The Weak Foundation of Calls for Climate Action

Permafrost used to be down south as far as 42-45 degrees North during the Glaciation time which means southern France was the boundary line and middle America was the boundary line which then melted back over 1,250 to around 1,750 miles to the north without any evidence of a …. “tipping point” having occurred.

These hack scientists are incompetent.

 

No kidding?

https://phys.org/news/2023-12-ecological-evidence-policy-makers-unstable.html

 

Data again undermine the alarmist narrative.
German Prof On German Floods: “Difference Between Facts And Political Narratives Is Breathtaking.”
By P Gosselin on 5. January 2024

As Germany’s flooding takes hold, the media have been quick to seize upon the extreme weather as proof of the climate crisis. But the data show us that it’s normal bad weather that’s also been seen many times before in the past.

Yet, despite the widespread flooding, there are those who still believe that flooded north Germany is still suffering from severe to exceptional drought.

Not unprecedented

But the media have not wasted time in spinning the latest flooding as another sign of climate change. “Never have we seen such amount of rain in such a short period”, or, “it’s more proof the weather is getting increasingly extreme”.

All of it of course is just lots of click-baiting hype.

No significant rainfall trend since 1881

As retired professor Stefan Homburg pointed out at Twitter: “More rain than usual, but not a record. The number of heavy rain days was also within the normal range. Neither “droughts” nor “extreme weather” are recognizable in the official statistics. The difference between facts and political narratives is breathtaking.”

He presents two charts from the DWD German national weather service. The first is the total annual precipitation in millimeters since 1881.



Source: DWD.de

The second chart shows the number of days with rally heavy rainfall each year. As readers will see, annual rainfall has in fact increased modestly, thus refuting claims Germany will get drier in the future, and extreme weather events as defined by very rainy days shows no real trend.

In other words, claims that the heavy rainfall over the past weeks is a sign of climate change is an absolute nothing-burger.

Drought is over, until the next one

Here where I live in northern Germany, I’ve been tracking monthly rainfall for about 3 and half years using a simple measurement cup from the local garden center. Here’s what I’ve recorded so far:



Chart: NoTricksZone

The chart clearly depicts how the months of October, November and December were very wet, with just under 500 mm of precipitation. This rain will more than compensate for the drought period seen earlier, especially in 2022.

Most of Germany at the moment is a soaked sponge.

 

Just finished reading this. Great piece highlighting the role of randomness in weather events. Apparently Climate alarmists skipped the date randomness was covered in math class. Probably somewhere protesting fossil fuels.

 

Another alarmist falsehood exposed.
How Bogus Arctic Warming Attribution Enabled the Climate Crisis Scam
Guest Blogger
Abnormal warming over the Arctic Ocean and Arctic sea ice loss have been falsely blamed on rising CO2. Such alarmist graphic propaganda is common similar to Yale 360’s climate alarmists pushed, emphasizing Arctic Ocean’s warming of several degrees in November 2022, while ignoring the cooling over North America and Eurasia. But any critical thinking person can see warm Arctic temperatures are due to inflows of warm Atlantic water not a CO2 climate crisis. . . . .

 

Once again, alarmist claims are refuted by data.
Global Historical Hurricane & Cyclone Statistics Establish that 2023 Experienced a Very “Normal Year.”
Guest Blogger
Climate alarmist claims that hype the year 2023 hurricane and cyclone season as being an “extreme weather outcome” are absurd and incompetent as is NOAA’s “Selected Significant Climate Events” hurricane…

 

The models are part of the problem, not the solution.
New Article on Climate Models vs. Observations
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 Heritage.org 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 Heritage.org. As a sneak peek, here’s a comparison between models and observations for the U.S. Corn Belt near-surface air temperatures in summer:

 

Once again the models are shown to be highly misleading.
U.S.A. Temperature Trends, 1979-2023: Models vs. Observations
February 2nd, 2024

Updated through 2023, here is a comparison of the “USA48” annual surface air temperature trend as computed by NOAA (+0.27 deg. C/decade, blue bar) to those in the CMIP6 climate models for the same time period and region (red bars). Following Gavin Schmidt’s concern that not all CMIP6 models should be included in such comparisons, I am only including those models having equilibrium climate sensitivities in the IPCC’s “highly likely” range of 2 to 5 deg. C for a doubling of atmospheric CO2.


