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Here’s an extract from a paper by Mehran et al 2014, comparing climate models with observations, over the same 1979-2005 time period:

From Mehran et al 2014

Click to enlarge

The graphs show the ratios of models to observations. Therefore, green is optimum, red means the model is producing too much rain, while blue means the model is producing too little rain (slightly counter-intuitive for rainfall and I’ll be showing data with colors reversed).

You can easily see that as well as models struggling to reproduce reality, models can be quite different from each other, for example the MPI model has very low rainfall for lots of Australia, whereas the NorESM model has very high rainfall. In other regions sometimes the models mostly lean the same way, for example NW US and W Canada.

For people who understand some level of detail about how models function it’s not a surprise that rainfall is more challenging than temperature (see Opinions and Perspectives – 6 – Climate Models, Consensus Myths and Fudge Factors).

But this challenge makes me wonder about drawing a solid black line through the median and expecting something useful to appear.

Here is an extract from the recent IPCC 1.5 report:

Global Warming of 1.5°C. An IPCC Special Report

I’ll try to shine some light on the outputs of rainfall in climate models in subsequent articles.

References

Note: these papers should be easily accessible without a paywall, just use scholar.google.com and type in the title.

Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations, Mehran, AghaKouchak, & Phillips, Journal of Geophysical Research: Atmospheres (2014)

The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), Adler et al, American Meteorological Society (2003)

Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht, J. Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5ºC Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)].

The datasets are accessible in websites below – there are options to plot specific regions, within specific dates, and to download the whole dataset as a .nc file.

GPCC – https://psl.noaa.gov/data/gridded/data.gpcc.html

GPCP – https://psl.noaa.gov/data/gridded/data.gpcp.html

I have just been looking at the GPCC dataset, using Matlab to extract and plot monthly data for different time periods including comparisons. I’d like to compare actual with the output of various climate models over similar time periods – and against future simulations under different scenarios.

Have any readers of the blog done this? If so I’d appreciate a few tips having run into a few dead ends.

What I’m looking for – monthly gridded surface precipitation.

GPCC has 0.5ºx0.5º and 2.5ºx2.5º datasets that I’ve downloaded so the same gridded output from models would be wonderful.

I have found:

–  The CMIP5 Data is now available through the new portal, the Earth System Grid – Center for Enabling Technologies (ESG-CET), on the page http://esgf-node.llnl.gov/

–  https://www.wcrp-climate.org/wgcm/references/IPCC_standard_output.pdf

Table A1a: Monthly-mean 2-d atmosphere or land surface data (longitude, latitude, time:month).

CF standard_name; output; variable name;  units;  notes  –
precipitation_flux; pr; kg m-2 s-1;   includes both liquid and solid phases.

So I think this is what I am looking for.

–  https://www.ipcc-data.org/sim/gcm_monthly/AR5/Reference-Archive.html gives a list of different experiments within each climate model. For example – the MPI model, I expect that historical and rcp.. are the ones I want. I would have to dig into MPI-ESM-LR and -MR which I assume are different model resolutions.

But when I work my way through the portal, e.g. https://esgf-data.dkrz.de/search/cmip5-dkrz/ I find a bewildering array of options and after hopefully culling it down to just monthly rainfall from the MPI-LR model, there are 213 files:

I can easily imagine spending 100+ hours trying to establish which files are correct, trying to verify.. So, if any readers have the knowledge it would be much appreciated.

————

Just for interest, here are a few graphs produced from GPCC using Matlab. I checked a couple of outputs against samples produced from their website and they seemed correct.

I set the max monthly rainfall on the color axis to increase contrast for most places in the world – 4 different 10-year periods:

GPCC Precipitation data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/

And a delta, % difference:

GPCC Precipitation data provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their Web site at https://psl.noaa.gov/

The IPCC 5th Assessment Report (AR5) from 2013 shows the range of results that climate models produce for global warming. These are under a set of conditions which for simplicity is doubling CO2 in the atmosphere from pre-industrial levels. The 2xCO2 result. Also known as ECS or equilibrium climate sensitivity.

The range is about 2-4ºC. That is, different models produce different results.

