Process-oriented Diagnostics in CMIP6 Models and Beyond


There is growing community interest in moving beyond typical model evaluation metrics to process-oriented diagnostics. These diagnostics better constrain poorly-represented physics components in climate models, provide actionable feedback to model developers, and are expected to play a key role in advancing the next-generation climate and earth system models.

The scope of this collection encompasses studies developing new process-oriented diagnostics—and the underlying understanding of climate system processes—as well as those applying existing diagnostics to climate models. Of particular interest are applications to models participating in the Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models but the scope is open to diagnostics of models beyond CMIP6, including higher-resolution models.

The special collection solicits studies from all realms of the climate system, and therefore spans several American Meteorological Society (AMS) journals. The special collection is organized by members of the NOAA Model Diagnostics Force (MDTF). The collection contains contributions from current task force members as well as community-wide contributions.

J David Neelin, University of California, Los Angeles
John Krasting, Geophysical Fluid Dynamics Laboratory
Fiaz Ahmed, University of California, Los Angeles
Allison Wing, Florida State University
Eric Maloney, Colorado State University

Process-oriented Diagnostics in CMIP6 Models and Beyond

R. M. Holmes
T. Sohail
, and
J. D. Zika


Anthropogenically induced radiative imbalances in the climate system lead to a slow accumulation of heat in the ocean. This warming is often obscured by natural modes of climate variability such as El Niño–Southern Oscillation (ENSO), which drive substantial ocean temperature changes as a function of depth and latitude. The use of watermass coordinates has been proposed to help isolate forced signals and filter out fast adiabatic processes associated with modes of variability. However, how much natural modes of variability project into these different coordinate systems has not been quantified. Here we apply a rigorous framework to quantify ocean temperature variability using both a quasi-Lagrangian, watermass-based temperature coordinate and Eulerian depth and latitude coordinates in a free-running climate model under preindustrial conditions. The temperature-based coordinate removes the adiabatic component of ENSO-dominated interannual variability by definition, but a substantial diabatic signal remains. At slower (decadal to centennial) frequencies, variability in the temperature- and depth-based coordinates is comparable. Spectral analysis of temperature tendencies reveals the dominance of advective processes in latitude and depth coordinates while the variability in temperature coordinates is related closely to the surface forcing. Diabatic mixing processes play an important role at slower frequencies where quasi-steady-state balances emerge between forcing and mixing in temperature, advection and mixing in depth, and forcing and advection in latitude. While watermass-based analyses highlight diabatic effects by removing adiabatic variability, our work shows that natural variability has a strong diabatic component and cannot be ignored in the analysis of long-term trends.

Significance Statement

Quantifying the ocean warming associated with anthropogenically induced radiative imbalances in the climate system can be challenging due to the superposition with modes of internal climate variability such as El Niño. One method proposed to address this issue is the analysis of temperature changes in fluid-following (or “watermass”) coordinates that filter out fast adiabatic processes associated with these modes of variability. In this study we compare a watermass-based analysis with more traditional analyses of temperature changes at fixed depth and latitude to show that even natural modes of climate variability exhibit a substantial signal in watermass coordinates, particularly at decadal and slower frequencies. This natural variability must be taken into account when analyzing long-term temperature trends in the ocean.

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