Practice and philosophy of climate model tuning across six US modeling centers

Abstract

Abstract. Model calibration (or "tuning") is a necessary part of developing and testing coupled ocean-atmosphere climate models regardless of their main scientific purpose. There is an increasing recognition that this process needs to become more transparent for both users of climate model output and other developers. Knowing how and why climate models are tuned and which targets are used is essential to avoiding possible misattributions of skillful predictions to data accommodation and vice versa. This paper describes the approach and practice of model tuning for the six major US climate modeling centers. While details differ among groups in terms of scientific missions, tuning targets, and tunable parameters, there is a core commonality of approaches. However, practices differ significantly on some key aspects, in particular, in the use of initialized forecast analyses as a tool, the explicit use of the historical transient record, and the use of the present-day radiative imbalance vs. the implied balance in the preindustrial era as a target.

The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies

“…Our tuning has been entirely based on the standard AMIP period (1980–2014), tuning to estimates of PD energy balance using observed SSTs as boundary conditions. Some of the advantages of trying to tune instead to zero net balance in PI conditions are discussed in Schmidt et al (). We feel it is important to optimize the SW and LW fluxes separately, and not just the net difference, for which we have no PI constraints.…”

Improved Aerosol Processes and Effective Radiative Forcing in HadGEM3 and UKESM1

“…The ability of a GCM to simulate the observed change in historical global mean surface temperature is seen as a critical measure of a models ability to predict future climate change. This inevitably leads to the sometimes undesirable but often pragmatic approach of climate model tuning with many GCMs directly tuning the aerosol indirect effects (Hourdin et al, ; Schmidt et al, ). Given the large uncertainties in the aerosol‐cloud forcing reported here and elsewhere, this practice is not surprising.…”

The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty

“…In light of the importance of the uncertainty estimate, we also need to think carefully about the methods by which it is derived. One source is GCM intermodel spread (e.g., [36,42,52,145,161]), but this source is plagued by problems of errors versus uncertainty, representativeness of the model diversity [66], and common lineages of model components ( [6], and references therein); further, model spread may be biased by requiring the model ERF aci to lie within the consensus range [119]. Attempts to set bounds with more rigorous methods are discussed in "Hypothesis Refutation".…”

“…Autoconversion schemes use power-law fits in liquid-water mixing ratio and N d to observational or process-scale modeling datasets [63,155], with "enhancement" corrections applied to account for the discrepancy between the available GCM gridbox mean mixing ratio and the high-liquid part of the subgrid variability that drives warm rain in reality; at the same time, the autoconversion scheme, in its capacity as a sink for low cloud, is a popular mechanism for tuning the TOA shortwave flux [45, 58,80,88,119].…”