TheClimateChanger

That sounds logical, but it’s not how the real Earth behaves. A true global average does have a seasonal cycle, and it’s not a sampling problem—it’s physics. The key issue is that the hemispheres aren’t equal. The Northern Hemisphere has a lot more land, and land heats and cools much faster than oceans. The Southern Hemisphere is mostly ocean, which responds slowly and dampens temperature swings. So when the Northern Hemisphere warms in summer, it pushes the global average up more strongly than the Southern Hemisphere can offset during its winter. The result is a real, global annual oscillation. If both hemispheres were identical (same land/ocean mix, same heat capacity), then yes—your cancellation idea would work. But they’re not, so it doesn’t. Also, every independent global dataset—NASA GISS, NOAA, HadCRUT—shows the same seasonal wiggle. That wouldn’t happen if it were just “Iowa with a fancy name.” So the graph is doing two things at once: The up-and-down is the seasonal cycle (dominated by Northern Hemisphere land) The overall rise is the long-term warming trend Seeing both together is exactly what you’d expect from a properly constructed global temperature record.

Lol, what's up with these June contract temperatures?

Indeed. Looks like May flips the script a bit though with a rather chilly look, at least to start.

Well, it’s clear that there’s a long-term warming trend on top of oscillations like El Niño–Southern Oscillation—and it’s especially noticeable over the past decade. ENSO explains short-term variability, not the rising baseline.

Yes—strong El Niños do cause short-term spikes in global temperature. But ENSO is an internal oscillation—it redistributes heat, it doesn’t create it. Over time, those effects average out. The fact that each ‘step up’ tends to land higher than the last is the signal of underlying greenhouse warming. That’s exactly the kind of ‘stair-step’ behavior that James Hansen has pointed to.

I don't know. Recent heating seems to be overpowering any natural variability/oscillations. Obviously, not every year is warmer than the previous, but the change seems to be more pronounced over the past several years - and I would expect that trend to continue at least through next year with the likely strong El Nino.

One angle I don’t see discussed much is how “feels like” temperatures are changing relative to actual air temperature. We usually focus on raw temperature trends, but that’s not necessarily what people experience. In winter, wind chill matters; in summer, humidity does. So I pulled PIT data back to 1960 and looked at mean “feels like” temperatures (wind chill in winter, heat index in summer) alongside the air temperature trends. A couple of interesting contrasts: January: Mean “feels like” temperature is increasing at ~10–11°F per century, versus about ~6–7°F per century for air temperature. So winters aren’t just warming — they’re becoming less harsh even faster than the thermometer suggests. July: The heat index trend is also higher than the air temperature trend (by roughly ~1°F/century on average). But that actually understates the real effect. Nighttime heat indices are typically equal to the air temperature (they’re not additive until you get into ~80°F+ conditions), so averaging over the full day mutes the signal. That implies that daytime heat indices are likely increasing on the order of ~2–3°F/century in addition to the air temperature trend.

Here are the trends since 1960 by month, sorted May to April. Month Trend (°F/decade) Trend (°F/century) May +0.60 +6.0 Jun +0.47 +4.7 Jul +0.56 +5.6 Aug +0.45 +4.5 Sep +0.54 +5.4 Oct +0.42 +4.2 Nov +0.22 +2.2 Dec +0.92 +9.2 Jan +0.68 +6.8 Feb +0.94 +9.4 Mar +0.60 +6.0 Apr +0.74 +7.4

