New Paper: Investigative Journalism Professor Slams Today’s ‘Fake News’ Climate Science Reporting

Professor: Climate Journalism Awash In

‘Emotional Propaganda’, ‘Mythological Constructs’

Too Much Reliance On Models, ‘Consensus’

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A University of Wollongong (Australia) investigative journalism professor with a research interest in ecological science and exposing environmental fraud has just published a scathing indictment of the climate science journalism industry in the academic journal Asia Pacific Media Educator.

Pulling no punches, Dr. David Blackall lambastes the modern climate science journalism practice of relying more on theoretical models, “expert” predictions, and authoritative “consensus” than on empirical observation and real-world physical measurements in reporting stories on global warming.

Instead of evaluating alarming claims of impending climate catastrophe with reasonable skepticism and critical review, today’s journalists not only reflexively accept the planetary meltdown narratives they promulgate, they simultaneously conceal the growing body of scientific evidence that may ameliorate the scariness of the modern climate narrative.

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Journalists Refuse To Report Non-Alarmist Scientific Evidence

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(1) Multiple papers have been published within the last year (Turner et al., 2016, Oliva et al., 2017) indicating that the rapid warming trend observed during the late 20th century for the Antarctic Peninsula (AP) has now reversed, and the AP has been dramatically cooling (-0.47 °C per decade) and glaciers have stopped receding in the region since 1999.   This cooling trend has not been reported by mainstream media outlets.

(2) Earlier this year, a paper (Fettweis et al ., 2017, with a review available here) was published in The Cryosphere indicating that the Greenland Ice Sheet melt had (a) contributed just 1.5 cm (0.6 of an inch) to sea levels between 1900 and 2010; (b) there was no net ice sheet loss during the 60 years between 1940 and 2000 despite explosive growth in anthropogenic CO2 emissions during that period; and (c) net ice sheet losses were similar to today during the 1930s, when CO2 concentrations were about 100 ppm lower.  This long-term Greenland Ice Sheet mass balance in an era of “alarming” warmth has not been reported by mainstream media outlets.

(3) In the 2007 IPCC report, it was claimed that glaciers in the Himalayas were melting so rapidly that the “likelihood is very high” that they would “disappear” by the year 2035.   And yet many published scientific papers have shown (here, here, here, here, here, here, and here) that the Himalayan region has not only not been warming in recent decades, but 88% of the glaciers in the region are either stable or advancing, with a net change of just 0.2% since 2000 (Bahuguna et al., 2014, Bolch et al., 2016, Holzer et al., 2015, Zhang et al., 2016).

(4) About a year ago, a NoTricksZone review of 8 recently published scientific papers revealed (a) land area across the world is expanding more rapidly than sea levels are rising; (b) climate change (warming) is not the primary determinant of sea level changes (coastal erosion and accretion, tectonic uplift and subsidence are more influential); (c) globally, sea levels are only rising by about 1 mm per year according to tide gauges; and (d) an anthropogenic signal could not be detected in regional sea level rise trends.  Of course, no mainstream media outlet publicized these scientific findings.  They don’t support the alarmist narrative.

(5) There were 133 peer-reviewed scientific papers published in 2016 linking solar forcing to climate changes.  There have already been 84 Sun-Climate papers published thus far in 2017.   More and more solar scientists are predicting a Grand Solar Minimum and concomitant global cooling in the coming decades.  Journalists have not been inclined to report on these developments in solar science.  The Sun-Climate link does not fit with narrative that humans are the predominant cause of climate changes.

(6) Finally, a collection of over 300 graphs of reconstructed historical (Holocene) temperatures has been made available in recent months. These graphs, taken from hundreds of peer-reviewed scientific papers, reveal that modern warming trends are neither unusual or unprecedented, and they do not even fall outside the range of natural (pre-anthropogenic influence) variability.   And yet what do mainstream journalists report in their headlines on a routine basis?   That today’s temperature changes are “shocking”, “stunning” and “unprecedented”.

