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Title: CMIP5 Scientific Gaps and Recommendations for CMIP6

Journal Article · · Bulletin of the American Meteorological Society
 [1];  [2];  [3];  [4];  [5];  [6];  [7]
  1. National Oceanic and Atmospheric Administration (NOAA), Princeton, NJ (United States). Geophysical Fluid Dynamics Lab.
  2. German Aerospace Center (DLR), Oberpfaffenhofen (Germany). Inst. of Atmospheric Physics
  3. National Center for Atmospheric Research, Boulder, CO (United States)
  4. Centre National de la Recherche Scientifique (CNRS), Paris (France). Lab. de Meteorologie Dynamique, IPSL
  5. Met Office Hadley Centre, Exeter (United Kingdom)
  6. Max-Planck-Inst. for Meteorology, Hamburg (Germany)
  7. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Program for Climate Model Diagnosis and Intercomparison
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The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP, that is, CMIP6: (1) How does the Earth system respond to changes in forcing? (2) What are the origins and consequences of systematic model biases? (3) How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. In conclusion, these biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Contributing Organization:
French CNRS; German Ministry of Education and Research (BMBF); Max Planck Society
Grant/Contract Number:
AC52-07NA27344; FC02-97ER62402; GA01101; ANR-10-LABX-0018
OSTI ID:
1377777
Report Number(s):
LLNL-JRNL-698186
Journal Information:
Bulletin of the American Meteorological Society, Vol. 98, Issue 1; ISSN 0003-0007
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 171 works
Citation information provided by
Web of Science

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Cited By (10)

Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties journal January 2018
Real‐Options Water Supply Planning: Multistage Scenario Trees for Adaptive and Flexible Capacity Expansion Under Probabilistic Climate Change Uncertainty journal July 2018
Taking climate model evaluation to the next level journal January 2019
The tropical forest carbon cycle and climate change journal July 2018
Uncertainty in forecasts of long-run economic growth journal May 2018
Integrated Modeling to Evaluate Climate Change Impacts on Coupled Social-Ecological Systems in Alaska journal January 2020
Coupled Stratospheric Chemistry–Meteorology Data Assimilation. Part I: Physical Background and Coupled Modeling Aspects journal January 2020
The Ross Sea Dipole – temperature, snow accumulation and sea ice variability in the Ross Sea region, Antarctica, over the past 2700 years journal January 2018
Towards improved and more routine Earth system model evaluation in CMIP journal January 2016
Taking climate model evaluation to the next level text January 2019

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