bluewave

SSTs near +30°C will produce forcing that overlaps with the primary El Nino standing wave. Plus large areas of mid-latitude record SST warmth will add a -PDO La Niña-like influence. The coming heatwave for late June into early July is something we more have associated with strong La Niña or -PDO patterns. The analog composite and July coorelations are mostly comprised of established La Ninas or developing La Nina’s with a strong +SOI.

Yeah, the fastest El Niño development experienced during modern record keeping.

Islip has experienced 15 out of the last 22 months since the drought started in September 2024 with below average precipitation bolded below. Monthly Total Precipitation for ISLIP-LI MACARTHUR AP, NY Click column heading to sort ascending, click again to sort descending. 2026 2.58 3.66 4.19 2.15 2.68 0.66 M M M M M M 15.92 2025 0.60 3.72 4.76 1.98 4.67 1.88 5.64 0.53 1.58 5.06 2.72 3.77 36.91 2024 7.32 2.40 9.54 3.45 4.67 2.44 2.55 6.50 0.24 0.12 3.34 6.23 48.80

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.

The first time that Nino 1+2 ONI went above +3 only 3 years apart. So the last super El Niño left a warm imprint without the stronger trades or much in the way of La Niña developing. The first clue the El Niño would reload so quickly was the 1+2 warming in November 2025 into December 2025. A record +PNA followed with a strongest Aleutian low in years and Nino-like elements to the pattern. Will be interesting to see how this record breaking event leaves the Pacific Basin SST and wind structure for what happens later in the 2020s. The current PDO would be in the +1.60 range just based on the EPAC warmth like July 2015. But the lingering warmth and ridging from Japan to North of Hawaii are having an overlapping influence leading to alternating Nino-like and Niña-like 500mb patterns across North America.

Did you get a chance to see the gorgeous Asperitas display? My friend was near where the SSP and RTE 110 met. It looked more impressive closer to the South Shore than further to the north. https://www.facebook.com/photo?fbid=1377119724231766&set=a.160564152554002 https://en.wikipedia.org/wiki/Asperitas_(cloud)

End of August 2003. End of July 2019. The old heatwaves are being ridiculed when we're only in June! ➡️France has just experienced by far the hottest day ever measured since at least 1900, with an average national temperature around 30°C. ➡️With spectacular temperatures of 44 to 45°C across several French departments. ➡️131 absolute records broken. ➡️44 million people are overwhelmed by a red "heatwave" alert. ➡️Tomorrow, with the wind dying down, a "foehn wind" episode or so-called "flash drought" is expected in the Centre-West region, with a fire risk index at "extreme" to "very extreme." We've just rewritten history. Before tomorrow, when it could get even hotter.

The LI crew would really be complaining if this was a snowstorm.

The Euro forecast chart may show what was discussed in that post more clearly.

Models begin to build 90° heat to our west as we move into early July. Some models hold onto low pressure just to the east of New England. So they are currently split on whether the 90s make it here or stay to our south.

Strongest Southern Hemisphere +AAO since May 2023 as their winter gets underway. The SAM index reached a strongly positive value of +4.23 on June 21, which is a three-year high. That means that mean sea level pressure is currently trending higher than normal near Australia's latitudes, and the westerly wind belt that flows between Australia and Antarctica is located further south than usual for this time of year. This has been evident in the sort of weather we’ve seen lately across southeastern Australia, with fewer cold fronts, frequent blocking high pressure systems, and unseasonably warm temperatures. The last time the SAM index reached 4 (or higher) was in May 2023, when it peaked at 5.5. The values in the index are a measure of standard deviation from the norm in terms of mean sea level pressure. In very basic terms, it means we’ve seen a lot more highs than lows.

The extensive ridge driving the warm pool from east of Japan to the north of Hawaii is more of a 2nd EOF -PDO type pattern. This is why most of the analog dates for early July are established or developing La Niña years. You would want to see a deep trough set up from Japan to north of Hawaii heading into next winter to turn the PDO more positive.

May 2026 was a little cooler than May 2023 around Japan. But much warmer than 2015 and 1997. This relationship is reflected in the PDO values for the month of May. Plus the area off the Baja was much warmer than 2023. https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/v6/index/ersst.v6.pdo.dat May 2026 PDO -1.60 May 2023 PDO -2.46 May 2015 PDO +0.40 and +1.65 by July May 1997 PDO +1.29 and +2.35 by June Traditional strong +PDO pattern

When the warm pool extends from Japan to California it allows the PDO to move closer to neutral. The key to watch going forward will it be able to get positive and hold it. Recent years the daily PDO values have rebounded back closer to neutral but couldn’t get into sustained positive territory. When the PMM was this strong going into the summer of 2015, the PDO was at +1.65. July 2015 +1.65 PDO vs May 2026 -PDO at -1.60 https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/v6/index/ersst.v6.pdo.dat