Practice and Philosophy of Climate Model Tuning Across Six U.S. Modeling Centers

Model calibration (or tuning) is a necessary part of developing and testing coupled ocean-atmosphere climatemodels 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 U.S. 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 pre-industrial as a target

The art and science of climate model tuning

PublishedThis is the final version of the article. Available from American Meteorological Society via the DOI in this record.We survey the rationale and diversity of approaches for tuning, a fundamental aspect of climate modeling which should be more systematically documented and taken into account in multi-model analysis. The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate sub-models. Most sub-models depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called ‘objective‘ methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.The authors would like to thank the World Climate Research Program and its Working Group on Coupled Modeling for initiating and helping organize the workshop on model tuning in October 2014 in Garmisch-Partenkirchen, Germany. Work at LLNL was performed under the auspices the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract No. DE-AC52-07NA27344. The National Center for Atmospheric Research is sup- ported by the U.S. National Science Foundation. The contribution of Yun Qian was supported by the U.S. Department of Energy’s Office of Science as part of the Earth System Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL0183

Evaluation of large-Eddy simulations via observations of nocturnal marine stratocumulus

Data from the first research flight (RF01) of the second Dynamics and Chemistry of Marine Stratocumulus (DYCOMS-II) field study are used to evaluate the fidelity with which large-eddy simulations (LESs) can represent the turbulent structure of stratocumulus-topped boundary layers. The initial data and forcings for this case placed it in an interesting part of parameter space, near the boundary where cloud-top mixing is thought to render the cloud layer unstable on the one hand, or tending toward a decoupled structure on the other hand. The basis of this evaluation consists of sixteen 4-h simulations from 10 modeling centers over grids whose vertical spacing was 5 m at the cloud-top interface and whose horizontal spacing was 35 m. Extensive sensitivity studies of both the configuration of the case and the numerical setup also enhanced the analysis. Overall it was found that (i) if efforts are made to reduce spurious mixing at cloud top, either by refining the vertical grid or limiting the effects of the subgrid model in this region, then the observed turbulent and thermodynamic structure of the layer can be reproduced with some fidelity; (ii) the base, or native configuration of most simulations greatly overestimated mixing at cloud top, tending toward a decoupled layer in which cloud liquid water path and turbulent intensities were grossly underestimated; (iii) the sensitivity of the simulations to the representation of mixing at cloud top is, to a certain extent, amplified by particulars of this case. Overall the results suggest that the use of LESs to map out the behavior of the stratocumulus-topped boundary layer in this interesting region of parameter space requires a more compelling representation of processes at cloud top. In the absence of significant leaps in the understanding of subgrid-scale (SGS) physics, such a representation can only be achieved by a significant refinement in resolution—a refinement that, while conceivable given existing resources, is probably still beyond the reach of most centers

Phospholipid Scramblase-1-Induced Lipid Reorganization Regulates Compensatory Endocytosis in Neuroendocrine Cells

Calcium-regulated exocytosis in neuroendocrine cells and neurons is accompanied by the redistribution of phosphatidylserine (PS) to the extracellular space, leading to a disruption of plasma membrane asymmetry. How and why outward translocation of PS occurs during secretion are currently unknown. Immunogold labeling on plasma membrane sheets coupled with hierarchical clustering analysis demonstrate that PS translocation occurs at the vicinity of the secretory granule fusion sites. We found that altering the function of the phospholipid scramblase-1 (PLSCR-1) by expressing a PLSCR-1 calcium-insensitive mutant or by using chromaffin cells from PLSCR-1−/−mice prevents outward translocation of PS in cells stimulated for exocytosis. Remarkably, whereas transmitter release was not affected, secretory granule membrane recapture after exocytosis was impaired, indicating that PLSCR-1 is required for compensatory endocytosis but not for exocytosis. Our results provide the first evidence for a role of specific lipid reorganization and calcium-dependent PLSCR-1 activity in neuroendocrine compensatory endocytosis

Energy Budget Constraints on the Time History of Aerosol Forcing and Climate Sensitivity

