We have now analysed the first 47 simulations that participants have run and they are plotted in the Figure below:


We will be posting the latest results plus analysis on the Live Results page on our website.

The blue dots represent the 2013/2014 winter as observed, and for this experiment, we received 20 simulations. For the �world that might have been� experiment, we received 27 simulations, which are shown as green dots.

The smaller light green dots represent the individual patterns of sea surface temperature we estimated as the response of the climate system to man-made climate change. The smaller light green dots therefore represent the uncertainty in the human influence.

At this stage, there are not enough simulations to make any conclusions about the role of climate change in the record wet winter 2013/2014. However, we want to illustrate to the public why we need such large ensembles, which is why we will show the �results� as they evolve.

With this size of ensemble, the 2013/2014 winter as observed and as in the �world that might have been� are not distinguishable from another. Interestingly, our current wettest simulation comes from one of the �worlds that might have been� simulations (the uppermost green dot) � but this could be entirely due to chance.

We will also compare the rainfall totals with observations. For this, we will need to calculate the rainfall total from a dataset from the Met Office for exactly the same region as the one we defined for the simulations (land rainfall for South England and Wales).

As this was a record wet winter, it has never been observed in the roughly 250 years of meteorological records, so we are looking at a 1/100-year event at least. As you can see from the figure, we are still only seeing 1/10-year events. We need more simulations to see any pattern, so please keep crunching!

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015

I know that the origional promise of daily updates may have been overly helpful - but do you know when we can expect the next update?

Yes, perhaps a bit optimistic! Turns out processing the data and turning it into a plot takes a bit more work than we thought.

We're just processing more data right now, and we plan to update the website tomorrow.

Cheers,
Hannah

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015

2nd Batch of Results from Weather@Home 2014 UK Flooding Experiment

Thank you again to everyone who has been running models for us on this experiment. We�re now able to show you the 2nd batch of results, with just over 500 models.

Here is a plot of the second batch of results from the weather@home UK Flooding experiment we�re currently running:



Each dark blue and green dot represents one model that was run on a participant�s computer. If climate change had changed the odds of getting the severe flooding last winter, then we should expect to see a significant gap between the blue and green dots. For more information about how to read this Return Time Plot, please see the Expected Results page.

We�re hoping to eventually get several thousand model runs done, but we thought it would be interesting to plot the results so far, so you can see why we need so many results.

With only 47 model runs (see First Results below), there simply aren�t enough dots to state statistically if there is a difference between the blue and green dots.

Now, with 510, we�re starting to see a better pattern emerge. As you can see in the plot, there isn�t currently any significant gap between the blue and green dots.

This short video shows an animation of the plot, as more and more results are added in:

http://www.youtube.com/watch?v=8l5QW0pj-os

The lighter green dots represent the individual patterns of sea surface temperature we estimated as the response of the climate system to man-made climate change. The smaller light green dots therefore represent the uncertainty in the human influence. If the blue dots lie within the area covered by the light green dots, then we can�t attribute these results to climate change.

But we need even more results to be able to make a statistical statement about the influence of climate change on the risk of extreme flooding.

We will be posting further results in the next few days, when we should be into the 1000�s of model runs.

So, please keep on crunching!

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015

Hello Hannah

I haven't yet fully understood what the light green dots represent. Is each one of these dots the sea surface temperature value for one model? As the models only run for one year, I presume the SST values do not rise in response to global warming even in the winter as observed? Or is there an upward temperature creep even during the single year?

Is each light green dot a single value or a pattern? I consider that a pattern should include a range of values.

How many light green dots are there for each model? Two perhaps - one for the global SST and one for the regional SST?

Sorry if these questions are way off base.

I haven't understood either how the attribution (or non-attribution) of the heavy rainfall to GW can depend on the position of the blue rainfall dots with regard to the light green temperature range. Isn't this a bit like saying it depends on whether the rainfall lies within a particular temperature range?

