Guiling Wang
University of Connecticut, CT·Earth & Environmental Science
Climate largely decides where forests, grasslands and other vegetation can grow. Vegetation in turn affects regional and global climate, partly through the global carbon cycle.
- Fields
Earth & Environmental Science
Computer Science
Ecology & Evolution
- Topics
vegetation-climate feedback
Earth system models
climate projection
- Grants
- 1 known grant, through Jun 2027
What they work on
- Develop machine learning and deep learning models that predict global vegetation properties from selected climate variables, trained on satellite remote sensing, reanalysis data and CMIP6 Earth System Model output.
- Test whether machine learning models trained on historical data transfer to future periods and new regions, capturing both gradual and abrupt climate-driven shifts in vegetation.
- Test whether the structure of process-based vegetation models, rather than differences in simulated climate, is the main source of uncertainty in Earth System Model projections of vegetation change.
- Project late-21st-century vegetation with optimized machine learning models, assess how realistic each Earth System Model's vegetation-climate relationship is, and map hotspots of vegetation change.
More about the research
Earth System Models (ESMs) are the computer simulations used to project future climate, and they represent vegetation in very different ways. These differences are a major source of uncertainty in projections of both climate and vegetation. This project asks how much these vegetation-climate interactions contribute to projected climate change, and how the related uncertainties can be identified and reduced.
Why it matters
Uncertainty about how vegetation will respond to and feed back on climate weakens the projections that scientists and government stakeholders rely on. This work could help guide future climate model development and open a path to machine learning vegetation components inside Earth System Models. Its maps of vegetation-change hotspots could guide where field experiments and long-term monitoring are placed.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Imme Ebert-Uphoff
Colorado State University-Fort Collins, CO·Earth & Environmental Science
Build simple, interpretable AI models that map large-scale environmental variables from reanalysis products to satellite-derived cloud properties, and use them to estimate cloud variability before the satellite era.
Alexandra Konings
Stanford University, CA·Earth & Environmental Science·Early-career
Use the CARDAMOM data assimilation framework, which combines a simple ecosystem model, satellite data and Markov Chain Monte Carlo sampling, to map optimal plant and ecosystem parameters for each pixel across the globe.
Qinghua Ding
University of California-Santa Barbara, CA·Earth & Environmental Science
Develop regional models of summertime Arctic sea ice that combine global observational data through machine learning, to forecast ice from weeks to a season ahead and find how far ahead prediction is possible.
The school
University of Connecticut: 31 labs on PI Pages, $434M a year on research, 360 research doctorates a year.
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