<p>Here's one idea to account for meteorological extremes in our climate models, though I'm not sure if it answers your question and am less certain if it qualifies as reliable (open for feedback on that too):</p>

<p>Take a regional climate model which has simulated into the past and the future. We also have a few decades of observed data to compare the same time period of simulated past. The observed data is going to capture the extreme events much better in every way of course. So, in projecting into the future, if we want to maintain those extremes, we can use the change factor (based on averages, e.g. monthly means) from past-simulation==>future-simulation in order to project past-observed==>future-predicted. Those future-predictions will have the extreme events as they came in the past but with the changed magnitude based on the climate model's past versus future simulations. </p>

<p>This could be applied to meteorological data such as precipitation and other weather variables in order to inform us about the driving weather forces we'll face in future scenarios. In this way our climate models account for meteorological extremes we've observed recently and in the past, which the models may otherwise fall short on.</p>

<hr>

<p>Otherwise, as folks have explained in comments on the OP, we simply don't have extensive enough observed data that we can rely upon to calibrate these rarer frequency events. I think your last two bullet points sum up our predicament fairly well:</p>

<blockquote>
  <ul>
  <li>The available measurements are from a 100-200 year observation period (at best);</li>
  <li>We do not have too much data from different climatic conditions (there might be some phenomena which are not (or not adequately) taken into account in current models, we are somehow biased by the observed, prior 100 year conditions.</li>
  </ul>
</blockquote>

<p>However, here are a few studies and leads you could explore which try to overcome these challenges in modeling extreme event return intervals and Probable Maximum Precipitation. These seem like they might be addressing your last question but hopefully it helps clarify the potential out there.</p>

<p>This one using <a href="https://www.google.com/search?q=extreme%20value%20theory%20modeling%20return%20interval" rel="nofollow">extreme-value theory</a>: <a href="http://onlinelibrary.wiley.com/doi/10.1002/joc.1155/pdf" rel="nofollow">Van Den Brink et al, 2004. Estimating return periods of extreme events from ECMWF seasonal forecast ensembles.</a></p>

<blockquote>
  <p>...applications to meteorological and hydrological situations are
  always  hampered  by  the  brevity  of  the  available  datasets,
  as  the  required  return  levels  exceed  the  record  lengths  by  a
  factor of 10 to 100. In order to overcome this problem, we u
  se archived data from all past seasonal forecast ensemble
  runs  of  the  European  Centre  for  Medium-Range  Weather  F
  orecasts  (ECMWF)  since  1987  as  input  for  extreme-value
  statistics analysis. We make use of the fact that the seasonal forecast has little seasonal skill for the Netherlands, which
  implies that the ensembles can be regarded as i
  ndependent sets that cumulate to over 1500 years.
  We investigate the hydraulic response in the Netherlands
  to extreme synoptic-scale weather systems by studying the
  extreme-value  distributions  of  sea  storm  surge  levels,  wa
  ves  and  river  discharges.  The  application  is  detailed  in  four
  practical examples originating from coastal protection, ri
  ver flooding protection, and water management problems. The
  long record length of the ECMWF data reduces the uncertainty in the 10
  3
  -year and the 10
  4
  -year return values considerably
  with respect to the results based on observational time ser
  ies. The ECMWF dataset gives the opportunity to explore the
  distribution of events that depend on several kinds of extreme (Van Den Brink et al, 2004).</p>
</blockquote>

<p>This one using the growth factor method, or "NERC M5 method", as explained on page 10: <a href="http://met.no/filestore/metnoReport13-2011.pdf" rel="nofollow">Skaugen and Førland, 2011. Future change in extreme precipitation estimated in Norwegian catchments.</a></p>

<p>And here is a study which sums the methods up pretty well and proposes a new one for further research, which sounds good to me and relatively like what I described in pretty broad, simple terms in the first part of my answer: <strong><a href="http://met.no/filestore/metnoReport_082012.pdf" rel="nofollow">Dyrrdal, 2012. Estimation of extreme precipitation in Norway and a summary of the state-of-the-art.</a></strong> There's many quote-worthy passages in this one so I think I'll leave it to you to explore. Sections 3, 4, and 5 are what I imagine you're most interested in because they cover, respectively, the state-of-the-art guidelines/methods from the World Meteorological Organization and various nations; a proposed new-and-improved method; and a conclusion summarize the above. </p>

