-resolution Models Improve Representation of Rainfall
Cloud Classification with Deep Learning
The Global Gridded Crop Model Intercomparison Project (GGCMIP) Phase II Experiment
Innovative Training in Data Science for Energy and Environmental Research
New Statistical Methods for Studying Changes in Climate
Coding and Data Exploration with Smart Lamps
Reduced Future Wintertime Variability Affects Mortality

Funded under NSF Decision Making Under Uncertainty Program.  Award No. SBE-1463644

© The University of Chicago     |     5735 South Ellis Avenue, Chicago, IL, 60637     |     773-702-5446

 
20200430 UChicago Logo in Footer of RDCEP-org.png