EGU24-11831, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Analyzing Climate Scenarios Using Dynamic Mode Decomposition With Control

Nathan Mankovich1, Shahine Bouabid2, and Gustau Camps-Valls3
Nathan Mankovich et al.
  • 1University of Valencia, Image and Signal Processing, Electrical Engineering, Spain (
  • 2University of Oxford, Computational Statistics and Machine Learning, Statistics (
  • 3Image and Signal Processing Group, University of Valencia, Spain

Analyzing climate scenarios is crucial for quantifying uncertainties, identifying trends, and validating models. Objective statistical methods provide decision support for policymakers, optimize resource allocation, and enhance our understanding of complex climate dynamics. These tools offer a systematic and quantitative framework for effective decision-making and policy formulation amid climate change, including accurate projections of extreme events—a fundamental requirement for Earth system modeling and actionable future predictions. 

This study applies dynamic mode decomposition with control (DMDc) to assess temperature and precipitation variability in climate model projections under various future shared socioeconomic pathways (SSPs). We leverage global greenhouse gas emissions and local aerosol emissions as control parameters to unveil nuanced insights into climate dynamics.Our approach involves fitting distinct DMDc models over a high-ambition/low-forcing scenario (SSP126), a medium-forcing scenario (SSP245) and a high-forcing scenario (SSP585). By scrutinizing the eigenvalues and dynamic modes of each DMDc model, we uncover crucial patterns and trends that extend beyond traditional climate analysis methods. Preliminary findings reveal that temporal modes effectively highlight variations in global warming trends under different emissions scenarios. Moreover, the spatial modes generated by DMDc offer a refined understanding of temperature disparities across latitudes, effectively capturing large-scale oscillations such as the El Niño Southern Oscillation. 

The proposed data-driven analytical framework not only enriches our comprehension of climate dynamics but also enhances our ability to anticipate and adapt to the multifaceted impacts of climate change. Integrating DMDc into climate scenario analysis may help formulate more effective strategies for mitigation and adaptation.


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How to cite: Mankovich, N., Bouabid, S., and Camps-Valls, G.: Analyzing Climate Scenarios Using Dynamic Mode Decomposition With Control, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11831,, 2024.