As many of you know, I attended and spoke at ICCC13 in Washington DC in July.
While there was a live stream available, not everyone could watch it.
Here is my presentation:
Anthony Watts, senior fellow for environment and climate at The Heartland Institute, speaks on Panel 3: Scientific Observations at The Heartland Institute’s Thirteenth International Conference on Climate Change.
Here is the playlist for the entire conference:
I am sitting here scratching my head on how you homogenize clean data (that has it’s average) with dirty data (that has it’s own average) and get worse data (even warmer average) as a result. Literally the temperature was higher after mixing in the lower temperature (clean) data.
The only way this can occur is if there is more than just averaging occurring between the data sets – they have to be adding additional warming through some process. I imagine it like this – they over use certain dirty station data where the data is the warmest so that by the time you mix in cooler data the average has risen past the dirty data set’s own original average. So this would mean that certain station data is over-represented in the final outcome, making the entire mess even worse. This would be a huge red-flag to anyone paying attention and knowing basic math.
Very good presentation.
Ty, AW. This issue has been getting ever more attention since your Surface Stations project. Notably recently in the UK, Germany, and Australia. Well done.
It would not surprise me that there is both weighting and averaging going on in the dataset. Why is a very different question.
And the presentation was short and to the point with clear verbiage. I would like to have seen the questions if any after the talk.
Anthony,
Thanks for all the thoughtful data and weather thinking. De-weaponized data!
Steve
Both presentations very good. Is anyone listening?
Thanks, Anthony. It’s always a pleasure to listen to you speak about the problems with the U.S. temperature records. I especially enjoy our phone conversations. Maybe one day again soon. Sorry I couldn’t make it to the ICCC#13 conference.
I saw your ICCC#13 presentation when it first appeared on YouTube, but I hadn’t yet seen Joe Bastardi’s about hurricanes. That was also a treat, especially Joe’s surprise caller. Thanks for including Joe’s presentation too on this post.
Regards,
Bob
The “playlist for the entire conference” seems to be missing…should there be a link to this somewhere?
https://climateconference.heartland.org/
Joe Born
August 12, 2019 at 5:33 pm
Many thanks Joe.
All those links will keep me busy for a few hours.
There is no doubt in Australia that the raw temperature data is simply “wrong” as it does not match the climate models. Over time it is gradually being homogenised, according to world best practice standard, to match the models. Each iteration of the ACORN dataset brings the trend closer to the models.
I will be disappointed if Roy Spencer’s comment on the CMIP6 models showing steeper or similar trend to the CMIP5 models, is proven true.
Chico?
The monthly data starts to cut out after 2003, and picks up again after 2017… in the actual official
record which is on the ftp site.
Why are the months missing?
go to this page and pick a year and month to see the daily paper reports.
https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00041715/detail
What you will see is that the daily record is scattered with missing days between 2004 and 2016
Stations with too many missing days are dropped from the monthly reports.
take march 2008 as an example.
or take November 2008, 15 days missing.
The daily record (GHCND) is turned into a monthly record, but if there are too many missing days
the station is dropped from monthly.
Great job that the Heartland does at these conferences and thanks for posting as I haven’t able to stream any of these conferences since 2015.
A favor if you please– in Jay Lehr’s talk he had a chart of CO2 distribution. Can you upload a copy that I can link to email being forwarded to PA Legislators– even they could understand. /sarc
Many thanks
Excellent presentation, easy to understand and completely relevant to long term weather and climate monitoring that is and will be used for future climate policy making decisions. A proper Data Quality Act that is actually used and had teeth might be the best thing that could happen for long term climate policies. It would ensure that we didn’t have to worry about all data sources being corrupted with mistakes, poor collecting methods or downright misrepresentation of actual data. Long term data collection is everything, so asking for it to be subject to proper procedure in a functioning Data Quality Act should be in everyone’s interest and not asking for too much. Just having to mention that much of the data set is subject to adjustment, and has been adjusted possibly for nefarious reasons should be enough to hold a much higher standard on the manipulation of data for long term policy decision making. Without accurate and honest data collection over time, we have nothing.
A very good talk – thank you Anthony.
Best personal regards, Allan
Great stuff from Anthony!
and, those commenting in the recent post about Katrina should view Joe Bastardi in “Weaponizing Hurricanes”.
Anthony did not highlight the real problem sufficiently. Most scientists and others taking an interest in temperature measurement know that UHI exists and that UHI is larger in larger cities. There is a peer reviewed paper that gives a formula by which one can calculate the UHI effect (although I think that was done in a temperate Mediterranean type climate area in Australia which could be different in other areas). Present measured temperatures should actually be reduced to compare with measurements in good location in the past. But of course the climate alarmists (note Mosher) do not want to do that because there will then be nothing to be alarmed about. It is complete scientific fraud to cool the past. Homogenisation is also fraud because measurements in other locations differ by large amounts. One only has to go a few kilometres inland from the ocean at night time. One of the suburbs of Brisbane is called Beaudesert. On a clear night in winter the minimum temperature can be below zero while a suburb on the coast has a minimum temperature 9-11C (note the winter ocean temperature along the SE Qld coast is 20-21C with plenty going swimming and surfing)
Someone may argue about increasing past temperatures to match present temperatures with UHI but a simple answer is why alter past raw temperatures. It is more scientific to eliminate badd stations and install new well locared stations.