Tuning the climate of a global model

By T. Mauritsen, Bjorn Stevens, E. Roeckner, T. Crueger, M. Esch, Marco A. Giorgetta, Helmuth Haak, Johann Jungclaus, D. Klocke, D. Matei and 5 more. 2012

498 citations recorded by the source corpus.

During a development stage global climate models have their properties adjusted or tuned in various ways to best match the known state of the Earth's climate system. These desired properties are observables, such as the radiation balance at the top of the atmosphere, the global mean temperature, sea ice, clouds and wind fields. The tuning is typically performed by adjusting uncertain, or even non‐observable, parameters related to processes not explicitly represented at the model grid resolution. The practice of climate model tuning has seen an increasing level of attention because key model properties, such as climate sensitivity, have been shown to depend on frequently used tuning parameters. Here we provide insights into how climate model tuning is practically done in the case of closing the radiation balance and adjusting the global mean temperature for the Max Planck Institute Earth System Model (MPI‐ESM). We demonstrate that considerable ambiguity exists in the choice of parameters, and present and compare three alternatively tuned, yet plausible configurations of the climate model. The impacts of parameter tuning on climate sensitivity was less than anticipated.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/deac724ecf5df5ef37f25652cb451f4855c56fae

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Citations in the corpus

Arctic sea‐ice evolution as modeled by Max Planck Institute for Meteorology's Earth system model (2013, methodology): “A short description of this tuning of our sea-ice model is provided by Mauritsen et al. [2012],”
Physics-informed machine learning: case studies for weather and climate modelling (2021, methodology): “Developers typically perform this calibration by adjusting uncertain, or even non-observable, parameters related to processes not explicitly represented at the model grid resolution [69].”
The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies (2018, methodology): “Following earlier work by Mauritsen et al. (2012) and Hourdin et al. (2017) on climate model tuning processes, Schmidt et al. (2017) provides a convenient summary of tuning strategies at a number of major US climate modeling centers, allowing a comparison of the strategy used in AM4.”
Evaluating process-based integrated assessment models of climate change mitigation (2017, background): “This also implies difficulties in selecting from multiple possible combinations of parameters (47, 48).”
The ‘too few, too bright’ tropical low‐cloud problem in CMIP5 models (2012, unknown): “For this purpose, atmospheric model parameters may be adjusted (or ‘ tuned ’ ) to ensure the global LW and SW TOA radiative fluxes roughly match observations and balance each other on long time scales [e.g., Mauritsen et al. , 2012; Hourdin et al. , 2012].”
Quantifying Progress Across Different CMIP Phases With the ESMValTool (2020, background): “MPI‐ESM 1‐2‐HR MPI‐ESM 1‐2‐LR Max Planck Institute, Germany Mauritsen et al. (2019); Muller et al. (2018)”
Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2 (2019, background): “Subsequently, it was decided to cool the model to about 13.6 ◦ C starting from run khw0062, because it was not possible to obtain acceptable amounts of Arctic sea ice at the warmer state around 14 ◦ C (run khw0061) using regular sea ice tuning parameters (Mauritsen et al., 2012).”
An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles (2020, background): “Independence predictors should be fields with minimal internal variability, such large-scale, long-term averages, and ideally fields that model developers do not explicitly tune, such as absolute global temperature (Mauritsen et al., 2012; Hourdin et al., 2017).”
Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP) (2018, methodology): “We used the ERA-Interim reanalysis data (Dee et al., 2011) averaged from 1979 to 2005 as the reference for the atmospheric mean state, as HR was tuned to this period (Mauritsen et al., 2012).”
ICON‐A, the Atmosphere Component of the ICON Earth System Model: I. Model Description (2018, methodology): “Vertical diffusion Mauritsen et al. (2007) Total turbulent energy scheme, implicitly coupled to the land surface model ∂T/∂t, ∂qv,c,i/∂t, ∂u/∂t, ∂v/∂t Land surface “JSBACH4-lite,” Raddatz et al. (2007) Five layers for water and heat storage, one surface type per cell -”