Would it be so difficult for journalists to actually seek scientific verification of their claims before publishing?

Or is the pursuit of real-world scientific confirmation too much to expect from journalists and media sources bent on advancing an agenda in this “Post Truth World”?

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Blackall, 2017

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‘Forlorn’ Polar Bears An Example of  ‘Emotional Propaganda’, ‘Fake News’ Reporting

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“One particularly emotive story line attached to this topic is the so-called pending extinction of the polar bear (Ursus maritimus) population. In recent times, there have been a number of claims that polar bears are threatened with extinction because global warming was melting their habitat. Yet the scientific evidence suggests to the contrary: population counts conducted between 2007 and 2017 suggest that bear numbers are on the increase. This has led Crockford (2017a) to label such claims as emotional propaganda. In the last decade, cherrypicked and unverified photographic material, ‘emotional’ videos, even animation, then used in news, of forlorn bears floating on ice was the practice (Crockford, 2016; Rode, 2014). This is a good example of ‘fake news’.”

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Climate Models Not Confirmed, Harmonious Pre-Industrial Climate A ‘Mythical Construct’

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“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).”

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A Non-Warming Climate Doesn’t Fit The Narrative – So It’s Unreported, Manipulated

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“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). New Zealand’s National Institute of Water and Atmospheric Research and Victoria University found that ‘regional cooling’ over 25 years had correlated with growing glaciers (Mackintosh et al., 2017).”

“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. This phenomenon was due to rainfall over Central Australia, which filled vast inland lakes. It was not predicted in the models, nor was it reported in the news. 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.”

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Scientific Uncertainty Replaced By ‘Consensus’ (Post-Normal) Science, Model ‘Validation’

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“Scientists test, measure, observe and retest, and they must be able to verify and repeat results (Errington et al., 2014). Uncertainty is always present (van Der Sluijs, 2005), but when uncertainty is replaced by ‘consensus’ (post-normal science), a culture of gatekeeping ensues (Lindzen, 2009). Post-normal science is said to be appropriate when ‘traditional methodologies are ineffective. In those circumstances, the quality assurance of scientific inputs to the policy process requires an ‘extended peer community’, consisting of all those with a stake in the dialogue on the issue’ (Funtowicz & Ravetz, 1993). Then, and dangerously, dissenters are silenced so that chosen and ‘necessary’ discourses arrive in journals, conferences and boardrooms. In such a 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.”

“Journalism conveys a ‘professional authority’—touting its discourse as ‘fact checked’, within ‘editorial consensus’—its validation process. However, ‘validation’ in climate science means something completely different—a model is validated, ‘acceptable for its intended use’, because it meets specified computer performance requirements (Rykiel, 1996).”

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Correcting Climate Journalism’s ‘False Narratives’: Offer Public Alternative Perspectives

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‘An online survey revealed similarity between climate change deniers and believers in terms of preference for climate change news sources and rating of reliability of authorities. It was also discovered that both groups do not believe in conspiracy theories. Thus the results show that participants on both sides in the discussion on climate change are similar, rational, and are basing their judgments by using similar types of sources.’ (Grabbe, 2015)

“As there is uncertainty with greenhouse gas theory, students should be given alternative perspectives to help find ways to publish stories that question, challenge and enlighten. With technological change in the traditional newsroom, which brings ‘heightened accountability’ (Bivens, 2008), and instantaneous research capability, there are plenty of opportunities to correct false narratives.”

An Alternative Perspective Example: Clouds As Climate Control Mechanism

“One avenue is to suggest the alternative narrative: clouds are crucial in climatic control, yet their role and production is not thoroughly understood. Clouds control terrestrial and ocean surface temperatures and this has been known for decades—in agronomy, geography and meteorology.  Could the great environmental catastrophe instead involve clouds and the water cycle?”

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20+ New Papers Affirm The Failures Of Climate Modeling

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Hedemann et al., 2017  (full paper)

“During the first decade of the twenty-first century, the Earth’s surface warmed more slowly than climate models simulated. This surface-warming hiatus is attributed by some studies to model errors in external forcing, while others point to heat rearrangements in the ocean caused by internal variability, the timing of which cannot be predicted by the models. The observed trend deviated by as much as −0.17 ◦C per decade from the CMIP5 (Coupled Model Intercomparison Project Phase 5) ensemble-mean projection—a gap two to four times the observed trend. The hiatus therefore continues to challenge climate science.”

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Power et al., 2017

“All of the model simulations examined simulate multi-decadal warming in the Pacific over the past half-century that exceeds observed values.”

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Zhou and Wang, 2017

“Land surface air temperature (Ta) is one of the fundamental variables in weather and climatic observations, modeling, and applications. Despite the ongoing increase in atmospheric greenhouse gases, the global mean surface temperature (GMST) has remained rather steady and has even decreased in the central and eastern Pacific since 1998. This cooling trend is referred to as the global ‘warming hiatus’“

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Xie et al., 2017

“As the recent global warming hiatus has attracted worldwide attention, we examined the robustness of the warming hiatus in China and the related dynamical mechanisms in this study. Based on the results confirmed by the multiple data and trend analysis methods, we found that the annual mean temperature in China had a cooling trend during the recent global warming hiatus period, which suggested a robust warming hiatus in China.”

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Xian and Fu, 2017

“Despite continually increasing concentrations of greenhouse gas, there has been a hiatus in rising global temperatures during the 21st century.”

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Liu and Zhou, 2017

“[C]limate models designed to represent the physics and dynamics of the climate system project that GMST [global mean surface temperature] continued to rise in the early 2000s. Dominant mechanisms proposed to understand the hiatus included the internal climate variability and ocean heat uptake and transport; however, the differences in the atmospheric footprint of recent warming slowdown remains unclear in terms of the dynamical and physical processes.”

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Rosenblum and Eisenman, 2017

“Observations indicate that the Arctic sea ice cover is rapidly retreating while the Antarctic sea ice cover is steadily expanding. State-of-the-art climate models, by contrast, typically simulate a moderate decrease in both the Arctic and Antarctic sea ice covers. … [T]he models are not consistent with the observations.”

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Zanchettin, 2017

“[U]ncertainties and gaps of knowledge in the characterization of forced decadal climate responses remain large, and only a few studies have systematically tackled the implication of these forcing agents for decadal predictability and prediction. For all forcing agents, major limitations in understanding arise from incompleteness and shortness of the instrumental observations concerning the forcing as well as the climate response. Further issues concern the deficient representation of key processes in climate models and limitations inherent to reconstructed evidence.”

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Kravtsov, 2017

“[S]tate-of-the-art global models used to predict climate fail to adequately reproduce such multidecadal climate variations. In particular, the models underestimate the magnitude of the observed variability and misrepresent its spatial pattern. Therefore, our ability to interpret the observed climate change using these models is limited.”

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Zhang et al., 2017

“Climate models, including typical regional climate models, do not directly simulate all extreme rainfall producing processes, such as convection.”

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Zuidema et al., 2017

“The ‘double ITCZ’ error is further implicated in the simulated Hadley circulation, seasonal cycle and winds on the equator, and equatorial modes of variability, such as El Niño–Southern Oscillation (ENSO) in the Pacific, casting doubt on the ability to model and predict both regional and global climate. … OAFlux allows for more ocean warming than is observed, an error that implies the CMIP5 model net flux biases are even larger, by at least 10 W m−2 …  Mean CMIP5 net CRE biases are very large, up to 40 W m−2, relative to CERES values. … The CMIP5 models generally continue to underestimate subtropical stratocumulus cloud cover relative to observations“

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Ahlström et al., 2017

“Our results suggest that climate biases could be responsible for a considerable fraction of the large uncertainties in ESM [Earth system models] simulations of land carbon fluxes and pools, amounting to about 40% of the range reported for ESMs. We conclude that climate bias-induced uncertainties must be decreased to make accurate coupled atmosphere-carbon cycle projections.”

