Solar variability weakens the Walker cell

Tallbloke's Talkshop

Credit: PAR @ Wikipedia
This looks significant, pointing directly at solar influences on climate patterns. The researchers found evidence that atmosphere-ocean coupling can amplify the solar signal, having detected that wind anomalies could not be explained by radiative considerations alone.

An international team of researchers from United Kingdom, Denmark, and Germany has found robust evidence for signatures of the 11-year sunspot cycle in the tropical Pacific, reports Phys.org.

They analyzed historical time series of pressure, surface winds and precipitation with specific focus on the Walker Circulation—a vast system of atmospheric flow in the tropical Pacific region that affects patterns of tropical rainfall.

They have revealed that during periods of increased solar irradiance, the trade winds weaken and the Walker circulation shifts eastward.

View original post 249 more words

Advertisements

Severe Weather Due To——–Weather!

NOT A LOT OF PEOPLE KNOW THAT

By Paul Homewood

WOW!! A BBC video about severe weather, and no mention of climate change:

image

https://www.bbc.co.uk/weather/features/47725850

Off to the re-education camp for Darren Bett!

View original post

Superstition’s Fingerprint In Climate Science

Reblogged from RealClimateScience.com:

Our top climate scientists are blaming floods in Nebraska on global warming

Manmade greenhouse gases trap heat in the atmosphere, warming the oceans and making the air above them more humid, scientists said. When a storm picks up and eventually spits out that moisture, it can be devastating for people caught below.

“The atmosphere is pretty close to fully saturated, it’s got all the water it can take,” said Michael Wehner, a senior scientist at the Lawrence Berkeley National Laboratory.

Big storms like the bomb cyclone and Hurricane Harvey, which smacked Houston in 2017 with record downpours, are where the impact of climate change can most clearly be seen, he said, adding that climate change’s fingerprints were all over the recent storm.

“I don’t think it’s a starring role, but it’s a strong supporting role,” said Kevin Trenberth, a senior scientist at the U.S. National Center for Atmospheric Research, a federally-funded office, of climate change’s role in the Midwest floods.

He said the bomb cyclone was carrying vast amounts of moisture from the Pacific up to 1,500 miles (2,400 km) away.

The atmosphere is pretty close to fully saturated, it’s got all the water it can take,” said Michael Wehner, a senior scientist at the Lawrence Berkeley National Laboratory.

Climate change’s fingerprints are on U.S. Midwest floods: scientists | News | The Mighty 790 KFGO

It has been the coldest February/March on record in Nebraska so far. (Average temperatures will rise a little before the end of the month, and may move this year out of the coldest spot.)

The reason the atmosphere is saturated, is because of the cold air – which can hold less moisture. This is something most science students learn in high school, but apparently our top PhD climate scientists are unaware of it.

Sea surface temperatures are also mostly below normal west of the US.  The claims by the climate scientists have no basis in reality, which is standard practice for their profession.

anomnight.3.21.2019.gif (1174×640)

Nebraska has a long history of floods.

The 1935 Nebraska flood killed more than 100 people and was associated with the world record rainfalls in Texas and Colorado.

03 Jun 1935, Page 11 – Muncie Evening Press at Newspapers.com

03 Jun 1935, Page 1 – Great Falls Tribune at Newspapers.com

On May 31, 1935 Woodward Ranch, Texas set the world record with 22 inches of rain in less than three hours.

Colorado got nearly that much rain a few hours earlier.

Extreme Weather: A Guide & Record Book – Christopher C. Burt – Google Books

1940 Nebraska flood

05 Jun 1940, 1 – Fremont Tribune at Newspapers.com

1941 Nebraska flood.

10 Jun 1941, Page 1 – Lincoln Journal Star at Newspapers.com

1947 Nebraska flood

26 Jun 1947, 1 – Sioux City Journal at Newspapers.com

1950 Nebraska flood.

11 May 1950, 1 – The Columbus Telegram at Newspapers.com

1951 Nebraska flood

15 Jul 1951, Page 62 – The Lincoln Star at Newspapers.com

1962 Nebraska flood.

27 Mar 1962, 1 – Lincoln Journal Star at Newspapers.com

1963 Nebraska flood

27 Jun 1963, Page 10 – Las Vegas Daily Optic at Newspapers.com

1978 Nebraska flood

28 Mar 1978, 6 – The Lincoln Star at Newspapers.com

Similarly, the record floods of 1936 came after the coldest February on record in the US.

20 Mar 1936 – ALL EASTERN AMERICA UNDER FLOOD WATERS 

Climate science and journalism – all lies, all the time.

Hurricanes & climate change: recent U.S. landfalling hurricanes

BLUF:  6.6   Conclusions

Convincing detection and attribution of individual extreme weather events such as hurricanes requires:

  • a very long time series of high-quality observations of the extreme event
  • an understanding of the variability of extreme weather events associated with multi-decadal ocean oscillations, which requires at least a century of observations
  • climate models that accurately simulate both natural internal variability on timescales of years to centuries and the extreme weather events

Of the four hurricanes considered here, only the rainfall in Hurricane Harvey passes the detection test, given that it is an event unprecedented in the historical record for a continental U.S. landfalling hurricane. Arguments attributing the high levels of rainfall to near record ocean heat content in the western Gulf of Mexico are physically plausible. The extent to which the high value of ocean heat content in the western Gulf of Mexico can be attributed to manmade global warming is debated. Owing to the large interannual and decadal variability in the Gulf of Mexico (e.g. ENSO), it is not clear that a dominant contribution from manmade warming can be identified against the background internal climate variability (Chapter 4).

