Stress Testing for Media Bias

I was recently reminded (H/T pHil R) about Michael Crichton’s insight into our vulnerability to media bias.  He called it the Gell-Mann Amnesia Effect, named after his friend, physicist Gell-Mann. 

“Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray’s case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the “wet streets cause rain” stories. Paper’s full of them.

In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.”

Howard Wetsman MD takes it from there in his article A New Corollary to the Gell-Mann Amnesia Effect, suggesting how to approach media reports with critical intelligence. Excerpts in italics with my bolds.

The corollary came to me the other day when I was reading an email string on Addiction Medicine. A couple of fathers of the field had written an article in one of those non-peer reviewed clinical newspapers that each specialty has and shared it with the group. They were showered with praise, so I started reading what they wrote. I was struck that the assumptions they made in their article directly contradicted several of the working assumptions of the group, yet the group expressed nearly universal approval with the conclusions of the article.

So the Hunt Assumption Amnesia Corollary is when experts start reading a paper, note that they disagree with some basic assumptions of the work, but keep reading and accept the conclusions, forgetting they had rejected the assumptions. This effect is rife in Addiction Medicine, and, I suspect, much of academia.

When I first learned to read a scientific paper, I was taught to go through the various sections to understand the limitations of the conclusions I’d read at the end. Did they select the subjects correctly? Did they use the right test for the question? Did they have enough subjects to power the study sufficiently? And many other important questions.

But I’ve come to find in the fullness of time that there are really only two questions I need to know when reading a paper. Were the authors aware that their assumptions are assumptions, and are they questioning them?

I want to pose my own testable hypothesis about how this corollary effect occurs. I think, if I’m right, that we’ll see it in all media.

First, the assumption is stated as fact, but in a muted way so that it slides past the readers assumption filter rather than slamming headlong into it. Then data is piled up to bolster the writer’s thesis by generalizing findings in particular situations to all situations. So, by the end of the piece any disagreement with the assumption is forgotten under the weight of “the evidence.”

A previous post is reprinted below showing how a journalism professor prepares his students to read critically media reports concerning climate change/global warming.

Decoding Climate News

Journalism professor David Blackall provides a professional context for investigative reporting I’ve been doing on this blog, along with other bloggers interested in science and climate change/global warming. His peer reviewed paper is Environmental Reporting in a Post Truth World. The excerpts below show his advice is good not only for journalists but for readers.  h/t GWPF, Pierre Gosselin

Overview: The Grand Transnational Narrative

The dominance of a ‘grand transnational narrative’ in environmental discourse (Mittal, 2012) over other human impacts, like deforestation, is problematic and is partly due to the complexities and overspecialization of climate modelling. A strategy for learning, therefore, is to instead focus on the news media: it is easily researched and it tends to act ‘as one driving force’, providing citizens with ‘piecemeal information’, making it impossible to arrive at an informed position about science, society and politics (Marisa Dispensa et al., 2003). After locating problematic news narratives, Google Scholar can then be employed to locate recent scientific papers that examine, verify or refute news media discourse.

The science publication Nature Climate Change this year, published a study demonstrating Earth this century warmed substantially less than computer-generated climate models predict.

Unfortunately for public knowledge, such findings don’t appear in the news. Sea levels too have not been obeying the ‘grand transnational narrative’ of catastrophic global warming. Sea levels around Australia 2011–2012 were measured with the most significant drops in sea levels since measurements began. . .The 2015–2016 El-Niño, a natural phenomenon, drove sea levels around Indonesia to low levels such that coral reefs were bleaching. The echo chamber of news repeatedly fails to report such phenomena and yet many studies continue to contradict mainstream news discourse.

facebook2bnew2blike2bbuttons2bfinal-970-80I will be arguing that a number of narratives need correction, and while I accept that the views I am about to express are not universally held, I believe that the scientific evidence does support them.

The Global Warming/Climate Change Narrative

The primary narrative in need of correction is that global warming alone (Lewis, 2016), which induces climate change (climate disruption), is due to the increase in global surface temperatures caused by atmospheric greenhouse gases. Instead, there are many factors arising from human land use (Pielke et al., 2016), which it could be argued are responsible for climate change, and some of these practices can be mitigated through direct public action.

Global warming is calculated by measuring average surface temperatures over time. While it is easy to argue that temperatures are increasing, it cannot be argued, as some models contend, that the increases are uniform throughout the global surface and atmosphere. Climate science is further problematized by its own scientists, in that computer modelling, as one component of this multi-faceted science, is privileged over other disciplines, like geology.

Scientific uncertainty arises from ‘simulations’ of climate because computer models are failing to match the actual climate. This means that computer models are unreliable in making predictions.

Published in the eminent journal Nature (Ma, et. al., 2017), ‘Theory of chaotic orbital variations confirmed by Cretaceous geological evidence’, provides excellent stimulus material for student news writing. The paper discusses the severe wobbles in planetary orbits, and these affect climate. The wobbles are reflected in geological records and show that the theoretical climate models are not rigorously confirmed by these radioisotopically calibrated and anchored geological data sets. Yet popular discourse presents Earth as harmonious: temperatures, sea levels and orbital patterns all naturally balanced until global warming affects them, a mythical construct. Instead, the reality is natural variability, the interactions of which are yet to be measured or discovered (Berger, 2013).

In such a (media) climate, it is difficult for the assertion to be made that there might be other sources, than a nontoxic greenhouse gas called carbon dioxide (CO2), that could be responsible for ‘climate disruption’. A healthy scientific process would allow such a proposition. Contrary to warming theory, CO2 levels have increased, but global average temperatures remain steady. The global average temperature increased from 1983 to 1998; then, it flat-lined for nearly 20 years. James Hansen’s Hockey Stick graph, with soaring and catastrophic temperatures, simply did not materialize.

As Keenan et al. (2016) found through using global carbon budget estimates, ground, atmospheric and satellite observations, and multiple global vegetation models that there is also now a pause in the growth rate of atmospheric CO2. They attribute this to increases in terrestrial sinks over the last decade, where forests consume the rising atmospheric CO2 and rapidly grow—the net effect being a slowing in the rate of warming from global respiration.

Contrary to public understanding, higher temperatures in cities are due to a phenomenon known as the ‘urban heat effect’ (Taha, 1997; Yuan & Bauer, 2007). Engines, air conditioners, heaters and heat absorbing surfaces like bitumen radiate heat energy in urban areas, but this is not due to the greenhouse effect. Problematic too are data sets like ocean heat temperatures, sea-ice thickness and glaciers: all of which are varied, some have not been measured or there are insignificant measurement time spans for the data to be reliable.

Contrary to news media reports, some glaciers throughout the world (Norway [Chinn et al., 2005] and New Zealand [Purdie et al., 2008]) are growing, while others shrink (Paul et al., 2007).

Conclusion

This is clearly a contentious topic. There are many agendas at play, with careers at stake. My view represents one side of the debate: it is one I strongly believe in, and is, I contend, supported by the science around deforestation, on the ground, rather than focusing almost entirely on atmosphere. However, as a journalism educator, I also recognize that my view, along with others, must be open to challenge, both within the scientific community and in the court of public opinion.

