PMIP Newsletter 6


 

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            *********************************************
            PALEOCLIMATE MODELING INTERCOMPARISON PROJECT
            *********************************************

                          Newsletter N. 6
                         -----------------
                           28 July 1995


Dear participant,

Now that most modeling groups have completed some of their PMIP
simulations, it is time to prepare the output for immediate use in
model-model comparisons and for later use in model-data comparisons.
We hope this will lead to a better understanding of our models' ability
to simulate PMIP paleoclimatic periods and will indicate how sensitive
the results are to various model formulations.

As you may already know, we have planned a first PMIP workshop devoted
to model-model comparisons for the week of October 1 - 6, 1995 in
Collonges-la-Rouge (France).  Representatives of each modeling group
(see table in newsletter 5) along with members of our scientific
advisory committee and data-model subcommittee (see newsletter 4) have
been invited to this workshop, making up a group of about 45
scientists.  The workshop is sponsored both by the European Union and
the NOAA Office of Global Programs (Paleoclimatology Program).

The purpose of the workshop is for the participating modeling groups to
share results from the 21 ka and 6 ka PMIP simulations and to begin
exploring differences.  We envision that special common interests will
draw together groups of participants into sub-projects which will focus
on various specific aspects of the simulations.  We shall also initiate
planning for systematic comparisons with paleoclimate data.

In preparation for the comparisons, we are now requesting that
participating groups fulfill their obligation to contribute model 
output to an archive that will be accessible by the other PMIP
participants (initially only by those who have contributed data to the
archive).  The data will be stored at PCMDI (Program for Climate Model
Diagnosis and Intercomparison), and the following specifications for
preparing data for transfer to PCMDI are based largely on their
experience in collecting and archiving data for AMIP (Atmospheric Model
Intercomparison Project).

Although this newsletter necessarily contains a rather detailed
description of the procedure for preparing your output for the PMIP
archive, it is really quite straight-forward.  You will need to:

*** compute climatological means of monthly, seasonal and annual data 
    from your model, and compute the variance of  the individual samples
    comprising the climatologies (i.e., interannual variances of the 
    monthly, seasonal, and annual data).  These means and variances will
    need to be calculated for each of the variables comprising the PMIP 
    standard output (listed in Appendix I of this newsletter) and for 
    each of the model simulations you perform (6k, 21k, and controls). 

*** convert your data (if necessary) to the PMIP standard units (given
    in Appendix I) and order your data as specified in section -A- of 
    this newsletter.

*** output the data in the PMIP standard ascii format by calling a 
    fortran subroutine described in section -B- and obtainable from Karl
    Taylor.

*** prepare short README files (one for each simulation), according to
    directions given in section -C-, and ftp them to PCMDI.

*** collect your output files into a few "tar" files (constructed
    according to the directions given in section -D-), and then compress
    and ftp them to the PMIP archive at PCMDI.

As soon as you contribute your output to the archive, you will be given
access to the output from the other models.  In the present newsletter
we therefore provide information you will need to archive your model
output.  Specifically, we provide instructions for:
        
     -A-  Computing and Structuring Standard Output 
     -B-  Writing PMIP Output in the Standard ascii Format
     -C-  Creating Standard README Files
     -D-  Transferring Data to the PCMDI Archive
     Appendix I  List of Variables Requested for Archival Storage
     Appendix II  Standard File Names Constructed by subroutine wrtascii
     Appendix III  Sample Output File Produced by subroutine wrtascii
        


***********************************************
-A-  Computing and Structuring Standard Output
***********************************************

Data should be sent to PCMDI by ftp or, if that is impossible, by
exabyte tape (details to follow in -D- below).  (Note that your data
will only be made available to other contributing PMIP members.)  Ascii
files in the PMIP standard format are required.  It will be easy to put
your data in this format because a fortran subroutine (called wrtascii)
has been written for this purpose and is available from anonymous ftp
at PCMDI.  Documentation of this subroutine and directions on how to
obtain a copy of it are given in -B- below.  The PMIP standard ascii
files are written consistent with CDL (network Common Data form
Language), which makes it trivial to translate the data into netCDF
format (which like DRS and grads files can be ingested by the PCMDI
visualization package called VCS). Through this established PMIP
standard file format, the tasks of archiving the data and performing
quality control checks can be simplified and automated.  Furthermore
you will be able to obtain the data in this format from all the PMIP
models along with programs for reading it or translating it into netCDF
or DRS format.