CC BY-SA 3.0
2016-01-09T07:19:41.567Z
Thank you for this great answer! The referred studies are very helpful and more or less answering my question. The Van Den Brink et al. paper is especially promising regarding the "reliability" of climate projections to infer extremes; of course with acknowledging all uncertainties in our inputs. An additional paper to the question: [Reliability of regional and global climate models to simulate precipitation extremes over India](http://onlinelibrary.wiley.com/doi/10.1002/2014JD021636/abstract). Your proposal for accounting change in the future is interesting, I will give it a try.
rozsasarpi
CC BY-SA 3.0
2016-01-08T03:35:25.347Z
2016-01-08T16:07:12.820Z
2016-01-08T16:07:12.820Z
cr0
<p>This question is related to and extension of my previous question which was about the methodology used to predict future climatic extremes:</p>

<p><a href="https://earthscience.stackexchange.com/questions/2789/what-is-the-methodology-to-analyze-future-climatic-extremes-using-the-results-of">What is the methodology to analyze future climatic extremes using the results of climate models?</a></p>

<p>Although the answer to that question is quite detailed and informative, it is still not clear to me <strong>whether the models used in global climate (GC) simulations can predict rare (~1000-year return period events)</strong>.</p>

<ul>
<li>I understand that if we have multi-thousand simulations, we can
calculate the 0.1% quantile, but I do not know whether the applied
models can predict rare events reliably. Under reliably, I mean that
in some alignment with the observed extremes or with the extremes
statistically inferred from the observations.</li>
<li><strong>All the papers I saw so far, which dealing with extremes due to
climate change, are considering 20-year return period events as
maximum</strong> and comparing the simulations to observed quantiles. This is
one source of my suspicion regarding the reliability of GCMs.</li>
<li>Are GCMs calibrated to observed mean or multiple quantiles? Daily,
monthly, annual, etc. averages are used? Averages of larger areas or
grid points (corresponding to the applied resolutions) are adopted in
the calibration, validation processes? What are the typically used
target parameters used in the calibrations?</li>
<li>Here is an example to make it more clear: one wants to determine the
probability distribution function of extreme wind speed in a given
location from daily measurements/simulation results. The question: If we take one
simulation for the 1950-2000 period and assume for a moment that
model uncertainty is negligible compared to climate variability, can
we treat the simulation data as a sample with more or less equivalent
confidence than that of the actual observations, corresponding to the
same period, to infer large return period extremes?</li>
</ul>

<p>One way to check this is would be to compare the large return period extremes inferred from observed data and from multiple simulations, I have not seen any papers with above 20-year return level comparison yet.
I see that we cannot expect very accurate models regarding e.g. 1000-year extremes, because we cannot reliably calibrate and validate GCMs:</p>

<ul>
<li>The available measurements are from a 100-200 year observation period (at best);</li>
<li>We do not have too much data from different climatic conditions (there might be some phenomena which are not (or not adequately) taken into account in current models, we are somehow biased by the observed, prior 100 year conditions.</li>
</ul>

<p>Any help, idea, reference, link are welcomed.</p>

CC BY-SA 3.0
2015-11-11T18:49:06.147Z
Just two points to add. Firstly, when we are dealing with climate change we are, by definition, dealing with a non-stationary data set, in which case the entire concept of return periods, especially long period returns, becomes meaningless - quite literally, little more than guesswork. Secondly, it depends which kind of extremes you are looking at. GCMs are pretty good at forward projections of temperature, but crap-lousy at forward predictions of rainfall extremes, and most other water related extremes, such as soil-moisture deficits.
Gordon Stanger
CC BY-SA 3.0
2015-01-12T10:54:35.160Z
My hunch on this is "I doubt that they can predict frequency very accurately, but I suspect that they could predict *relative changes* in frequency due to changes in forcings reasonably well". However, I think that the real problem is that we probably don't have enough reliable data. As I said in the other question, we can run thousands of simulations of the same 100 years, and use that to base our statistics on. We obviously can't do that with the earth - so we actually *need* thousands of years of data to accurately estimate likelihood of very extreme events, and we just don't have it...
naught101
CC BY-SA 3.0
2015-01-12T11:19:01.523Z
@naught101 Thank you for the comment, I agree that the scarcity of measurement data is a(the) bottleneck. But I think that using a probabilistic approach, one could reach meaningful probabilistic conclusions, e.g. inferring 100-year return level wind from observation and determining what is the probability of this, if we infer from the simulations. The 'strength' of the inference would decrease with return period, but it would be more than the current 20-year return period.
rozsasarpi
CC BY-SA 3.0
2015-01-12T18:14:25.190Z
Great question. Cliff Mass, at University of Washington, has been considering this topic quite a bit, and trying to peer through that veil of uncertainty.  You can google his publications, but here's a description of some of that work in his own words: http://cliffmass.blogspot.com/2014/10/will-extreme-precipitation-intensity-in.html
Adam
CC BY-SA 3.0
2015-01-12T23:49:27.757Z
With a name like that, why would you go in to meteorology, and not geology??
naught101
<meteorology><climate-change><precipitation><climate-models><simulation>
Can climate models reliably predict meteorological extremes?
rozsasarpi
# Can climate models reliably predict meteorological extremes?