As a journalism educator, it is my responsibility to provide my students with the research skills they need to question—and test—the arguments put forward by the key players in any debate. Given the complexity of the climate warming debate, and the contested nature of the science that underpins both sides, this will provide challenges well into the future. It is a challenge our students should relish, particularly in an era when they are constantly being bombarded with ‘fake news’ and so-called ‘alternative facts’.

To do so, they need to understand the science. If they don’t, they need to at least understand the key players in the debate and what is motivating them. They need to be prepared to question these people and to look beyond their arguments to the agendas that may be driving them. If they don’t, we must be reconciled to a future in which ‘fake news’ becomes the norm.

Examples of my investigative reports are in Data Vs. Models posts listed at Climate Whack-a-Mole

See also Yellow Climate Journalism

Some suggestions for reading critically National Climate Assessment reports is at Impaired Climate Vision

 

 

Oceans Cold to Start 2021


The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.  More on what distinguishes HadSST3 from other SST products at the end.

The Current Context

The year end report below showed 2020 rapidly cooling in all regions.  The anomalies have continued to drop sharply and are now well below the mean since 1995.  This Global Cooling was also evident in the UAH Land and Ocean air temperatures (See 2021 Starts with Cool Land and Sea )

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through January 2021. After three straight Spring 2020 months of cooling led by the tropics and SH, NH spiked in the summer, along with smaller bumps elsewhere.  Now temps everywhere are dropping the last six months, with all regions well below the Global Mean since 2015, matching the cold of 2018, and lower than January 2015. 

A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016.  In 2019 all regions had been converging to reach nearly the same value in April.

Then  NH rose exceptionally by almost 0.5C over the four summer months, in August 2019 exceeding previous summer peaks in NH since 2015.  In the 4 succeeding months, that warm NH pulse reversed sharply. Then again NH temps warmed to a 2020 summer peak, matching 2019.  This has now been reversed with all regions pulling the Global anomaly downward sharply.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year.  A fourth NH bump was lower and peaked in September 2018.  As noted above, a fifth peak in August 2019 and a sixth August 2020 exceeded the four previous upward bumps in NH.

And as before, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.  The major difference between now and 2015-2016 is the absence of Tropical warming driving the SSTs, along with SH anomalies reaching nearly the lowest in this period. Presently both SH and the Tropics are quite cool, with NH coming off its summer peak.  Note the tropical temps descending into La Nina levels.  At this point, the 2016 El Nino and its NH after effects have dissipated completely.

A longer view of SSTs

The graph below  is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July.

To enlarge image, single-click or open in new tab.

1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino.  The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99.  For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.2C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.4C.  Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs.  As a result the Global average fluctuates around that same 0.4C, which also turns out to be the average for the entire record since 1995.

2007 stands out with a sharp drop in temperatures so that Jan.08 matches the low in Jan. ’99, but starting from a lower high. The oceans all decline as well, until temps build peaking in 2010.

Now again a different pattern appears.  The Tropics cool sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peak came in 2019, only this time the Tropics and SH are offsetting rather adding to the warming. Since 2014 SH has played a moderating role, offsetting the NH warming pulses. Now September 2020 is dropping off last summer’s unusually high NH SSTs. (Note: these are high anomalies on top of the highest absolute temps in the NH.)

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

But the peaks coming nearly every summer in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.
The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows August warming began after 1992 up to 1998, with a series of matching years since, including 2020.  Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.

This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. The black line shows that 2020 began slightly warm, then set records for 3 months. then dropped below 2016 and 2017, peaked in August and is now below 2016.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? If the pattern of recent years continues, NH SST anomalies may rise slightly in coming months, but once again, ENSO which has weakened will probably determine the outcome.

Footnote: Why Rely on HadSST3

HadSST3 is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST3 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

 

IPCC Scenarios Ensure Unreal Climate Forecasts

 

Figure 5. CO2 emissions (a) and concentrations (b), anthropogenic radiative forcing (c), and global mean temperature change (d) for the three long-term extensions. As in Fig. 3, concentration, forcing, and temperature outcomes are calculated with a simple climate model (MAGICC version 6.8.01 BETA; Meinshausen et al., 2011a, b). Outcomes for the CMIP5 versions of the long-term extensions of RCP2.6 and RCP8.5 (Meinshausen et al., 2011c), as calculated with the same model, are shown for comparison.

Roger Pielke Jr. has a new paper at Science Direct Distorting the view of our climate future: The misuse and abuse of climate pathways and scenarios.  Excerpt in italics with my bolds.

Abstract

Climate science research and assessments under the umbrella of the Intergovernmental Panel on Climate Change (IPCC) have misused scenarios for more than a decade. Symptoms of misuse have included the treatment of an unrealistic, extreme scenario as the world’s most likely future in the absence of climate policy and the illogical comparison of climate projections across inconsistent global development trajectories.

Reasons why such misuse arose include (a) competing demands for scenarios from users in diverse academic disciplines that ultimately conflated exploratory and policy relevant pathways, (b) the evolving role of the IPCC – which extended its mandate in a way that creates an inter-relationship between literature assessment and literature coordination, (c) unforeseen consequences of employing a temporary approach to scenario development, (d) maintaining research practices that normalize careless use of scenarios, and (e) the inherent complexity and technicality of scenarios in model-based research and in support of policy.

Consequently, much of the climate research community is presently off-track from scientific coherence and policy-relevance.

Attempts to address scenario misuse within the community have thus far not worked. The result has been the widespread production of myopic or misleading perspectives on future climate change and climate policy. Until reform is implemented, we can expect the production of such perspectives to continue, threatening the overall credibility of the IPCC and associated climate research. However, because many aspects of climate change discourse are contingent on scenarios, there is considerable momentum that will make such a course correction difficult and contested – even as efforts to improve scenarios have informed research that will be included in the IPCC 6th Assessment.

Discussion of How Imaginary Scenarios Spoil Attempts to Envision Climate Futures

The article above is paywalled, but a previous post reprinted below goes into the background of the role of scenarios in climate modelling, and demonstrates the effects referring to results from the most realistic model, INMCM5

Roger Pielke Jr. explains that climate models projections are unreliable because they are based on scenarios no longer bounded by reality.  His article is The Unstoppable Momentum of Outdated Science.  Excerpts in italics with my bolds.

Much of climate research is focused on implausible scenarios of the future, but implementing a course correction will be difficult.

In 2020, climate research finds itself in a similar situation to that of breast cancer research in 2007. Evidence indicates the scenarios of the future to 2100 that are at the focus of much of climate research have already diverged from the real world and thus offer a poor basis for projecting policy-relevant variables like economic growth and carbon dioxide emissions. A course-correction is needed.

In a new paper of ours just out in Environmental Research Letters we perform the most rigorous evaluation to date of how key variables in climate scenarios compare with data from the real world (specifically, we look at population, economic growth, energy intensity of economic growth and carbon intensity of energy consumption). We also look at how these variables might evolve in the near-term to 2040.

