Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow. People who describe the unprecedented comfort and ease of modern life as a climate disaster, in my opinion have no idea what a real problem is.
There has been criticism of the UK Met Officeโs methodology for monitoring long-term changes in UK-average temperatures, starting with Tallblokeโs (Ray Sandersโ)ย blog postย on 31 October 2024. A major criticism that Tallbloke has is the fact that most UK stations do not meet the World Meteorological Organization (WMO) criteria for a good climate monitoring station. The UKMO doesnโt actually use the WMO quality classification system, butย their own 4-tiered system. Another criticism is that many UK stations have closed in recent years, and so those stations are, in effect, estimated (โfabricatedโ?) from surrounding stations.
My analysis of UK daily high (Tmax) and low (Tmin) temperatures during 1960-2025 using a station relative bias removal method produces UK-average summer temperature variations essentially identical to the very different UKMO methodology.
In both my and the UKMO analysis, 1976 (not 2025) was the hottest summer in daily high temperatures (Tmax), with 2025 taking 3rd or 4th place; the โrecordโ hot year of 2025 was due to nightly low temperatures (Tmin) being anomalously warm.
The average of the three hottest daytime temperatures in each summer month put the summer of 2025 in 4th place since 1960, behind 1976, 1995, and 2022 (which were essentially identical).
There has been criticism of the UK Met Officeโs methodology for monitoring long-term changes in UK-average temperatures, starting with Tallblokeโs (Ray Sandersโ) blog post on 31 October 2024. A major criticism that Tallbloke has is the fact that most UK stations do not meet the World Meteorological Organization (WMO) criteria for a good climate monitoring station. The UKMO doesnโt actually use the WMO quality classification system, but their own 4-tiered system. Another criticism is that many UK stations have closed in recent years, and so those stations are, in effect, estimated (โfabricatedโ?) from surrounding stations.
No Station is Perfect
On the subject of which WMO (or UKMO) class of station is suitable for long-term climate monitoring, I think itโs important to note that a station could be placed in a non-natural, anomalously warm urban environment, but as long as that environment stays the same over time,it can probably still be used for climate change monitoring.
For example, the urban heat island (UHI) effect of London was described over 200 years ago by Luke Howard. Even if London is significantly warmer than the surrounding rural areas, it might be that there has been little additional UHI warming since then, and so a downtown London weather station might be adequate for monitoring large-scale climate change, since I have no reason to believe that (say) 1 deg. C of large-scale warming will lead to city warming substantially different from 1 deg. C.
On the additional subject of replacing a closed station with estimates from surrounding stations (which NOAA also does because so many of their UNHCN stations in the U.S. have closed, a process that has also been criticized), I believe it is a little disingenuous to claim those data are โfabricatedโ. Rather than continuing the closed station record with estimates from surrounding stations, one could just use the surrounding stations, which is the same thing.
The UKMOโs Fancy High-Resolution Mapping of UK Temperatures
The Met Office divides the UK land mass into tiny (1ร1 km) grid cells, and the temperature in each one is estimated from the nearest station(s) using average, regression-based adjustments for elevation, latitude, longitude, terrain shape, coastal proximity, and land use variations. The result is a seemingly complete coverage of UK for the purpose of temperature monitoring:
And I get why this is done: the UKMO primary mission is to provide daily weather monitoring and forecasts, and given limited station data providing actual measurements, their system provides useful temperature estimates in areas far removed from actual weather stations.
Of course, all of this high-resolution fanciness must be anchored by actual measurements, and in the daily Global Historical Climate Network (GHCNd) database, only ~100 stations exist across the UK in recent years. (There were very few GHCNd stations before 1960, so I will address temperature change only since then here). This means only 1 in ~2,400 UK grid cells have an actual temperature monitoring station in the GHCNd dataset, which is the dataset all global temperature monitoring efforts rely upon. While the UKMO might have access to somewhat more stations than are included in the GHCNd dataset, my point will remain valid.
Nevertheless, this doesnโt mean that long-term climate change canโt be monitored with the existing station network. What complicates matters is that stations come and go over time, and this can introduce biases that change over time and corrupt long-term estimates of temperature change. How one accounts for, and adjusts for, these changes is not a settled matter.
Removing Relative Biases Between Stations
From what Iโve been able to glean, the UKMO does not actually calculate and remove relative biases between stations. Instead, they use the above-described strategy to evaluate how station temperatures vary with latitude, longitude, elevation, proximity to the coast, land use (e.g. urbanization), etc., then apply regression-based techniques to estimate temperatures on the 1ร1 km grid. This has no doubt involved considerable effort, and having done similar kinds of data analysis myself, itโs a complex task.
A simpler way of monitoring climate change is to assume that long-term (in the current example, 65 years) warming trends that actually exist in nature are pretty uniform across the UK. If this assumption holds, we can just take whatever stations exist over time, no matter where they are located or what their local microclimate-induced biases are, and quantify how the temperatures at each one varies over time, and then average all of those variations together. This methodology is somewhat similar to that of Hansen and Lebedeff, 1987, as well as our UAH satellite global temperature dataset.
In my implementation of this relative bias removal method, I start with the stations having the longest periods of record. In the UK, only 3 stations have had continuous records in all 126 years from 1900 to 2025: CET Central England, Armagh, and Stornoway Airport. (Only 31% of the UK stations had periods of record at least half as long, 63+ years). I average those 3 stations together. Then, I take the station(s) with the next-longest record (Oxford, 124 years), compute the average difference with the original series, and add it to the series to make a new 4-station average. This is done sequentially for all (148) stations in the UK since 1900 that have at least 2 years of record, going down the list from the longest periods of record to the shortest. Again, since there were few stations before 1960, the following plots cover variations since 1960.
Amazingly, the result using this simple relative bias removal method produces yearly summer-average temperatures (average of daily high [Tmax] and daily low [Tmin]) that are nearly identical to the much fancier UKMO methodology:
In this plot (as well as the others, below) for display purposes I have removed a small (~1-2 deg C) temperature offset due to my use of the original 3 stations for an absolute temperature baseline, whereas the UKMO uses their gridded estimate of the entire area of the UK. The linear trends in the above plot are essentially identical, at +0.27 C/decade.
But that record high did not exist for the daytime high temperatures. As seen in the next plot, 2025 was very similar to several years since 1995, while 1976 holds the record for hottest summertime daily high temperatures:
So, where did the 2025 record come from? It was due to the nighttime temperatures being so warm (although I donโt see how 53 deg. F is is insufferably warm). This was true in both analyses of the station data:
Finally, since I am analyzing daily temperature data, I can compute the average of the three hottest daytime temperatures in each summer month, which produces this:
For this statistic we see that the record is a 3-way tie between 1976, 1995, and 2022. We also see a stronger warming trend (+0.40 C/decade vs ~+0.26 C/decade in all-days average Tmax and Tmin). I suspect this is due to more Saharan air intrusions in recent decades, which are the primary cause of excessively hot days in the UK.
Conclusions
Despite criticisms of the UKMO data and methods for computing UK-average temperatures, I find that a simple bias-removal method of combining all available UK stations produces essentially identical results to the much more complex UKMO methodology. It should provide some vindication for the UKMO methodology in the context of climate temperature trend monitoring.
The record hot summer of 2025 in the UK was in the nightly minimum temperatures, not in the daytime maximum temperatures. This is true in both my analysis and that of the UKMO.
Finally, neither my nor the UKMO method accounts for possible changes in stations over time, such as an increasing urban heat island (UHI) effect at some stations. Based upon our work on this in recent years I suspect this effect since 1960 would be small, but I donโt know that for sure.