(Asked by: username_0 on 2015-01-12)

This question is related to and extension of my previous question which was about the methodology used to predict future climatic extremes:

[What is the methodology to analyze future climatic extremes using the results of climate models?](https://earthscience.stackexchange.com/questions/2789/what-is-the-methodology-to-analyze-future-climatic-extremes-using-the-results-of)

Although the answer to that question is quite detailed and informative, it is still not clear to me **whether the models used in global climate (GC) simulations can predict rare (~1000-year return period events)**.

* I understand that if we have multi-thousand simulations, we can
  calculate the 0.1% quantile, but I do not know whether the applied
  models can predict rare events reliably. Under reliably, I mean that
  in some alignment with the observed extremes or with the extremes
  statistically inferred from the observations.
* **All the papers I saw so far, which dealing with extremes due to
  climate change, are considering 20-year return period events as
  maximum** and comparing the simulations to observed quantiles. This is
  one source of my suspicion regarding the reliability of GCMs.
* Are GCMs calibrated to observed mean or multiple quantiles? Daily,
  monthly, annual, etc. averages are used? Averages of larger areas or
  grid points (corresponding to the applied resolutions) are adopted in
  the calibration, validation processes? What are the typically used
  target parameters used in the calibrations?
* Here is an example to make it more clear: one wants to determine the
  probability distribution function of extreme wind speed in a given
  location from daily measurements/simulation results. The question: If we take one
  simulation for the 1950-2000 period and assume for a moment that
  model uncertainty is negligible compared to climate variability, can
  we treat the simulation data as a sample with more or less equivalent
  confidence than that of the actual observations, corresponding to the
  same period, to infer large return period extremes?

One way to check this is would be to compare the large return period extremes inferred from observed data and from multiple simulations, I have not seen any papers with above 20-year return level comparison yet.
I see that we cannot expect very accurate models regarding e.g. 1000-year extremes, because we cannot reliably calibrate and validate GCMs:

* The available measurements are from a 100-200 year observation period (at best);
* We do not have too much data from different climatic conditions (there might be some phenomena which are not (or not adequately) taken into account in current models, we are somehow biased by the observed, prior 100 year conditions.

Any help, idea, reference, link are welcomed.

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

> **Comments:**
> **username_2**: My hunch on this is "I doubt that they can predict frequency very accurately, but I suspect that they could predict *relative changes* in frequency due to changes in forcings reasonably well". However, I think that the real problem is that we probably don't have enough reliable data. As I said in the other question, we can run thousands of simulations of the same 100 years, and use that to base our statistics on. We obviously can't do that with the earth - so we actually *need* thousands of years of data to accurately estimate likelihood of very extreme events, and we just don't have it...  
> **username_0**: @username_2 Thank you for the comment, I agree that the scarcity of measurement data is a(the) bottleneck. But I think that using a probabilistic approach, one could reach meaningful probabilistic conclusions, e.g. inferring 100-year return level wind from observation and determining what is the probability of this, if we infer from the simulations. The 'strength' of the inference would decrease with return period, but it would be more than the current 20-year return period.  
> **username_3**: Great question. Cliff Mass, at University of Washington, has been considering this topic quite a bit, and trying to peer through that veil of uncertainty.  You can google his publications, but here's a description of some of that work in his own words: http://cliffmass.blogspot.com/2014/10/will-extreme-precipitation-intensity-in.html  
> **username_2**: With a name like that, why would you go in to meteorology, and not geology??  
> **username_1**: Just two points to add. Firstly, when we are dealing with climate change we are, by definition, dealing with a non-stationary data set, in which case the entire concept of return periods, especially long period returns, becomes meaningless - quite literally, little more than guesswork. Secondly, it depends which kind of extremes you are looking at. GCMs are pretty good at forward projections of temperature, but crap-lousy at forward predictions of rainfall extremes, and most other water related extremes, such as soil-moisture deficits.  