We find that the most commonly-used scenarios in climate research have already diverged significantly from the real world, and that divergence is going to only get larger in coming decades. You can see this visualized in the graph above, which shows carbon dioxide emissions from fossil fuels from 2005, when many scenarios begin, to 2045. The graph shows emissions trajectories projected by the most commonly used climate scenarios (called SSP5-8.5 and RCP8.5, with labels on the right vertical axis), along with other scenario trajectories. Actual emissions to date (dark purple curve) and those of near-term energy outlooks (labeled as EIA, BP and ExxonMobil) all can be found at the very low end of the scenario range, and far below the most commonly used scenarios.

Our paper goes into the technical details, but in short, an important reason for the lower-than-projected carbon dioxide emissions is that economic growth has been slower than expected across the scenarios, and rather than seeing coal use expand dramatically around the world, it has actually declined in many regions.

It is even conceivable, if not likely, that in 2019 the world has passed “peak carbon dioxide emissions.” Crucially, the projections in the figure above are pre-Covid19, which means that actual emissions 2020 to 2045 will be even less than was projected in 2019.

While it is excellent news that the broader community is beginning to realize that scenarios are increasingly outdated, voluminous amounts of research have been and continue to be produced based on the outdated scenarios. For instance, O’Neill and colleagues find that “many studies” use scenarios that are “unlikely.” In fact, in their literature review such “unlikely” scenarios comprise more than 20% of all scenario applications from 2014 to 2019. They also call for “re-examining the assumptions underlying” the high-end emissions scenarios that are favored in physical climate research, impact studies and economic and policy analyses.

Make no mistake. The momentum of outdated science is powerful. Recognizing that a considerable amount of climate science to be outdated is, in the words of the late Steve Rayer, “uncomfortable knowledge” — that knowledge which challenges widely-held preconceptions. According to Rayner, in such a context we should expect to see reactions to uncomfortable knowledge that include:

  • denial (that scenarios are off track),
  • dismissal (the scenarios are off track, but it doesn’t matter),
  • diversion (the scenarios are off track, but saying so advances the agenda of those opposed to action) and,
  • displacement (the scenarios are off track but there are perhaps compensating errors elsewhere within scenario assumptions).

Such responses reinforce the momentum of outdated science and make it more difficult to implement a much needed course correction.

Responding to climate change is critically important. So too is upholding the integrity of the science which helps to inform those responses. Identification of a growing divergence between scenarios and the real-world should be seen as an opportunity — to improve both science and policy related to climate — but also to develop new ways for science to be more nimble in getting back on track when research is found to be outdated.

[A previous post is reprinted below since it demonstrates how the scenarios drive forecasting by CMIP6 models, including the example of the best performant model: INMCM5]

Background from Previous Post : Best Climate Model: Mild Warming Forecasted

Links are provided at the end to previous posts describing climate models 4 and 5 from the Institute of Numerical Mathematics in Moscow, Russia.  Now we have forecasts for the 21st Century published for INM-CM5 at Izvestiya, Atmospheric and Oceanic Physics volume 56, pages218–228(July 7, 2020). The article is Simulation of Possible Future Climate Changes in the 21st Century in the INM-CM5 Climate Model by E. M. Volodin & A. S. Gritsun.  Excerpts are in italics with my bolds, along with a contextual comment.

Abstract

Climate changes in 2015–2100 have been simulated with the use of the INM-CM5 climate model following four scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5 (single model runs) and SSP3-7.0 (an ensemble of five model runs). Changes in the global mean temperature and spatial distribution of temperature and precipitation are analyzed. The global warming predicted by the INM-CM5 model in the scenarios considered is smaller than that in other CMIP6 models. It is shown that the temperature in the hottest summer month can rise more quickly than the seasonal mean temperature in Russia. An analysis of a change in Arctic sea ice shows no complete Arctic summer ice melting in the 21st century under any model scenario. Changes in the meridional stream function in atmosphere and ocean are studied.

Overview

The climate is understood as the totality of statistical characteristics of the instantaneous states of the atmosphere, ocean, and other climate system components averaged over a long time period.

Therefore, we restrict ourselves to an analysis of some of the most important climate parameters, such as average temperature and precipitation. A more detailed analysis of individual aspects of climate change, such as changes in extreme weather and climate situations, will be the subject of another work. This study is not aimed at a full comparison with the results of other climate models, where calculations follow the same scenarios, since the results of other models have not yet been published in peer reviewed journals by the time of this writing.

The INM-CM5 climate model [1, 2] is used for the numerical experiments. It differs from the previous version, INMCM4, which was also used for experiments on reproducing climate change in the 21st century [3], in the following:

  • an aerosol block has been added to the model, which allows inputting anthropogenic emissions of aerosols and their precursors;
  • the concentrations and optical properties of aerosols are calculated, but not specified, like in the previous version;
  • the parametrizations of cloud formation and condensation are changed in the atmospheric block;
  • the upper boundary in the atmospheric block is raised from 30 to 60 km;
  • the horizontal resolution in the ocean block is doubled along each coordinate; and,
  • the software related to adaptation to massively parallel computers is improved, which allows the effective use a larger number of compute cores.

The model resolution in the atmospheric and aerosol blocks is 2° × 1.5° in longitude and latitude and 73 levels and, in the ocean, 0.5° × 0.25° and 40 levels. The calculations were performed at supercomputers of the Joint Supercomputer Center, Russian Academy of Sciences, and Moscow State University, with the use of 360 to 720 cores. The model calculated 6–10 years per 24 h in the above configuration.

Four scenarios were used to model the future climate: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The scenarios are described in [4]. The figure after the abbreviation SSP (Shared Socioeconomic Pathway) is the number of the mankind development path (see the values in [4]). The number after the dash means the radiation forcing (W m–2) in 2100 compared to the preindustrial level. Thus, the SSP1-2.6 scenario is the most moderate and assumes rapid actions which sharply limit and then almost completely stop anthropogenic emissions. Within this scenario, greenhouse gas concentrations are maximal in the middle of the 21st century and then slightly decrease by the end of the century. The SSP5-8.5 scenario is the warmest and implies the fastest climate change. The scenarios are recommended for use in the project on comparing CMIP6 (Coupled Model Intercomparison Project, Phase 6, [5]) climate models.  Each scenario includes the time series of:

  • carbon dioxide, methane, nitrous oxide, and ozone concentrations;
  • emissions of anthropogenic aerosols and their precursors;
  • the concentration of volcanic sulfate aerosol; and
  • the solar constant. 

One model experiment was carried out for each of the above scenarios. It began at the beginning of 2015 and ended at the end of 2100. The initial state was taken from the so-called historical experiment with the same model, where climate changes were simulated for 1850–2014, and all impacts on the climate system were set according to observations. The results of the ensemble of historical experiments with the model under consideration are given in [6, 7]. For the SSP3-7.0 scenario, five model runs was performed differing in the initial data taken from different historical experiments. The ensemble of numerical experiments is required to increase the statistical confidence of conclusions about climate changes.

[My Contextual Comment inserted Prior to Consideration of Results]

Firstly, the INM-CM5 historical experiment can be read in detail by following a linked post (see Resources at the end), but this graphic summarizes the model hindcasting of past temperatures (GMT) compared to HadCrutv4.

