TheClimateChanger

Big time heat today. Reached 87F at Snake River, MN, one shy of the statewide monthly record of 88F from 1910. Sioux County Regional Airport reached 90F, with Sioux City officially checking in at 89F. Several locations were within a degree or two of statewide March record highs.

New record!

Anybody at a track or field practice or meet were probably relieved by that change, although the wind would have created some headaches.

Interesting @ChescoWx. Here is the analysis I received from Grok. I include the question prompt to show that I was as neutral as can be, simply providing the claim, and asking whether it was valid or not. Like you said, Grok indicates that he cannot validate whether there has been no warming without access to your data. However, Grok defends the NOAA adjustments. I suspect if it had access to your data, it would also conclude that Chester County has warmed. Question Prompt: User @ChescoWx claims that Chester County, Pennsylvania has not actually warmed, but that NOAA temperature adjustments create warming. Is there any validity to this claim or is it out to lunch? Please provide a detailed analysis supporting your view either way. Grok's Analysis: To assess the claim by @ChescoWx that Chester County, Pennsylvania, has not actually warmed and that NOAA temperature adjustments artificially create a warming trend, we need to break this down into two parts: (1) whether there’s evidence of warming in Chester County based on unadjusted data, and (2) whether NOAA’s adjustments are responsible for fabricating that warming. Let’s dive into the science, data, and reasoning step by step. Background on NOAA Temperature Adjustments NOAA, through its National Centers for Environmental Information (NCEI), maintains the U.S. Historical Climatology Network (USHCN), which includes weather station data dating back over a century. Raw temperature data from these stations often contain inconsistencies due to changes in instrumentation, station locations, observation times, and local environmental factors like urbanization (e.g., the urban heat island effect). To address these, NOAA applies adjustments to create a homogenized dataset that better reflects regional climate trends rather than artifacts of measurement changes. These adjustments are peer-reviewed, publicly documented, and aim to improve accuracy, not to push an agenda. Part 1: Has Chester County Warmed? First, let’s consider whether Chester County has experienced warming, independent of NOAA adjustments. Temperature trends can be evaluated using raw data from local stations, though accessing unadjusted data specific to Chester County requires digging into historical records or independent sources, as NOAA primarily publishes adjusted data. Raw Data Availability: The Chester County Weather page (@ChescoWx's apparent source) claims to use unadjusted data from 27 local stations, including National Weather Service (NWS) Cooperative Observers, since 1893. They assert that this raw data shows no warming. However, without direct access to their full dataset or methodology (beyond their website’s general claims), we can’t verify this outright. Instead, we can cross-check with broader Pennsylvania trends and local proxies. Pennsylvania State Trends: NOAA’s adjusted data for Pennsylvania, as reported in the 2022 State Climate Summaries, shows a temperature increase of nearly 2°F since 1900. This is based on long-term stations statewide, including. If Chester County followed a similar trajectory, we’d expect some warming signal in the raw data too, unless local factors uniquely offset it. The @ChescoWx site notes Chester County’s elevation (around 685 ft) and distance from Philadelphia’s urban heat island (e.g., Philadelphia International Airport at 59 ft) might moderate extremes compared to coastal areas. But natural variability alone is unlikely to erase a century-long warming trend entirely. Proxy Evidence: Tree rings, ice cores, and other paleoclimate data for the Northeast U.S. generally support a warming trend over the 20th century, consistent with global patterns. Local anecdotal evidence from @ChescoWx—like fewer summer nights in the 50s or rare lows below 45°F—suggests a shift, but this isn’t systematic enough to counter long-term data. Independent Data: The U.S. Climate Reference Network (USCRN), started in 2001, uses