On April 15th (2015) the (UK) National Maritime Museum is hosting a special one-day seminar organised by the (UK) Royal Meteorological Society. The meeting is in honour of the remarkable Matthew Fontaine Maury (1806-1873), who established the value of marine weather observations for scientific research.
The meeting covers everything from using the very earliest records to make circulation indices, to modern satellite observations. The speakers include several members of the oldWeather science team, and one of the talks is about the leading current method of recovering marine observations: I’m talking about oldWeather at 14:40. (Full agenda).
It’s an open meeting – all are welcome.
The Met Office, where I work, has just finalised an agreement to buy a new supercomputer. This isn’t that rare an event – you can’t do serious weather forecasting without a supercomputer and, just like everyday computers, they need replacing every few years as their technology advances. But this one’s a big-un, and the news reminded me of the importance of high-performance computing, even to observational projects like oldWeather.
To stand tall and proud in the world of supercomputing, you need an entry in the Top500: This is a list, in rank order, of the biggest and fastest computers in the world. These machines are frighteningly powerful and expensive, and a few of them have turned part of their power to using the oldWeather observations:
- Currently at number 34 in the world is Hopper: A Cray XE6 at the US National Energy Research Scientific Computing Centre (NERSC). Hopper is the main computing engine for the current developments of the Twentieth Century Reanalysis (20CR).
- At numbers 60 and 61 in the list are the pair of IBM Power775s (1,2) which used to support the European Centre for Medium-Range Weather Forecasts (ECMWF). Operational centres, like ECMWF, tend to buy supercomputers in pairs so they can keep working even if one system needs repair or maintenance – we have to issue weather forecasts every day, we can’t just stop for a while while we fix the computer. These two machines were used to produce ERA-20C.
Two other machines have not used our observations yet (except for occasional tests), but are gearing up to do so in the near future:
- At number 18 in the world is Edison: NERSC’s latest supercomputer, a Cray XC30.
- At number 64 is Gaea C2 – the US National Oceanic and Atmospheric Administration (NOAA)‘s supercomputer at Oak Ridge.
My personal favourite, though, is none of these: Carver is not one of the really big boys. An IBM iDataPlex with only 9,984 processor cores, it ranked at 322 in the list when it was new, in 2010, and has since fallen off the Top500 altogether; overtaken by newer and bigger machines. It still has the processing power of something like 5000 modern PCs though, and shares in NERSC’s excellent technical infrastructure and expert staff. I use Carver to analyse the millions of weather observations and terabytes of weather reconstructions we are generating – almost all of the videos that regularly appear here were created on it.
The collective power of these systems is awe-inspiring. One of the most exciting aspects of working on weather and climate is that we can work (through collaborators) right at the forefront of technical and scientific capability.
But although we need these leading-edge systems to reconstruct past weather, they are helpless without the observations we provide. All these computers together could not read a single logbook page, let alone interpret the contents; the singularity is not that close; we’re still, fundamentally, a people project.
Today is the fourth birthday of oldWeather, and it’s almost two years since we started work on the Arctic voyages. So it’s a good time to illustrate some more of what we’ve achieved:
I’m looking at the moment at the Arctic ships we’ve finished: Bear, Corwin, Jeannette, Manning, Rush, Rodgers, Unalga II, and Yukon have each had all of their logbook pages read by three people; so it’s time to add their records to the global climate databases and start using them in weather reconstructions. From them we have recovered 43 ship-years of hourly observations – more than 125,000 observations concentrating on the marginal sea-ice zones in Baffin Bay and the Bering Strait – an enormous addition to our observational records.
The video above shows the movements of this fleet (compressed into a single year). They may occasionally choose to winter in San Pedro or Honolulu, but every summer they are back up against the ice – making observations exactly where we want them most.
So in our last two years of work, we’ve completed the recovery of 43-ship years of logbooks, and actually we’ve done much more than that: The eight completed ships shown here make up only about 25% of the 1.5 million transcriptions we’ve done so far. So this group is only a taster – there’s three times as much more material already in the pipeline.
The answer, as we know, is 42 – but does that mean that it’s exactly 42; or somewhere between 41.5 and 42.5; or is 42 just a ball-park estimate, and the answer could actually be, say, 37?
The value of science is its power to generate new knowledge about the world, but a key part of the scientific approach is that we care almost as much about estimating the accuracy of our new knowledge as about the new knowledge itself. This is certainly my own experience: I must have spent more time calculating how wrong I could be – estimating uncertainty ranges on my results – than on anything else.
