Climate Modellers Waiting for Observations to Catch Up with Their Predictions

Reblogged from Watts Up With That:

Guest essay by Eric Worrall

h/t Dr. Willie Soon; In climate science, when your model predictions are wrong, you wait for the world to correct itself.

New climate models predict a warming surge
By Paul VoosenApr. 16, 2019 , 3:55 PM

For nearly 40 years, the massive computer models used to simulate global climate have delivered a fairly consistent picture of how fast human carbon emissions might warm the world. But a host of global climate models developed for the United Nations’s next major assessment of global warming, due in 2021, are now showing a puzzling but undeniable trend. They are running hotter than they have in the past. Soon the world could be, too.

In earlier models, doubling atmospheric carbon dioxide (CO2) over preindustrial levels led models to predict somewhere between 2°C and 4.5°C of warming once the planet came into balance. But in at least eight of the next-generation models, produced by leading centers in the United States, the United Kingdom, Canada, and France, that “equilibrium climate sensitivity” has come in at 5°C or warmer. Modelers are struggling to identify which of their refinements explain this heightened sensitivity before the next assessment from the United Nations’s Intergovernmental Panel on Climate Change (IPCC). But the trend “is definitely real. There’s no question,” says Reto Knutti, a climate scientist at ETH Zurich in Switzerland. “Is that realistic or not? At this point, we don’t know.”

Many scientists are skeptical, pointing out that past climate changes recorded in ice cores and elsewhere don’t support the high climate sensitivity —nor does the pace of modern warming. The results so far are “not sufficient to convince me,” says Kate Marvel, a climate scientist at NASA’s Goddard Institute for Space Studies in New York City. In the effort to account for atmospheric components that are too small to directly simulate, like clouds, the new models could easily have strayed from reality, she says. “That’s always going to be a bumpy road.”

In assessing how fast climate may change, the next IPCC report probably won’t lean as heavily on models as past reports did, says Thorsten Mauritsen, a climate scientist at Stockholm University and an IPCC author. It will look to other evidence as well, in particular a large study in preparation that will use ancient climates and observations of recent climate change to constrain sensitivity. IPCC is also not likely to give projections from all the models equal weight, Fyfe adds, instead weighing results by each model’s credibility.

Read more: https://www.sciencemag.org/news/2019/04/new-climate-models-predict-warming-surge

It’s nice to learn that the IPCC is considering using observations to constrain model projections.

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GHCN v3.3 vs v4 Anomaly North America

Reblogged from Musings from the Chiefio:

GHCN v3.3 vs v4 Anomaly North America

In prior postings I did a sample of various countries around the world, and a full set of South America and Antarctica. This extends that set with North America.

I’m goiing to group things into North Continental (USA, Canada, Greenland), Central (Mexico and Central American countries) and Carib, those islands in the Caribbean Sea. Why? Because countries in those areas ought to look a lot more like each other in terms of Anomaly than like those in other groups. The Caribbean is dominated by water and tropical conditions. Central America and Mexico are about the same but with a bit less water influence. The USA, Canada and to some extent Greenland are large land masses prone to cold winters and significantly further north (so more summer / winter sun changes). This also lets me group making the graphs into smaller work units and it is less oppressive 😉

Here’s the Koppen Climate graph for North America so you have something for comparison.

Koppen Climate Zones for North America

From that I think you can see why I’d put Cuba into the Caribbean rather than count it in South America… like GHCN did…

The Graphs

Northern Big 3 (Canada, USA, Greenland)

Greenland

GHCN v3.3 vs v4 Greenland Difference

So about 1 C range of changes both ways. So an error band of about 1 C? Is that what this means? So we can’t know if there is 1/2 C of warming… The the actual anomalies below bounce around by a 3 C range (that might have a flier off the graph – I didn’t check). Looks to me like Greenland data is just chaotic weather. I do note that the really big DIP happens right on the “baseline period” used by GISStemp (1950-1980) or Hadley (1960-1990)…

GHCN v3.3 vs v4 Greenland Anomaly

Canada

GHCN v3.3 vs v4 Canada Difference

Interesting that it’s a roll off to colder. The anomalies (below) look a mess, but without evident warming. More like the loss of some very low going extremes recently. (One wonders if the big freeze in the last couple of years will show up in future data?)

GHCN v3.3 vs v4 Canada Anomaly

USA

GHCN v3.3 vs v4 USA Anomaly Difference

Again with recent data cooled. Wonder if they were embarrassed by all the attention over the last few years. We also again see no real warming tops, only loss of cold excursions and a general narrowing of the range. Or perhaps being early to the party, they had already “cooked” the v2 data so no more needed here. /snark;

GHCN v3.3 vs v4 USA Anomaly

The Caribbean & Bermuda

These are all in one large shallow water basin with common weather. They ought to be nearly identical.

Antigua & Barbuda

Antigua has no data in GHCN v3.3:

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="AC";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.00 sec)

So all we get is the v4 anomaly graph:

GHCN v4 Antigua & Barbuda Anomaly

Bermuda

GHCN v3.3 vs v4 Bermuda Difference

Not a lot of change in the data. They seem to have cooled since 1980 (see graph below) and were hotter back around 1880 – 1860.

GHCN v3.3 vs v4 Bermuda Anomaly

The Bahamas

GHCN v3.3 vs v4 Bahamas Difference

About 1/2 of recent data warmed about 1/2 C, and a cool dip about 1940-1980; but now about the same as the mid 1800s. Cyclical changes?

GHCN v3.3 vs v4 Bahamas Anomaly

Barbados

GHCN v3.3 vs v4 Barbados Difference

Drop the past about 0.4 C and warm the recent about 1/4 C – Presto! a trend! Except that those pesky 1800s look about the same as now. Better use that cold snap from 1940 to 1980 as the “baseline period” and ony measure warming against it.

GHCN v3.3 vs v4 Barbados Anomaly

Cayman Islands

GHCN v3.3 vs v4 Cayman Islands Difference

Again with cooling the past a bit. But from 1990 to date is basically flat. Ought not the “warming from CO2” be more now and less in 1970?