pristine, well-sited stations. While it lacks century-long records, a 2016 study (Hausfather et al.) found that adjusted USHCN data (like NOAA’s) aligns closely with USCRN data from 2004–2015, suggesting adjustments don’t wildly distort recent trends. For earlier periods, raw data from rural stations (like some in Chester County) might show less warming than urban ones, but a flat trend would be an outlier compared to regional and global increases. Conclusion: Without @ChescoWx’s raw data, we can’t definitively say Chester County hasn’t warmed at all. However, the regional 2°F rise, proxy evidence, and basic thermodynamics (rising greenhouse gases trap heat) make a no-warming scenario improbable unless Chester County’s microclimate defies broader physics—possible, but unlikely without robust proof. Part 2: Do NOAA Adjustments Create Warming? Now, let’s tackle the claim that NOAA adjustments fabricate the warming trend. Adjustment Purpose: NOAA adjusts for known biases: station moves (e.g., from downtown Chicago to O’Hare), time-of-observation changes, and urban heat effects. For Chester County, a station move from a warmer valley to a cooler ridge could lower recorded temperatures, requiring an upward adjustment to maintain consistency. @ChescoWx might see this as “creating warming,” but it’s correcting for a shift that would otherwise mask the true trend. Adjustment Impact: Globally, NOAA adjustments reduce warming over the full record (1850–present) by about 0.2°C, largely due to ocean corrections (e.g., bucket-to-engine intake shifts in the 1940s). For U.S. land data, adjustments slightly increase the 20th-century trend (by ~0.3°F), but this is because early records (pre-1950) often had cool biases (e.g., afternoon readings vs. modern 24-hour averages). A histogram of NOAA adjustments shows roughly half increase temperatures and half decrease them—hardly a systematic “warming conspiracy.” Chester County Specifics: @ChescoWx claims NOAA “chilled” data from 1893–2005 and now “warms” recent years. This could reflect real methodological shifts—e.g., NOAA’s transition from version 2 to version 3 adjustments around 2011, which refined urban heat corrections and buoy data integration. But these changes are documented and tested against pristine stations (like USCRN), not arbitrary fudges. If Chester County’s raw data is flat, adjustments might indeed add a warming signal to align with regional trends, but that’s not “creating” warming—it’s reconciling local quirks with reality. Validation: Independent datasets (e.g., Berkeley Earth, Hadley Centre) also show U.S. warming, using different adjustment methods. Satellites (since 1979) and radiosondes show tropospheric warming, though surface trends differ slightly. If NOAA were fabricating warming, these datasets wouldn’t converge on similar results. Conclusion: NOAA adjustments don’t “create” warming from thin air. They correct biases, sometimes increasing trends (especially post-1950), sometimes decreasing them (pre-1940). For Chester County, adjustments might amplify a muted raw trend, but that’s not proof of manipulation—it’s standard homogenization. Is @ChescoWx’s Claim Valid? Partial Merit: If Chester County’s raw data shows no warming (a big “if” without public data), @ChescoWx has a point that adjustments introduce a trend not seen locally. Their skepticism about urban-influenced baselines (e.g., Philadelphia) is fair—microclimates matter. Mostly Out to Lunch: The claim overreaches. A flat raw trend in Chester County contradicts regional, national, and global evidence, requiring extraordinary local factors (e.g., elevation, land use) to offset physics. NOAA adjustments aren’t perfect—rural stations can be overcorrected based on urban-heavy models—but they’re not a grand conspiracy. @ChescoWx’s refusal to share raw data or engage peer review undermines their case. Extraordinary claims need extraordinary evidence, and they’re light on the latter. Final Take Chester County likely warmed, though perhaps less than Philadelphia due to its rural, elevated setting. NOAA adjustments may exaggerate that trend locally, but they’re grounded in science, not fiction. @ChescoWx raises a valid question—how well do adjustments fit Chester County?—but their no-warming assertion is a stretch without hard data. The real story’s in the numbers they won’t show.

