bluewave

This summer so far really hasn’t been that rainy compared to the past. Only a few areas in the interior have had 10” this month. Notice how far down in the rankings we are for wet summers at the major stations like NYC and ISP which missed the heaviest rains this month. Time Series Summary for NY CITY CENTRAL PARK, NY - Jun through Aug Wettest Summers Click column heading to sort ascending, click again to sort descending. 1 2011 25.23 0 2 2021 24.03 0 3 1975 22.40 0 4 1989 22.36 0 5 2009 21.38 0 6 2006 20.79 0 7 2007 20.62 0 8 1928 20.50 0 9 1903 20.43 0 10 1927 20.01 0 11 2003 19.87 0 12 1942 19.82 0 13 2018 19.15 0 14 1971 18.88 0 15 1889 18.55 0 16 1990 18.37 0 17 1871 18.22 0 18 1919 18.16 0 19 1884 17.62 0 20 1967 17.57 0 21 1922 17.23 0 22 1937 17.22 0 23 1911 17.11 0 24 1872 17.05 0 25 1938 16.87 0 26 1887 16.64 0 27 1875 16.55 0 28 1902 16.37 0 29 1960 16.29 0 30 1897 16.28 0 31 2000 15.97 0 32 1991 15.86 0 33 2013 15.79 0 34 1972 15.76 0 35 1904 15.75 0 36 1878 15.67 0 37 1873 15.45 0 38 1926 15.37 0 39 1933 15.24 0 40 1901 15.19 0 41 1946 15.16 0 42 1920 15.13 0 43 2019 14.93 0 - 1880 14.93 0 44 1941 14.76 0 45 1879 14.68 0 46 1896 14.66 0 47 1984 14.17 0 48 1915 14.12 0 49 1955 13.89 0 50 1930 13.85 0 51 1909 13.69 0 52 2004 13.68 0 53 1890 13.53 0 54 2023 13.52 0 - 1973 13.52 0 55 1997 13.50 0 56 1983 13.48 0 57 1994 13.40 0 58 2020 13.37 0 59 1996 13.34 0 60 2008 13.12 0 61 1969 13.06 0 62 1948 13.02 0 63 1987 12.95 0 64 2024 12.93 0 - 1959 12.93 0 65 1982 12.92 0 66 1992 12.75 0 67 1893 12.44 0 68 2017 12.29 0 69 2014 12.10 0 70 1945 11.92 0 71 1888 11.79 0 72 1978 11.67 0 73 1986 11.66 0 - 1968 11.66 0 74 1905 11.63 0 75 1988 11.62 0 76 2016 11.59 0 - 1952 11.59 0 77 1985 11.40 0 - 1918 11.40 0 78 1950 11.39 0 79 1877 11.30 0 80 1895 11.17 0 81 2015 11.12 0 82 1962 11.11 0 83 1925 11.09 0 84 1908 11.08 0 85 1961 10.91 0 86 2005 10.73 0 87 1976 10.72 0 88 1934 10.69 0 89 1935 10.64 0 90 1974 10.61 0 91 1885 10.58 0 92 1936 10.50 0 93 2002 10.44 0 - 1940 10.44 0 94 1913 10.42 0 95 1917 10.40 0 96 1931 10.39 0 97 1874 10.27 0 98 1980 10.26 0 99 1892 10.15 0 100 1899 10.14 0 101 2012 10.09 0 102 1914 10.07 0 103 1977 10.00 0 104 1900 9.93 0 - 1891 9.93 0 105 2001 9.89 0 106 1998 9.81 0 107 1916 9.65 0 - 1870 9.65 0 108 1939 9.62 0 109 1981 9.51 0 - 1924 9.51 0 110 1921 9.38 0 111 1869 9.36 0 112 1943 9.35 0 113 2022 9.18 0 114 1951 9.17 0 115 1898 9.13 0 116 1932 8.98 0 117 2010 8.94 0 118 2025 8.70 0 119 1956 8.68 0 120 1993 8.60 0 121 1958 8.59 0 - 1876 8.59 0 122 1947 8.56 0 123 1949 8.52 0 124 1995 8.40 0 125 1954 8.37 0 126 1883 8.35 0 127 2026 8.28 31 Time Series Summary for ISLIP-LI MACARTHUR AP, NY - Jun through Aug Wettest Summers Click column heading to sort ascending, click again to sort descending. 1 1990 22.31 0 2 1989 20.44 0 3 2014 18.82 0 4 2012 18.54 0 5 1992 17.71 0 6 2011 17.02 0 7 2003 16.60 0 8 2006 16.51 0 9 1967 15.64 0 10 2009 14.91 0 11 2000 14.49 0 12 1998 14.26 0 13 2023 14.18 0 14 1982 14.14 0 15 1984 13.97 0 16 1976 13.87 0 17 1969 13.76 0 18 1985 13.57 0 19 2021 13.35 0 20 1996 13.24 0 21 2013 13.21 0 22 2018 12.94 0 23 1991 12.71 0 24 1975 12.54 0 25 2007 12.33 0 26 2024 11.49 0 27 1977 10.85 0 28 1983 10.83 0 29 1979 10.66 0 30 2019 10.64 0 - 2001 10.64 0 31 1997 10.39 0 32 2008 10.04 0 33 2020 9.96 0 34 1973 9.94 0 35 2002 9.93 0 36 1986 9.82 0 37 2017 9.41 0 38 1978 9.33 1 39 1972 9.25 0 40 1971 9.06 0 41 1987 8.89 0 42 1970 8.72 0 43 2004 8.71 0 44 1981 8.67 0 45 1994 8.53 0 46 1968 8.49 0 47 2026 8.43 31

That will be the test going forward. We will have to see how things evolve in the coming months. Since the seasonal models missed this summer pattern. So the seasonal models may even have lower skill than they traditionally have had beyond the one month forecast period. This summer is featuring a weaker Aleutian low than usual for a developing super El Niño and a much stronger mid-latitude ridge across the Pacific and the CONUS. So if this continues into the winter , then it could mean the traditional super El Niño ridge gets displaced further south and perhaps east into the CONUS than usual. Since a -PDO atmospheric state even overlapping at 10%-20% relative to the winter super El Niño forcing could lead to shifts. This would have implications on the warmth and possibly where the heaviest precipitation zones set up. The mid-latitude ridges being stronger than the troughs has been the main feature of our winters and other seasons since the 2015-2016 super El Niño.

2026 is near the warmest July on record in my first post using 1991-2020 climate normals at +2.5 since that is the base period Brian used. The departure would be even higher if an older base period was used. My 2nd post uses 1971-2000 climo and even that cooler one shows that 1987 wasn’t a warm summer for the CONUs even during the 1980s. Notice the warmest summer of that decade was 1988 which came in the summer after the El Niño and not before. So this El Niño is getting off to a much warmer start than usual. It’s El Ninoish since it matches most of the pattern for an El Niño summer from the Pacific across to the Atlantic. Every spot isn’t going to be a 100% match. But most of the area from the Pacific to the Atlantic is a reasonable 500 mb match whatever the base period is used for an El Niño summer. A hallmark of a developing very strong El Niño pattern during the summer is higher 500mb heights further north and lower heights underneath from the Pacific to the Atlantic. This summer the pattern is reversed which lead to all the record warmth across the CONUS. The record ridge north of Hawaii this month is the reason that the PDO had the steep drop in July. This is not the 500 mb pattern that we have typically had during any El Niños which were on their way to super status. So this event is in a league of its own in numerous ways.

Deflect from what? 1987 had a summer pattern that was a reasonable match for El Nino expectations.This summer is completely different.

It wasn’t even a hot summer for the cooler 1980s era. The warmest 1980s summer was 1988 and the 2nd warmest 1980. This current July is close to the warmest July on record. So there really isn’t a comparison to the summer of 1987.

That was still a cooler summer by recent standards. June-August 1987 71.77°F -0.54°F

One of the better recent seasons so far for sea ice retention on the Pacific side of the Arctic. So the weaker dipole pattern since 2013 with lower pressure north of Alaska continues. This major circulation reversal since 2007-2012 is the main reason that the September 2012 low extent hasn’t been surpassed for 14 years now. Rick Thoman @alaskawx.bsky.social Follow Sea ice in the Chukchi Sea remains well above the 1991-2020 and amazingly is the highest for the this point in the season since 1994 in NSIDC data. For the first time this year, there's an open water path to Utqiaġvik. Ice extent in the Beaufort Sea is the highest since 2006. #akwx #Arctic #SeaIce ALT 1:47 PM · Jul 26, 2026

The summer of 1991 only had a weak El Niño in progress as opposed the strongest on record this summer. July did have some similarity to the composite. But sometimes weaker El Niño summers in the past had more heat than the stronger developing ones. This was the case in 2002 also. So it has been common in the past for weaker El Niños to have less influence in the midlatitudes than during developing super El Niños like 2023, 2015, 1997, 1982, and 1972 which had the more typical cooler summers. The big story this summer is how out of phase the mid-latitudes and tropics are for the magnitude of this event.

While it would be nice to end a longer term drought with just one storm, it’s a process that requires months of above average rainfall. Only a small portion of the total area got over 10.00” this month. But it’s a nice a nice start for most of us and our first month with above average precipitation in a while.

July 1991 actually had a cooler El Niño-like -0.52 pattern across the CONUS compared to the record warmth this July at +2.4. July 1991 73.87°F -0.52°F Climatologist49 @climatologist49.bsky.social · 1d It's going to be a close call, but it now appears more likely than not that July 2026 will be the warmest month on record in the Contiguous U.S. As of today, we are essentially tied with July 1936 and July 2012.

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.