Less than a month has passed since the official end of the 2025 hurricane season for the Northern Hemisphere. Interestingly, 2025 statistics show that Accumulated Cyclone Energy (ACE) in the northern hemisphere was roughly 20% below the 1991-2020 mean while the number of major hurricanes and major hurricane days were down about 27% and 36%, respectively, compared to their 30-year averages
Less than a month has passed since the official end of the 2025 hurricane season for the Northern Hemisphere. Interestingly, 2025 statistics show that Accumulated Cyclone Energy (ACE) in the northern hemisphere was roughly 20% below the 1991-2020 mean while the number of major hurricanes and major hurricane days were down about 27% and 36%, respectively, compared to their 30-year averages (shown in parentheses).1
Despite the otherwise lackluster hurricane season, the climate activist community had their poster child in hurricane Melissa, one of the most intense tropical cyclones in history. Of course, the media had a field day blaming it on climate change. Here are some quotes and headlines โฆ
The last article begs the question โฆ. Did climate change, or more accurately anthropogenic global warming (AGW), really make Melissa stronger? Enter the world of single-event attribution science, where climate change can be shown to enhance any hurricane, even if itโs the only hurricane of the season! All thatโs needed is to show that higher water temperatures will intensify existing hurricanes and that water temperatures have increased due to AGW. Just like that the hurricane is proven to be enhanced by climate change. News organizations feed the frenzy by publishing the results without dispute and lawyers take oil companies to court to pay for their part of the damages. Now single-event attribution science is significantly more complicated than the above over-simplification, and admittedly their methods are quite intuitive and mathematically rigorous. After all, they are based on โpeer-reviewedโ science (tongue-in-cheek). There are several organizations that specialize on attribution science and ambulance-chase storms around the globe hoping to find a climate change connection. Several of the more prominent outfits include World Weather Attribution, Climate Central, and ClimaMeter. Although their methods differ significantly, all three of these companies determined that human-driven climate change enhanced Melissaโs wind speeds by about 7-10%, which doesnโt seem quite as dramatic or impactful as the news headlines indicated above.
Nonetheless, despite their diverse methodologies, all of their single-event hurricane attribution studies contain the same critical flaw. Specifically, they donโt assess the role of climate change in areas where there are no storms to gauge theย totalย effect of climate change on hurricane activity or to see if climate change might have actually prevented an intense hurricane in another location. This point was alluded to at least a couple times in the past on WUWT, for example see Rotter (2023) and Vislocky (2018) below. However itโs an important point that needs to be exposed and repeated often given the meteoric rise in single-event climate attributions.
As it is right now, single-event attribution science is the equivalent of a pathologist performing an autopsy on one victim who passed away as a result of an allergic reaction to a vaccine and concluding that the vaccine is causing excess deaths. After all thereโs no denying how the person died or the science used in determining the cause of death. But such a superficial analysis is incomplete because the total efficacy of the vaccine hasnโt been taken into account. Was a study performed to see if the vaccine saved thousands or millions of other lives from a deadly pathogen, or whether people who took the vaccine perished at a lower rate than those who did not take the vaccine? Until those additional analyses are completed there would be no way to determine theย *overall*ย role of the vaccine on human health based on just that one autopsy. Similarly, thereโs no way single-event attribution science can measure the influence of climate change on the totality of hurricane activity. Sure, thereโs probably little doubt that AGW enhanced water temperatures made Melissa up to 10% stronger, but it canโt measure whether climate change is increasing overall hurricane activity unless additional analyses are completed. Even more preposterous is itโs entirely conceivable in a hypothetical season where only one hurricane occurs that single-event attribution science could still ascribe a portion of that hurricaneโs strength on climate change!
Now this notion (that climate change can occasionally prevent extreme storms from forming) may seem preposterous to those who think that climate change can only create more โstorm energyโ and make everything worse. However, there are some known mechanisms as a result of climate change that can actually stabilize the atmosphere and potentially offset some of enhanced storminess thatโs found through the single-event attribution studies. For one such example, itโs well known that arctic amplification is reducing the north-south temperature gradient which, in turn, could reduce the strength of cold fronts in mid-latitudes. Perhaps this is a reason why powerful tornadoes (EF3+) have decreased substantially since the 1950s. In terms of hurricanes, itโs pretty clear that the vertical lapse rate is also lessening with climate change as there appears to be more warming aloft in the 850-700 mb layer than at the surface across tropical latitudes (see graph below).2ย This could cause an increase in ambient stability making it more difficult for hurricanes to form in the first place. Perhaps this could explain why overall hurricane frequencies are slightly down but the proportion of major to total hurricanes is up (i.e., the more stable air decreases the odds of initial hurricane formation, but the warmer sea surface temperature increases its intensity once one forms). The point is there are a myriad of physical mechanisms in which climate change could impact hurricanes, some factors can make them worse, and perhaps some could make them less impactful. Without a deeper analysis we just wonโt know the total impact of climate change on hurricane activity, and we certainly canโt assess that through a forensic analysis of one storm. Climate models are of little help as they canโt resolve hurricanes or reproduce our atmosphere accurately (otherwise seasonal hurricane forecasting would be easy), and single-event attribution models donโt provide an answer either as already pointed out.
This is why I feel that trend analysis right now is the best way to determine the broader impact of AGW on hurricane activity. However which trends should be computed? One trend would be to estimate the cyclonic energy of each hurricane in the US Landfalling Hurricane database.3 This calculation is simply the annual sum of the squared sustained wind speeds for each hurricane at landfall. The cyclonic energy estimate is similar to the ACE index except in this case itโs just computed at landfall, not over the life of the storm, and I didnโt divide by 10,000 like the ACE. The advantage in using the landfalling hurricane database is its long history back into the 1800s so hurricane activity can be viewed through a wide lens as it impacted the United States. Certainly by the late 1800s most coastlines were populated enough that it would be unlikely to miss a big tropical cyclone, so this would be a fair starting point.
The chart below shows the running 5-year cyclonic energy totals ending at the year shown. Although thereโs been a lot of choppiness the most recent couple of decades, the overall trend (dashed line) actually shows a slight but probably statistically insignificant decline. Most importantly thereโs no strong evidence that climate change is making US landfalling hurricanes worse. Even the more active periods after 2000 were on par with some of the active periods in the late 19th century through the middle of the 20th century.
Unfortunately, the use of the US landfalling hurricane dataset to uncover a trend is not without criticism as it only looks at only a small subset of the total hurricane picture. Specifically, the dataset leaves out non-landfalling hurricanes and storms in basins outside the Atlantic. The US landfalling dataset also does not include measurements of hurricanes over their entire life cycle but rather only a snapshot at landfall. Those are all fair criticisms but sadly the ability to detect a trend using all the available hurricane data becomes a challenge as the ability to remote-sense and detect hurricanes over the open oceans has increased greatly over time. As a result historical hurricane data over the open oceans are subject to potential observational biases, more specifically under-sampling biases going back in time, which can significantly distort the real trend. Many hurricanes were missed prior to 1970 due to the lack of 24/7 global satellite coverage. During the 1970s and 1980s satellite technology evolved considerably, and it wasnโt until the 1990s that aircraft reconnaissance could measure surface wind speeds with high accuracy. Despite increased coverage over the last quarter of the 20th century, a number of very short-lived tropical cyclones over the open waters were still likely missed or their intensity underestimated prior to 2000 as remote-sensing and detection technology gradually improved. Since 2000, there have been a plethora of new tools available to observe hurricanes including scatterometers, GPS dropsondes, stepped frequency microwave radiometers, etc., that werenโt available earlier. Landsea and Blake (2021) discuss on the National Hurricane Centerโs blog site, Inside the Eye, the evolution of technology for monitoring tropical cyclones and its impact on counting storm totals (see first link below). Below that are links which further describe the evolution of weather satellite and aircraft recon technology.
With that preface in mind, shown below are plots of annual ACE values for all tropical cyclones in the northern hemisphere and the globe during the post-satellite era.4 However, as mentioned above, since detection technology improved significantly over time, and because itโs a relatively short time period overall, the trend in the ACE may be sensitive to the year the graph is started. Therefore, several graphs are provided with different starting dates: 1971, 1980, 1990, and 2000. Note that hurricane data was available for the northern hemisphere and the globe beginning in 1971 and 1980, respectively.
Results show an upward trend in accumulated cyclonic energy for the northern hemisphere in the chart starting in 1971. However every chart with a starting point from 1980 onward shows a slight decline in ACE for both the northern hemisphere and the globe. In light of all of these graphs itโs certainly difficult to make a case that AGW is making hurricanes more powerful and destructive, at least on large time and/or space scales, especially considering the fact that it seems like every successive year in the past couple decades a new record is set for the โhottest year ever recordedโ. One would think that if hurricanes were becoming significantly worse from global warming that solid confirmation signals would show up in these charts. Of course this is contrary to the news media articles and the attribution reports from the climate cabal. However, it is consistent with IPCC reports which expressed low confidence in long-term trends in tropical cyclone intensity, frequency, and duration. In the least, it is hoped that these analyses will at least draw doubt and speculation to the efficacy of single-event attribution science which has serious flaws. It is further hoped that word will spread of this speculative science to lawyers and judges so that unnecessary and costly lawsuits can hopefully be avoided.
4 Data from the Department of Atmospheric Science, Colorado State University
4 Data from the Department of Atmospheric Science, Colorado State University
Rationing gasoline and diesel under the Climate Act is a predictable prescription for chaos. It is the mobility these motor fuels provide that guarantees rationing to meet the 2030 emissions target will not work.
manhattan skyline, brooklyn bridge and the statue of liberty at night lights, new york city
Rationing gasoline and diesel under the Climate Act is a predictable prescription for chaos. It is the mobility these motor fuels provide that guarantees rationing to meet the 2030 emissions target will not work.
First a little background. When the Climate Act was passed back in 2019, the utopian assumption was that a massive switch to EVs would quickly occur. So, they set a very aggressive 2030 emission reduction target of 40%. Getting rid of internal combustion exhaust was thought to be beneficial, so the law actually specifies that poor communities should be targeted for the biggest cuts.
Of course, the EV switch never happened. Emission reductions overall have only gone down 10%, mostly from switching from coal to gas in power generation. So, under the Climate Act, the State faces an incredible mandatory 30% emission reduction to be achieved in just four years.
The proposed draconian mechanism for achieving this reduction is by rationing the sale of fossil fuel, including gasoline and diesel. This is to be done under the โCap and Invest Program,โ which I explain here.
Reducing emissions by rationing motor fuel simply does not work, for several reasons, all due to mobility.
First and foremost, the people living or working relatively close to the state line can just drive over to fill up where their fuel is not rationed. In New York State, this is a large fraction of the folks. In New York City, you can walk to New Jersey. In this case, the amount of driving actually goes way up, which increases emissions. This has to be factored into modeling how to meet the ridiculous Climate Act 2030 target.
Second, note that if the New York gas stations near the borders lose enough business to other states they might then have a lot more fuel to sell to those further interior. This too could increase driving and hence emissions.
Third, there are the huge numbers of people that drive into New York and back out again, using less than a tank of fuel in the process. Some are visitors, others just passing through. These drivers simply have to buy their fuel in another state. Their driving does not increase, but their emissions do not go down.
These three cases taken together strongly suggest that emissions cannot be significantly reduced by rationing the sale of motor fuel. They certainly cannot be reduced by 30%. But the fuel distribution system could take a big hit financially.
Some other adverse behavioral changes are also likely to occur. The first is people carrying a trunk full of jerry cans full of fuel, especially if they have to drive a long way to get it. This practice can increase the number of people who can go out of state to tank up.
The second is one we saw in the 1970โs gas shortage scare. This is likely in the deep interior where the rationing actually works. Fearing a shortage, people keep their tanks full by topping off after using just a few gallons. This creates long lines at the pumps with a lot of anger.
And, of course, where there is rationing, there is bootlegging. An additional feature of the Cap and Invest scheme is that the price of fuel is to be driven way up as an โinducementโ to using less. This hits the poor especially hard. If the price is a lot higher than in neighboring states it will pay people to put big tanks in their pickups and run fuel like it was moonshine. They might even buy tank trucks.
In summary, the idea that New York State can significantly reduce emissions by rationing gas and diesel is ridiculous. The Climate Act assumed massive sales of EVโs which did not happen. The answer is not rationing; it is to change the law.
In his clownishย articleย the other day about the falling price of gas, Ambrose Evans-Pritchard made reference to high gas prices in New England, blaming them on Americaโs โbadly integrated energy infrastructureโ.
In his clownish article the other day about the falling price of gas, Ambrose Evans-Pritchard made reference to high gas prices in New England, blaming them on Americaโs โbadly integrated energy infrastructureโ.
This, you wonโt be surprised to hear(!), is not the whole story.
New England is now totally reliant on natural gas, without which the region would collapse economically and people die.
Gas provides over half New Englandโs electricity, ramping up and down as required, even during summer heatwaves. It is also, of course, vital for domestic heating.
Youโll need a magnifying glass to see the contribution from AEPโs favourite wind and solar power.
Reliance on gas is even greater now, as nearly all the local coal power plants have been shut down since 2010. As a result, the increased demand for gas means that gas pipeline capacity often gets maxed out in winter. As the US EIA explain, this causes spot gas prices to spike:
The answer is to expand pipeline capacity, not renewables as AEP prefers. Natural gas from Pennsylvaniaโs Marcellus fields is still dirt cheap โ the problem is getting it to market.
One project that would have done that and should have been up and running now is the Constitution Pipeline, which would have carried shale gas from Pennsylvania to upstate New York, where it would have connected to the existing Tennessee and Iroquois pipelines, which in turn supply the Northeast.
It was in planning as long ago as 2014, but guess what? It was blocked by the New York State Department of Environmental Conservation in 2016 on environmental grounds.
Thanks to Trump and his Energy Secretary, Chris Wright, the project is now back on the table, as the Connecticut Mirror reported in September:
At first glance, the route of one of Americaโs most controversial pipeline projects might not seem to have much to do with New England.
The proposed 125-mile pipeline between Pennsylvaniaโs Marcellus Shale field and upstate New York, however, is part of a long-standing effort to increase the flow of natural gas into parts of Connecticut and, by extension, the neighboring states of Massachusetts, Rhode Island and New Hampshire.
Such efforts have recently drawn interest from a politically-diverse group of figures such as Democratic Gov. Ned Lamont as well as Republican President Donald J. Trump and Interior Secretary Doug Burgum, all of whom view natural gas as a way of reducing the regionโs cost-of-energy burden.
Opponents, meanwhile, argue that building a new pipeline will only worsen pollution and the release of climate-altering greenhouse gases, while doing little to bring down prices.
What is the Constitution Pipeline?
The Constitution Pipeline is being developed by Williams Companies, a large operator of pipelines across the United States. The pipeline would have the ability to carry up to 650 million cubic feet of gas a day, according to the company, enough to serve about 3 million homes.
Williams first received federal approvals to build the pipeline more than a decade ago, but the project hit a snag when the New York State Department of Environmental Conservation declined to issue a water quality permit in 2016.
The project continued to languish until the developers resubmitted applications to state and federal regulators earlier this year.
โWilliams remains committed to advancing the Constitution Pipeline project and has submitted permit applications,โ to regulators in New York and Pennsylvania, the company said in a statement this month. โWe are also continuing to work with the states, Congress, and the Administration to strengthen U.S. energy markets, lower costs for American families, and support long-term economic growth.โ
Pending those approvals, Williams estimates that the pipeline should begin construction next year and enter service near the end of 2027.
Less than a month has passed since the official end of the 2025 hurricane season for the Northern Hemisphere. Interestingly, 2025 statistics show that Accumulated Cyclone Energy (ACE) in the northern hemisphere was roughly 20% below the 1991-2020 mean while the number of major hurricanes and major hurricane days were down about 27% and 36%, respectively, compared to their 30-year averages
Less than a month has passed since the official end of the 2025 hurricane season for the Northern Hemisphere. Interestingly, 2025 statistics show that Accumulated Cyclone Energy (ACE) in the northern hemisphere was roughly 20% below the 1991-2020 mean while the number of major hurricanes and major hurricane days were down about 27% and 36%, respectively, compared to their 30-year averages (shown in parentheses).1
Despite the otherwise lackluster hurricane season, the climate activist community had their poster child in hurricane Melissa, one of the most intense tropical cyclones in history. Of course, the media had a field day blaming it on climate change. Here are some quotes and headlines โฆ
The last article begs the question โฆ. Did climate change, or more accurately anthropogenic global warming (AGW), really make Melissa stronger? Enter the world of single-event attribution science, where climate change can be shown to enhance any hurricane, even if itโs the only hurricane of the season! All thatโs needed is to show that higher water temperatures will intensify existing hurricanes and that water temperatures have increased due to AGW. Just like that the hurricane is proven to be enhanced by climate change. News organizations feed the frenzy by publishing the results without dispute and lawyers take oil companies to court to pay for their part of the damages. Now single-event attribution science is significantly more complicated than the above over-simplification, and admittedly their methods are quite intuitive and mathematically rigorous. After all, they are based on โpeer-reviewedโ science (tongue-in-cheek). There are several organizations that specialize on attribution science and ambulance-chase storms around the globe hoping to find a climate change connection. Several of the more prominent outfits include World Weather Attribution, Climate Central, and ClimaMeter. Although their methods differ significantly, all three of these companies determined that human-driven climate change enhanced Melissaโs wind speeds by about 7-10%, which doesnโt seem quite as dramatic or impactful as the news headlines indicated above.
Nonetheless, despite their diverse methodologies, all of their single-event hurricane attribution studies contain the same critical flaw. Specifically, they donโt assess the role of climate change in areas where there are no storms to gauge the total effect of climate change on hurricane activity or to see if climate change might have actually prevented an intense hurricane in another location. This point was alluded to at least a couple times in the past on WUWT, for example see Rotter (2023) and Vislocky (2018) below. However itโs an important point that needs to be exposed and repeated often given the meteoric rise in single-event climate attributions.
As it is right now, single-event attribution science is the equivalent of a pathologist performing an autopsy on one victim who passed away as a result of an allergic reaction to a vaccine and concluding that the vaccine is causing excess deaths. After all thereโs no denying how the person died or the science used in determining the cause of death. But such a superficial analysis is incomplete because the total efficacy of the vaccine hasnโt been taken into account. Was a study performed to see if the vaccine saved thousands or millions of other lives from a deadly pathogen, or whether people who took the vaccine perished at a lower rate than those who did not take the vaccine? Until those additional analyses are completed there would be no way to determine the *overall* role of the vaccine on human health based on just that one autopsy. Similarly, thereโs no way single-event attribution science can measure the influence of climate change on the totality of hurricane activity. Sure, thereโs probably little doubt that AGW enhanced water temperatures made Melissa up to 10% stronger, but it canโt measure whether climate change is increasing overall hurricane activity unless additional analyses are completed. Even more preposterous is itโs entirely conceivable in a hypothetical season where only one hurricane occurs that single-event attribution science could still ascribe a portion of that hurricaneโs strength on climate change!
Now this notion (that climate change can occasionally prevent extreme storms from forming) may seem preposterous to those who think that climate change can only create more โstorm energyโ and make everything worse. However, there are some known mechanisms as a result of climate change that can actually stabilize the atmosphere and potentially offset some of enhanced storminess thatโs found through the single-event attribution studies. For one such example, itโs well known that arctic amplification is reducing the north-south temperature gradient which, in turn, could reduce the strength of cold fronts in mid-latitudes. Perhaps this is a reason why powerful tornadoes (EF3+) have decreased substantially since the 1950s. In terms of hurricanes, itโs pretty clear that the vertical lapse rate is also lessening with climate change as there appears to be more warming aloft in the 850-700 mb layer than at the surface across tropical latitudes (see graph below).2 This could cause an increase in ambient stability making it more difficult for hurricanes to form in the first place. Perhaps this could explain why overall hurricane frequencies are slightly down but the proportion of major to total hurricanes is up (i.e., the more stable air decreases the odds of initial hurricane formation but the warmer sea surface temperature increases its intensity once one forms). The point is there are a myriad of physical mechanisms in which climate change could impact hurricanes, some factors can make them worse, and perhaps some could make them less impactful. Without a deeper analysis we just wonโt know the total impact of climate change on hurricane activity, and we certainly canโt assess that through a forensic analysis of one storm. Climate models are of little help as they canโt resolve hurricanes or reproduce our atmosphere accurately (otherwise seasonal hurricane forecasting would be easy), and single-event attribution models donโt provide an answer either as already pointed out.
This is why I feel that trend analysis right now is the best way to determine the broader impact of AGW on hurricane activity. However which trends should be computed? One trend would be to estimate the cyclonic energy of each hurricane in the US Landfalling Hurricane database.3 This calculation is simply the annual sum of the squared sustained wind speeds for each hurricane at landfall. The cyclonic energy estimate is similar to the ACE index except in this case itโs just computed at landfall, not over the life of the storm, and I didnโt divide by 10,000 like the ACE. The advantage in using the landfalling hurricane database is its long history back into the 1800s so hurricane activity can be viewed through a wide lens as it impacted the United States. Certainly by the late 1800s most coastlines were populated enough that it would be unlikely to miss a big tropical cyclone, so this would be a fair starting point.
The chart below shows the running 5-year cyclonic energy totals ending at the year shown. Although thereโs been a lot of choppiness the most recent couple of decades, the overall trend (dashed line) actually shows a slight but probably statistically insignificant decline. Most importantly thereโs no strong evidence that climate change is making US landfalling hurricanes worse. Even the more active periods after 2000 were on par with some of the active periods in the late 19th century through the middle of the 20th century.
Unfortunately, the use of the US landfalling hurricane dataset to uncover a trend is not without criticism as it only looks at only a small subset of the total hurricane picture. Specifically, the dataset leaves out non-landfalling hurricanes and storms in basins outside the Atlantic. The US landfalling dataset also does not include measurements of hurricanes over their entire life cycle but rather only a snapshot at landfall. Those are all fair criticisms but sadly the ability to detect a trend using all the available hurricane data becomes a challenge as the ability to remote-sense and detect hurricanes over the open oceans has increased greatly over time. As a result historical hurricane data over the open oceans are subject to potential observational biases, more specifically under-sampling biases going back in time, which can significantly distort the real trend. Many hurricanes were missed prior to 1970 due to the lack of 24/7 global satellite coverage. During the 1970s and 1980s satellite technology evolved considerably, and it wasnโt until the 1990s that aircraft reconnaissance could measure surface wind speeds with high accuracy. Despite increased coverage over the last quarter of the 20th century, a number of very short-lived tropical cyclones over the open waters were still likely missed or their intensity underestimated prior to 2000 as remote-sensing and detection technology gradually improved. Since 2000, there have been a plethora of new tools available to observe hurricanes including scatterometers, GPS dropsondes, stepped frequency microwave radiometers, etc., that werenโt available earlier. Landsea and Blake (2021) discuss on the National Hurricane Centerโs blog site, Inside the Eye, the evolution of technology for monitoring tropical cyclones and its impact on counting storm totals (see first link below). Below that are links which further describe the evolution of weather satellite and aircraft recon technology.
With that preface in mind, shown below are plots of annual ACE values for all tropical cyclones in the northern hemisphere and the globe during the post-satellite era.4 However, as mentioned above, since detection technology improved significantly over time, and because itโs a relatively short time period overall, the trend in the ACE may be sensitive to the year the graph is started. Therefore, several graphs are provided with different starting dates: 1971, 1980, 1990, and 2000. Note that hurricane data was available for the northern hemisphere and the globe beginning in 1971 and 1980, respectively.
Results show an upward trend in accumulated cyclonic energy for the northern hemisphere in the chart starting in 1971. However every chart with a starting point from 1980 onward shows a slight decline in ACE for both the northern hemisphere and the globe. In light of all of these graphs itโs certainly difficult to make a case that AGW is making hurricanes more powerful and destructive, at least on large time and/or space scales, especially considering the fact that it seems like every successive year in the past couple decades a new record is set for the โhottest year ever recordedโ. One would think that if hurricanes were becoming significantly worse from global warming that solid confirmation signals would show up in these charts. Of course this is contrary to the news media articles and the attribution reports from the climate cabal. However, it is consistent with IPCC reports which expressed low confidence in long-term trends in tropical cyclone intensity, frequency, and duration. In the least, it is hoped that these analyses will at least draw doubt and speculation to the efficacy of single-event attribution science which has serious flaws. It is further hoped that word will spread of this speculative science to lawyers and judges so that unnecessary and costly lawsuits can hopefully be avoided.
4 Data from the Department of Atmospheric Science, Colorado State University
Ok folks, looking for some ideas. We are starting to plan out the Climate Solutions dinner for next May. This is our big annual event that typically has about 700 or so people attend.
We are thinking about who a great keynote speaker might be. In the past we have had folks like Gina McCarthy, Van Jones, Bill McKibben, Bill Gates, Mindy Lubber, and Jigar Shah.
Who have you seen recently that lights things up and brings energy and excitement to a keynote speech. Who do you want to hear from? Huge thanks for any ideas that you have. Feel free to pop ideas into the feed or send suggestions over a message.
Smallโs list are all Statists now peddling false hopes. Each is at odds with the new political reality of energy affordability first, alleged climate harm last. To correct this, I suggested in a comment:
Alex Epstein on Fossil Future would explain why the anti-CO2 crusade is both failing and anti-human betterment.
Other nominees from Smallโs climate crowd were Gavin Newsom (โCap-and-Trade market โฆ re-branded to Cap-and-Invest b/c he liked WAโs approachโ), Katharine Hayhoe, Mary Nichols, Greta Thunberg, and a number of names I had never heard of. Some longed for a new inspiring voice now that past voices (think Gore, Kerry, Podesta) had lost their luster.
So what was the response to my suggestion? Nothing. Not a single like or a criticismโcrickets. Yet Alex Epstein would challenge the crowd to understand why the anti-CO2, anti-fossil-fuel crusade was futile and wasteful.
This is not what the crowd wants to hear in their moment of growing crisis. Invested emotional capital (change my mind now?) and jobs (what will I do in the real world?) are controlling.
Open Minds?
But some minds (more will follow) might entertain a rethink, if only to understand their predicament and the reasons behind it. It will be a lonely endeavor, beginning from within. As Milton and Rose Friedman stated in Free to Choose (1980: xii):
The only person who can truly persuade you is yourself. You must turn the issues over in your mind at leisure, consider the many arguments, let them simmer, and after a long time turn your preferences into convictions.
IEA forecasts new all-time high in coal consumption โ wind and hydropower, of all things, are partly to blame
Do you still remember the forecasts? Coal was supposed to be in retreat, fossil fuels were to be displaced, and the energy transition was to be unstoppable. The International Energy Agency (IEA) now has to announce remarkable news: global demand for coal will reach a historic high of 8.85 billion tons in 2025 โ an increase of 0.5 percent compared to the previous year.
USA: Up eight percent
In the USA, coal demand will increase by eight percent in 2025. After 15 years of continuous decline โ an average of six percent per year โ coal is returning. The reasons: higher natural gas prices and slower shutdowns of coal-fired power plants. What was sold as structural change turns out to be a cyclical snapshot.
Europe: When wind and water fail
The real punch line takes place in the European Union. Here, coal demand declined, but “significantly less strongly than in the last two years”. The reason, according to the IEA: “because wind and hydropower delivered less energy in the first half of the year”.
The technologies that were supposed to replace fossil fuels fail exactly when they are needed. The first half of 2025 was characterized by calm winds and low water levels. The result: more coal burning. The replacement of base-load-capable power plants with weather-dependent energy sources means that fossil fuel backups have to be used in the event of a lull.
The Global Perspective
The figures in detail:
Chinaย (56% of global consumption): demand stagnates at 4,953 million tons
India: Consumption declines for the first time in decades due to intense monsoon season โ temporarily
USA:ย Increase of 37 million tonnes in absolute terms
EU: Slowdownย in decline due to lower wind and hydropower production
The IEA expects global demand to decline slightly to 2023 levels by 2030. However, with the note: “If China sees a faster-than-expected increase in electricity consumption or slower integration of renewables, global coal demand could be above forecasts.”
Coal’s share of electricity generation
Keisuke Sadamori, IEA Director of Energy Markets, admits: “In 2013, coal accounted for 41 percent of electricity generation, and in 2025 we expect it to be around 34 percent. This is the lowest value in the history of IEA statistics.” Sounds like success โ if it weren’t for the absolute consumption record. Total electricity generation increased by about 7,000 TWh from 2013 to 2025. Coal is losing shares, but not absolutely.
Why electricity demand is exploding
Global electricity production reached around 27,000 TWh in 2024 and will rise to over 30,500 TWh by 2027 โ an increase of 3,500 TWh in three years. This is equivalent to the annual addition of Japan’s total electricity demand. The IEA forecasts growth of just under four percent per year until 2027, well above the historical average of 2.6 percent (2015-2023).
The drivers of this growth are manifold:
Air conditioning: The demand for air conditioning systems is booming. In India, 14 million units were sold in 2024 โ 27 percent more than in 2023. Air conditioning systems already account for 60 GW of peak load, although only 20 percent of households own an appliance at all. By 2030, cooling could account for a third of India’s peak load (140 GW). Each additional degree Celsius of average temperature increases the peak load by 7 GW โ in 2027 it could be 11 GW per degree.
Data centers and AI: In the U.S., data center electricity demand will increase from 25 GW (2024) to over 80 GW (2030). Globally, data centers currently consume 500-650 TWh annually โ by 2030, it will be 770 to 1,280 TWh, depending on the scenario. The AI revolution is the single biggest driver. The IEA expects data center consumption to double by 2030 in the USA, China and the EU alone.
Electrification of industry: In China, the production of solar panels, batteries and electric vehicles alone consumes 300 TWh per year โ as much as the whole of Italy. These sectors accounted for about 35 percent of the growth in industrial electricity demand in 2022-2024. The industrial sector will continue to drive about 50 percent of China’s demand growth.
Electromobility: 17 million electric vehicles were sold in 2024 (+25 percent), electricity consumption in the transport sector increased by eight percent.
Emerging market growth: 85 percent of global electricity demand growth by 2027 will come from developing and emerging countries โ 60 percent of which will come from China and India alone. China will reach the 10,000 TWh mark in annual consumption in 2025 and will thus be responsible for half of the global demand growth.
The IEA expects global electricity demand to exceed 29,000 TWh by 2026. The demand for electricity is thus growing twice as fast as the total energy demand. And this is exactly where the problem lies: This explosive growth has to be covered โ depending on the weather or not.
The intermittency problem remains unsolved
This massive growth in demand reveals the basic problem: an energy system based on weather-dependent sources requires backup capacities. In Europe, reduced wind and water production in the first half of 2025 directly led to higher coal consumption. The consequence: COโ emissions are rising instead of falling.
For 2025, the IEA forecasts global electricity generation of 32,200 TWh for coal-fired power generation alone. Renewables are being expanded “in record numbers” and are expected to produce more than coal for the first time in 2025. Nevertheless, coal-fired power generation remains “largely stable” at 5,964 million tonnes, supported by “seasonal heating demand and system safety requirements, especially in Asia”. With every lull, the coal steps in.
