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AR6 WG1 Chapter 3 Figure 3.28 Halosteric Sea Level Change

DOI

Figure number: Figure 3.28 From the IPCC Working Group I Contribution to the Sixth Assessment Report: Chapter 3

Figure 3.28 Halosteric Sea Level Change

Description:

This is a six pane figure. The left two panes are scatter plots and the right four panes are maps.

The two left panes show the behaviour of the halosteric sea level change in the top pane and the thermosteric sea level change in the lower pane. The x axis of these figures shows the mean behaviour of the Pacific region and the y axis shows the mean of the Atlantic. The scatter points include CMIP6 historical data, CMIP6 hist-nat data, and three observational datasets.

The right-hand side shows the halosteric sea level trend over the whole time period. The top three maps are observational data and the lowest pane is the CMIP6 multi model mean.

The halosteric content was calculated using the TEOS-10 gsw python toolkit: https://teos-10.github.io/GSW-Python/

In all cases, the model temperature and salinity were de-drifted against the pi-control. From there, we ensured that the pressure was calculated correctly, the cell volume was available, we use absolute salinity & conservative temperature.

The multi model mean is calculated such that each modelled ensemble has the same weight, i.e. one-model, one vote.

Author list:

  • Lee de Mora, Plymouth Marine Laboratory, UK; ledm@pml.ac.uk
  • Paul J. Durack, Lawrence Livermore National Laboratory, USA; durack1@llnl.gov
  • Nathan Gillett, University of Victoria, Canada
  • Krishna Achutarao, Indian Institute of Technology, Delhi, India
  • Shayne McGregor, Monash University, Melbourne, Australia
  • Rondrotiana Barimalala, University of Cape Town, South Africa
  • Elizaveta Malinina-Rieger, Environment and Climate Change Canada
  • Valeriu Predoi, University of Reading, UK
  • Veronika Eyring, DLR, Germany

Publication sources:

ESMValTool Branch:

ESMValCore Branch:

Recipe & diagnostics:

Recipe used:

Diagnostic used:

Expected image path:

This is the path of the image relative to the automatically generated ESMValTool output location:

  • plots/diag_ohc/diagnostic/halosteric_multipane_historical_1950-2015.png

Recipe generations tools:

Were any tools used to populate the recipe? if so what were they? N/A if not applicable.

Two scripts are included to populate this recipe:

  • check_TSV.py
  • recipe_filler.py

Check_TSV is a tool to generate the dataset list in the recipe_ocean_heat_content_TSV_all.yml recipe.

This tool is relatively complex, as it needs to find all possible cases where the following six datasets exist for a given model & ensemble member:

  • historical temperature (thetao)
  • historical salinity (so)
  • piControl temperature (thetao)
  • piControl salinity (so)
  • volcello: both historical anbd piControl for models where volume varies with time.
  • volcello: piControl only for models where volume is fixed in time.

The tool checks that the data for all these 5 or 6 datasets must be available for the entire time range. In addition, the tool checks where the historical was branched from the piControl and adds the relevant picontrol years.

The recipe filler is an earlier and more general version of the check_TSV.py tool. It can be used to add whatever data is available into a recipe. I believe that a version of it was added to the ESMValTool master by Valeriu Predoi.

Ancillary figures and datasets:

In addition to the main figure, diagnostics may produce several figures and datasets along the way or several versions of the main figure. Please use this space to highlight anything that may be useful for future iterations:

The OHC diagnostic produces the OHC, SSS trends and Halosteric SLR figures. This code is particularly complex and several ancillary figures are produced along the way for each model and each ensemble member.

