Dynamical Downscaling for Climate Model Projections

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Does anyone have experience with running high-resolution climate models on a regional sub-domain, i.e. dynamical downscaling for climate studies and climate projections?

Specifically, I am interested in the SRES A2 Scenario (Special Report on Emissions Scenario) in PRECIS Model.

Could you please give me some advice on how to use it and share some research papers on the subject?

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2 Answers

  1. Dear Muhammad,

    You have asked some very good questions. First of all the modeling has moved on somewhat from the SRES to Representative Concentration Pathways (RCPs) and downscaling for generation of RCMS using the latest CMIP 5 data has been carried out by CORDEX ((Coordinated Regional Climate Downscaling Experiment). There is considerable regional variation on the number of CORDEX models. When it comes to the application of post-processed GCM data your application/problem statement becomes very important i.e. there is a lot of GCM/RCM data available (daily and sub daily) but it very often needs to be processed carefully before it is applied. We are involved in these sorts of activities everyday. Send me a note: peter@climsystems.com so we can look at your specific application. I have attached some documents to spur discussion. All the very best. Peter

    3 Comments

    1. Dear Mr. Peter,

      I am sorry for late response. I am interested to research on the following title " Impacts of Climate Change in Groundwater Recharge in the Irrigated Area of Pakistan". I need to develop the climate parameters for the future prediction of climate. Sir kindly guide me about the following questions.

      1. How to select the GCM?

      2. How to  use PRECIS Model for performing Dynamical Downscaling.

      Best Regards,

      Muhammad Saeed

       

    2. Ok I will send you a note on a specific area and topic.

       

    3. Thank you very much Mr. Peter

  2. Global climate models (GCMs) are our best tools for providing climate projections. A climate model is a mathematical representation of the climate system. Mathematical equations are solved on a super-computer at points on a 3-dimensional grid in the ocean and atmosphere, over a number of time-steps. Due to practical computing constraints, the spatial resolution (horizontal size of grid-cells) is about 200 km. the general name for a procedure to take information known at large scales to make predictions at local scales. The two main approaches to downscaling climate information are dynamical and statistical. Dynamical downscaling requires running high-resolution climate models on a regional sub-domain, using observational data or lower-resolution climate model output as a boundary condition.  These models use physical principles to reproduce local climates, but are computationally intensive.'s higher resolution atmosphere allows simulation of tropical cyclone activity.  CM2.5 has been applied to the problem of the future of global snow cover in a warming climate where its resolution allows improved simulation of changes in mountainous regions.A third stream of development attempted to improve CM2.1’s simulation by increasing the resolution of the atmospheric and oceanic components. This effort produced CM2.5 and its variants CM2.5FLOR and CM2.6. The resolution of the atmospheric and oceanic components of CM2.5 is increased by a factor of four compared to CM2.1, along with a 33% increase in the number of vertical levels in the atmosphere. A further refinement of the ocean component to 1/10o produced CM2.6 which can claim to resolve ocean eddies based on agreement with an observational estimate of the eddy kinetic energy. Statistical downscaling is a two-step process consisting of i) the development of statistical relationships between local climate variables (e.g., surface air temperature and precipitation) and large-scale predictors (e.g., pressure fields), and ii) the application of such relationships to the output of global climate model experiments to simulate local climate characteristics in the futureThe main tools used to project climate are General Circulation Model (GCMs), which are computer models that mathematically

    represent various physical processes of the global climate system. These processes are generally well known but often cannot be fully represented in the models due to limitations on computing resources

    and input data. Thus, GCM results should only be considered at global or continental scales for climatic conditions averaged at monthly, seasonal, annual, and longer time scales. 

    https://www.climatechangeinaustralia.gov.au/

     

    http://earthsystemcog.org/projects/ncpp /

     

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