chubbs

BS, its well known that glaciers around the world are shrinking rapidly due to climate change. The Himalayas are no exception. The increasing local risk from glacier melting was described as a "crisis" earlier this year: https://www.icimod.org/press-releases/hindu-kush-himalaya-glaciers-losing-ice-at-double-the-rate-since-2000-new-icimod-report-confirm/ Kathmandu, 18 March 2026 – Glaciers across the Hindu Kush Himalaya (HKH) are melting at an accelerating rate, with ice loss rates doubling since the year 2000, according to two new landmark reports that will be released on 21 March 2026 by the International Centre for Integrated Mountain Development (ICIMOD) to mark World Day for Glaciers. The reports, Changing Dynamics of Glaciers in the Hindu Kush Himalaya Region from 1990 to 2020 and HKH Glacier Outlook 2026: Insights from 50 Years of Himalayan Glacier Monitoring, provide the most comprehensive evidence yet of glacier change in the region. They reveal a total loss of up to 27 metres of ice thickness since 1975, sounding an alarm for the nearly two billion people downstream who depend on meltwater from the ‘Water Towers of Asia’. “This isn’t a distant problem; it’s a crisis unfolding in real-time, with new disasters every summer and monsoon. The fact that ice loss rates have doubled this century should shock us all into action,” said Pema Gyamtsho, Director General of ICIMOD. “The Hindu Kush Himalaya is at a crossroads. The rapidly escalating impacts we’re seeing from water uncertainty to catastrophic floods underscore that we are in a critical decade for the cryosphere. We must scale up monitoring and invest in adaptation now. These aren’t blind spots becoming surprises anymore; they are our new reality.”

I estimate 1.77C is needed to break a record this year vs Jacobson's 1.84. In any case August's warmth has made a record likely this year and put 2023's gobsmacking September of 1.74C in play. Sept 2023 is still the highest ERA monthly anomaly.

“We don’t see a time in the past where El Niños have been as strong as today, and we show that the strength of El Niño changes in parallel with the warming of global temperature,” said lead author Julia Cole, professor and chair of the U-M Department of Earth and Environmental Sciences. “Our findings tell us that the big El Niño events of the last 40 years are not normal in the context of the last thousand years.” https://news.umich.edu/el-ninos-more-intense-over-last-40-years-than-previous-1000-according-to-u-m-study/

Blog article on the Nepal floods by a glaciologist providing background and recent history of the area impacted. https://www.antarcticglaciers.org/2026/08/august-2026-nepal-tibet-floods/

Satellite OISST (through August) has risen to a new peak, 0.08C warmer than the previous peak in September 2023. So far, the nino spike this year is very similar in magnitude to the 2023 spike. Both larger and faster than previous El Nino.

The chart I posted shows temperature anomaly, while your chart shows temperature. August will rank a little higher on a temperature basis than it will on an anomaly basis because August is a relatively warm month.

The rise in global temperature anomalies that began in June continues through the end of August in this gfs-based forecast. August will easily break the previous August record and is tracking as the fourth highest monthly anomaly in any month on this metric, fifth highest on ERA. End of the month readings are near the peak monthly value for this metric, 1.04 in November 2023. Note that anomalies are usually highest in the colder months. We'll see what September brings. http://www.karstenhaustein.com/climate.php

Here's Zeke H latest forecasts (updated daily). An August record is almost guaranteed, while a record for 2026 is currently a coin flip. Monthly and yearly record odds have both been increasing as the month continues to run warm. https://dashboard.theclimatebrink.com/#global

This isn't a new thought, scientists have been looking for weather impacts from accelerated warming in the arctic for a while. One hypothesis is a weaker, wavier jetstream, amplifying and slowing the progression of weather patterns. As far as I am aware this hasn't been proven with studies on both sides. Not aware of any findings on baroclinic storms either. Note that increased moisture compensates for weaker temperature gradients in baroclinic storms through increased heat release from condensation in clouds. One weather effect that has been found is a northward shift in the jet stream and storm track.

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.