Occasional Thoughts on Climate Change

[bluewave]

 

 

[donsutherland1]

12 minutes ago, bluewave said:

 

 

The official announcement from Météo-France:

Translation:

June 24, 2026

France has just experienced its hottest day ever recorded.

Across the country, the average temperature over 24 hours reached 30°C, exceeding the 29.9°C measured... the day before, which was already the hottest day ever recorded since measurements began in 1947.

 

[bluewave]

40 minutes ago, donsutherland1 said:

The official announcement from Météo-France:

Translation:

June 24, 2026

France has just experienced its hottest day ever recorded.

Across the country, the average temperature over 24 hours reached 30°C, exceeding the 29.9°C measured... the day before, which was already the hottest day ever recorded since measurements began in 1947.

 

Another extreme heatwave matching the findings of this recent paper. 
 

Conclusion

Actionable climate assessment for effective climate adaptation and mitigation requires skillful and reliable projections of extreme weather risks under different emission scenarios on a regional to local level. This holds particularly true for the representation of recently observed extremes of large magnitude that might be rare under current climatic conditions but will become more likely under continued GHG emissions (1, 56, 64). Skillful projections of trends in such “extreme-extremes” (unprecedented or record-shattering extremes) must build on a thorough physical understanding of why they are emerging and the nonlinear behavior responsible so that model simulations can be benchmarked and potential biases can be accounted for.

In large and densely populated areas such as western Europe and China and other areas that feature important biomes for the world climate such as the Amazon, and polar regions around Greenland and Canada, some of which have been discussed in the context of climate tipping points (65, 66), the multimodel mean of climate simulations of the past decades does not show the enhanced warming of the temperature distributions’ upper tails observed in these regions (Fig. 1 and SI Appendix, Fig. S5). Note that for the Amazon, the strongest trends have emerged over the past 23 y and are found for ERA5 only (SI Appendix, Fig. S4).

Often, the multimodel mean is used and prioritized in many assessments of climate risks, while upper percentiles are treated as implausible scenarios and are at times rejected as outliers. For instance, the 1.5 °C warming target established by the Paris Agreement was set largely based on avoiding “dangerous climate change,” in part associated with critical tipping elements and/or thresholds in the Earth system (65, 67). However, if impacts of global warming, such as amplified extreme heat, proceed faster than expected based on the multimodel mean projections used to support such a warming target, its utility may deserve reconsideration. We find that in numerous regions (Figs. 2 and 3), trends in the tail-widening of extreme heat distribution over the past 65 y exceed the 95th percentile of the model spread and, in some cases, even exceed the spread entirely. Trends shown in ERA5 reanalysis are outside of the modeled range for southern South America, the Arabian Peninsula, and Arctic Canada (Fig. 3 D, E, and H), irrespective of any model configuration investigated here, while the observed uncertainty intervals determined by bootstrapping overlap with the model spread. These findings hold for model simulations at higher resolution, or forced with historical SSTs, as well as with greatly expanded ensemble sizes (SI Appendix, Figs. S5, S8, and S9).

Newer modeling initiatives such as super-high-resolution frameworks suggested, e.g., in the Earth Virtualization Engine (EVE) (68) promise convection permitting resolution and may offer possibilities in improving the depiction of important mechanisms. However, no substantial improvement for the higher resolved subset of the investigated models was found. Super-high-resolution, convection-resolving models may better represent processes that link SSTs with Rossby waves and associated extremes (45), regional blocking, and realistic surface response of heat events to such atmospheric patterns (50, 51). However, limitations due to data storage and computing costs might be significant constraints for the study of extreme events with high-resolution modeling frameworks, as the long time series lengths and large ensemble sizes needed for adequate statistics and trend attribution may be too resource intensive and not readily available. Newer generation models have also shown an improved skill in modeling blocking events which is more pronounced in high-resolution models (47, 69). Given the importance of nonlinear feedbacks involving hydroclimatic processes, a proper representation of the seasonal relationships of the flow of energy and water in the soil–vegetation–atmosphere continuum needs to be assured (7). Reasonable forecasts of past extreme heatwaves suggest that models can in principle produce such extreme-extremes when directly forced with the correct boundary conditions (11, 70). Ensemble boosting techniques can be used to create large ensembles of extraordinary extremes at reduced computational cost (71, 72). In an evolutionary manner, these algorithms preserve those that follow an extreme trajectory while filtering out others. This allows a sampling around a specific event characteristic. A large ensemble of highly anomalous events, which would be featured only at an extremely low rate in large ensembles (20), allows for an in-depth and statistically robust analysis of the governing physics of extreme-extremes in models.

However, disentangling the relative importance of externally forced and internal variability in the observed trends may be key to attributing the sources of model–observation discrepancies. Coordinated single forcing large ensemble experiments such as the new Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP) (73) might help in improving our understanding in the relative role of various external or internal drivers in extreme event trends.

Further, machine learning (ML) approaches have shown promising results for providing more reliable bias adjustment of climate model output. These are based on methods from image processing and are better in retaining the relationships between variables compared to more traditional quantile-mapping approaches. This is particularly important when analyzing risks and impacts from compound extremes. ML techniques could also assist in detecting nonlinear and regime-changing behavior in the ocean–atmosphere–land–vegetation system and provide causality where common drivers experience strong coupling and feedbacks (9, 74). Beyond using ML for analysis, recent advances in ML-driven weather forecasts exemplify its potential in climate modeling (75, 76). In addition, ML might offer accurate and less computationally costly solutions for resolving important subgrid processes (77, 78), compared to purely numerical frameworks. ML approaches, however, must be combined with others that can physically explain and understand the causal flows identified by ML. New assimilation techniques that integrate observational datasets and exploit advanced interpolation frameworks have been proven to improve the depiction of extremes compared to reanalysis datasets (79) and provide climate information at a higher resolution.