The standard PMIP fortran program for writing model output (subroutine
wrtascii) will correctly format your data if you prepare it as
described here:


*1* For each simulation and each variable (listed in Appendix I)
    you should produce the following climatological statistics which
    will reside in separate files:

    a) climatological annual mean data (i.e., averages over at least 10 
       years)
    b) interannual variance of the annual mean data (see 2g below)
    c) climatological seasonal mean data (i.e., averages over several
       winters, several springs, etc. as defined in 2b and 2e below)
    d) interannual variance of the seasonal mean data
    e) climatological monthly mean data (i.e., averages over several
       Januarys, several Februarys, etc.)
    f) interannual variance of the monthly mean data

    Thus for each simulation and each variable you will call subroutine
    wrtascii 6 times (with different arguments) to produce the above 6
    files.


*2* In preparing to call subroutine wrtascii note the following: 

    a) Remember that means for the months and seasons should be
    defined in the conventional way with the calendar defined in all
    experiments such that vernal equinox coincides with noon of March
    21, and months kept the same as for the control run
    (See Newsletters 1, 2 and 3 for further discussion.)

    b) Seasons are defined as: N.H. winter = DJF (December, January,
    February); N.H. spring = MAM (March, April, May); N.H. summer =
    JJA (June, July, August); N.H. autumn = SON (September, October,
    November)

    c) Although not a requirement of PMIP, some of you may want to
    ensure exact correspondence between the data used to define your
    climatological monthly, seasonal and annual means.  If you do, we
    recommend that you define your climatological year as beginning in
    December and ending in November so that the same months that
    contribute to the climatological monthly mean data also contribute
    to the climatological seasonal and annual mean data. 

    d) Although the climatological seasonal mean data and the
    climatological annual mean data can obviously be calculated from
    the climatological monthly mean data, the interannual variance of
    the seasonal and annual data cannot be calculated from the
    interannual variance of the monthly mean data.  For completeness
    and consistency, therefore, please contribute all 6 files listed in
    *1* above to the archive.

    e) When computing seasonal means, weight the monthly data by the
    number of days in that month.  For example, if your model has a
    365 day year, then the mean autumn = (SEP*30 + OCT*31 +
    NOV*30)/91, where SEP, OCT, and NOV represent the monthly mean 
    data for those months.

    f) When computing the annual mean, weight the monthly data by the
    number of days in that month.  (Alternatively, compute the annual
    means from the seasonal means, weighting the seasonal data by the
    number of days in the season.)

    g) An unbiased estimate of the interannual variance should be
    computed according to the conventional formula:  sum over N years
    {(x(n) - x-bar)**2/(N-1)}  where x-bar is the climatological mean
    and n is the year index.

    h) Be sure to carry out sufficiently long simulations such that 
    the climatologies you calculate are good estimates of the true
    model climatology.  The spin up time for fixed SST experiments
    will normally be less than a year (unless your soil moisture and
    snow cover require longer adjustment times), but for computed SST 
    experiments with ocean mixed layers, the spin-up period may
    vary from one to several decades, depending on the model.  
    Normally you should average over at least 10 years of data, but
    for computed SST experiments, you may need to average over longer
    periods to account for variability on longer time-scales. 


*3* In preparing to call wrtascii be sure your array of data is filled
    in the following order: 

    a) For each longitude - latitude field, the data should be stored
    with the longitude index varying most rapidly, latitude next, and
    finally time (i.e., in FORTRAN:  A(i,j,m) where i is the longitude 
    index, j is the latitude index, and m is the month or season 
    index).  For annual average data there will be no time index.

    b) For each longitude - latitude - pressure level field, the data
    should be stored with the longitude index varying most rapidly,
    latitude next, pressure level next, and finally time (i.e., in
    FORTRAN:  A(i,j,k,m) where i is the longitude index, j is the  
    latitude index, k is the pressure level index and m is the month 
    or season index).  For annual average data there will be no time
    index.

    c) For each latitude - pressure zonal cross-section, the data
    should be stored with the latitude index varying most rapidly,
    pressure next, and finally time (i.e., in FORTRAN:  A(j,k,m) where
    j is the latitude index, k is the pressure index, and m is the
    month or season index).  For annual average data there will be no
    time index.

    d) Longitudes should be stored west to east beginning at 0 degrees
    or if your model does not have a grid point at 0 degrees, then
    beginning at the first longitude to the east of 0 degrees.  Do not
    include a "wrap-around" point (i.e., do not duplicate your first
    grid point as your last grid point).

    e) Latitudes should be stored north to south, beginning with the
    northern most grid cell.

    f) Pressure levels should be stored in order of increasing
    pressure (i.e., top of atmosphere to surface)

    g) Seasons should be stored in the order: N.H. winter (DJF), N.H.
    spring (MAM), N.H. summer (JJA), N.H. autumn (SON).

    h) Months should be stored in calendar order beginning with
    January and ending with December.


