Task 1 – National platform for climate modeling

Leader: J-L Dufresne (IPSL), D. Salas (CNRS-GAME)

Contributors: IPSL, CNRS-GAME, CERFACS, IDRIS, MDLS

Objectives:

The overall objective of this task is to ensure that the platform is operational and reaches its objectives, and to ensure and to promote the dissemination and the large use of the platform. First, within two years, a first version of the platform will be achieved and tested through an implementation of a standard version of IPSL-CM. Second, within the two last years, the platform will be used to implement three new climate model configurations, all components of the platform will continue to evolve to reach their final objectives. At the end of the project, the platform will be fully assessed through a challenging test. The platform is composed by a set of tools. Implementing a platform consists of assembling tools and models into different configurations, with different resolution, allowing a set of simulations, producing a set of diagnostics, running efficiently and reliably on different super-computers. All the tools developed will follow usual best practices: versioning, source code management, tracking system, documentation, examples, assembled prototypes, regular release associated with regression tests. Platform component having cross-dependencies, each versions of the platform will include implementation tests to insure the compatibility and the consistency of the new developments.

Training sessions will be organized and training material will be made available. We already organized training sessions on specifics tools. Two to three training sessions on libIGCM have been organized each year for the last 3 years with 10 to 20 participants each. The 3 days training session for OASIS is organized regularly each year.

  • Task 1.1. : Platform release, documentation and training

Each version of the platform will be released with associated documentation. Codes, documentations and training materials will be made available via the project web site. To ensure the consistency and the effectiveness of all the tools, each version of the platform will be evaluated with at least the reference version of the IPSL-CM model. During the project, different training session will be organized for different public: (1) Platform tools for climate model developers (mainly during M1 and M24), (2) How to use implemented platform for regular climate model user (mainly during M24 and M48), (3) How to use implemented platform for new user (M 36 and M48). Our past experience teaches us that the writing of documentation is often difficult to complete, and that dedicated seminar of few days with a small writing team are very efficient to do it. We will organize such seminars within the project.

  • Task 1.2 : Model implementations using the platform

In addition to the implementation with the standard IPSL-CM model, the platform will be used to implement three new climate model configurations: a version of the global climate model with the CNRM-CM model, a version of the global climate model IPSL-CM with a zoomed version of the atmospheric model, and a version of a regional climate model using the LMDZ atmospheric model. The variety of all these implementations will be an illustration of the flexibility and the suitability of the platform. They are typical of the simulations performed by the climate community and will illustrate the benefit allowed by the platform compared to existing current practices. The implementation with CNRM-CM and with the standard IPSL-CM will be relevant for usual climate experiments like those of CMIP. The implementation with the zoomed version, both coupled and uncoupled with the ocean, will be relevant to study regional climates and, for the later, to contribute to the CORDEX project

  • Task 1.3: Our « grand chalenge »: a multi-step, multi-criteria procedure to define next version of IPSL-CM model

GCMs include many parameterizations, which are approximate descriptions of sub-grid processes. These parameterizations are formulated via a series of parameters that are usually not directly observable and must be tuned so that the parametrizations fit as well as possible the statistical behaviour of the physical processes. The tuning process is an important aspect of climate model development and is usually performed at different stages: for individual parameterizations, for individual model components (atmosphere, ocean, land surface,…) and for the full coupled climate model. However, this tuning process is non linear, includes iterations among these three stages and is very time consuming. This slow down the development of the model and the overall evaluation at the end of this process is satisfactory yet.

The goal of this task is to design and perform a procedure that optimize the parameter values using a suite of IPSL-CM tests (from simple well documented tests over short periods to multi-decadal tests with the coupled climate model) and a wide range of evaluation diagnostics. This task using the platform as a whole will be a nice illustration of its added value. The first step will be to design the overall procedure, to specify configurations and for each of them the most relevant simulations and diagnostics. A first challenge will be to run in a short time a few hundreds of simulations, from a few years to a few centuries, with very different configurations (individual parameterizations, individual model components, coupled climate model with all or part of its components…). As an additional test, we will perform these very time consuming runs between two tier-1 national centre: IDRIS and TGCC. A second challenge will be to automatically analyse the huge amount of resulting data and to extract a few set of indicators that will help us to choose the model version that is the most relevant to our goals.

Success criteria:

  • The number of persons that will participate to our training sessions and that will use the platform.
  • The ease to implement the platform with a new model, configuration or experiment.
  • The length of the “tuning” phase and quality of the model obtained at the end.

Risks and envisaged solutions:

Some tools or diagnostics may be missing for the final tuning. However, we anticipate that enough of them will be available to strongly facilitate and improve the quality of the model compared to current versions.