While our findings provide many avenues for interesting and relevant new research, the authors stress that the best way to reduce both uncertainty in and exposure to climate impacts is a rapid transition of relevant societal sectors away from fossil fuels to stabilize global temperature rise.

[Typhoon Tip]

One aspect that intrigues me about that final/ending statement, "While our findings provide many avenues for interesting and relevant new research, the authors stress that the best way to reduce both uncertainty in and exposure to climate impacts is a rapid transition of relevant societal sectors away from fossil fuels to stabilize global temperature rise",   is that the climate response has actually lagged behind the anthropomorphic contributed forcing.  Or in other words, the latter has outpaced the former.

I keep reading statements - no fault to the author as it's not specific to their study - like this, where it "seems" or intimates a 1::1 causality in time.   As though if the ideal reality could ever be achieved, where there were a sudden and abrupt cessation of fossil fuel use, there would thus begin an immediately response and stabilizing climate.  

That is unfortunately not the case. 

In any such idealized state of affairs, the Earth would like keep warming until it satisfies the total thermal regulation/balance.  Another way to look at it is, there is room for the present atmospheric chemistry to store yet more thermal energy that it is.

Another possibility ( intuitive speculation) is that the modulating aspect of the global oceanic quasi coupling to this mess we are in, might also continue to absorb the lion's share of the warming human activity should otherwise have realized.  90% of which has sunk into the oceans (btw) since the Industrial Revolution.  So in simpler terms, it's possible that a sudden stoppage of fossil fuel combustion might register more slowing of the warming due to this factor.

[frontranger8]

4 hours ago, bluewave said:

Another extreme heatwave matching the findings of this recent paper. 
 

 

 

It's interesting that 2 of the 3 areas that have seen the biggest increase in extreme heatwaves, western North America with a focus on BC/the PNW and NW Europe, have pretty similar climates. Traditionally very temperate and mild in both the winter and summer, and similar latitude.

[donsutherland1]

The Paris-Montouris station, for which records go back to 1872, has recorded its first case of two consecutive 40°C (104°F) or above days. Paris-Jardin du Luxembourg topped out at 41.2°C (106.2°F). Jersey (Maison St. Louis: 39.3°C/102.7°F) and Switzerland (Basel: 38.0°C/100.4°F) set national all-time records. The UK (Merrifield: 36.7°C/98.1F) set a national June monthly record.

[donsutherland1]

Today was another day of furnace-like heat in France, Jersey, and the UK. The extreme heat will shift eastward over the next few days. 

[ChescoWx]

A really interesting statistic for the entire USA which exactly mirrors the data I share here from the philly burbs of Chester County. This below analysis measures not the typical metric of 90+ days but unusual hot periods of at least 4 days on average across the entire country where the average temperature reaches a level based on historical records that would be expected to only occur once every 10 years. Look how much hotter the entire nation was back in 1930's and 1940's compared to today! No matter how much we hear about how hot it is today we have still not reached that peak heating from the 1930 through 1942 period which matches the hottest period here in Chester County PA.

[donsutherland1]

Excerpts from World Weather Attribution concerning Europe's record-breaking June heatwave:

Fossil fuel emissions have rapidly worsened European heatwaves in just a few decades

• Over the region studied this heatwave is the most severe ever recorded. 
• In 1976, when some of the previous European records were set, the 2026 temperatures would have been virtually impossible to occur in June, while also highly unlikely at any time of the year. In 2003, the first major heatwave of this century, daytime heat like this would still have been very rare, about 10 times less likely than today, while nighttime temperatures such as this June would have been more than a hundred times less likely in 2003. 
• Across large parts of Western Europe, June is warming faster than any other month. In addition, daily maximum temperatures are warming faster than night time temperatures, though both are warming much faster than global warming. The hottest daily temperatures are warming at about triple the rate of global warming and night time temperatures at about twice the rate. Many capital cities are experiencing not only their hottest June 3-day period but also the hottest three-day period since 1950, according to the ERA5 dataset. However, due to global warming, these very high temperatures are now expected regularly during the summer months in many capitals. 
• This means that a similar heatwave in June would have been about 3.5°C cooler during the day in 1976 and about 2°C cooler in 2003. The nighttime temperatures would have been about 2.4°C cooler in June 1976 and about 1.3°C cooler in June 2003. 
• This June 2026 heatwave occurred under a circulation pattern broadly similar to historical analogues – Southerly Flow. However, a similar circulation pattern now produces significantly hotter temperatures than it did in the mid-20th century because the climate baseline has warmed...
• This summer shows that at 1.4°C of global warming, extreme heat is already reaching the limits of our societies’ ability to cope. Our analysis here shows that intense heat is increasing rapidly even in living memory, with such events tens to hundreds of times more likely since only 2003 and virtually impossible just 50 years ago. A rapid phase-out of fossil fuels is critical if we are to avoid even higher temperatures and their consequences in the future.

https://www.worldweatherattribution.org/fossil-fuel-emissions-have-rapidly-worsened-european-heatwaves-in-just-a-few-decades/