*4*  Please note:

    a)  All variables from your model must be reported on the same
    grid.  If you have a staggered grid (e.g., some variables
    calculated at the center of a grid cell and others calculated at
    the edges or corners)  or if your model uses a grid with the number
    of longitude grid-cells varying with latitude (e.g., the new
    ARPEGE/IFS model with a grid that "thins" at higher latitudes),
    then you should interpolate to a common rectangular
    longitude-latitude grid.  Note that it is not necessary that the
    grid spacing be uniform (Gaussian grids, for example, used in
    spectral models are perfectly acceptable.)

    b)  Interpolate, when necessary, to standard pressure levels (see
    Appendix I for the list of standard levels).  Ideally this should
    be done before computing averages.
    


*****************************************************
-B-  Writing PMIP Output in the Standard ascii Format
*****************************************************

You can use anonymous ftp (to PCMDI) to obtain subroutine wrtascii,
which will write data in the PMIP standard ascii format, as follows:

enter:
ftp sprite.llnl.gov
        
     Respond to login prompt:
anonymous

     we ask you to enter your e-mail address as the password, for
     example:
myemail@myhost.mydomain

     change directories:
cd /pub/ktaylor/pmip

     transfer files, for example:
get wrtascii.f

quit


For each variable in the list given in Appendix I and for each
simulation you will need to read your data, calculate appropriate time
means and interannual variances, possibly reorder the data (to be
consistent with the specifications given in -A- above), and call
subroutine wrtascii 6 times (once for each file written).

The arguments for the subroutine are documented here:

      subroutine wrtascii(modelid, modelnm, run, varname, stattype,
     &     nlon, nlat, nlev, ntim, lensim, undef, tol, alon, alat, 
     &     alev, field, ierr)

c ----------------------------------------------------------------------
c
c     This subroutine creates a file and stores a single output field
c     in it.  The ascii output written by this subroutine is in a form
c     called CDL (network Common Data form Language) that can be easily
c     translated into a binary netCDF file or a DRS file.  The output
c     is the standard form required by the PMIP project (ca. 1995).
c
c     Author:  Karl E. Taylor
c
c     Date last revised:  27 July 1995
c
c
c  INPUT:
c
c     character strings: 
c
c         modelid = ascii string (<= 8 characters) containing 
c                abbreviated model i.d. for use in file names (e.g., 
c                gen, ccm2, ech2, ccm-llnl, etc.)  No blanks or upper  


