February 1, 2016 at 4:14 pm #459
Communicating what data trends we are confident of can a challenge.
During the training workshop in Wageningen (april 2016), there was a Best Tip in communicating uncertainty: use a continuous meeting approach with client. Use probabilities and slowly introduce the client to the result in a way that the client can consider a solution. There are no short cuts!
How do you communicate confidence/uncertainty to your client? What is your Best Tip??
Here is a link to a User Guide on Communicating Uncertainty from ECLISE project: https://drive.google.com/open?id=0BxIX7GkCBBxMYlhyM010anlNSEE
Here is the tutorial on Uncertainty in SWICCA: http://swicca.climate.copernicus.eu/information-videos/
Please reply with what you have found has worked best so far!August 4, 2017 at 9:13 am #2215
Communicating uncertainty is indeed a tricky part because the clients usually prefer a straight answer, a uniquely explained result, a “single value”. However, uncertainty is exactly the opposite.
We took advantage of the SWICCA demonstrator graphical presentation of climate change results in order to convey the nature of uncertainty to our clients. We found out than once all scenarios are presented then it is easier to be accepted that all scenarios are based on current scientific knowledge and they are all equally possible. Afterwards, clients are more willing to look for trends in the results and to discuss actions based on a cloud of results which are similar rather than look for a unique answer. In fact, the initial doubts on the scientific ability to predict future change is raised when the equal probability of different scenarios is understood. We believe that SWICCA demonstrator can be a great tool for conveying the nature of uncertainty which characterises climate change.
AlexandrosAugust 7, 2017 at 8:58 am #2218
Good to hear that you managed (with the help of the Demonstrator) to explain to your clients the nature of the uncertainty in climate change projections. And that together you analysed the robust messages from the data cloud rather than the uncertainties.
This once more confirms the fact that climate services, in order to be successful, require close and sustained collaboration between purveyor and client.
If, based on your interactions with the client vis-a-vis the Demonstreator, you still have concerns on things that were are hard to explain or if you have tips for us to improve the Demonstrator we’d like to hear!August 10, 2017 at 6:54 am #2219
In my case I feel that my client is used to dealing with uncertainties. They (the Contact persons at Jönköping County) regard the results rather as a size of magnitude than an exact number.
My concern though, is that when the results are passed on to “second hand users”, they will be too litterally interpreted. I need to be very clear about the uncertainty in report, diagrams and tables and that the results should be used to get a hint of the magnitude of the change rather than that the report give them exact numbers of the change. I want to do this without diminishing the importance of the results. This is a challenge.
/KatarnaAugust 10, 2017 at 10:04 am #2222
The response of Alexandros is very encouraging this actually means that a visualization tools as is developed in the demonstrator is helping.
what also helps is discussing which decision/investment/strategy will be affected by climate change uncertainty. For some decisions high uncertainty is more acceptable than others. For some decisions and strategies already knowing the trend (warmer/dryer/more extreme events) is already helpful. While for other decisions more precision on for example the exact flood risks are necessary.August 18, 2017 at 12:09 am #2243
Maria J. PoloParticipant
In our case, most of our clients were used to the use of probabilistic forecasts and thus the concept of uncertainty was not an issue itself. However, the difference between scenarios and forecasts was not that straightforward to them. The visual capabilities of the SWICCA platform were really helpful to provide them with clear examples to base our explanations upon.
The direct meetings, during which we used the platforme together with the clients, proved to be the most efficient way to move towards confidence on the information. Their initial experience with the platform led to assuming the scenarios as forecasts, and they asked us about the probability of each one of them. Once we went through the SWICCA services with them, it was easy and clear how to deal with the information. This leads me to wonder about the potential new users and their initial expertise in this framework; we know that many people ignore the tutorials and “Readme instructions” and directly step into the stuff, especially if maps and graphs are readily available at one click. Maybe some automatic comments in balloon formats could pop out when clicking on a mapo r downloading data with key messages addressing this. I’m just thinking aloud…August 31, 2017 at 9:17 am #2363
We particpated to the training in Wageningen and went through the tutorial ,then, after discussion with one Client ( case study on River water balance) we found out that the most effective way to let the Client accept and understand the uncertainty in the results was:
– first reduce the number of possible combinations of Climate change projections of used variables by identifyng the most suitable idrological model among the 3 available one for the area of interest (comparison of hydrological modelled discharges Vs. recorded values for actual period)
– present final results using probabilities (i.e. probaility that the choosen output indicator in Climate change conditions falls in a range of values or in another)August 31, 2017 at 9:47 am #2364
in order to better communicate the climate projections for the climate-proof irrigation indicator we have computed the ENSEMBLE Cumulative Distribution Function (CDF) combing the different results.
Please have a look at the following image (right click link below to view image)
Comments are welcomeSeptember 16, 2017 at 7:33 am #2375
From a purveyors perspective I want to stress the importance of first understanding the purpose that the client has. Purpose determines quality requirements. A large investment decision requires more specific information (including probabilities) than, for instance awareness raising. In the case of Heineken, the purpose was to raise a sense of urgency and to make climate change ‘tangible’ for non-experts. Therefore we decided to focus on making clear and simple visualizations, only showing the RCP45 and 85 maps and graphs. We left out further details on probability and uncertainty margins, and the information was used to facilitate ‘what if’ types of discussions. What if climate change continues in this direction?
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