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Published June 9, 2025 | Version v1
Dataset Open

ForceSMIP Tier 1 Data Repository

  • 1. EDMO icon ETH Zürich
  • 2. ROR icon NSF National Center for Atmospheric Research
  • 3. University of California, Los Angeles
  • 4. ROR icon Lawrence Livermore National Laboratory
  • 5. ROR icon Leipzig University

Contributors

Description

Data archive for Tier 1 of the ForceSMIP project, which is described in "Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)" by Wills et al., submitted to Journal of Climate. Please cite that paper for any usage of this data. 

Types of data included here are:

  • Evaluation-Tier1: Raw data for 10* evaluation members, including reanalysis/observations (member "1I")
  • ensmeans-Tier1: The "true forced response", from the corresponding large ensemble mean, for the 9* evaluation members from models (all except "1I")
  • ForceSMIP-estimates-Tier1: ForcesSMIP method estimates of the forced response in each evaluation member

Each of these types of data is provided at monthly temporal resolution over 1950-2022, for each of 8* variables: tos (sea-surface temperature), tas (surface air temperature), pr (precipitation), psl (sea-level pressure), monmaxtasmax (monthly maximum daily maximum temperature), monmintasmin (monthly minimum daily minimum temperature), monmaxpr (monthly maximum daily precipitation), and zmta (zonal-mean atmospheric temperature).

*At the time of initial submission, only 6 of 10 evaluation members are included (the 5 unseen models and observations), and data is only provided for 3 out of 8 variables. The full dataset requires a Zenodo quota increase, which will be requested as the publication is finalized. Please also note that in this version, the "member" labels are swapped between the methods "RegGMST" and RegGMST-LENSem". 

Files

ensmeans-Tier1.zip

Files (32.8 GB)

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md5:a8498fe08d8d8771e49aee2a65a1bb50
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Additional details

Funding

Swiss National Science Foundation
Constraining Future Changes in the Large-Scale Atmospheric Circulation to Improve Projections of Regional Climate Impacts PCEFP2-203376
U.S. National Science Foundation
NSF National Center for Atmospheric Research 185297