Climate science from climate scientists...
Raymond Bradley is a Distinguished Professor in the Department of Geosciences at the University of Massachusetts, Amherst. His research focuses on climate variations over recent centuries and millennia. He has carried out extensive fieldwork in the Arctic and North Atlantic region (Canadian High Arctic, Greenland, Svalbard, the Faroe Islands and northern Norway).
Bradley did his undergraduate work at Southampton University (U.K.) and his post-graduate studies (M.A., Ph.D.) at the Institute of Arctic and Alpine Research, University of Colorado, Boulder. He also earned a D.Sc. from Southampton University, for his contributions in paleoclimatology.
Bradley has received honorary degrees (D.Sc honoris causa) from Lancaster University (U.K.), Queen’s University (Canada) and the University of Bern (Switzerland). He was awarded the Oeschger Medal of the European Geosciences Union and he received the Zuckerberg Leadership Chair from the University of Massachusetts Foundation.
He is a Fellow of the Royal Society of Canada, the American Geophysical Union, the American Association for the Advancement of Science, the Arctic Institute of North America, the Finnish Academy of Science and Letters, and the European Academy of Science.
Bradley has written or edited thirteen books on climatic change, and authored/co-authored more than 200 peer-reviewed articles on the topic.
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.
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.
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.
A survey is being conducted by researchers of Cambridge University and Wageningen University. They have asked us to post information about it. Please share your views on climate change and reading blogs by filling out this survey. The data will be used to get a better understanding of climate change blog audiences’ views on climate change and their blog reading behavior.
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Update (22 Dec 2020): The paper describing the results of this survey has now been published (van Eck et al., 2020).
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.
The underlying mission of my job is to safeguard lives and property through climate change adaptation based on science. In other words, to help society to prepare itself for risks connected with more extreme rainfall and temperatures.
For many people, “climate” may seem to be an abstract concept. I have had many conversations about climate, and then realised that people often have different interpretations. In my mind, climate is the same as weather statistics (which I realise can be quite abstract to many).
To avoid miscommunication, I want to make sure that we are on the same page when I discuss climate. Maybe it helps if I talk about more familiar and specific aspects, such as the temperature, rainfall, snow, or wind?
Guest post by Mike Favetta
The goal of “Climate without Borders” (CwB) is to unite TV weather presenters from all over the world and bring scientific knowledge to a broader public. This, in turn, creates climate awareness and creates support for the urgent climate action needed. Although the name suggests a kind of connection with Doctors without Borders, members of Climate without Borders won’t be traveling to island nations about to be submerged, like Tuvalu, or areas sub and physically volunteering in the refugee efforts. Rather, Climate without Borders is a network of TV weathercasters around the world who aim to communicate the science, and impact of climate change, and give warnings to their local viewing populations. This makes the organization unique in the world. TV weathercasters are trusted sources of information, and they know the nuances of their audience’s cultures to make messages more understandable. Exploiting this relationship is an effective way of sharing climate information that people will listen to and comprehend.
[Read more…] about Climate without Borders: putting changing climate into a new perspective
An important milestone was passed during the second general assembly of the Copernicus Climate Change Service, which took place in Berlin on Sept 24-28 (twitter hashtag '#C3SGA18'). The European climate service has become operational, hosted by the European Centre for Medium-Range Forecasts (ECMWF).
[Read more…] about European climate services take an important leap forward