I like your football analogy. I got excited about football for the first time in my life when Australia got to the women's semi-final. Oops! I meant to watch the final tonight and forgot! So I just see now Spain won 1-0. I remember as a child coming to Europe from Australia with my parents and visiting friends of theirs who ran a kind of hotel, I think the name of the places was Downside House in the Somerset village, Shepton Mallet.
Very, very interesting and pretty easy to understand even for me I think.
Have you looked at the missing days for individual stations, John, because it would be interesting to see if it occurred more in some than others? You really have to wonder how this could happen.
Glad you enjoyed it! I have just downloaded individual station records to see what has been happening and shall be presenting the results in part 2. NASAs fancy global maps are based on such data and I'm guessing they're sweeping a whole bunch of issues under the carpet.
An odd period to choose given WMO prefer everyone to use 1981-2010, but since there was global cooling in 1960 then 1951-1980 is going to maximise the anomaly values they plot. I thought I had done an article on how the choice of climate normal affects the values attained but I can't find anything in the archive - I've made a note to churn one out so we can get to see how the trick is performed. It is possible to derive a moving 30-year anomaly such that we get to see whether they've chosen the optimum period to play silly buggers.
The oceanic data derives from ERSST v5, which is based on statistical interpolation of ICOADS 3.0 and uses a 2 x 2° grid. It's worth noting that ERSST is a reconstruction dataset and not observed data. It is also worth noting that the underlying raw data is not methodologically homogeneous in that different techniques of collecting sea surface temperature have been bolted together over time, each with its own quirks, shortcomings and biases. This is being sold to us as a coherent time series when it is nothing of the sort.
I like your football analogy. I got excited about football for the first time in my life when Australia got to the women's semi-final. Oops! I meant to watch the final tonight and forgot! So I just see now Spain won 1-0. I remember as a child coming to Europe from Australia with my parents and visiting friends of theirs who ran a kind of hotel, I think the name of the places was Downside House in the Somerset village, Shepton Mallet.
Very, very interesting and pretty easy to understand even for me I think.
Have you looked at the missing days for individual stations, John, because it would be interesting to see if it occurred more in some than others? You really have to wonder how this could happen.
Glad you enjoyed it! I have just downloaded individual station records to see what has been happening and shall be presenting the results in part 2. NASAs fancy global maps are based on such data and I'm guessing they're sweeping a whole bunch of issues under the carpet.
The maps compare July 2023 to an average for 1951 to 1980. Why do you think they chose that base period?
Do you know the size of the grids in the middle of the oceans?
An odd period to choose given WMO prefer everyone to use 1981-2010, but since there was global cooling in 1960 then 1951-1980 is going to maximise the anomaly values they plot. I thought I had done an article on how the choice of climate normal affects the values attained but I can't find anything in the archive - I've made a note to churn one out so we can get to see how the trick is performed. It is possible to derive a moving 30-year anomaly such that we get to see whether they've chosen the optimum period to play silly buggers.
The oceanic data derives from ERSST v5, which is based on statistical interpolation of ICOADS 3.0 and uses a 2 x 2° grid. It's worth noting that ERSST is a reconstruction dataset and not observed data. It is also worth noting that the underlying raw data is not methodologically homogeneous in that different techniques of collecting sea surface temperature have been bolted together over time, each with its own quirks, shortcomings and biases. This is being sold to us as a coherent time series when it is nothing of the sort.
Another tour de force John. Why isn't there wider coverage of your brilliant work?
Ta muchly! A good question. Maybe the heavy numbers approach puts people off.
But they are not heavy numbers! You write really well and it's all very understandable.