# Marine Heatwave Variance Project

## July 7,2021
- Julius
- Hillary
- Paige

### Discussion
- combining variance, MHWs, CMIP6
- would first step be census of MHWs in models?
- has been done in obs, but not in models yet (as far as we know)
- maybe we need map of probability of MHW at each location
- then can look at if there is either an increase or decrease in MHWs
- two approaches
- Lagrangian - follow specific MHW
- Eulerian - if area has more heatwaves occuring then there must be significant changes in heat budget, even in frequency space
- do analysis at each grid point, not Lagrangian
- variance budget terms and map of MHW drivers
- the frequency-domain technique may not make sense to use for specific MHW events
- two mechanisms:
- mean increases in specific region, so more premanent MHW
- more high-freq variability that has low-frequency envelope
- time of emergence in MHW literature
- MHW intensity, frequency, when that signal exceeds the noise of climate variability
- already been looked at in CMIP5
- could compare low and hi-res models
- Julius has looked at MHWs and effect on corals in CM2.6 and low-res version
- see Pilo et al (2019) below
- MHW literature is pretty saturated - what could we bring to the table?
- CESM Large Ensemble hasn't been tapped into yet
- drivers of MHWs - active topic of research particularly in Australia
- have eg mixed layer budget and
- IDF plots: intensity, duration, frequency
- often used in flood research
- can we get SST from a frequency spectrum?
- what if we take out the mean, and look only at variability
- with increasing mean SST, it makes sense that MHWs will increase in future
- would it even make sense to detrend and look at variability?
- not if looking at biology, since living things can't evolve quickly enough to adapt
- the Olvier et al paper uses statistical model
- Hillary: want to use ML to predict MHWs
- let's just apply ocetrac to CMIP6 models and compare across models!
- at least as a first step
- could compare to obs

**Action item: apply ocetrac to CMIP6 models and compare across the models**

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## December XX, 2020
* *Next meeting should be on the theoretical framework*
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## November 24, 2020
* Paige
* Hillary

**–Brainstorming Session–**

### Questions/Ideas:

* Do the drivers of SST variance match the drivers of MHWs?
* Can we formulate the variance budget for MHWs?
* use temperature tendency
* look at the budget leading up to and during MHW, and during times when there is not a MHW
* compute a climatology of variance budget terms and look at anomalies during MHWs
* How will MHW variance change in a warming world? Any why?
* Is the change purely dominated by a shift in the mean?
* Look at spread across ensemble members
* Can we link any change in variance to a potential change in drivers?
* atmospheric blocking
* change in the location of ocean fronts, current, etc.
* Increase in ocean stratification
* Link time scales to drivers and quantify
* Can we do a more formal analysis of [Figure 2](https://www.nature.com/articles/s41467-019-10206-z/figures/2) from [Holbrook et al. 2019](https://www.nature.com/articles/s41467-019-10206-z), which is based purely on past knowlege
* It would be interesting to look at temperature variance with depth, however the surface budget seems first-order.
* Challenges:
* Noisy spectra at low frequencies because of the short record.
* Could we reduce noise by leveraging ensemble members?
* How will we interpret residuals in the budget terms?

### Datasets:

* Large ensmbles (same forcing, different initial conditions)
* CESM-LE
* MPI–GE
* Idealized models (Paige's wheelhouse)
* partial coupling (i.e., the elimination diet) is really cool!

### What software packages might we use?
* xarray
* dask
* xrft
* xgcm (regridding)
* xESMF (regridding)
* CMIP6 preprocessing

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## Relevant Litterature

* **Martin et al.** (in review): Drivers of atmospheric and oceanic surface temperature variance: a frequency domain approach
* **[Tesdal and Abernathey](https://eartharxiv.org/repository/view/435/)** (in review): Drivers of Local Ocean Heat Content Variability in ECCOv4
* **[Jacox et al., 2020](https://www.nature.com/articles/s41586-020-2534-z)**: Thermal displacement by marine heatwaves
* **[Hakase Hayashida et al., 2020](https://www.nature.com/articles/s41467-020-18241-x)**: Insights into projected changes in marine heatwaves from a high-resolution ocean circulation model
* **[Sen Gupta et al., 2020](https://www.nature.com/articles/s41598-020-75445-3)**: (Among other aspects in the paper) assess forcing mechanisms of marine heatwaves, as well as the processes that drive the end of a MHW. They separate out different forcing terms, e.g. wind speed, Ekman transport, latent heat flux, etc. (see Figure 6)
* **[Small et al., 2020](https://journals.ametsoc.org/jcli/article-abstract/33/2/577/346234/What-Drives-Upper-Ocean-Temperature-Variability-in)**: What Drives Upper-Ocean Temperature Variability in Coupled Climate Models and Observations?
* **[Holbrook et al., 2019](https://www.nature.com/articles/s41467-019-10206-z)**: A global assessment of marine heatwaves and their drivers
* **[Oliver et al., 2019](https://www.frontiersin.org/articles/10.3389/fmars.2019.00734/full)**: Projected Marine Heatwaves in the 21st Century and the Potential for Ecological Impact
* **[Oliver, 2019](https://link.springer.com/article/10.1007/s00382-019-04707-2)**: Mean warming not variability drives marine heatwave trends
* **[Pilo et al., 2019](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL084928)**: Sensitivity of Marine Heatwave Metrics to Ocean Model Resolution
* **[Schlegel et al., 2019](https://www.frontiersin.org/articles/10.3389/fmars.2019.00737/full)**:
Detecting Marine Heatwaves With Sub-Optimal Data
* **[Schmeisser et al., 2019](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019JD030780)**: The Role of Clouds and Surface Heat Fluxes in the Maintenance of the 2013–2016 Northeast Pacific Marine Heatwave
* **[Alexander et al., 2018](https://psl.noaa.gov/people/michael.alexander/alexander.etal.SST-LME.elementa.1-18.pdf)**: Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans