NorCPM User Manual

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PLEASE READ THIS BEFORE EDITING


Ingo Bethke
Francois Counillon
enter your name here (alphabetical order)

Overview

The Norwegian Climate Prediction Model (NorCPM) is aiming at providing prediction from seasonal-to-decadal time scale. It is based on the Norwegian earth system model ([1]) and the Ensemble Kalman Filter ([2]) data assimilation method. NorESM is a state of the art Earth system model that is based on CESM ([3]), but that used different Aerosol/chemistry scheme and ocean model ( evolve from the MICOM). The EnKF is a sequential data assimilation method that allows for fully multivariate and flow dependent correct using the covariance matrix from a Monte-carlo model integration.

Norwegian Earth System Model

The Norwegian Earth System Model (NorESM) is one out of ~20 climate models that has produced output for the CMIP5 (http://cmip-pcmdi.llnl.gov/cmip5). The NorESM-family of models are based on the Community Climate System Model version 4 (CCSM4) of the University Corporation for Atmospheric Research, but differs from the latter by, in particular, an isopycnic coordinate ocean model and advanced chemistry-aerosol-cloud-radiation interaction schemes. The main version NorESM1-M has a horizontal resolution of approximately 2deg for the atmosphere and land components and 1deg for the ocean and ice components. NorESM is also available in a lower resolution version (NorESM1-L), a medium-low resolution version (NorESM1-ML), a high-top version with specified and full chemistry (NorESM1-MLHT and NorESM1-MLHTC) and a version that includes prognostic biogeochemical cycling (NorESM1-ME).

NorESM configurations in NorCPM
Model acronym Ocean Atmosphere References
NorESM1-L Micom (3.6deg) CAM4 (T31) Zhang et al. 2012, Counillon et al. 2014
NorESM1-ML Micom (2deg) CAM4 (2deg)
NorESM1-MLHT Micom (2deg) CAM4-WACCMSC (2deg)
NorESM1-MLHTC Micom (2deg) CAM4-WACCM (2deg)
NorESM1-ME Micom (1deg) CAM4-OSLO (2deg) Tjiputra et al. 2013

Ensemble Kalman Filter

The EnKF is a sequential ensemble based data assimilation method that consists of two steps, a propagation and a correction. The propagation step is a Monte Carlo method. The ensemble spread (i.e. ensemble variability) is used to estimate the forecast error, because they are expected to be related in locations (and times) where (and when) the system is more chaotic. Assuming that the distribution of the error is Gaussian and the model is not biased one can proceed with the tBayesian update and find new estimate of the ensemble mean and model covariance. The method is often called as flow dependent as the covariance matrix evolves with the system and thus provide correction that are in agreement with the state of the system. The method allows fully multivariate updated - meaning that observation of for example SST can be used to apply correction on all other model variables. However one should bear in mind that the update assume linearity, which is not suitable for all variable and that correlation are subject to sampling error. Currently NorCPM uses the Deterministic Ensemble Kalman Filter (DEnKF, Sakov et al. 2008), which is a square root filter version of the EnKF.

Getting started with NorESM

Prerequisites

User-support for NorCPM is currently limited to Norway.

Step 1: New users need to apply for access to computational and storage resources at the Norwegian Metacenter for Computational Science (link to application page: https://www.notur.no/user-account). NorCPM activities are usually tied to the cpu and storage accounts nn9039k and ns9039k, which are held by Noel Keenlyside (noel.keenlyside[at]gfi.uib.no). NorCPM is currently set up on the computational platform HEXAGON (https://www.notur.no/hardware/hexagon).

Step 2: After gaining access to HEXAGON, the user needs to contact the local support (support-uib@notur.no) to be added to the unix-groups "noresm" and "nn9039k".


Obtaining and installing the model

To install NorCPM on your account, follow the step: 1) install NorESM and link the script necessary : cd ${HOME} mkdir -p NorESM cd NorESM

  1. If you have a NoreSM svn access do:

svn checkout https://svn.met.no/NorESM/noresm/tags/projectEPOCASA-3 projectEPOCASA-3

  1. if you don't do:

tar xvf /work-common/shared/nn9039k/NorCPM/Code/NorESM/projectEPOCASA-3.tar.gz mkdir -p Script

  1. Now you will use the default Script version

ln -s /work/shared/nn9039k/NorCPM/Script/* . rm personal_setting.sh cp /work/shared/nn9039k/NorCPM/Script/personal_setting.sh . cd ${HOME}/NorESM/ mkdir -p bin cd bin

  1. Same with bin, I would recommend linking the executable for now. If you want to create your own,
  2. Copy and compile the code in /work-common/shared/nn9039k/NorCPM/Code/EnKF/
  3. delete the link in bin and move your own executable there

ln -sf /work/shared/nn9039k/NorCPM/bin/* .

2) Select of a model version and experiment : Need to edit ${HOME}/Script/personal_setting.sh to chose a model version, ensemble size, starting date, ... Launch the creation of the ensemble structure. cd ${HOME}/NorESM/Script ./create_ensemble.sh You structure of ensemble as well as initial condition should be ready to run reanalysis

Model and directory structure

You get many shared files on hexagon: /work/shared/nn9039k/NorCPM/ The subfolder: -Code contains source code of all fortran code needed (NorESM, EnKF, Post processing) -Script contains all bash script necessary to run the reanalysis or prediction -Restart contains the initial condition (restart files) for two different configuration of NorESM in 1980-01-15 -Obs contains observation that are available for assimilation (SST,SSH) -bin contains compiled executable from the Code subfolder -Input contains input files both for NorESM and EnKF -matlab contains code used for validation purpose

In your home folder ${HOME}/NorESM/ you have your personal file

bin, Script and projectEPOCASA-3 and copy or linked from the /work/shared cases contains the specification of your ensemble of experiment. Each ensemble members have its own separate experiment with limitation of the duplicate.


Component, resolution and forcing options

Setting up experiments

Creating new experiments

Cloning existing experiments

Configuring the initialisation

Customisation of output

Building the model

Setting up ensembles

Setting up atmospheric nudging

Running the model

Initial run

Continuation of existing run

Running ensembles as single jobs

Getting started with EnKf

Post-processing and long-term storage

Output compression

Archiving output on NorStore

Disk storage

Tape storage

National archive

Diagnostics and analysis