Other lines of research have tried to assess the past from observations. Over the last 200 years we have some knowledge of changes in CO2 and other “greenhouse” gases, along with changes in aerosols (these usually cool the climate). We also have some knowledge of how the surface temperature has changed and how the oceans have warmed. From this data we can calculate ECS.

This comes out at around 1.5-2ºC.

Some people think there is a conflict, others think that it’s just the low end of the model results. But either way, the result of observations sounds much better than the result of models.

The reason for preferring observations over models seems obvious – even though there is some uncertainty, the results are based on what actually happened rather than models with real physics but also fudge factors.

The reason for preferring models over observations is less obvious but no less convincing – the climate is non-linear and the current state of the climate affects future warming. The climate in 1800 and 1900 was different from today.

“Pattern effects”, as they have come to be known, probably matter a lot.

And that leads me to a question or point or idea that has bothered me ever since I first started studying climate.

Surely the patterns of warming and cooling, the patterns of rainfall, of storms matter hugely for calculating the future climate with more CO2. Yet climate models vary greatly from each other even on large regional scales.

Articles in this Series

Opinions and Perspectives – 1 – The Consensus

Opinions and Perspectives – 2 – There is More than One Proposition in Climate Science

Opinions and Perspectives – 3 – How much CO2 will there be? And Activists in Disguise

Opinions and Perspectives – 3.5 – Follow up to “How much CO2 will there be?”

Opinions and Perspectives – 4 – Climate Models and Contrarian Myths

Opinions and Perspectives – 5 – Climate Models and Consensus Myths

Opinions and Perspectives – 6 – Climate Models, Consensus Myths and Fudge Factors

Opinions and Perspectives – 7 – Global Temperature Change from Doubling CO2

Opinions and Perspectives – 8 – Pattern Effects Primer

For people with maths, physics and chemistry (and biology) backgrounds non-linear processes are familiar. For people without this background they are often quite obscure.

I’ll give a simple example. It’s not based on reality but it seems like the easiest way to explain non-linear effects.

Here we go..

Half the world is snow-covered land and half the world is ocean. Snow reflects about half of sunlight and ocean reflects no sunlight (this is not accurate, the actual figure is something like 10%, but we’ll stick with 0% for simplicity).

We also have clouds in this world. Clouds reflect 100% of sunlight.

Half of the sky has cloud cover. In our mythical world the land has cloudy skies and the ocean has clear skies.

So the cloud over the land reflects 100% of solar radiation while the ocean, with clear skies, absorbs all of its radiation.

Result – the mythical world absorbs 50% of solar radiation and so reaches some steady state temperature.

Now some climate change takes place. The winds are stronger and all the clouds move over the ocean. So the ocean has cloudy skies and the land has clear skies. Now the land reflects 50% of its sunlight (because of the snow) and the ocean region – because it’s covered by clouds – reflects 100% of sunlight.

Result – under the changed climate, the mythical world absorbs only 25% of solar radiation and cools dramatically

The important point is that clouds still cover 50% of the skies, and the ocean and land haven’t changed. But simply moving the clouds halves the sunlight absorbed.

A more realistic example is given by in Clouds & Water Vapor – Part Five – Back of the envelope calcs from Pierrehumbert which looks at regions of low humidity and high humidity.

Articles in this Series

Opinions and Perspectives – 1 – The Consensus

Opinions and Perspectives – 2 – There is More than One Proposition in Climate Science

Opinions and Perspectives – 3 – How much CO2 will there be? And Activists in Disguise

Opinions and Perspectives – 3.5 – Follow up to “How much CO2 will there be?”

Opinions and Perspectives – 4 – Climate Models and Contrarian Myths

Opinions and Perspectives – 5 – Climate Models and Consensus Myths

Opinions and Perspectives – 6 – Climate Models, Consensus Myths and Fudge Factors

Opinions and Perspectives – 7 – Global Temperature Change from Doubling CO2

How much will global temperature rise if we double CO2 from pre-industrial levels? Based on current behaviour that’s roughly what we are on course to do by the end of the century (see 3 – How much CO2 will there be? And Activists in Disguise and 3.5 – Follow up to “How much CO2 will there be?”).

For this we need a model. But to begin with we can use a much simpler model than a current GCM (global climate model).

The key is to say “all other things remaining equal”. So we double CO2 but assume (in our model) that the vertical temperature structure of the atmosphere doesn’t change, clouds don’t change, water vapor doesn’t change, etc.

This allows us to calculate how the radiation balance gets disturbed. Then we can find the new surface temperature which brings everything back into balance. We don’t need a GCM that attempts to model turbulent flows of the atmosphere and ocean.

It turns out that the global temperature change will be about 1.2ºC from pre-industrial levels.

Well, kind of.

This is with the absolute amount of water vapor staying the same. Water vapor is the strongest “greenhouse” gas in the atmosphere and the amount of water vapor is one key to understanding future climate change.

If you stand next to the ocean in the tropics, barring a strong wind coming from inland, it’s pretty humid. If you stand next to the ocean in the Arctic it’s pretty dry. The reason is that the amount of water vapor that the air can hold depends strongly on temperature. Next to the ocean the air can be close to 100% relative humidity, but 100% relative humidity in the tropics has lots more water vapor than 100% in the Arctic.

So a slightly different simulation has relative humidity staying constant. The result is some amplification from the water vapor. The Earth’s surface gets a little hotter, so there’s more water vapor – which is also a greenhouse gas – so the surface gets hotter still.

In this experiment the global temperature change will be about 2.4ºC from pre-industrial levels. This second experiment is intuitively a better experiment than the first one – at least to get a “finger in the air” kind of result. It doesn’t mean it’s correct, but most people working in climate would expect relative humidity to be more likely to be constant than absolute humidity, when you increase the temperature.

So, our “no feedback” result is 1.2ºC, and our slightly more realistic “some feedback” result is 2.4ºC (currently the global temperature has increased about 0.8ºC from pre-industrial levels).

Both of these results can be obtained without relying on models that have “giant fudge factors” which is what you need to model the atmosphere and ocean “fluid flows” – see 6 – Climate Models, Consensus Myths and Fudge Factors. They only rely on being able to accurately calculate how radiation is absorbed and emitted by the atmosphere – an extremely well understood physics problem. (I can reproduce the results on my home computer using Matlab and the spectroscopic properties of CO2 and water vapor – see Visualizing Atmospheric Radiation).

The real story, of course, is more complicated. However, to understand anthropogenic global warming (AGW) – a better name than “climate change” – it’s useful to know these results (first calculated in the late 1960s by Manabe & Wetherald) – and to understand the difference between simple radiation models and global climate models.

Articles in this Series

Opinions and Perspectives – 1 – The Consensus

Opinions and Perspectives – 2 – There is More than One Proposition in Climate Science

Opinions and Perspectives – 3 – How much CO2 will there be? And Activists in Disguise

Opinions and Perspectives – 3.5 – Follow up to “How much CO2 will there be?”

Opinions and Perspectives – 4 – Climate Models and Contrarian Myths

Opinions and Perspectives – 5 – Climate Models and Consensus Myths

Opinions and Perspectives – 6 – Climate Models, Consensus Myths and Fudge Factors

In Opinions and Perspectives – 5 – Climate Models and Consensus Myths we looked at (and I critiqued) a typical example of the storytime edition – why we can trust climate models. On that same web page they outlined a “Climate Myth”:

Models are unreliable
“[Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observed data. But there is no reason to believe that the same fudge factors would give the right behaviour in a world with different chemistry, for example in a world with increased CO2 in the atmosphere.” (Freeman Dyson)

Fudge factors does kind of imply a lot of unreliability. But I was recently giving some thought to how I would explain some of the issues in climate models to a non-technical group of people. My conclusion:

Models are built on real physics (momentum, conservation of heat, conservation of mass) and giant fudge factors.

Depending on the group I might hasten to add that “giant fudge factors” isn’t a nasty term, aimed at some nefarious activity of climate scientists. It’s just to paint a conceptual picture to people. If I said “sub-grid parameterization” I’m not sure the right mental picture would pop into peoples’ heads. I’m not sure any mental picture would pop into peoples’ heads. Instead, how do we change the subject and stop this nutter talking?

