Member Since 2006
Claudia Tebaldi
Earth Scientist, Pacific Northwest National Laboratory, Joint Global Change Research Institute
Professional Experience
Pacific Northwest National Laboratory, Joint Global Change Research Institute
Earth Scientist
2019 - Present
National Center for Atmospheric Research
Project Scientist
2013 - 2019
Climate Central
Staff Scientist
2008 - 2013
National Center for Atmosperic Research
Project Scientist
2001 - 2007
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Education
Duke University
Doctorate
1997
Honors & Awards
Union Fellow
Received December 2023
Citation
For outstanding contributions in statistics and climate science leading to improved understanding of climate change including extreme events.
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Outstanding Reviewer Award - Geophysical Research Letters
Received December 2022
Outstanding Reviewer Award - Geophysical Research Letters
Received December 2013
Publications
DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models With 3D Diffu...
Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computati...
October 17, 2024
Scenario Discovery Analysis of Drivers of Solar and Wind Ene...
August 21, 2023
The Climatic Impact‐Driver Framework for Assessment of Risk‐...
November 16, 2022
Characteristics of Future Warmer Base States in CESM2
September 18, 2020
AGU Abstracts
ScenarioMIP for CMIP7: new scenarios, new and old sources of variability and uncertainties.
AGU 2024
biogeosciences | 12 december 2024
Claudia Tebaldi, Brian C. O'Neill, Detlef P. Van V...
In this presentation I will give an overview of the new experimental design that ScenarioMIP is proposing for the 7th phase of the Coupled Model Inter...
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The Detection and Attribution Model Intercomparison Project: CMIP6 highlights and plans for CMIP7
AGU 2024
global environmental change | 11 december 2024
Isla Simpson, Nathan Gillett, Gabriele C. Hegerl, ...
The Detection and Attribution Model Intercomparison Project (DAMIP) coordinates single forcing climate model simulations for detection and attribution...
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Emulating the Global Change Analysis Model with Deep Learning
AGU 2024
global environmental change | 10 december 2024
Brian Hutchinson, Andrew Holmes, Matthew Jensen, S...
The Global Change Analysis Model (GCAM) simulates complex interactions between coupled Earth and human systems. In doing so, it provides insights into...
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