Stratosphere - Autoassess diagnostics#


Polar night jet / easterly jet strengths are defined as the maximum / minimum wind speed of the climatological zonal mean jet, and measure how realistic the zonal wind climatology is in the stratosphere.

Extratropical temperature at 50hPa (area averaged poleward of 60 degrees) is important for polar stratospheric cloud formation (in winter/spring), determining the amount of heterogeneous ozone depletion simulated by models with interactive chemistry schemes.

The Quasi-Biennial Oscillation (QBO) is a good measure of tropical variability in the stratosphere. Zonal mean zonal wind at 30hPa is used to define the period and amplitude of the QBO.

The tropical tropopause cold point (100hPa, 10S-10N) temperature is an important factor in determining the stratospheric water vapour concentrations at entry point (70hPa, 10S-10N), and this in turn is important for the accurate simulation of stratospheric chemistry and radiative balance.

Performance metrics:

  • Polar night jet: northern hem (January) vs. ERA Interim

  • Polar night jet: southern hem (July) vs. ERA Interim

  • Easterly jet: southern hem (January) vs. ERA Interim

  • Easterly jet: northern hem (July) vs. ERA Interim

  • 50 hPa temperature: 60N-90N (DJF) vs. ERA Interim

  • 50 hPa temperature: 60N-90N (MAM) vs. ERA Interim

  • 50 hPa temperature: 90S-60S (JJA) vs. ERA Interim

  • 50 hPa temperature: 90S-60S (SON) vs. ERA Interim

  • QBO period at 30 hPa vs. ERA Interim

  • QBO amplitude at 30 hPa (westward) vs. ERA Interim

  • QBO amplitude at 30 hPa (eastward) vs. ERA Interim

  • 100 hPa equatorial temp (annual mean) vs. ERA Interim

  • 100 hPa equatorial temp (annual cycle strength) vs. ERA Interim

  • 70 hPa 10S-10N water vapour (annual mean) vs. ERA-Interim

Diagnostic plot:

  • Age of stratospheric air vs. observations from Andrews et al. (2001) and Engel et al. (2009)

Available recipes and diagnostics#

Recipes are stored in esmvaltool/recipes/

  • recipe_autoassess_stratosphere.yml

Diagnostics are stored in esmvaltool/diag_scripts/autoassess/

  • wrapper for autoassess scripts

  • stratosphere/ calculation of metrics

  • stratosphere/ calculate age of stratospheric air

  • stratosphere/ zonal mean wind and QBO plots

  • plot normalised assessment metrics

User settings in recipe#

The stratosphere area metric is part of the esmvaltool/diag_scripts/autoassess diagnostics, and, as any other autoassess metric, it uses the as general purpose wrapper. This wrapper accepts a number of input arguments that are read through from the recipe.

This recipe is part of the larger group of Autoassess metrics ported to ESMValTool from the native Autoassess package from the UK’s Met Office. The diagnostics settings are almost the same as for the other Atoassess metrics.


Time gating for autoassess metrics.

To preserve the native Autoassess functionalities, data loading and selection on time is done somewhat differently for ESMValTool’s autoassess metrics: the time selection is done in the preprocessor as per usual but a further time selection is performed as part of the diagnostic. For this purpose the user will specify a start: and end: pair of arguments of scripts: autoassess_script (see below for example). These are formatted as YYYY/MM/DD; this is necessary since the Autoassess metrics are computed from 1-Dec through 1-Dec rather than 1-Jan through 1-Jan. This is a temporary implementation to fully replicate the native Autoassess functionality and a minor user inconvenience since they need to set an extra set of start and end arguments in the diagnostic; this will be phased when all the native Autoassess metrics have been ported to ESMValTool review has completed.


Polar Night/Easterly Jets Metrics

Polar Night Jets (PNJ) metrics require data available at very low air pressures ie very high altitudes; both Olar Night Jet and Easterly Jets computations should be preformed using ta and ua data at << 100 Pa; the lowest air pressure found in atmospheric CMOR mip tables corresponds to plev39 air pressure table, and is used in the AERmonZ mip. If the user requires correct calculations of these jets, it is highly advisable to use data from AERmonZ. Note that standard QBO calculation is exact for plev17 or plev19 tables.

