Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

What is Climate?01:16

What is Climate?

Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
Global Climate Change01:50

Global Climate Change

Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Macroecological assessment of invasive species impact on Japanese lake fish communities using eDNA metabarcoding.

Scientific reports·2026
Same author

Identification of hybrids between the Japanese giant salamander (<i>Andrias japonicus</i>) and Chinese giant salamander (<i>Andrias</i> cf. <i>davidianus</i>) using deep learning and smartphone images.

Ecology and evolution·2023
Same author

Individual identification of endangered amphibians using deep learning and smartphone images: case study of the Japanese giant salamander (Andrias japonicus).

Scientific reports·2023
Same author

Automatic detection of alien plant species in action camera images using the chopped picture method and the potential of citizen science.

Breeding science·2022
Same author

Assessing streetscape greenery with deep neural network using Google Street View.

Breeding science·2022
Same author

Explainable identification and mapping of trees using UAV RGB image and deep learning.

Scientific reports·2021

Related Experiment Video

Updated: Jul 3, 2026

Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

1.3K

Forecasting Climatic Trends Using Neural Networks: An Experimental Study Using Global Historical Data.

Takeshi Ise1,2, Yurika Oba1

  • 1Field Science Education and Research Center (FSERC), Kyoto University, Kyoto, Japan.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary

Artificial intelligence (AI) models using neural networks can accurately forecast global temperatures. This top-down approach shows high performance for decadal climate prediction, complementing traditional physics-based models.

Keywords:
NVIDIA DIGITSbig dataclimate changeglobal environmental changegraphical image classificationhistorical dataneural networkstop-down approach

Related Experiment Videos

Last Updated: Jul 3, 2026

Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

1.3K

Area of Science:

  • Climate Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Climate change poses significant global challenges.
  • Traditional climate forecasting relies on mechanistic, bottom-up models like general circulation and earth system models.
  • These models are complex and computationally intensive.

Purpose of the Study:

  • To explore the performance of a phenomenological, top-down model for climate forecasting.
  • To evaluate the effectiveness of a neural network approach using big data of global mean monthly temperature.
  • To compare the accuracy of AI-based forecasting with conventional physics-based models for decadal-scale predictions.

Main Methods:

  • A neural network system, specifically LeNet for convolutional neural networks, was employed.
  • The model was trained using 30 years of global mean monthly temperature data, visualized as graphical images.
  • The system generated predictions for temperature fluctuations over the next 10 years.

Main Results:

  • The neural network system successfully predicted temperature trends with high accuracy.
  • The best global model achieved an accuracy of 97.0%, with accuracy increasing with more training data.
  • Model performance was influenced by image color schemes, climatic zones, and temporal ranges.

Conclusions:

  • Phenomenological, top-down AI models demonstrate high accuracy for decadal climate forecasting, outperforming conventional bottom-up approaches.
  • AI-based forecasting methods can complement traditional physics-based models, offering a synergistic approach to climate prediction.
  • Further research into AI applications in climate science is warranted.