Surely the light green dots represent a different graph altogether, except that we have no vertical scale for it. It seems to me that here we have two graphs, one for rainfall and one for temperature, superimposed on one another; where the two graphs lie in relation to each other on the vertical axis looks to me arbitrary. Or are these positions constrained and determined by the results of lots of other climate models and real-world observations?

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Cpdn news

Hi Mo,

I know these return time plots are confusing to understand!

I'm currently working on a new page for the website which will hopefully explain what they mean in relatively straight forward way.

Perhaps I could get you to read over it and let me know if it makes sense?

Best wishes,
Hannah

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015

Over 5,000 results in for Weather@home 2014 UK Flooding experiment

We�ve now have several thousand results back, which is fantastic! Thank you so much to everyone who has been running models for us.

Here is the latest plot with over 5,000 models:



As you can see, with this many models, the curves are really filling in at the extreme weather risks to the right of the plot.

For more information about how to rest this return time plot, please see the Expected Results page. As I mentioned above, I'm also working a new "how to read return time plots" page for the website - watch this space!

We�ve also updated the animation for this plot, showing the plot as it builds up from just a handful all the way to 5,000 models:
http://www.youtube.com/watch?v=oeKXYeVFbJE

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015

One of your crunchers here.

So, looking at this, it would so far seem that we cannot (yet) attribute the recent flooding to climate change?

Bonus questions:

Do the models we are running take into account the perturbed jet stream and/or the higher than normal sea-surface temperatures in the Pacific and, if so, to what extent can we be confident these are linked to climate change? http://www.metoffice.gov.uk/research/news/2014/uk-storms-and-floods

Feel encouraged to elaborate your answers with reference to published papers.[/i]

Hi Niall,

In response to your first question, that's correct - we can't yet attribute the risk of flooding to climate change, but it's possible with more results (we're hoping to get 10s of thousands eventually), we will see something.

As for your bonus questions, I think I will pass that over to the climate scientists and see if I can get an answer for you!

Thanks,
Hannah

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015

I do think the publication, in semi-real-time, of the results for this experiment as they come in is a great step forward. Many of us have crunched away for many years, secure in the personal knowledge that we are doing something a bit worthwhile, without finding it easy to track the findings of our work. But this accessible form of presentation gives a way of seeing the output in a much more relevant fashion. Or is it just me?

Yes!

Every project should have someone like Hannah Rowlands, great work spreading the science!

Most of the work done here is for researchers in centres external to Oxford Uni, so it's up to them what they do with the results.
And that may be restricted until official publication, as well as being intended for other climatologists.

There is this in our news section, linked from the front page: Weatherathome papers published in Bulletin of the American Meteorological Society.

There's also lots of papers that have been published by people associated with this project, also linked from the front page under Publications.

[Hannah Rowlands wrote:]... I'm also working a new "how to read return time plots" page for the website - watch this space!

Based on a straw poll (principally my wife, who kindly interrupted her Saturday morning crossword) people tend to assume that the x-axis is the independent variable and the y-axis the dependent variable. They therefore look at the vertical offset when presented with two curves. For this graph this assumption leads to the probably erroneous conclusion that the two curves are more or less the same - exacerbated somewhat by the size of the blobs, which obscures what differences there are. It is not hard to foresee reactions along the lines of, "what does it matter if the 10 year precipitation changes by a small amount?"

It seems to me that the ensemble is intended to assess the change in risk of particular events. In other words, what matters is the horizontal offset between the two flattish curves, which may already be significant (though not in the statistical sense) in the data as presented. The return-time presentation is entirely conventional, so you're stuck with it - but it may be a hard sell!

For what it's worth, what continually confuses me is the log-reciprocal-cumulative-probability axis.

I'd personally really like to see a regular probability density plot for these results (i.e. y axis the approximated derivative, x axis rainfall in mm).

I don't believe that would actually be any more use for displaying the change you are studying - I just find it far easier to read probability distributions off probability density plots than cumulative distribution plots.

