Journal cover Journal topic
Earth System Dynamics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.351 IF 4.351
  • IF 5-year value: 5.124 IF 5-year
    5.124
  • CiteScore value: 4.44 CiteScore
    4.44
  • SNIP value: 1.250 SNIP 1.250
  • IPP value: 4.10 IPP 4.10
  • SJR value: 2.203 SJR 2.203
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 29 Scimago H
    index 29
  • h5-index value: 31 h5-index 31
Volume 6, issue 1
Earth Syst. Dynam., 6, 311-326, 2015
https://doi.org/10.5194/esd-6-311-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Climate Change and Environmental Pressure: Adaptation and...

Earth Syst. Dynam., 6, 311-326, 2015
https://doi.org/10.5194/esd-6-311-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 28 May 2015

Research article | 28 May 2015

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

N. Forsythe, S. Blenkinsop, and H. J . Fowler N. Forsythe et al.
  • Centre for Earth Systems Engineering Research (CESER), School of Civil Engineering and Geosciences, Newcastle University, Newcastle, UK

Abstract. A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

Publications Copernicus
Special issue
Download
Short summary
A three-step climate classification – input variable selection, principal components analysis and k-means clustering – was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. This revealed a reanalysis ensemble consensus for eight macro-climate zones. Zonal statistics revealed consistent, distinct climatologies. This approach has implications for resource assessments and data set bias characterisations.
A three-step climate classification – input variable selection, principal components analysis...
Citation
Share