c                case characters are permitted.
c
c                You should be sure to plan ahead in choosing your
c                model i.d.; if you plan on running your model again 
c                at a different resolution or after modifying it, for 
c                example, then leave room for some suffix modifier,
c                such as ccm2_2a    
c 
c         modelnm = ascii string (<= 48 characters) containing full
c                model name and version along with resolution 
c                information (e.g. "Genesis 2.3 T42L18" or
c                "LMCE - modele du LMD version 4ter - 5x7L11").  
c                Blanks, lower and upper case characters are o.k.
c
c                Although the model name and version are up to you, you
c                must include the resolution information (e.g., T42L18
c                or 5x7L11)  consistent with the following rules:
c                For spectral models use the common spectral truncation
c                abbreviations (e.g., T42, R30). For finite-difference
c                models use average grid cell size (i.e., 180/nlat by 
c                360/nlon) and write resolution similar to 5x7 (for a 
c                5 degree latitude by 7 degree longitude grid (round
c                to nearest degree).  Indicate the number of levels 
c                with an L prefix (e.g., L18 or L11)
c
c         run = ascii string (<= 5 characters)
c             = '6fix' (for 6 ka BP simulation with fixed SSTs)
c             = '21cal' (for 21 ka BP simulation with computed SSTs)
c             = '21fix' (for 21 ka BP simulation with fixed SSTs)
c             = '0fix' (for fixed SST control run)
c             = '0cal' (for calculated SST control run)
c
c         varname = ascii string (<= 8 characters) containing variable
c                   name (exactly as specified in standard list given in
c                   Appendix I)
c
c         stattype = ascii string (<= 8 characters)
c                  = 'mean' (for time-mean field)
c                  = 'variance' (for interannual variance data)
c
c      integers:
c
c          nlon = longitude dimension (i.e., number of longitudes). 
c                  nlon should be set to 0 for fields that are 
c                  independent of longitude (i.e., for zonal mean cross 
c                  sections)
c
c          nlat = latitude dimension (i.e., number of latitudes)
c
c          nlev = pressure level dimension (i.e., number of pressure 
c                  levels).  nlev should be set to 0 for fields that 
c                  are independent of pressure level (i.e., for data
c                  that represent a surface field (e.g., pmsl),
c                  or a field at the top of the atmosphere (e.g., rsdt),
c                  or a column integrated total (e.g., clt), then nlev 
c                  should be set to 0).  Allowed values of nlev are: 0,
c                  2, 3, 15.
c
c          ntim = time dimension 
c               =  0 for annual mean data 
c               =  4 for seasonal data
c               = 12 for monthly data
c
c          lensim = number of years of data from which the 
c                    climatological statistics were calculated (i.e., 
c                    the averaging period).
c
c      real:
c
c          undef = the value you use to identify missing or undefined 
c                   data.  (This value will be replaced with the PCMDI
c                   standard missing value identifier, 1.0e20, before
c                   the ascii output file is written by this 
c                   subroutine.)
c
c          tol  = tolerance for determining how close data must be to
c                  to your "undef" value before it will be considered 
c                  missing.  if (abs(field-undef) .le. tol) then field
c                  will be reset to 1.0e20, which identifies it as  
c                  "missing" or "undefined".
c
c          alon = a vector containing the longitude coordinate values 
c                  (e.g., 0., 5., 10., ... 355.)  The values stored in
c                  alon are immaterial (i.e., they are ignored) if you
c                  are writing a zonal mean cross-section.
c
c          alat = a vector containing the latitude coordinate values
c                  (e.g., 88. 84., 80, ... -88.)
c
c          alev = a vector containing the pressure-level coordinate 
c                  values (e.g., 200., 850.).  The values stored in alev
c                  are immaterial (i.e., they are ignored) if the data 
c                  set you are writing represents a surface field 
c                  (e.g., pmsl), or a field at the top of the atmosphere
c                  (e.g., rsdt), or a column integrated total (e.g.,
c                  clt).
c
c          field = array containing your data stored in the order 
c                   described in -A- above.  
c                = field(nlon,nlat,nlev,ntim) for monthly or seasonal
c                      data that are functions of longitude, latitude 
c                      and pressure level.
c                = field(nlon,nlat,ntim) for monthly or seasonal data
c                      that are functions of longitude and latitude.
c                = field(nlat,nlev,ntim) for monthly or seasonal zonal
c                      mean cross-sections that are functions of 
c                      latitude and pressure level.
c                = field(nlon,nlat,nlev) for annual data that are 
c                      functions of longitude, latitude and pressure 
c                      level.
c                = field(nlon,nlat) for annual data that are functions 
c                      of longitude and latitude.
c                = field(nlat,nlev) for annual mean cross-sections that
c                      are functions of latitude and pressure level.
c
c  OUTPUT:
c     integer: 
c
c          ierr = error flag
c               = 0 if no error detected by subroutine wrtascii
c               > 0 if fatal error detected by subroutine wrtascii
c               < 0 if warning message has been written by subroutine
c                      wrtascii
c
c ----------------------------------------------------------------------



************************************
-C-  Creating Standard README Files
************************************

For each of your simulations, please provide a README file which will
contain some important information about your model and simulation.
The README file names should be of the form:

              modelid_run_README

where modelid and run are defined in -B- above.

     Example: ccm-llnl_21cal_README

To create a standard format README file, you will probably find it
easiest to simply edit the following example:

----------------------------------------------------------------------
# Comments or additional information should be given on lines with 
# a "#" in the first column.
# Blank lines are allowed.

# KEYWORD               YOUR INPUT

MODEL_ID                ccm-llnl
MODEL_NAME              CCM1/LLNL Version 1 R15/L12
PMIP_RUN                21cal

YEARS_MEAN              20
YEARS_SPUNUP            25
NB_SAMPLES              24

DIM_LONGITUDE           48
DIM_LATITUDE            40
DIM_LEVELS              12

MODEL_MONTHS            31  28  31  30  31  30  31  31  30  31  30  31

PMIP_CONTACT            Karl E. Taylor
ADDRESS                 P.O. Box 808  L-264
ADDRESS                 Lawrence Livermore National Lab
ADDRESS                 Livermore, CA 94550
ADDRESS                 USA
PHONE                   510 423 3623
FAX                     510 422 7675 
EMAIL                   taylor13

# MODEL_DOCUMENTATION:
#
#     Williamson, D.L., J.T. Kiehl, V. Ramanathan, R.E. Dickinson, J.J.
#        Hack, 1987: Description of NCAR Community Climate Model (CCM1).
#        NCAR Technical Note, NCAR/TN-285+STR, 112 pp.
#
#     Taylor, K.E., and S.J. Ghan, 1992: An analysis of cloud liquid
#        water feedback and global climate sensitivity in a general 
#        circulation model, J. Climate, 5, 907-919. 

# COMMENTS:
#
# The model was started from an equilibrium state of a 2XCO2
# experiment.
#
# The model was run on a CRAY 2 under the UNICOS operating system.
#
# The model uses a sigma-coordinate in the vertical with the following
# levels:  0.009, 0.025, 0.060, 0.110, 0.165, 0.245, 0.355, 0.500
# 0.664, 0.811, 0.926, 0.991
#
# [Please be sure to include any special problems encountered   
# that might affect interpretation of the results or any known biases
# in your model's climate.]  
#

----------------------------------------------------------------------

Note that in the above README file:

    MODEL_ID should be identical to the "modelid" supplied to
       subroutine wrtascii and described in -B- above.

    MODEL_NAME should be identical to the "modelnm" supplied to
       subroutine wrtascii and described in -B- above.
   
    PMIP_RUN should be identical to the "run" supplied to subroutine
       wrtascii and described in -B- above.

    YEARS_MEAN should be the number of years contributing to
       climatological statistics (i.e. averaging period for data) and
       should be identical to "lensim" supplied to subroutine wrtascii
       (described in -B- above).

    YEARS_SPUNUP should be the number of years the model was "spun up"
       prior to the climatological averaging period (i.e., the spin-up
       time).

    NB_SAMPLES is the number of samples per day contributing to your
       time-means. If you accumulate over each time-step, then the
       number of samples should equal the number of time-steps per day
       even though you may only save the accumulations a few times per
       day.  If different variables are sampled at different intervals,
       enter the fewest samples per day that contribute to any of your
       time means.  You might want to list the sampling interval for
       each variable in the comments section of this file.

    DIM_LONGITUDE is your model's longitude dimension (i.e. the number
       of longitude grid cells in your model)

    DIM_LATITUDE is your model's latitude dimension (i.e. the number of
       latitude grid cells in your model)

    DIM_LEVELS is your model's vertical coordinate dimension (i.e.  the
       number of levels in your model)

    MODEL_MONTHS is a list of the number of days in each month,
       according to your model.  You should enter these as a list of
       blank-separated integers.

    PMIP_CONTACT is the person to contact concerning this simulation.

    ADDRESS is the contact's address.

    PHONE is the contact's phone number.

    FAX is the contact's fax number.

    EMAIL is the contact's e-mail address.

  NOTE: The following final two items must appear on comment lines
 (i.e., lines starting with '#')

    MODEL_DOCUMENTATION is followed by a list of references that
       document your model.

    COMMENTS is followed by any further information of interest
       concerning your model and the simulation (see example above).



***********************************************
-D-  Transferring Data to the PCMDI Archive
***********************************************

When you are ready to archive your data at PCMDI, you should contact:
                   
                        Karl E. Taylor
                email:  taylor13 (NEW! Please check the PMIP 'Contacts' web page)

Karl will provide instructions on where to place the data using ftp.
(Note again, that your data will be made available only to other
contributing PMIP participants.)  You may prefer to send output from
each of your simulations as they are completed.  This is encouraged.
The output files created for each simulation should be put into 6 tar
files.  You should also ftp the README file (named as specified in -C-
above).  The directory structure for the 6 tar files should be as
follows:

For each simulation and each statistic (e.g., climatological annual
mean, interannual monthly variance) place all files in a single
compressed tar file named

     modelid_run_stat.tar.Z

where 

    modelid should be identical to the "modelid" supplied to
    subroutine wrtascii, described in -B- above.

    run should be identical to the "run" supplied to subroutine
    wrtascii, described in -B- above. (Choose from: 6fix, 21fix, 21cal,
    0fix, 0cal.)

    stat should be identical to the "stat" component of the file name
    (see Appendix II).  Stat should be one of the following: cm, vm, cs, 
    vs, ca, va.  Do not confuse "stat" with " stattype".


Each tar file should contain all the variable fields for a single
statistic and simulation, and should be structured with directories and
subdirectories as follows:

      modelid/run/stat/modelid_run_*_stat_*

where modelid, run, and stat were defined just above and 


    modelid_run_*_stat_* represents the set of files you created by
    calling wrtascii (for each variable) which will be stored in the
    directory, modelid/run/stat (see Appendix 2 for interpretation of
    file names).  The first * represents one of the standard PMIP
    variable names and the second * represents one of the three "views"
    (map, 3d, or xsec).

