Climate Dynamics

, Volume 51, Issue 1–2, pp 207–219 | Cite as

Intercomparison of model response and internal variability across climate model ensembles

  • Devashish KumarEmail author
  • Auroop R. Ganguly


Characterization of climate uncertainty at regional scales over near-term planning horizons (0–30 years) is crucial for climate adaptation. Climate internal variability (CIV) dominates climate uncertainty over decadal prediction horizons at stakeholders’ scales (regional to local). In the literature, CIV has been characterized indirectly using projections of climate change from multi-model ensembles (MME) instead of directly using projections from multiple initial condition ensembles (MICE), primarily because adequate number of initial condition (IC) runs were not available for any climate model. Nevertheless, the recent availability of significant number of IC runs from one climate model allows for the first time to characterize CIV directly from climate model projections and perform a sensitivity analysis to study the dominance of CIV compared to model response variability (MRV). Here, we measure relative agreement (a dimensionless number with values ranging between 0 and 1, inclusive; a high value indicates less variability and vice versa) among MME and MICE and find that CIV is lower than MRV for all projection time horizons and spatial resolutions for precipitation and temperature. However, CIV exhibits greater dominance over MRV for seasonal and annual mean precipitation at higher latitudes where signals of climate change are expected to emerge sooner. Furthermore, precipitation exhibits large uncertainties and a rapid decline in relative agreement from global to continental, regional, or local scales for MICE compared to MME. The fractional contribution of uncertainty due to CIV is invariant for precipitation and decreases for temperature as lead time progresses towards the end of the century.


Climate predictability Near-term climate change Climate adaptation Multiple initial condition CMIP5 



The research was funded by the US National Science Foundation’s (NSF) Expeditions in Computing award #1029711, NSF CyberSEES award #1442728, and NSF BIGDATA award #1447587. We thank Jouni Räisänen for sharing his code to compute relative agreement metric. We used Climate Data Operators (CDO), the R Language for Statistical Computing, and National Aeronautics and Space Administration (NASA) Panoply for data processing, analysis, and visualization. CMIP5 model data were obtained from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) website. Multiple initial condition data were obtained from the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) Large Ensemble Community project.

Supplementary material

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Supplementary material 1 (PDF 41573 KB)


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Sustainability and Data Sciences Laboratory (SDS Lab), Department of Civil and Environmental EngineeringNortheastern UniversityBostonUSA

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