the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Time averaging for tendency budget analysis
Abstract. Climate models can be used to quantify the processes that contribute to a property's change through analysis of the tendency terms in the budget of the property. For example, a heat budget can be performed to understand the processes driving an increase in the ocean's heat content under climate change. However, conventional budget analyses in climate models are typically performed without proper consideration of their relationship to the property of interest. Specifically, most studies consider standard arithmetic time-averages of budget terms. For the tendency term, this average is related to the difference between two time instants (snapshots) of the property and thus is strongly impacted by rapid fluctuations, or "weather". Such an approach to budget analysis is thus less relevant to the "climate change" problem, that is, the difference between two long-term averages, or "epochs", of the property. Here we present a time-averaging method, referred to as the hat average, which exactly relates the processes contributing to a property's tendency budget to the epoch difference, or "climate change", of that property. We demonstrate the utility of this method by applying it to the horizontally integrated heat budget of a global ocean model. We find that the hat average removes high-frequency variability, typically associated with rapid and noisy advective processes, and so allows for a clear measure of how processes contribute to changes in climate.
- Preprint
(2648 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-3672', Anonymous Referee #1, 04 Sep 2026
-
RC2: 'Comment on egusphere-2026-3672', Anonymous Referee #2, 08 Sep 2026
The manuscript provides a relation of epoch differences of a quantity to a weighted average of the related tendencies. They show that differences can mathematically be related to hat averaged tendencies and that those can easily be constructed from additionally saving rising or falling weighted averages. The relation is important because for climate change studies epoch differences are a popular means to improve signal-to-noise ratios.
Overall I find the articel very readable as ever detail is well explained, which leaves little to improve. Maybe one thing one could add: I find the example difference between snapshot vs differences in epochs less instructive. Usually people will no do difference between snapshot because they are not availabe. Instead they will use differences of means but still trying to relate those to tendencies during the period of change accepting a certain amount of error. I think it would be useful to mention this approach and illustrate the error. To illustrate the error, Fig. 5 could show the boxcar or the hat-average and the difference instead of two sets figures that look very similar.
Some details:
Section 2.3 First half of this section is not about frequency domain and probably fits better to the section 2.2.
l 215-116 With delta z being time dependent, does D also become time dependent and is the heat content change affected by changes of D? The contribution from D changes may become big in particular if theta is measured in Kelvin. Or do you scale the values to 100m depth?
Citation: https://doi.org/10.5194/egusphere-2026-3672-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 170 | 55 | 17 | 242 | 19 | 18 |
- HTML: 170
- PDF: 55
- XML: 17
- Total: 242
- BibTeX: 19
- EndNote: 18
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
“Time averaging for tendency budget analysis” by Bladwell et al submitted to GMD
This manuscript presents a novel time-averaging method for tendency budget analysis, the hat average. This new method differs from the standard time average widely used in the literature by considering the difference among epochs (decades over the historical period) of a particular property field (e.g. ocean heat content, as shown in this study) rather than snapshot (instantaneous) differences. Epoch differences are argued to be a better quantity for estimating the long-term net changes, as they filter out large part of the climate variability. The authors demonstrated that the hat average method provides a better representation of the long-term heat tendency, filtering out rapid fluctuations that are intrinsically included in the standard method, allowing a clearer quantification of the climate change component.
The manuscript is well written and clearly demonstrate the mathematical derivation of the method, its application to a global ocean-sea ice model, and the differences compared to the standard time-averaging method. It addresses an important issue of separating the climate change component from the “weather-like” fluctuations in ocean heat content changes and clearly deserved to be published.
However, I have a couple of questions regarding the usability and efficiency of the hat average approach.
Minor comments:
References:
Kuhlbrodt, T., J. M. Gregory, and L. C. Shaffrey, 2015: A process-based analysis of ocean heat uptake in an AOGCM with an eddy-permitting ocean component. Climate Dyn., 45, 3205–3226, https://doi.org/10.1007/s00382-015-2534-0.
Dias, F. B., C. M. Domingues, S. J. Marsland, S. M. Griffies, S. R. Rintoul, R. Matear, and R. Fiedler, 2020: On the Superposition of Mean Advective and Eddy-Induced Transports in Global Ocean Heat and Salt Budgets. J. Climate, 33, 1121–1140, https://doi.org/10.1175/JCLI-D-19-0418.1.
Dias, Fabio Boeira, Fiedler, R., Marsland, S. J., Domingues, C. M., Clément, L., Rintoul, S. R., Mcdonagh, E. L., Mata, M. M., and Savita, A., 2020, "Ocean Heat Storage in Response to Changing Ocean Circulation Processes" Journal of Climate Vol. 33, No. 21, pp 9065, 1520-0442
Saenko, O. A., J. M. Gregory, S. M. Griffies, M. P. Couldrey, and F. B. Dias, 2021: Contribution of Ocean Physics and Dynamics at Different Scales to Heat Uptake in Low-Resolution AOGCMs. J. Climate, 34, 2017–2035, https://doi.org/10.1175/JCLI-D-20-0652.1.
Savita, A., J. D. Zika, C. M. Domingues, S. J. Marsland, G. D. Evans, F. B. Dias, R. M. Holmes, and A. McC. Hogg, 2021: Super Residual Circulation: A New Perspective on Ocean Vertical Heat Transport. J. Phys. Oceanogr., 51, 2443–2462, https://doi.org/10.1175/JPO-D-21-0008.1.
C. L. Wolfe, P. Cessi, J. L. McClean, and M. E. Maltrud: Vertical heat transport in eddying ocean models, Geophysical Research Letters, Vol. 35, L23605, doi:10.1029/2008GL036138, 2008