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.
“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.
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).
If you want to make a difference as a scientist, you need to make sure that people understand the importance of your work. Conferences give you one opportunity to explain what you’ve found out.
I sometimes wonder if the value of attending conferences is sufficiently appreciated. You can save time getting an overview over your field of research and catch up on the latest developments, which would take many weeks just from reading papers (and it gets harder these days to find the time reading papers).
Another benefit is being able to meet colleagues and discuss the latest findings and your results. In addition to sharing your thoughts, you represent your institution and enhance its visibility. Organisations pay a lot of money for increased visibility.
This week, I have listened to many good and interesting talks at the European Meteorological Society’s (EMS) annual meeting in Budapest (#emsannual2018), a meeting place for weather and climate experts across Europe and the rest of the world.
I get a lot of questions about the connection between heatwaves and climate change these days. Particularly about the heatwave that has affected northern Europe this summer. If you live in Japan, South Korea, California, Spain, or Canada, you may have asked the same question.
The climate system is complex, and a complete description of its state would require huge amounts of data. However, it is possible to keep track of its conditions through summary statistics.
The most common indicator is the atmospheric background CO2 concentration, the global mean temperature, the global mean sea level, and the area with snow or Arctic sea ice. Other indicators include rainfall statistics, drought indices, or other hydrological aspects. The EPA provides some examples.
One challenge has been that the state of the hydrological cycle is not as easily summarised by one single index in the same way as the global mean temperature or the global mean sea level height. However, Giorgi et al. (2011) suggested a measure of hydro-climatic intensity (HY-INT) which is an integrated metric that captures the precipitation intensity as well as dry spell length.
There are also global datasets of indices representing the more extreme aspects of climate called CLIMDEX, providing a list of 27 core climate extremes indices (so-called the ‘ETCCDI’ indices, referring to the ‘CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices’).
In addition, there is a website hosted by the NOAA that presents various U.S. Climate Extremes Index (CEI) in an interactive way.
So there are quite a few indicators for various aspects of the climate. One question we should ask, however, is whether they capture all the important and relevant aspects of the climate. I think that they don’t, and that there are still some gaps.
- F. Giorgi, E. Im, E. Coppola, N.S. Diffenbaugh, X.J. Gao, L. Mariotti, and Y. Shi, "Higher Hydroclimatic Intensity with Global Warming", Journal of Climate, vol. 24, pp. 5309-5324, 2011. http://dx.doi.org/10.1175/2011JCLI3979.1
A recent story in the Guardian claims that new calculations reduce the uncertainty associated with a global warming:
A revised calculation of how greenhouse gases drive up the planet’s temperature reduces the range of possible end-of-century outcomes by more than half, …
It was based on a study recently published in Nature (Cox et al. 2018), however, I think its conclusions are premature.
- P.M. Cox, C. Huntingford, and M.S. Williamson, "Emergent constraint on equilibrium climate sensitivity from global temperature variability", Nature, vol. 553, pp. 319-322, 2018. http://dx.doi.org/10.1038/nature25450
There have been a number of studies which show that we can expect more extreme rainfall with a global warming (e.g. Donat et al., 2016). Hence, there is a need to increase our resilience to more rainfall in the future.
We can say something about how the rainfall statistics will be affected by a global warming, even when the weather itself is unpredictable beyond a few days.
Statistics is remarkably predictable for a large number of events where each of them is completely random (welcome to thermodynamics and quantum physics).
The normal distribution has often been used to describe the statistical character of daily temperature, but it is completely unsuitable for 24-hr precipitation. Instead, the gamma distribution has been a popular choice for describing rainfall.
I wonder, however, if there is an even better way to quantify rainfall statistics.
- M.G. Donat, A.L. Lowry, L.V. Alexander, P.A. O’Gorman, and N. Maher, "More extreme precipitation in the world’s dry and wet regions", Nature Climate Change, vol. 6, pp. 508-513, 2016. http://dx.doi.org/10.1038/nclimate2941