Climate Etc.

by Judith Curry

An assessment of whether any of the impacts of recent  U.S. landfalling hurricanes were exacerbated by global warming.

View original post 2,140 more words

There is no [statistically significant] snow cover trend due to global warming since 1972 in the Northern Hemisphere

Reblogged from Watts Up With That:

From the “alarmists and their cats are grumpy over this” department.

There’s been some recent hubbub over decreasing snowfall in the northern hemisphere by the usual suspects, who claim that AGW is reducing snow cover.

And then of course, there’s Dr. David Viner of CRU, who famously said in a story in the UK Independent titled: Snowfalls are now just a thing of the past:

However, the warming is so far manifesting itself more in winters which are less cold than in much hotter summers. According to Dr David Viner, a senior research scientist at the climatic research unit (CRU) of the University of East Anglia,within a few years winter snowfall will become “a very rare and exciting event”.

“Children just aren’t going to know what snow is,” he said.

It got disappeared from the Internet, but I saved a copy here: One of the longest running climate prediction blunders has disappeared from the Internet

That’s opinion, then there’s data, such as this data from the highly respected Rutgers Snow Lab, as plotted by climate scientists Ole Humlum.

No trend, period.

 

Don’t believe it? Plot it yourself: use this link to download the original data.

More here: http://www.climate4you.com/SnowCover.htm


Added: Willis Eschenbach writes in comments

Hmmm … I took you up on your invitation to plot it myself. Actually, there is a trend … in fact, there are two trends.

Here’s the first trend, starting in 1972 …

As you can see, there’s a slight negative trend overall, about a tenth of a million square km. per decade. However, an examination of the blue Gaussian average in the bottom panel shows a faster drop to 1990, then a slow rise to the present. So I took a closer look at the post 1990 data.

As you can see, since 1990 the snow area has been increasing at about a quarter million square km per decade.

Go figure …

w.


Steve Mosher writes in comments: (and I reply)

Jeez

You cant say there is NO trend or zero trend. FFS

Here

library(dplyr)
library(ggplot2)

nhURL <-“https://climate.rutgers.edu/snowcover/files/moncov.nhland.txt&quot;

N <- tbl_df(read.table(nhURL, header=F))
N % rename(Year=V1, Month=V2, Extent=V3) %>%
mutate(Extent=Extent/1000000)%>% dplyr::filter(Year > 1971)

A % group_by(Year) %>% summarise(Average=mean(Extent))

ggplot( A, aes(x=Year,y=Average))+geom_line()+geom_smooth(method=”lm”) +
ggtitle(“Global Snow Extent Annual Average”)+
labs(y= “Average Snow Extent (millions sq km”) +
scale_x_continuous(breaks=seq(min(A$Year),max(A$Year),2))+

theme( axis.text.x = element_text(angle=90, vjust=0.5, size=10))

Call:
lm(formula = Average ~ Year, data = A)

Residuals:
Min 1Q Median 3Q Max
-2.02403 -0.44104 0.03122 0.30382 1.91688

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.005641 15.978734 3.38 0.00151 **
Year -0.014501 0.008009 -1.81 0.07690 .

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7448 on 45 degrees of freedom
Multiple R-squared: 0.0679, Adjusted R-squared: 0.04718
F-statistic: 3.278 on 1 and 45 DF, p-value: 0.0769

  • Your p-value is 0.0769 or ~0.08

    A definition of p-values says:

    – A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis.

    – A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis.

    – p-values very close to the cutoff (0.05) are considered to be marginal (could go either way). Always report the p-value so your readers can draw their own conclusions.

    Source: https://www.dummies.com/education/math/statistics/what-a-p-value-tells-you-about-statistical-data/

    So at 0.08 no statistically significant trend. I’ve added to the title to reflect that.

[UPDATE] I thought I might add one more plot. Here are the trends of the Rutgers snow data by quarter.

As you can see, there is no significant trend in the winter data. Snow in the fall has increased, snow in the spring and summer have decreased.

What’s Natural? A Look at Wildfires

Reblogged from Watts Up With That:

Jim Steele writes: I am excited to announce my local weekly paper the Pacifica Tribune has added me as a columnist. Every 2 weeks I will post my column “What’s Natural”. The publisher has 5 other papers in the SF paper which might also carry the column. To publish a more skeptical and scientific opinion, while deep in the heart of this blue state is a bold move and reveals a commitment to objectivity and I am eager to see what kind of reaction it gets. Pacifica is just south of San Francisco. Next column will be a look at drought.

What’s Natural?