India’s future
India will see the largest absolute increase by 2030: over 200 million tons of additional demand, an average of three percent per year. 14 gigawatts of new coal capacity will be connected to the grid in 2025. The IEA states: “Although renewables are growing rapidly, they alone cannot keep pace with the growth in electricity demand.”
Conclusion
8.85 billion tons of coal in 2025 โ a new record despite the massive expansion of renewable energies. The IEA figures show that the energy transition in its current form does not replace fossil fuels, but makes us more dependent on them. Instead of providing reliable energy, it creates an unstable system that relies on coal when there is no wind.
Coal consumption is peaking not despite, but because of, inadequate backup strategies for volatile energy sources. Until a solution to the storage and base load problem is found, coal will remain โ because it is needed when wind and water fail.
Socialists have long wanted to take away our cars. I remember as long ago as the 1970s them arguing for this.
To them, the ability to travel where and when you want is not compatible with the sort of state control of our lives that they yearn for.
Making EVs mandatory was another step on this long planned path, given that they are impractical for drivers with no off street parking. Now it seems they are opening another front in their war on motorists.
From the Telegraph:
Labour to step up war on motorists by limiting bays on housing developments
Sir Keir Starmer is starting a crackdown on parking spaces in a fresh โwar on motoristsโ.
Under plans published by the Government, councils will be required to impose sweeping parking restrictions limiting the number of spaces on new housing developments without any justification.
The move aims to discourage car use in favour of greener alternatives such as using public transport, walking or cycling.
But critics warn that the policy will squeeze drivers and lead to โparking miseryโ in the latest move targeting motorists in the name of net zero.
Campaigners say the plans could backfire as a shortage of off-road spaces for cars could force drivers to resort to โillegal and dangerousโ parking practices.
Richard Holden MP, shadow transport secretary, said: โLabour have ripped up the rulebook that protected families from parking misery and replaced it with a green light for councils to squeeze drivers โ [it means] blocked pavements, congested roads, and tickets through the post.
โThis is how the war on drivers works. Motorists arenโt the problem. Once again, theyโre the revenue stream.โ
A dairy farmer with low milk production asks a physicist for help. After some months, the physicist eventually reports back: โI have found out how to solve the problem.โ
โAbout time!โ, said the farmer, โWhat is it?โ
The physicist replies, โFirst, assume a spherical cow in a vacuum.โ
Hereโs a science joke about the dangers of oversimplified models.
A dairy farmer with low milk production asks a physicist for help. After some months, the physicist eventually reports back: โI have found out how to solve the problem.โ
โAbout time!โ, said the farmer, โWhat is it?โ
The physicist replies, โFirst, assume a spherical cow in a vacuum.โ
In 2023 I wrote post entitled Testing a Constructal Climate Model, where I took a first cut at making a computer implementation of Adrian Bejanโs ideas about a Constructal model of the climate.
For those who missed my earlier post, the Constructal Law is the most recently discovered fundamental law of thermodynamics. It was first described by Adrian Bejan in 1996. Heโs the JA Jones Distinguished Professor of Mechanical Engineering at Duke University, and his writings have over 100,000 citations.
The Constructal Law says that flow systems far from equilibrium evolve to maximize access to flow. Rivers donโt meander randomlyโthey organize to maximize water transport. Animal circulatory systems donโt just happenโthey evolve to maximize nutrient flow. And according to Adrian Bejan, the climate should organize itself to maximize heat flow from the hot tropics to the cold poles.
Now, that sounded reasonable enough โฆ but does it actually work? Can you build a working climate model based on that principle?
Turns out you can. And as my post above showed, itโs a very simple model.
In any case, after writing that post, I got invited to present my work in November at the 15th Constructal Law Conference, hosted by the Florida International University College of Engineering and Computing in Miami.
I went in part because I wanted to meet Adrian Bejan, who was slated to be in attendance. I have a number of scientific heroes, and heโs one of them. I introduced myself to him, and he said Oh, youโre Willis Eschenbach. Iโve seen your work. Itโs very impressive!
Zowie, sez I. A win for the reformed cowboy!
There were many interesting presentations at the Constructal Conference, but that interaction alone was worth the ticket to Miami โฆ however, I digress.
Originally, my plan for the conference was to just present the work shown in my post linked above. But then I realized that was a lazy copout โ that model was two years old. I had to take it further forward. So I did more research and analysis, which is what this post is about. Basically, itโs an expanded version of my presentation at the conference.
The model Iโm about to show you treats the Earth as a smooth sphereโno land, no ocean, no mountains, no ice sheets, nothing but a ball heated by the sun. It divides this ball into two zones: a hot equatorial zone and cold polar zones. Thatโs it. Two zones. And from those two zones, using the Constructal Law to maximize heat flow between them, the model reproduces the actual Earthโs temperature, circulation patterns, and year-to-year variations with remarkable accuracy.
Let me show you how it works, and then weโll look at what it tells us about climate sensitivity. Spoiler alert: the news is good if youโre worried about catastrophic warming.
The Basic Setup
Figure 1 shows the concept. We divide Earth into a hot zone,extending from the equator to some latitude ฮธ, and two cold zones, poleward of ฮธ. Heat flows from hot to cold, driven by the temperature difference.
Figure 1. The spherical cow โฆ er, spherical Earth model. There is a hot equatorial zone of area A_H with an average temperature T_H. There are two cold polar zones of total area A_L with an average temperature T_L). Heat flow q moves from hot to cold.
Now, before anyone objects that this is ridiculously oversimplified, let me show you what the real Earth looks like in terms of energy balance:
Figure 2a. The actual Earthโs hot and cold zones, as determined by the gridded top-of-atmosphere radiation balance from CERES satellite data. The tropics export energy (red), and the polar regions import it (blue). Note that the desertsโSahara, Arabia, Australia, Gobiโare actually in the cold zone despite being hot. They radiate more energy than they absorb.
The similarity is striking. The real Earth organizes itself into these zones naturally. The model just captures this organization mathematically. And the division into these zones is surprisingly stable over time.
Figure 2b. Annual average hot and cold zones, 2001 to 2024
Note how the Sahara, Arabia, and Gobi deserts protrude down into the hot zone. This leads to some offsets in the results, as discussed below.
The Energy Balance
Being a simple fellow, I started with Bejanโs simple energy balance equations. The hot zone receives solar energy based on its projected area, reduced by its albedo (the fraction of incoming solar radiation reflected by the planet, โalbedo_Hโ):
Energy in = (projected area) ร (1 โ albedo_H) ร (solar constant) [Equation 1]
The hot zone radiates energy to space, reduced by the greenhouse fraction โgreenhouse_Hโ. This is the percentage of upwelling thermal radiation from the surface that is absorbed by the clouds, water vapor, and the radiatively active gases.
Energy out = (area) ร (1 โ greenhouse_H) ร ฯ ร T_H^4 [Equation 2]
where ฯ is the Stefan-Boltzmann constant and greenhouse_H is the fraction of surface radiation absorbed by the atmosphere.
The same things are true for the cold zones. They absorb solar radiation mediated by albedo and emit thermal radiation to space, regulated by the greenhouse fraction.
The difference between incoming and outgoing power in each zone must equal the heat flow q between the zones. Energy is conservedโit has to go somewhere.
So far, nothing fancy. Just bookkeeping.
The Heat Transport
Hereโs where it gets interesting. How much heat flows from the hot zone to the cold zone? Bejan and Reis showed that for buoyancy-driven atmospheric circulation, the heat flow should vary as:
q = C ร (T_H โ T_L)(3/2) [Equation 3]
where q is the heat flow and C is a thermal conductance factor representing how easily the heat is conducted from the hot to the cold zones.
