<p>The RIP-nomenclature is used to distinguish between different runs of the same scenario <em>within</em> a modeling center rather than to indicate any similarity <em>across</em> modeling centers.  Strictly it's within and across models not centers, because some centers have more than one model, e.g., the MIROC center submitted models MIROC5, MIROC4h, MIROC-ESM, etc.  So r1i1p1 for some scenario is typically just the ensemble member that happened to be run first for each model.  One caveat is that whichever historial ensemble member you pick for each model, you should use the same member from the corresponding RCP scenario, e.g., historical r3i1p1 should join seamlessly with rcp8.5 r3i1p1:</p>

<blockquote>
  <p>A  recommendation  for  CMIP5  is  that  each  so-called  RCP  (future  scenario)  simulation  should when  possible  be  assigned  the  same  realization  integer  as  the  historical  run  from  which  it  was initiated. </p>
  
  <p>From Taylor et al (2012) <em>CMIP5 Data Reference Syntax (DRS) and Controlled Vocabularies</em> (<a href="https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf?id=3" rel="nofollow noreferrer">pdf</a>)</p>
</blockquote>

<p>There's loads more detail in that Data Reference Syntax doc that I've linked to, and a summary of it <a href="https://pmip4.lsce.ipsl.fr/doku.php/database:drs" rel="nofollow noreferrer">on the IPSL website</a>.  Karl Taylor gave a presentation that summarised it even more succinctly:</p>

<blockquote>
  <ul>
  <li>r = “realization”: simulations started from equally likely initial conditions
  that lead to equally likely realizations of the true climate trajectory</li>
  <li>i = “initialization”: only used in decadal predictions, to distinguish among
  different initialization procedures</li>
  <li>p = “physics”: to identify simulations that are very closely related (e.g.,
  “perturbed physics” ensemble members or simulations forced by slightly
  modified parameterizations)</li>
  </ul>
</blockquote>

<p>In practice, the "initialization" and "physics" ensemble members are a bit specialist and the vast majority of CMIP5 users just stick to the rNi1p1 members, and most of them probably just stick to r1i1p1 from each model (e.g, <a href="https://doi.org/10.1007/s00382-019-05014-6" rel="nofollow noreferrer">Peng et al, 2019</a>) for simplicity.  You should feel free to use any of the "realisation" members from each model, and you could pick r1i1p1 from one model and r10i1p1 from another.  I'd advise sticking to one ensemble member from each model though, to avoid your analysis over-representing the forced responses of models that submitted more realizations to the CMIP5 database.</p>

<p>To address your final question, it's more common to look at the variance across the models for a given scenario.  But there are also plenty of studies that use the variance within each model ensemble to estimate the uncertainty of a model property due to internal variability, e.g., <a href="https://www.nature.com/articles/s41561-019-0463-y" rel="nofollow noreferrer">emergent constraints on climate sensitivity</a>.  It really depends what you're investigating.</p>

CC BY-SA 4.0
2019-11-13T15:11:14.157Z
Thank you so much - this is extremely helpful.
user178237
CC BY-SA 4.0
2019-11-09T16:02:47.293Z
2019-11-09T16:02:47.293Z
Deditos
<p>I am a statistician working on a climatological downscaling project in the Caribbean Sea.</p>

<p>I am just starting to learn about the CMIP5 GCM data project. I plan to download CMIP5 data from 6 climate centers and examine 3 scenarios: historical, RCP4.5, and RCP8.5 I was hoping that someone could further explain the CMIP5 ensemble RIP-nomenclature to me:</p>

<p>R: realization</p>

<p>I: initialization</p>

<p>P: physics</p>

<p>More specifically, if I were to use the same ensemble number (say r1i1p1) across all 6 centers - would I have the exact same data for each scenario (historical, RCP4.5, and RCP8.5)?  If not, should I keep the ensemble number consistent across models or should I vary it somewhat?</p>

<p>I guess the main question here: is it more common in geography to examine the variance between climate center projections or the variance between runs within a single ensemble?</p>

<meteorology><climate-change><climate><climate-models>
How to better understand the RIP-nomenclature used in the CMIP5 project?
user178237
# How to better understand the RIP-nomenclature used in the CMIP5 project?

(Asked by: username_0 on 2019-11-06)

I am a statistician working on a climatological downscaling project in the Caribbean Sea.

I am just starting to learn about the CMIP5 GCM data project. I plan to download CMIP5 data from 6 climate centers and examine 3 scenarios: historical, RCP4.5, and RCP8.5 I was hoping that someone could further explain the CMIP5 ensemble RIP-nomenclature to me:

R: realization

I: initialization

P: physics

More specifically, if I were to use the same ensemble number (say r1i1p1) across all 6 centers - would I have the exact same data for each scenario (historical, RCP4.5, and RCP8.5)? If not, should I keep the ensemble number consistent across models or should I vary it somewhat?

I guess the main question here: is it more common in geography to examine the variance between climate center projections or the variance between runs within a single ensemble?

**Tags:** meteorology, climate-change, climate, climate-models


---

## 1 answers

### Answer by username_1 on 2019-11-09. Score: 3 | Accepted answer

The RIP-nomenclature is used to distinguish between different runs of the same scenario *within* a modeling center rather than to indicate any similarity *across* modeling centers. Strictly it's within and across models not centers, because some centers have more than one model, e.g., the MIROC center submitted models MIROC5, MIROC4h, MIROC-ESM, etc. So r1i1p1 for some scenario is typically just the ensemble member that happened to be run first for each model. One caveat is that whichever historial ensemble member you pick for each model, you should use the same member from the corresponding RCP scenario, e.g., historical r3i1p1 should join seamlessly with rcp8.5 r3i1p1:

> A recommendation for CMIP5 is that each so-called RCP (future scenario) simulation should when possible be assigned the same realization integer as the historical run from which it was initiated.
>
> From Taylor et al (2012) *CMIP5 Data Reference Syntax (DRS) and Controlled Vocabularies* ([pdf](https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf?id=3))

There's loads more detail in that Data Reference Syntax doc that I've linked to, and a summary of it [on the IPSL website](https://pmip4.lsce.ipsl.fr/doku.php/database:drs). Karl Taylor gave a presentation that summarised it even more succinctly:

> * r = “realization”: simulations started from equally likely initial conditions
>   that lead to equally likely realizations of the true climate trajectory
> * i = “initialization”: only used in decadal predictions, to distinguish among
>   different initialization procedures
> * p = “physics”: to identify simulations that are very closely related (e.g.,
>   “perturbed physics” ensemble members or simulations forced by slightly
>   modified parameterizations)

In practice, the "initialization" and "physics" ensemble members are a bit specialist and the vast majority of CMIP5 users just stick to the rNi1p1 members, and most of them probably just stick to r1i1p1 from each model (e.g, [Peng et al, 2019](https://doi.org/10.1007/s00382-019-05014-6)) for simplicity. You should feel free to use any of the "realisation" members from each model, and you could pick r1i1p1 from one model and r10i1p1 from another. I'd advise sticking to one ensemble member from each model though, to avoid your analysis over-representing the forced responses of models that submitted more realizations to the CMIP5 database.

To address your final question, it's more common to look at the variance across the models for a given scenario. But there are also plenty of studies that use the variance within each model ensemble to estimate the uncertainty of a model property due to internal variability, e.g., [emergent constraints on climate sensitivity](https://www.nature.com/articles/s41561-019-0463-y). It really depends what you're investigating.

> **Comments:**
> **username_0**: Thank you so much - this is extremely helpful.  

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