Fudge Factors

In the least technical terms I can think of.. imagine that we divided the world up into a 3d grid. Our climate model has one (and only one) value in each cell for east-west winds, north-south winds, up-down winds, high cloud fraction, low cloud fraction, water vapor concentration.. basically everything you might think is important for determining climate.

The grid dimensions, even with the latest and best supercomputers, is something like 100km x 100km x 1km (for height).

So something like N-S winds, and E-W winds, might work quite well with large scale processes operating.

But now think about clouds and rain. Especially in the tropics, it’s common to have strong upwards vertical winds (strong convection) over something like a few km x a few km, and to have slower downward winds over larger areas. So within one cell we have both up and down winds. How does the climate model deal with this?

We also have water vapor condensing out in small areas, creating clouds, later turning into rain.. yet other places within our cell this is not happening.

How does the model, which only allows one value for each parameter in each cell, deal with this?

It uses “sub-grid parameterizations”. Sorry, it uses giant fudge factors.

Here is an example from a review paper in 2000 on water vapor (the concepts haven’t changed in the intervening years) – the box is one cell in the grid, with clouds and rainfall as light blue and dark blue:

Held and Soden (2000)

Held and Soden (2000)

Figure 1

The problem is, when you create a fudge factor you are attempting to combine multiple processes operating over different ranges and different times and get some kind of average. In the world of non-linear physics (the real world), these can change radically with very slight changes to conditions. Your model isn’t picking any of this up.

Here’s just one example from a climate science paper. It picks out one parameter in cloud microphysics, and uses three different values to run the climate model from 1860 to today and looks at the resulting temperature anomalies (see the last article 5 – Climate Models and Consensus Myths).

 

From Golaz et al 2013

Figure 2

The problem is that the (apparently) most accurate value of this parameter (blue) produces the “worst” result (the least accurate) for temperature anomalies. As the authors of the papers say:

Conversely, CM3c uses a more desirable value of 10.6μm but produces a very unrealistic 20th century temperature evolution. This might indicate the presence of compensating model errors. Recent advances in the use of satellite observations to evaluate warm rain processes [Suzuki et al., 2011; Wang et al., 2012] might help understand the nature of these compensating errors. (More about this paper in Models, On – and Off – the Catwalk – Part Five – More on Tuning & the Magic Behind the Scenes).

Conclusion

This is the reality in climate models. They do include giant fudge factors (sub-grid parameterizations). The “right value” is not clear. Often when some clarity appears and a better “right value” does appear, it throws off some other aspects of the climate model. And anyway, there may not be a “right value” at all.

This is well-known and well-understood in climate science, at least among those who work closely with models.

What is not well-known or understood is what to do about it, or what this means for the results produced by climate models. At least, there isn’t any kind of consensus.

Articles in this Series

Opinions and Perspectives – 1 – The Consensus

Opinions and Perspectives – 2 – There is More than One Proposition in Climate Science

Opinions and Perspectives – 3 – How much CO2 will there be? And Activists in Disguise

Opinions and Perspectives – 3.5 – Follow up to “How much CO2 will there be?”

Opinions and Perspectives – 4 – Climate Models and Contrarian Myths

Opinions and Perspectives – 5 – Climate Models and Consensus Myths

References

Cloud tuning in a coupled climate model: Impact on 20th century warming, Jean-Christophe Golaz, Larry W. Horowitz, and Hiram Levy II, GRL (2013) – free paper

 

In the last article – Opinions and Perspectives – 4 – Climate Models and Contrarian Myths – we looked at a few ideas that are common in many blogs but have no basis in reality.

The title of this article “Consensus Myths” doesn’t refer to what you will find if you read a broad range of papers in climate science. It refers to popular myths put forward by those defending climate models. The reality is more complicated.

While the contrarian myths are just plain ignorance of science, the consensus myths are closer to the truth, and more subtly misleading.

1. Here is a typical example:

While there are uncertainties with climate models, they successfully reproduce the past and have made predictions that have been subsequently confirmed by observations.

2. And a followup from the same site:

A common argument heard is “scientists can’t even predict the weather next week – how can they predict the climate years from now”. This betrays a misunderstanding of the difference between weather, which is chaotic and unpredictable, and climate which is weather averaged out over time. While you can’t predict with certainty whether a coin will land heads or tails, you can predict the statistical results of a large number of coin tosses. In weather terms, you can’t predict the exact route a storm will take but the average temperature and precipitation over the whole region is the same regardless of the route.

Reproducing the Past

So let’s look at #1. Here is a graph from that page:

The bottom figure is the key. We can see the model and the observations match up on temperature anomalies. Clearly the author of that site has a point.

Here is another version of this graph (with also future projections based on various scenarios) – the gray lines are the model results:

From Mauritsen et al 2012

Figure 1

Now we are looking at actual temperatures (rather than anomalies) reproduced by climate models. Climate models – reproducing the past – are running between 12.5ºC and 15.5ºC. A range of 3ºC. Why should we trust a model that runs 2ºC cold or a model that runs 1ºC hot? Should we? Shouldn’t we? Is this successfully reproducing the past?

Here’s another example. I happen to live in Australia, not the country of my birth which is England, but it’s definitely the lucky country. Take a look at Australia (it’s bottom right for people confused by geography!). This graph shows rainfall simulated by different models over 25 years (1979-2005) compared with observations.

It’s nothing sophisticated like how accurate are models on seasonal rainfall, or decadal variation. It is simply the total rainfall over 25 years. The color indicates the ratio of model results to observations:

From Mehran et al 2014 – click to enlarge

Figure 2

I did try to access the complete datasets for myself but ran into problems. It’s possible that the scale 0-2 has just maxed on 2. I don’t know. Best case, some models have Australian rainfall at double the actual, some models have rainfall at 50% or less. Is this success?

At best we can say that models do some things well and some things badly. Just highlighting success in modeling past temperature anomalies is not the end of the story.

Some confident souls might suggest that if they get past temperature anomalies right, then they will get future temperature anomalies right. For consideration in due course..

Weather and Climate and Chaos

Now let’s look at item #2.

Climate models don’t attempt to tell us the weather on a given day. They do attempt to tell us the average of weather over a period. On this, the website cited is correct.

There is a large fly in the ointment unfortunately. It’s true that if you flip lots of coins you can predict the statistical outcome. Non-linear chaotic systems are a little different.

There is an extremely simple chaotic system, described by the famous Edward Lorenz. He simplified a convection model with fluid being heated from the bottom into a system of 3 variables and looked at the results.

The simple idea is that when you have a “deity-like view” (which means over a long enough time period) you can be confident that you know the statistics – the mean, the standard deviation and so on. But when you don’t have this deity-like view you can’t have any confidence in the statistics. You might watch the system over a “really long time” and calculate the statistics, but over twice that time, and 10x that time, the statistics may change. You don’t know how long you need to watch it for. More on this in the series Natural Variability and Chaos, especially Natural Variability and Chaos – Four – The Thirty Year Myth.

Here is an example of the quasi-periodic fluctuations – note that the graphs are showing the running average over a time period for each of the three variables (graphs 1,2,3), for three very slightly different starting conditions (red, blue, green):

Figure 3 – Click to expand

The climate system is massively more complicated than the Lorenz 3-equation problem. No one has any idea how concepts from the “simple” Lorenz problem map to the complex problem. And the “simple” Lorenz problem is a gift that keeps on giving.

Conclusion

The storytime edition of why we can trust climate models isn’t one you find very much in climate science papers. Instead the question is what we can learn from climate models. Relationship status with climate models – it’s complicated.

Articles in this Series

Opinions and Perspectives – 1 – The Consensus

Opinions and Perspectives – 2 – There is More than One Proposition in Climate Science

Opinions and Perspectives – 3 – How much CO2 will there be? And Activists in Disguise

Opinions and Perspectives – 3.5 – Follow up to “How much CO2 will there be?”

Opinions and Perspectives – 4 – Climate Models and Contrarian Myths

References

Tuning the climate of a global model, by Mauritsen et al (2012)

Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations, A. Mehran et al, JGR (2014)