As an example, suppose you have completed the 6 ka BP simulation.  You
will create 6 tar files plus a README file:

     ccm2_6fix_cm.tar.Z
     ccm2_6fix_vm.tar.Z
     ccm2_6fix_cs.tar.Z
     ccm2_6fix_vs.tar.Z
     ccm2_6fix_ca.tar.Z
     ccm2_6fix_va.tar.Z
     ccm2_6fix_README

Each compressed tar file will comprise the whole suite of variable files
for the given simulation and given statistic.  For example,
ccm2_6fix_cm.tar.Z will contain, in the modelid/6fix/cm directory

     ccm2_6fix_orog_cm_map
     ccm2_6fix_sftland_cm_map
     ccm2_6fix_snow_cm_map
     ccm2_6fix_sim_cm_map
             .
             .
             .
     ccm2_6fix_zg_cm_3d
     ccm2_6fix_ta_cm_3d
     ccm2_6fix_u_cm_3d
     ccm2_6fix_v_cm_3d
             .
             .
             .
     ccm2_6fix_hur_cm_xsec
     ccm2_6fix_clf_cm_xsec
     ccm2_6fix_stfmm_cm_xsec





We look forward to receiving your output.  Remember, you will be given
access to the output from the other models only after you contribute
your own output to the archive.

Please let us know if you have questions.

                                     Yours sincerely,


                                Sylvie Joussaume (LMCE)
                                Karl Taylor      (PCMDI)
                            



---------------------------------------------------------------------

Contact Address:
################
        Laboratoire de Modelisation du Climat et de l´Environnement
        D.S.M. / Orme des Merisiers / Bat. 709
        C.E. Saclay
        9119 Gif-sur-Yvette cedex
        FRANCE

        Tel.:   (33) 1 69.08.77.11
        Fax.:   (33) 1 69.08.77.16
        email:  paleo (NEW! Please check the PMIP 'Contacts' web page)
---------------------------------------------------------------------


************************************************************
Appendix I:   List of Variables Requested for Archival Storage
*************************************************************

Please send us the following fields for inclusion in the PMIP archive
(listed here with standard names that you are required to use when
calling subroutine wrtascii).  Also listed are the units required of
your data and titles and notes for your information and the sign
convention for fluxes is given in the NOTES column (either positive up
or positive down):


GROUP 1:  The following are functions of longitude, latitude, time (in
the case of seasonal and monthly data), and for zg, ta, u, v, hus, hur,
stfuv, and potuv they are also a function  of pressure level (as
indicated in the NOTES column).


NAME      UNITS                  TITLE                         NOTES

orog     m         Surface Elevation                             
sftland  %         Percentage Land                      % of grid cell that is 
                                                          land (# see footnote)
seaice   %         Percentage of Area Covered by Sea Ice  % of area of grid cell
                                                          that is sea-ice 
                                                          covered; i.e., 100
                                                          times the product of
                                                          (fraction of time sea
                                                          ice is present in
                                                          cell) and (fraction of
                                                          cell covered) 
                                                          (## see footnote)
snow     %         Percentage of Area Covered by Snow   % of area of grid cell
                                                          that is snow covered;
                                                          i.e., 100 times the 
                                                          product of (fraction
                                                          of time snow is
                                                          present in cell) and
                                                          (fraction of cell
                                                          covered) 
                                                          (## see footnote)
sim      kg/(m^2)  Sea Ice Mass per Unit Area           
rsdt     W/(m^2)   TOA Incident Shortwave Radiation     insolation (+ down)
rsut     W/(m^2)   TOA Reflected Shortwave Radiation    + up
rlt      W/(m^2)   TOA Outgoing Longwave Radiation      + up
rsds     W/(m^2)   Surface Incident Shortwave Radiation + down
rsus     W/(m^2)   Surface Reflected Shortwave Radiation + up
rls      W/(m^2)   Surface Net Longwave Radiation       + down
hfss     W/(m^2)   Surface Sensible Heat Flux           + down
hfls     W/(m^2)   Surface Latent Heat Flux             + down
mrso     kg/(m^2)  Soil Moisture                        AMIP units were cm 
snm      kg/(m^2)  Snow Mass per Unit Area              mass of snow on ground 
                                                          per unit area
mrro     mm/day    Runoff                               
pr       mm/day    Total Precipitation                  include liquid & solid
                                                          phase, convective &
                                                        large scale precip.
prc      mm/day    Convective Precipitation             if available;
                                                          (include both liquid &
                                                          solid phase precip.)
prsnwe   mm/day    Snowfall (Liquid Water Equivalent)   include both convective
                                                          & large scale
evs      mm/day    Surface Evaporation                  include both evaporation
                                                          & sublimation
prw      kg/(m^2)  Total Precipitable Water             AMIP units were mm
tas      C         Surface Air Temperature              2-meter screen temp.
tg       C         Ground Temperature                   skin temperature
pmsl     hPa       Mean Sea Level Pressure     
usfc     m/s       Eastward Surface Wind Speed          at 10 m
vsfc     m/s       Northward Surface Wind Speed         at 10 m
tauu     N/(m^2)   Eastward Surface Wind Stress         at surface
tauv     N/(m^2)   Northward Surface Wind Stress        at surface
hursfc   %         Surface Relative Humidity            at 2 m
clt      %         Total Cloudiness                     % of atmospheric column
                                                         covered by clouds
clw      g/(m^2)   Cloud Water Content                  if available
rltcs    W/(m^2)   TOA Outgoing Longwave Clear Sky      Method II **; + up 
                       Radiation
rstcs    W/(m^2)   TOA Net Shortwave Clear Sky          Method II **; 
                     Radiation                          (net=incident-reflected)
                                                        + down **
rlscs    W/(m^2)   Surface Net Longwave Clear Sky       Method II **; + down
                     Radiation 
rsdscs   W/(m^2)   Surface Incident Shortwave Clear     Method II **; + down
                     Sky Radiation 
rsuscs   W/(m^2)   Surface Reflected Shortwave Clear    Method II **; + up
                     Sky Radiation 