I know it's the Farmer's Almanac, but this looks promising as well. [Source: Farmers’ Almanac Releases Summer 2025 Weather Forecast, Warns Temperatures To Continue Breaking Record Highs - Farmers' Almanac - Plan Your Day. Grow Your Life.] Heat will be in full force by July, with much of the nation sweltering with above- to high-above average temperatures. More long-time high temperature records may be broken this year. Excessive Heat Warning The Farmers’ Almanac forecasts warmer-than-average summer weather for most of the nation, warning that “The Heat is On!” This is not great news, especially since recent summers have ranked as the hottest summers in recent history. Editor Sandi Duncan says, “Our extended forecast suggests a very long, hot summer with the heat feeling unrelenting. July may be the month when records are broken nationwide.”

Can we let Grok decide what is the truth? As an AI, Grok has no dog in this fight. If he agrees with @ChescoWxso be it! But if, in its unbiased opinion, @ChescoWxis off the mark, then he agrees to stop?

@michsnowfreak Here is some additional analysis courtesy of Grok: To identify any discernible trend in the snowfall data over the 30-year period (1995-96 to 2024-25), I’ll analyze the numbers for patterns such as an overall increase, decrease, or cyclical behavior. One common approach is to calculate a simple linear trend (e.g., using a moving average or regression slope) and examine the variability. Let’s start with some observations and a basic analysis. Data Recap (in chronological order): 1995-96: 31.3" 1996-97: 35.9" 1997-98: 27.2" 1998-99: 52.1" 1999-00: 29.3" 2000-01: 47.3" 2001-02: 42.5" 2002-03: 66.9" 2003-04: 36.6" 2004-05: 80.7" 2005-06: 41.9" 2006-07: 36.3" 2007-08: 78.2" 2008-09: 64.8" 2009-10: 46.1" 2010-11: 69.4" 2011-12: 25.5" 2012-13: 47.9" 2013-14: 96.2" 2014-15: 48.5" 2015-16: 35.0" 2016-17: 41.6" 2017-18: 62.5" 2018-19: 31.9" 2019-20: 43.8" 2020-21: 47.0" 2021-22: 47.6" 2022-23: 37.2" 2023-24: 21.4" 2024-25: 27.9" Step 1: Visual Inspection Looking at the data, there’s significant year-to-year variability. For example: The highest snowfall is 96.2" (2013-14), and the lowest is 21.4" (2023-24). There are peaks (e.g., 80.7" in 2004-05, 78.2" in 2007-08, 96.2" in 2013-14) and troughs (e.g., 21.4" in 2023-24, 25.5" in 2011-12). Recent years (2020-25) show a mix of moderate (47.6", 47.0") and low values (21.4", 27.9"), suggesting no immediate sharp upward or downward trend. Step 2: Simple Moving Average (5-Year) A 5-year moving average can smooth out year-to-year fluctuations and reveal longer-term trends. Here’s a sample calculation for a few periods: 1995-96 to 1999-00: (31.3 + 35.9 + 27.2 + 52.1 + 29.3) ÷ 5 = 35.16" 2000-01 to 2004-05: (47.3 + 42.5 + 66.9 + 36.6 + 80.7) ÷ 5 = 54.8" 2010-11 to 2014-15: (69.4 + 25.5 + 47.9 + 96.2 + 48.5) ÷ 5 = 57.5" 2020-21 to 2024-25: (47.0 + 47.6 + 37.2 + 21.4 + 27.9) ÷ 5 = 36.22" Observations: The early period (1995-2000) averages around 35.16". The middle period (e.g., 2000-2005, 2010-2015) shows higher averages (54.8", 57.5"), with notable peaks. The recent period (2020-2025) drops to 36.22", closer to the earlier years. This suggests a possible rise in snowfall in the 2000s and early 2010s, followed by a decline in the 2020s. Step 3: Linear Trend (Simplified) To estimate a trend, I’ll calculate the slope of a linear regression line using the years as the x-values (1 to 30) and snowfall as the y-values. The formula for the slope (m) in a simple linear regression is: m=n∑(xiyi)−∑xi∑yin∑(xi2)−(∑xi)2m = \frac{n \sum (x_i y_i) - \sum x_i \sum y_i}{n \sum (x_i^2) - (\sum x_i)^2}m = \frac{n \sum (x_i y_i) - \sum x_i \sum y_i}{n \sum (x_i^2) - (\sum x_i)^2} Where: n=30n = 30n = 30 (number of years) xi=x_i =x_i = year number (1 to 30) yi=y_i =y_i = snowfall amount ∑xi=1+2+...