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 Geographic, Science News, the New York Times, etc. These popular reports leave out some very important caveats.
- The NCA4 results are from computer climate model runs, some of them implausible.
- 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 myself, Judith 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.
Credit: PAR @ Wikipedia
Climate Collections 
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"
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