#  In calculating the "Percentage Land" for a grid cell, include sea
ice as "ocean" and glacial ice as "land".  For models with
non-fractional coverage of land or ocean within a grid cell, the
"Percentage Land" should either be 0.0 or 100.0.

##  In calculating "Percentage Sea Ice" for a grid cell, compute
(100/n) * [sum-over-n f-sub-n] where n is the time-index and f-sub-n is
the fraction of sea ice in the grid cell at time n.  For models that
allow a grid cell to be partially ocean and partially land, the
fraction of sea-ice in the grid cell should be calculated relative to
the total area of the cell, not just the ocean area.

**  Method II for calculating clear-sky fluxes is the normal procedure
used in model-model intercomparisons (see, for example,  Cess, R.D. and
G.L. Potter, 1987: Exploratory studies of cloud radiative forcing with
a general circulation model.  Tellus, 39A, 460-473) The clear-sky
fluxes are computed during each sampling interval and then
time-averaged (without weighting by cloud fraction).
  

GROUP 2:  The following are functions of longitude, latitude, pressure
level (as indicated in the NOTES column), and time (in the case of
seasonal and monthly data).


NAME      UNITS                  TITLE                         NOTES  
          
zg       m         Geopotential Height                  at 200, 500 and 850 hpa 
ta       C         Temperature                          at 200 and 850 hpa
u        m/s       Eastward Wind Speed                  at 200 and 850 hpa
v        m/s       Northward Wind Speed                 at 200 and 850 hpa
hus      g/kg      Specific Humidity                    at 200 and 850 hpa
hur      %         Relative Humidity                    at 200 and 850 hpa
stfuv    m^2/s     Horizontal Wind Velocity Stream Function  at 200 and 850 hpa
potuv    m^2/s     Horizontal Wind Velocity Potential   at 200 and 850 hpa



GROUP 3: The following are functions of latitude, pressure level and
time (in the case of seasonal and monthly data).  For these
cross-sections, output should be reported at the standard pressure
levels: 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700,
850, 1000 hPa.


NAME      UNITS                  TITLE                         NOTES            

u        m/s       Eastward Wind Speed                       
v        m/s       Northward Wind Speed
ta       C         Temperature
hus      g/kg      Specific Humidity
hur      %         Relative Humidity
clt      %         Percent Clouds                       % of layer occupied by
                                                          clouds
stfmm    m^2/s     Mean Meridional Stream Function


GROUP 4 [You are welcome to propose that other fields you think might
be of general interest (especially for model-data comparisons) be
archived, but please contact Karl Taylor before including non-standard
variables in your contribution to the PMIP archive.]



********************************************************************
Appendix II:  Standard File Names Constructed by subroutine wrtascii
********************************************************************

Subroutine wrtascii described in section -B- will use your input to
create and write to a file with a descriptive name of the form:

              modelid_run_var_stat_view

    Example:  ccm2_21fix_pmsl_cm_map

   The filename comprises 5 segments:

