Cluster-based analysis of multi-model climate ensembles

Richard Hyde, Ryan Hossaini, Amber A. Leeson

Research output: Contribution to journalArticle

1 Citation (Scopus)
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Abstract

Clustering - the automated grouping of similar data - can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model-observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry-climate model (CCM) output of tropospheric ozone - an important greenhouse gas - from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ∼20% in the global absolute mean bias between the MMM and an observed satellitebased tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ∼62% of all locations, with the largest bias reductions occurring in the Northern Hemisphere - where ozone concentrations are relatively large. However, the bias is unchanged at 9% of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.

Original languageEnglish
Pages (from-to)2033-2048
Number of pages16
JournalGeoscientific Model Development
Volume11
Issue number6
DOIs
Publication statusPublished - 4 Jun 2018
Externally publishedYes

Fingerprint

Climate models
Multi-model
Climate
Climate Models
climate modeling
Ozone
Ensemble
Clustering
Chemistry
Hemisphere
Atmospheric chemistry
Data Model
atmospheric chemistry
Medical Image Processing
Bias Reduction
Subsampling
Greenhouse Gases
Model
ozone
Output

Bibliographical note

This work is distributed under the Creative Commons Attribution 4.0 License.

ASJC Scopus subject areas

  • Modelling and Simulation
  • Earth and Planetary Sciences(all)

Cite this

Cluster-based analysis of multi-model climate ensembles. / Hyde, Richard; Hossaini, Ryan; Leeson, Amber A.

In: Geoscientific Model Development, Vol. 11, No. 6, 04.06.2018, p. 2033-2048.

Research output: Contribution to journalArticle

Hyde, Richard ; Hossaini, Ryan ; Leeson, Amber A. / Cluster-based analysis of multi-model climate ensembles. In: Geoscientific Model Development. 2018 ; Vol. 11, No. 6. pp. 2033-2048.
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