The traditional problem with plotting probability density is that the probability scale then becomes arbitrary or conventional (i.e. defined by a rainfall bin size, often conventionally implied when people draw curves instead of blobs), whereas the cumulative probability doesn't have that problem. The big advantage of the PDF is that for most people it would convey the reality more effectively: it would be obvious, for example, what the most frequent rainfall event is from the PDF, but not from the CDF. Nonetheless, the risk being considered here is high accumulated rainfall, so I reckon it's easier just to read the exceedence probability from a CDF, even though the return-time plot could (should?) be drawn as a curve but isn't (perhaps the worry is the inevitably ragged low-probability tail).

The swapping of the axes and the scaling keeps leading me astray. It may be that for people not used to CDFs at all, this conventional meteorological format doesn't cause the same problems as it does for people whose habits have been formed in other domains.

It is, of course, rather nice to have feedback on ensemble runs of any sort.

Here's an update on the results of the UK 2014 Flooding Experiment.

We're now at more than 15,000 results, and we still have more coming in - we're hoping to get to 10s of thousands of models back before we make a final statement about the results.

Here's a plot of the latest results:



And here's the animation, now showing all 15,000+ models:

http://www.youtube.com/watch?v=kdnPGAuo_K4

It's really exciting to see so many results coming in! Thank you, as always, to everyone who is helping us by running the models on their computers.

Best wishes,
Hannah

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015

Latest results with nearly 30,000 models in:



You can see that there is some �wobble� in the curve for the blue models. This makes it difficult to say anything conclusive about the difference between the blue (�winter as observed�) and green (�winter that might have been without climate change�) results.

So, we are going to keep running the experiment for another week or so to get some more model results back which will hopefully help give us a clearer overall result.

And as usual, we've made an animation of the plot so far:

http://youtu.be/KAWSpFTgTso

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015

Niall - here's a reply to your question about the jet stream from Nathalie Schaller, the lead scientist on the UK 2014 Flooding experiment:

Qu: Do the models we are running take into account the perturbed jet stream and/or the higher than normal sea-surface temperatures in the Pacific and, if so, to what extent can we be confident these are linked to climate change? http://www.metoffice.gov.uk/research/news/2014/uk-storms-and-floods

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A: Well, there are no publication yet on that event, that's for sure! About climate change and jet stream in general there are many papers, but the ones I am aware of are more about how increased CO2 concentrations affect the whole troposphere and the jet stream (and for the next 100 years).

And understanding or being confident that SSTs anomalies are due or not to climate change is a very difficult question, this is what many people are doing research on. Natural variability (the fact that some years are warmer than others, or for example El Nino events) will always be here, there is never no SST anomalies somewhere.

But yes, the simulations are forced with the observed SSTs so they definitely have the positive anomaly in the north Pacific (see Fig 1 http://www.climateprediction.net/weatherathome/weatherhome-2014/experiment-setup/). But then the model calculates how the atmosphere responds to that SST pattern, and we haven't analysed yet what it did in terms of the position of the jet stream.

We will definitely look into that but that is a lengthier analysis. Even if the model doesn't simulate the jet stream as it was this winter, what they say in the met office report is true, by that I mean that our results will not contradict what the met office reports say. There are many angles to look at such events, some attribution studies are based on observational datasets only, what we do here is just a particular way of understanding how extreme events might change by using models.

In general, only one study does not give you a definitive answer, you need a lot of studies, which takes time, to look at events from different angles. And even if two studies come to different conclusions, it doesn't mean that one or the other is wrong (the Otto et al 2012 paper shows that nicely).
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I hope this helps!

Best wishes,
Hannah

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015

Thank you, and please also thank Dr Schaller.

Follow the latest results on our website:
http://www.climateprediction.net/weatherathome/weatherhome-2014/results/

Here is the latest plot for the weather@home 2014 UK flooding experiment:



We have now analysed over 33 thousand models!

The curves are quite hard to tell apart, so we've zoomed in on an interesting part of the plot so you can see the blue and green curves separately.

The further apart they are, the more climate change increased the risk of last winter's flooding event.

However, we still have to wait for more models to come back and do more analysis before we can say if this change is statistically significant.

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Hannah Rowlands
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No longer Communications Officer for climateprediction.net, as of October 2015