Approximately 6 times as many models (23) have more warming than the NOAA observations than those having cooler trends (4). The model trends average 42% warmer than the observed temperature trends. As I allude to in the graph, there is evidence that the NOAA thermometer-based observations have a warm bias due to little-to-no adjustment for the Urban Heat Island effect, but our latest estimate of that bias (now in review at Journal of Applied Meteorology and Climatology) suggests the UHI effect in the U.S. has been rather small since about 1960.

Note I have also included our UAH lower tropospheric trend, even though I do not expect as good agreement between tropospheric and surface temperature trends in a regional area like the U.S. as for global, hemispheric, or tropical average trends. Theoretically, the tropospheric warming should be a little stronger than surface warming, but that depends upon how much positive water vapor feedback actually exists in nature (It is certainly positive in the atmospheric boundary layer where surface evaporation dominates, but it’s not obviously positive in the free-troposphere where precipitation efficiency changes with warming are largely unknown. I believe this is why there is little to no observational evidence of a tropical “hot spot” as predicted by models).

If we now switch to a comparison for just the summer months (June, July, August), the discrepancy between climate model and observed warming trends is larger, with the model trends averaging 59% warmer than the observations:


For the summer season, there are 26 models exhibiting warmer trends than the observations, and only 1 model with a weaker warming trend. The satellite tropospheric temperature trend is weakest of all.

Given that “global warming” is a greater concern in the summer, these results further demonstrate that the climate models depended upon for public policy should not be believed when it comes to their global warming projections.

 

The debate is not going well for Gavin Schmidt.

Two model-observation comparisons confirm: CMIP6 models run too hot
Posted on February 2, 2024 by niclewis | 9 comments
By Frank Bosse and Nic Lewis

A recent article by Roy Spencer was (strongly) criticized by Gavin Schmidt over at “Real Climate”.

In the summary Gavin S. wrote:

“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.”

Let’s have a detailed and objective look if the wording “…to mislead the readers” is sound.

Continue reading →

 

The models come up short, no matter how you try to use them.
What Period of Warming Best Correlates with Climate Sensitivity?
February 6th, 2024
When computing temperature trends in the context of “global warming” we must choose a region (U.S.? global? etc.) and a time period (the last 10 years? 50 years? 100 years?) and a season (summer? winter? annual?). Obviously, we will obtain different temperature trends depending upon our choices. But what significance do these choices have in the context of global warming?

Obviously, if we pick the most recent 10 years, such a short period can have a trend heavily influenced by an El Nino at the beginning and a La Nina at the end (thus depressing the trend) — or vice versa.

Alternatively, if we go too far back in time (say, before the mid-20th Century), increasing CO2 in the atmosphere cannot have much of an impact on the temperatures before that time. Inclusion of data too far back will just mute the signal we are looking for.

One way to investigate this problem is to look at climate model output across many models to see how their warming trends compare to those models’ diagnosed equilibrium climate sensitivities (ECS). I realize climate models have their own problems, but at least they generate internal variability somewhat like the real world, for instance with El Ninos and La Ninas scattered throughout their time simulations.

I’ve investigated this for 34 CMIP6 models having data available at the KNMI Climate Explorer website which also have published ECS values. The following plot shows the correlation between the 34 models’ ECS and their temperature trends through 2023, but with different starting years.


The peak correlation occurs around 1945, which is when CO2 emissions began to increase substantially, after World War II. But there is a reason why the correlations start to fall off after that date.

The CMIP6 Climate Models Have Widely Differing Aerosol Forcings

The following plot (annotated by me, source publication here) shows that after WWII the various CMIP6 models have increasingly different amounts of aerosol forcings causing various amounts of cooling.


If those models had not differed so much in their aerosol forcing, one could presumable have picked a later starting date than 1945 for meaningful temperature trend computation. Note the differences remain large even by 2015, which is reaching the point of not being useful anyway for trend computations through 2023.

So, what period would provide the “best” length of time to evaluate global warming claims? At this point, I honestly do not know.

 

Game, set and match to Spencer and Christy in their debate with Gavin Schmidt.
Proof that the Spencer & Christy Method of Plotting Temperature Time Series is Best
February 9th, 2024
Since the blogosphere continues to amplify Gavin Schmidt’s claim that the way John Christy and I plot temperature time series data is some form of “trickery”, I have come up with a way to demonstrate its superiority. Following a suggestion by Heritage Foundation chief statistician Kevin Dayaratna, I will do this using only climate model data, and not comparing the models to observations. That way, no one can claim I am displaying the data in such a way to make the models “look bad”.

