Climatology

The [climatology] section of a configuration file contains options used to determine the start and end years of climatologies, the reference years for anomalies and to control remapping of climatologies to comparions grids:

[climatology]
## options related to producing climatologies, typically to compare against
## observations and previous runs

# the year from which to compute anomalies if not the start year of the
# simulation.  This might be useful if a long spin-up cycle is performed and
# only the anomaly over a later span of years is of interest.
# anomalyRefYear = 249

# the first year over which to average climatalogies
startYear = 11
# the last year over which to average climatalogies
endYear = 20

# The comparison lat/lon grid resolution in degrees
comparisonLatResolution = 0.5
comparisonLonResolution = 0.5

# The comparison Antarctic polar stereographic grid size and resolution in km
comparisonAntarcticStereoWidth = 6000.
comparisonAntarcticStereoResolution = 10.

# interpolation order for model and observation results. Likely values are
#   'bilinear', 'neareststod' (nearest neighbor) or 'conserve'
mpasInterpolationMethod = bilinear

# should remapping be performed with ncremap or with the Remapper class
# directly in MPAS-Analysis
useNcremap = True

# The minimum weight of a destination cell after remapping. Any cell with
# weights lower than this threshold will therefore be masked out.
renormalizationThreshold = 0.01

Start and End Year

A custom config file should specify a start and end year for climatologies. Simulation data must exist for all 12 months of each year in this range. Otherwise, the range will be reduced to those years with complete data and a warning message will be displayed.

Anomaly Reference Year

Anomalies between a climatology and the associated field from a reference year is used in a few analysis tasks. By default, the reference year is not specified in the configuration file and it taken to be the start of the simulation (determined from the contents of a restart file). Under certain circumstances (e.g. repetition of forcing data for several cycles, as in the Common Ocean Reference Experiments, CORE), it may be desirable to specify a different year to use for computing anomalies:

anomalyRefYear = 249

Comparison Grids

See Comparison Grids.

Remapping Options

Climatologies are remapped from MPAS meshes and observations grids to common comparison grids. The remapping can be performed with any of three methods: bilinear, neareststod (nearest neighbor) or conserve. Mapping files are created with the ESMF_RegridWeightGen tool. The default method is bilinear and these are the mapping files distributed from the E3SM public data repository. The conseve method is know to be much slower to compute and should only be used if it is necessary (e.g. because remapped data will be checked for conservation).

MPAS-Analysis typically uses the NCO tool ncremap to perform to perform remapping. However, ncreamp does not support the Antarctic stereographic grids used by some MPAS-Analysis tasks so a python remapping function is used for these grids. The user can force all remapping to use the internal remapping function by specifying:

useNcremap = False

This capability is available largely for debugging purposes.

Remapped data typically only makes sense if it is renormalized after remapping. For remapping of conserved quatntities like fluxes, renormalization would not be desirable but for quantities like potential temperature, salinity and potential density commonly used in MPAS-Anlaysis tasks, values become physically meaningless near land boundaries and regions without data unless renormalization is performed. A threshold is needed to determine how much of a cell’s area on the output grid must contain valid data from the input grid or mesh, below which that cell is considered invalid and is masked out of the destination data set. This threshold is specified as a fraction:

renormalizationThreshold = 0.01

If noisy or unphysical values occur near maked regions on the comparison grid, it might be necessary to increase this threshold. If too much data appears to be being masked out unnecessarily on the comparison grid, perhaps this value should be made smaller.