Difference between revisions of "Online Help"

Revision as of 11:21, 17 June 2013

With the live.magicc.org interface for running MAGICC6 on our servers, you have a simple 3-step process to generate your own climate scenario output. Below, we provide a little bit of help with the choices you have during this three step process;

Step 1: Choosing Emissions

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Wonrdfuel explanation of facts available here.

Upload your own emission scenario

You have the option to upload your own MAGICC emission scenario as an ASCII file. You can either provide only globally aggregate emissions or regional emissions. If you want to put your own emission data into such a scenario input file, see this page of how to create your own .SCEN file .

Step 2: Select Model Settings

You can run MAGICC in two distinct modes, a "standard" one and a "probabilistic" one.

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Selecting "Probabilistic" run mode

If you choose the "probabilistic" setting, your selected emission scenarios will be run multiple times by MAGICC, each time with a slightly different parameter setting. The results will not be single temperature or CO₂ concentration outcomes for each scenario, but actually uncertainty distributions.

Note: Since probabilistic runs will require the climate model to be run 171 or even 600 times for each scenario, some patience is required. Normally, finishing a probabilistic run will take just a couple of minutes, maybe up to 10min. You will be provided with distributions of key climate outputs for every decade, but not for every year, as under the "standard" runs.

Choose the "multi-model ensemble" probabilistic run mode

If you select this option, we will run your emission scenarios 171 times, with all combinations of 19 AOGCM calibrations and 9 carbon cycle model calibrations. These AOGCMs and carbon cycle models are from the IPCC Fourth Assessment Report and belong to the so-called "CMIP3" and "C4MIP" intercomparisons. If you assume that all those 19 AOGCMs and 9 carbon cycle models are equally likely and sampling the full uncertainty space (and there are good reasons, by the way, not to make that assumption), then you can interpret your outcome as a probabilistic distribution of expected future climate change. Otherwise, simply call it what it is: a "multi-model ensemble" without assigning this statistical property to it.

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