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

While these very long range forecasts aren’t the strong suit of these seasonal models, the subsurface cold pool is near the Dateline by next March looks weaker than 1998. The warmth in the east looks similar to March 1998. But Nino 3.4 is also much warmer than 1998.

A little later than the developing super El Niños in 2023, 2015, and 1997. Those years all had record amplitudes in MJO 7-2 during March. AMJ 2023 and 1997 were also focused in these phases also

At least the record cold was extensive enough that month for the entire CONUS to average a little below the long term February average. https://www.ncei.noaa.gov/access/monitoring/monthly-report/national/201502 The February contiguous U.S. temperature was 33.1°F, 0.7°F below the 20th century average, ranking near the median value in the 121-year period of record. The average February maximum (daytime) temperature for the contiguous U.S. was 44.6°F, 0.2°F below average, while the average minimum (nighttime) temperature was 21.7°F, 1.2°F below average.

Good to finally see the first daily rainfall over 1.20 in NYC since 4-25. Data for NY CITY CENTRAL PARK, NY Click column heading to sort ascending, click again to sort descending. 2026-06-23 M 2026-06-22 1.24 2026-06-21 0.00 2026-06-20 0.00 2026-06-19 0.00 2026-06-18 T 2026-06-17 T 2026-06-16 0.00 2026-06-15 0.02 2026-06-14 0.25 2026-06-13 0.00 2026-06-12 0.90 2026-06-11 0.21 2026-06-10 T 2026-06-09 0.00 2026-06-08 0.00 2026-06-07 0.04 2026-06-06 0.26 2026-06-05 0.00 2026-06-04 0.00 2026-06-03 0.00 2026-06-02 0.00 2026-06-01 T 2026-05-31 0.00 2026-05-30 T 2026-05-29 0.00 2026-05-28 0.00 2026-05-27 T 2026-05-26 0.00 2026-05-25 0.18 2026-05-24 1.10 2026-05-23 0.75 2026-05-22 0.00 2026-05-21 0.15 2026-05-20 0.09 2026-05-19 0.00 2026-05-18 0.00 2026-05-17 0.00 2026-05-16 0.00 2026-05-15 0.00 2026-05-14 0.01 2026-05-13 0.01 2026-05-12 0.00 2026-05-11 0.00 2026-05-10 0.01 2026-05-09 0.24 2026-05-08 0.01 2026-05-07 0.16 2026-05-06 0.32 2026-05-05 0.00 2026-05-04 0.00 2026-05-03 0.00 2026-05-02 0.02 2026-05-01 T 2026-04-30 0.16 2026-04-29 0.20 2026-04-28 0.00 2026-04-27 0.00 2026-04-26 0.03 2026-04-25 1.36 2026-04-24 0.00

Yeah, these seasonal models are more or less just defaulting to ENSO correlations depending on the state ENSO is in at the time. So they really aren’t forecasts in the traditional sense. This is why none of the seasonal forecasts issued relying on them came close to the magnitude of the warmth experienced since December 2015. The common denominator to all the forecasts is that the ridge was magnitudes stronger than the original forecasts and the trough areas were generally weaker. None of these records were forecast beyond a week or two before they actually occurred. These extremes used to be very rare before December 2015 during months like March 2012 or January 2006. This is not getting very much attention since we have tended to normalize all the warmth. Plus record warmth during the winter doesn’t generate as much attention as the periods of extreme cold which have become few and far between. The last -10 month in the Northeast during February 2015 got much more attention than most of the +10 months since then have. I chalk this up to human nature which was conditioned to fear cold from thousands of years back to the ice ages which made survival so difficult. This is why so much of the population has moved to the sun belt areas. Back in the 1970s when -10 months were much more common, all the talk was about an impending return of another ice age. So imagine how much attention the 17 months below would be getting if they were all -10s instead of +10s. The super El Niño in 2015-2016 had 2 months go 10+ from December to March with 2023-2024 also having 2 months reach this mark. None of those months like the others were forecast from the long range seasonal guidance. It would be very challenging for any seasonal forecast to pick out the specific month and geographic location this coming 2026-2027 super El Niño that would potentially experience one of these extremes. Forecasters just don’t feel comfortable including +10s to their seasonal forecast maps. We seldom see much beyond a +1 to +3 area and sometimes up to +5. DEC…2015….NYC….+13.3 MAR…2016…MOT….+10.5 JAN…2017….BTV…..+11.0 FEB….2017….ORD….+10.3 FEB…..2018…ATL….+10.6 FEB….2019…MGM….+10.5 JAN….2020…YAM….+9.8 DEC….2021….DFW….+13.2 JAN….2023….DXR….+12.3 FEB….2023…..SSI…..+9.8 DEC….2023….INL…..+15.8 FEB…..2024….FAR…..+17.5 DEC….2024…..LND…..+11.3 DEC….2025….CPR…..+12.1 JAN….2026….RIW……+10.2 FEB…..2026….LND…..+11.3 MAR….2026….PHX…..+12.5