    1.  modelid -- abbreviated model i.d. supplied by user and
        described in the documentation of the subroutine arguments given 
        in section -B-
    2.  run -- model simulation
        a)  6fix (for 6 ka BP simulation with fixed SSTs)
        b)  21fix (for 21 ka BP simulation with fixed SSTs)
        c)  21cal (for 21 ka BP simulation with computed SSTs)
        d)  0fix (for fixed SST control run)
        e)  0cal (for calculated SST control run)
    3.  variable name (exactly as specified in the PMIP standard list
        given in Appendix I; for example, pmsl)
    4.  stat -- statistic 
        a)  cm (climatological monthly mean)
        b)  vm (interannual variance of monthly means)
        c)  cs (climatological seasonal mean)
        d)  vs (interannual variance of seasonal means)
        e)  ca (climatological annual mean)
        f)  va (interannual variance of annual means)
    5. view
       a) map (longitude-latitude field)
       b) 3d (longitude-latitude-pressure field)
       c) xsec (zonal mean latitude-pressure cross-section)

    

*****************************************************************
Appendix III:  Sample Output File Produced by subroutine wrtascii
*****************************************************************

Here is a sample, partial listing of the output produced by subroutine
wrtascii for the case of a longitude-latitude-season file:

netcdf lmcelmd4_6fix_tas_cs_map  {

dimensions:
    longitude =  48;
    latitude =   36;
 // plev =        0;
    time =        4;
    time_3char = 12;

variables:

    float longitude(longitude), latitude(latitude), time(time);
             longitude:units    = "degrees";  
             longitude:title    = "Longitude";
             longitude:long_name= "LONGITUDE";
             latitude:units     = "degrees";  
             latitude:title     = "Latitude"; 
             latitude:long_name = "LATITUDE"; 
             // no plev units                 
             // no plev title                 
             // no plev long_name             
             time:units         = "season";   
             time:title         = "Season";   
             time:long_name     = "SEASON";   

    char  timelabel(time_3char);

    float tas(time,latitude,longitude);                                         
             tas:units           = "C";
             tas:stat_name       = "Climatological Seasonal Mean";
             tas:missing_value   =  1.00000E+20;
             tas:valid_min       = -4.87557E+01;
             tas:valid_max       =  4.29864E+01;
             tas:mean_val        =  1.47086E+01;
             tas:mean_absval     =  1.89489E+01;
             tas:warning_flag    =            0;
             tas:missing_count   =            0;
             tas:title           =
"Surface Air Temperature";
             tas:source          =
"PMIP 6 ka BP  15-Year Simulation with Prescribed SST's by LMCE - modele du LMD 
version 4ter - 5x7L11";
             tas:notes           =
"2-meter screen temperature";

          // global attributes
               :file_name   = "lmcelmd4_6fix_tas_cs_map";
               :model_name  = "LMCE - modele du LMD version 4ter - 5x7L11";
               :experiment  = "6 ka BP Simulation with Prescribed SST's";


data:
    longitude =   3.7500,  11.2500,  18.7500,  26.2500,  33.7500,  41.2500,
                 48.7500,  56.2500,  63.7500,  71.2500,  78.7500,  86.2500,
                 93.7500, 101.2500, 108.7500, 116.2500, 123.7500, 131.2500,
                138.7500, 146.2500, 153.7500, 161.2500, 168.7500, 176.2500,
                183.7500, 191.2500, 198.7500, 206.2500, 213.7500, 221.2500,
                228.7500, 236.2500, 243.7500, 251.2500, 258.7500, 266.2500,
                273.7500, 281.2500, 288.7500, 296.2500, 303.7500, 311.2500,
                318.7500, 326.2500, 333.7500, 341.2500, 348.7500, 356.2500;
     latitude =  76.4638,  66.4435,  59.4416,  53.6639,  48.5904,  43.9830,
                 39.7090,  35.6853,  31.8554,  28.1786,  24.6243,  21.1684,
                 17.7916,  14.4775,  11.2123,   7.9836,   4.7802,   1.5918,
                 -1.5918,  -4.7802,  -7.9836, -11.2123, -14.4775, -17.7916,
                -21.1684, -24.6243, -28.1786, -31.8554, -35.6853, -39.7090,
                -43.9830, -48.5904, -53.6639, -59.4416, -66.4435, -76.4638;
         time =   1.0,   2.0,   3.0,   4.0;
    timelabel = "DJF", "MAM", "JJA", "SON";
          tas =-2.77484E+01,-3.01865E+01,-3.42403E+01,-3.70435E+01,-3.81412E+01,
               -3.86561E+01,-3.87973E+01,-3.87936E+01,-3.87416E+01,-3.86108E+01,
               -3.84682E+01,-3.85719E+01,-3.98430E+01,-3.95486E+01,-3.76386E+01,
               -3.66105E+01,-3.54578E+01,-3.38125E+01,-3.21477E+01,-3.00725E+01,
               -2.86386E+01,-2.73882E+01,-2.64791E+01,-2.60693E+01,-2.63416E+01,
                  ..., 
                  ...,

 

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