<p>Precipitation is the results of physical processes. As each model implement convective and thermodynamic processes differently (mainly through parametrization or imposed by different grid cell resolution), each model will produce different precipitation. Hence, in other words, you can't tune precipitation, but you can tune the parameters that will produce precipitation. As rainfall is not an easy observable, especially in the period usually used for tuning (~1850-1900), climate modelers does not use such information for their tuning.</p>

<p>Mauritsen et al. (2012) gives an interesting overview of this subject. The abstract is given below. From this article, it states (emphasis are mine) : </p>

<p>"[...] Formulating and prioritizing our goals is challenging. To us, a <strong>global mean temperature</strong> in close absolute agreement with observations is of highest priority because <strong>it sets the stage for temperature-dependent processes to act</strong>. For this, we target the 1850–1880 observed global mean temperature of about 13.7 \deg C [Brohan et al., 2006]. Beyond that, we prioritize having <strong>globally averaged TOA shortwave absorption and outgoing longwave radiation in good agreement with satellite observations</strong>, along with a <strong>representation of important climate variability modes</strong>. [...]"</p>

<p><strong>Abstract</strong></p>

<p>"During a development stage global climate models have their properties adjusted or tuned in various ways to best match the known state of the Earth’s climate system. These desired properties are observables, such as the radiation balance at the top of the atmosphere, the global mean temperature, sea ice, clouds and wind fields. The tuning is typically performed by adjusting uncertain, or even non-observable, parameters related to processes not explicitly represented at the model grid resolution. The practice of climate model tuning has seen an increasing level of attention because key model properties, such as climate sensitivity, have been shown to depend on frequently used tuning parameters. Here we provide insights into how climate model tuning is practically done in the case of closing the radiation balance and adjusting the global mean temperature for the Max Planck Institute Earth System Model (MPI-ESM). We demonstrate that considerable ambiguity exists in the choice of parameters,and present and compare three alternatively tuned, yet plausible configurations of the climate model. The impacts of parameter tuning on climate sensitivity was less than anticipated."</p>

<p><strong>Citation</strong></p>

<p>Mauritsen, Thorsten, Bjorn Stevens, Erich Roeckner, Traute Crueger, Monika Esch, Marco Giorgetta, Helmuth Haak, et al. 2012. “Tuning the Climate of a Global Model.” Journal of Advances in Modeling Earth Systems 4 (3): n/a – n/a. doi:10.1029/2012MS000154.</p>

CC BY-SA 3.0
2016-06-09T17:10:20.087Z
2016-06-09T17:10:20.087Z
Balinus
<p>Part of calibrating Global Climatic Models involves 'hindcasts' or 'history matching' against historic climate records, including rainfall. Such rainfall records are all in error because almost all raingauges incur at least a 7% aerodynamic under-catch. Conventionally this is not 'corrected' but for model calibration they should be corrected. So are GCM using corrected or uncorrected rainfall inputs? If yes, how are the rainfall records corrected - just multiplying by 1.07, or some other way? If no, why not? This is important because, worldwide, the GCM outputs are being used for forward projections of water resources (even though that is not what GCMs are designed to support).   </p>

CC BY-SA 3.0
2016-05-03T20:51:59.087Z
From what I have heard from colleagues about the spatial variability of (real world) precipitation is that precipitation measurements in some meters distance to each other can differ considerably (I don't have a reference and I don't know the order of the difference). However based on that knowledge, I would assume that the error by not correcting the historical precipitation measurements is low with respect to the real-world spatial variabilty of precipitation. But I am not on expert on this topic... .
daniel.heydebreck
CC BY-SA 3.0
2016-05-04T08:50:00.960Z
"_... involves... matching against historic climate records, including rainfall_."  Can you give an example of a model doing this?  In my experience, calibration is limited to better observed variables like TOA radiation and surface temperature, and rainfall is used only for model evaluation.
Deditos
<climate><models><water><rainfall>
When GCMs are calibrated do they use 'corrected rainfall' input?
Gordon Stanger
# When GCMs are calibrated do they use 'corrected rainfall' input?

(Asked by: username_0 on 2016-05-03)

Part of calibrating Global Climatic Models involves 'hindcasts' or 'history matching' against historic climate records, including rainfall. Such rainfall records are all in error because almost all raingauges incur at least a 7% aerodynamic under-catch. Conventionally this is not 'corrected' but for model calibration they should be corrected. So are GCM using corrected or uncorrected rainfall inputs? If yes, how are the rainfall records corrected - just multiplying by 1.07, or some other way? If no, why not? This is important because, worldwide, the GCM outputs are being used for forward projections of water resources (even though that is not what GCMs are designed to support).

**Tags:** climate, models, water, rainfall

> **Comments:**
> **username_1**: From what I have heard from colleagues about the spatial variability of (real world) precipitation is that precipitation measurements in some meters distance to each other can differ considerably (I don't have a reference and I don't know the order of the difference). However based on that knowledge, I would assume that the error by not correcting the historical precipitation measurements is low with respect to the real-world spatial variabilty of precipitation. But I am not on expert on this topic... .  
> **username_2**: "_... involves... matching against historic climate records, including rainfall_."  Can you give an example of a model doing this?  In my experience, calibration is limited to better observed variables like TOA radiation and surface temperature, and rainfall is used only for model evaluation.  

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## 1 answers

### Answer by username_3 on 2016-06-09. Score: 2

Precipitation is the results of physical processes. As each model implement convective and thermodynamic processes differently (mainly through parametrization or imposed by different grid cell resolution), each model will produce different precipitation. Hence, in other words, you can't tune precipitation, but you can tune the parameters that will produce precipitation. As rainfall is not an easy observable, especially in the period usually used for tuning (~1850-1900), climate modelers does not use such information for their tuning.

Mauritsen et al. (2012) gives an interesting overview of this subject. The abstract is given below. From this article, it states (emphasis are mine) :

"[...] Formulating and prioritizing our goals is challenging. To us, a **global mean temperature** in close absolute agreement with observations is of highest priority because **it sets the stage for temperature-dependent processes to act**. For this, we target the 1850–1880 observed global mean temperature of about 13.7 \deg C [Brohan et al., 2006]. Beyond that, we prioritize having **globally averaged TOA shortwave absorption and outgoing longwave radiation in good agreement with satellite observations**, along with a **representation of important climate variability modes**. [...]"

**Abstract**

"During a development stage global climate models have their properties adjusted or tuned in various ways to best match the known state of the Earth’s climate system. These desired properties are observables, such as the radiation balance at the top of the atmosphere, the global mean temperature, sea ice, clouds and wind fields. The tuning is typically performed by adjusting uncertain, or even non-observable, parameters related to processes not explicitly represented at the model grid resolution. The practice of climate model tuning has seen an increasing level of attention because key model properties, such as climate sensitivity, have been shown to depend on frequently used tuning parameters. Here we provide insights into how climate model tuning is practically done in the case of closing the radiation balance and adjusting the global mean temperature for the Max Planck Institute Earth System Model (MPI-ESM). We demonstrate that considerable ambiguity exists in the choice of parameters,and present and compare three alternatively tuned, yet plausible configurations of the climate model. The impacts of parameter tuning on climate sensitivity was less than anticipated."

**Citation**

Mauritsen, Thorsten, Bjorn Stevens, Erich Roeckner, Traute Crueger, Monika Esch, Marco Giorgetta, Helmuth Haak, et al. 2012. “Tuning the Climate of a Global Model.” Journal of Advances in Modeling Earth Systems 4 (3): n/a – n/a. doi:10.1029/2012MS000154.


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