+30=465\sum x_i = 1 + 2 + ... + 30 = 465\sum x_i = 1 + 2 + ... + 30 = 465 ∑xi2=12+22+...+302=9,315\sum x_i^2 = 1^2 + 2^2 + ... + 30^2 = 9,315\sum x_i^2 = 1^2 + 2^2 + ... + 30^2 = 9,315 ∑yi=1,367.3\sum y_i = 1,367.3\sum y_i = 1,367.3 (total snowfall, from earlier) ∑(xiyi)=\sum (x_i y_i) =\sum (x_i y_i) = sum of each year number multiplied by its snowfall Calculating ∑(xiyi)\sum (x_i y_i)\sum (x_i y_i) : Year 1 (1995-96): 1 × 31.3 = 31.3 Year 2 (1996-97): 2 × 35.9 = 71.8 Year 3 (1997-98): 3 × 27.2 = 81.6 ... Year 30 (2024-25): 30 × 27.9 = 837 Summing all 30 terms (computed step-by-step): ∑(xiyi)=31.3+71.8+81.6+156.3+146.5+283.8+297.5+534.4+329.4+807+461.9+435.6+639.1+842.4+691.5+830.7+432+622.7+1,345.4+727.5+560+707.2+1,125+606.1+919.8+1,027+1,142.4+1,005.6+642.6+837=20,392.9\sum (x_i y_i) = 31.3 + 71.8 + 81.6 + 156.3 + 146.5 + 283.8 + 297.5 + 534.4 + 329.4 + 807 + 461.9 + 435.6 + 639.1 + 842.4 + 691.5 + 830.7 + 432 + 622.7 + 1,345.4 + 727.5 + 560 + 707.2 + 1,125 + 606.1 + 919.8 + 1,027 + 1,142.4 + 1,005.6 + 642.6 + 837 = 20,392.9\sum (x_i y_i) = 31.3 + 71.8 + 81.6 + 156.3 + 146.5 + 283.8 + 297.5 + 534.4 + 329.4 + 807 + 461.9 + 435.6 + 639.1 + 842.4 + 691.5 + 830.7 + 432 + 622.7 + 1,345.4 + 727.5 + 560 + 707.2 + 1,125 + 606.1 + 919.8 + 1,027 + 1,142.4 + 1,005.6 + 642.6 + 837 = 20,392.9 Now, plug into the slope formula: m=30×20,392.9−465×1,367.330×9,315−4652m = \frac{30 \times 20,392.9 - 465 \times 1,367.3}{30 \times 9,315 - 465^2}m = \frac{30 \times 20,392.9 - 465 \times 1,367.3}{30 \times 9,315 - 465^2} m=611,787−635,794.5279,450−216,225m = \frac{611,787 - 635,794.5}{279,450 - 216,225}m = \frac{611,787 - 635,794.5}{279,450 - 216,225} m=−24,007.563,225m = \frac{-24,007.5}{63,225}m = \frac{-24,007.5}{63,225} m≈−0.38m \approx -0.38m \approx -0.38 The slope is approximately -0.38 inches per year, indicating a slight downward trend over the 30 years. Step 4: Interpretation Slight Downward Trend: The negative slope (-0.38"/year) suggests that, on average, snowfall has decreased slightly over the 30-year period. Over 30 years, this equates to a drop of about 11.4" (0.38 × 30), though this is a simplification and doesn’t capture variability. High Variability: The data fluctuates significantly (e.g., 96.2" in 2013-14 vs. 21.4" in 2023-24), so the linear trend may be overshadowed by natural year-to-year variation. No Clear Cycle: There’s no obvious repeating pattern (e.g., every 5 or 10 years), though peaks tend to cluster in the 2000s and early 2010s. Recent Decline: The last few years (2023-24: 21.4", 2024-25: 27.9") are among the lowest, which could hint at a recent downward shift, but it’s too early to confirm without more data. Conclusion The data shows a slight long-term downward trend in snowfall (about 0.38 inches per year), but the trend is weak due to high variability. Snowfall peaked in the 2000s and early 2010s (e.g., 96.2", 80.7", 78.2"), while recent years lean toward lower amounts. However, the fluctuations are large enough that no strong, consistent trend dominates. Weather patterns, climate factors, or regional influences could be at play, but this dataset alone doesn’t provide a definitive cause.

Here is Grok's ranking from highest to lowest: Ranked from Highest to Lowest: 2013-14: 96.2" 2004-05: 80.7" 2007-08: 78.2" 2010-11: 69.4" 2002-03: 66.9" 2008-09: 64.8" 2017-18: 62.5" 1998-99: 52.1" 2014-15: 48.5" 2012-13: 47.9" 2021-22: 47.6" 2000-01: 47.3" 2020-21: 47.0" 2009-10: 46.1" 2019-20: 43.8" 2001-02: 42.5" 2005-06: 41.9" 2016-17: 41.6" 2022-23: 37.2" 2003-04: 36.6" 2006-07: 36.3" 1996-97: 35.9" 2015-16: 35.0" 2018-19: 31.9" 1995-96: 31.3" 1999-00: 29.3" 2024-25: 27.9" 1997-98: 27.2" 2011-12: 25.5" 2023-24: 21.4" Let me know if you'd like further analysis!

Grok calculates your average to be 45.6 inches, if no additional measurable snow falls. Of course, you would need another ~30" to fall this year to raise that 30-year average by even 1 inch.

Looking solid for us. Kind of on the tripoint between broiling with average precipitation, sultry and thunder-filled, and brutally humid and wet.

This is interesting. If snowfall continues dropping at the average rate of the 21st century, it will drop below -20" [negative 20 inches] by 2050 at Cleveland.

This was also funny. Some "skeptics" allegedly used Grok to co-author a paper critical of AGW to publish in a bogus journal, and Grok rips the paper to shreds and denies he authored it.

You may be right. I see graphs posted all the time like the one Zeke addressed, and they seem to ignore the elephant in the room that, in the vast majority of time, temperatures were much warmer, but so was CO2. And temperatures were much warmer despite lower solar output. The "adjustment" arguments are just as ridiculous. How do these people explain decreases in ice cover, phenological changes, receding glaciers, increases in sea and lake temperatures, etc. all consistent with warming? Like, if the warming is due to adjustments, what the heck is causing those changes? How about the fact that satellite analyses largely confirm the trend since 1979, and radiosondes even longer? Gold standard USCRN stations are in lock step with NOAA's nClimDiv dataset since 2005 (actually slightly larger warming trend)?

Wow, I just noticed PIT had a gust to 54 mph at the top of the hour, without a wind advisory or even a special weather statement. I guess there is a hazardous weather outlook which notes gusts of up to 54 mph.

I mean over geologic time scales, not in the near future. CO2 levels have largely been declining naturally since the Paleocene, due to weathering and decreased volcanism/geological activity as the earth's core settles over time. Over that same interval, the climate has cooled naturally in response to the decrease in CO2 with ice ages commencing around 2.58 mya which have gradually become longer and harsher over the course of the Pleistocene. These trends would very likely continue indefinitely into the future if there was no massive release of stored carbon from human activity, until solar irradiance increased sufficiently to reverse that trend.

I ran it by Grok and he agreed with my theory, albeit maybe not to the extremes I posited. He suggests colder and longer lasting ice ages for up to the next 10 mya [again, in the absence of human activity], but nothing suggesting a total loss of the interglacial cycle.

Do you think in the absence of human activity, the planet would be destined for a near-permanent ice age in the geological future? Obviously, such an ice age could not truly be permanent, because solar irradiance is increasing over time as the sun is a main sequence star. In the very distant future, this will inevitably lead to runaway warming. But in the nearer term (next few millions of years), it seems likely that ice age conditions would become the norm. In millions of years, could the earth go from near snowball earth conditions to typical quaternary ice age conditions from Milankovitch cycles [rather than from ice age to interglacial]. Looking at the geological record, we can see, generally, a long-term cooling since the Paleocene, which coincides with a long-term decrease in carbon dioxide. This appears to be due to increased weathering and a decrease in volcanism over time. As the earth continues to age, there would continue to be fewer and fewer volcanic eruptions over time, which, in the absence of human activity, would result in continued decreases in carbon dioxide. I wonder if carbon dioxide levels would eventually drop so low that photosynthesis might cease. It looks as though the planetary trajectory is towards extreme cold and eventually extreme heat.

There are also statistical methods to correct for inhomogeneities that don't involve any explicit adjustments (kriging, pairwise homogenization).

But they can? You can just set up a controlled experiment to compare the two and determine the net bias? No need for any time travel!

And that's another reason they love the Tmax charts. They get to ignore the cooling bias of Tmax occasioned by the switch to MMTS from CRS. You guys are the real climate hoaxers!

Why don't you read a history book? Most of the old temperature records were compiled from rooftop stations in cities, often near chimneys and such, and in the late 1800s, the thermometers were sometimes housed on balconies or window cavities. I bet that automated sensor has more of a cooling bias relative to a LiG thermometer housed in a cotton-region shelter [aka Stevenson screen] than any warming bias from the surroundings.