Given a global mean temperature pathway, generate random global climate fields consistent with it and with spatial and temporal correlation derived from an ESM
In the past, we've run only future scenarios, but for an upcoming experiment we need to be able to run fldgen over datasets that span the entire historical and future periods. The easiest way to do this is to add an (optional) argument to specify a historical scenario to splice onto the beginning of each future scenario.
Once we've done that, we can treat each spliced scenario almost exactly as if it were provided as a single scenario. One exception is that when doing the linear fits, we might want to arrange for duplicated historical scenarios to be included in the fit only once, so as not to bias the fit. This will require a bit of extra record keeping in the tagout structure, but otherwise should be pretty easy to do.
In the past, we've run only future scenarios, but for an upcoming experiment we need to be able to run fldgen over datasets that span the entire historical and future periods. The easiest way to do this is to add an (optional) argument to specify a historical scenario to splice onto the beginning of each future scenario.
Once we've done that, we can treat each spliced scenario almost exactly as if it were provided as a single scenario. One exception is that when doing the linear fits, we might want to arrange for duplicated historical scenarios to be included in the fit only once, so as not to bias the fit. This will require a bit of extra record keeping in the
tagoutstructure, but otherwise should be pretty easy to do.