Bitterly cold – what happens when we run out of superlatives?

Just as a reminder a superlative is described by the dictionary in Google as:-

Figure 1 – Courtesy of Google

I’ve heard so many reports across social media describing just how cold, very cold, bitter or raw this weekend will be across the country as colder northeasterly air spreads southwestward across the country, that I thought I’d take a look. Here are the forecast temperatures across the country for Sunday (fig 2).

Figure 2 – Courtesy of Twitter and the BBC

And here are what the mean maximum temperatures for January look like across the UK (fig 3).

Figure 3 background map courtesy of BBC and Twitter

So all this incessant hype I’ve heard in social media about the coming change of weather type, and talk of just how cold this weekend will be, should be taken with a pinch of salt, because although it’s turned colder, in reality and for a variety of reasons it’s not that cold at all.

No doubt, the ‘feel like’ or ‘wind-chill’ temperature figures will be brought into play as winds strengthen across southern areas on Sunday, but let’s not get carried away with the fact that these temperature are nothing out of the ordinary for January and put them into perspective.

That’s not a knife –  that’s a knife!

Here’s a look back at the 12th of January 1987 (fig 4) to see a situation that does merit the superlative ‘bitter’, to describe the coldest and snowiest days spells of weather that I can remember in my lifetime in the British Isles.

Figure 4
Figure 5

Chances of a really cold winter 11.4% less than in 1960

I’ve just read a recent article in the weather and climate forum about severe winters and the enigmatic and elusive Scandinavian anticyclone. I could have used reanalysis data from NOAA, but decided instead to analyse the frequency of this particular weather feature with the help of the daily objective Lamb Weather Types from the UEA. It’s quite simple really, all you have to do is add up the occurrence of all days of a certain LWT, in this case these twelve types which I thought were the best associated with a blocked circulation and Scandinavian or Siberian anticyclone:

  1. ANE
  2. AE
  3. ASE
  4. AN
  5. NE
  6. E
  7. SE
  8. N
  9. CNE
  10. CE
  11. CSE
  12. CN

Then examine the LWT of each day in a Winter [DJF] and if it matches any of the types listed above add it to a total. At the end of each Winter, calculate the frequency by dividing the total by the number of days in the season and multiplying by a 100, then repeat the process for each Winter since 1871, before plotting the results as a scatter graph, with a 10 year centred average and linear trends (fig 1), and adding the results to a table of ranked frequencies in descending order (fig 2).

Figure 1 – Data courtesy of the UEA/CRU

No big surprises really, all the old favourites are showing frequencies of 30% or more. 1962-63, 1946-47, 1978-79, 2009-10 & 1985-86. The interesting thing is the steady decrease in frequency of these combined types since 1871, they are down 4.3%. Even more striking is the decline of 11.4% since 1960! It’s no wonder that people of a certain age feel like someone’s shut the fridge door since their school days in the 1960’s.

Figure 2 – Data courtesy of the UEA/CRU

The Grimness Index

I came across a unique type of weather index a few months ago when I was looking at the climate statistic on the website of Edinburgh’s Royal Botanic Garden. They had devised a “grimness” value, where grimness was calculated by dividing the monthly precipitation with the monthly sunshine, for example the September grimness value for Edinburgh was 0.6.

I loved the idea, but since developing a viewer for the Summer Index last year I realised that if the “grimness” value was to work for each month of the year, it should also include a reference to mean monthly temperatures and use quintiles to allow inter-comparison of other regions within the UK. The gridded climate data values from the Met Office are perfect to calculate percentiles of temperature, precipitation and sunshine with back to 1929. I used those percentiles in this simple formula to calculate the monthly grimness values [GI] with.

    • S:=Monthly sunshine percentile (0..99)
    • R:=Monthly precipitation percentile (0..99)
    • T:=Monthly mean temperature (0..99)
    • GI:=((100-S)+(100-T)+(R+1))/3

The formula should generate a grimness index between 0 and 100. The higher the GI for wetter, duller and colder weather, the lower the GI for drier, sunnier and warmer weather.

It’s grim up north.

Here’s an example of the GI for last month across the UK, November 2017 (fig 1). The drier and sunnier weather during the month reduced the grimness index for regions further south and east.

Figure 1

Here’s a look back at the UK grimness index for the last 10 years (fig 2), and as you can see, grim months don’t just occur in the depths of winter, they can can occur in any month of the year. This year for example saw a lovely April and May (5), but quite a grim summer, with a particular grim September (4). June 2012 (1) had one of the highest grim values on record at 92.7. A grim index closer to zero does highlight exceptionally pleasant months, such as April 2011 (3), and March 2012 (2) that we may have completely forgotten all about.

Figure 2

I’ll work out some monthly long-term averages for grimness over the coming weeks, just to see which month is statistically the grimmest and if there is any kind of annual cycle of grimness going on.

My meteorological heroes

Figure 1 – Courtesy of the WMO

I have a few ‘meteorological’ heroes in my life, and it may come as a surprise to many that Peter Ewins or Julian Hunt don’t feature in the list. At the top of it must come Gordon Manley (1902-1980), whose name I first came across when reading his 1952 book ‘Climate and the British scene’ as a teenager. He is best known for his work on reconstructing the past climate of Central England with his CET series, which was adopted and sanitized by the Met Office, which I noticed that he joined in 1925, but had the good sense to resign the very next year!

Along side Manley at the top of my list of meteorological heroes is Hubert Lamb (1913-1987). It was Lamb who catalogued the circulation patterns across the British Isles from 1861 and came up with the idea of ‘Lamb’ weather types, which as far as I know extended the earlier work on weather types done by Van Bebber and Gold. That work has been carried forward by the CRU at the UEA, although they switched to an objective rather than Lamb’s original subjective way of classifying each days weather type. In 1964 he was asked to write a new edition of a book called the ‘English Climate’, another very readable book that I first came across when I joined the Met Office. Lamb did work on and off for many years with the Met Office, in fact from before the war until 1971. The second world war was a problem for him because as a Quaker he couldn’t directly get involved in it. The answer was that he work in the Republic of Ireland for the duration of the war with Met Éireann. He finally saw the light late in his career, and had the good sense to leave the Met Office for academia and launch the CRU at the UEA.

Looking at both of my heroes, I can see the connection between the two is that they were both instrumental in creating important climate data series with the CET and LWT. For some reason that appealed to me, and as soon as I had invested in a BBC micro in 1982, it was the first thing that I felt impelled to write a program for – the rest as they say is history!

The connection between these two esteemed gents and myself, apart from our innate love of the climate and weather of the British Isles and resigning from the Met Office, is a fondness for its hills and mountains.

So when I came across an interview with Hubert Lamb recently on the WMO website I just had to include some of the more interesting questions that he was asked. It was in a publication called the Bulletin Interviews and here are a few of the questions that he was asked that I found interesting. As far as I can see the interview took place before 1981.

Figure 2 – Courtesy of the WMO

Hands up how many of us have a love of weather that started when we experienced a snowy winter as a child?

Figure 3 – Courtesy of the WMO

I never realised that Richardson was also a Quaker, he too would also feature in my list of meteorological heroes.

Figure 4 – Courtesy of the WMO

This story of Lamb’s reminds me of the thick smoke haze that we would get in the Vale of York when I started my observing career at RAF Leeming in 1970. I can still remember reporting 800 metres in smoke behind a light northeasterly sea breeze that was blowing down from Middlesbrough. Thankfully things are a little cleaner these days.

Figure 5 – Courtesy of the WMO

It’s a shame that the Met Office don’t share Lamb’s passion for past climate data, they’ve recently left the digitising of the DWR records to the Weather Rescue volunteers. What would Lamb have thought? No wonder he left.

Figure 6 – Courtesy of the WMO

I like the bit where he says about climate warning – “…there is also the dangerous tendency to think that Man is responsible for all occurrences. That should be viewed with considerable scepticism“.

Unfortunately the ‘Bulletin interviews’ doesn’t feature an interview with Gordon Manley which is a shame.

Shouldn’t this be the Met Office’s job?

Figure 1 – Courtesy of the Met Office

I have tremendous admiration for the people involved with Weather Rescue in digitising all the Ben Nevis observations from the old paper records for the years between 1883 and 1904, which is now I see 88% complete. Weather Rescue are now encouraging people to get involved with the even bigger project of transcribing all the daily weather reports issued by the Met Office from 1860 to 1950. Obviously this is a massive undertaking, but the records have all been scanned and made available by the Met Office on their website to make this happen.

Figure 2 – Courtesy of Weather Rescue

From what I can see from the Weather Rescue website (fig 2) most of the data in the DWR before 1950 have not been digitised. The one simple question that I have about this new projects is:

Shouldn’t this be the Met Office’s job?

As each daily weather report was published, they were obviously produced by copying from the original record that had been telegraphed into the Met Office. I imagine that the original record was then filed away as subset of a much more extensive collection of daily climatological records that they collected by post at the end of each month. You would think that the Met Office, as one of the first government departments to early use of computers in the 1960’s, would have moved to digitised all such climatological records to magnetic disk as quickly as possible. I think this latest undertaking of the Weather Rescue volunteers throws up some awkward and embarrassing questions about the Met Office as custodians of the nation’s climatological records:

  1. What have they been doing sitting on a mountain of paper climatological records for the last 50 years?
  2. As custodian,  isn’t it their responsibility to get these paper records digitised?
  3. Why do we have to depend on Weather Rescue to do the work that should have already been done by them?
  4. If records before 1950 haven’t been digitised that means they aren’t being used, and that makes the efforts of past generations worthless – Isn’t climate data from the past just as important as recent data in verifying the accuracy of climate models?

Memories of late November 1973

Looking at the latest long-range forecast charts from the GFS for T+192 (fig 1) and T+336 (fig 2) reminds me of the last few days of November 1973, which saw an early cold spell from a similar outbreak of northerly winds (fig 3). I had just left the Met Office (for the first time), and had started working as a trainee bank clerk for the Trustees Savings Bank at a new branch they had just opened in Dronfield, northeast Derbyshire, and can still remember the snow.

Figure 1 – 8 day forecast – courtesy of www.netweather.tv
Figure 2 – 14 day forecast – courtesy of www.netweather.tv
Figure 3

It also extends the run of northerly outbreaks which started on the 29th of October with a period of approximately 7 days, that’s of course if the GFS forecasts come to pass, although the northerlies now seem to have taken a liking to Thursday rather than Sunday!

  • Sunday 29th October [N]
  • Sunday 5th November [N/NW]
  • Sunday 12th November [N]
  • Sunday 19th November [NW] although the main thrust is east of the meridian at T+84
  • Thursday 23rd November [CN] 7 day cycle slipped a bit!
  • Thursday 30th November [N]

Now the acid test – what do the Met Office think of this northerly theme? Well to be perfectly honest not a lot, if you look at their latest extended forecast out to the end of November (fig 4). This outlook is couched with so many vague and bland phrases I can’t see what practical use it has for most people.

Figure 4 – Courtesy of the Met Office

A critique of the October news release

Figure 1 – Courtesy of the Met Office

I should skip this post now if you don’t like me having a go at what the Met Office push out in their various social media outlets, because I’m just going to give an honest critique of what I think of their latest news release that’s looking back at the weather of October 2017:

Textual content

I can’t really fault what they say about last month’s weather, I’ve written quite a few monthly weather diaries for the UK myself, and know that it’s not easy to condense everything into one and keep it readable, although I do wish they would fully justify it!

Colour contoured charts

I’ve always like the colour contoured climate charts that the Met Office produce. Although they haven’t changed for at least twenty years or more it still looks professional, but I think it’s about time to see what ArcGIS can do.

Climate tables

What let’s the whole article down in my opinion, is the inclusion of the badly formatted HTML tables of climate values (fig 1). This is what’s wrong with them in my opinion:

  • Both tables are left aligned on the page and not centred.
  • The values in the columns are left and not right aligned.
  • The titles in the header are left aligned and not centred.
  • Why not use the word ‘anomaly’ or simply ‘anom’ rather than ‘Diff from average’?
  • Why not combine the two tables into one like this (fig 2):
Figure 2

I’ve generated this graphic from a bespoke application that I use to downloads and visualise the free gridded monthly data that the Met Office make available. Obviously this is a graphic file, but I could have just as easily output HTML. I think you’ll agree that it’s much more professional and more importantly easy to read than the Met Office offering? (If not get an appointment with your optician as soon as you can). The good thing about doing this in software, is that it’ll work just as well for next month, as it would for January 1963. Here’s a screen shot with all the regional values for January 1963 as an example (fig 3).

Figure 3