A Look at Wildfires

In early December I surveyed the horrific Camp Fire disaster in Paradise. Having been director for 25 years of a university field station located in the heart of the Tahoe National Forest, I’ve been a “student” of fire ecology for 30 years and wanted a closer look at why row after row of homes completely incinerated while surrounding trees were merely scorched, with leaves and needles browned but not burnt?

clip_image002

Large fires have recently ravaged about 1.8 million California acres a year, prompting media and politicians to proclaim a “new normal” that’s “evidence of global warming”. But UC Berkeley fire ecologists have calculated that before 1800, fires burned 4 million California acres each year (despite cooler temperatures). So what natural fire dynamics promote such extensive burning?

Wildfires have indeed increased since 1970, but that’s relative to previous decades of intensive fire prevention. As fire was recognized as a natural and necessary phenomenon for healthy ecosystems a new era began. In the 70s the US Forest Service moved away from extinguishing all fires by 10 AM the day after detection, switching to a “let it burn policy” if human structures were not endangered.

Paradise, unfortunately, sprung up amidst a forest dominated by Ponderosa pines. Largely due to frequent lightning strikes and dry summers, Ponderosa habitat endures fires about every 11 years. Fortunately for California’s coastal residents, lightning is rare. However, both regions are vulnerable to human ignitions, which start 85-95% of all fires. Recognizing this growing problem, a bipartisan bill was presented to Governor Brown two years ago to secure our power grid. Shockingly he vetoed it. That was a bad choice given the Camp Fire, Wine Country Fires and many more were sparked by an ageing electrical infrastructure. Recent studies show larger fires result from a confluence of human ignitions and high winds. But it is not just random coincidence. The high winds that spread these massive fires also blow down power lines that ignite those fires.

In 2008 the world’s foremost expert on fire history, Stephen Pyne lamented, “global warming has furnished political cover to encourage certain fire management decisions while allowing climate to take the blame.” How true. Both PGE and Governor Brown have blamed wildfires on climate change.

When you build a camp fire, you intuitively understand fire ecology basics. You do not hold a match to a log no matter how dry. You start a camp fire with kindling. Fire ecologists call forest kindling, like dead grass, leaves and small shrubs, “fine fuels”. In dry weather “fine fuels” become highly combustible in a matter of hours, or at most days, even during the winter. Furthermore, California’s summer climate is naturally dry for 3-4 months, creating highly combustible habitat each and every summer.

Additionally, camp fires only smolder without enough air, so we huff and puff to get a burst of flames. Likewise, high winds turn a spark into a major conflagration. It was strong winds that rapidly spread the Camp Fire. The fast-moving flames, feeding on “fine fuels” littering the forest floor, generated enough heat to ignite flammable homes that then burned from the inside out; but only enough heat to char the bark of most surrounding trees.

Miraculously spared buildings dotting a devastated landscape made the case for creating “defensible spaces” by managing the “fine fuels”. Surveying one unscathed church, the fire clearly came within 100 feet, scorching the base of every encircling tree. But due to a parking lot and a well-manicured lawn, the lack of “fine fuels” stopped the fire in its tracks. Trees on the lawn were not even charred. The public would benefit greatly if wildfire news stories emphasized the need to create adequate defensible spaces.

With high deserts to the east and the ocean to the west, California’s winds shift with the seasons. Land temperatures always change faster than the ocean’s. In the summer, warmer land surfaces draw in moist sea breezes. The resulting fog moistens coastal landscapes and reduces fire danger there. Thus, any warming, whether natural or CO2 driven, should increase the fog.

In the autumn, the land cools faster than the ocean causing the winds to reverse direction. The colder it gets, the stronger the winds blow from the high deserts towards the coast, peaking in December. These winds are called Santa Annas in southern California. The Wine Country fires were spread by the Diablo winds. But regardless of the name, the science is the same. Accordingly, it was November winds that fanned a spark into an inferno aimed directly at the heart of Paradise.

It has long been known that due to these autumn and winter winds, much of California endures a dangerous fire season year-round. On the optimistic side, any warming of the land during the cool seasons, whether natural or CO2 driven, should reduce these winds. Indeed, the natural drivers of wildfire are very complex, and maintaining a defensible space is our safest bet.

Jim Steele is author of “Landscapes and Cycles: An Environmentalist’s Journey to Climate Skepticism”. Contact him at naturalclimatechange@earthlink.net

Fraud In The National Climate Assessment

Here are Tony Heller’s videos on the 2018 National Climate Assessment

The Dryer Gets Wetter

Guest Post by Willis Eschenbach

I keep reading that one of the things that we are already seeing (or that is predicted) is that the wet areas of the planet are getting wetter and the dry areas are getting dryer, viz:

Expect the Wet to Get Wetter, and the Dry, Drier – Center for Climate …

May 31, 2017 – As the world warms due to human-induced climate change, many scientists have been … summer, wet areas will get wetter and dry areas will get drier. … United States, inner Asia and the Middle East will become even dryer.

Will the Wet Get Wetter and the Dry Drier – Geophysical Fluid …

NOAA GFDL Climate Research Highlights Image Gallery Will the Wet Get Wetter … in precipitation near 20°S and 20°N – latitudes in the subtropical dry zones.