Figure 1. The 5-year mean GMST (K) anomaly with respect to 1850–1899 for HadCRUTv4 (thick solid black); model mean (thick solid red). Dashed thin lines represent data from individual model runs: 1 – purple, 2 – dark blue, 3 – blue, 4 – green, 5 – yellow, 6 – orange, 7 – magenta. In this and the next figures numbers on the time axis indicate the first year of the 5-year mean.

Secondly, the scenarios are important to understand since they stipulate data inputs the model must accept as conditions for producing forecasts according to a particular scenario (set of assumptions).  The document with complete details referenced as [4] is The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6.

All the details are written there but one diagram suggests the implications for the results described below.

Figure 5. CO2 emissions (a) and concentrations (b), anthropogenic radiative forcing (c), and global mean temperature change (d) for the three long-term extensions. As in Fig. 3, concentration, forcing, and temperature outcomes are calculated with a simple climate model (MAGICC version 6.8.01 BETA; Meinshausen et al., 2011a, b). Outcomes for the CMIP5 versions of the long-term extensions of RCP2.6 and RCP8.5 (Meinshausen et al., 2011c), as calculated with the same model, are shown for comparison.

As shown, the SSP1-26 is virtually the same scenario as the former RCP2.6, while SSP5-85 is virtually the same as RCP8.5, the wildly improbable scenario (impossible according to some analysts).  Note that FF CO2 emissions are assumed to quadruple in the next 80 years, with atmospheric CO2 rising from 400 to 1000 ppm ( +150%).  Bear these suppositions in mind when considering the INMCM5 forecasts below.

Results [Continuing From Volodin and Gritsun]

Fig. 1. Changes in the global average surface temperature (K) with respect to the pre-industrial level in experiments according to the SSP1-2.6 (triangles), SSP2-4.5 (squares), SSP3-7.0 (crosses), and SSP5-8.5 (circles) scenarios.

Let us describe some simulation results of climate change in the 21st century. Figure 1 shows the change in the globally averaged surface air temperature with respect to the data of the corresponding historical experiment for 1850–1899. In the warmest SSP5-8.5 scenario (circles), the temperature rises by more than 4° by the end of the 21st century. In the SSP3-7.0 scenario (crosses), different members of the ensemble show warming by 3.4°–3.6°. In the SSP2-4.5 scenario (squares), the temperature increases by about 2.4°. According to the SSP1-2.6 scenario (triangles) , the maximal warming by ~1.7° occurs in the middle of the 21st century, and the temperature exceeds the preindustrial temperature by 1.4° by the end of the century.

[My comment: Note that the vertical scale starts with +1.0C as was seen in the historical experiment. Thus an anomaly of 1.4C by 2100 is an increase of only 0.4C, while the SSP2-4.5 result adds 1.4C to the present]. 

The results for other CMIP6 models have not yet been published in peer-reviewed journals. However, according to the preliminary analysis (see, e.g.  https://cmip6workshop19.sciencesconf.org/ data/Session1_PosterSlides.pdf, p.29), the INM-CM5 model shows the lowest temperature increase among the CMIP6 models considered for all the scenarios due to the minimal equilibrium sensitivity to the CO2 concentration doubling, which is ~2.1° for the current model version, like for the previous version, despite new condensation and cloud formation blocks. [For more on CMIP6 comparisons see post Climate Models: Good, Bad and Ugly]

Fig. 2. Differences between the annual average surface air temperatures (K) in 2071–2100 and 1981–2010 for the (a) SSP5-8.5 and (b) SSP1-2.6 scenarios.

The changes in the surface air temperature are similar for all scenarios; therefore, we analyze the difference between temperatures in 2071–2100 and 1981–2010 under the SSP5-8.5 and SSP1-2.6 scenarios (Fig. 2). The warming is maximal in the Arctic; it reaches 10° and 3°, respectively. Other features mainly correspond to CMIP5 data [8], including the INMCM4 model, which participates in the comparison. The warming on the continents of the Northern Hemisphere is about 2 times higher than the mean, and the warming in the Southern Hemisphere is noticeably less than in the Northern Hemisphere. The land surface is getting warmer than the ocean surface in all the scenarios except SSP1-2.6, because the greenhouse effect is expected to weaken in the second half of the 21st century in this scenario, and the higher heat capacity of the ocean prevents it from cooling as quickly as the land.

The changes in precipitation in December–February and June–August for the SSP3-7.0 scenario averaged over five members of the ensemble are shown in Fig. 4. All members of the ensemble show an increase in precipitation in the winter in a significant part of middle and high latitudes. In summer, the border between the increase and decrease in precipitation in Eurasia passes mainly around or to the north of 60°. In southern and central Europe, all members of the ensemble show a decrease in precipitation. Precipitation also increases in the region of the summer Asian monsoon, over the equatorial Pacific, due to a decrease in the upwelling and an increase in ocean surface temperature (OST). The distribution of changes in precipitation mainly corresponds to that given in [6, Fig. 12.22] for all CMIP5 models.

The change in the Arctic sea ice area in September, when the ocean ice cover is minimal over the year, is of interest. Figure 5 shows the sea ice area in September 2015–2019 to be 4–6 million km2 in all experiments, which corresponds to the estimate from observations in [11]. The Arctic sea ice does not completely melt in any of the experiments and under any scenario. However, according to [8, Figs. 12.28 and 12.31], many models participating in CMIP6, where the Arctic ice area is similar to that observed at the beginning of the 21st century, show the complete absence of ice by the end of the 21st century, especially under the RCP8.5 scenario, which is similar to SSP5-8.5.

The reason for these differences is the lower equilibrium sensitivity of the INM-CM5 model.

Note that the scatter of data between experiments under different scenarios in the first half of the 21st century is approximately the same as between different members of the ensemble under the SSP3-7.0 scenario and becomes larger only after 2070. The sea ice area values are sorted in accordance with the radiative forcing of the scenarios only after 2090. This indicates the large contribution of natural climate variability into the Arctic ice area. In the SSP1-2.6 experiment, the Arctic ice area at the end of the 21st century approximately corresponds to its area at the beginning of the experiment.

Climate changes can be also traced in the ocean circulation. Figure 6 shows the change in the 5-year averaged intensity of the Atlantic meridional circulation, defined as the maximum of the meridional streamfunction at 32° N. All experiments show a decrease in the intensity of meridional circulation in the 21st century and natural fluctuations against this decrease. The decrease is about 4.5–5 Sv for the SSP5-8.5 scenario, which is close to values obtained in the CMIP5 models [8, Fig. 12.35] under the RCP8.5 scenario. Under milder scenarios, the weakening of the meridional circulation is less pronounced. The reason for this weakening of the meridional circulation in the Atlantic, as far as we know, is not yet fully understood.

Conclusion

Numerical experiments have been carried out to reproduce climate changes in the 21st century according to four scenarios of the CMIP6 program [4, 5], including an ensemble of five experiments under the SSP3-7.0 scenario. The changes in the global mean surface temperature are analyzed. It is shown that the global warming predicted by the INM-CM5 model is the lowest among the currently published CMIP6 model data. The geographical distribution of changes in the temperature and precipitation is considered. According to the model, the temperature in the warmest summer month will increase faster than the summer average temperature in Russia.