An example of standard inputs as read by and passed over to the diagnostic/metric is listed below.

  autoassess_strato_test_1: &autoassess_strato_test_1_settings
    script: autoassess/  # the base wrapper
    title: "Autoassess Stratosphere Diagnostic Metric"  # title
    area: stratosphere  # assesment area
    control_model: UKESM1-0-LL-hist  # control dataset name
    exp_model: UKESM1-0-LL-piCont  # experiment dataset name
    obs_models: [ERA-Interim]  # list to hold models that are NOT for metrics but for obs operations
    additional_metrics: [ERA-Interim]  # list to hold additional datasets for metrics
    start: 2004/12/01  # start date in native Autoassess format
    end: 2014/12/01  # end date in native Autoassess format


Variable/Field name




Eastward wind (ua)


monthly mean

original stash: x-wind, no stash

Air temperature (ta)


monthly mean

original stash: m01s30i204

Specific humidity (hus)


monthly mean

original stash: m01s30i205

The recipe takes as input a control model and experimental model, comparisons being made with these two CMIP models; additionally it can take observational data s input, in the current implementation ERA-Interim.

Observations and reformat scripts#

ERA-Interim (ta, ua, hus - cmorizers/data/formatters/datasets/


  • Andrews, A. E., and Coauthors, 2001: Mean ages of stratospheric air derived from in situ observations of CO2, CH4, and N2O. J. Geophys. Res., 106 (D23), 32295-32314.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc, 137, 553-597, doi:10.1002/qj.828.

  • Engel, A., and Coauthors, 2009: Age of stratospheric air unchanged within uncertainties over the past 30 years. Nat. Geosci., 2, 28-31, doi:10 .1038/NGEO388.

Example metrics and plots#

Below is a set of metrics for UKESM1-0-LL (historical data); the table shows a comparison made between running ESMValTool on CMIP6 CMORized netCDF data freely available on ESGF nodes and the run made using native Autoassess performed at the Met Office using the pp output of the model.

Metric name

UKESM1-0-LL; CMIP6: AERmonZ; historical, ESGF

UKESM1-0-LL; pp files; historical, u-bc179

Polar night jet: northern hem (January)



Polar night jet: southern hem (July)



Easterly jet: southern hem (January)



Easterly jet: northern hem (July)



QBO period at 30 hPa



QBO amplitude at 30 hPa (westward)



QBO amplitude at 30 hPa (eastward)



50 hPa temperature: 60N-90N (DJF)



50 hPa temperature: 60N-90N (MAM)



50 hPa temperature: 90S-60S (JJA)



50 hPa temperature: 90S-60S (SON)



100 hPa equatorial temp (annual mean)



100 hPa equatorial temp (annual cycle strength)



100 hPa 10Sto10N temp (annual mean)



100 hPa 10Sto10N temp (annual cycle strength)



70 hPa 10Sto10N wv (annual mean)



Results from u-bc179 have been obtained by running the native Autoassess/stratosphere on .pp data from UKESM1 u-bc179 suite and are listed here to confirm the compliance between the ported Autoassess metric in ESMValTool and the original native metric.

Another reference run comparing UKESM1-0-LL to the physical model HadGEM3-GC31-LL can be found here .


Fig. 60 Standard metrics plot comparing standard metrics from UKESM1-0-LL and HadGEM3-GC31.#


Fig. 61 Zonal mean zonal wind in January for UKESM1-0-LL.#


Fig. 62 Zonal mean zonal wind in January for HadGEM3-GC31-LL.#


Fig. 63 QBO for UKESM1-0-LL.#


Fig. 64 QBO for HadGEM3-GC31-LL.#


Fig. 65 QBO at 30hPa comparison between UKESM1-0-LL and HadGEM3-GC31-LL.#


Fig. 66 Equatorial temperature at 100hPa, multi annual means.#

Prior and current contributors#

Met Office:

  • Prior to May 2008: Neal Butchart

  • May 2008 - May 2016: Steven C Hardiman

  • Since May 2016: Alistair Sellar and Paul Earnshaw


  • Since April 2018: Porting into ESMValTool by Valeriu Predoi


Met Office:

  • Prior to May 2008: Neal Butchart

  • May 2008 - May 2016: Steven C Hardiman


  • Since April 2018: Valeriu Predoi