These figures include the following directories related to the de-drifting process:

  • piControl:
    • maps showing the raw temperature and salinity data at the surface at the final time step of the PI control run.
  • piTrend:
    • histograms showing the distribution of the de-drifting linear regression (slope & intersect)
  • slope:
    • maps showing the slope over the surface for the entire PI control
  • intersect:
    • maps showing the intersect over the surface for the entire PI control
  • trend_intact:
    • maps showing the raw temperature and salinity data at the surface at the final time step of historical and hist-nat run
  • detrended:
    • maps showing the dedrifted temperature and salinity data at the surface at the final time step of historical and hist-nat run.
  • detrended_quad:
    • 4 pane figure showing the surface map for the historical detrended, trend-intact, the difference and the quoitent.
  • vw_timeseries:
    • time series figure showing the volume Weighted mean for the detrended and trend intact.
  • detrending_ts:
    • time series figure showing the global volume weighted mean (or total) temperature, salinity or OHC for the historical and piControl.
  • multi_model_mean:
    • shows maps of the multi-model mean surface temperature and salinity at various points in time and specific time ranges.

The following figure directories contain figures for the Dynamic Height calculation:

  • dyn_height_timeseries:
    • Shows a timeseries of the mean dynamic height.
  • slr_height_*_detrended:
    • Surface height map for various time points.
  • SLR_Regional_trend_scatter:
    • scatter plots for the regional thermostericd and halosteric data. Like panes a and b of the halosteric SLR figure.
  • SLR_timeseries_all:
    • time series plots show the time development of each of the total, thermo and halo SLR mean for the global, atlantic and pacific regions.
  • multi_model_agrement_with_*:
    • map showing where the CMIP data agrees with the observations.
  • halosteric_multipane:
    • The full Halosteric sea level figure.

Additional datasets:

What additional datasets were used to produce this figure? Where are they on the computational machine or in the repository? Can they be re-created? What are their access permissions/Licenses?

The file names are:

  • 210201_EN4.2.1.g10_annual_steric_1950-2019_5-5350m.nc

  • 210201_Ishii17_v7.3_annual_steric_1955-2019_0-3000m.nc

  • 210127_DurackandWijffels_V1.0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc

These are the observational datasets that were added to panes a, b, and to panes c,d and e. The variables steric_height_halo_anom_depthInterp and steric_height_thermo_anom_depthInterp are used. These files were downloaded directly from Paul Durack via the invite-only google drive page: https://drive.google.com/drive/folders/1VO2FehHCz1zJu8tLvp1dNPF2IURJudJN

In addition, shapefiles are required to calculate the regional boundaries:

  • Pacific.shp

  • Atlantic.shp

These regions should be standardized through AR6, and were emailed to me by chapter author Lisa Bock, (mailto):lisa.bock@dlr.de)

Software description:

Hardware description:

What machine was used: Jasmin

When was this machine used? December 2020 to March 2021

Any further instructions:

While this code was written for the IPCC report, there are several limitations and potential sources of error. In this section, we document some potential problems.

This code uses shelve files, which are sometimes not portable between different versions of python.

We cannot guarantee that the auxiliary data will remain available indefinitely.

If the hatching is turned on in the Halosateric SLR figure, and the multi_model_agrement_with_* figures do not exist, then the code will try to create a new figure while another is unfinished. This will break matplotlib.

The dedrifting is calculated using the entire picontrol instead of limiting it to the period specific in the historical run. Other analyses have used shorter periods for their dedrifting range. This method was chosen due to the time constraints.

Other analyses have used polymetric dedrifting, to remove even more of the picontrol trend from the historical run, where as we used a much linear regression straight line fit.

The DAMIP experiment has the flaw that the Omon wasn't required to contribute the cell volume. This means that the hist-nat datasets do not include any time-varying cell volume data. To maximize the data available, we assume that the hist-nat data can use the mean along of the time axis of the pre-industrial control data.

We have interchangably used the terms de-drifting and de-trending, but the correct term for the process that we've applied is de-drifting. When something is marked as de-trended, it is actually dedrifted.

Additional information can be found in IPCC_README_files/IPCC_AR6_Chap3_Ocean_figures_notes_ledm_README.md