The goal here is to plot multiple temperature time series on a single graph in such a way the their different rates of long-term warming (usually measured by linear warming trends) are best reflected by their placement on the graph, without hiding those differences.

A. Raw Temperatures

Let’s start with 32 CMIP6 climate model projections of global annual average surface air temperature for the period 1979 through 2100 (Plot A) and for which we have equilibrium climate sensitivity (ECS) estimates (I’ve omitted 2 of the 3 Canadian model simulations, which produce the most warming and are virtually the same).

Here, I am using the raw temperatures out of the models (not anomalies). As can be seen in Plot A, there are rather large biases between models which tend to obscure which models warm the most and which warm the least.


B. Temperature Anomalies Relative to the Full Period (1979-2100)

Next, if we plot the departures of each model’s temperature from the full-period (1979-2100) average, we see in Plot B that the discrepancies between models warming rates are divided between the first and second half of the record, with the warmest models by 2100 having the coolest temperature anomalies in 1979, and the coolest models in 2100 having the warmest temperatures in 1979. Clearly, this isn’t much of an improvement, especially if one wants to compare the models early in the record… right?


C. Temperature Anomalies Relative to the First 30 Years

The first level of real improvement we get is by plotting the temperatures relative to the average of the first part of the record, in this case I will use 1979-2008 (Plot C). This appears to be the method favored by Gavin Schmidt, and just looking at the graph might lead one to believe this is sufficient. (As we shall see, though, there is a way to quantify how well these plots convey information about the various models’ rates of warming.)


D. Temperature Departures from 1979

For purposes of demonstration (and since someone will ask anyway), let’s look at the graph when the model data are plotted as departures from the 1st year, 1979 (Plot D). This also looks pretty good, but if you think about it the trouble one could run into is that in one model there might be a warm El Nino going on in 1979, while in another model a cool La Nina might be occurring. Using just the first year (1979) as a “baseline” will then produce small model-dependent biases in all post-1979 years seen in Plot D. Nevertheless, Plots C and D “look” pretty good, right? Well, as I will soon show, there is a way to “score” them.


E. Temperature Departures from Linear Trends (relative to the trend Y-intercepts in 1979)

Finally, I show the method John Christy and I have been using for quite a few years now, which is to align the time series such that their linear trends all intersect in the first year, here 1979 (Plot E). I’ve previously discussed why this ‘seems’ the most logical method, but clearly not everyone is convinced.

Admittedly, Plots C, D, and E all look quite similar… so how to know which (if any) is best?


How the Models’ Temperature Metrics Compare to their Equilibrium Climate Sensitivities

What we want is a method of graphing where the model differences in long-term warming rates show up as early as possible in the record. For example, imagine you are looking at a specific year, say 1990… we want a way to display the model temperature differences in that year that have some relationship to the models’ long-term rates of warming.

Of course, each model already has a metric of how much warming it produces, through their diagnosed equilibrium (or effective) climate sensitivities, ECS. So, all we have to do is, in each separate year, correlate the model temperature metrics in Plots A, B, C, D, and E with the models’ ECS values (see plot, below).

When we do this ‘scoring’ we find that our method of plotting the data clearly has the highest correlations between temperature and ECS early in the record.


I hope this is sufficient evidence of the superiority of our way of plotting different time series when the intent is to reveal differences in long-term trends, rather than hide those differences.

 

Any period less than ~1Ky is going -- and is clearly intended -- to conflate the natural recovery from the LIA with the effect of CO2. That is the purpose of disingenuously calling LIA temperatures "pre-industrial," while ignoring the fact that the much higher temperatures millions of years ago were also pre-industrial. The honest way to characterize the earth's surface temperature in the century before the mid-19th century when the large-scale instrument record begins would be "Holocene extended temperature minimum."

Because there is a one-way secular trend in the development of instrumentation systems and records, only proxy reconstructions can be used to measure temperatures over such long periods. The proxies cannot include tree rings, as they are affected by CO2's fertilization effect, creating a spurious warming signal. This lets out such obviously fraudulent reconstructions as Michael "Piltdown" Mann's hockey stick graph. The instrument record over the last ~200y, which is needed to calibrate the proxies, must be carefully curated to focus on long-term, pristine rural sites where land use changes, urban heating effects, etc. can be unambiguously ruled out. Temperature records that are not honestly curated in this way, such as the GIS, Hadley, and NASA/NOAA datasets, are intended to create a false impression of recent rapid warming.