Wet regions getting wetter, dry regions drier as planet warms …

Wet regions getting wetter, dry regions drier as planet warms … simulations of the climate show reductions in rainfall over the drier tropical land regions … that drying of the drier regions continues (right) while wet regions will experience more …

The world’s wet regions are getting wetter and the dry regions are …

Dec 12, 2016 – The world’s wet regions are getting wetter and the dry regions are … warming climate; as the world gets warmer wet regions will continue to get …

Dry lands getting drier, wet getting wetter: Earth’s water cycle …

May 21, 2012 – … get richer’ mechanism, where wet regions get wetter and dry regions drier. … “Salinity shifts in the ocean confirm climate and the global water …

I thought about that while reading a recent study called Rapid Drying of Northeast India in the Last Three Decades: Climate Change or Natural Variability? (Their conclusion, by the way, was that it was NOT from human actions, but instead that “the recent decreasing trend of NEI summer monsoon rainfall is, rather associated with the strong interdecadal variability of the subtropical Pacific Ocean“.)

So I went to the marvelous KNMI Climate Explorer and got the CRU TS 4.01 gridded precipitation dataset, which covers from 1901 through 2016 . I started by looking at the trend of the data since 1901.

Figure 1. Global rainfall, land-only, 1901 – 2016.

Now, there is a trend … but the increase in monthly rainfall is only 2 millimeters per century. This represents an increase of about an inch (25 mm) in the yearly average rainfall. Small.

Next, since the Indian study concerned the recent decades, I looked more closely at the average rainfall over that period. Figure 2 shows the average rainfall around the globe for the period 1980 through 2016.

Figure 2. Average rainfall, 1980 – 2016. Rainfall is shown on a 1° latitude by 1° longitude gridcell basis. All areas with over 2.2 metres/year are shown in darkest blue.

Here you can see the great deserts of the Sahara, the Gobi, the Atacama, and central Australia. You can also see the wet zones of the Amazon, the African rainforests, and Indonesia and Papua New Guinea.

Next, I looked at the individual trends for each 1°x1° gridcell over that same period, from 1980 through 2016.

Figure 3. Precipitation trends, monthly precipitation, millimeters/month per decade. Areas with trends greater than 7 or less than -7 mm/decade are shown in bright blue or bright red. Click to embiggen.

Here we can see the problem indicated in the Indian study, the drying of Northeast India.

Finally, using the combination of precipitation and precipitation trend data, we can see if it is true that the dry areas are getting dryer and the wet areas are getting wetter. Figure 4 shows a scatterplot of the two datasets.

Figure 4. Scatterplot, annual precipitation versus precipitation trend. The colored lines show the trends for the areas with varying amounts of rainfall, from dry to wet.

What this shows is that while the wetter areas are getting wetter, it is not true that the dryer areas are getting dryer. All areas are getting more rain. Not a lot more rain, of course, but more rain. Once again, the climate models are wrong.

And so one more beautiful climate myth runs aground on a reef of hard facts … the wet is getting wetter, but the dry isn’t get dryer—instead, the dry is getting wetter as well


Here, we’re in the “wetter” part of the equation, a rainy Christmas Eve. Well, since it’s 2:03 AM, I guess it’s actually a rainy Christmas morning … so the very best of wet Christmas morning wishes to everyone.

w.

An Assessment of the 4th National Climate Assessment

By Andy May

The U.S. Fourth National Climate Assessment (NCA4) Volume II is out and generating a lot of discussion. Volume II, Impacts Risks and Adaptation in the United States to climate change can be downloaded here (Reidmiller, et al. 2018). Volume I, published last year, on the physical science behind the assessment is here (Wuebbles, et al. 2017).

The mainstream media (MSM) is breathlessly reporting about it using the following template or something similar:

“[Volume II] of the Fourth National Climate Assessment shows how [America/city/state/poor/people of color/old people/young people, etc.] are already feeling the effects of climate change from [wildfires/droughts/floods/disease/hurricanes/etc.].

Examples of these statements can be seen in National GeographicScience News, the New York Times, etc. These popular reports leave out some very important caveats.

  1. The NCA4 results are from computer climate model runs, some of them implausible.
  2. The climate models used to compute the effects of human influence on climate have never successfully predicted the weather, weather cycles (such as El Niño or La Niña events), or climate.

All climate models fail to predict the weather or climate, with the possible exception of the Russian model INM-CM4(Volodin, Dianskii and Gusev 2010). This model is mostly ignored by the climate community, presumably because it does not predict anything bad. As you can see in Figure 1, INM-CM4 matches observations reasonably well and that makes it an outlier among the 32 model output datasets plotted. This success also makes INM-CM4 the only validated model in the group.

Figure 1. A comparison of 32 climate models and observations. The observations are from weather balloon and satellite data. The two observational methods are independent of one another and support each other. The plot is after Dr. John Christy of the University of Alabama in Huntsville (Christy 2016).

A computer model is developed for a specific purpose and its validity must be determined with respect to that purpose (Sargent 2011). Climate models have been developed to predict future climate, with an emphasis on predicting global average temperatures, due to concerns that human fossil fuel use will result in dangerously higher global average temperatures. A secondary purpose of the models is to determine how much warming is due to humans and how much is due to natural variation. This is a tall order, since the warming over the past 120 years is less than one degree Celsius, a very small number relative to annual or daily temperature variations.

