bluewave

Some improvement mostly north of I -78 corridor.

This July was the opposite temperature composite from the other developing super El Niño years. Notice the record warmth focused in the Rockies and Upper Plains. Rapid City, SD just tied their warmest July. That’s where the coolest anomalies during the July super El Niños were focused in the past. Notice the past developing super El Niños were among the coolest. Time Series Summary for Rapid City Area, SD (ThreadEx) - Month of Jul Warmest Average Temperatures Click column heading to sort ascending, click again to sort descending. 1 2026 79.7 2 - 2007 79.7 0 2 2006 79.3 0 3 2012 78.4 0 4 2002 78.3 0 5 1954 77.7 0 6 1974 77.3 0 7 2017 77.1 0 8 2003 77.0 0 - 1989 77.0 0 9 1955 76.9 0 10 1960 76.8 0 Time Series Summary for Rapid City Area, SD (ThreadEx) - Month of Jul Coolest Average Temperatures Click column heading to sort ascending, click again to sort descending. 1 1992 64.3 0 2 1993 65.1 0 3 1972 65.6 0 4 1950 66.9 0 5 1958 67.0 0 6 1971 68.0 0 7 2009 68.1 0 8 1944 69.0 0 9 1968 69.1 0 10 1962 69.2 0 11 2014 69.5 0 12 2018 70.2 0 13 2010 70.4 0 - 1979 70.4 0 14 2023 70.5 0 15 2019 70.6 0 - 1995 70.6 0 - 1967 70.6 0 16 1994 70.7 0 - 1982 70.7 0 - 1973 70.7 0 - 1948 70.7 0 17 1996 70.8 0 - 1986 70.8 0 - 1951 70.8 0 18 1997 70.9 0 19 2015 71.0 0

The Pacific mid-latitude SSTs are also near the record for the date. This has helped to drive the daily -PDO closer to -2. So no surprise that global SSTs are at record levels also.

Low-level clouds over the earth's oceans play a prominent role in keeping our planet cool by reflecting sunlight away from the surface. But their response to climate change has been hard to model. Now, researchers at Caltech and Google have uncovered important new insights about how clouds might respond to warming sea-surface temperatures and rising CO2 levels using a large and powerful dataset of simulations developed by the group. "One of the largest open questions in climate prediction is how low clouds will respond to global warming," says Zhaoyi Shen, lead research scientist at Caltech's Ronald and Maxine Linde Center for Global Environmental Science and a co-author on a paper outlining the team's findings published July 24 in Science Advances. "Our results show potentially large rapid adjustments of low clouds to high CO2 concentrations, which suggests the earth's climate might be more sensitive to high CO2 levels than some climate models currently project." The team also found that the thinning of low clouds—uniform layers or large, lumpy expanses below 6,000 feet that cover massive portions of subtropical seas—amplify global warming through a feedback loop: Rising sea surface temperatures lead to fewer clouds, meaning less reflected sunlight and a warmer planet. "This supports the growing body of evidence from the last few years," says Tapio Schneider, the Theodore Y. Wu Professor of Environmental Science and Engineering at Caltech and co-author of the paper; Schneider is also a principal scientist at Google. "We can now confidently rule out the idea that this effect is zero or that it somehow dampens global warming." While virtually all global climate models show that the earth is getting warmer, they differ widely on predictions of the exact long-term temperature rise triggered by sustained increases in atmospheric CO2. A large part of the challenge in reaching a scientific consensus has been the inability to resolve the fine-scale atmospheric turbulence that drives low-level cloud formation and dissipation. By combining a modeling framework for simulations developed by Shen with the power of Google computing resources, the research team used simulated large-scale weather data from a trusted global climate model developed by the National Oceanic and Atmospheric Administration to drive thousands of high-resolution large-eddy simulations. The simulations used atmospheric and surface-level conditions from 500 randomly selected locations across the tropical Pacific Ocean, taken during four different months to represent seasonal changes. Each location–season combination was then used to drive large-eddy simulations for four climate change scenarios: a 4 degrees Celsius sea-surface temperature increase from baseline; a quadrupling of atmospheric CO2 alone; a 4 degrees Celsius warming with doubled CO2; and a 4 degrees Celsius warming with quadrupled CO2. "We found that clouds respond directly to CO2 changes, a fact well understood in physics but perhaps a surprise to many," Schneider says. "Simply altering atmospheric CO2 shifts how infrared radiation moves through the air, directly affecting clouds even if temperatures are artificially held steady. Crucially, this effect is nonlinear; it accelerates as CO2 levels rise." 3D renderings show examples of large-eddy simulations (LES) generated by the research team. The center panel represents locations sampled in the Pacific Ocean. In each corner, volumetric renderings of cloud water mass fraction in LES driven by a global climate model output for today's climate at four representative locations (black circles) during July reveal distinct low-cloud patterns (clockwise from lower left: shallow cumulus, stratocumulus, coastal stratocumulus with fog, and stratocumulus over cumulus). The bottom plane renders surface buoyancy.Credit: Sheide Chammas / Google The team used more than 7,000 simulations in their investigation, which represents a massive increase in sample size compared to earlier work. For example, an earlier study by Shen combined data from 500 simulations, which was itself an increase from the dozens of simulations in previous experiments. The massive upscaling was made computationally feasible using a large-eddy simulation code that leverages Google's tensor processing unit (TPU) clusters to build a robust collection of cloud states under different conditions. TPUs are specialized computer chips designed to accelerate artificial intelligence (AI) and machine learning workloads. "This experiment demonstrates the scale of computation made possible by Google's hardware capabilities," says Yi-Fan Chen, a software engineering director at Google and lead of the Google team that carried out the research. "It's computing at an extraordinary scale, demonstrating how processors originally developed for AI and machine learning can accelerate scientific discovery, enabling simulations that were previously out of reach." In addition to modeling the future, the dataset can help researchers elucidate the planet's past climates, such as during the Eocene Epoch (which began 56 million years ago and ended 33.9 million years ago), when CO2 levels were up to four times higher than today. Plus, the dataset is public, meaning anyone with a laptop can use it to build their own models. "This new public dataset has the potential to help the broader scientific community evaluate and train turbulence, convection, and cloud models for global climate models," says Shen. Shen is also part of the Climate Modeling Alliance (CliMA), a Caltech-based coalition of scientists, engineers, and applied mathematicians from Caltech and MIT. "At the Climate Modeling Alliance, we are currently developing a new climate model designed to learn from data using AI and machine learning, and I am using this dataset to calibrate our model's new turbulence and convection schemes." Schneider, who leads CliMA, says the group is already using the data extensively and that he is excited to see what others can glean from it. "I hope researchers come up with new and creative ways of representing clouds in climate models and that we finally reduce these massive uncertainties in climate predictions," he says. "It's not going to happen overnight to change all sorts of climate models around the world, but over time, I think this can happen." The Science Advances paper is titled "High-resolution simulations reveal positive global warming feedback from Pacific low clouds." Additional authors from Google are Sheide Chammas, Qing Wang, Rob Carver, Jeffrey B. Parker, Cenk Gazen, Matthias Ihme, Yi-Fan Chen, and John Anderson. The work was supported by the National Science Foundation and Schmidt Sciences.