---

## 1 answers

### Answer by username_4 on 2016-01-08. Score: 4 | Accepted answer

Here's one idea to account for meteorological extremes in our climate models, though I'm not sure if it answers your question and am less certain if it qualifies as reliable (open for feedback on that too):

Take a regional climate model which has simulated into the past and the future. We also have a few decades of observed data to compare the same time period of simulated past. The observed data is going to capture the extreme events much better in every way of course. So, in projecting into the future, if we want to maintain those extremes, we can use the change factor (based on averages, e.g. monthly means) from past-simulation==>future-simulation in order to project past-observed==>future-predicted. Those future-predictions will have the extreme events as they came in the past but with the changed magnitude based on the climate model's past versus future simulations.

This could be applied to meteorological data such as precipitation and other weather variables in order to inform us about the driving weather forces we'll face in future scenarios. In this way our climate models account for meteorological extremes we've observed recently and in the past, which the models may otherwise fall short on.

---

Otherwise, as folks have explained in comments on the OP, we simply don't have extensive enough observed data that we can rely upon to calibrate these rarer frequency events. I think your last two bullet points sum up our predicament fairly well:

> * The available measurements are from a 100-200 year observation period (at best);
> * We do not have too much data from different climatic conditions (there might be some phenomena which are not (or not adequately) taken into account in current models, we are somehow biased by the observed, prior 100 year conditions.

However, here are a few studies and leads you could explore which try to overcome these challenges in modeling extreme event return intervals and Probable Maximum Precipitation. These seem like they might be addressing your last question but hopefully it helps clarify the potential out there.

This one using [extreme-value theory](https://www.google.com/search?q=extreme%20value%20theory%20modeling%20return%20interval): [Van Den Brink et al, 2004. Estimating return periods of extreme events from ECMWF seasonal forecast ensembles.](http://onlinelibrary.wiley.com/doi/10.1002/joc.1155/pdf)

> ...applications to meteorological and hydrological situations are
> always hampered by the brevity of the available datasets,
> as the required return levels exceed the record lengths by a
> factor of 10 to 100. In order to overcome this problem, we u
> se archived data from all past seasonal forecast ensemble
> runs of the European Centre for Medium-Range Weather F
> orecasts (ECMWF) since 1987 as input for extreme-value
> statistics analysis. We make use of the fact that the seasonal forecast has little seasonal skill for the Netherlands, which
> implies that the ensembles can be regarded as i
> ndependent sets that cumulate to over 1500 years.
> We investigate the hydraulic response in the Netherlands
> to extreme synoptic-scale weather systems by studying the
> extreme-value distributions of sea storm surge levels, wa
> ves and river discharges. The application is detailed in four
> practical examples originating from coastal protection, ri
> ver flooding protection, and water management problems. The
> long record length of the ECMWF data reduces the uncertainty in the 10
> 3
> -year and the 10
> 4
> -year return values considerably
> with respect to the results based on observational time ser
> ies. The ECMWF dataset gives the opportunity to explore the
> distribution of events that depend on several kinds of extreme (Van Den Brink et al, 2004).

This one using the growth factor method, or "NERC M5 method", as explained on page 10: [Skaugen and Førland, 2011. Future change in extreme precipitation estimated in Norwegian catchments.](http://met.no/filestore/metnoReport13-2011.pdf)

And here is a study which sums the methods up pretty well and proposes a new one for further research, which sounds good to me and relatively like what I described in pretty broad, simple terms in the first part of my answer: **[Dyrrdal, 2012. Estimation of extreme precipitation in Norway and a summary of the state-of-the-art.](http://met.no/filestore/metnoReport_082012.pdf)** There's many quote-worthy passages in this one so I think I'll leave it to you to explore. Sections 3, 4, and 5 are what I imagine you're most interested in because they cover, respectively, the state-of-the-art guidelines/methods from the World Meteorological Organization and various nations; a proposed new-and-improved method; and a conclusion summarize the above.

> **Comments:**
> **username_0**: Thank you for this great answer! The referred studies are very helpful and more or less answering my question. The Van Den Brink et al. paper is especially promising regarding the "reliability" of climate projections to infer extremes; of course with acknowledging all uncertainties in our inputs. An additional paper to the question: [Reliability of regional and global climate models to simulate precipitation extremes over India](http://onlinelibrary.wiley.com/doi/10.1002/2014JD021636/abstract). Your proposal for accounting change in the future is interesting, I will give it a try.  

---