One reason I like working with the 20th Century Reanalysis (20CR) is that it comes with uncertainty ranges for all of its results. It achieves this by being an ensemble analysis – everything is calculated 56 times, and the mean of the 56 estimates is the best estimate of the answer, while their standard deviation provides an uncertainty range. This uncertainty range is the basis for our calculation of the ‘fog of ignorance‘.
We are testing the effects of the new oldWeather observations on 20CR – by doing parallel experiments reconstructing the weather with and without the new observations. We have definitely produced a substantial improvement, but to say exactly how much of an improvement, where, and when, requires careful attention to the uncertainty in the reconstructions. In principle it’s not that hard: if the uncertainty in the reanalysis including the oldWeather observations is less than the uncertainty without the new observations, then we’ve produced an improvement (there are other possible improvements too, but let’s keep it simple). So I calculated this, and it looked good. But further checks turned up a catch: we don’t know the uncertainty in either case precisely, we only have an estimate of it, so any improvement might not be real – it might be an artefact of the limitations of our uncertainty estimates.
To resolve this I have entered the murky world of uncertainty uncertainty. If I can calculate the uncertainty in the uncertainty range of each reanalysis, I can find times and places where the decrease in uncertainty between the analysis without and with the oldWeather observations is greater than any likely spurious decrease from the uncertainty in the uncertainty. (Still with me? Excellent). These are the times and places where oldWeather has definitely made things better. In principle this calculation is straightforward – I just have to increase the size of the reanalysis ensemble: so instead of doing 56 global weather simulations we do around 5600; I could then estimate the effect of being restricted to only 56. However, running a global weather simulation uses quite a bit of supercomputer time; running 56 of them requires a LOT of supercomputer time; and running 5600 of them is – well, it’s not going to happen.
So I need to do something cleverer. But as usual I’m not the first person to hit this sort of problem, so I don’t have to be clever myself – I can take advantage of a well-established general method for faking large samples when you only have small ones – a tool with the splendid name of the bootstrap. This means estimating the 5600 simulations I need by repeatedly sub-sampling from the 56 simulations I’ve got. The results are in the video below:
By bootstrapping, we can estimate a decrease in uncertainty that a reanalysis not using the oldWeather observations is unlikely to reach just by chance (less than 2.5% chance). Where a reanalysis using the oldweather observations has a decrease in uncertainty that’s bigger than this, it’s likely that the new observations caused the improvement. The yellow highlight in this video marks times and places where this happens. We can see that the regions of improvement show a strong tendency to cluster around the new oldweather observations (shown as yellow dots) – this is what we expect and supports the conclusion that these are mostly real improvements.
It’s also possible, though unlikely, that adding new observations can make the reanalysis worse (increase in estimated uncertainty). The bootstrap also gives an increase in uncertainty that a reanalysis not using the oldWeather observations is unlikely to reach just by chance (less that 2.5% probable) – the red highlight marks times and places where the reanalysis including the observations has an increase in uncertainty that’s bigger than this. There is much less red than yellow, and the red regions are not usually close to new observations, so I think they are spurious results – places where the this particular reanalysis is worse by chance, rather than systematically made worse by the new observations.
This analysis meets it’s aim of identifying, formally, when and where all our work transcribing new observations has produced improvements in our weather reconstructions. But it is still contaminated with random effects: We’d expect to get spurious red and yellow regions each 2.5% of the time anyway (because that’s the threshold we chose), but there is a second problem: The bootstrapped 2.5% thresholds in uncertainty uncertainty are only estimates – they have uncertainty of their own, and where the thresholds are too low we will get too much highlighting (both yellow and red). To quantify and understand this we need to venture into the even murkier world of uncertainty uncertainty uncer… .
No – that way madness lies. I’m stopping here.
OK, as you’re in the 0.1% of people who’ve read all the way to the bottom of this post, there is one more wrinkle I feel I must share with you: The quality metric I use for assessing the improvement caused by adding the oW observations isn’t simply the reanalysis uncertainty, it’s the Kullback–Leibler divergence of the climatological PDF from the reanalysis PDF. So for ‘uncertainty uncertainty’ above read ‘Kullback–Leibler divergence uncertainty’. I’d have mentioned this earlier, except that it would have made an already complex post utterly impenetrable, and methodologically it makes no difference, as one great virtue of the bootstrap is that it works for any metric.
The UK is too small to have its own weather, we participate in the weather of the North Atlantic region, or indeed the whole world. So to understand why the last UK winter was record-breakingly wet, we need to look at atmospheric behaviour on a large scale. I’ve turned to MERRA – an hour-by-hour reconstruction of the whole atmosphere – and made the video of northern hemisphere weather above. (There’s a lot going on, I recommend watching it in full-screen, press the ‘X’ on the control bar).
