About Rasmus Benestad

After an absurd period with a real-life gloomy corona pandemic, lock-down and unrest, it was quite refreshing to see visions for a sustainable future in a new documentary ‘2040‘ (link to trailer). Its message, through the voice of Damon Gameau, is about hope and is based on rational thinking.

The video takes us to twenty years fast-forward to an imagined future. It makes good use of effects that communicate. For instance, Damon Gameau speaks with children about green and sustainable solutions and then makes the time travel to show what such a future may look like when climate change has stopped.

The documentary also makes use of some cool effects to demonstrate how things work. But it is mostly about a positive message on solutions rather than emphasising climate science and harmful consequences of climate change.

There is an interesting timing with the release of ‘2040’, and hopefully it will contribute to discussions about new solutions and how we can make use of both technology and new behaviour to improve our lives and the health of the planet. This is something that is already being discussed in Europe.

I thought the documentary made some interesting points about energy production, how to make agriculture more sustainable through mixed crops and good soil health, and how to use ocean resources. Another important point is the importance of empowering girls and women. However, I’m not in the position to say how successful the suggested solutions would be. I guess we may know answers in 2040.

There is a growing need for local climate information in order to update our understanding of risks connected to the changing weather and prepare for new challenges. This need has been an important motivation behind the World Meteorological Organisation’s (WMO) Global Framework for Climate Services (GFCS).

There has also been a lot of work carried out to meet these needs over time, but I’m not convinced that people always get the whole story.  

[Read more…] about Regional climate modeling and some common omissions

The American Geophysical Union (AGU) started to stream sessions at their annual meeting in San Francisco a few years ago. This kind of participation over the Internet is a nice alternative since many scholars are unable to attend the AGU meetings due to distance, time constraints, time difference and cost.

  [Read more…] about A problem with YouTube

Last week, a colleague shared a tweet with a link to a very unusual paper. I first thought it must be a joke, but then realised that since it was the last days in March when I read it, it could not be an April’s fool joke. It seems to be a serious paper.

So I thought it would be perfect to share the reference McCarthy et al. (2020) today. The paper has a few useful take-home messages, such as the C.R.A.P. framework.

Update: here is a presentation slide deck to accompany the paper. 

References

1. I.P. McCarthy, D. Hannah, L.F. Pitt, and J.M. McCarthy, "Confronting indifference toward truth: Dealing with workplace bullshit", Business Horizons, vol. 63, pp. 253-263, 2020. http://dx.doi.org/10.1016/j.bushor.2020.01.001

I have recently been asked whether the present corona pandemic will have any consequence on climate change. Gavin has already discussed the coronavirus and climate here on RealClimate, and I like to follow up on his post.

Rather than emphasising analogies, I would highlight additional common denominators between the present world-wide Covid-19 pandemic and climate change.

[Read more…] about Further perspectives on pandemics and climate change

There is a clever mathematical trick for comparing different data sets, but it does not seem to be widely used. It is based on so-called empirical orthogonal functions (EOFs), which Edward Lorenz described in a Massachusetts Institute of Technology (MIT) scientific report from 1956. The EOFs are similar to principal component analysis (PCA). 

The EOFs and PCAs provide patterns of spatio-temporal covariance structure. Usually these techniques are applied to datasets with many parallel variables to show coherent patterns of variability. Myles Allen used to lecture on EOFs at Oxford University about twenty years ago and convinced me about their value. Many scientists do indeed use EOFs to analyse their data. 

It is not that there is little use of EOFs (they are widely used), but the question is how the EOFs are used and how the results are interpreted. I learned that EOFs can be used in many different ways from Doug Nychka, when I visited University Corporation for Atmospheric Research (UCAR) in 2011.

The clever trick is to apply these techniques to data compiled from more than one source of data. When used this way, the technique is labelled “common EOFs” or “common PCA”. There are some scientific studies that have made use of common EOFs or common PCA, such as Flurry (1988), Barnett (1999), Sengupta & Boyle (1993), Benestad (2001), and Gilett et al (2002). 

Nevertheless, a Scholar Google recent search with “common EOFs” only gave 101 hits (2020-03-05). I find this low interest for this technique a bit puzzling, since it in many ways has lots in common to machine learning and artificial intelligence (AI), both which are hot topics these days. 

Common EOFs are also particularly useful for quantifying local effects of global warming through a process known as empirical-statistical downscaling (ESD). It's pity that common EOFs aren't even mentioned in the recent textbook on ESD by Maraun and Widmann (2019)  (they are discussed in Benestad et al. (2008)). 

Figure. Examples showing how common EOFs can be used to compare the annual cycle in T(2m) in the upper set of panels and precipitation (lower panels) simulated by global climate models from the CMIP5 experiment (red) and compared with the ERAINT reanalysis (black).

 

The take-home message from these common EOFs, eigenvalues and principal components, is that the models do reproduce the large-scale patterns in the mean annual cycle. For those interested, common EOFs can easily be calculated with the R-based tool:

github.com/metno/esd.

