Land-surface Surface Radiation - Autoassess diagnostics#

Overview#

The simulation of surface radiation is central to all aspects of model performance, and can often reveal compensating errors which are hidden within top of atmosphere fluxes. This recipe provides metrics that evaluate the skill of models’ spatial and seasonal distribution of surface shortwave and longwave radiation against the CERES EBAF satellite dataset.

Performance metrics:

  • median absolute error (model minus observations) net surface shortwave (SW) radiation

  • median absolute error (model minus observations) net surface longwave (LW) radiation

Metrics are calculated using model and observation multi-year climatologies (seasonal means) for meteorological seasons: * December-January-February (djf) * March-April-May (mam) * June-July-August (jja) * September-October-November (son) * Annual mean (ann)

Plots:

  • Normalised assessment metrics plot comparing control and experiment

The recipe takes as input a control model and experimental model, comparisons being made with these two models.

Available recipes and diagnostics#

Recipes are stored in esmvaltool/recipes/

  • recipe_autoassess_landsurface_surfrad.yml

Diagnostics are stored in esmvaltool/diag_scripts/autoassess/

  • autoassess_area_base.py: wrapper for autoassess scripts

  • land_surface_surfrad/surfrad.py: script to calculate surface radiation metrics

  • plot_autoassess_metrics.py: plot normalised assessment metrics

User settings in recipe#

  1. Script autoassess_area_base.py

    Required settings for script

    • area: must equal land_surface_surfrad to select this diagnostic

    • control_model: name of model to be used as control

    • exp_model: name of model to be used as experiment

    • start: date (YYYY/MM/DD) at which period begins (see note on time gating)

    • end: date (YYYY/MM/DD) at which period ends (see note on time gating)

    • climfiles_root: path to observation climatologies

    Optional settings for script

    • title: arbitrary string with name of diagnostic

    • obs_models: unused for this recipe

    Required settings for variables

    none

    Optional settings for variables

    none

  2. Script plot_autoassess_metrics.py

    Required settings for script

    • area: must equal land_surface_surfrad to select this diagnostic

    • control_model: name of model to be used as control in metrics plot

    • exp_model: name of model to be used as experiment in metrics plot

    • title: string to use as plot title

    Optional settings for script

    none

    Required settings for variables

    none

    Optional settings for variables

    none

Variables#

  • rsns (atmos, monthly mean, longitude latitude time)

  • rlns (atmos, monthly mean, longitude latitude time)

  • sftlf (mask, fixed, longitude latitude)

Observations and reformat scripts#

2001-2012 climatologies (seasonal means) from CERES-EBAF Ed2.7.

References#

  • Loeb, N. G., D. R. Doelling, H. Wang, W. Su, C. Nguyen, J. G. Corbett, L. Liang, C. Mitrescu, F. G. Rose, and S. Kato, 2018: Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product. J. Climate, 31, 895-918, doi: 10.1175/JCLI-D-17-0208.1.

  • Kato, S., F. G. Rose, D. A. Rutan, T. E. Thorsen, N. G. Loeb, D. R. Doelling, X. Huang, W. L. Smith, W. Su, and S.-H. Ham, 2018: Surface irradiances of Edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data product, J. Climate, 31, 4501-4527, doi: 10.1175/JCLI-D-17-0523.1

Example plots#

Surfrad_Metrics.png

Fig. 64 Normalised metrics plot comparing a control and experiment simulation#

Inputs and usage#

The landsurface_soilmoisture area metric is part of the esmvaltool/diag_scripts/autoassess diagnostics, and, as any other autoassess metric, it uses the autoassess_area_base.py 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 Autoassess metrics.

Note

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.

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

scripts:
  autoassess_landsurf_surfrad: &autoassess_landsurf_surfrad_settings
    script: autoassess/autoassess_area_base.py
    title: "Autoassess Land-Surface Diagnostic Surfrad Metric"
    area: land_surface_surfrad
    control_model: UKESM1-0-LL
    exp_model: UKESM1-0-LL
    obs_models: [CERES-EBAF]
    obs_type: obs4MIPs
    start: 1997/12/01
    end: 2002/12/01