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#
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
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#

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