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Related Experiment Video

Updated: May 12, 2026

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

Mathematics applied to the climate system: outstanding challenges and recent progress.

Paul D Williams¹, Michael J P Cullen, Michael K Davey

¹Department of Meteorology, University of Reading, Earley Gate, Reading RG6 6BB, UK. p.d.williams@reading.ac.uk

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|April 17, 2013

Summary

Reliable climate predictions require addressing uncertainties from model formulation. This study outlines three mathematical challenges in developing and evaluating climate models and statistical frameworks for better climate science.

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Last Updated: May 12, 2026

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

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Published on: June 8, 2015

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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

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Published on: December 12, 2013

Area of Science:

Climate Science
Applied Mathematics
Statistical Modeling

Background:

Societal need for accurate climate predictions and uncertainty assessment is critical.
Uncertainties in climate predictions stem from initial conditions, forcing scenarios, and model formulation.

Purpose of the Study:

Identify and document key mathematical challenges in climate system modeling.
Highlight recent progress and inspire future research directions in climate science.

Main Methods:

Focus on three classes of problems in mathematics applied to the climate system.
Evaluation of simple physically based global climate models.
Development and assessment of components within complex climate models, such as general circulation models.
Development and evaluation of appropriate statistical frameworks for climate data.

Main Results:

Identified three major challenges in developing and evaluating climate models and statistical frameworks.
Recent progress in addressing these challenges is presented.
Emphasized the need for interdisciplinary collaboration.

Conclusions:

Addressing mathematical challenges in climate modeling is crucial for reliable predictions.
Closer collaboration between climate scientists, mathematicians, and statisticians is essential.
This work aims to inspire future research and collaborations in climate science.