According to Bejan, the 3/2 power law comes from the physics of convection. Warmer air rises, cooler air sinks, and the flow rate depends on the temperature difference in a very specific way. The constant C depends on atmospheric properties and circulation patterns.
Now we have five unknowns (T_H, T_L, q, the fraction x of Earthโs surface in the hot zone, and the conductance factor C), and three equations. How do we close the system?
Enter the Constructal Law
Hereโs where Bejanโs insight comes in. The system evolves to maximize the heat flow q. Why? Because the climate is a heat engine, with the tropics as the hot reservoir and the poles as the cold reservoir. But unlike a car engine that delivers power to the wheels, Earthโs climate engine is permanently coupled to its โbrakeโโall the power it generates gets dissipated immediately through friction in winds and ocean currents.
For such a systemโan engine hard-wired to its brakeโmaximum power production equals maximum dissipation, which means maximum heat flow. The atmosphere and ocean organize themselves to achieve this.
So the fourth equation is simply:
dq/dx = 0 [Equation 4]
Making It Work
I implemented this as a nested optimization problem in R. For any given value of x, I first solve for the values of T_H, T_L, and q that satisfy the three energy balance equations. Then I vary x to find the value that maximizes q. Finally, I optimize the value of C, the conductance, to agree with the distance between T_H and T_L. (T_H minus T_L) Itโs optimization within optimization.
The original Bejan/Reis model used global average values for albedo and greenhouse effect. I improved this by using separate values for each zone, derived from CERES satellite data. Tropical regions have lower albedo (less ice and snow) and higher greenhouse effect (more water vapor) than polar regions. This matters.
I also added one more refinement. As you can see in my previous model analysis linked to above, the original model had a slight drift in temperatures over time.
Figure 3a. Results of myย previous modelย incarnation, which shows an anomalous trend in the modeled temperatures
After too much investigation, I realized that this is because my model neglected the heat absorbed by the oceans. This flow was also not included in the Bejan model shown in Figure 1 above. I added this as a small tunable parameter for each zone.
So how many parameters does this model have? Letโs count:
Albedo in hot zone (measured from CERES data)
Albedo in cold zone (measured from CERES data)
Greenhouse fraction in hot zone (measured from CERES data)
Greenhouse fraction in cold zone (measured from CERES data)
Conductance C^(3/2) (tuned parameter)
Ocean heat absorption, hot zone (tuned)
Ocean heat absorption, cold zone (tuned)
Thatโs four measured parameters and three tuned ones. Compare that to the thousands of parameters in comprehensive climate models. Itโs a spherical cow.
The Results: Temperatures
OK, enough theory. Does it work? Figure 3b shows 24 years of data (2001-2024) comparing CERES satellite surface temperature observations to the Constructal model emulation. This is the corrected version of Figure 3a above.
Figure 3b. Annual mean temperatures. Blue/cyan: CERES satellite observations. Red/orange: Constructal model predictions. Top: hot zone temperature T_H. Bottom: cold zone temperature T_L.
The agreement with the actual real-world temperatures of the hot and cold zones is remarkable, given that nowhere is the model tuned to produce them. At this scale, they are so close that you can only see the difference between reality and the model around 2010.The model gets the absolute temperatures right within 0.1ยฐC on average. It also tracks the year-to-year variations. It captures the slight warming trend. And rememberโthis is a model of a featureless sphere with no continents, no oceans, no mountains, no ice sheets, nothing.
Next, hereโs a close-up of the hot zone temperatures shown in Figure 3b above:
Figure 4. Detailed view of hot zone temperatures. CERES data (blue) and model (red). Root mean square error: 0.20ยฐC. Correlation of interannual variations: r = 0.56, p < 0.001.
The main difference between the model and reality is that the swings in the modeled temperature are somewhat larger than the swings in the real world. I suspect this is because the model assumes that the losses are all temperature-controlled, and also, in the model, thereโs no atmospheric absorption of the sunlight. Both of these omissions increase the size of the swings.
Next, the cold zone results:
Figure 5. Cold zone temperatures. CERES data (cyan) and model (orange). RMSE: 0.13ยฐC. Correlation: r = 0.71, p < 0.001.
The fit in the cold zone is better than in the hot zone, although again the swings are exaggerated in the model.
This shows that the model isnโt just matching the average temperaturesโitโs capturing the dynamic annual changes in temperature as well.
The Hot Zone Boundary
The model predicts that the boundary between hot and cold zones should be at about 36ยฐ North and South latitude. The actual boundary, based on where top-of-atmosphere radiation balance crosses zero, is at around 34ยฐ. The model is off by about 2ยฐ, or about 220 kilometers.
Not bad for a model that doesnโt know about Hadley cells, polar cells, El Nino/La Nina, jet streams, Gulf Streams, or any of the detailed atmospheric and oceanic circulation patterns. Those patterns emerge naturally from maximizing heat transport.
Figure 6 shows how the boundaries compare by year:
Figure 6. Boundaries of Earthโs hot zone. Blue: CERES observations. Red: Model predictions. The model boundary is about 2.4ยฐ wider than observed.
The model tracks the variations well. The offset in the boundaries exists because in the real Earth, the major deserts (Sahara, Arabia, Australia, Gobi) are in the cold zoneโthey radiate more than they absorb. See Figures 2a and 2b above. But in the smooth-sphere model, those latitudes are in the hot zone. This makes the modelโs hot zone larger than reality.
Despite this, the model captures the year-to-year changes quite well. Figure 7 shows the anomalies (variations around the mean) of the latitude of the boundary between hot and cold:
Figure 7. Anomalies in hot zone area. Blue: CERES. Red: Model. When you remove the offset, the model tracks the variations quite well.
This is doing a workmanlike job of calculating both the annual variations in the size of the hot zone and the overall trend of hot zone size over the period of record.
And to close the circle on this part of the analysis, hereโs Figure 2 with the Constructal model hot zone boundaries overlaid on it.
Figure 8. As in Figure 2, but with the modeled boundaries overlaid on it (yellow/gray dotted lines).
Here you can see why the actual earthโs total hot zone area is slightly smaller than that of the modelโthe areas in green which are cold zone intruding into the model-simulated hot zone.
Heat Flow
Now for the crucial test. The Constructal Law says the system maximizes heat flow. Does the model get the heat flow right?
Figure 9 shows the total poleward heat transport:
Figure 9. Annual heat flow from tropics to poles, in petawatts (PW = 10^15 watts, left scale) and the equivalent global heat flow (petawatts divided by global surface area, W/m2, right scale). Blue: CERES. Red: Model.
The model shows a significantly higher heat flow than observed (14.9 PW modeled vs. 12.3 PW observed). This is for the same reason the area of the hot zone is larger as discussed aboveโthose deserts that are in the cold zone in the real world but in the hot zone in the model. This means the model is absorbing more solar power than the real world in the hot zones and transferring it to the poles. So the total flow is too large, by a globally averaged value of ~ 5 W/m2. But look at the heat flow anomalies below:
Figure 10. Heat flow anomalies. Blue: CERES. Red: Model. RMSE: 0.04 PW. The model captures year-to-year variations almost perfectly.
That RMSE of 0.04 PW is tiny compared to the year-to-year variations. The model is faithfully capturing the dynamics of heat transport using only annually updated albedo and greenhouse parameters as input. That validates that we are seeing the Constructal Law at work.
Climate Sensitivity
Finally, what does this model tell us about climate sensitivityโhow much warming would we get from doubling atmospheric COโ?
To determine that, I ran the model with a uniform increase in downwelling radiation of 3.7 W/mยฒ, the IPCC standard assumed forcing from doubled COโ. The model predicts:
So the equilibrium climate sensitivity is about 1.1ยฐC per doubling of COโ.
