Tag: observations

Lesson 13: Total Solar Irradiance

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Total Solar Irradiance Composite. From: https://soho.nascom.nasa.gov/gallery/helioseismology/large/vir011.jpg

The Sun, providing almost all the energy we receive, is the driver of our climate. Therefore one of the core parameters needed to understand the climate is a quantity called “total solar irradiance” (TSI). TSI is measured in watts per metre squared and is a measure of the incoming energy from the Sun into a square metre every second. Note that even that definition needs some caveats – the irradiance of the Sun will depend on the angle the ground is to the Sun and will depend on the distance between the Earth and the Sun which changes a little over our year’s orbit. So, it’s defined as the “straight on” area – something like at the Equator at noon – and for the average distance between the Earth and the Sun over the whole year. The “Total” in total solar irradiance means that this is the Sun’s output at any wavelength of light and distinguishes it from “spectral solar irradiance” where we measure how much light there is at each wavelength individually.

The graph at the top represents the satellite observations of total solar irradiance over the last 40 years. Because the Sun is the driver of the Earth’s climate, it is absolutely essential to understand these data. The coloured lines you see represent the daily values – there’s a lot of natural variation. This is because the Sun has something akin to “weather” – the Sun’s activity can vary significantly and it becomes more and less active depending on the exact processes going on in the upper regions of the Sun. The grey line is a rolling average of that weather – akin to a measure of the Sun’s climatic state.

We’ve been monitoring the Sun’s activity since 1611 when the first telescope observations of Sun spots were made. (The Wikipedia article on Sunspots also says that sunspot observations go right back to the Chinese Book of Changes in 800 BC). When the Sun is particularly active there are lots of Sunspots and when the Sun is not very active there are fewer Sunspots.

If you look at Sunspot numbers over the last 400 years, you see there is a regular 11 year cycle for most of this time where Sunspot numbers increase, then go to almost zero and then increase again. This is known as the “solar cycle” and it is also visible in the satellite observations at the top of the page – the total solar irradiance is higher when the number of sunspots is higher.

Sunspot counts since 1610, from Wikipedia article on Sunspots

You can also see from the 400 year record that there were times when the number of sunspots was extremely low. This is especially true in the very early record with a long “Maunder Minimum” with almost no Sunspots observed at all from 1650 to 1700. That time period also corresponds to the “Little Ice Age” which may have had multiple causes, including because of the Sun’s lower total solar irradiance.

Clearly, the total solar irradiance is a variable quantity and therefore it is essential that climate models include TSI in their analyses. The satellite observations that make up the graph at the top are our best estimates of this quantity – mostly because they are measuring the pure sunlight, unfiltered by the atmosphere. Any observations from the ground (and the best of those are made in Davos, Switzerland at the “World Radiometric Reference”) will lose some light to the atmosphere and that loss will depend on the weather conditions.

In my last blog I showed how even with something as simple as “temperature” there needed to be some thinking about how to interpret and analyse the data to give meaningful information that could be used by climate scientists. On my facebook page someone asked me how you can tell if data are “manipulated” and I’ve been meaning to talk about TSI since then because TSI data must be analysed carefully before being used.

The first clue is in the title of the graph at the top of the page. It describes this record as a “composite”. That means that people have combined data from multiple sources and that almost always means that some analysis is required. If you know how to find scientific data, you can relatively quickly find the graph of the “raw” data.

See the source image
Total Solar Irradiance raw data from different satellites. From Kopp (2014): http://dx.doi.org/10.1051/swsc/20140

The colour scale is slightly different from the top graph, but you can see from the names of the satellites that these are the same satellite observations. When you see the raw data you see why analysis is required – there are noticeable step changes between satellites. Furthermore, at times when more than one satellite was observing simultaneously, you can see that some of the detailed shape is also different.

These differences are because the satellites themselves have slightly different methods for measuring the TSI. All of them use a basic “electrical substitution” technique – they have black cavities that absorb the sunlight and heat up and they compare the temperature rise from the sunlight with the temperature rise that they get using an electrical heater. But there are differences in exactly how they absorb the sunlight and in exactly how they compare the solar heating with the electrical heating. Each satellite instrument manufacturer has made the best attempt at getting that heating equivalent – but there are real differences between satellites because there are real differences between approaches. When I first showed this graph in talks in 1999, I used to say “but you can see that more recently the lines are closer together” and then ACRIM3 and TIM V15 were launched. TIM V15 used a far more accurate technique to do the electrical substitution and that showed a step change. Instruments also change once they are in space – the sunlight they are absorbing contains considerable amounts of extreme ultraviolet that is very damaging to the instruments – the black absorber might go a bit grey, the electrical heater might not be as powerful – and they also get hit by solar wind particles which are even more damaging.

