Cloud Regime Error Metric (CREM)#
The radiative feedback from clouds remains the largest source of uncertainty in determining the climate sensitivity. Traditionally, cloud has been evaluated in terms of its impact on the mean top of atmosphere fluxes. However it is quite possible to achieve good performance on these criteria through compensating errors, with boundary layer clouds being too reflective but having insufficient horizontal coverage being a common example (e.g., Nam et al., 2012). Williams and Webb (2009) (WW09) propose a Cloud Regime Error Metric (CREM) which critically tests the ability of a model to simulate both the relative frequency of occurrence and the radiative properties correctly for a set of cloud regimes determined by the daily mean cloud top pressure, cloud albedo and fractional coverage at each grid-box. WW09 describe in detail how to calculate their metrics and we have included the CREMpd metric from their paper in ESMValTool, with clear references in the lodged code to tables in their paper. This has been applied to those CMIP5 models who have submitted the required diagnostics for their AMIP simulation (see Figure 8 below). As documented by WW09, a perfect score with respect to ISCCP would be zero. WW09 also compared MODIS/ERBE to ISCCP in order to provide an estimate of observational uncertainty. This was found to be 0.96 and this is marked on Figure 8, hence a model with a CREM similar to this value could be considered to have an error comparable with observational uncertainty, although it should be noted that this does not necessarily mean that the model lies within the observations for each regime. A limitation of the metric is that it requires a model to be good enough to simulate each regime. If a model is that poor that the simulated frequency of occurrence of a particular regime is zero, then a NaN will be returned from the code and a bar not plotted on the figure for that model.
The original publication recommends to use sea ice fields from one model also for other models that do not provide daily sea ice concentration. This is possible as sea ice concentrations are prescribed in the AMIP simulations and has been done to produce the example figure shown below.
Available recipes and diagnostics#
Recipes are stored in recipes/
Diagnostics are stored in diag_scripts/crem/
albisccp (atmos, daily mean, longitude latitude time)
cltisccp (atmos, daily mean, longitude latitude time)
pctisccp (atmos, daily mean, longitude latitude time)
rlut (atmos, daily mean, longitude latitude time)
rlutcs (atmos, daily mean, longitude latitude time)
rsut (atmos, daily mean, longitude latitude time)
rsutcs (atmos, daily mean, longitude latitude time)
sic/siconc (seaice, daily mean, longitude latitude time)
snc (atmos, daily mean, longitude latitude time)
If snc is not available then snw can be used instead. For AMIP simulations, sic/siconc is often not submitted as it a boundary condition and effectively the same for every model. In this case the same daily sic data set can be used for each model.
Note: in case of using sic/siconc data from a different model (AMIP), it has to be checked by the user that the calendar definitions of all data sets are compatible, in particular whether leap days are included or not.
Observations and reformat scripts#
All observational data have been pre-processed and included within the routine. These are ISCCP, ISCCP-FD, MODIS, ERBE. No additional observational data are required at runtime.
Nam, C., Bony, S., Dufresne, J.-L., and Chepfer, H.: The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, L21801, doi: 10.1029/2012GL053421, 2012.
Williams, K.D. and Webb, M.J.: A quantitative performance assessment of cloud regimes in climate models. Clim. Dyn. 33, 141-157, doi: 10.1007/s00382-008-0443-1, 2009.