An observationally-constrained time series of historical aerosol effective radiative forcing (ERF) from 1750 to 2019 is developed in this paper. We find that the time history of aerosol ERFs diagnosed in CMIP6 models exhibits considerable variation and explore how the time history of aerosol forcing influences the probability distributions of present-day aerosol forcing and emergent metrics such as climate sensitivity. Using a simple energy balance model, trained on CMIP6 climate models and constrained by observed near-surface warming and ocean heat uptake, we derive estimates for the historical aerosol forcing. We find 2005-2014 mean aerosol ERF to be -1.1 (-1.8 to -0.5) W m-2 relative to 1750. Assuming recently published historical emissions from fossil fuel and industrial sectors and biomass burning emissions from SSP2-4.5, aerosol ERF in 2019 is -0.9 (-1.5 to -0.4) W m-2. There is a modest recovery in aerosol forcing (+0.025 W m-2 decade-1) between 1980 and 2014. This analysis also gives a 5-95% range of equilibrium climate sensitivity (ECS) of 1.8-5.1°C (best estimate 3.1°C) with a transient climate response (TCR) of 1.2-2.6°C (best estimate 1.8°C)

An Overview of the Atmospheric Component of the Energy Exascale Earth System Model

The Energy Exascale Earth System Model Atmosphere Model version 1, the atmospheric component of the Department of Energy’s Energy Exascale Earth System Model is described. The model began as a fork of the wellâ known Community Atmosphere Model, but it has evolved in new ways, and coding, performance, resolution, physical processes (primarily cloud and aerosols formulations), testing and development procedures now differ significantly. Vertical resolution was increased (from 30 to 72 layers), and the model top extended to 60 km (~0.1 hPa). A simple ozone photochemistry predicts stratospheric ozone, and the model now supports increased and more realistic variability in the upper troposphere and stratosphere. An optional improved treatment of lightâ absorbing particle deposition to snowpack and ice is available, and stronger connections with Earth system biogeochemistry can be used for some science problems. Satellite and groundâ based cloud and aerosol simulators were implemented to facilitate evaluation of clouds, aerosols, and aerosolâ cloud interactions. Higher horizontal and vertical resolution, increased complexity, and more predicted and transported variables have increased the model computational cost and changed the simulations considerably. These changes required development of alternate strategies for tuning and evaluation as it was not feasible to â brute forceâ tune the highâ resolution configurations, so shortâ term hindcasts, perturbed parameter ensemble simulations, and regionally refined simulations provided guidance on tuning and parameterization sensitivity to higher resolution. A brief overview of the model and model climate is provided. Model fidelity has generally improved compared to its predecessors and the CMIP5 generation of climate models.Plain Language SummaryThis study provides an overview of a new computer model of the Earth’s atmosphere that is used as one component of the Department of Energy’s latest Earth system model. The model can be used to help understand past, present, and future changes in Earth’s behavior as the system responds to changes in atmospheric composition (like pollution and greenhouse gases), land, and water use and to explore how the atmosphere interacts with other components of the Earth system (ocean, land, biology, etc.). Physical, chemical, and biogeochemical processes treated within the atmospheric model are described, and pointers to previous and recent work are listed to provide additional information. The model is compared to presentâ day observations and evaluated for some important tests that provide information about what could happen to clouds and the environment as changes occur. Strengths and weaknesses of the model are listed, as well as opportunities for future work.Key PointsA brief description and evaluation is provided for the atmospheric component of the Department of Energy’s Energy Exascale Earth System ModelModel fidelity has generally improved compared to predecessors and models participating in past international model evaluationsStrengths and weaknesses of the model, as well as opportunities for future work, are describedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151811/1/jame20932_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151811/2/jame20932.pd

A framework for convection and boundary layer parameterization derived from conditional filtering

A new theoretical framework is derived for parameterization of subgrid physical processes in atmospheric models; the application to parameterization of convection and boundary layer fluxes is a particular focus. The derivation is based on conditional filtering, which uses a set of quasi-Lagrangian labels to pick out different regions of the fluid, such as convective updrafts and environment, before applying a spatial filter. This results in a set of coupled prognostic equations for the different fluid components, including subfilter-scale flux terms and entrainment/detrainment terms. The framework can accommodate different types of approaches to parameterization, such as local turbulence approaches and mass-flux approaches. It provides a natural way to distinguish between local and nonlocal transport processes, and makes a clearer conceptual link to schemes based on coherent structures such as convective plumes or thermals than the straightforward application of a filter without the quasi-Lagrangian labels. The framework should facilitate the unification of different approaches to parameterization by highlighting the different approximations made, and by helping to ensure that budgets of energy, entropy, and momentum are handled consistently and without double counting. The framework also points to various ways in which traditional parameterizations might be extended, for example by including additional prognostic variables. One possibility is to allow the large-scale dynamics of all the fluid components to be handled by the dynamical core. This has the potential to improve several aspects of convection-dynamics coupling, such as dynamical memory, the location of compensating subsidence, and the propagation of convection to neighboring grid columns