It’s also important to remember that scientists do put “uncertainty estimates” on their observations. And those “uncertainty estimates” are larger than the differences between satellites.

The TSI composite you see at the top is the best estimate by scientists of how to take all this into account. They choose the most stable satellites, they correct for instrument drifts based on models of how the instruments degrade, they “bias correct” the step changes between instruments, they link to the ground observations from Davos and they make their best composite analysis of what the Sun is doing. Different groups around the world have their own best composite and those different composites disagree – and in meeting rooms all over the world scientists argue about the exact details of this composite.

These are real data and real data are always messy. They always need analysing and interpreting by real experts who understand why those differences exist. I’ll write a separate “opinion blog” about how this is over-interpreted by climate sceptics. However, here I’ll just note that when TIM V15 was launched, the TSI was changed downwards. That was taken into account in the modelling and is part of why the older models showed subtle differences to the newer models. But none of that changed the underlying story that anthropogenic greenhouse gases are the dominant cause of recent warming. (Just because we don’t know everything [e.g. about the exact value of TSI] doesn’t mean we know nothing [e.g. the relative effects of anthropogenic greenhouse gases and solar changes].)

 

 

Lesson 11: Carbon dioxide measurements from Mauna Loa

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Obtained from https://www.esrl.noaa.gov/gmd/ccgg/trends/full.html. This shows the atmospheric carbon dioxide measurements from the Mauna Loa Observatory

Today I’d like to talk a bit about the observations of climate change. Observations are used both to set up climate models and to test them. That is a bit circular – and where independent data sets exist, different data sets are used for these two roles – but usually the observations are used to tune the model using a method called “data assimilation” which is a mathematical process that tries to minimise the average difference between prediction and observation.

There are three types of observation we need to consider: observations of the quantities that affect the climate, observations of the changing climate and observations of the effects of changing climate. In practice, these three categories are blurred (many observations are both cause and effect).

Today we’ll consider the first of these, and in particular the graph that was published widely in the last week because it measured the highest carbon dioxide levels yet: the Mauna Loa observation of carbon dioxide levels in the atmosphere. As we considered in lesson 7, carbon dioxide is a powerful greenhouse gas that affects the Earth’s radiative energy balance (though not in a simple manner). The Mauna Loa Observatory is on a volcano in Hawaii – right in the middle of the Pacific, and, most significantly, a very, very long way from any meaningful industry. The instruments are at the top of the mountain – 3397 m above sea level – again conditions that keep the observations pure. The observatory has measured carbon dioxide daily since March 1958 by taking samples of air and analysing which gases are inside them.

There is an excellent video at https://youtu.be/gH6fQh9eAQE, which I will embed here:

In the video you can see the observations of carbon dioxide from observatories since 1989. The red dot is Mauna Loa (the black dots are other stations around the world – over time the number of black dots changes as stations come in and out of operation). The upward trend is clear – and this has to be factored into the climate models. The zig-zag pattern is due to the seasons – and in particular due to the summer leaf growth in the northern hemisphere which temporarily removes carbon dioxide from the atmosphere. But the unceasing upward trend behind this is because we’re burning fossil fuels (and, to a more minor extent, because we’re cutting down forests and there are more forest fires).

One problem with these observations is that they are made at only a few sites and these sites are intentionally chosen to be well away from the places where fossil fuels are burnt. There are some satellites that are now measuring global CO2 levels – and these can show where the CO2 is. These work by observing the absorption of the spectrum (seeing how black the black lines are) of sunlight reflected by the Earth in wavelengths we know carbon dioxide absorbs (see back to earlier lessons). In particular they make measurements in a “weak-CO<sub>2<\sub>” band, a “strong-CO<sub>2<\sub>” band and an oxygen O<sub>2<\sub> band. The strong band is a band where carbon dioxide strongly absorbs: this band gives information about the overall absorption of carbon dioxide. The weak band is one where carbon dioxide only partly absorbs. This means it goes through most of the atmosphere undisturbed and gives information about carbon dioxide absorption near the surface: in other words it gives information about whether the surface is a source (e.g. factory) or sink (e.g. forest) of carbon dioxide and to what extent. The oxygen band is a reference band to compare the carbon dioxide against.

The main current CO2 sensor is the NASA OCO-2 satellite which has run since 2014 (OCO failed on launch in 2009).

You can get a video of OCO-2’s observations on YouTube too (https://youtu.be/x1SgmFa0r04)

There’s a joint French-British satellite mission called Microcarb that is currently being built to be launched in 2021 that will also perform satellite-based carbon dioxide measurements.