The key feature is the sequence of storms that spin off North America, and then head out into the North Atlantic in the Prevailing Westerly Circulation (anti-clockwise in the projection of the video). In November these storms mostly follow the standard path northwest to Greenland or Iceland and Scandinavia, but in December the weather changes: North America becomes much colder and the path of the storms moves south, driving the bad weather straight at the UK. This persistent pattern of a cold North America and southerly Atlantic Storm Track is the outstanding feature of the winter, and it shows up even more clearly a bit higher in the atmosphere – the Upper-Level Winds have a simpler structure, as they are not complicated by contact with the Earth’s surface.
The temperature difference between cold polar air and warmer southerly air stirs up an overturning circulation, and the rotation of the Earth turns this into a strong anti-clockwise (westerly) rotating wind – the Polar Vortex. As early as 1939, Carl-Gustaf Rossby realised that this circulation would not be smooth and stable, and the characteristic undulations (Rossby waves) have a major impact on our weather. It’s a series of these waves that push cold polar air much further south than usual over eastern North America, producing a Very Cold Winter in those parts, shifting the storm tracks south and causing the wet, stormy weather in the UK.
But of course I’m not really interested in modern weather – that’s too easy, with ample satellite observations and tremendous tools like MERRA to show us what’s going on. The challenge is in providing the long-term context needed to understand these modern events – is there a consistent pattern, if not, what’s changed. And it just happens that a previous Markedly Wet UK Winter occurred 99 years earlier, in 1914/5, and we’ve been rescuing logbook observations for that time so we can use them to make improved studies of that winter.
This time we use the Twentieth Century Reanalysis (more precisely a test version of 20CR updated to benefit from oldWeather-rescued observations). In some areas (most obviously the high Arctic) there are no observations so the analysis is too uncertain to be useful, but over the US, UK, and Atlantic storm-track region we can reconstruct the weather of that year.
Again, the picture is clearer if we look at the upper-level circulation:
Do we see the same picture in 1914/5 as in 2013/4? Reality tends to be somewhat messier than the simple explanations that scientists treasure – but I think we do see the same pattern: a persistent tendency for cold, polar air to extend south over North America, and a North Atlantic storm track shifted to the south.
We can say quite precisely what happened last winter, and (thanks, in part, to oldWeather) how last winter compared to previous Exceptional Winters. However the obvious follow-on question is ‘Why did the polar vortex behave like that, and can we predict when it’s going to do it again? We’re still working on that one.
This week, atmospheric scientists are gathering in Queenstown, New Zealand, for the fifth general assembly of the SPARC program (Stratosphere-troposphere Processes And their Role in Climate). We’ve mentioned New Zealand before: both as a country who’s isolation means that its historical weather is poorly documented, and as a Battlecruiser in the original oldWeather fleet. In September 1919 the two met: the battlecruiser visited the country, giving us an opportunity to make a major improvement in reconstructing the climate of the region.
As we showed back in October, we’re now re-doing our analysis of global weather, so we can see exactly how much the observations we’ve recovered from HMS New Zealand have improved our knowledge of the climate of New Zealand (the country). The figure above (made for the SPARC meeting) shows our estimates of the weather in each region visited by HMS New Zealand during her circumnavigation in 1919: blue for before oldWeather, and red a new revision using our observations. The width of the band indicates uncertainty – narrower is better – and the improvement we’ve made is very large.
One of the fun parts of working as a scientist is going to conferences, and in the geosciences, conferences don’t come much bigger than AGU. The American Geophysical Union’s 46th annual Fall Meeting ran last week in San Francisco, and it brought together more than 22,000 scientists for a week of presentations, discussions, celebrations, and beer.
Our man at AGU this year was Gil Compo, and he represented oldWeather at an important side event: The prize ceremony for the 2013 International Data Rescue Award in the Geosciences. We didn’t quite win this prize (the winner was the excellent Nimbus Data Rescue Project), but the judges liked us a lot, and we were awarded an honourable mention. So well done to all the oldWeather participants on a further well-deserved honour, and thanks to the award sponsors and organisers.
Every scientist’s must-have accessory, at any large conference or meeting, is a poster: This is a large sheet of paper (typically A0, or about 4′ by 3′) covered with artistically arranged images and results from your project, which you attach to a wall or display board, and use as a visual aid. Kevin made an excellent poster for us, combining images from all aspects of the project. You can see it on display in the background of the photo above, and if you’d like your own copy, it’s on our resources page.
