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Related Experiment Video

Updated: Jan 5, 2026

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Predicting future climate at high spatial and temporal resolution.

Ilya M D Maclean¹

¹Environment and Sustainability Institute, University of Exeter, Penryn, UK.

Global Change Biology

|October 23, 2019

Summary

This summary is machine-generated.

Future climate change impacts on organisms require fine-grained microclimate data. This study provides methods for hourly temperature and soil moisture estimates, revealing less extreme future conditions than previously thought.

Keywords:

ecology mechanistic model microclimate soil moisture soil temperature species distributions

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Last Updated: Jan 5, 2026

Using Generative Art to Convey Past and Future Climate Transitions

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Published on: March 31, 2023

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Ecology
Climate Science
Environmental Modeling

Background:

Current climate change studies often use coarse, seasonally aggregated data, masking vital microclimate variability.
This approach inadequately represents the actual environmental conditions organisms face in the wild.
A significant gap exists in understanding fine-resolution climatic changes and their biological implications.

Purpose of the Study:

To present methods for generating fine-grained, hourly microclimate estimates (temperature and soil moisture) over decadal timescales.
To improve the accuracy of predicting future climatic conditions experienced by organisms.
To provide tools for biologists to assess climate change impacts at a relevant scale.

Main Methods:

Disaggregation of coarse climate data to hourly resolution to drive physical microclimate models.
Incorporation of mesoclimatic effects (e.g., elevation, coastal exposure) and local factors (terrain, vegetation) using energy balance and hydrological equations.
Development of R packages to process data and run thermal and hydrological microclimate models.

Main Results:

Application of the methods to the Lizard Peninsula, UK, yielded hourly temperature estimates at high spatial resolution.
Fine-resolution variability in climatic changes was observed, driven by landscape-climate interactions.
Future extreme climatic conditions were predicted to be less novel than those estimated using aggregated data.

Conclusions:

Accurate microclimate modeling is crucial for understanding organismal responses to climate change.
The developed methods and tools enable more realistic assessments of future environmental conditions.
This approach highlights the importance of fine-scale data for ecological predictions.