To validate any model, we must specify the required accuracy of the model output to meet our needs (Sargent 2011). The total temperature change over the past 120 years is about one degree and we want to know how much of that is due to nature and how much is due to humans. To meet this objective, the model must be accurate to better than 0.5 degree per century. Figure 1 suggests that most models do not meet the minimum threshold of 0.5 degrees/century for the period 1979 to 2015. On average (the red line) the models are 0.5 degrees above the observations by 2015, only 36 years after they were initialized in 1979. INM-CN4 is labeled and it, alone, is tracking the observations with enough accuracy, yet it does not predict dangerous temperatures in the future or any significant human influence on climate. The spread of model results, in 2015, after 36 years, is 0.9 degrees C., which is comparable to the entire change in temperature for the past 120 years. Thus, the spread in model results, argues that the accuracy is inadequate for the stated purpose of the models.

Both volumes of NCA4 argue that humans are mostly responsible for the recent observed global warming, that the recent warming is causing climate change, and that the climate change is dangerous. Figure 2 illustrates this chain of logic, the shades of gray indicate the uncertainty in each step, very low uncertainty is black, light gray is very uncertain.

Figure 2. The NCA4 chain of logic, deep black indicates very low uncertainty, very light gray indicates high uncertainty.

We can be very certain that climate is changing, we can observe the changes and see it in history and the archaeological record. It is well documented, to read more about the record see my posts on climate and civilization here and here. Humans are currently burning large quantities of fossil fuels and causing the concentration of CO2 to increase in the atmosphere, it has increased by 27% (from 0.032% to 0.04%) since 1959 according to data collected at the Mauna Loa observatory in Hawaii. CO2 is a greenhouse gas and increasing its concentration in the atmosphere will slow the loss of thermal energy from the Earth’s surface and, thus, cause some warming in the lower atmosphere. So far, we are in the deep black, very certain boxes.

Where considerable uncertainty enters the flow of logic, is when we get to “how much do humans contribute” to warming. NCA4 volume one tells us:

“… it is extremely likely that human activities, especially emissions of greenhouse gases, are the dominant cause of the observed warming since the mid-20th century. For the warming over the last century, there is no convincing alternative explanation supported by the extent of the observational evidence.”

The phrase “extremely likely” is not well supported in the volume, or anywhere else. The lack of a “convincing alternative” (in their opinion) is not evidence that humans are the dominant cause of the warming, it just says we don’t understand the warming very well. They must rely on unvalidated climate models to tell us how much humans contribute, because the only validated model suggests the contribution from additional CO2 (and thus humans) is quite small. We observe warming, but we cannot observe human-caused warming. How much of the warming is due to nature? This is a complex problem and very poorly quantified. Chapter 3 of NCA4, volume one:

“The likely range of the human contribution to the global mean temperature increase over the period 1951–2010 is 1.1° to 1.4°F (0.6° to 0.8°C), and the central estimate of the observed warming of 1.2°F (0.65°C) lies within this range (high confidence). This translates to a likely human contribution of 93%–123% of the observed 1951–2010 change. It is extremely likely that more than half of the global mean temperature increase since 1951 was caused by human influence on climate (high confidence).”

Surface temperature models were used to compute the 0.65°C central estimate, yet as we can see in Figure 1, the range of model results is larger than this in the mid-troposphere, just for the period from 1979 to 2015. This fact alone invalidates their conclusion. We will not discuss the problems with the NCA4 determination of the human impact on climate here, this has been well covered in other posts, by myselfJudith Curry and others. We will just point out that the models and process they used are the same as those used by the IPCC in their fifth assessment (IPCC 2013).

NCA4 Volume II

NCA4 volume one provides the climate change projections for the future and volume two discusses the current and projected impacts on society due to these projections. It also discusses how we might mitigate and adapt to the changes. Because they have already concluded (by accepting dubious climate model output as fact without enough evidence, in our opinion) that human fossil fuel use is the cause of 93%-123% of recent climate changes, their discussion of mitigation revolves around eliminating fossil fuel use. However, the calculation of the impact of human fossil fuel use is swamped by the uncertainty in their models and unvalidated. Since volume two is entirely based on the human impact calculations in volume one, it is almost entirely invalid.

Climate change is real, climate has changed throughout the Earth’s history and will change in the future. Many times in human history climate has changed more rapidly than it is changing today, these changes are documented hereand here. Probably the best example is from the end of the last glacial period, 11,700 years ago, after the Younger Dyas cold period, when temperatures rose 5-10°C in just a few decades in the Northern Hemisphere (Severinghaus, et al. 1998). This is an astounding 9°F to 18°F in much less than 100 years. Humans adapted and even thrived during this change, which occurred at the dawn of human civilization. Despite this evidence, NCA4 insists that recent warming is unprecedented, this is a clear error in the report.

Due to the considerable doubt about the magnitude of the human contribution to climate change it would seem foolish to destroy the fossil fuel industry, throwing millions out of work and crushing the world’s economy with higher energy prices. Anything this foolish and destructive should certainly wait until (and if) the climate models used to create the projections used in NCA4 volume two are validated and produce a much tighter set of projections than seen in Figure 1. However, the chapter on adaptation is still valid. If some climate changes are harmful in some areas, these ideas are useful. Regardless of how much climate change is man-made, communities should adapt by improving their infrastructure to resist climate-related threats. Coastal areas should improve storm-surge and flood barriers, the western U.S. should improve their forest management to make fighting forest fires easier, every part of the U.S. should improve their surface water drainage, etc. Adaptation is an obvious thing to do, the benefits of mitigation (reducing fossil fuel use) are far more speculative and much less likely to be effective (May 2018). Bjorn Lomborg has also written extensively about this in his book Cool It and in articles such as this one. NCA4 reports that construction of adaptation infrastructure in the U.S. has increased since 2014, which is a good thing (page 53, Report-in-Brief).

I may have missed it, these reports are very long, and I didn’t read every word, but I don’t think the benefits of global warming and increasing CO2 levels are discussed or considered, outside of a few vague throw-away comments without documentation. There is a throw-away comment on page 37 of the Report-in-Brief: “Some aspects of our economy may see slight improvements …” but no discussion of the benefits. This is a major oversight, since the only impacts of climate change that can be verified to date, are beneficial. The additional CO2 in the atmosphere acts as a strong plant fertilizer and it also makes plants more resistant to drought. This has helped increase farm and forest productivity in the U.S. and around the world (Zhu, et al. 2016). Zhu et al. show that CO2 fertilization effects explain 70% the greening of the Earth since 1982. This is discussed in more detail here.

As the planet has warmed the past 120 years new land has also opened for agriculture in the far north of Canada and Asia, which has also increased agricultural productivity in those countries. Kip Hansen has discussed global greening here in a very good post with abundant references. NASA also has a page devoted to CO2 greening of the planet here. Yet, while the report acknowledges that U.S. forested area has increased (see Chapter 6) they neglect to say it was mostly because of additional CO2. While they mention that forests and wildlife are expanding to higher elevations and northward due to warming, they do not acknowledge that a large part of this expansion is due to additional CO2 and global warming. Then they inevitably ignore that this is a good thing and characterize the expansion and greening as “aiding the spread of invasive species” (Report-in-Brief, page 44). Every effect of warming or increasing CO2 is presented only in a negative light, showing a complete lack of lack of scientific reasoning or methods and displaying blatant political advocacy.

The report mentions that if the world warms, there will be more deaths due to extreme heat, which is true. Then, they project that the reduction in cold-related deaths due to warming will be smaller and the number of temperature related deaths will increase, not decrease as most previous studies have concluded (their study is here). In fact, in all parts of the Northern Hemisphere mid-latitudes, most deaths occur in the winter and the optimum temperature (meaning the time of fewest deaths) is near the average local summer temperature. Thus, humans are very adaptable and when they adapt, they adapt best to the local summer temperature. The statistical method used does not appear to consider adaptation, and the result is contrary to previous studies, which conclude that warming will decrease net deaths. They write the opposite in their report and state (without enough documentation in my opinion) that “the increase in heat deaths due to climate change will likely be larger than the decrease in cold deaths.” This is a difficult area to study and fraught with uncertainty, but it seems likely that they are wrong, and the net effect will be fewer deaths due to weather (Dixon, et al. 2005), not more. Besides the excellent paper by Dixon, I’ve written on climate-related mortality here. It is revealing that this group would do a risk assessment of man-made climate change and not consider all sides of this argument. It hurts their credibility.

As the world population grows and becomes more affluent, people build more expensive buildings and houses, and some build them in areas that are very vulnerable to disasters caused by weather and climate. It is the increase in development and population in dangerous locations that has increased the cost of climate-related damages over the 20th century (Mohleji and Pielke 2014). Mohleji and Pielke successfully separated the portion of disaster losses due to population growth and affluence (“socioeconomic” change) in disaster-prone areas and those due to climate. They found that it was all population growth and affluence, and none could be attributed to global climate change. We often hear public claims that climate change is causing an increase in disaster losses, but the peer-reviewed literature is clear that population growth in disaster prone areas explains the increase in losses, Mohleji and Pielke address this directly and write:

“As concluded by the IPCC (2012), socioeconomic change can explain the long-term increase in global losses. Thus, the apparent disconnect between peer-reviewed research and public claims is reconciled, and there is no disconnect at all. Even assuming anthropogenic climate change occurs as projected under a suite of models, it may be a very long time before attribution of economic losses to greenhouse gas emissions is possible. Crompton et al. (2011) conclude that an anthropogenic climate signal will not be identifiable in U.S. tropical cyclone losses for another 120–550 years with even longer timescales expected for other global weather-related natural disasters.”

So, we see that disaster losses have increased recently, but attributing these losses to climate change (man-made or otherwise) is not possible at this time. Pielke Jr. in testimony to the House of Representatives Committee on Science, Space and Technology (Pielke Jr. 2017) has shown that disaster losses, as a percent of global GDP, have gone down since 1990. There is no trend in the frequency of storms, droughts, or floods over the last 100 years. We actually have fewer acres of land burned today than we did in the 1930s.

Conclusions

NCA4 volume two assumes that human CO2 emissions dominate climate and that we can change our climate future by reducing our fossil fuel emissions. But we have already seen that the uncertainty in this conclusion is much larger than the changes we have observed. They falsely equate “climate change” and “man-made climate change.” By doing this, they can take any negative effects of climate change and blame us for it.

Both NCA4 reports contain some useful data, but they interpret it in a very one-sided and biased way. The report has errors of omission, such as omitting all the positive aspects of global warming and more CO2, such as a greener planet, more drought resistant plants, fewer climate-related deaths, and more arable land. Volume II of the report also accepts demonstrably uncertain model output from volume one as fact, justifying this solely because it is already published in volume one. Then it compounds the error and uses the dubious results to make highly uncertain projections about our economy and health. Finally, every projection is interpreted in the most negative way possible

Some of the more egregious errors and omissions are pointed out above, a more comprehensive list can be found on the NCA4 web site in their document “NCA4 Public Comments and Author Responses with names.” The good stuff starts on page 4 where you will see Ross McKitrick dissect portions of the report and the author responses. The print is very small so remember to use “ctrl +” to enlarge the print. Dr. McKitrick’s questions are precise, to the point and accurate as far as I can tell. It is telling that the authors usually simply say they disagree with Dr. McKitrick and, in a blatant example of circular reasoning, refer him to volume one. They do not try to debate him on the merits. A comment by Sean Birkel (Dr. Birkel is the Maine State Climatologist and an Assistant Professor at the University of Maine) on page 8 is pertinent:

“If these claims [Summary Findings, Chapter 1] were true then how is it that the US has grown so prosperous since the 1900s? You have just finished stating that massive, historically unprecedented climate changes occurred in the past century, especially in the past few decades. It is a matter of historical record that throughout this period the quality of life in the US just kept going up and up. Now you say that the next increment of warming will be completely different and will lead to ruin across the land. No exceptions, no caveats, no qualifications: you are asking the reader to forget the pattern that held up to now and take your word for it that disaster is coming. If you really believe that, then you owe it to the readers to be convincing, not cartoonish and apocalyptic. As one example, the opening phrase “cascading disruptions and damages in interdependent networks of infrastructure, ecosystems and social systems” reads like a Hollywood disaster flick – i.e. fiction. You have a very evocative style, but it sets a tone at odds with the expectation that this is a serious scientific document.”

Precisely so, the whole document does read like a Hollywood movie script. At any moment we expect Dwayne Johnson, Ben Affleck and Bruce Willis to jump out of the pages to save the world from Armageddon. A serious scientific report would cover the whole subject, good and bad. This reads like it was written first and then references selected to fit the narrative.

I have some respect for the most recent IPCC reports (IPCC 2013) and (IPCC 2014b) and refer to them often, but they cover both sides (at least in the actual report, the summaries often don’t). I’m afraid the NCA4 does not and as a result, it is a national embarrassment.

President Trump stated on November 26 that he didn’t believe the economic projections in the report and I certainly agree with him on that. Dr. Steven Koonin (Professor at NYU and former Obama undersecretary of science) recently wrote the following on this topic in the Wall Street Journal:

“The report’s numbers, uncertain as they are, turn out not to be all that alarming. The final figure of the final chapter [Chapter 29, page 170] shows that an increase in global average temperatures of 9 degrees Fahrenheit (beyond the 1.4-degree rise already recorded since 1880) [RCP8.5, an implausible scenario, “that does not provide a useful benchmark for policy studies.”] would directly reduce the U.S. gross domestic product in 2090 by 4%, plus or minus 2%—that is, the GDP would be about 4% less than it would have been absent human influences on the climate. That “worst-worst case” estimate assumes the largest plausible temperature rise and only known modes of adaptation. To place a 4% reduction in context, conservatively assume that real annual GDP growth will average 2% in the coming decades (it has averaged 3.2% since 1935 and is currently 3%). That would result in a U.S. economy roughly four times as large in 2090 as today. A 4% climate impact would reduce that multiple to 3.8—a correction much smaller than the uncertainty of any projection over seven decades. … The U.S. economy in 2090 would be no more than two years behind where it would have been absent man-caused climate change. Experts know that worst-case climate projections show minimal impact on the overall economy. Buried in the Intergovernmental Panel on Climate Change’s 2014 report is a chart showing that a global temperature rise of 5 degrees Fahrenheit would have a global economic impact of about 3% in 2100—negligibly diminishing projected global growth over that period to 385% from 400%. If we take the new report’s estimates at face value, human-induced climate change isn’t an existential threat to the overall U.S. economy through the end of this century—or even a significant one. … There are many reasons to be concerned about a changing climate, including disparate impact across industries and regions. But national economic catastrophe isn’t one of them. It should concern anyone who supports well-informed public and policy discussions that the report’s authors, reviewers and media coverage obscured such an important point.”

The worst possible scenario in NCA4 results in a GDP decrease that is far less than the margin of error in the estimate. In other words, it amounts to nothing. This is pretty much what the report itself amounts to.

Works Cited

Charney, J., A. Arakawa, D. Baker, B. Bolin, R. Dickinson, R. Goody, C. Leith, H. Stommel, and C. Wunsch. 1979. Carbon Dioxide and Climate: A Scientific Assessment. National Research Council, Washington DC: National Academy of Sciences. http://www.ecd.bnl.gov/steve/charney_report1979.pdf.

