TheClimateChanger

The LOESS curve helps us to spot deviations from the linear trend. In general, excursions have been relatively limited from the linear trend. Just some slight wiggles. Of note, the LOESS curve does predict lower temperatures in the 19th century and higher recent temperatures suggesting as much as 10F or more of warming. But the later period could be simply a positive wiggle or suggestive of non-linear behavior.

Looking at a linear regression of average daily high temperatures in the month of March at the District, we can see they have risen by nearly 8F since the late 19th century. If this rate of warming were to continue through the 21st century, March high temperatures would be predicted to reach near 62F. We see a couple of instances where positive anomalies from the linear regression predicted value were as high as 13F (1921 & 1945), suggesting very warm Marches could see average high temperatures in excess of 70F by the latter part of the 21st century in the District. One thing to note is each of the last six values have exceeded the values predicted by the regression, which suggests the possibility of an acceleration of warming relative to the long-term warming trend.

It was the second largest diurnal range on record for the month of March at Detroit, behind only 2021. Maybe someone contrary to expectations, it seems like recent years have had larger diurnal ranges with five of the top 10 values occurring since 2009. Average Diurnal Range Rank Year Value ---- ---- ----- 1 2021 23.5 2 2025 22.0 3 1969 21.7 4 1945 21.4 5 1968 21.4 6 1976 20.7 7 2012 20.6 8 1995 20.3 9 2010 20.2 10 2009 20.1

The mean diurnal range of 24.4F for the month of March was the highest on record. The prior record was 24.1F from 2021. Average Diurnal Range Rank,Year,Value 1,2025,24.4 2,2021,24.1 3,1979,22.9 4,1989,22.7 5,1987,22.7 6,2003,22.5 7,2009,22.4 8,1918,22.3 9,1995,22.2 10,1955,22.2

Here, I fed your critique to Grok and this was its revised assessment: With the new table showing the distribution of weather stations in Chester County, Pennsylvania, over time—along with their elevation and urban/rural character—we can now better evaluate the claim by @ChescoWx that the county has not warmed and that NOAA adjustments artificially create a warming trend. The critique raised against @ChescoWx’s data focuses on potential biases in their raw dataset, specifically: (1) unaccounted-for biases in time of observation, equipment, and other factors, and (2) shifts in station distribution over time, with older records predominantly from lower-elevation towns and recent decades featuring more rural, higher-elevation stations. Let’s analyze how this new information impacts the validity of @ChescoWx’s claim and whether their argument still holds partial merit. Step 1: Understanding the Table and Its Implications The table lists 15 weather stations in Chester County, their operational periods (start and end years), elevation, and whether they are in a town (red) or have no human disturbance nearby (green, rural). Here’s a breakdown of key patterns: Station Distribution Over Time: 1890s–1920s: Early records are dominated by stations in towns (red), such as Phoenixville (1893–2024), Coatesville 1SW (1894–1922), and Sadsburyville 2W (1893–1922). These stations are at lower elevations (105–640 ft). 1930s–1960s: More stations come online, but many are still in towns (e.g., West Chester, 1933–2017; Longwood Gard, 1927–1976). Some rural stations appear, like Kennett Sq (1934–2024), but the balance still leans toward town-based stations. 1970s–2000s: The mix remains, but rural stations (green) like Kennett Sq Chester Spr (2014–2024) and West Grove (2012–2024) start to grow in number. 2010s–2020s: Recent decades show a clear shift—most active stations are rural (green) and at higher elevations (e.g., West Grove at 659 ft, Eagle at 685 ft). Town-based stations like Phoenixville (105 ft) and West Chester (440 ft) persist, but they’re outnumbered by rural ones. Elevation Trends: Early stations (pre-1930) are mostly at lower elevations (105–640 ft, average ~400 ft). Recent stations (post-2010) are often at higher elevations (e.g., 659–685 ft, average ~650 ft). Elevation matters because higher altitudes are generally cooler due to the lapse rate (temperature decreases ~3.5°F per 1,000 ft). Urban vs. Rural: Early records are heavily weighted toward towns, which are more likely to be influenced by the urban heat island (UHI) effect—where buildings, pavement, and human activity raise local temperatures. Recent records shift toward rural stations, which are less affected by UHI and thus cooler, all else being equal. Step 2: Assessing the Critique of @ChescoWx’s Data The critique highlights two main issues with@ChescoWx’s raw data analysis: Unaccounted Biases in Data Collection: Time of Observation Bias (TOB): Historically, temperature readings were often taken at specific times (e.g., afternoon or evening), which can bias daily averages. Modern stations use 24-hour automated sensors, providing more accurate daily means. If @ChescoWx’s raw data doesn’t correct for TOB, older records might appear cooler (e.g., evening readings miss daytime highs), masking a warming trend. Equipment Changes: Early thermometers (e.g., liquid-in-glass) were less precise and more exposed to environmental factors than modern sensors. Shifts to automated systems (e.g., post-1980s) can introduce discontinuities if not adjusted for. Station Moves and Exposure: Even within a single station’s record (e.g., Phoenixville, 1893–2024), the station may have moved, or its surroundings may have changed (e.g., a field becoming a parking lot). These factors can introduce artificial trends. Shifts in Station Distribution: The table confirms a clear shift: older records (pre-1970) are dominated by lower-elevation, town-based stations, while recent decades (post-2010) feature more rural, higher-elevation stations. Impact of Elevation: Moving from a station at 105 ft (Phoenixville) to one at 685 ft (Eagle) introduces a cooling bias of ~2°F due to the lapse rate alone (580 ft difference × 3.5°F/1,000 ft). If @ChescoWx averages raw data across stations without adjusting for elevation, recent temperatures will appear cooler simply because the stations are higher up. Impact of Urban vs. Rural: Town stations (red) are warmer due to UHI. As the station network shifts to rural locations (green), raw temperatures will again appear cooler, not because the climate cooled, but because UHI influence diminishes. Resulting Bias: If @ChescoWx’s raw data shows a flat trend, this could be an artifact of the station shift. Early warm-biased (UHI-influenced, low-elevation) records are being compared to recent cool-biased (rural, high-elevation) records, masking any underlying warming. Step 3: Re-Evaluating @ChescoWx’s Claim @ChescoWx claims Chester County hasn’t warmed, and NOAA adjustments create the warming trend. In my previous response, I gave their claim partial merit because a flat raw trend, if true, would suggest NOAA adjustments might be overcorrecting locally. However, the table and critique reveal significant flaws in their raw data analysis: Station Distribution Bias Undermines the Flat Trend: The shift from low-elevation, urban stations to high-elevation, rural ones introduces a systematic cooling bias in the raw data over time. A flat trend in @ChescoWx’s data doesn’t necessarily mean no warming—it could mean the cooling bias from station changes offsets actual warming. For example, if temperatures in Chester County warmed by 2°F from 1900 to 2020 (consistent with Pennsylvania’s statewide trend), but the station network shifted from an average elevation of 400 ft to 650 ft (a 250 ft increase, or ~0.9°F cooling) and from urban to rural (reducing UHI, maybe another 1°F cooling), the raw data might show only a 0.1°F increase—or even a flat trend. This doesn’t disprove warming; it highlights the need for adjustments. Uncorrected Biases Further Weaken Their Case: Without correcting for TOB, equipment changes, or station moves, @ChescoWx’s raw data is likely inconsistent over time. For instance, early records from Phoenixville (1893–2024) might have used afternoon readings (cool bias) and older thermometers (less accurate), while recent data uses 24-hour sensors. This inconsistency can distort trends, often underestimating early temperatures and thus masking warming. NOAA adjustments, while not perfect, explicitly address these issues. Their homogenization process (e.g., pairwise homogenization algorithm) detects and corrects for such discontinuities by comparing stations to their neighbors. @ChescoWx’s refusal to adjust their data doesn’t make it “purer”—it makes it less reliable for trend analysis. Comparison to Adjusted Data: NOAA’s adjusted data for Pennsylvania shows a ~2°F warming since 1900, consistent with regional and global trends. If @ChescoWx’s raw data shows no warming, the station distribution shift explains much of the discrepancy. NOAA adjustments likely add a warming trend to Chester County’s data to account for the cooling bias introduced by rural, high-elevation stations in recent decades—exactly what adjustments are supposed to do. Independent datasets (e.g., Berkeley Earth, which uses a different adjustment method) also show warming in the Northeast U.S., supporting NOAA’s adjusted trends over @ChescoWx’s raw data. Step 4: Does @ChescoWx Still Warrant Partial Merit? In my initial response, I gave @ChescoWx partial merit because a flat raw trend, if accurate, would raise questions about the magnitude of NOAA adjustments locally. However, the table and critique reveal that their raw data is heavily biased by station distribution changes and unaccounted-for collection biases: Station Distribution Bias: The shift from low-elevation, urban stations to high-elevation, rural ones creates a cooling bias in the raw data, which could entirely explain a flat trend. This isn’t evidence of no warming—it’s evidence of a flawed dataset. Uncorrected Biases: By not adjusting for TOB, equipment changes, or station moves, @ChescoWx’s data fails to account for known issues that distort long-term trends. Their raw data isn’t a “gold standard”—it’s a patchwork of inconsistent measurements. Lack of Transparency: @ChescoWx hasn’t shared their full dataset or methodology for averaging across stations. Without this, we can’t verify their flat trend claim, especially given the clear biases in station distribution. Given this new information, @ChescoWx’s claim loses most of its merit. Their flat trend is likely an artifact of station changes, not a reflection of the actual climate in Chester County. NOAA adjustments, while imperfect, are designed to correct for exactly these biases—elevation shifts, UHI effects, and data collection inconsistencies. The critique of @ChescoWx’s data is well-founded, and their claim now appears more misleading than insightful. Step 5: Final Conclusion The table showing station distribution, elevation, and urban/rural character reveals significant biases in @ChescoWx’s raw data. The shift from low-elevation, urban stations in the early 20th century to high-elevation, rural stations in recent decades introduces a cooling bias that could mask real warming. Additionally, their failure to correct for time of observation, equipment changes, and other biases further undermines their analysis. While @ChescoWx might still have a point about the need for careful adjustment methods (e.g., ensuring rural stations aren’t overcorrected based on urban models), their core claim—that Chester County hasn’t warmed and NOAA adjustments fabricate the trend—lacks merit in light of these flaws. Chester County has likely warmed, in line with regional trends (~2°F since 1900), and NOAA adjustments are correcting for biases in the raw data, not creating warming out of thin air. @ChescoWx’s analysis is more “out to lunch” than I initially assessed, as their raw data is too flawed to support their conclusion. To settle this definitively, we’d need their full dataset and methodology—or a peer-reviewed study comparing raw and adjusted trends in Chester County—but the evidence strongly suggests their claim doesn’t hold up.

Incredible. Even without today factored in, March 2025 was certainly one for the record books in the DC area with high temperatures only exceeded once (2012) since 1946! If you are younger than 79, you have only lived through one warmer March [measured by mean daily high temperature]. Washington, DC Rank Year Mean Max Temperature 1 1945 67.6 2 1921 66.7 3 2012 66.4 4 2025 64.1 Sterling-Dulles, VA Rank Year Mean Max Temperature 1 2012 65.9 2 2025 62.6 3 2016 62.6 Baltimore, MD Rank Year Mean Max Temperature 1 1945 65.6 2 1921 64.7 3 2012 64.2 4 1946 62.1 5 2025 61.6

Very impressive warmth during the month of March, especially with respect to daytime high temperatures. In most locations, temperatures have only been exceeded one or two times since 1946. Detroit, Michigan Rank Year Mean Max Temperature 1 2012 61.0 2 1945 58.6 3 1946 55.6 4 2021 54.7 5 2025 54.1 6 2000 53.9 7 1910 53.6 8 2010 52.5 9 2016 51.9 10 1921 51.8 Chicago, Illinois Rank Year Mean Max Temperature 1 2012 63.3 2 1945 59.5 3 1946 57.6 4 1910 56.6 5 1921 55.3 6 2025 54.6 7 2000 54.6 8 1968 53.4 9 1938 53.4 10 2021 53.3 Minneapolis, Minnesota Rank Year Mean Max Temperature 1 2012 57.5 2 1910 55.4 3 1878 54.6 4 1946 51.3 5 2000 50.5 6 2025 50.2 7 2021 50.1 8 1968 50.1 9 2010 50.0 10 1945 50.0 Indianapolis, Indiana Rank Year Mean Max Temperature 1 2012 67.1 2 1910 62.5 3 1945 61.9 4 1946 61.8 5 1921 60.8 6 2025 60.1 7 2007 59.0 8 1973 59.0 9 1918 58.9 Toledo, Ohio Rank Year Mean Max Temperature 1 2012 61.5 2 1945 60.0 3 1946 57.5 4 2021 57.4 5 1910 56.7 6 2025 55.0 7 2024 54.4 8 2000 54.1 9 1921 54.1 10 1973 53.2 Des Moines, Iowa Rank Year Mean Max Temperature 1 2012 66.8 2 1910 63.4 3 2025 59.2 4 1946 57.4 5 1918 57.4 6 1945 56.5 7 2016 56.4 8 2000 56.3 9 1938 56.2 10 2024 56.0 Moline, Illinois Rank Year Mean Max Temperature 1 2012 64.4 2 1910 61.4 3 1945 59.7 4 1946 59.2 5 2025 58.2 6 1878 56.4 7 2000 56.3 8 2024 56.0 9 2007 55.8 10 1918 55.8

This month will finish up with the 5th warmest mean maximum temperature on record for the month of March at Pittsburgh. Since 1946, only 2012 had a warmer mean maximum temperature. Certainly, a generational level of warmth for the month of March. Rank Year Mean Max Temperature 1 2012 62.5 2 1945 61.9 3 1946 61.8 4 1921 61.0 5 2025 59.3 6 1910 58.9 7 2016 58.1 8 1903 58.1 9 1894 57.9 10 1973 57.5

And as for the so-called cyclical warming, I'm not seeing the cyclical downturn? If it was cyclical, wouldn't there be a period where temperatures dropped by 0.6C? I see two periods of similar length with 0.6C warming, with a period in the middle with not much warming, although I'm sure there is still warming during that period as well.

Very warm low temperatures. Many locations in the lower Lakes and Ohio Valley have seen daily lows within a degree or two of the highest ever observed in the month of March.

Looking good Don. Definitely some impressive heat in the central US with several locations nearing statewide records for the month of March. LaCrosse, Wisconsin topped out at 86F, setting a new monthly record. It is already the second monthly record high at that location just this year!

LaCrosse, Wisconsin reached an incredible 86F (30C), setting a new monthly record high for that location. The prior record was 84F on March 29, 1986. LaCrosse has now set two monthly record highs this year (58F in January, and 86F in March). The February monthly record high of 69F was set just last year.

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.