Harrison, NJ away from the bay breeze that the Newark ASOS gets is currently in the lead for our area. Time Series Summary for HARRISON, NJ Most 90° Days by July 28th Click column heading to sort ascending, click again to sort descending. 1 2010-07-28 34 17 2 2024-07-28 28 0 3 1999-07-28 27 1 4 2021-07-28 26 0 - 2011-07-28 26 15 - 2002-07-28 26 5 5 2026-07-28 25 0 - 2018-07-28 25 0 - 2012-07-28 25 21 Data for January 1, 2026 through July 28, 2026 NJ 90° days Click column heading to sort ascending, click again to sort descending. ESTELL MANOR COOP 26 HARRISON COOP 25 ATLANTIC CITY INTL AP WBAN 23 TETERBORO AIRPORT WBAN 23 Atlantic City Area ThreadEx 23 TETERBORO AIRPORT COOP 23 NEW BRUNSWICK 3 SE COOP 22 New Brunswick Area ThreadEx 22 NEWARK LIBERTY INTL AP WBAN 21 Newark Area ThreadEx 21 MCGUIRE AFB WBAN 20 PHILADELPHIA/MT. HOLLY WFO COOP 20 Data for January 1, 2026 through July 28, 2026 NY 90° Days Click column heading to sort ascending, click again to sort descending. LAGUARDIA AIRPORT WBAN 15 PORT AUTH DOWNTN MANHATTAN WALL ST HEL ICAO 15 New York-LGA Area ThreadEx 15 Poughkeepsie Area ThreadEx 14 POUGHKEEPSIE/HUDSON VALLEY REGIONAL AIRPORT WBAN 14 STEWART FIELD WBAN 12 BAITING HOLLOW COOP 12 NY CITY CENTRAL PARK WBAN 11 SHRUB OAK COOP 11 ELMIRA COOP 11 SARATOGA SPRINGS 4 SW COOP 11 VICTOR 2NW COOP 11 New York-Central Park Area ThreadEx 11 MONTGOMERY ORANGE COUNTY AP WBAN 10 SARA NEW YORK RAWS 10

The fewer 90s in late July allowed the warm spots like Newark to pull back to the 10th highest number of 90s by July 28th following a very fast start by July 15th. Time Series Summary for NEWARK LIBERTY INTL AP, NJ Most 90° days by July 28th Click column heading to sort ascending, click again to sort descending. 1 2010-07-28 36 0 2 1993-07-28 31 0 3 2022-07-28 29 0 - 1987-07-28 29 0 4 1994-07-28 28 0 5 2025-07-28 27 0 - 2021-07-28 27 0 - 1991-07-28 27 0 6 2011-07-28 25 0 7 2024-07-28 24 0 - 2012-07-28 24 0 - 1999-07-28 24 0 - 1952-07-28 24 0 - 1949-07-28 24 0 8 2002-07-28 23 0 - 1988-07-28 23 0 - 1966-07-28 23 0 - 1955-07-28 23 0 9 2016-07-28 22 0 - 1944-07-28 22 0 10 2026-07-28 21 0 - 2013-07-28 21 0 - 2005-07-28 21 0 - 1943-07-28 21 Time Series Summary for NEWARK LIBERTY INTL AP, NJ Most 90° days by July 15th Click column heading to sort ascending, click again to sort descending. 1 2010-07-15 23 0 - 1993-07-15 23 0 2 2021-07-15 22 0 3 2024-07-15 21 0 - 1994-07-15 21 0 - 1987-07-15 21 0 4 2026-07-15 20 0 - 2022-07-15 20 0 - 1991-07-15 20 0 - 1966-07-15 20 0

Updating for July 2026 with several spots in the northern forecast zones going over 10.00” for the month. https://dex.cocorahs.org/stations/NY-PT-2 Viewing Station: NY-PT-2 : Beacon 4.2 ESE Month-To-Date: 16.30"

The area near Beacon in Putnam County picked up 8.22” and 16.30” for July. https://dex.cocorahs.org/stations/NY-PT-2 Today: 8.22" Month-To-Date: 16.30"

It would be interesting to know what caused the WWBs to be so much stronger this year than in 2023-2024 and 2015-2016? We noticed the record WWBs developing back in December north of Australia. These record WWBs are the reason that this is turning into an east based event over the last month. Nino 1+2 has pulled just ahead of 1997-1998 and 3 and 3.4 are significantly ahead of 1997-1998. Nino 4 has been declining this month. This is going to be a really extreme case study. Since these anomalies would allow the +30 C warm pool to get further east in the ENSO regions than it ever has been before.

1997-1998 was a great example of what happens when the STJ overpowers the pattern with too much warmth. NYC only finished with 5.5 inches of snow that season. Many lows running to the Ohio Valley and then tracking very close to the area. I made a composite of the 12 heaviest precipitation days that winter. 12 heaviest precipitation storm days in NYC during DJF 1997-1998 and the storm track progression

Mike Flannigan @mikeflannigan.bsky.social Follow Graphs showing cumulative area burned and carbon emissions in Ontario. Note in terms of area burned 2021 is the top spot but 2026 is top spot in terms of carbon emissions. Both are correct. The reason for this is that the 2026 fires are more severe - consumed more biomass (greater depth of burn). ALT ALT 7:12 PM · Jul 19, 2026 Everybody can reply

Our area will finish July much wetter than much of the CONUS which has been experiencing record heat and drought.

Just saying it’s going to be positive isn’t much of a stretch. But my examples of 2015-2016 and 2023-2024 both featured positive +PNA patterns. The magnitude and character of the positive is the question here which is a function of where the main Nino ridge and Aleutian trough complex becomes established. Plus we have the difference between the CPC PNA and the 500 mb PNA at times. Sometimes we get bootleg +PNA patterns like in January 2023 with a deep trough underneath the Canadian ridge out West which many consider a -PNA. We may have to be patient before getting something more closely resembling an El Niño pattern across the mid-latitudes of the Northern Hemisphere.

We may not start to get a better idea about things until the Northern Hemisphere mid-latitude 500 mb pattern actually couples with the El Niño. This continues to be a strong -PDO 500 mb pattern which has translated into the drop in the SST PDO and the near record ridge north of Hawaii. Have never seen the Southern Hemisphere so fully coupled with the El Niño standing wave while the Northern Hemisphere mid-latitudes are so out of sync. I will compare the VP charts for July in early August against previous super events when JMA posts them. My only guess is that the standing wave appears to be centered more to the south of the equator than usual. But not really sure since we haven’t seen this disparity with the summers of 2023, 2015, 1997, 1982, and 1972. Perhaps the pattern will slowly begin to shift in the Northern Hemisphere to more of a standard Nino look in the next few months.