The warm pool to the east of Japan began developing during the mid to late 2010s. It’s the first time the ocean there down to the subsurface has warmed this much in the modern monitoring era. It’s appears to be due to the record 500 mb heights leading to light winds and clear skies allow the ocean below to warm. When we had the colder pool in the EPAC in recent years it lead to the record low -PDOs. In the old days the -PDOs were driven by mostly the cooler SSTs in the EPAC rather than the warm anomalies from Japan to south of the Aleutians. Most researchers avoid the term permanent and use persistent or new as a description. What would need to have happen to reverse this pattern would be for low pressure and strong winds to persist in this location with more clouds. If this could be sustained for more than a few months, then there would be a shot at cooling the surface and subsurface. As long as the warm pool persisted off of California, then the PDO could transition to a more strongly positive level like we last saw back in 2015. Current model forecasts have this warm pool east of Japan persisting through December at the same time there is a warm pool off of California. So this effectively brings the PDO closer to neutral with overlapping warm pools from the West and East. Since these models aren’t the greatest for reliably beyond 8-15 days, we are just going to have to wait and see what the details will be. Plus they have missed the summer -PDO declines recent summers as the ridge to the East of Japan has verified much stronger than seasonal model forecasts. It appears that the subsurface reservoir of record warmth reaching to the surface has resulted in a feedback process between the ocean and atmosphere sustaining the pattern. While it’s still very early in the El Niño process, the big increase in WWBs near and off the equator so far hasn’t had the stronger winds and lower pressures to the East of Japan and to the south of the Aleutians like the developing super El Niño 1997 had during the spring. We would want to see the westerlies increase to the east of Japan and south of the Aleutians especially by next winter to have a chance to begin to get the PDO into more of a positive state.

It’s a favorable pressure pattern for sea ice retention with the deep low north of Alaska. We have been seeing this weak dipole pattern much of the time since 2013. Pretty much the opposite of the strong dipole pattern which was in place from 2007-2012 which lead to the multiple records. But the post below shows that the Euro has a cold bias on the DMI chart below. Still a colder pattern but nothing as cold as the Euro shows.

My guess with these seasonal models are that they are very simple tools that aren’t actually making a forecast. They are showing the correlations and filling in the temperatures to match. These are the forecasts issued for DJF 2023-2024 made in August. Notice how it looks like the models are just cutting and pasting a correlation map for Nino 3.4 North American temperatures without much regard to this being a super El Niño or weak El Niño. I converted the verification below to °C to match the forecasts so everything lines up correctly. I also included the cooler 1981-2010 base period for the CanSIPS which the forecast was issued in. Verification was the warmest winter on record for the CONUS

We can see the influence of the tree growth adding artificial cooling to the NYC record when we look at very warm June 1-20 periods for high temperatures going back in time. NYC used to be more evenly matched with Newark. Notice how the spread widened following the tree growth since the 1990s. NYC mean maximum temperature compared to Newark 2026….-4.1 2008….-2.4 1994…..-2.5 1984…..-1.4 1966….+0.3 1957….…0.0 1952…..-0.7 Data for June 1, 2026 through June 20, 2026 Click column heading to sort ascending, click again to sort descending. NJ NEWARK LIBERTY INTL AP WBAN 86.0 NJ TETERBORO AIRPORT WBAN 85.4 NJ HARRISON COOP 85.1 NJ TETERBORO AIRPORT COOP 85.0 NY LAGUARDIA AIRPORT WBAN 84.0 NY PORT AUTH DOWNTN MANHATTAN WALL ST HEL ICAO 83.9 NJ CALDWELL ESSEX COUNTY AP WBAN 83.7 NY BAITING HOLLOW COOP 83.1 CT MERIDEN MARKHAM MUNICIPAL AP WBAN 82.9 CT DANBURY COOP 82.7 NY CENTERPORT COOP 82.6 NY JFK INTERNATIONAL AIRPORT WBAN 82.5 NY SHRUB OAK COOP 82.4 CT NORWICH PUBLIC UTILITY PLANT COOP 82.4 NY PORT JERVIS COOP 82.2 NY MONTGOMERY ORANGE COUNTY AP WBAN 82.2 NY RIVERHEAD RESEARCH FARM COOP 82.2 NY NY CITY CENTRAL PARK WBAN 81.9 Data for June 1, 2008 through June 20, 2008 Click column heading to sort ascending, click again to sort descending. NJ NEWARK LIBERTY INTL AP WBAN 84.9 NJ TETERBORO AIRPORT WBAN 84.9 NJ ELIZABETH COOP 84.8 NJ CRANFORD COOP 84.8 NJ CANOE BROOK COOP 84.7 NJ TETERBORO AIRPORT COOP 84.7 NY BRONX COOP 84.5 NJ HARRISON COOP 84.2 NY DOBBS FERRY-ARDSLEY COOP 84.1 NJ CALDWELL ESSEX COUNTY AP WBAN 83.8 NY LAGUARDIA AIRPORT WBAN 83.5 CT DANBURY COOP 83.4 NJ ESSEX FELLS SERVICE BLDG COOP 83.0 NY MINEOLA COOP 82.7 NY NY CITY CENTRAL PARK WBAN 82.5 NY WEST POINT COOP 82.5 NY PORT JERVIS COOP 82.1 NY MONTGOMERY ORANGE COUNTY AP WBAN 82.0 NY RIVERHEAD RESEARCH FARM COOP 81.5 NY JFK INTERNATIONAL AIRPORT WBAN 81.3 Data for June 1, 1994 through June 20, 1994 Click column heading to sort ascending, click again to sort descending. NJ NEWARK LIBERTY INTL AP WBAN 87.2 NJ CRANFORD COOP 86.1 CT DANBURY COOP 84.9 NJ LITTLE FALLS COOP 84.8 NY NY CITY CENTRAL PARK WBAN 84.7 NY LAGUARDIA AIRPORT WBAN 84.6 NJ CANOE BROOK COOP 83.6 NY MIDDLETOWN 2 NW COOP 83.5 NY DOBBS FERRY-ARDSLEY COOP 83.4 NY RIVERHEAD RESEARCH FARM COOP 83.4 NJ JERSEY CITY COOP 83.3 NY SETAUKET STRONG COOP 83.1 NY WEST POINT COOP 83.0 CT STAMFORD 5 N COOP 82.9 NY PORT JERVIS COOP 82.8 NJ ESSEX FELLS SERVICE BLDG COOP 82.8 NJ TETERBORO AIRPORT WBAN 82.7 NJ CHARLOTTEBURG RESERVOIR COOP 82.3 NY WEST NYACK COOP 81.6 NY SUFFERN COOP 81.1 NY PATCHOGUE 2 N COOP 81.0 CT MIDDLETOWN 4 W COOP 81.0 NJ WANAQUE RAYMOND DAM COOP 80.8 NY NEW YORK AVE V BROOKLYN COOP 80.7 CT IGOR I SIKORSKY MEMORIAL AIRPORT WBAN 80.7 CT NORWICH PUBLIC UTILITY PLANT COOP 80.7 NY MINEOLA COOP 80.6 NY WALDEN 1 ESE COOP 80.3 NY WESTCHESTER CO AP WBAN 80.3 NY YORKTOWN HEIGHTS 1W COOP 79.6 NY ISLIP-LI MACARTHUR AP WBAN 79.6 NY JFK INTERNATIONAL AIRPORT WBAN 78.9 Data for June 1, 1984 through June 20, 1984 Click column heading to sort ascending, click again to sort descending. NY SCARSDALE COOP 87.5 NY NY WESTERLEIGH STAT IS COOP 85.4 CT NORWALK GAS PLANT COOP 84.9 NJ CRANFORD COOP 84.8 NY DOBBS FERRY-ARDSLEY COOP 84.8 NY WEST POINT COOP 84.8 NJ NEWARK LIBERTY INTL AP WBAN 84.7 CT STAMFORD 5 N COOP 84.5 NY GARNERVILLE COOP 84.2 NJ WANAQUE RAYMOND DAM COOP 83.8 NJ LITTLE FALLS COOP 83.7 NY MIDDLETOWN 2 NW COOP 83.5 NY NY CITY CENTRAL PARK WBAN 83.3 NJ ESSEX FELLS SERVICE BLDG COOP 83.3 NY NEW YORK AVE V BROOKLYN COOP 83.1 NY LAGUARDIA AIRPORT WBAN 83.1 CT MOUNT CARMEL COOP 83.1 NY PORT JERVIS COOP 83.0 NJ CANOE BROOK COOP 83.0 NY WESTBURY COOP 83.0 NY RIVERHEAD RESEARCH FARM COOP 82.9 CT MIDDLETOWN 4 W COOP 82.9 NJ TETERBORO AIRPORT WBAN 82.8 CT DANBURY COOP 82.6 NY JFK INTERNATIONAL AIRPORT WBAN 82.5 Data for June 1, 1966 through June 20, 1966 Click column heading to sort ascending, click again to sort descending. NY NEW YORK LAUREL HILL COOP 82.6 NY NY CITY CENTRAL PARK WBAN 82.6 NJ ELIZABETH COOP 82.3 NJ NEWARK LIBERTY INTL AP WBAN 82.3 NJ PATERSON COOP 81.9 NY WEST POINT COOP 81.5 NJ LITTLE FALLS COOP 81.3 NY PORT JERVIS COOP 81.2 NY SCARSDALE COOP 80.9 CT WATERBURY RADIO WBRY COOP 80.5 NY NEW YORK AVE V BROOKLYN COOP 80.4 NY NY WESTERLEIGH STAT IS COOP 80.3 NY BEDFORD HILLS COOP 79.5 NJ CANOE BROOK COOP 79.4 NJ ESSEX FELLS SERVICE BLDG COOP 79.4 CT STAMFORD 5 N COOP 79.0 NJ WANAQUE RAYMOND DAM COOP 78.9 CT SAUGATUCK RESERVOIR COOP 78.8 CT DANBURY COOP 78.8 NJ JERSEY CITY COOP 78.8 NY LAGUARDIA AIRPORT WBAN 78.8 NY DOBBS FERRY-ARDSLEY COOP 78.6 NY MIDDLETOWN 2 NW COOP 78.5 NY CARMEL COOP 78.3 CT NORWICH PUBLIC UTILITY PLANT COOP 78.3 NY YORKTOWN HEIGHTS 1W COOP 78.2 NY SHRUB OAK COOP 78.1 CT NORWALK GAS PLANT COOP 78.1 NY SUFFERN COOP 78.0 NY STEWART FIELD WBAN 77.9 NY WESTCHESTER CO AP WBAN 77.9 NY HEMPSTEAD GARDEN CITY COOP 77.7 NY MINEOLA COOP 77.1 NY SETAUKET STRONG COOP 76.9 CT MOUNT CARMEL COOP 76.8 NY JFK INTERNATIONAL AIRPORT WBAN 76.7 Data for June 1, 1957 through June 20, 1957 Click column heading to sort ascending, click again to sort descending. NY WEST POINT COOP 85.0 NJ ELIZABETH COOP 84.9 NJ LITTLE FALLS COOP 84.9 NJ RIDGEFIELD COOP 84.5 NJ PATERSON COOP 84.5 NY SCARSDALE COOP 84.3 NY DOBBS FERRY-ARDSLEY COOP 84.1 NY SUFFERN COOP 84.1 NY PORT JERVIS COOP 83.5 NY BEDFORD HILLS COOP 83.2 NJ CANOE BROOK COOP 83.0 NY STEWART FIELD WBAN 82.8 NY CARMEL COOP 82.8 CT NORWICH PUBLIC UTILITY PLANT COOP 82.7 NJ NEWARK LIBERTY INTL AP WBAN 82.5 NY NEW YORK LAUREL HILL COOP 82.5 NY LAGUARDIA AIRPORT WBAN 82.5 NY NY CITY CENTRAL PARK WBAN 82.5 CT MOUNT CARMEL COOP 82.5 NY MIDDLETOWN 2 NW COOP 82.4 CT STAMFORD 5 N COOP 82.4 NY HEMPSTEAD MALVERNE COOP 82.2 CT DANBURY COOP 82.2 NY SHRUB OAK COOP 82.1 Data for June 1, 1952 through June 20, 1952 Click column heading to sort ascending, click again to sort descending. NJ ELIZABETH COOP 88.6 NJ PATERSON COOP 85.8 NJ LITTLE FALLS COOP 84.6 NY BEDFORD HILLS COOP 84.5 NY SCARSDALE COOP 84.4 NY WEST POINT COOP 84.3 NY NEW YORK LAUREL HILL COOP 84.2 NJ NEWARK LIBERTY INTL AP WBAN 83.7 NJ RIDGEFIELD COOP 83.5 NJ CANOE BROOK COOP 83.3 CT WATERBURY ANACONDA COOP 83.0 NY LAGUARDIA AIRPORT WBAN 83.0 NY NY CITY CENTRAL PARK WBAN 83.0 NY PORT JERVIS COOP 82.8 CT WATERBURY CITY HALL COOP 82.7 NY MINEOLA COOP 82.6 NY MIDDLETOWN 2 NW COOP 82.5 CT STAMFORD COOP 82.3 CT NORWALK COOP 82.2 NJ WANAQUE RAYMOND DAM COOP 81.8 NY HEMPSTEAD MALVERNE COOP 81.8

Big shift to a negative tendency last few days as we see a more Niña-like pattern again for a time near the end of the month.