Christy, John. 2016. Testimony of John R. Christy. Washington, D.C.: U.S. House Committee on Science, Space and Technology, 23. https://docs.house.gov/meetings/SY/SY00/20160202/104399/HHRG-114-SY00-Wstate-ChristyJ-20160202.pdf.

Curry, J. 2017. Climate Models for the layman. GWPF Reports. https://www.thegwpf.org/content/uploads/2017/02/Curry-2017.pdf.

Dixon, P., D. Brommer, B. Hedquist, A. Kalkstein, G. Goodrich, J. Wlter, C. Dickerson, S. Penney, and R. Cerveny. 2005. “HEAT MORTALITY VERSUS COLD MORTALITY A Study of Conflicting Databases in the United States.” AMERICAN METEOROLOGICAL SOCIETY 937-943. https://journals.ametsoc.org/doi/pdf/10.1175/BAMS-86-7-937.

IPCC. 2013. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, by T. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley. Cambridge: Cambridge University Press. https://www.ipcc.ch/pdf/assessment-report/ar5/wg1/WG1AR5_SPM_FINAL.pdf.

IPCC. 2014b. “Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.” In Climate Change 2014, by C.B. Field, V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, et al. Cambridge University Press. http://www.ipcc.ch/pdf/assessment-report/ar5/wg2/WGIIAR5-PartA_FINAL.pdf.

Lomborg, Bjorn. 2007. Cool It. Vintage Books. http://www.lomborg.com/cool-it.

May, Andy. 2018. Climate Catastrophe! Science or Science Fiction? American Freedom Publications LLC. https://www.amazon.com/CLIMATE-CATASTROPHE-Science-Fiction-ebook/dp/B07CPHCBV1/ref=sr_1_1?ie=UTF8&qid=1535627846&sr=8-1&keywords=climate+catastrophe+science+or+science+fiction.

Mohleji, Shalini, and Roger Pielke. 2014. “Reconciliation of Trends in Global and Regional Economic Losses from Weather Events: 1980–2008.” ASCE National Hazards Review 15 (4). https://ascelibrary.org/doi/abs/10.1061/(ASCE)NH.1527-6996.0000141.

Pielke Jr., Roger. 2017. “STATEMENT OF DR. ROGER PIELKE, JR. to the COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY of the UNITED STATES HOUSE OF REPRESENTATIVES.” U.S. House of Representatives, Washington, DC. https://science.house.gov/sites/republicans.science.house.gov/files/documents/HHRG-115-SY-WState-RPielke-20170329.pdf.

Reidmiller, D.R., C.W. Avery, D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C. Stewart. 2018. “mpacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II.” USGCRP, Washington D.C. doi:10.7930/NCA4.2018.

Sargent, Robert. 2011. “VERIFICATION AND VALIDATION OF SIMULATION MODELS.” Proceedings of the 2011 Winter Simulation Conference. https://www.informs-sim.org/wsc11papers/016.pdf.

Severinghaus, Jeffrey P., Todd Sowers, Edward J. Brook, Richard B. Alley, and Michael L. Bender. 1998. “Timing of abrupt climate change at the end of the Younger Dryas interval from thermally fractionated gases in polar ice.” Nature, January 8: 141-146. http://shadow.eas.gatech.edu/~jean/paleo/Severinghaus_1998.pdf.

Volodin, E. M., N. A. Dianskii, and A.V. Gusev. 2010. “Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations.” Atmospheric and Oceanic Physics 46 (4): 414-431. https://link.springer.com/article/10.1134%2FS000143381004002X.

Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock. 2017. Climate Science Special Report: Fourth National Climate Assessment, Volume I. Washington, D.C.: USGCRP, 470. doi: 10.7930/J0J964J6.

Zhu, Zaichun, Shilong Piao, Ranga B. Myneni, Mengtian Huang, Zhenzhong Zeng, Josep G. Canadell, Philippe Ciais, et al. 2016. “Greening of the Earth and its drivers.” Nature Climate Change 6: 791-795. https://www.nature.com/articles/nclimate3004.

Climate change is making hurricanes even more destructive, research finds

NOT A LOT OF PEOPLE KNOW THAT

By Paul Homewood

The Guardian hypes up the latest attempt to claim that climate change is making hurricanes worse:

image

Climate changeworsened the most destructive hurricanes of recent years, including Katrina, Irma and Maria, by intensifying rainfall by as much as 10%, new research has found.

High-resolution climate simulations of 15 tropical cyclones in the Atlantic, Pacific and Indian Oceans found that warming in the ocean and atmosphere increased rainfall by between 5% and 10%, although wind speeds remained largely unchanged.

This situation is set to worsen under future anticipated warming, however. Researchers found that if little is done to constrain greenhouse gas emissions and the world warms by 3C to 4C this century then hurricane rainfall could increase by a third, while wind speeds would be boosted by as much as 25 knots.

“Climate change has exacerbated rainfall and is set to enhance the wind speed,” said Christina Patricola…

View original post 972 more words

%d bloggers like this: