Archive for the ‘Climate Change’ Category

Hot dry summers

August 10, 2018

Apparently its been hot all around the northern hemisphere this summer.

And that got me thinking about the long hot summer of 1976 when I was 16.

I have the general impression that summers now are warmer than they used to be. But I am aware that such impressions can be misleading.

Being the age I am (58), I fear my own mis-remembering of times past.

So was 1976 really exceptional? And will this year (2018) also prove to be really exceptional?

I decided to download some data and take a look.

Heathrow Data.

I popped over to the Met Office’s Climate pages and downloaded the historical data from the nearby Heathrow weather station.

I had downloaded this data before when looking at long-term climate trends, but this time I was looking for individual hot months rather than annual or decadal trends.

When I plotted the monthly average of the daily maximum temperature, I was surprised that 1976 didn’t stand out at all as an exceptional year.

Heathrow Monthly Climate Data July Maxima Analysis

The monthly average of the daily temperature maxima are plotted as black dots connected by grey lines. I have highlighted the data from July each year using red squares. Notice that since 1976 there have been many comparable July months.

In the graph above I have highlighted July average maximum temperatures. I tried similar analyses for June and August and the results were similar. 1976 stood out as a hot year, but not exceptionally so.

Ask an Expert

Puzzled, I turned to an expert. I sent an e-mail to John Kennedy at the UK’s Met Office  and to my astonishment he responded within a few hours.

His suggestion was to try plotting seasonal data.

His insight was based on the fact that it is not so unusual to have a single warm month. But it is unusual to have three warm months in a row.

So I re-plotted the data and this time I highlighted the average of daily maximum temperatures for June, July and August.

Heathrow Monthly Climate Data June July August Maxima Analysis

The monthly average of the daily temperature maxima are plotted as black dots connected by grey lines as in the previous figure. Here I have highlighted the seasonal average data (from June July and August) using red squares. Notice that 1976 now stands out as an exceptionally warm summer.

Delightfully, 1976 pops out as being an exceptional summer – in line with my adolescent recollection.

More than just being hot

But John suggested more. He suggested looking at the seasonal average of the minimum daily temperature.

Recall that in hot weather it is often the overnight warmth which is particularly oppressive.

In this graph (below) 1976 does not stand out as exceptional, but it is noticeable that warming trend is easily visible to the naked eye. On average summer, summer nights are about 2 °C warmer now than they were at the start of my lifetime.

Heathrow Monthly Climate Data JJA Minimum Analysis

The monthly average of the daily temperature minima are plotted as black dots connected by grey lines. Here I have highlighted the seasonal average data (from June July and August) using red squares. Notice that 1976 does not stand out exceptionally.

John also suggested that I look at other available data such as the averages of

  • daily hours of sunshine
  • daily rainfall

Once again seasonal averages of these quantities show 1976 to have been an exceptional year. Below I have plotted the Rainfall totals on two graphs, one showing the overall rainfall, and the other detail of the low rainfall summers.

Heathrow Monthly Monthly Rainfall

The monthly average of the daily rainfall total are plotted as black dots connected by grey lines. Here I have highlighted the seasonal average data (from June July and August) using red squares. Notice that 1976 was a dry summer. The data below 50 mm of rainfall are re-plotted in the next graph.

Heathrow Monthly Monthly Rainfall detail

Detail from the previous figure showing the low rainfall data. The monthly average of the daily rainfall total are plotted as black dots connected by grey lines. Here I have highlighted the seasonal average data (from June July and August) using red squares. Notice that 1976 was a dry summer.

de Podesta ‘Hot Summer’ Index

Following on from John’s suggestion, I devised the ‘de Podesta Long Hot Summer Index‘. I defined this to be:

  • the sum of the seasonal averages of the minimum and maximum temperatures (for June July and August),
  • divided by the seasonal average of rainfall (for June July and August).

Plotting this I was surprised to see 1976 pop out of the data as a truly exceptional hot dry summer – my memory had not deceived me.

But I also noticed 1995 ‘popped out’ too and I had no recollection of that being an exceptional summer. However this data (and Wikipedia) confirms that it was.

Now I just have to wait until the end of August to see if this year was exceptional too – it most surely felt exceptional, but we need to look at the data to see if our perceptions are genuinely grounded in reality.

Heathrow Hot Dry Summer Index

The de Podesta Hot Dry Summer (HDS) index as described in the text.  Construct an ‘index’ in this way really flags up the exceptional nature of 1976, and also 1995.

John Kennedy’s blog

In typical self-deprecating manner, John calls himself a ‘diagram monkey’ and blogs under that pseudonym. 

His is one of just two blogs to which I subscribe and I recommend it to you highly.

The view from 10 kilometres

June 3, 2018

At the start of May I travelled by air to and from California.

The flight takes an extraordinary route, crossing the southern tip of Greenland, the vast shield of northern Canada, the American mid-west and the south-western deserts.

But despite the extreme terrain covered by the plane, for me the journey was easy. It was nothing more than an exercise in advanced sitting, and I am good at sitting.

And looking out the window, I saw two extraordinary things.

London to LA

Greenland

I had chosen a window seat on the right-hand side of the plane on the off-chance that visibility would be good as we flew over Greenland. I also brought my camera with a pointy lens.

The camera’s field of view on the ground was roughly 1 km at best, and I could see detailed features of the spring-melt of the sea-ice around Greenland.

Greeland Ice

At times I could see the surface texture of what I guess was a glacier as it reached the sea in an ice-cliff.

Greeland Ice 5

The scale of the ice was overwhelming. It didn’t look like a ‘snowy polar cap’ on the globe. It looked like a vast and utterly alien ice world.

I found it interesting to compare this ‘bird’s-eye’ view with the data gathered by satellites that have charted the decades long decline in the extent of the sea ice.

California-Nevada

As we flew over the Nevada-California border I was delighted  to catch a  glimpse of the immense Ivanpah solar power plant (Link & Wikipedia article).

One of three solar collectors at the Ivanpah solar power plant.

One of three solar collectors at the Ivanpah solar power plant.

The three solar collectors of the Ivanpah solar plant together with a vast solar photo-voltaic array

The three solar collectors of the Ivanpah solar plant together with a vast solar photo-voltaic array. It is clear that solar generation is not limited by available land!

Next to Ivanpah was a vast conventional solar photo-voltaic plant.

As I had been when I flew over Greenland, I was struck by the vastness of the landscape and the boldness of these engineering ventures in that inhospitable climate.

The link

Momentarily I allowed my self to hope – forgive me: I was on holiday.

I allowed myself to hope that solar engineering might really provide a way to de-carbonise electricity production.

From 10 km above the ground  it was breathtakingly clear that a lack of suitable land for solar power plants was not a limitation on production. Surely not even 1% of the available land was being used.

And as we flew over the Hoover Dam – with water sadly still at historically low levels – I allowed myself to imagine a world powered by renewable energy.

And as result, eventually there would be a slowdown in the rate of loss of arctic sea ice.

Hoover Dam  from 10 km

Hoover Dam from 10 km

It struck me that the first step required to make this happen was to imagine that it could even be possible.

From 10 kilometres up, briefly it all seemed clear

 

 

Air Temperature

April 1, 2018

Recently, two disparate strands of my work produced publications within a week of each other.

Curiously they both concerned one of the commonest measurements made on Earth today – the measurement of air temperature.

  • One of the papers was the result of a humbling discovery I made last year concerning a common source of error in air temperature measurements. (Link to open access paper)
  • On the other  paper I was just one amongst 17 authors calling for the establishment of global reference network to monitor the climate. My guess is that most people imagine such a network already exists – but it doesn’t! (Link to open access paper)

I am writing this article because I was struck by the contrasting styles of these papers: one describing an arcane experimental detail; and the other proposing a global inter-governmental initiative.

And yet the aim of both papers was identical: to improve measurement so that we can more clearly see what is happening in the world.

Paper 1

In the middle of 2018 I was experimenting with a new device for measuring air temperature by measuring the speed of sound in air.

It’s an ingenious device, but it obviously needed to be checked. We had previously carried out tests inside environmental chambers, but the temperature stability and uniformity inside the chambers was not as good as we had hoped for.

So we decided to test the device in one of NPL’s dimensional laboratories. In these laboratories, there is a gentle, uniform flow of air from ceiling to floor, and the temperature is stable to within a hundredth of a degree Celsius (0.01 °C) indefinitely.

However, when I tried to measure the temperature of the air using conventional temperature sensors I got widely differing answers – varying by a quarter of a degree depending on where I placed the thermometer. I felt utterly depressed and humiliated.

Eventually I realised what the problem was. This involved stopping. Thinking carefully. And talking with colleagues. It was a classic case of eliminating the impossible leaving only the improbable.

After believing I understood the effect, I devised a simple experiment to test my understanding – a photograph of the apparatus is shown below.

tubes-in-a-lab-photo.png

The apparatus consisted of a set of stainless steel tubes held in a clamp stand. It was almost certainly the cheapest experiment I have ever conducted.

I placed the tubes in the laboratory, exposed to the downward air flow, and  left them for several hours to equilibrate with air.

Prior to this experience, I would have bet serious amounts of money on the ‘fact’ that all these tubes would be at the same temperature. My insight had led me to question this assumption.

And my insight was correct. Every one of the tubes was at a different temperature and none of them were at the temperature of the air! The temperature of the tubes depended on:

  • the brightness of the lights in the room – which was understandable but a larger effect than I expected, and
  • the diameter of the tubes – which was the truly surprising result.

Results 1

I was shocked. But although the reason for this is not obvious, it is also not complicated to understand.

When air flows air around a cylindrical (or spherical) sensor only a very small amount of air actually makes contact with the sensor.

Air reaching the sensor first is stopped (it ‘stagnates’ to use the jargon). At this point heat exchange is very effective. But this same air is then forced to flow around the sensor in a ‘boundary layer’ which effectively insulates the sensor from the rest of the air.

Air flow

For small sensors, the sensor acquires a temperature close to that of the air. But the air is surprisingly ineffective at changing the temperature of larger sensors.

The effect matters in two quite distinct realms.

Metrology

In metrology – the science of measurement – it transpires that knowledge of the temperature of the air is important for the most accurate length measurements.

This is because we measure the dimensions of objects in terms of the wavelength of light, and this wavelength is slightly affected by the temperature of the air through which the light passes.

In a dimensional laboratory such as the one illustrated below, the thermometer will indicate a temperature which is:

  • different from the temperature of artefacts placed in the room, and
  • different from the temperature of the air.

Laboratory

Unless the effect is accounted for – which it generally isn’t – then length measurements will be slightly incorrect.

Climatology

The effect is also important in climatology. If a sensor is changed in a meteorological station people check that the sensor is calibrated, but they rarely record its diameter.

If a calibrated sensor is replaced by another calibrated sensor with a different diameter, then there will be a systematic effect on the temperatures recorded by the station. Such effects won’t matter for weather forecasting, but they will matter for people using the stations for a climate record.

And that brings me to Paper 2

Paper 2

Hadcrut4 Global Temperature

When we see graphs of ‘global temperatures’ over time, many people assume that the data is derived from satellites or some ‘high-tech’ network of sensors. Not so.

The ‘surface’ temperature of the Earth is generally estimated in two quite distinct parts – sea surface temperature and land surface temperature. But both these terms are slight misnomers.

Considering just the land measurements, the actual temperature measured is the air temperature above the land surface. In the jargon, the measurement is called LSAT – the Land Surface Air Temperature.

LSAT is the temperature which human beings experience and satellites can’t measure it.

LSAT data is extracted from temperature measurements made in thousands of meteorological stations around the world. We have data records from some stations extending back for 150 years.

However, it is well known that data is less than ideal: it is biased and unrepresentative in many ways.

The effect described in Paper 1 is just one of many such biases which have been extensively studied. And scientists have devised many ways to check that the overall trend they have extracted – what we now call global warming – is real.

Nonetheless. It is slightly shocking that a global network of stations designed specifically with the aim of climate monitoring does not exist.

And that is what we were calling for in Paper 2. Such a climate network would consist of less than 200 stations world-wide and cost less than a modest satellite launch. But it would add confidence to the measurements extracted from meteorological stations.

Perhaps the most important reason for creating such a network is that we don’t know how meteorological technology will evolve over the coming century.

Over the last century, the technology has remained reasonably stable. But it is quite possible that the nature of data acquisition for meteorological applications will change  in ways we cannot anticipate.

It seems prudent to me that we establish a global climate reference network as soon as possible.

References

Paper 1

Air temperature sensors: dependence of radiative errors on sensor diameter in precision metrology and meteorology
Michael de Podesta, Stephanie Bell and Robin Underwood

Published 28 February 2018
Metrologia, Volume 55, Number 2 https://doi.org/10.1088/1681-7575/aaaa52

Paper 2

Towards a global land surface climate fiducial reference measurements network
P. W. Thorne, H. J. Diamond, B. Goodison , S. Harrigan , Z. Hausfather , N. B. Ingleby , P. D. Jones ,J. H. Lawrimore , D. H. Lister , A. Merlone , T. Oakley , M. Palecki , T. C. Peterson , M. de Podesta , C. Tassone ,  V. Venema, K. M. Willett

Published: 1 March 2018
Int. J. Climatol 2018;1–15. https://doi.org/10.1002/joc.5458

Santa Rosa Fire: Update

October 18, 2017

The fires surrounding Santa Rosa are slowly coming under control. And – thankfully – rains are due tomorrow (Thursday 19th October).

From the San Francisco Chronicle’s interactive graphic page, I compiled the animated gif above to show how closely the fires approached Santa Rosa from two directions.

Each frame shows one day’s fire extent starting with day 0 – the day before the fires – to  day 11: the 18th October 2017.

What is particularly striking is the rapidity of the spread of the so-called ‘Tubbs Fire’ on Day 2.

It completely outpaced any attempt to contain it, and it devastated the north-eastern suburb of Fountaingrove.

I confess I immediately thought that this extraordinary fire, following an extended drought, had the fingerprints of ‘Climate Change’ written all over it.

And I was sure that Suzanne would feel that way too. But this is not quite the open-and-shut case that I thought.

It turns out that there was a great ‘Hanley’ fire in 1964 that has an uncanny geographical overlap with the the 2017 fires. Several newspapers have featured recollections of the blaze (here and here) and Suzanne sent me the graphic below.

The ‘hatched’ regions correspond to the extent of the 1964 fires, and the coloured dots correspond to the 2017 fires. The overlap is… suspicious. And it rather changes ‘the story’ of the fires.

Santa Rosa Fires 1964-2017

The narrative now appears to be less “OMG! Climate change induced fire-meggedon“, and more “Were the proper planning procedures followed?“. i.e. rather less dramatic, but just as important if it happened to be your house that was burned.

It appears that the fire burned in the same place as it did previously, but in the intervening 53 years, thousands of homes were built.

One irony that Suzanne reports is that a couple of years ago, there was a move to build a fire station in Fountaingrove, but there were objections because it might lower property values in the area.

The fire station was eventually built, but – like everything else there – it has been devastated.

I suspect that when they re-build having a fire station nearby will not be seen with quite the negativity that it once was.

UPDATE: The Washington Post have also run with this story under the headline Santa Rosa ignored nature’s warning . Perhaps after reading my blog?

 

The Santa Rosa Fire

October 16, 2017

Santa Rosa FiresIt’s been a few year’s since we visited my friend Suzanne in Santa Rosa. But I remember the street on which she lives.

In the early mornings, I would go jogging around the neighboured which was sleepy and suburban.

But this memory contrast strongly with the news that the largest of the recent Northern California wildfires (The Tubbs Fire) stopped just about 100 metres short of Suzanne’s home.

The map at the head of the page (stolen from the excellent San Francisco Chronicle) shows how the fire swept across the suburbs from the north east, and by nothing more than good chance, stopped just short of her home.

It’s not just people who have lost homes. Businesses have been burned out too.

But Keysight (formerly Agilent, formerly Hewlett Packard) who have their HQ in Santa Rosa seem to have lost only a few out-buildings.

Aside from the unimaginable personal and financial losses, this must be devastating for the entire community.

I don’t want to say anything about this now because it’s too shocking and the fires are not yet out. But until this moment, I would never have believed this possible.

 

Not everything is getting worse!

April 19, 2017

Carbon Intensity April 2017

Friends, I find it hard to believe, but I think I have found something happening in the world which is not bad. Who knew such things still happened?

The news comes from the fantastic web site MyGridGB which charts the development of electricity generation in the UK.

On the site I read that:

  • At lunchtime on Sunday 9th April 2017,  8 GW of solar power was generated.
  • On Friday all coal power stations in the UK were off.
  • On Saturday, strong winds and solar combined with low demand to briefly provide 73% of power.

All three of these facts fill me with hope. Just think:

  • 8 gigawatts of solar power. In the UK! IN APRIL!!!
  • And no coal generation at all!
  • And renewable energy providing 73% of our power!

Even a few years ago each of these facts would have been unthinkable!

And even more wonderfully: nobody noticed!

Of course, these were just transients, but they show we have the potential to generate electricity which has a significantly low carbon intensity.

Carbon Intensity is a measure of the amount of carbon dioxide emitted into the atmosphere for each unit (kWh) of electricity generated.

Wikipedia tells me that electricity generated from:

  • Coal has a carbon intensity of about 1.0 kg of CO2 per kWh
  • Gas has a carbon intensity of about 0.47 kg of CO2 per kWh
  • Biomass has a carbon intensity of about 0.23 kg of CO2 per kWh
  • Solar PV has a carbon intensity of about 0.05 kg of CO2 per kW
  • Nuclear has a carbon intensity of about 0.02 kg of CO2 per kWh
  • Wind has a carbon intensity of about 0.01 kg of CO2 per kWh

The graph at the head of the page shows that in April 2017 the generating mix in the UK has a carbon intensity of about 0.25 kg of CO2 per kWh.

MyGridGB’s mastermind is Andrew Crossland. On the site he has published a manifesto outlining a plan which would actually reduce our carbon intensity to less than 0.1 kg of CO2 per kWh.

What I like about the manifesto is that it is eminently doable.

And who knows? Perhaps we might actually do it?

Ahhhh. Thank you Andrew.

Even thinking that a good thing might still be possible makes me feel better.

 

Climate Reflections

March 28, 2017

I am currently in Exeter attending the 22nd meeting of the WMO GCOS/WCRP AOPC. Let me translate:

In short, I am here to talk about monitoring the global climate with some of the best climate scientists from around the world.

The topics being discussed are diverse, and I am here to talk about one small part of the work. However, I feel honoured to take coffee with these people and to be able to legitimately call them ‘colleagues’.

My contribution is to speak on Thursday about creating a reference network of climate monitoring stations.

Historically, we have used records from normal weather stations to monitor the changing climate. But these stations have known biases that have to be detected and corrected.

It would have been really helpful if 100 years ago, scientists had thought to create a reference network where every time a new thermometer screen was installed, they recorded the fact. But they didn’t.

So the idea is to create that reference network now so that in 100 year’s time when climate scientists look back they will say:

“Thank heaven for AOPC-22: that’s when our job got easier! They created a Climate Reference Network that has allowed us to detect anomalies in the climate signal inferred from analysing regular weather stations.

But that’s not what I wanted to talk about.

The mood of the meeting

This meeting is busy. People are mindful of the ability of a roomful of scientists to chat endlessly about details. And to counter this there is a powerful focus on getting things done.

However President Trump casts a shadow over the meeting.

Trump collage

Headlines from news sites today.: BBC, Guardian and Ars Technica

And the news today is that he has signed executive orders that effectively scrap energy policies based on avoiding the worst effects of climate change.

Most people at the meeting find this depressing. And it would be an understatement to say that colleagues from the US are ‘concerned’.

Trump’s policies are ultimately based on a simple belief which is summed up in the graph below from the Gapminder foundation.

2013 data for the countries of the world showing GDP per person versus carbon dioxide emissions per person. Each bubble represents a country and the size of each bubble is proportional to its  population.

2013 data for the countries of the world showing GDP per person versus carbon dioxide emissions per person. Each bubble represents a country and the size of each bubble is proportional to its population.

The graph shows that countries that emit a lot of carbon per person are richer.

However the graph shows correlation not causation. Emitting carbon dioxide of itself does not make anyone richer.

Burning carbon produces energy, and it is access to energy that makes countries rich, and unequivocally improves the quality of people’s lives.

But emitting ~30 billion tonnes of carbon dioxide per year also has another effect which is not documented on the ‘bubble graph’. As the people at this meeting have helped make clear, it has warmed the surface of the planet and will continue to do so for centuries to come. But we no longer need to emit carbon to produce energy.

Currently renewable energy sources are (generally) more expensive than fossil fuels. But there is no reason why that will always be the case.

Indeed, if Trump’s aim is to make America independent of foreign energy sources, the best thing he could do would be to increase exploitation of renewable energy which would reduce its cost.

Personally, I think that it is already too late for coal and that Trump’s efforts to open coal mines and burn more coal will fail, just like efforts to create ‘clean coal’ have utterly failed.

 

How would you take a dinosaur’s temperature?

March 15, 2017
A tooth from a tyrannosaurus rex.

A tooth from a tyrannosaurus rex.

Were dinosaurs warm-blooded or cold-blooded?

That is an interesting question. And one might imagine that we could infer an answer by looking at fossil skeletons and drawing inferences from analogies with modern animals.

But with dinosaurs all being dead these last 66 million years or so, a direct temperature measurement is obviously impossible.

Or so I thought until earlier today when I visited the isotope facilities at the Scottish Universities Environmental Research Centre in East Kilbride.

There they have a plan to make direct physical measurements on dinosaur remains, and from these measurements work out the temperature of the dinosaur during its life.

Their cunning three-step plan goes like this:

  1. Find some dinosaur remains: They have chosen to study the teeth from tyrannosaurs because it transpires that there are plenty of these available and so museums will let them carry out experiments on samples.
  2. Analyse the isotopic composition of carbonate compounds in the teeth. It turns out that the detailed isotopic composition of carbonates changes systematically with the temperature at which the carbonate was formed. Studying the isotopic composition of the carbon dioxide gas given off when the teeth are dissolved reveals that subtle change in carbonate composition, and hence the temperature at which the carbonate was formed.
  3. Study the ‘formation temperature’ of the carbonate in dinosaur teeth discovered in a range of different climates. If dinosaurs were cold-blooded, (i.e. unable to control their own body temperature) then the temperature ought to vary systematically with climate. But if dinosaurs were warm-blooded, then the formation temperature should be the same no matter where they lived (in the same way that human body temperature doesn’t vary with latitude).
A 'paleo-thermometer'

A ‘paleo-thermometer’

I have written out the three step plan above, and I hope it sort of made sense.

So contrary to what I said at the start of this article, it is possible – at least in principle – to measure the temperature of a dinosaur that died at least 66 million years ago.

But in fact work like this is right on the edge of ‘the possible’. It ought to work. And the people doing the work think it will work.

But the complexities of the measurement in Step 2 appeared to me to be so many that it must be possible that it won’t work. Or not as well as hoped.

However I don’t say that as a criticism: I say it with admiration.

To be able to even imagine making such a measurement seems to me to be on a par with measuring the cosmic microwave background, or gravitational waves.

It involves stretching everything we can do to its limits and then studying the faint structures and patterns that we detect. Ghosts from the past, whispering to us through time.

I was inspired.

=============================

Thanks to Adrian Boyce and Darren Mark for their time today, and apologies to them both if I have mangled this story!

Do you really want to know if global warming is real?

January 28, 2017

About a year ago, I thought that Climate Change Deniers had lost the argument.

I thought that we were all moving on to answering more interesting questions, such as what to do about it.

But it seems I was wrong. It seems that in this post-truth world, climate change deniers are uninterested in reality – preferring instead alternative facts.

I am left speechless in the face of this kind of intellectual dishonesty.

Actually I am only almost speechless. I intend to continue trying to empower people by fighting this kind deception.

Rather than trying to woo people over to my view, my aim is simply to offer people the chance to come to their own informed opinion.

See for yourself

As part of my FREE University of Chicago Course on Global Warming, I have been using some astonishing FREE software. And its FREE!

1

The ‘Time Series Browser’ allows one to browse a 7000 station subset of our historical temperature records from meteorological stations around the world.

  • The data are the local station temperatures averaged over 1 month, 1 year or 1 decade. Whichever you choose you can also download this data into a spreadsheet to have fun with on your own!
  • One can select sets of data based on a variety of criteria – such as country, latitude band, altitude, or type of geographical location – desert, maritime, tropical etc. Or you can simply pick a single station – maybe the one nearest you.

Already this is enormously empowering: this is the pretty much the same data set that leading climate scientists have used.

For this article I randomly chose a set of stations with latitudes between 20°N and 50°N.

7

The bold dots on the map show the station locations, and the grey dots (merging into a continuous fill in parts) are the available locations that I could have chosen.

The data from the selected stations is shown below.  Notice the scale on the left hand side runs from -10 °C to + 30 °C.

2

In this form it is not obvious if the data is warming or cooling: And notice that only a few data sets span the full time range.

So how do we discover if there are trends in the data?

The first step

Once you have selected a set of stations one can see that some stations are warm and others cool. In order to be able to compare these data fairly, we subtract off the average value of each data set between 1900 and 1950.

This is called normalisation and allows us to look in detail at changes from the 1900-1950 average independent of whether the station was in a warm place or a cold place.

3

Notice that the scale on the left-hand side is now just ± 3.5 °C.

The second step

One can then average all the data together. This is has the effect of reducing the fluctuations in the data.

One can then fit a trend-line to see if there is a recent warming or cooling trend.

5

For this particular set of stations its pretty clear that since 1970, there is a warming trend. The software tells me it is approximately 0.31 ± 0.09 °C per decade.

What I have found is that for any reasonably diverse set of stations a warming trend always emerges. I haven’t investigated this thoroughly, but the trend actually seems to emerge quite clearly above the fluctuations.

But you can check that for yourself if you want!

Is it a cheat? No!

You can check the maths of the software by downloading the data and checking it for yourself.

Maybe the data is fixed? You download the source data yourself – it comes from the US Global Historical Climatology Network-Monthly (GHCN-M) temperature data-set.

But accessing the raw data is quite hard work. If you are a newbie, it will probably take you days to figure out how to do it.

There is more!

This ability to browse, normalise, average and fit trends to data is cool. But – at the risk of sounding like a shopping channel advertorial – there is more!

It can also access the calculations of eleven different climate models.

For the particular set of stations that you have selected, the software will select the climate model predictions (a) including the effect of human climate change and (b) without including human-induced climate change.

For my data selection I chose to compare the data with the predictions of the CCSM4 Climate model. The results are shown below

6

You can judge for yourself whether you think the trend in the observed data is consistent with the idea of human-induced climate change.

For the particular set of stations I chose, it seems the CCSM4 climate model can only explain the data by including the effect of human-induced climate change.

But Michael: this is just too much like hard work!

Yes and no. This analysis is conceptually challenging. But it is not crazily difficult. For example:

  • Schoolchildren could do this with help from a teacher.
  • Friends could do it as a group and ask each other for help.
  • University students could do this.
  • Scout groups could do it collectively.

It isn’t easy, but ultimately, if you really want to know for yourself, it will take some work. But then you will know.

So why not have a go?  The software is described in more detail here, and you can view a video explaining how to use the software here.

[January 28th 2017: Weight this morning 71.2 kg: Anxiety: Sick to my stomach: never felt worse]

5.What was all that about?

January 3, 2017

duty_calls

Interviewer: So Michael, why did you write the last four articles (1,2,3,4) on the transmission of infrared radiation through the atmosphere: that stuff is already well known?

Me: I know, but I was irritated by a friend of a friend who wrote an “exposé” of why carbon dioxide can’t cause global warming.

Interviewer: Curious. Were they an expert in Climate Science? Or had they made a study of radiative transfer through the atmosphere?

Me: Neither. I think they were an electrical engineer.

Interviewer: An electrical engineer? Why did they think that their assessment outweighed the view of the large number of experts who had studied this intensively over the last century or so?

Me: I think it is an example of the Dunning-Kruger effect in which people who don’t know about a subject fail to appreciate how little they know. We are all affected by it at times.

Interviewer: OK, So you wrote all this just to set them straight?

Me: Yes, and hopefully to help others who are curious about radiative transfer. It is complicated.

Interviewer: And how do you feel about it now?

Me: Numb and Tired. But OK. I like one or two of the graphs I have created, and I enjoyed learning how to make animated GIFs. I have also learned quite a bit about MODTRAN.

Interviewer: But…

Me: But the articles took literally weeks to prepare and I still don’t feel satisfied with them. However now, if I see anyone else write stuff like this:

The bottom line is that once Carbon Dioxide reaches a concentration that makes the atmosphere completely opaque in the band where it resonates,  further increases in the concentration cannot result in any additional blocking

I will know exactly where to send them. And so will you.

 


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