# How can I create multi model ensemble from output of 3 CMIP5 models?

I have a output of 3 models of CMIP5, they are CCSM4, IPSL and MPI. I have two scenarios RCP 2.6 and RCP 8.5 for each model. I want to know that how can I create a multi model ensemble from these 3 models? Because when I plot different graphs of various variables, I end up with so many lines. So is multi model a sensible approach? But how can I create a multi model of these models in R? and what is the science behind multi model ensemble of cmip5 models (because I have seen in many research papers that authors have taken ensemble means).

Suppose I have 3 lines of each model in a graph, and then showing the mean of all lines by ONE line, is that considered as an ensemble?

• Wecome the EarthScience.SE! Maybe you misunderstood "ensemble". What you have in the beginning is already an ensemble: a set (or ensemble) of several (three) model simulations. Particularly, it is a multi-model ensemble. You can also create an ensemble with only one model by performing several simulations -- each simulation disturbed by some noise. If you calculate the mean of your models runs, you call it the "ensemble mean". If you calculate the standard deviations, you call it "ensemble standard deviation". The "ensemble" just means that you calculated it from an ensemble of simulations. – daniel.neumann Apr 5 at 11:26
• @daniel.neumann can you convert that comment into an answer ? I would upvote it. – gansub Apr 5 at 14:14

# What is an ensemble?

An ensemble is a set of several model simulations. One simulation of an ensemble is denoted as ensemble member.

A multi-model ensemble is an ensemble created by simulations with several models (e.g. each model is run twice).

Alternatively, one might create an ensemble by performing several simulations performed with one model. Each simulation is disturbed by some noise so that the model produces slightly different result for each model run. We denote each run as a realization. The generation of the appropriate noise is model dependent (the results should cover the natural variability) and a scientific topic itself. Additionally, we might use other initial conditions or different physical parameterizations.

The individual simulations of a CMIP5 model are identified according to the rip nomenclature: rXiYpZ (r: realization; i: initialisation; p: physics; X, Y, and Z are integers), e.g. r12i1p1 or r1i1p1.

It is also explained here: https://portal.enes.org/data/enes-model-data/cmip5/datastructure

# Summarizing ensemble simulations

If you calculate the mean of your models runs, you call it the "ensemble mean". If you calculate the standard deviations, you call it "ensemble standard deviation". The "ensemble" prefix just means that you calculated the mean/std.dev./etc. from an ensemble of simulations.

Example: This plot of the 5th Assessment Report of the IPCC (synthesis report of Topic 2 - Future Climate Changes, Risks and Impacts, online version) shows the ensemble mean (solid thick colored lines) and the space covered by the 5% to 95% percentiles (shaded area). Please have a look here for the caption and more details: http://ar5-syr.ipcc.ch/topic_futurechanges.php#figure_2_1