I was looking at some data for Pittsburgh and, with GPT’s help, calculated the annual and monthly trends for periods starting in 1960, 1970, 1980, 1990, and 2000 (all using May–April data). For the first four start dates, warming is pretty insensitive to the choice of starting point — it comes out to roughly ~6°F per century in each case. However, starting in 2000, the trend is nearly double that rate (see table of trends below). Start Year Period Trend (°F/decade) Trend (°F/century) 1960 1960–2026 +0.59 +5.9 1970 1970–2026 +0.60 +6.0 1980 1980–2026 +0.62 +6.2 1990 1990–2026 +0.58 +5.8 2000 2000–2026 +1.14 +11.4 The obvious caveat is the shorter time window. I intentionally didn’t go any shorter than 2000 because it quickly becomes too noisy to draw meaningful conclusions. With that in mind, I wanted to get a sense of what the climate might look like 50 years from now. The table/graphic shows a range: Low end: continuation of the long-term trend (1960–present) High end: continuation of the more accelerated warming seen since 2000 This shouldn’t be interpreted as a true “high-end forecast.” If anything, one could argue warming may continue to accelerate as CO₂ increases and feedbacks come into play. This is simply a trend-based extrapolation, not a model projection. A couple interesting takeaways: Winter shows the largest absolute changes Summer warms less in °F, but still shifts meaningfully given its low variability November consistently stands out as a relative laggard Curious what others think, especially regarding the seasonal differences and whether similar patterns show up in nearby regions. A couple quick notes on the table: “Recent” refers to the most recent 7 years (May 2019 through April 2026), so it should be thought of as a snapshot of the current climate rather than a formal 30-year normal. The 2070s range is not a forecast — it’s simply an extrapolation of observed trends: Low end = continuation of the long-term (~1960–present) trend High end = continuation of the more accelerated warming seen since ~2000 Importantly, this should not be interpreted as a true upper bound. If anything, actual warming could end up higher than shown here if the recent acceleration continues or increases due to rising CO₂ and amplifying feedbacks. This is just a simple trend-based framework to give a sense of scale.

That’s not inconsistent at all—those are two completely different contexts. When I post here, I’m discussing weather in the U.S. on a U.S.-focused forum. That’s the scope of the discussion, and everyone does the same thing. But when you’re evaluating a scientific paper making broader claims about climate behavior, scope absolutely matters. If a study is U.S.-only, that’s an important limitation that needs to be acknowledged before drawing bigger conclusions. On the Canada point, focusing on a cold spell there would actually be the cherry-pick. Short-term regional cold anomalies happen all the time, even in a warming world. The broader context right now is near-record global warmth, so isolating one region’s cold period doesn’t really tell you much about the overall climate signal. So the distinction is pretty simple: Talking about regional weather → it’s perfectly fine to focus on the U.S. Evaluating climate claims in a paper → you have to be clear about geographic limits and not overextend them Those aren’t contradictory standards—they’re just applying the right frame to the right situation.

I’ve read through the Christy & Spencer paper and I think it’s worth discussing, but a lot of the conclusions depend heavily on how the analysis is set up. First, it’s U.S.-only, which already limits how broadly you can interpret it. The U.S. is a small, noisy region with a lot of land-use influence, and it’s not necessarily representative of global behavior. Second, they lean on minimally adjusted station data, which is not a neutral choice. One big issue there is time of observation bias (TOBs)—earlier stations often used afternoon observation times, which can effectively double-count hot days across two calendar days. That tends to inflate extreme heat in the early part of the record relative to today, when observations are mostly taken in the morning. If you don’t correct for that, you’re giving early decades a built-in advantage in heat extremes. On top of that, the way “extremes” are counted matters a lot. If you’re relying on record-setting events and not handling ties properly (or only counting first occurrences), later decades are inherently disadvantaged. Early in the record, everything is a “new record.” Later on, even in a warmer climate, you’re more likely to tie or narrowly exceed previous values. If ties aren’t treated equally, you can manufacture a downward trend in “new extremes” even if the underlying distribution is shifting warmer. Another big limitation is that the analysis focuses almost entirely on summer high extremes and winter low extremes, which is a pretty narrow slice of the climate system. It doesn’t really tell you anything about extreme warmth in winter, spring, or fall—which is where a lot of the warming signal actually shows up—or about the broader shift in temperatures. Summer daytime highs (Tmax) are one of the least responsive variables in many parts of the U.S., especially in the East. And this is where land-use changes become critical. The eastern and central U.S. have undergone significant reforestation over the past century after being heavily clear-cut, which increases evapotranspiration and tends to suppress daytime highs. At the same time, the Midwest has seen an explosion in corn and soybean agriculture, and those crops transpire enormous amounts of water—far more than the historical landscape. That effectively adds a massive, artificial cooling mechanism during the growing season. So you’ve got large parts of the country where land surface changes are actively dampening summer heat extremes, even as the broader climate warms. Meanwhile, more arid regions in the West—where you don’t have that same vegetation-driven cooling—show increasing heat extremes, which is more in line with expectations. On top of that, the 1930s Dust Bowl looms large in any long-term U.S. extremes analysis. That period featured extreme heat driven in large part by land degradation and drought—largely human-influenced surface conditions. If your methodology already favors early extremes (via TOBs and record-counting asymmetry), and you anchor the dataset with the most extreme decade in the record for partly non-climatic reasons, you’re stacking the deck toward showing a long-term decline. There are also the usual issues with long-term station data—station moves, instrument changes, siting differences, etc. Homogenization is meant to deal with those. Skipping it doesn’t remove bias, it just means you’re accepting a different set of biases, many of which tend to inflate earlier extremes relative to modern ones. To be fair, the paper is very likely correct that cold extremes are declining—that’s one of the most consistent and physically well-understood signals in the observational record. It’s also true that regional trends (especially in the U.S.) can look different due to land-use effects. But the headline claim about declining heat extremes is much more questionable and looks heavily dependent on methodology. At a minimum, this analysis is incomplete—it’s looking at a narrow set of metrics that are least likely to show strong warming signals and then generalizing from them. If you actually want to understand how extremes are changing, you need to look at the full temperature distribution, across all seasons, with methods that account for known observational biases and major land surface changes.