GHCN v3.3 vs v4 Cayman Islands Anomaly

Dominica

Dominica has no data in v3.3, so all we get is the v4 anomaly graph.

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="DO";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.06 sec)

GHCN v4 Dominica Anomaly

Dominican Republic

GHCN v3.3 vs v4 Dominican Republic Difference

So they had a crazy high 2 C “warming” rate, chop 1 C out of it and get a more respectable 1 C rate. Or is there just 1.5 C of “random” in temperature measuring on islands?

GHCN v3.3 vs v4 Dominican Republic Anomaly

Grenada

GHCN v3.3 vs v4 Grenada Difference

Again with 2 C of warming, so chop a degree C out of some of it, yet the most recent data points (see below graph) are below normal. But that’s just weather, right?

GHCN v3.3 vs v4 Grenada Anomaly

Guadaloupe

GHCN v3.3 vs v4 Guadeloupe Difference

A whopping 5 C range of temperatures, but currently about the same as 1940. Natural variability or error band, one of the other, is way higher than that 1/2 C of Project Fear Warming.

GHCN v3.3 vs v4 Guadeloupe Anomaly

Haiti

GHCN v3.3 vs v4 Haiti Difference

Looks like nobody fooled with the temperatures between versions. Guess a major hurricane will stop that kind of thing for a while. Anomaly graph below is rather flat too. Had a rise in to 1960, then was ignored for about 20 years, and now the temperatures are like “1940 all over again”.

GHCN v3.3 vs v4 Haiti Anomaly

Jamaica

GHCN v3.3 vs v4 Jamaica Difference

Push up the 40s (outside the baseline interval) a 1/2 C and drop the baseline spots about 1/2 C but with some noise in it, then lift the very recent by about 1/2 C. I think we’ve seen that before.

Per the anomaly graph below, Jamaica has warmed about 3 C. All that without setting regular record temperatures and with nearby islands not having the same rise. Airport tarmac anyone? Didn’t Jamaica have a big pop in tourism with the whole Bob Marley / Reggae thing? I know I went. Locals were complaining about added traffic, all the airplanes and hotels… I note in passing the hot late 1800s highs are about the same as now.

GHCN v3.3 vs v4 Jamaica Anomaly

Martinique

GHCN v3.3 vs v4 Martinique Difference

Differences look like about 1 C range of random. Anomaly trend looks like it was cold in the ’60s and recovered.

GHCN v3.3 vs v4 Martinique Anomaly

Netherlands Antilles

GHCN v3.3 vs v4 Netherlands Antilles Difference

A nice 1/4 C or so cooling of the past, and another 1/4 C to 1/2 C warming of the recent data, pretty soon you got yourself a trend. Except now is about the same as 1980.

GHCN v3.3 vs v4 Netherlands Antilles Anomaly

Puerto Rico

GHCN v3.3 vs v4 Puerto Rico Difference

Well that’s interesting. In v3.3 it was heating up by 1 C in Puerto Rico, so now the change is a 1 C cooling, and Puerto Rico is showing mostly flat trend in the red anomaly spots below. Almost like a decade of scrutiny and being pulled before congress might have caused some folks to fear being caught…

GHCN v3.3 vs v4 Puerto Rico Anomaly

St. Kits & Nevis

GHCN v3.3 vs v4 Saint Kits & Nevis Difference

Nothing goning on in St. Ktis & Nevis. Oddly, as all these islands are in the bathtub together, their anomaly graphs ought to all look alike. Yet they don’t…

GHCN v3.3 vs v4 Saint Kits & Nevis Anomaly

St. Lucia

Saint Lucia has no data in either version, so no graphs at all:

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="DO";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.06 sec)


MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom4 WHERE abrev="ST";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.31 sec)

Kinda makes you wonder why it is in the inventory at all.

St. Pierre & Miquelon

GHCN v3.3 vs v4 Saint Pierre & Miquelon Difference

Nice gentle almost unnoticed 1/4 C cooling of the past, a narrow “dip” in the baseline interval. All in all, nicely done sculpting.

GHCN v3.3 vs v4 Saint Pierre & Miquelon Anomaly

ST. Vincent & The Grenadines

Saint Vincent & The Grenadines have no data in version v3.3.

MariaDB [temps]> SELECT COUNT(deg_C) 
FROM anom3 AS A 
INNER JOIN country AS C on A.country=C.cnum 
WHERE C.abrev="VC";
+--------------+
| COUNT(deg_C) |
+--------------+
|            0 |
+--------------+
1 row in set (0.01 sec)

So all we get is this v4 anomaly graph:

GHCN v4 Saint Vincent & The Grenadines Anomaly

Trinidad & Tobago

GHCN v4 Trinidad & Tobago Difference

WOW, that 2 C of change down in the “baseline interval” is about the same as the dip in the anomaly graph then… Without that 2 C of down, Trinidad & Tobago would look sort of flat; with “now” temps about the same as the 1920-1930 temps.

GHCN v4 Trinidad & Tobago Anomaly

Virgin Islands (US)

One wonders what happened to the British Virgin Islands in terms of temperatures… but moving on…

GHCN v4 U.S. Virgin islands Difference

Sure doesn’t look like warming to me. Given some islands flat, and some with 3 C of warming, I think we’re looking at land use issues, thermometer location, or error bands / data fudging.

GHCN v4 U.S. Virgin islands Anomaly

Mexico & Central America

Mexico

GHCN v3.3 vs v4 Mexico Difference

Gee, that recent rise at the tail of the anomaly data (below) seems to match in shape the rise in the difference graph (above).

GHCN v3.3 vs v4 Mexico Anomaly

Belize

GHCN v3.3 vs v4 Belize Difference

The actual anomaly data (below) isn’t warming on the top end, though we do see the loss of low going anomalies in the recent data. As though tons of concrete at the airport held heat over night… The change graph isn’t doing much either, though around 1980 got a spike. Sleepy little Belize…

GHCN v3.3 vs v4 Belize Anomaly

Guatemala

GHCN v3.3 vs v4 Guatemala Difference

Not really warming though we lose some of the low going anomalies in recent data (see graph below) and the changes between v3.3 and v4 are not much. Looks like nobody is paying attention to Guatemala.