Nobody succeeds alone, and that’s doubly true of oldWeather: not only are we legion in ourselves – a community of thousands working on logbook weather, but even as a project we are embedded in a community – we have friends and relations.
Our close relations, of course, are the other Zooniverse projects: That’s a diverse family – from the paterfamilias to the newest member, united by shared principles and the talents of the core team. But we also have more distant relatives. oldWeather is neither the first, nor the biggest, climate and weather citizen science project. climateprediction.net (CPDN) turned ten this year, and they have a very different way of doing science.
Many of the experiments climate scientists would like to do are impossible in practice: What would happen to the weather, for example, if we were to induce artificial volcanoes as a way to cool the planet? To investigate these questions, we do simulations – we build computer models of the climate system and do the experiment in the model. We have learned an enormous amount by doing this, but it does take a lot of computer time. CPDN asks volunteers to let their desktop computers contribute to this work – most of the time we use only a small fraction of the power of our computers, so this work can be done entirely in your computer’s spare time – it does not interfere with your normal use.
CPDN is also part of a family: There are lots of volunteer computing projects sharing the infrastructure provided by the Berkeley Open Infrastructure for Network Computing (BOINC) and you can contribute to any you choose.
Several of the oldWeather community have doubled their efficiency by doing citizen science and volunteer computing simultaneously: while the people are reading logbooks, their computers are simulating the climate, or Neutron stars, or malaria, or the Milky Way, or … I’d like to congratulate the oldWeather BOINC group on their tremendous contribution both to oldWeather and to volunteer computing.
Last year, Kevin was out making such measurements from a ship, on a research cruise in the Bering Strait. This field season he’s back out there, but he’s gone up in the world. For some purposes ground level is too low, and satellites are too high, and to fill this gap NOAA have two research aircraft (affectionally known as ‘Kermit’ and ‘Miss Piggy’). Kevin’s group have got some time on one of them, they are trying to “quantify the air-ice-sea interactions and lower atmospheric structure in the marginal ice zone, with the ultimate goal of being able to infer how recent reductions in sea ice extent in autumn will impact the atmosphere“.
The research aircraft is complex and well-equipped: According to Kevin “The NOAA WP-3 is instrumented like ten satellites. So we are able to collect a vast array of data from deep oceanography with AXCTD and AXBT expendables, SST and surface microwave emission (wind/waves/ice), upward/downward radiation, up to 22 thousand feet where we deploy dropsondes from above the clouds to characterize the structure of the atmosphere. On a survey we collect flight level data continuously while deploying AX instruments about every six minutes.”
To do all that effectively requires close cooperation between the crew of the aircraft and the scientists – that’s Kevin’s job. He’s sent back this video to give us a taste of what it’s like. It looks exciting – they spend a lot of time travelling at 200 knots, only 200 feet off the ground, much to the distress of the auto-pilot – but it’s hard work: one flight means 8-10 hours flight time + 2 hours for briefings before and after.
See more about this mission on the NOAA website.
We’ve looked at the world from the top; this is the view from beneath: Antarctica in the centre, South America at top, South Africa right, Australia and New Zealand bottom left. Streamlines show near-surface wind, colours indicate temperature, dots mark rain and snow. All data are from the Met Office global analysis.
One reason why weather forecasting and climate research are hard is that the atmosphere is complicated: There’s a lot going on – all sorts of different motions and changes occurring simultaneously all over the world. So while it’s often useful to use simplified views – perhaps to look only at mean-sea-level pressure, for example – it’s also good sometimes to embrace the complexity, and remind ourselves why we need a supercomputer to keep track of it all.
So this time I’ve put as much as possible in the video: sea-ice, wind speed and direction, temperature and even rainfall. It’s still only a tiny fraction of the full three dimensional atmospheric state that our forecast models have to simulate, but there’s plenty to look at: We can see not only the small-scale complexity of the winds, but also some larger-scale patterns: the strong clockwise circulation around Antarctica formed by the southern hemisphere westerlies, the cyclones forming in that strong flow, and atmospheric waves folding outwards.
This isn’t really old weather, it’s almost new – from only last month. But I used this example because it illustrates that the weather is not only complicated and interesting, it also matters. If you set the video to September 16th you’ll see a low pressure (clockwise circulation) off Marie Byrd land, linking with a high pressure (anti-clockwise circulation) in the south-east Pacific. These combined to channel cold Antarctic air up toward central Chile, which contributed to a late frost which cost their fruit industry an estimated $1 billion. Expect to pay extra for peaches, cherries, and even Cabernet Sauvignon, as a result.