Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

What is Climate?

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 Change

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.

Precipitation Processes

Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...

What is Weather?

What is Weather?

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

You might also read

Related Articles

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

Sort by

Same journal

Are patient-centered constructs measurable?

Studies in history and philosophy of science·2026

Same journal

Transferring ways of thinking and mathematizing: The statistical approach between physics and biology.

Studies in history and philosophy of science·2026

Same journal

The Dynamics of Quantum Gravity: The Missing Piece in the Spacetime Emergentist Account.

Studies in history and philosophy of science·2026

Same journal

A frame-based approach for reconstructing theories.

Studies in history and philosophy of science·2026

Same journal

Strategic ignorance, and the management of performative effects: Lessons from climate economics.

Studies in history and philosophy of science·2026

Same journal

Fictionalism and scientific realism: A response to ungrounded criticism.

Studies in history and philosophy of science·2026

Search research articles

Related Experiment Video

Updated: Nov 29, 2025

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

Understanding climate phenomena with data-driven models.

Benedikt Knüsel¹, Christoph Baumberger²

¹Institute for Environmental Decisions, ETH Zürich, Universitätstrasse 16, 8092, Zürich, Switzerland; Institute for Atmospheric and Climate Science, ETH Zürich, Universitätstrasse 16, 8092, Zürich, Switzerland.

Studies in History and Philosophy of Science

|November 21, 2020

Summary

We developed a framework to evaluate climate models for scientific understanding, assessing accuracy, depth, and graspability. Data-driven models can offer valuable insights, challenging initial perceptions of their inadequacy.

Keywords:

Climate models Data-driven models Grasping Machine learning Representation Understanding

More Related Videos

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

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Related Experiment Videos

Last Updated: Nov 29, 2025

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

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

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Climate Science
Scientific Modeling

Background:

Climate models are crucial tools for understanding climate phenomena.
Assessing the 'fitness-for-understanding' of these models is essential for scientific progress.

Purpose of the Study:

To develop and apply a novel framework for evaluating the understanding-generating capacity of climate models.
To compare the fitness-for-understanding of traditional process-based models with modern data-driven (machine learning) models.

Main Methods:

A three-dimensional framework was developed, assessing representational accuracy, representational depth, and graspability.
Comparative analysis of process-based and data-driven climate models using the developed framework.
A case study in atmospheric research was employed to validate the findings.

Main Results:

The framework validates the intuitive understanding provided by classical process-based climate models.
State-of-the-art models excel in representational accuracy and depth, while simpler models offer greater graspability.
Data-driven models, initially seeming inadequate, can be valuable for understanding when their coherence with background knowledge is established and complexity is managed.

Conclusions:

A robust framework for assessing climate model understanding is established.
Data-driven models are not inherently inadequate for scientific understanding and can complement traditional approaches.
The utility of data-driven models depends on verifiable representational accuracy and sufficient graspability.