With spring about halfway through, currently warmest April to date and 3rd warmest season to date [warmest in the KPIT era]. April is running about 1°F above 2010, and spring to date 0.5°F above 2012. Honestly, I'm more surprised it has been the 2nd wettest spring to date. I know it has been fairly wet, but I didn't know it had been that wet (we really haven't had any significant, widespread flooding or anything). 3rd warmest spring:

Incredible! Downloaded data from SERCC (observations from March 1-April 14, forecast values through April 19) reveals nearly 80 long-term threaded stations are in the midst of their warmest spring on record, calculated by average daily high temperature. Led by Huntington, West Virginia, where the first 50 days of spring has seen a mean high temperature of 72.8F, an astounding 12.1F above the 1991-2020 mean. Again, that's a 50-day average!

Here's where we could be sitting on Friday, with current forecast values. Hottest spring on record to date: Columbus, OH; Cincinnati, OH; all of Kentucky; Evansville, IN; and Springfield, MO. Top 2-5: Kansas City, MO; Des Moines, IA; Waterloo, IA; Moline, IL; Rockford, IL; Peoria, IL; Springfield, IL; Columbia, MO; Indianapolis, IN; South Bend, IN; Fort Wayne, IN; Toledo, OH; Dayton, OH; Mansfield, OH; Cleveland, OH; Akron/Canton, OH; Youngstown, OH. Top 6-10: Detroit, MI; Flint, MI; Lansing, MI; Grand Rapids, MI; Chicago, IL; Milwaukee, IL; Sioux City, IA; Dubuque, IA; La Crosse, WI; Minneapolis, MN; Rochester, MN. Really feel for those in the far north who apparently have missed out. Dare I say it - best climo ever?

Best spring in the history of America.

Hmm, maybe because the old measurements are biased low? Snowfall measurement: a flaky history | NCAR & UCAR News

Unreal. This may go into the annals as the most pleasant spring in the history of the City of Pittsburgh. Already up to 29 days at or above 60F (T-most), and 13 days at or above 70F (T-3rd most). Tomorrow will be close to 60F, but the ensuing 6 days are 70s and 80s. Count on those two should be 35/36 & 19, respectively, by next Friday, with even warmer temperatures possible next weekend.

I'm sure the observer in Toronto was lying about how much snow used to fall as well.

Are we sure these correlations apply in this new regime? If we look at April 1997, it was 5th coldest on record, but this year seems destined for back to back record warm months. The NBM numbers would bring us up to about +5.5F (1991-2020 climo) by the 18th/19th timeframe, whereas the current record is +3.4 in 2006.

Looks like good, sound records to me. The snowfall and liquid equivalents make sense.

What motive would they have to lie about how much snow fell? Newark is not the same climate as Detroit. It could have been colder and drier in Detroit than present.