References

1. R.E. Benestad, "A comparison between two empirical downscaling strategies", International Journal of Climatology, vol. 21, pp. 1645-1668, 2001. http://dx.doi.org/10.1002/joc.703
2. N.P. Gillett, F.W. Zwiers, A.J. Weaver, G.C. Hegerl, M.R. Allen, and P.A. Stott, "Detecting anthropogenic influence with a multi-model ensemble", Geophysical Research Letters, vol. 29, pp. 31-1-31-4, 2002. http://dx.doi.org/10.1029/2002GL015836

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References

1. C.W. van Eck, B.C. Mulder, and S. van der Linden, "Echo Chamber Effects in the Climate Change Blogosphere", Environmental Communication, vol. 15, pp. 145-152, 2020. http://dx.doi.org/10.1080/17524032.2020.1861048

A consensus is usually established when one explanation is more convincing than alternative accounts, convincing the majority. This is also true in science. However, science-based knowledge is also our best description of our world because it is built on testing hypotheses that are independently reexamined by colleagues.

[Read more…] about More than 500 people misunderstand climate change

“The weather forecast looks sunny and particularly hot from Sunday to Friday, with afternoon temperatures above 30°C every day, and likely exceeding 35°C by the middle of the week. One consequence is that the poster sessions (Tuesday and Thursday) have been moved to the morning as they will be held outside under a marquee.”

 

I have never received a notification like this before a conference. And it was then followed up by a warning from the Guardian: ‘Hell is coming’: week-long heatwave begins across Europe.

 

The heatwave took place and was an appropriate frame for the International meeting on statistical climatology (IMSC), which took place in Toulouse, France (June 24-28). France set a new record-high temperature 45.9°C on June 28th, beating the previous record 44.1°C from 2003 by a wide margin (1.8°C).

 

One of the topics of this meeting was indeed heatwaves and one buzzword was “event attribution”. It is still difficult to say whether a single event is more likely as a result of climate change because of model inaccuracies when it comes to local and regional details.

 

Weather and climate events tend to be limited geographically and involve very local processes. Climate models, however, tend to be designed to reproduce more large-scale features, and their output is not exactly the same as observed quantity. Hence, there is often a need for downscaling global climate model results in order to explain such events.

 

A popular strategy for studying attribution of events is to run two sets of simulations: ‘factual’ (with greenhouse gas forcing) and ‘counterfactual’ (without greenhouse gas forcings) runs for the past, and then compare the results. Another question is how to “frame” the event, as different definitions of an event can give different indicators.

 

Individual heatwaves are still difficult to attribute to global warming because soil moisture may be affected by irrigation wheras land surface changes and pollution (aerosols) can shift the temperature. These factors are tricky when it comes to modeling and thus have an effect on the precision of the analysis.

 

Nevertheless, there is little doubt that the emerging pattern of more extremes that we see is a result of the ongoing global warming. Indeed, the results presented at the IMSC provide further support for the link between climate change and extremes (see previous post absence of evidence).

 

I braved the heat inside the marquee to have a look at the IMSC posters. Several of them presented work on seasonal and decadal forecasting, so both seasonal and decadal prediction still seem to be hot topics within the research community.

 

A major hurdle facing decadal predictions is to design climate models and give them good enough information so that they are able to predict how temperature and circulation evolve (see past post on decadal predictions). It is hard enough to predict the global mean temperature (link), but regional scales are even more challenging. One question addressed by the posters was whether advanced statistical methods improve the skill when applied to model output.

 

A wide range of topics was discussed during the IMSC. For instance, how the rate of new record-breaking events (link) can reveal trends in extreme statistics. There was one talk about ocean wave heights and how wave heights are likely to increase as sea-ice retreats. I also learned how severe thunderstorms in the US may be affected by ENSO and climate change.

 

Another interesting observation was that so-called “emergent constraints” (and the Cox et al, (2018) paper) are still debated, in addition to methods for separating internal variability from forced climate change. And there is ongoing work on the reconstruction of temperature over the whole globe, making use of all available information and the best statistical methods.

 

It is probably not so surprising that the data sample from the ARGO floats shows an ongoing warming trend, however, by filling in the spaces with temperature estimates between the floats, the picture becomes less noisy. It seems that a better geographical representation removes a bias that gives an underestimated warming trend.

While most talks were based on statistics, there was one that was mostly physics-based on the transition between weather regimes. Other topics included bias-adjustment (multi-variate), studies of compound events (straining the emergency service), the connection between drought and crop yields, how extreme weather affects health, snow avalanches, precipitation from tropical cyclones, uncertainties, downscaling based on texture analysis, and weather generators. To cover all of these would take more space than I think is appropriate for a blog like this.

 

One important issue was about data sharing which merits wider attention. The lack of open and free data is still a problem, especially if we want to tackle the World Climate Research Programme’s grand challenges. European and US data are freely available and the Israeli experience indicate that open access is beneficial.