Hot weather is due to CO2. Cold weather is due to CO2. Heavy rain is due to CO2. Drought is due to CO2. Today I look at the variance inherent in the HadCRUT5 global mean temperature.
The urban heat island effect has a LOT to answer for. In 2022 the Heartland Institute published a new report which found 'that 96% of the National Oceanic and Atmospheric Administration (NOAA) temperature stations produce corrupted data due to purposeful placement in man-made hot spots. These stations fail to meet the published standards deemed “acceptable” by NOAA itself.
This nationwide study follows up widespread corruption and heat biases found at NOAA stations in 2009, and the heat-bias distortion problem is even worse now'
Processed beyond recognition, it being a gridded dataset with judicious infill by modelling following extensive homogenisation. By way of example, back in 2017 I was studying the jungle of Thailand when up popped temperature data from northern Australia! A mere handful of stations date back to 1900 and they're mostly in the northern hemisphere. This was always going to be the case, for even with 20,000 land surface stations come the '60s you're not even going to get close to comprehensive global coverage, with the situation being worse for sea surface temperature datasets. It's like making a cake with 0.0001g of butter!
I was intrigued if unpredictability was increasing, as so often claimed, it is unpredictability that messes us up because we can't prepare for the heat, wet, cold etc. So my approach was to graph the difference between one year's temperature, or rainfall, record and the previous year's.
So for instance 2022's mean temperature was 10 deg C and 2021's 10.3 so I record that as a drop of 0.3 for 2022. Obviously the larger the gains and drops the more "unpredictable" the weather is.
Morning! I like this a lot. What you've intuited is actually a cornerstone of time series analysis, whereby we take the first order difference to remove any underlying trend in order to make the series stationary prior to modelling. That is to say we model the residuals in terms of periodic behaviour. I'd like to incorporate this approach in my new article series for it would sit nicely alongside my work with variance.
We just had this report in Cape Town by our very own Climate Alarmist brigade, the Daily Maverick who have their "Burning Planet" section and the entire paper goes under the self proclaimed Defend Truth epithet - the report entitled "THERMAL INEQUALITIES", can you believe it? LOL!
So here is what they found: "A key takeaway was that neighbourhoods with densely packed buildings and no trees had temperatures of as much as 15°C higher than cooler neighbourhoods, while vegetation, shade and building materials can reduce surface temperature by more than 10°C". They are quick to blame Climate Change on one hand, but seem to definitively prove massive urban heat island effect on the other. Clowns.
The urban heat island effect has a LOT to answer for. In 2022 the Heartland Institute published a new report which found 'that 96% of the National Oceanic and Atmospheric Administration (NOAA) temperature stations produce corrupted data due to purposeful placement in man-made hot spots. These stations fail to meet the published standards deemed “acceptable” by NOAA itself.
This nationwide study follows up widespread corruption and heat biases found at NOAA stations in 2009, and the heat-bias distortion problem is even worse now'
It sure has! If it was the elephant in the room it'd be so big all you could see is grey.
Is the source data from HADCRUT adjusted, raw, mixed or dunno?
Processed beyond recognition, it being a gridded dataset with judicious infill by modelling following extensive homogenisation. By way of example, back in 2017 I was studying the jungle of Thailand when up popped temperature data from northern Australia! A mere handful of stations date back to 1900 and they're mostly in the northern hemisphere. This was always going to be the case, for even with 20,000 land surface stations come the '60s you're not even going to get close to comprehensive global coverage, with the situation being worse for sea surface temperature datasets. It's like making a cake with 0.0001g of butter!
I was intrigued if unpredictability was increasing, as so often claimed, it is unpredictability that messes us up because we can't prepare for the heat, wet, cold etc. So my approach was to graph the difference between one year's temperature, or rainfall, record and the previous year's.
I used the UK's Met Office records. https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html and https://www.metoffice.gov.uk/hadobs/hadukp/data/download.html
So for instance 2022's mean temperature was 10 deg C and 2021's 10.3 so I record that as a drop of 0.3 for 2022. Obviously the larger the gains and drops the more "unpredictable" the weather is.
I think you can guess the results - see https://www.sarsen.org/2023/12/unpredictable-weather-not-unprecedented.html
I would be interested in your more scientific thoughts.
(There was an odd 60 year pattern in the Mean spring temperatures - https://www.sarsen.org/2022/12/is-this-years-tumultuous-weather-set-to.html )
Morning! I like this a lot. What you've intuited is actually a cornerstone of time series analysis, whereby we take the first order difference to remove any underlying trend in order to make the series stationary prior to modelling. That is to say we model the residuals in terms of periodic behaviour. I'd like to incorporate this approach in my new article series for it would sit nicely alongside my work with variance.
We just had this report in Cape Town by our very own Climate Alarmist brigade, the Daily Maverick who have their "Burning Planet" section and the entire paper goes under the self proclaimed Defend Truth epithet - the report entitled "THERMAL INEQUALITIES", can you believe it? LOL!
So here is what they found: "A key takeaway was that neighbourhoods with densely packed buildings and no trees had temperatures of as much as 15°C higher than cooler neighbourhoods, while vegetation, shade and building materials can reduce surface temperature by more than 10°C". They are quick to blame Climate Change on one hand, but seem to definitively prove massive urban heat island effect on the other. Clowns.