Archive for January, 2024

Spencer vs. Schmidt: My Response to Criticisms

Wednesday, January 31st, 2024

What follows is a response to Gavin Schmidt’s blog post at entitled Spencer’s Shenanigans in which he takes issue with my claims in Global Warming: Observations vs. Climate Models. As I read through his criticism, he seems to be trying too hard to refute my claims while using weak (and even non-existent) evidence.

To summarize my claims regarding the science of global warming:

  1. Climate models relied upon to guide public policy have produced average surface global warming rates about 40% greater than observed over the last half-century (the period of most rapid warming)
  2. The discrepancy is much larger in the U.S. Corn Belt, the world-leader in corn production, and widely believed to be suffering the effects of climate change (despite virtually no observed warming there).
  3. In the deep-troposphere (where our weather occurs, and where global warming rates are predicted to be the largest), the discrepancy between models and observations is also large based upon multiple satellite, weather balloon, and multi-data source reanalysis datasets.
  4. The global energy imbalance involved in recent warming of the global deep oceans, whatever its cause, is smaller than the uncertainty in any of the natural energy flows in the climate system. This means a portion of recent warming could be natural and we would never know it.
  5. The observed warming of the deep ocean and land has led to observational estimates of climate sensitivity considerably lower (1.5 to 1.8 deg. C here, 1.5 to 2.2 deg. C, here) compared to the IPCC claims of a “high confidence” range of 2.5 to 4.0 deg. C.
  6. Climate models used to project future climate change appear to not even conserve energy despite the fact that global warming is, fundamentally, a conservation of energy issue.

In Gavin’s post, he makes the following criticisms, which I summarize below and which are followed by my responses. Note the numbered list follows my numbered claims, above.

1.1 Criticism: The climate model (and observation) base period (1991-2020) is incorrect for the graph shown (1st chart of 3 in my article). RESPONSE: this appears to be a typo, but the base period is irrelevant to the temperature trends, which is what the article is about.

1.2 Criticism: Gavin says the individual models, not the model-average should be shown. Also, not all the models are included in the IPCC estimate of how much future warming we will experience, the warmest models are excluded, which will reduce the discrepancy. RESPONSE: OK, so if I look at just those models which have diagnosed equilibrium climate sensitivities (ECS) in the IPCC’s “highly likely” range of 2 to 5 deg. C for a doubling of atmospheric CO2, the following chart shows that the observed warming trends are still near the bottom end of the model range:

And since a few people asked how the results change with the inclusion of the record-warm year in 2023, the following chart shows the results don’t change very much.

Now, it is true that leaving out the warmest models (AND the IPCC leaves out the coolest models) leads to a model average excess warming of 28% for the 1979-2022 trends (24% for the 1979-2023 trends), which is lower than the ~40% claimed in my article. But many people still use these most sensitive models to support fears of what “could” happen, despite the fact the observations support only those models near the lower end of the warming spectrum.

1.3 Criticism: Gavin shows his own comparison of models to observations (only GISS, but it’s very close to my 5-dataset average), and demonstrates that the observations are within the envelope of all models. RESPONSE: I never said the observations were “outside the envelope” of all the models (at least for global average temperatures, they are for the Corn Belt, below). My point is, they are near the lower end of the model spread of warming estimates.

1.4 Criticism: Gavin says that in his chart “there isn’t an extra adjustment to exaggerate the difference in trends” as there supposedly is in my chart. RESPONSE: I have no idea why Gavin thinks that trends are affected by how one vertically align two time series on a graph. They ARE NOT. For comparing trends, John Christy and I align different time series so that their linear trends intersect at the beginning of the graph. If one thinks about it, this is the most logical way to show the difference in trends in a graph, and I don’t know why everyone else doesn’t do this, too. Every “race” starts at the beginning. It seems Gavin doesn’t like it because it makes the models look bad, which is probably why the climate modelers don’t do it this way. They want to hide discrepancies, so the models look better.

2.1 Criticism: Gavin doesn’t like me “cherry picking” the U.S. Corn Belt (2nd chart of 3 in my article) where the warming over the last 50 years has been less than that produced by ALL climate models. RESPONSE: The U.S. Corn Belt is the largest corn-producing area in the world. (Soybean production is also very large). There has been long-standing concern that agriculture there will be harmed by increasing temperatures and decreased rainfall. For example, this publication claimed it’s already happening. But it’s not. Instead, since 1960 (when crop production numbers have been well documented), (or since 1973, or 1979…it doesn’t matter, Gavin), the warming has been almost non-existent, and rainfall has had a slight upward trend. So, why did I “cherry pick” the Corn Belt? Because it’s depended upon, globally, for grain production, and because there are claims it has suffered from “climate change”. It hasn’t.

3.1 Criticism: Gavin, again, objects to the comparison of global tropospheric temperature datasets to just the multi-model average (3rd of three charts in my article), rather than to the individual models. He then shows a similar chart, but with the model spread shown. RESPONSE: Take a look at his chart… the observations (satellites, radiosondes, and reanalysis datasets) are ALL near the bottom of the model spread. Gavin makes my point for me. AND… I would not trust his chart anyway, because the trend lines should be shown and the data plots vertically aligned so the trends intersect at the beginning. This is the most logical way to illustrate the trend differences between different time series.

4. Regarding my point that the global energy imbalance causing recent warming of the deep oceans could be partly (or even mostly) natural, Gavin has no response.

5. Regarding observational-based estimates of climate sensitivity being much lower than what the IPCC claims (based mostly on theory-based models), Gavin has no response.

6. Regarding my point that recent published evidence shows climate models don’t even conserve energy (which seems a necessity, since global warming is, fundamentally, an energy conservation issue), Gavin has no response.

Gavin concludes with this: “Spencer’s shenanigans are designed to mislead readers about the likely sources of any discrepancies and to imply that climate modelers are uninterested in such comparisons — and he is wrong on both counts.”

I will leave it to you to decide whether my article was trying to “mislead readers”. In fact, I believe that accusation would be better directed at Gavin’s criticisms and claims.

P.S. For those who haven’t seen it, Gavin and I were interviewed on John Stossel’s TV show, where he refused to debate me, and would not sit at the table with me. It’s pretty revealing.

How Much Ocean Heating is Due To Deep-Sea Hydrothermal Vents?

Monday, January 29th, 2024

I sometimes see comments to the effect that recent ocean warming could be due to deep-sea hydrothermal vents. Of course, what they mean is an INCREASE in hydrothermal vent activity since these sources of heat are presumably operating continuously and are part of the average energy budget of the ocean, even without any long-term warming.

Fortunately, there are measurements of the heat output from these vents, and there are rough estimates of how many vents there are. Importantly, the vents (sometimes called “smokers”) are almost exclusively found along the mid-oceanic ridges, and those ridges have an estimated total length of 75,000 km (ref).

So, if we had (rough) estimates of the average heat output of a vent, and (roughly) know how many vents are scattered along the ridges, we can (roughly) estimate to total heat flux into the ocean per sq. meter of ocean surface.

Direct Temperature Measurements Near the Vents Offer a Clue

A more useful observation comes from deep-sea surveys using a towed sensor package which measures trace minerals produced by the vents, as well as temperature. A study published in 2016 described a total towed sensor distance of ~1,500 km just above where these smokers have been located. The purpose was to find out just how many sites there are scattered along the ridges.

Importantly, the study notes, “temperature anomalies from such sites are commonly too weak to be reliably detected during a tow“.

Let’s think about that: even when the sensor package is towed through water in which the mineral tracers from smokers exist, the temperature anomaly is “too weak to be reliably detected”.

Now think about that (already) extremely weak warmth being mixed laterally away from the (relatively isolated) ocean ridges, and vertically through 1,000s of meters of ocean depth.

Also, recall the deep ocean is, everywhere, exceedingly cold. It has been calculated that the global-average ocean temperature below 200m depth is 4 deg. C (39 deg. F). The cold water originates at the surface at high latitudes where it becomes extra-salty (and thus dense) and it slowly sinks, filling the global deep oceans over thousands of years with chilled water.

The fact that deep-sea towed probes over hydrothermal vent sites can’t even measure a temperature increase in the mineral-enriched water means there is no way for buoyant water parcels to reach up several kilometers to reach the thermocline.

Estimating The Heat Flux Into the Ocean from Hydrothermal Vents

We can get some idea of just how small the heat input is based upon various current estimates of a few parameters. The previously mentioned study comes up with a possible spacing of hydrothermal sites every ~10 km. So, that’s 7,500 sites around the world along the mid-oceanic ridges. From deep-sea probes carrying specialized sampling equipment, the average energy output looks to be about 1 MW per vent (see Table 1, here). But how many vents are there per site? I could not find a number. They sampled several vents at several sites. Let’s assume 100, and see where the numbers lead. The total heat flux into the ocean from hydrothermal vents in Watts per sq. meter (W m-2) would then be:

Heat Flux = (7,500 sites)x(100 vents per site)x(1 MW per vent)/(360,000,000,000,000 sq. m ocean sfc).

This comes out to 0.00029 W m-2.

That is an exceedingly small number, about 1/4,000th of the 1 W m-2 estimated energy imbalance from Argo float measurements of (very weak) ocean warming over the last 20 years or so. Even if the estimate is off by a factor of 100, the resulting heat flux is still 1/40th of global ocean heating rate. I assume that oceanographers have published some similar estimates, but I could not find them.

Now, what *is* somewhat larger is the average geothermal heat flux from the deep, hot Earth, which occurs everywhere. That has a global average value of 0.087 W m-2. This is approximately 1/10 of the estimate current ocean heating rate. But remember, it’s not the average geothermal heat flux that is of interest because that is always going on. Instead, that heat flux would have to increase by a factor of ten for decades to cause the observed heating rate of the global deep oceans.

Evidence Ocean Warming Has Been Top-Down, Not Bottom-Up

Finally, we can look at the Argo-estimate vertical profile of warming trends in the ocean. Even though the probes only reach a little more than half-way to the (average) ocean bottom, the warming profile supports heating from above, not from below (see panel B, right). Given these various pieces of evidence, it would difficult to believe that deep-sea hydrothermal vents — actually, an increase in their heat output — can be the reason for recent ocean warming.

New Article on Climate Models vs. Observations

Thursday, January 25th, 2024

UPDATE: Since commenter Nate objects to my inclusion of the Corn Belt graph (yes, it is a small area), please go to the actual article link at where 2 out of the 3 graphs I provide are for global average temperatures. But also remember that we are being told (through the National Climate Assessment’s authors’ belief in climate models) that U.S. agriculture is at risk from warming and drying– the first claim is mostly wrong, and the second claim is (so far) totally wrong. I’ve blogged on this before, folks.

I was asked by Heritage Foundation to write an article on the exaggerated global warming trends produced by climate models over the last 50 years or so. These are the models being used to guide energy policy in the U.S. and around the world. The article is now up at As a sneak peek, here’s a comparison between models and observations for the U.S. Corn Belt near-surface air temperatures in summer:

UAH Global Temperature Update for December, 2023: +0.83 deg. C

Wednesday, January 3rd, 2024

2023 Was the Warmest Year In the 45-Year Satellite Record

The Version 6 global average lower tropospheric temperature (LT) anomaly for December, 2023 was +0.83 deg. C departure from the 1991-2020 mean, down from the November, 2023 anomaly of +0.91 deg. C.

The 2023 annual average global LT anomaly was +0.51 deg. C above the 1991-2020 mean, easily making 2023 the warmest of the 45-year satellite record. The next-warmest year was +0.39 deg. C in 2016. The following plot shows all 45 years ranked from the warmest to coolest.

The linear warming trend since January, 1979 still stands at +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.19 C/decade over global-averaged land).

It might be partly coincidence, but the +0.51 deg. C number for 2023 from satellites is the same as the surface air temperature estimate from the NOAA/NCEP/NCAR Climate Data Assimilation System (CDAS). Note that the CDAS estimate is only partly based upon actual surface air temperature observations… it represents a physically consistent model-based estimate using a wide variety of data sources (surface observations, commercial aircraft, weather balloons, satellites, etc.). [UPDATE: it appears the CDAS anomalies are not relative to the 1991-2020 base period… I recomputed them, and the CDAS anomaly appears to be +0.45 deg. C, not +0.51 deg. C]:

Various regional LT departures from the 30-year (1991-2020) average for the last 24 months are:


The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for December, 2023, and a more detailed analysis by John Christy, should be available within the next several days here.

The monthly anomalies for various regions for the four deep layers we monitor from satellites will be available in the next several days:

Lower Troposphere:



Lower Stratosphere: