chubbs

In El Nino mode. 2026 started #5 in GISS in Jan and Feb, but reached #1 in July. August on track to easily top 2024. Can expect monthly records well into until 2027. The one exception is Sept, the "gobsmacking" warmth of 2023 will be tough to beat.

This study provides an estimate of transient climate response (TCR) using CERES net radiation estimates and updates temperature-based estimates through 2025. The new TCR estimates for temperature are a little higher than previous studies (orange bars) and the CERES TCR estimates (red bars) are higher still, inline with TCR estimated with the warmer CMIP6 models. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2026EF008356

I don't think area burned is telling you much about CC; because, other human factors are driving the long-term trends. Per Our World in Data, the vast majority of wildfires occur in grassland and savanna in the sub-tropics and tropics, roughly 50% in Africa. Forest burning is a small portion of area burned in all regions. Our World in Data cites a Science paper that attributes the long-term decrease in area burned to the spread and intensification of agriculture. In Europe and North America, reduction in cropland burned is another important long-term trend. Have to look at other stats to see the effect of climate change. The blog I linked makes the point that the intensity of the largest fires is increasing due to climate change. Big, intense fires are the most problematic but aren't well captured by the area burned stats. Here's a recent paper that finds that a small number of extreme fires are having an out-sized impact on the forest ecosystem in western and boreal North America. https://www.science.org/doi/full/10.1126/sciadv.aeg5802

Blog on wildfire smoke in the US. There's been an increase in smoky years since the late 2010s. https://www.theclimatebrink.com/p/smoke-gets-in-your-eyes

I would look at Tavg, annual and monthly, to start.

Yes, Reading and West Chester are stable in the 1929 to 1960 period and I am going to add Reading to my analyses. A couple of comments. As I said above looking at shorter time periods significantly expands the list of reference stations for individual station breaks as all stations are stable between the station breaks. Second, there is no need to restrict to short distances from Chester County. Weather station data is well correlated over hundreds of miles. An interesting first step would be to check how far good correlation with Chester County extends.

Lets look at the big picture. All four estimates you've listed agree that Phoenixville experienced spurious cooling in 1948. Better to recognize that Phoenixville had a cooling move vs ignoring the move and using the raw data. However things aren't as uncertain as the four numbers make it seem. First the BEST estimate is 1.58F on Mike's chart not 1.88F, closer to NCEI. Second If you look at Mike's charts there are many small differences between NCEI and Berkeley that cancel out over a longer time period. I don't see a big difference in the NCEI and Berkeley local climate history. Third, I wouldn't expect my estimates to be that accurate. I haven't taken the time to optimize my approach and more importantly there are many other stations besides West Chester and Coatesville that can be used to estimate the impact of Phoenixville's move. I say the NCEI method is bullet-proof because it has a strong scientific foundation. Science says that there is a strong correlation in year-to-year temperature changes over a wide area and that's exactly what I have found. This chart is an example. These stations are well correlated at the time of the Phoenixville move indicating that they all can be used to estimate the timing and impact of the Phoenixville move. Moreover, I have only scratched the surface, there are just the ones that had the time to check.

The only way to estimate the magnitude of station breaks is from raw temperature data, by comparing stable and changed stations. As documented in this thread, my tests using raw temperature data in Chester County agree very well with the NCEI bias adjustments. Not surprising because the bias adjustments are calculated from the raw data. My experience in Chester County has convinced me that the NCEI method is bullet proof. Below is raw temperature info on the Phoenixville 1948 move. NCDC has monthly means for Phoenixville for Janaury through July 1948, partial data from August through November but no monthly mean until December 1948. Phoenixville cooled relative to Coatesville and West Chester when the monthly mean started again in December 1948. Phoenixville cooled 1.6F relative to Coatesville, and 2F relative to West Chester, due to the move. The stats using the monthly data are highly significant for both Coatesville and West Chester. The post-war moves at Coatesville (1946), West Chester (1970) and Phoenixville (1948) are all highly significant statistically. A coincidence that all of the major county COOP stations moved in the same direction and roughly the same magnitude after the war. Clearly not weather because the cooling occurred at different times at each station.