None of the experiments show the complete melting of the Arctic ice cover by the end of the 21st century. Some changes in the ocean dynamics, including the flow velocity and the meridional stream function, are analyzed. The changes in the Hadley and Ferrel circulation in the atmosphere are considered.

Resources:

Climate Models: Good, Bad and Ugly

2018 Update: Best Climate Model INMCM5

Temperatures According to Climate Models

Advance Briefing for Glasgow COP 2021

 

Presently the next climate Conference of Parties is scheduled for Glasgow this November, Covid allowing.  (People used to say “God willing”, or “Weather permitting”, but nowadays it’s a virus in charge.)  Actually, climate hysteria is like a seasonal sickness.  Each year a contagion of anxiety and fear is created by disinformation going viral in both legacy and social media in the run up to the autumnal COP (postponed last year due to pandemic travel restrictions).  Now that climatists have put themselves at the controls of the formidable US federal government, we can expect the public will be hugely hosed with alarms over the next few months.  Before the distress signals go full tilt, individuals need to inoculate themselves against the false claims, in order to build some herd immunity against the nonsense the media will promulgate. This post is offered as a means to that end.

Media Climate Hype is a Cover Up

Back in 2015 in the run up to Paris COP, French mathematicians published a thorough critique of the raison d’etre of the whole crusade. They said:

Fighting Global Warming is Absurd, Costly and Pointless.

  • Absurd because of no reliable evidence that anything unusual is happening in our climate.
  • Costly because trillions of dollars are wasted on immature, inefficient technologies that serve only to make cheap, reliable energy expensive and intermittent.
  • Pointless because we do not control the weather anyway.

The prestigious Société de Calcul Mathématique (Society for Mathematical Calculation) issued a detailed 195-page White Paper presenting a blistering point-by-point critique of the key dogmas of global warming. The synopsis with links to the entire document is at COP Briefing for Realists

Even without attending to their documentation, you can tell they are right because all the media climate hype is concentrated against those three points.

Finding: Nothing unusual is happening with our weather and climate.
Hype: Every metric or weather event is “unprecedented,” or “worse than we thought.”

Finding: Proposed solutions will cost many trillions of dollars for little effect or benefit.
Hype: Zero carbon will lead the world to do the right thing.  Anyway, the planet must be saved at any cost.

Finding: Nature operates without caring what humans do or think.
Hype: Any destructive natural event is blamed on humans burning fossil fuels.

How the Media Throws Up Flak to Defend False Suppositions

The Absurd Media:  Climate is Dangerous Today, Yesterday It was Ideal.

Billions of dollars have been spent researching any and all negative effects from a warming world: Everything from Acne to Zika virus.  A recent Climate Report repeats the usual litany of calamities to be feared and avoided by submitting to IPCC demands. The evidence does not support these claims. An example:

 It is scientifically established that human activities produce GHG emissions, which accumulate in the atmosphere and the oceans, resulting in warming of Earth’s surface and the oceans, acidification of the oceans, increased variability of climate, with a higher incidence of extreme weather events, and other changes in the climate.

Moreover, leading experts believe that there is already more than enough excess heat in the climate system to do severe damage and that 2C of warming would have very significant adverse effects, including resulting in multi-meter sea level rise.

Experts have observed an increased incidence of climate-related extreme weather events, including increased frequency and intensity of extreme heat and heavy precipitation events and more severe droughts and associated heatwaves. Experts have also observed an increased incidence of large forest fires; and reduced snowpack affecting water resources in the western U.S. The most recent National Climate Assessment projects these climate impacts will continue to worsen in the future as global temperatures increase.

Alarming Weather and Wildfires

But: Weather is not more extreme.


And Wildfires were worse in the past.
But: Sea Level Rise is not accelerating.


Litany of Changes

Seven of the ten hottest years on record have occurred within the last decade; wildfires are at an all-time high, while Arctic Sea ice is rapidly diminishing.

We are seeing one-in-a-thousand-year floods with astonishing frequency.

When it rains really hard, it’s harder than ever.

We’re seeing glaciers melting, sea level rising.

The length and the intensity of heatwaves has gone up dramatically.

Plants and trees are flowering earlier in the year. Birds are moving polewards.

We’re seeing more intense storms.

But: Arctic Ice has not declined since 2007.

But: All of these are within the range of past variability.

In fact our climate is remarkably stable, compared to the range of daily temperatures during a year where I live.

And many aspects follow quasi-60 year cycles.

The Impractical Media:  Money is No Object in Saving the Planet.

Here it is blithely assumed that the court can rule the seas to stop rising, heat waves to cease, and Arctic ice to grow (though why we would want that is debatable).  All this will be achieved by leaving fossil fuels in the ground and powering civilization with windmills and solar panels.  While admitting that our way of life depends on fossil fuels, they ignore the inadequacy of renewable energy sources at their present immaturity.

 

An Example:
The choice between incurring manageable costs now and the incalculable, perhaps even irreparable, burden Youth Plaintiffs and Affected Children will face if Defendants fail to rapidly transition to a non-fossil fuel economy is clear. While the full costs of the climate damages that would result from maintaining a fossil fuel-based economy may be incalculable, there is already ample evidence concerning the lower bound of such costs, and with these minimum estimates, it is already clear that the cost of transitioning to a low/no carbon economy are far less than the benefits of such a transition. No rational calculus could come to an alternative conclusion. Defendants must act with all deliberate speed and immediately cease the subsidization of fossil fuels and any new fossil fuel projects, and implement policies to rapidly transition the U.S. economy away from fossil fuels.

But CO2 relation to Temperature is Inconsistent.

But: The planet is greener because of rising CO2.

But: Modern nations (G20) depend on fossil fuels for nearly 90% of their energy.

But: Renewables are not ready for prime time.

People need to know that adding renewables to an electrical grid presents both technical and economic challenges.  Experience shows that adding intermittent power more than 10% of the baseload makes precarious the reliability of the supply.  South Australia is demonstrating this with a series of blackouts when the grid cannot be balanced.  Germany got to a higher % by dumping its excess renewable generation onto neighboring countries until the EU finally woke up and stopped them. Texas got up to 29% by dumping onto neighboring states, and some like Georgia are having problems.

But more dangerous is the way renewables destroy the economics of electrical power.  Seasoned energy analyst Gail Tverberg writes:

In fact, I have come to the rather astounding conclusion that even if wind turbines and solar PV could be built at zero cost, it would not make sense to continue to add them to the electric grid in the absence of very much better and cheaper electricity storage than we have today. There are too many costs outside building the devices themselves. It is these secondary costs that are problematic. Also, the presence of intermittent electricity disrupts competitive prices, leading to electricity prices that are far too low for other electricity providers, including those providing electricity using nuclear or natural gas. The tiny contribution of wind and solar to grid electricity cannot make up for the loss of more traditional electricity sources due to low prices.

These issues are discussed in more detail in the post Climateers Tilting at Windmills

The Irrational Media:  Whatever Happens in Nature is Our Fault.

An Example:

Other potential examples include agricultural losses. Whether or not insurance
reimburses farmers for their crops, there can be food shortages that lead to higher food
prices (that will be borne by consumers, that is, Youth Plaintiffs and Affected Children).
There is a further risk that as our climate and land use pattern changes, disease vectors
may also move (e.g., diseases formerly only in tropical climates move northward).36 This
could lead to material increases in public health costs

But: Actual climate zones are local and regional in scope, and they show little boundary change.

But: Ice cores show that it was warmer in the past, not due to humans.

The hype is produced by computer programs designed to frighten and distract children and the uninformed.  For example, there was mention above of “multi-meter” sea level rise.  It is all done with computer models.  For example, below is San Francisco.  More at USCS Warnings of Coastal Floodings

In addition, there is no mention that GCMs projections are running about twice as hot as observations.

Omitted is the fact GCMs correctly replicate tropospheric temperature observations only when CO2 warming is turned off.

Figure 5. Simplification of IPCC AR5 shown above in Fig. 4. The colored lines represent the range of results for the models and observations. The trends here represent trends at different levels of the tropical atmosphere from the surface up to 50,000 ft. The gray lines are the bounds for the range of observations, the blue for the range of IPCC model results without extra GHGs and the red for IPCC model results with extra GHGs.The key point displayed is the lack of overlap between the GHG model results (red) and the observations (gray). The nonGHG model runs (blue) overlap the observations almost completely.

In the effort to proclaim scientific certainty, neither the media nor IPCC discuss the lack of warming since the 1998 El Nino, despite two additional El Ninos in 2010 and 2016.

Further they exclude comparisons between fossil fuel consumption and temperature changes. The legal methodology for discerning causation regarding work environments or medicine side effects insists that the correlation be strong and consistent over time, and there be no confounding additional factors. As long as there is another equally or more likely explanation for a set of facts, the claimed causation is unproven. Such is the null hypothesis in legal terms: Things happen for many reasons unless you can prove one reason is dominant.

Finally, advocates and IPCC are picking on the wrong molecule. The climate is controlled not by CO2 but by H20. Oceans make climate through the massive movement of energy involved in water’s phase changes from solid to liquid to gas and back again. From those heat transfers come all that we call weather and climate: Clouds, Snow, Rain, Winds, and Storms.

Esteemed climate scientist Richard Lindzen ended a very fine recent presentation with this description of the climate system:

I haven’t spent much time on the details of the science, but there is one thing that should spark skepticism in any intelligent reader. The system we are looking at consists in two turbulent fluids interacting with each other. They are on a rotating planet that is differentially heated by the sun. A vital constituent of the atmospheric component is water in the liquid, solid and vapor phases, and the changes in phase have vast energetic ramifications. The energy budget of this system involves the absorption and reemission of about 200 watts per square meter. Doubling CO2 involves a 2% perturbation to this budget. So do minor changes in clouds and other features, and such changes are common. In this complex multifactor system, what is the likelihood of the climate (which, itself, consists in many variables and not just globally averaged temperature anomaly) is controlled by this 2% perturbation in a single variable? Believing this is pretty close to believing in magic. Instead, you are told that it is believing in ‘science.’ Such a claim should be a tip-off that something is amiss. After all, science is a mode of inquiry rather than a belief structure.

Summary:  From this we learn three things:

Climate warms and cools without any help from humans.

Warming is good and cooling is bad.

The hypothetical warming from CO2 would be a good thing.

 

2021 Starts with Cool Land and Sea

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With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you will hear a lot about 2020 temperatures matching 2016 as the highest ever, that spin ignores how fast is the cooling setting in.  The UAH data analyzed below shows that warming from the last El Nino is now fully dissipated with all regions heading down.

UAH has updated their tlt (temperatures in lower troposphere) dataset for January.  Previously I have done posts on their reading of ocean air temps as a prelude to updated records from HADSST3. This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

Note:  UAH has shifted their baseline from 1981-2010 to 1991-2020 beginning with January 2021.  In the charts below, the trends and fluctuations remain the same but the anomaly values change with the baseline reference shift.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

After a technical enhancement to HadSST3 delayed March and April updates, May resumed a pattern of HadSST updates mid month.  For comparison we can look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for January. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the new and current dataset.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI).  The graph below shows monthly anomalies for ocean temps since January 2015.

To enlarge open image in new tab.

Note 2020 was warmed mainly by a spike in February in all regions, and secondarily by an October spike in NH alone. End of 2020 November and December ocean temps plummeted in NH and the Tropics. In January SH dropped sharply, pulling the Global anomaly down despite an upward bump in NH. Both SH and the Tropics are now as cold as any time in the last five years, and all regions are comparable to to 2015 prior to the 2016 El Nino event.

Land Air Temperatures Tracking Downward in Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for January is below.

Here we have fresh evidence of the greater volatility of the Land temperatures, along with an extraordinary departure by SH land.  Land temps are dominated by NH with a 2020 spike in February, followed by cooling down to July.  Then NH land warmed with a second spike in November.  Note the mid-year spikes in SH winter months.  In December all of that was wiped out. Then January showed a sharp drop in SH, but a rise in NH more than offset, pulling the Global anomaly upward. All regions are roughly comparable to early 2015, prior to the 2016 El Nino.

The Bigger Picture UAH Global Since 1995

The chart shows monthly anomalies starting 01/1995 to present.  The average anomaly is 0.04, since this period is the same as the new baseline, lacking only the first 4 years.  1995 was chosen as an ENSO neutral year.  The graph shows the 1998 El Nino after which the mean resumed, and again after the smaller 2010 event. The 2016 El Nino matched 1998 peak and in addition NH after effects lasted longer, followed by the NH warming 2019-20, with temps now returning again to the mean.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, more than 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

Climate Science Victim of Fake News

A recent article in the legacy media needed some editorial work in the public interest. Published at the Business Post, it began this way:

Climate science has long been the victim of ‘fake news’ obscuring uncomfortable truths. By pouncing on supposed uncertainties in climate science, big business interests and their supporters in the media divert attention away from the real climate emergency.

Now that is so misleading that a “False Alarm” label should be attached by fact checkers. In their absence, the next best thing is to rewrite to set the record straight and eliminate the falsehoods and hype. So let’s begin again.

Climate science has long been the victim of ‘false alarms’ obscuring the remarkable stability of our climate system. By exaggerating the dangers from extreme weather, entrenched environmental lobbies and ignorant media supporters frighten people for the sake of their tax-subsidized enterprises. (There, fixed.) To Continue:

A climate change awareness rally in Sydney in 2019. Picture: Don Arnold/Getty Images

Climate change is a popular crusade with catchy slogans and many social gatherings to celebrate solidarity. Actual scientific understanding of the climate is hard, lonely work collecting and analyzing data. And simplistic notions about “fighting climate change” are nonsense without rigorous cost and benefit analysis.

To the political classes and wider public still reeling from social and mass media censorship and warped “fact-checking”, and astounded that the world’s leading democracy could see its elections invalidated by a blizzard of lies and backroom vote counting, climate scientists might well say: Don’t be so naive.

Take Phil Jones, a quietly spoken climatologist at the University of East Anglia in England. In 2009, he was caught up in a whistleblower’s leak of context from the university’s email servers which was later dubbed “climategate.”

There are three threads in particular in the leaked documents which sent a shock wave through informed observers across the world. Perhaps the most obvious was the highly disturbing series of emails which show how Dr Jones and his colleagues had for years been discussing the devious tactics whereby they could avoid releasing their data to outsiders under freedom of information laws.

But the question which inevitably arises from this systematic refusal to release their data is – what is it that these scientists seem so anxious to hide? The second and most shocking revelation of the leaked documents is how they show the scientists trying to manipulate data through their tortuous computer programs, always to point in only the one desired direction – to lower past temperatures and to ‘adjust’ recent temperatures upwards, in order to convey the impression of an accelerated warming. This is what Mr McIntyre caught Dr Hansen doing with his GISS temperature record last year (after which Hansen was forced to revise his record), and two further shocking examples have now come to light from Australia and New Zealand.

The third shocking revelation of these documents is the ruthless way in which these academics have been determined to silence any expert questioning of the findings they have arrived at by such dubious methods – not just by refusing to disclose their basic data but by discrediting and freezing out any scientific journal which dares to publish their critics’ work. It seems they are prepared to stop at nothing to stifle scientific debate in this way, not least by ensuring that no dissenting research should find its way into the pages of IPCC reports. (Source:  excerpt from John Walker, former Laboratory Medical Director/Pathologist (1984-2011) See: Q&A Why So Many Climate Skeptics

Background from previous post:

In 2021, there may well be a new deluge of hysterical claims from the usual suspects published at the usual venues comprising legacy and social media.

These outrageous appeals by alarmists in the face of contrary facts remind me of the story defining the term “chutzpuh.” A young man is convicted of killing his parents, and later appears before the judge for sentencing. Asked to give any last words, he replies: “Go easy on me, your Honor, I’m an orphan.”
alcoholics-anonymous-logo-e1497443623248

Fortunately, there is help for climate alarmists. They can join or start a chapter of Alarmists Anonymous. By following the Twelve Step Program, it is possible to recover and unite in service to the real world and humanity.

Step One: Fully concede (admit) to our innermost selves that we were addicted to climate fear mongering.

Step Two: Come to believe that a Power greater than ourselves causes weather and climate, restoring us to sanity.

Step Three: Make a decision to study and understand how the natural world works.

Step Four: Make a searching and fearless moral inventory of ourselves, our need to frighten others and how we have personally benefited by expressing alarms about the climate.

Step Five: Admit to God, to ourselves, and to another human being the exact nature of our exaggerations and false claims.

Step Six: Become ready to set aside these notions and actions we now recognize as objectionable and groundless.

Step Seven: Seek help to remove every single defect of character that produced fear in us and led us to make others afraid.

Step Eight: Make a list of all persons we have harmed and called “deniers”, and become willing to make amends to them all.

Step Nine: Apologize to people we have frightened or denigrated and explain the errors of our ways.

Step Ten: Continue to take personal inventory and when new illusions creep into our thinking, promptly renounce them.

Step Eleven: Dedicate ourselves to gain knowledge of natural climate factors and to deepen our understanding of nature’s powers and ways of working.

Step Twelve: Having awakened to our delusion of climate alarm, we try to carry this message to other addicts, and to practice these principles in all our affairs.

Summary:

With a New Year just beginning, let us hope that many climate alarmists take the opportunity to turn the page by resolving a return to sanity. It is not too late to get right with reality before the cooling comes in earnest.

This is your brain on climate alarm.  Just say No!

 

Oceans Confirm Global Cooling Year End 2020


The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.  More on what distinguishes HadSST3 from other SST products at the end.

The Current Context

The year end report below shows 2020 is rapidly cooling in all regions.  The anomalies have been dropping sharply and are now well below the averages since 1995.  This Global Cooling was also evident in the UAH Land and Ocean air temperatures (See Big Chill Over Land and Sea with 2020 End )

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through December 2020. After three straight Spring 2020 months of cooling led by the tropics and SH, NH spiked in the summer.  Now temps everywhere are dropping the last four months, with SH the lowest in this period, and Global and Tropical anomalies below average since 2015.

A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016.  In 2019 all regions had been converging to reach nearly the same value in April.

Then  NH rose exceptionally by almost 0.5C over the four summer months, in August 2019 exceeding previous summer peaks in NH since 2015.  In the 4 succeeding months, that warm NH pulse reversed sharply. Then again NH temps warmed to a 2020 summer peak, matching 2019.  This has now been reversed with all regions pulling the Global anomaly downward sharply.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year.  A fourth NH bump was lower and peaked in September 2018.  As noted above, a fifth peak in August 2019 and a sixth August 2020 exceeded the four previous upward bumps in NH.

And as before, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.  The major difference between now and 2015-2016 is the absence of Tropical warming driving the SSTs, along with SH anomalies reaching nearly the lowest in this period. Presently both SH and the Tropics are quite cool, with NH coming off its summer peak.  Note the tropical temps descending into La Nina levels.  At this point, the 2016 El Nino and its NH after effects have dissipated completely.

A longer view of SSTs

The graph below  is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July.

To enlarge image, double-click or open in new tab.

1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino.  The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99.  For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.2C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.4C.  Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs.  As a result the Global average fluctuates around that same 0.4C, which also turns out to be the average for the entire record since 1995.

2007 stands out with a sharp drop in temperatures so that Jan.08 matches the low in Jan. ’99, but starting from a lower high. The oceans all decline as well, until temps build peaking in 2010.

Now again a different pattern appears.  The Tropics cool sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peak came in 2019, only this time the Tropics and SH are offsetting rather adding to the warming. Since 2014 SH has played a moderating role, offsetting the NH warming pulses. Now September 2020 is dropping off last summer’s unusually high NH SSTs. (Note: these are high anomalies on top of the highest absolute temps in the NH.)

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

But the peaks coming nearly every summer in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.
The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows August warming began after 1992 up to 1998, with a series of matching years since, including 2020.  Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.

This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. The black line shows that 2020 began slightly warm, then set records for 3 months. then dropped below 2016 and 2017, peaked in August and is now below 2016.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? If the pattern of recent years continues, NH SST anomalies may rise slightly in coming months, but once again, ENSO which has weakened will probably determine the outcome.

Footnote: Why Rely on HadSST3

HadSST3 is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST3 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

 

Big Chill Over Land and Sea with 2020 End

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With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you will hear a lot about 2020 temperatures matching 2016 as the highest ever, that spin ignores how fast is the cooling setting in.  The UAH data analyzed below shows that warming from the last El Nino is now fully dissipated with all regions heading down.

UAH has updated their tlt (temperatures in lower troposphere) dataset for December.  Previously I have done posts on their reading of ocean air temps as a prelude to updated records from HADSST3. This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

After a technical enhancement to HadSST3 delayed March and April updates, May resumed a pattern of HadSST updates mid month.  For comparison we can look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for December. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the new and current dataset.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI).  The graph below shows monthly anomalies for ocean temps since January 2015.

Note 2020 is warmed mainly by a spike in February in all regions, and secondarily by an October spike in NH alone. Now in 2020 November and December ocean temps are plummeting in NH and the Tropics, while SH is little changed. Both NH and the Tropics are now as cold as any time in the last five years, and all regions are comparable to to 2015 prior to the 2016 El Nino event.

Land Air Temperatures Tracking Downward in Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for December is below.

Here we have fresh evidence of the greater volatility of the Land temperatures, along with an extraordinary departure by SH land.  Land temps are dominated by NH with a spike in February, followed by cooling down to July.  Then NH land warmed with a second spike in November.  Note the mid-year spikes in SH winter months. Now in December all of that has been wiped out. All regions are comparable to early 2015, prior to the 2016 El Nino.

The Bigger Picture UAH Global Since 1995

The chart shows monthly anomalies starting 01/1995 to present.  The average anomaly is 0.18, which was typical after the 1998 El Nino ended, and again after the smaller 2010 event. The 2016 El Nino matched 1998 with NH after effects lasting longer, followed by the NH warming 2019-20, with temps now returning again to the mean.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, more than 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

Ocean Temps Plunging Nov. 2020


The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.  More on what distinguishes HadSST3 from other SST products at the end.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through November 2020. After three straight Spring months of cooling led by the tropics and SH, NH spiked in the summer.  Now temps everywhere are dropping the last three months, with SH the lowest in this period, and Global anomalies below average since 2015.

A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016.  In 2019 all regions had been converging to reach nearly the same value in April.

Then  NH rose exceptionally by almost 0.5C over the four summer months, in August 2019 exceeding previous summer peaks in NH since 2015.  In the 4 succeeding months, that warm NH pulse reversed sharply. Then again NH temps warmed to a 2020 summer peak, matching 2019.  This has now been reversed with SH and Tropics pulling the Global anomaly downward sharply.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year.  A fourth NH bump was lower and peaked in September 2018.  As noted above, a fifth peak in August 2019 and a sixth August 2020 exceeded the four previous upward bumps in NH.

And as before, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.  The major difference between now and 2015-2016 is the absence of Tropical warming driving the SSTs, along with SH anomalies reaching nearly the lowest in this period. Presently both SH and the Tropics are quite cool, with NH coming off its summer peak.  Note the tropical temps descending into La Nina levels.

A longer view of SSTs

The graph below  is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July.

1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino.  The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99.  For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.2C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.4C.  Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs.  As a result the Global average fluctuates around that same 0.4C, which also turns out to be the average for the entire record since 1995.

2007 stands out with a sharp drop in temperatures so that Jan.08 matches the low in Jan. ’99, but starting from a lower high. The oceans all decline as well, until temps build peaking in 2010.

Now again a different pattern appears.  The Tropics cool sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peak came in 2019, only this time the Tropics and SH are offsetting rather adding to the warming. Since 2014 SH has played a moderating role, offsetting the NH warming pulses. Now September 2020 is dropping off last summer’s unusually high NH SSTs. (Note: these are high anomalies on top of the highest absolute temps in the NH.)

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

But the peaks coming nearly every summer in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.
The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows August warming began after 1992 up to 1998, with a series of matching years since, including 2020.  Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.

This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. The black line shows that 2020 began slightly warm, then set records for 3 months. then dropped below 2016 and 2017, peaked in August and is now below 2016.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? If the pattern of recent years continues, NH SST anomalies may rise slightly in coming months, but once again, ENSO which has weakened will probably determine the outcome.

Footnote: Why Rely on HadSST3

HadSST3 is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST3 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

Why Net Zero CO2 is Social Suicide

Greta and her handlers are upset that political leaders followed advice from epidemiologists and imposed lockdowns against Covid, but not against climate change.  Commenters at Not a Lot of People Know That exposed Greta’s ignorance for championing Net Zero policies to reduce atmospheric CO2.  One problem is the impracticality of removing CO2 to put into storage.  Broadlands noted:

NET stands for Negative Emission Technology. That means industrial removal and geological burial of billions of tons of CO2 under pressure. The irony is the fact that they cannot fit a lot into those geological locations…not even one part-per-million. Greta’s puppeteers don’t even realize that themselves. Certainly not Mr. Biden’s experts as they tool up to spend trillions.

Even more dangerous is activists failing to recognize we are presently suffering from a dearth of CO2, not a surplus  Pardonmeforbreathing drew the implications from the above graph (excerpted):

If you look at the Surface Temperature vs Atmospheric CO2 Concentration in the chart you will see something more concerning AND REAL than the over hyped COVID and Climate Change put together.  This is data is not new and shows  the problem with the Carbon Cycle is caused by a linear decrease in atmospheric CO2 for the last 160 million years. Far from being too much CO2 which is hyped by the priests of climatism, there is way too little. Reach160ppm CO2 in the atmosphere and photosynthesis is compromised. We were only 20ppm away from it during the first part of the current Ice Age. Us driving our SUVs to the supermarket inadvertently has temporarily halted the decline so mankind deserves a big pat on the back!

Greta and her fellow lemmings racing to the cliff edge who pull her strings want to spend BILLIONS and BILLIONS getting us to a real extinction event even quicker! .

Background from William Happer (2019)

From Happer’s Statement: CO₂ will be a major benefit to the Earth Excerpts in italics with my bolds.

Figure 1. The ratio, RCO2, of past atmospheric CO2 concentrations to average values (about 300 ppm) of the past few million years, This particular proxy record comes from analyzing the fraction of the rare stable isotope 13C to the dominant isotope 12C in carbonate sediments and paleosols. Other proxies give qualitatively similar results.

Fig. 1 summarizes the most important theme of this discussion. It is not true that releasing more CO2 into the atmosphere is a dangerous, unprecedented experiment. The Earth has already “experimented” with much higher CO2 levels than we have today or that can be produced by the combustion of all economically recoverable fossil fuels.

More CO2 in the atmosphere will be good for life on planet earth. Few realize that the world has been in a CO2 famine for millions of years — a long time for us, but a passing moment in geological history. Over the past 550 million years since the Cambrian, when abundant fossils first appeared in the sedimentary record, CO2 levels have averaged many thousands of parts per million (ppm), not today’s few hundred ppm, which is not that far above the minimum level, around 150 ppm, when many plants die of CO2 starvation.

Summary

The Earth is in no danger from increasing levels of CO2. More CO2 will be a major benefit to the biosphere and to humanity. Some of the reasons are:

  • As shown in Fig. 1, much higher CO2 levels than today’s prevailed over most last 550 million years of higher life forms on Earth. Geological history shows that the biosphere does better with more CO2.
  • As shown in Fig. 13 and Fig. 14, observations over the past two decades show that the warming predicted by climate models has been greatly exaggerated. The temperature increase for doubling CO2 levels appears to be close to the feedback-free doubling sensitivity of S =1 K, and much less than the “most likely” value S = 3 K promoted by the IPCC and assumed in most climate models.
  • As shown in Fig. 12, if CO2 emissions continue at levels comparable to those today, centuries will be needed for the added CO2 to warm the Earth’s surface by 2 K, generally considered to be a safe and even beneficial amount.
  • Over the past tens of millions of years, the Earth has been in a CO2 famine with respect to the optimal levels for plants, the levels that have prevailed over most of the geological history of land plants. There was probably CO2 starvation of some plants during the coldest periods of recent ice ages. As shown in Fig. 15–17, more atmospheric CO2 will substantially increase plant growth rates and drought resistance.

More at previous post: Climate Advice: Don’t Worry, Be Happer