But wait, thereโs more. And this is important. This value is a maximum estimate because the model doesnโt include negative feedbacks from a host of emergent climate phenomena like thunderstorm responses, El Nino/La Nina events, tornadoes, and other phenomena which Iโve written about extensively in my thunderstorm thermostat posts. Those feedbacks all oppose surface warming by transferring heat from the surface to the atmosphere. As a result, the real climate sensitivity is likely lower than shown above.
In addition, total known fossil fuel reserves contain about 4,780 gigatonnes of CO2. This is not total proven reserves, those we know we can get to within budget. This is everything we know about, whether or not it actually is economically recoverable with todayโs technology.
At 17.4 Gt CO2 added per each ppmv increase, that will increase the atmospheric CO2 by 275 ppmv โฆ and since we have ~ 420 ppmv of CO2, itโs unlikely that weโll ever see a doubling of CO2 from here.
And with a sensitivity of 1.1ยฐC from doubling, this implies a maximum temperature increase on the order of log2((275 + 420) / 420) * 1.1 = 0.8ยฐC โฆ Thermageddonโข cancelled, sorry, no refunds.
Is this sensitivity believable? The IPCCโs range for climate sensitivity is 1.5ยฐC to 4.5ยฐC per doubling. The Constructal model puts it at 1.1ยฐC, below the IPCCโs lower bound. However, this is in the same range of observational estimates from Lewis and Curry and others whoโve analyzed the actual warming weโve seen.
For comparison, hereโs the history of climate sensitivity estimates:
Figure 11. Historical estimates of equilibrium climate sensitivity. The range of the estimates has actually gotten wider over time. That tells you something about how well we understand the climate.
Climate sensitivity is a, perhaps the, biggest unknown in climate science. Note that the uncertainty of the climate sensitivity has increased, despite hundreds of thousands of hours of computer time and human research. Thatโs not how science is supposed to work. To me, it strongly suggests our current climate models have been barking up the wrong tree.
What This Tells Us
The success of this ultra-simple spherical cow model reveals some profound things about how climate works:
1. Optimization trumps complexity. You donโt need to simulate every cloud, every ocean eddy, every rainstorm to understand the big picture. The climate organizes itself according to simple principlesโit maximizes heat transport, subject to radiative constraints.
2. The oceanโs role is simpler than we thought. The model treats Earth as a uniform sphereโno explicit ocean, no currents, no thermohaline circulation. Yet it works, and works very well. This suggests the oceanโs main climate role is facilitating heat transport (captured in that conductance parameter C), not creating fundamentally new dynamics that require detailed representation.
3. Circulation patterns emerge. The model predicts a hot/cold boundary near 34ยฐN/Sโwhere the Hadley cell meets the Ferrel cell in the real atmosphere. The model doesnโt know about these circulation cells. They emerge from the optimization. The atmosphere organizes itself into Hadley and polar cells because that configuration maximizes heat transport.
4. Less is more. This model has seven parameters (four measured, three tuned). Comprehensive climate models have hundreds. Yet this simple model matches observations as well as or better than the complex ones for these fundamental variables of hot and cold zone temperatures, hot zone area, and heat flow. Occamโs razor suggests we should pay attention to that.
5. It demonstrates and validates the importance of the albedo and the poorly-named โgreenhouse effectโ. Clearly, those two variables alone have an overwhelming importance in the way that the climate organizes itself.
6. It makes intuitive sense. Albedo and the greenhouse fraction respectively control the amount of energy entering and leaving the system. And from basic physics, this makes senseโweโd expect the global temperature to be a function of the energy entering the system minus energy leaving the system. However, it is not a simple function of those two values. Instead, following the Constructal Law, the system constantly rearranges itself to maximize the energy flow given those two values.
7. Current climate models are going down the wrong path. This model clearly shows the underlying Constructal structure and action of the climate, a structure that is not emulated by the current generation of climate models.
Limitations
Now, Iโm not claiming this model can do everything. It canโt. It has obvious limitations:
Only two zones, so no gradients within zones and no longitudinal (east-west) variations
Annual averages only, so no seasonal or daily cycles
Simplified transport physics lumped into one parameter
No ocean, no land. If nothing else, the thermal conductance factor C will be different in those two regimes.
No explicit clouds, water vapor changes, ice-albedo feedback, or vegetation
Simple greenhouse treatment
Future work should address these. A multi-zone model with finer resolution. Adding seasonal cycles. Explicitly modeling thunderstorm convection and ocean circulation. Including the desert areas as part of the cold zones. Coupling to detailed radiative transfer models.
But the point is, it works now despite its simplicity. That tells us something important about natureโs organizing principles.
Not Maximum Entropy Production
Some folks confuse the Constructal Law with Maximum Entropy Production (MEP). Theyโre related but different.
MEP says systems evolve to maximize entropy generationโmaximum dissipation. For Earthโs climate, thatโs sort of true. But Bejanโs Constructal Law is more general. It says flow systems evolve to maximize flow access, not necessarily dissipation. For an engine that can deliver power externally (like your car), Constructal Law predicts minimum dissipation. For an engine coupled to its brake (like Earth), it predicts maximum dissipation.
Earth just happens to be the second case. The Constructal Law explains both. MEP only explains the Earth. The success of this model validates the more general Constructal principle.
Where We Stand
Let me summarize what weโve got here:
A computational climate model based on Constructal optimization of heat flow
Validated against 24 years of CERES satellite data
Matches absolute temperatures within 0.1ยฐC average (RMSE 0.13-0.20 K)
Matches hot zone area within 2ยฐ latitude
Captures interannual temperature variability and trends of the two zones (correlations 0.56-0.74)
Predicts heat flux variations with RMSE of 0.04 PW
Uses minimal parameterizationโfour measured quantities plus three tuned parameters
Predicts climate sensitivity of 1.1ยฐC per doubling of COโ as an emergent property, not a tuned value
This ultra-simple smooth sphere model, with no ocean or land or topography, successfully reproduces observed temperatures, circulation patterns, and power flow with unprecedented accuracy. I donโt know of any comprehensive climate model that achieves this level of skill, regardless of its level of complexity.
And it shows that every once in a while โฆ an รผber-simplified model of a spherical cow in a vacuum actually does work โฆ
CODA:
The Constructal climate model has two input variablesโgreenhouse fraction and albedo. Itโs worth noting that the albedo and the greenhouse fraction are both functions inter alia of thunderstorms and clouds.
The surface is constantly emitting thermal radiation upwards (โupwelling radiationโ). Some of this upwelling radiation is absorbed by the clouds and the โgreenhouse gasesโ. The greenhouse fraction is the percentage of the upwelling radiation that is absorbed in the atmosphere. Figure 12 below shows how that differs around the world.
Figure 12. Average greenhouse fraction, CERES data.
As you can see, the areas of largest absorption enclosed by the 45% greenhouse fraction contour line are the great band of thunderstorms that stretches around the world at the Inter-Tropical Convergence Zone (ITCZ) just north of the equator.
Next, here is a map of the albedo, overlaid with the 45% greenhouse fraction contour line.
Figure 13. Average albedo, CERES data
Note that the areas of high albedo line up with the areas of high greenhouse fraction.
And to verify that this is indeed related to thunderstorms, here is the rainfall from the Tropical Rainfall Measuring Mission satellite observation.
Figure 14. Average monthly rainfall as measured by the TRMM
And thatโs all I have for now, dear friends โฆ tomorrow is a new day, always more to learn.
Here on our Northern California coastal hillside, we had rain yesterdayโblessed rain after a dry spell. The weather systems that brought it organized themselves according to the same principles this model uses: maximize heat and moisture transport from where thereโs an excess to where thereโs a deficit. Itโs beautiful to see the same physics working at scales from individual storms to the global circulation.
My warmest regards to all, my thanks to Adrian Bejan for illuminating conversations about Constructal theory, and to Umit Gunes and Pezhman Manipour for organizing the conference.
w.
As Is My Custom: When you comment, please quote the exact words youโre discussing, so we can all be clear on the exact topic under discussion.
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