Nice analysis. Zeke Hausfather had a blog recently on how the 1930s warmth gets overestimated. Here's one of his plots. US stations were concentrated in the Great Plains in the 1930s, exactly where the warmth of the 1930s was focused. That is why it is important to grid the station data as you did. https://www.theclimatebrink.com/p/raw-data-wrong-answers

Nope. Homogenization is accepted science. Thoroughly tested over decades. Proven to obtain the correct climate information from raw temperature when station changes have occurred. The raw temperature data in Chester County is contaminated by myriad station moves and changes. Assuming that Coatesville and other Chesco COOPs stayed in exactly the same spot with zero equipment and method changes over decades will give you the wrong climate answer every time. The cooling of Coatesville between 1945 and 1949 is well documented in this thread. The regional raw temperature data is 100% conclusive on this point. Coatesville was much cooler in 1949 than it was in 1945 relative to all stable regional stations. Comparison of Coatesville to Phoenixville over the period 1941-75, is immaterial; because, as the bottom chart shows, Phoenixville had a station break in 1948. The post-war Phoenixville and Coatesville station breaks occurred at different times proving that they are not weather-related. Mike's analysis and the NCEI bias adjustments are completely independent, derived from 2 different sources of information. Mike uses the station records. Bias adjustments use raw temperature data. The fact that they agree so well shows how powerful and accurate homogenization is in Chester County. Your AI slop is wasting our time if it doesn't focus on the station breaks themselves. Regression over 35 years is not the right tool to identify station breaks. You need much more focused analyses to look at station breaks. The 1945-49 period for the Coatesville post-war move/changes for instance.

Good quick explainer.

July was a record for GISS 1.23 vs 1.20 in 2024. NOAA's July tied 2024. First of many monthly records over the next year.

No I don't mind running Claude as you describe. Should be useful as a learning tool. Berkeley Earth has a West Chester 1 station with data back to Dec 1845, link for West Chester 1 below. The Berkeley Earth write-up for West Chester 2NW says that West Chester 1 is an alternative name for the station. So West Chester 1 may be the older data for West Chester 2NW. Worth checking anyway. https://data.berkeleyearth.org/stations/35128

You understand what I did correctly. Yes it would be easy to use the stable portions of the Chesco station data to test the others. That's essentially what NCEI does (and other groups). The NCEI adjustments are based on raw temperature data not station information. NCEI also uses data from outside of Chester County to determine Chesco bias adjustments, taking advantage of the fact that weather data is correlated over hundreds of miles. That's one of the reasons I found your Coatesville analysis interesting. The NCEI adjustments match the detailed site information you developed very well; and. for the periods where your site information was more complete and accurate than NCDCs, the match between the site info and the bias adjustments improved. Interesting that the West Chester site has data from before 1894. Can you obtain the data?

Yes stations contaminated with station changes are not suitable references. Myriad station changes are readily apparent on Paul's chart. Just look for a single station with-year-to-year movement different than the others. Between 1941 and 1975, West Chester, Phoenixville, Holtwood and Ephrata all had large station changes making them bad references for Coatesville over that period. However, there's an easy way to turn these stations from bad into good references. Use shorter time periods without station changes. The station changes only effect a small portion of any one station's 35-year record. The other years are untainted. Several examples are posted upthread. Here's another : West Chester's 1970 move using monthly data from Phoenixville and Coatesville as the reference. Both stations were stable in this 3-year period. The West Chester station closed at the end of 1969 and re-opened at a new site in May 1970. West Chester cooled immediately on reopening. The stats show a statistically significant 1.4-1.7F cooling relative to Coatesville and Phoenixville due to the move. This simple analysis only uses 2 reference stations. Many more are available outside of Chester County to further improve the power of the statistics. It is clear that the most important NCEI bias adjustments are highly statistically significant.

When you focus on periods just before and just after station changes, the changes are have very strong statistical significance. Below is an example for Coatesville, using the monthly difference between Coatesville and West Chester. On average, Coatesville was 2.5F cooler relative to West Chester in 1949-50 vs 1944-45, before the 1946 move. The stats show that the move-related change was highly significant statistically.

https://dashboard.theclimatebrink.com/#global

The bulk of the post-war Coatesville cooling occurred in 1946 and 1947 which is consistent with a 1946 move. The cooling relative to West Chester and ABE also matches the timing and roughly 2F magnitude of the NCEI and Berkeley Earth bias adjustments that you posted above.

Very interesting. More evidence supporting bias adjustment in Chester County. The detailed documentation describes many more station changes than the NCDC documented. Indicates that the move from the City of Coatesville to Doe Run Road occurred in 1946. Per NCDC documentation Doe Run road site wasn't used until 1949.

Most of the Arctic Ice caps are having a record or near record melt year. The exceptions are Greenland which has above average losses and Ellsmere Island the which has below average losses. The loss distribution is consistent with the average temperature map since May 1. The Atlantic side of the arctic has been warm, while the Pacific side has been cool. Other than Ellesmere, the arctic ice caps are on the Atlantic side. https://www.climato.uliege.be/cms/c_5855702/fr/climato-arctic

Nope. Your station selection and AI's method are both faulty. The key period for evaluating the Coatesville post-war moves is 1945-1950, the period immediately after the war when the moves occurred. I didn't include Sadsbury, Kennett Square, and the other Chesco stations you listed, because they didn't operate in that period. After reading the AI analysis more closely, I see that AI used regression analysis to make its determination. As I said above, that is a poor choice because the station moves are focused in time not spread out over a long period of time. AI based its conclusion largely on regression results from Devault and Morgantown both of which didn't start operating until 1951. AI is drawing a conclusion from two stations that don't provide any information on the Coatesville post-war moves; because, they weren't operating at the time. A clear indication of a faulty analysis approach. With 2 of the 3 Chesco stations, Caotesville and Phoenixville, both experiencing spurious cooling between 1945 and 1950, It is important to include non-Chesco stations in the analysis. That's why my analysis included 10 non-Chesco stations. The non-Chesco stations and West Chester agreed completely with West Chester in identifying the common weather signal between 1945 and 1955. Having the common weather signal makes identification of the Coatesville and Phoenixville moves easy; and, with plenty of stations is highly significant statistically. A faulty analysis does not disprove a good one. To disprove my analysis, you need show that the significant difference between the move and non-move stations in the 1945-55 period changes when more data is added. Good luck with that, the P value is 0.0001.

Extended the stat analysis to the 1970s to include the West Chester move. Two comparisons were made: individual years using 1972 minus 1945 as a metric, and a 5-year average using 1971-75 minus 1941-45 as a metric. The results are the same for both metrics: the Chester County COOPs are significantly different statistically from the non-Chesco stations. As was the case for the 1950s there is no overlap between the move and non-move stations. The Chester County COOPs have spurious cooling of over 2F. This analysis and the one above provide strong evidence that the larger NCEI bias adjustments are very robust statistically. This analysis also shows the problems with AI. As Don stated above AI is handy for an initial screening; but, AI is unlikely to give a complete and accurate answer to a complex problem.

Here's a closer look at the statistics of the Coatesville and Phoenixville post war moves. Differences between stations was removed by setting 1945 to zero to facilitate comparison between stations. Coatesville and Phoenixville behaved differently than the other stations, cooling relative to the other stations at the time of the moves; and, remaining cooler after the moves.. Using the temperature difference between 1945 and 1952 as a metric, Coatesville and Phoenixville are significantly different from 11 stations without station changes. The P value is 0.0001 which means that there is virtually no chance that Phoenixville and Coatesville are the same as the other 11 stations. Furthermore Phoenixville and Coatesville are significantly different from the other stations in every year after the moves in this period. Finally note also the good agreement among the 11 stations without changes. Chester County shares the same weather with a much larger region. Many stations can provide useful information on the Chester County COOP stations; which makes the identification of Chester County station changes very robust.

A good first step in using the raw data to identify station changes, with several station changes identified. What's missing are most of the station changes in Chester County. A couple of issues cause station changes to be missed: 1) It appears that only station moves and TOB changes described in NCDC documentation were investigated. There are two problems: NCDC documentation is incomplete and there are many other causes of station changes besides moves and TOB. 2) The common weather signal across stations is not identified. Having this signal facilitates the identification of station changes. and 3) Stations outside of Chester County are not used. Comments 2 and 3 work together. It easier to identify the common weather signal with more stations. The fact that many station changes are missed causes station comparison to break down. In summary the analysis is much less thorough than NCEI's A couple of comments on write-up itself. First like most AI its very wordy and wishy-washy. Too long to review carefully in detail. More importantly, there are no charts or quantification of any kind. Impossible to verify or compare to other results without hard numbers.

That's the $64 question for this year. Will we see the same type of "gobsmaking" global warming we saw in 2023 or will this year behave more like a typical strong/super el nino. So far I'd put this year in the typical camp, with global air temperatures lagging the rise in ENSO and global SST. One aspect that has held back global temperatures in the past 2 months has been very cold temperatures in Antarctica associated with a strong AAO+ regime. In 2023 AAO was strongly positive in January (see AAO table below), reversing by March, and staying negative through September. So the AAO boosted global temperatures in the first half of 2023 but are holding them back this year. One of the many factors that contributes to a different outcome in 2026 vs 2023. If the AAO reverses in August there will be an added boost in global temperatures on top of ENSO. We will see. 2023 2.304 0.554 -0.258 -0.921 1.452 -0.438 -0.818 -0.038 -1.050 0.535 0.097 1.510 2024 0.922 1.043 -0.058 1.005 -0.073 0.210 -0.597 -2.150 0.098 -0.567 1.020 -0.826 2025 -0.080 -0.271 0.733 1.138 0.509 0.209 0.753 0.357 -0.709 -1.236 -1.324 -1.136 2026 0.848 0.489 0.492 -0.125 0.544 2.506