GHCN v3.3 vs v4 Guatemala Anomaly

El Salvador

GHCN v3.3 vs v4 El Salvador Difference

Looking at the anomaly plot below, there’s a nice “dip” in the baseline interval, and a gap about 1990-2005, but then some crazy changes in the above difference graph. Looks to me like +/- 1 C of error band and nobody really knowing just what the temperature is in tenths of C.

GHCN v3.3 vs v4 El Salvador Anomaly

Honduras

GHCN v3.3 vs v4 Honduras Difference

Already has a nice “dip” in the baseline interval, but those changes in the recent time are just wild.

GHCN v3.3 vs v4 Honduras Anomaly

Nicaragua

GHCN v3.3 vs v4 Nicaragua Difference

We have 1/2 C drop added to the baseline, and 1 C of rise added in recent data. Just about the same as the “warming” found. Looks like someone found a way to get Nicaragua data in line with goals.

GHCN v3.3 vs v4 Nicaragua Anomaly

Coasta Rica

GHCN v3.3 vs v4 Costa Rica Difference

Nice dip in the baseline interval, but what’s this with the cooing at the end? Costa Rica just not getting hot? And right next to Nicaragua too. Ought to be nearly identical, but isn’t.

GHCN v3.3 vs v4 Costa Rica Anomaly

Panama

GHCN v3.3 vs v4 Panama Difference

Gee.. Looks to me like Panama is being flat to slightly cooling (see below) and not much change at all between v3.3 and v4. I wonder if this is at a US Military Base and they are grumpy if you play with their data? I wonder if that’s why it cuts off in 1980… Seems to me someone ought to know the temperature in Panama right now.

GHCN v3.3 vs v4 Panama Anomaly

Tech Talk

The specifics on the report / graph making programs is in the first “by country” posting so will not be repeated here.

This SQL produced the list of countries in North America (Region 4):

SELECT cnum, abrev,region, cname 
FROM country WHERE region=4 ORDER BY cname;

Here’s the list:

MariaDB [temps]> source bin/Namerica.sql
+------+-------+--------+------------------------------------+
| cnum | abrev | region | cname                              |
+------+-------+--------+------------------------------------+
| 426  | AC    | 4      | Antigua and Barbuda                |
| 423  | BF    | 4      | Bahamas, The                       |
| 401  | BB    | 4      | Barbados                           |
| 402  | BH    | 4      | Belize                             |
| 427  | BD    | 4      | Bermuda [United Kingdom]           |
| 403  | CA    | 4      | Canada                             |
| 429  | CJ    | 4      | Cayman Islands [United Kingdom]    |
| 405  | CS    | 4      | Costa Rica                         |
| 430  | DO    | 4      | Dominica                           |
| 407  | DR    | 4      | Dominican Republic                 |
| 408  | ES    | 4      | El Salvador                        |
| 431  | GL    | 4      | Greenland [Denmark]                |
| 409  | GJ    | 4      | Grenada                            |
| 432  | GP    | 4      | Guadeloupe [France]                |
| 410  | GT    | 4      | Guatemala                          |
| 411  | HA    | 4      | Haiti                              |
| 412  | HO    | 4      | Honduras                           |
| 413  | JM    | 4      | Jamaica                            |
| 433  | MB    | 4      | Martinique [France]                |
| 414  | MX    | 4      | Mexico                             |
| 434  | NT    | 4      | Netherlands Antilles [Netherlands] |
| 415  | NU    | 4      | Nicaragua                          |
| 416  | PM    | 4      | Panama                             |
| 435  | RQ    | 4      | Puerto Rico [United States]        |
| 417  | SC    | 4      | Saint Kitts and Nevis              |
| 436  | ST    | 4      | Saint Lucia                        |
| 438  | SB    | 4      | Saint Pierre and Miquelon [France] |
| 437  | VC    | 4      | Saint Vincent and the Grenadines   |
| 424  | TD    | 4      | Trinidad and Tobago                |
| 425  | US    | 4      | United States                      |
| 440  | VQ    | 4      | Virgin Islands [United States]     |
+------+-------+--------+------------------------------------+
31 rows in set (0.00 sec)

In Conclusion

So there you have it. A few days of “seat time” at the computer. Hopefully it is of use to someone. I think it does point out what country’s data needs more scrutiny. Then there’s also the question of why trends in some nations are different from another very nearby and in the same body of water. Furthermore, there’s a great “cross check” for this in that there is surface water temperature data from decades of hurricane tracking. On these small islands, air temperature never strays far from water temperature. I was swimming in Jamaica once when rain started. The ocean, air, and rain all at 86 F.

It does look like there was a cyclical “dip” after the hot 1930s-40s into a cool ’50s-70s and we have newspaper and magazine articles from then shouting about a New Little Ice Age (and I personally remember it – IT Happened.) So despite the folks saying that picking it for a “baseline” doesn’t matter, I think it’s just too convenient. The method I used to calculate anomaly has no baseline. A given thermometer is only compared to itself over a selected month across the years. (So, for example, the Jamaica Airport thermometer would have all of its Januaries averaged then the January anomaly computed against that. Repeat for each other month of the year for each instrument.) Then there is just the shortness of most records. Many just start in that cold period and rise out of it, not having an old 1870 hot sample to see in their past.

Finally, with that much change showing up in some very small countries, you know they didn’t have a dozen thermometers to choose from in 1890. It must be some kind of “intervention” when decades of data all move by the same amount. It just screams “Fudging the data” (though I’m sure they would call it fixing errors in the past). But when all the “warming” comes from the “fixing up” and “adjusting” (even of this “unadjusted” data) or from splicing a 1/2 cycle and calling it a trend: Just where is the room for CO2?

It took me a few days of “seat time” to make this set and post it, so don’t worry if you just want to look at a few each day over time. After the first dozen even my eyes started to glaze over 😉 But these will be here for months or years to come, for your pondering.

The noble corruption of climate science

Reblogged from Fabius Maximus Website:

Science & Nature

Summary:  This is a story of climate science, tracing from its enthusiastic beginnings as small field – warning of a global threat –to its rich and increasingly desperate present. It is a long story, with a climax at the end.

