<p>This was a feature of CMIP5 too and the CMIP6 data request paper mentions it:</p>
<blockquote>
<p>... while near-surface relative humidity values of 140 % can, in principle, be realistic at a point in space and time, many of the high values in the CMIP5 archive, which represented time and grid cell averages, are likely to be caused by processing errors. Hence the upper-limit is set at 100.001 % and categorized as <code>suggested</code>, in contrast to the limit for sea ice extent that has a robust limit of 100.001 %</p>
</blockquote>
<p><em>Juckes et al (2020): The CMIP6 Data Request (DREQ, version 01.00.31), Geosci. Model Dev., 13, 201–224, <a href="https://doi.org/10.5194/gmd-13-201-2020" rel="nofollow noreferrer">https://doi.org/10.5194/gmd-13-201-2020</a></em></p>
<p>So modeling centers were permitted to clip their data to 100.001 %, but presumably many of them chose not to.  That papers cites <a href="https://doi.org/10.1002/2017JD026909" rel="nofollow noreferrer">Ruosteenoja et al (2017)</a> for the CMIP5 analysis, who suggest several reasons:</p>
<ul>
<li>in some models supersaturations originate from the core of the model algorithm</li>
<li>in others they have been formed only in the stage of creating the surface air moisture fields</li>
<li>in several models both factors play a role: some degree of supersaturation has developed within the dynamic calculations but the excessive relative humidities have further been amplified when building the surface air humidity fields</li>
<li>isobaric level fields [do not] represent the actual intrinsic model data but have been interpolated from the values given at model hybrid levels</li>
</ul>
<p>They also contacted the modeling groups, who gave a variety of answers that fall into three categories:</p>
<ul>
<li>the definition of RH with respect to ice rather than liquid water</li>
<li>contradictions in the determination of specific humidity and air temperature for the near-surface level</li>
<li>nonlinearity of saturated specific humidity as a function of temperature</li>
</ul>
<p>That paper is open access (I think) and worth a read for more details on all those points.  It's also worth pointing out that there are plenty of screwy features of CMIP data, which is inevitable given the volumes of data, complexity of post-processing chains and expense of running the models.  It gets better each round, but there will always be something odd in there.  The important thing is to do what you're doing and query anything that doesn't seem right, rather than taking it at face value.</p>

CC BY-SA 4.0
2021-09-21T15:45:18.180Z
2021-09-21T15:45:18.180Z
Deditos
<p>I have been looking at the CMIP6 model outputs for 'hur' (Relative Humidity/RH) and I see that they are not scaled to be between 1 and 100 (the units are percent).</p>
<p>Why is this? Is it just an oversight and everyone knows that they need to set anything above 100 to 100, or is there a significant reason for not scaling the data? From reading other answers here, it could be due to supersaturation, but I get the impression that that shouldn't be particularly widespread..the example I am looking at from cmip6 for a January climatology has values &gt;100 over large swathes of Russia, Greenland and Canada.</p>

<climate-change><climate-models>
Why are the CMIP6 model outputs for Relative Humidity not scaled to between 1-100%?
matlabcat
# Why are the CMIP6 model outputs for Relative Humidity not scaled to between 1-100%?

(Asked by: username_0 on 2021-09-20)

I have been looking at the CMIP6 model outputs for 'hur' (Relative Humidity/RH) and I see that they are not scaled to be between 1 and 100 (the units are percent).

Why is this? Is it just an oversight and everyone knows that they need to set anything above 100 to 100, or is there a significant reason for not scaling the data? From reading other answers here, it could be due to supersaturation, but I get the impression that that shouldn't be particularly widespread..the example I am looking at from cmip6 for a January climatology has values >100 over large swathes of Russia, Greenland and Canada.

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


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## 1 answers

### Answer by username_1 on 2021-09-21. Score: 4 | Accepted answer

This was a feature of CMIP5 too and the CMIP6 data request paper mentions it:

> ... while near-surface relative humidity values of 140 % can, in principle, be realistic at a point in space and time, many of the high values in the CMIP5 archive, which represented time and grid cell averages, are likely to be caused by processing errors. Hence the upper-limit is set at 100.001 % and categorized as `suggested`, in contrast to the limit for sea ice extent that has a robust limit of 100.001 %

*Juckes et al (2020): The CMIP6 Data Request (DREQ, version 01.00.31), Geosci. Model Dev., 13, 201–224, <https://doi.org/10.5194/gmd-13-201-2020>*

So modeling centers were permitted to clip their data to 100.001 %, but presumably many of them chose not to. That papers cites [Ruosteenoja et al (2017)](https://doi.org/10.1002/2017JD026909) for the CMIP5 analysis, who suggest several reasons:

* in some models supersaturations originate from the core of the model algorithm
* in others they have been formed only in the stage of creating the surface air moisture fields
* in several models both factors play a role: some degree of supersaturation has developed within the dynamic calculations but the excessive relative humidities have further been amplified when building the surface air humidity fields
* isobaric level fields [do not] represent the actual intrinsic model data but have been interpolated from the values given at model hybrid levels

They also contacted the modeling groups, who gave a variety of answers that fall into three categories:

* the definition of RH with respect to ice rather than liquid water
* contradictions in the determination of specific humidity and air temperature for the near-surface level
* nonlinearity of saturated specific humidity as a function of temperature

That paper is open access (I think) and worth a read for more details on all those points. It's also worth pointing out that there are plenty of screwy features of CMIP data, which is inevitable given the volumes of data, complexity of post-processing chains and expense of running the models. It gets better each round, but there will always be something odd in there. The important thing is to do what you're doing and query anything that doesn't seem right, rather than taking it at face value.


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