Sign of "Corruption above" - dreamstime_105297867
ID 105297867 © Adonis1969 | Dreamstime.

The climate change campaign hits a dead end

On 24 June 1988, James Hansen’s testimony to the Senate began the campaign to fight anthropogenic global warming. During the following 31 years we have heard increasingly dire forecasts of doom. Some describe the distant future, beyond any reasonable forecasting horizon (due to both technical and social uncertainties). Some describe the near future. Many attribute almost all current extreme weather to our emissions of greenhouse gases (GHG) – using impossible to validate methods.

Karl Popper said that successful predictions, especially of the unexpected, were the gold standard of science (see here). That is a problem for climate activists. The Earth has been warming since the mid-19th century, when the Little Ice Age ended. The rate of warming in the past four decades (since 1977) is roughly the same as that during the four decades up to 1945. Anthropogenic GHG became a major factor only after WWII. So warming has occurred as predicted, but a naive forecast (without considering GHG) would have also predicted warming. There are explanations for this, but it makes model validation difficult (perhaps why it is seldom attempted: see links in section f in the For More Info section of this post).

Worse, the weather has not cooperated. Major hurricanes avoided America for 11 years, ending in 2017. Warming slowed during what climate scientists called the “pause” or “hiatus” (see links about its causes). And most forms of extreme weather have no obvious increasing trend. So surveys show little public support in America for expensive measures to fight climate change.

Activists grow desperate.

The Uninhabitable Earth” by David Wallace-Wells in New York Magazine
“Famine, economic collapse, a sun that cooks us: what climate change could wreak
– sooner than you think.”
Expanded into a book: The Uninhabitable Earth: Life After Warming.

The five ways the human race could be WIPED OUT because of global warming.”
By Rod Ardehali at the Daily Mail. H/t to the daily links at Naked Capitalism.
Promo for Falter: Has the Human Game Begun to Play Itself Out?, a book by Bill McKibben.

Activists responded to the uncooperative weather by making ever-more dire predictions (many of which have passed their due date and been proven false).  All extreme weather was “climate change.” They made more vivid propaganda (e.g., the 10:10 video, showing a teacher exploding the heads of students who do not accept her propaganda). They increased the volume of their claims, with more 2-minute hate sessions for dissenters (with lies about even eminent climate scientists). The long-term effects of this are (hopefully) small, since these fear barrages have been the Left’s go-to tactic since the 1960s (see some classics of the genre).

But one tactic might have awful long-term consequences. Many activists are climate scientists (see the many stories about depression among them, overcome by fears about their worst-case scenarios, such as this and this). Some have reacted with noble lie corruption (from Plato’s The Republic). However well-intended, it might weaken the public’s trust in science (as might the replication crisis, of which this is an example, if they learn about it).

Broken stone with "Trust" carved in it.
ID 37813605 © Lane Erickson | Dreamstime.

The Noble Lie in action

Obvious evidence of this is climate scientists’ relentless focus on RCP8.5, the worst-case scenario in the IPCC’s Fifth Assessment Report. As a good worst-case should be, it is almost impossible to happen without unlikely assumptions (details here; also see Dr. Curry’s articles). Yet it receives the majority of mentions in the climate science literature – usually with no mention of its improbable nature (see this history). Activists exaggerate these papers, whose stories are uncritically reported by journalists. A decade of this bombardment has a fraction of the Left terrified, certain that we are doomed.

For a recent example, see “A glacier the size of Florida is on track to change the course of human civilization” by “Pakalolo” at the Daily Kos. Widely reposted, quite bonkers. See the details here.

Worse, climate scientists remain silent when activists exaggerate their work, even when they materially misrepresenting it. The most extreme doomster predictions are greeted by silence. Even over-top climate doomster claims receive only mild push-back. For example, see the reactions to “The Uninhabitable Earth” by David Wallace-Wells. WaPo: “Scientists challenge magazine story about ‘uninhabitable Earth’.” Climate Feedback: “Scientists explain what New York Magazine article on “The Uninhabitable Earth” gets wrong.” It was too much even for Michael Mann.

Yet leading climate scientists are quick to loudly condemn skeptics – even fellow climate scientists – for questioning aggressive claims about climate change. Allowing activists to call scientists “deniers” for challenging the current paradigm is imo among the most irresponsible actions by leaders of science, ever. By ancient law, silence means assent to activists’ behavior. They are guilty of “aiding and abetting.” For more about this, see About the corruption of climate science.

But in the past few years, activist scientists’ desperation appears to have pushed them to take another step away from science.

Papers to generate alarmist news!

As Marc Morano of Climate Depot says, recent studies often appear designed to produce media stories for alarmists. I see several of these every week. The most recent is “Key indicators of Arctic climate change: 1971–2017” in Environmental Research Letters (April 2019), by scientists at the International Arctic Research Center at the University of Alaska-Fairbanks and the Geological Survey of Denmark and Greenland in Copenhagen. Abstract:

“Key observational indicators of climate change in the Arctic, most spanning a 47 year period (1971–2017) demonstrate fundamental changes among nine key elements of the Arctic system. …Downward trends continue in sea ice thickness (and extent) and spring snow cover extent and duration, while near-surface permafrost continues to warm. Several of the climate indicators exhibit a significant statistical correlation with air temperature or precipitation, reinforcing the notion that increasing air temperatures and precipitation are drivers of major changes in various components of the Arctic system. …

“The Arctic biophysical system is now clearly trending away from its 20th Century state and into an unprecedented state, with implications not only within but beyond the Arctic. The indicator time series of this study are freely downloadable at AMAP.no.”

Ecowatch describes it in their usual apocalyptic fashion: “Researchers Warn Arctic Has Entered ‘Unprecedented State’ That Threatens Global Climate Stability.

The paper is odd in several ways. It is evidence showing the broken peer-review process. Five times they describe conditions in the arctic as “unprecedented.” But they start their analysis with data from the 1970’s. Given the various kinds of long-term natural fluctuations, five decades of data is too brief a period to draw such a bold conclusion.

The authors neglect to mention that the Arctic was also warm in the 1930’s. Which is strange since one of the authors, Uma S. Bhatt, was also a co-author of a major paper on the subject: “Variability and Trends of Air Temperature and Pressure in the Maritime Arctic, 1875–2000” in the Journal of Climate, June 2003. She did not even cite it in their new paper. Abstract …

“Arctic atmospheric variability during the industrial era (1875–2000) is assessed using spatially averaged surface air temperature (SAT) and sea level pressure (SLP) records. Air temperature and pressure display strong multidecadal variability on timescales of 50–80 yr [termed low-frequency oscillation (LFO)]. Associated with this variability, the Arctic SAT record shows two maxima: in the 1930s–40s and in recent decades, with two colder periods in between.

“In contrast to the global and hemispheric temperature, the maritime Arctic temperature was higher in the late 1930s through the early 1940s than in the 1990s. …Thus, the large-amplitude multidecadal climate variability impacting the maritime Arctic may confound the detection of the true underlying climate trend over the past century. LFO-modulated trends for short records are not indicative of the long-term behavior of the Arctic climate system.

“The accelerated warming and a shift of the atmospheric pressure pattern from anticyclonic to cyclonic in recent decades can be attributed to a positive LFO phase. It is speculated that this LFO-driven shift was crucial to the recent reduction in Arctic ice cover. Joint examination of air temperature and pressure records suggests that peaks in temperature associated with the LFO follow pressure minima after 5–15 yr. Elucidating the mechanisms behind this relationship will be critical to understanding the complex nature of low-frequency variability.”

Starting their analysis in the 1970s is misleading without disclosing that was a cold spell. There was concern then about global cooling (but not a consensus). See here and here for details. Starting in the 1970’s makes current conditions look extraordinary. Since we are in the warming period following the Little Ice Age, robust comparisons should include previous warm periods, such as the Medieval Warm Period and the Holocene climatic optimum.

A later paper provides more detail, showing the temperature anomaly in 2008 was aprox. 1°C warmer than the ~1940 peak: “Role of Polar Amplification in Long-Term Surface Air Temperature Variations and Modern Arctic Warming” by Roman V. Bekryaev et al. in Journal of Climate, 15 July 2010. Is that a one standard deviation from the long-term mean? Three? Are temperatures a normal distribution? They do not say. Climate science papers often use arcane statistics, but usually ignore the basics. (Here is an as yet unpublished estimate of arctic sea ice back to the 1880s. Here is a 2017 paper with arctic temperatures and sea ice extent back to 1900)

Two comments from climate scientists on this paper.

“It is normalization of data cherry picking.”
— Dr. Judith Curry (bio). She her analysis of arctic sea ice trends here and here. She writes at Climate Etc.

“Of course, if these changes are predominantly due to the Arctic Oscillation (AO) and/or the LFO, we should see a reversal. If not, the trend would continue. Time will eventually sort this out. But a proper literature summary should still be provided with papers that might disagree with the theme of a newer paper. All peer-reviewed perspectives on this subject should be given.”
— Dr. Roger Pielke Sr. (bio).

See other examples in the comments. These kind of stories are coming along like trolleys.

This is a follow-up to About the corruption of climate science.

Bleeding eye
“Bleeding Eye” by C. Bayraktaroglu.

Conclusions

Science has been politicized, distorting its results, before. It will be again. But climate science provides essential insights on several major public policy issues. Losing reliable guidance from it could have disastrous consequences. Worse, the high public profile of climate science means that a loss of public confidence in it might affect science as a whole.

Let’s hope that the leaders of climate science come to their senses soon, despite their personal, institutional, and ideological reasons to continue on this dark path.

For More Information

Hat tip on the ERL 2019 paper to Naked Capitalism’s daily links, who uncritically run climate alarmist articles, a one-side flow of information without context – terrifying their Leftist readers (other than that, their daily links are a valuable resource – which read every morning). Hat tip on the JoC 2003 paper to Marc Morano at Climate Depot; see his article about it.

Ideas! For some shopping ideas, see my recommended books and films at Amazon.

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GHCN v3.3 vs v4 Anomaly South America

Reblogged from Musings from the Chiefio:

Earlier I looked at a subset of countries globally, comparing their “change of temperature from the average” (anomaly) for a given set of thermometers in a given country as it is found in the Global Historical Climate Network version 3.3 when compared to version 4. Now you might think that since this is “historical” it ought not change. And since it is based on “anomalies”, if you have a couple of different thermometers in the set (as the Warmers constantly insist) it would not cause a change. (After all, if it’s 1 C warmer in your front yard it’s also 1 C warmer in your back yard…) So you would expect that there ought to either be NO change, or any change will be due to the application of changed “adjustments” to the temperatures (that just happen to exactly match ALL global warming found…) But these are the “unadjusted” data sets, so ought not to have any of that.

Yet they are different. Often for what ought to be the SAME thermometer in the same time and place in history. (You can’t go back to 1850 and add a new thermometer in Cuba…)

I’ll be presenting two graphs for each country. One has black spots for the Anomaly in a given year for GHCN v3.3 in that country, and red spots for what is the same country, year, and anomaly process but from GHCN v4. Often they are different (almost always). Sometimes up to whole degrees C. Now if your thermometer selection and processing can change THE SAME PLACE AND TIME in history by 1 C, what are the odds that 1/2 C of “global warming” comes from just that sort of instrument change? I’d rank it at about 100%.

For reference, here’s the climate zones from the Wiki on South America

South America Koppen – Geiger Climate Zones

The Graphs

Since we saw Argentina and Brazil in the earlier posting, I’ll Start with Brazil, then add the countries near it to the north and away from the Andes. Then we’ll travel down the spine of the Andes ending with Chile and Argentina, then finally fill in Paraguay and Uruguay on the south side of Brazil up against Argentina. Ending with the two island clusters of the Falkland islands and South Georgia & Sandwich Islands.

Brazil

GHCN v3.3 vs v4 Brazil Difference

GHCN v3.3 vs v4 Brazil Anomaly

Cuba

Cuba is included in South America, but personally I’d have accounted for it in with all the other Caribbean islands.

GHCN v3.3 vs v4 Cuba Difference

Mostly the old data is “cooled” and the recent data given a bit of a “lift”. Looking at the raw anomalies below, it looks like Cuba has some cycles in it, and like it was “way hot” in the long ago past.

GHCN v3.3 vs v4 Cuba Anomaly

Venezuela

GHCN v3.3 vs v4 Venezuela Difference

The past cooled by 1/4 to 3/4 C in a nice general slope. Has the past of Venezuela really cooled?…

GHCN v3.3 vs v4 Venezuela Anomaly

French Guiana

GHCN v3.3 vs v4 French Guiana Difference

Looking at the anomalies down below, not much to work with. So we get that tail in the present being changed a lot higher. But hey, what’s a full degree C of “fixing it up” anomaly change when you need to get a global 1/2 C of “warming’ out of stable actual data… But really, what a “dogs breakfast” that is. A “dip” of 1/2 C in the “baseline period” and then an added almost a full C in the most recent common data? What can possibly justify that? Remember, this is supposed to be the same place same times.. and many of the same instruments if not all of them the same.

GHCN v3.3 vs v4 French Guiana Anomaly

Guyana

GHCN v3.3 vs v4 Guyana Difference

Oh wow. A full degree C of “dip” in the baseline near 1980, then a 2 c “FLYER” negative before 2000, then up to 1 C of “uplift” in the recent tail. Sheesh.

GHCN v3.3 vs v4 Guyana Anomaly

Suriname

GHCN v3.3 vs v4 Suriname Difference

1.5 C of “rise” added recently. Really? WT?… Nice 1/2 C “dip” in the tail of the baseline period.

GHCN v3.3 vs v4 Suriname Anomaly

Colombia

GHCN v3.3 vs v4 Colombia Difference

Then we hit Colombia and it’s just not happening. Looks rather flat and dull. Guess they were too busy with the cocaine trade to give a fig about the UN Climate Graft money… Or maybe CO2 got kinda high and forgot to warm things up… Well, someone got high…

GHCN v3.3 vs v4 Colombia Anomaly

Ecuador

GHCN v3.3 vs v4 Ecuador Difference

Half a degree down in the baseline, to 3/4 C down, then 1/2 C up recently in the “fixing”. Now that’s someone on board with the agenda!

GHCN v3.3 vs v4 Ecuador Anomaly

Peru

GHCN v3.3 vs v4 Peru Difference

Actual anomalies (below) not going anywhere… Short record. What to do, what to do… How about dip it 0.4 C in the baseline and lift the near end another 1/2 C?

GHCN v3.3 vs v4 Peru Anomaly

Bolivia

GHCN v3.3 vs v4 Bolivia Difference

Highly volatile (see below) and not much trend, then the “fix” being all over the place. What a mess. On this we bet the global economy?

GHCN v3.3 vs v4 Bolivia Anomaly

Chile

GHCN v3.3 vs v4 Chile Difference

Not much really happening below, so what’s our “Go To” thing? Dip the baseilne around the ’50s and bump up the present by 1/4 C to 1/2 C.

GHCN v3.3 vs v4 Chile Anomaly

Argentina

GHCN v3.3 vs v4 Argentina Difference

GHCN v3.3 vs v4 Argentina Anomaly

Paraguay

GHCN v3.3 vs v4 Paraguay Difference

Just WOW. Drop the WHOLE past by 1/4 C, then pop up 1/5 C to a full 1.5 C in the recent data. Just WOW.

GHCN v3.3 vs v4 Paraguay Anomaly

Uruguay

Guess Uruguay is not all that interesting. Not a team players. Only gets about .4 C of dip at the very end of the “baseline period” and can’t get more than 1/4 C of “lift” in the recent data.

Then we once again leave the mainland for two groups of Islands in the Southern Ocean.

The Falkland Islands

GHCN v3.3 vs v4 Falkland Islands Difference

Nothing really happening in the Falklands. (In more ways than one). Gee, think a stable station in the middle of the south Atlantic Gyre might mean not much is happening? (Someone will need to fix that in v5… I’d /sarc; it but I’m not sure that’s valid…)

GHCN v3.3 vs v4 Falkland Islands Anomaly

South Georgia and Sandwich Island

GHCN v3.3 vs v4 South Georgia & Sandwich Islands Difference

This one is interesting. Some “High Fliers” in years where the prior data set had no data. How’d they do that? Go back and put data in where none was reported? Overall, another flat island in the ocean. But with mystery fliers. Though they did manage to cool almost the entire history by about 1/3 C, so there’s that…

GHCN v3.3 vs v4 South Georgia & Sandwich Islands Anomaly

Tech Talk

This would basically be a repeat of the tech stuff in the prior posting, so take a look there for example code and the hows / whys / and designs.

https://chiefio.wordpress.com/2019/04/09/ghcn-v3-3-vs-v4-selected-country-anomaly-differences

In Conclusion

I’ve scattered some detail comments through the graphs and as I get time to stare at them a bit, if I see something else I’ll add it in comments. You may well see something I’ve not seen, so stare at ’em and ponder…

In general, I’ve noticed some places hardly change at all. Often very minor places like an island somewhere. Larger places look more “manicured” with loss of low going excursions in the data lately. Then there’s the general tendency to cool the past, and put “dips” in the “baseline” period used by GISStemp and Hadley (1950 to 1990). Is it really the case that all those places had just those same needs to cool the past, dip the baseline and juice up the recent highs while clipping recent lows? What physicality could possible account for that? What systematic failure of thermometer tech Globally can account for those “errors”?

To me it looks like deliberately cooking the books.

In Aftermath of Volcanic Eruption, Photosynthesis Waxes, Carbon Dioxide Wanes

From Scientific American:

By Laura Wright on March 28, 2003

 

Read more from this special report:

A Guide to Volcanoes

In June 1991, when Mt. Pinatubo in the Philippines spewed tons of volcanic ash and gases into the atmosphere, it just so happened that halfway around the world scientists were beginning to obtain good data from carbon dioxide monitors high above the tree canopy in Harvard Forest, outside Boston, Mass. Now, more than a decade later, the measurements taken during the years following the eruption are providing new insight into how atmospheric aerosols affect photosynthesis. The findings, published today in the journal Science, are forcing scientists to rethink the factors that influence the cycling of carbon through the environment, particularly carbon dioxide, a major player in global warming.

 

Within three weeks of the Mt. Pinatubo eruption, the largest volcanic blast of the century, a band of sulfur aerosol had encircled the globe. By early 1992, the volcanic gases and aerosols had diffused through the stratosphere, veiling the earth. During that time, global carbon dioxide levels fell more sharply than any other decline on record. Some scientists suggested that global cooling caused ecosystem respiration to drop, lowering the amount of carbon dioxide emitted into the atmosphere. But Lianhong Gu of Oak Ridge National Laboratory, lead author of the Science report, didn’t think that could be the only explanation.

Gu knew that crop scientists had discovered that plants grow best in diffuse light. When sunlight is too intense, some leaves fall into shadow, unable to photosynthesize, while others bask in the direct beams but will reach a photosynthetic saturation point. Moderate cloud cover and aerosols block direct beams, but allow light to bounce back and forth off water vapor and other molecules, creating a “softer” light that reaches leaves that would otherwise be shaded. As a result, the plants photosynthesize more, using up carbon dioxide in the process. Gu and his collaborators determined that the same principles apply to forest canopies. The Harvard Forest data show that carbon dioxide levels dropped for two years following the eruption at Mt. Pinatubo findings that the scientists suggest represent a worldwide phenomenon given that the eruption had a global atmospheric effect. “Up until now we hadn’t linked aerosols and clouds with carbon studies,” Gu says. “In order to understand atmospheric carbon dioxide concentrations, which affect climate, we have to look at how aerosols and clouds affect the global carbon cycle.”

Predicting heat waves? Look half a world away

Reblogged from Watts Up With That:

[HiFast Note:  This study identifies the Madden-Julian Oscillation (MJO) and correlates one of its phases to California heat waves.  Nothing really new here.  Joe Bastardi has been talking about the MJO for many years.]

charles the moderator /

When thunderstorms brew over the tropics, California heat wave soon to follow

University of California – Davis

An orchard of young trees withstands drought in California's Central Valley in 2014. The ability to predict heat waves in the Central Valley could help better prepare and protect crops and people from the impacts. Credit UC Davis

An orchard of young trees withstands drought in California’s Central Valley in 2014. The ability to predict heat waves in the Central Valley could help better prepare and protect crops and people from the impacts. Credit UC Davis

When heavy rain falls over the Indian Ocean and Southeast Asia and the eastern Pacific Ocean, it is a good indicator that temperatures in central California will reach 100°F in four to 16 days, according to a collaborative research team from the University of California, Davis, and the Asia-Pacific Economic Cooperation (APEC) Climate Center in Busan, South Korea.

The results were published in Advances in Atmospheric Sciences on April 12.

FROM PREDICTION TO PROTECTION

Heat waves are common in the Central California Valley, a 50-mile-wide oval of land that runs 450 miles from just north of Los Angeles up to Redding. The valley is home to half of the nation’s tree fruit and nut crops, as well as extensive dairy production, and heat waves can wreak havoc on agricultural production. The dairy industry had a heat wave-induced economic loss of about $1 billion in 2006, for instance. The ability to predict heat waves and understand what causes them could inform protective measures against damage.

“We want to know more about how extreme events are created,” said Richard Grotjahn, corresponding author on the paper and professor in the UC Davis Department of Land, Air and Water Resources. “We know that such patterns in winter are sometimes linked with areas of the tropics where thunderstorms are enhanced. We wondered if there might be similar links during summer for those heat waves.”

The scientists analyzed the heat wave data from June through September from 1979 to 2010. The data were collected by 15 National Climatic Data Centers stations located throughout the Valley. From these data, the researchers identified 24 heat waves. They compared these instances to the phases of a large, traveling atmospheric circulation pattern called the Madden-Julian Oscillation, or MJO.

The MJO manifests as heavy rain that migrates across the tropical Indian and then Pacific Oceans, and researchers have shown that it influences winter weather patterns.

TROPICAL RAINFALL AND CALIFORNIA

“It’s well known that tropical rainfall, such as the MJO, has effects beyond the tropics,” said Yun-Young Lee of the APEC Climate Center in Busan, South Korea, the paper’s first author. “So a question comes to mind: Is hot weather in the Central California Valley partly attributable to tropical rainfall?”

Lee and Grotjahn found that, yes, enhanced rainfall in the tropics preceded each heat wave in specific and relatively predictable patterns. They also found that hot weather in the valley is most common after more intense MJO activity in the eastern Pacific Ocean, and next most common after strong MJO activity in the Indian Ocean.

“The more we know about such associations to large-scale weather patterns and remote links, the better we can assess climate model simulations and therefore better assess simulations of future climate scenarios,” Grotjahn said.

###

This work was supported by the National Science Foundation, the National Aeronautics and Space Administration, the Department of Energy Office of Science, the United States Department of Agriculture’s National Institute of Food and Agriculture, and the APEC Climate Center in the Republic of Korea.

Warmists Epic History Fail

Science Matters

Geologist Gregory Whitestone provides a climate history lesson for warmists who skipped history classes protesting against global warming.  Hist article at Town Hall is Ocasio-Cortez’s Climatology Lacks Historical Context. Excerpts in italics with my bolds. H/T Climate Depot.

When Sam Cooke sang “Don’t know much about history” in 1960 he could not have had U.S. Rep. Alexandria Ocasio-Cortez in mind, but only because she lives a half century later.

Whatever Ocasio-Cortez got from history classes during her time at Boston University, it wasn’t an appreciation of historical context because it is sorely lacking in her assertions about climate and its effect on humankind. She and others promoting the Green New Deal have the facts exactly backwardswhen they claim that warming temperatures are an existential threat to humanity.

Ocasio-Cortez recently warned in a House Oversight Committee hearing that the United States would have “blood on our hands” if legislation to…

View original post 850 more words

Inconvenient stumps

Reblogged from Watts Up With That:

Climate alarmists tell us that the Earth has never been warmer, and that we can tell by looking at tree rings, treelines, and other proxy indicators of climate.

Climate scientists claim the warmth is unprecedented.

We’ve been told it is warming so fast, we have only 12 years left!

Yet nature seems to not be paying attention to such pronouncements, as this discovery shows.

This photo shows a tree stump of White Spruce that was radiocarbon dated at 5000 years old. It was located 100 km north of the current tree line in extreme Northwest Canada.

The area is now frozen tundra, but it was once warm enough to support significant tree growth like this.

If climate was this warm in the past, how did that happen before we started using the fossil fuels that supposedly made our current climate unprecedentedly warm?

A Simple Model of the Atmospheric CO2 Budget

Reblogged from Dr. Roy Spencer:

April 11th, 2019 by Roy W. Spencer, Ph. D.

SUMMARY: A simple model of the CO2 concentration of the atmosphere is presented which fairly accurately reproduces the Mauna Loa observations 1959 through 2018. The model assumes the surface removes CO2 at a rate proportional to the excess of atmospheric CO2 above some equilibrium value. It is forced with estimates of yearly CO2 emissions since 1750, as well as El Nino and La Nina effects. The residual effects of major volcanic eruptions (not included in the model) are clearly seen. Two interesting finding are that (1) the natural equilibrium level of CO2 in the atmosphere inplied by the model is about 295 ppm, rather than 265 or 270 ppm as is often assumed, and (2) if CO2 emissions were stabilized and kept constant at 2018 levels, the atmospheric CO2 concentration would eventually stabilize at close to 500 ppm, even with continued emissions.

A recent e-mail discussion regarding sources of CO2 other than anthropogenic led me to revisit a simple model to explain the history of CO2 observations at Mauna Loa since 1959. My intent here isn’t to try to prove there is some natural source of CO2 causing the recent rise, as I think it is mostly anthropogenic. Instead, I’m trying to see how well a simple model can explain the rise in CO2, and what useful insight can be deduced from such a model.

The model uses the Boden et al. (2017) estimates of yearly anthropogenic CO2 production rates since 1750, updated through 2018. The model assumes that the rate at which CO2 is removed from the atmosphere is proportional to the atmospheric excess above some natural “equilibrium level” of CO2 concentration. A spreadsheet with the model is here.

Here’s the assumed yearly CO2 inputs into the model:

1
Fig. 1. Assumed yearly anthropogenic CO2 input into the model atmosphere.

I also added in the effects of El Nino and La Nina, which I calculate cause a 0.47 ppm yearly change in CO2 per unit Multivariate ENSO Index (MEI) value (May to April average). This helps to capture some of the wiggles in the Mauna Loa CO2 observations.

The resulting fit to the Mauna Loa data required an assumed “natural equilibrium” CO2 concentration of 295 ppm, which is higher than the usually assumed 265 or 270 ppm pre-industrial value:

2Fig. 2. Simple model of atmospheric CO2 concentration using Boden et al. (2017) estimates of yearly anthropogenic emissions, an El Nino/La Nina natural source/sink, after fitting of three model free parameters.

Click on the above plot and notice just how well even the little El Nino- and La Nina-induced changes are captured. I’ll address the role of volcanoes later.

The next figure shows the full model period since 1750, extended out to the year 2200:

3
Fig. 3. As in Fig. 2, but for the full model period, 1750-2200.

Interestingly, note that despite continued CO2 emissions, the atmospheric concentration stabilizes just short of 500 ppm. This is the direct result of the fact that the Mauna Loa observations support the assumption that the rate at which CO2 is removed from the atmosphere is directly proportional to the amount of “excess” CO2 in the atmosphere above a “natural equilibrium” level. As the CO2 content increases, the rate or removal increases until it matches the rate of anthropogenic input.

We can also examine the removal rate of CO2 as a fraction of the anthropogenic source. We have long known that only about half of what is emitted “shows up” in the atmosphere (which isn’t what’s really going on), and decades ago the IPCC assumed that the biosphere and ocean couldn’t keep removing excess CO2 at such a high rate. But, in fact, the fractional rate of removal has actually been increasing, not decreasing.And the simple model captures this:

4
Fig. 4. Rate of removal of atmospheric CO2 as a fraction of the anthropogenic source, in the model and observations.

The up-and-down variations in Fig. 4 are due to El Nino and La Nina events (and volcanoes, discussed next).

Finally, a plot of the difference between the model and Mauna Loa observations reveals the effects of volcanoes. After a major eruption, the amount of CO2 in the atmosphere is depressed, either because of a decrease in natural surface emissions or an increase in surface uptake of atmospheric CO2:

5
Fig. 5. Simple model of yearly CO2 concentrations minus Mauna Loa observations (ppm), revealing the effects of volcanoes which are not included in the model.

What is amazing to me is that a model with such simple but physically reasonable assumptions can so accurately reproduce the Mauna Loa record of CO2 concentrations. I’ll admit I am no expert in the global carbon cycle, but the Mauna Loa data seem to support the assumption that for global, yearly averages, the surface removes a net amount of CO2 from the atmosphere that is directly proportional to how high the CO2 concentration goes above 295 ppm. The biological and physical oceanographic reasons for this might be complex, but the net result seems to follow a simple relationship.

Javier on sunspot data

This comment by Javier [slightly edited for clarity] is reposted from Dr. Judith Curry’s Climate Etc.

The Modern Solar Maximum is clearly seen with a Gaussian smoothing of the sunspot data:

Or even better just by running a 70-year moving average through the 1750-2018 sunspot data:

Respected climatologists like Takuro Kobashi, Bo Vinther and Tom Blunier accept the existence of the Modern Solar Maximum, as they see its effects on climate:

Kobashi, T., Box, J.E., Vinther, B.M., Goto‐Azuma, K., Blunier, T., White, J.W.C., Nakaegawa, T. and Andresen, C.S., 2015. Modern solar maximum forced late twentieth century Greenland cooling. Geophysical Research Letters, 42 (14), pp.5992-5999.

The Modern Solar Maximum, a one in 600 years event that exactly coincides with Modern Global Warming is assigned a near-zero effect on climate by models. Not surprisingly, since it ended models performance has been abysmally poor.