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Zhou et al., 2017

“The evaluation results show that 5 out of 30 climate models can well capture the observed APO [Asian-Pacific Oscillation]-related features in a comprehensive way, including the strengthened South Asian high (SAH), deepened North Pacific trough (NPT) and northward East Asian jet (EAJ) in the upper troposphere.” [25 of 30 climate models cannot capture the APO features comprehensively.]

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Comiso et al., 2017

“The Antarctic sea ice extent has been slowly increasing contrary to expected trends due to global warming and results from coupled climate models. After a record high extent in 2012 the extent was even higher in 2014 when the magnitude exceeded 20 × 10⁶ km² for the first time during the satellite era. … [T]he trend in sea ice cover is strongly influenced by the trend in surface temperature [cooling]. …  [T]he ability of current climate models to forecast sea ice trend can be improved through better performance in reproducing observed surface temperatures in the Antarctic region.”

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Stenni et al., 2017

“A recent effort to characterize Antarctic and sub-Antarctic climate variability during the last 200 years also concluded that most of the trends observed since satellite climate monitoring began in 1979 CE cannot yet be distinguished from natural (unforced) climate variability (Jones et al., 2016), and are of the opposite sign [cooling] to those produced by most forced climate model simulations over the same post-1979 CE interval. …  [L]ack of confidence in climate model skill for the Antarctic region (Flato et al., 2013). … [N]o continent-scale warming of Antarctic temperature is evident in the last century.”

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Büntgen et al., 2017

“Little agreement is found with climate model simulations that consistently overestimate recent summer warming and underestimate pre-industrial temperature changes. … [W]hen it comes to disentangling natural variability from anthropogenically affected variability the vast majority of the instrumental record may be biased.”

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Schroeter et al., 2017

“Antarctic sea ice extent has increased by approximately 1.5 % per decade since satellite observations began in 1979 (Parkinson and Cavalieri, 2012; Turner et al., 2015). [M]odels in the Coupled Model Intercomparison Project Phase 5 (CMIP5) exhibit decreasing sea ice trends in all months (Turner et al., 2013a). The reasons for the disparity between observed and modelled trends are not yet well understood (Bindoff et al., 2013; Hobbs et al., 2016).”

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Stouffer et al., 2017

“There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. These biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.”

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Barcikowska et al., 2017

“How global temperature will evolve over the next decade or so remains unclear (Knutson et al. 2016), although the most recent warming hiatus, observed in surface temperature records over the period 1998–2014, has challenged the scientific community in terms of consistency of models versus observations and in the attribution of the phenomena (Kosaka and Xie 2013; England et al. 2014; McGregor et al. 2014; Fyfe et al. 2012).”

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Hope et al., 2017

“Comparison of the observed rise in GMST [global mean surface temperature] over the past 32 years with GCM output reveals these models tend to warm too quickly, on average by about a factor of two. Most GCMs [general circulation models] likely represent climate feedback in a manner that amplifies the radiative forcing of climate due to greenhouse gases (GHGs) too strongly.”

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Crockford, 2017

“Data collected between 2007 and 2015 reveal that polar bear numbers have not declined as predicted and no subpopulation has been extirpated. … [T]he hypothesis that repeated summer sea ice levels of below 5 mkm² will cause significant population declines in polar bears is rejected. This result indicates that the ESA and IUCN judgments to list polar bears as threatened based on future risks of habitat loss were hasty generalizations that were scientifically unfounded, which suggests that similar predictions for Arctic seals and walrus may be likewise flawed, while the lack of a demonstrable ‘sea ice decline = population decline’ relationship for polar bears invalidates updated survival model outputs that predict catastrophic population declines should the Arctic become ice-free in summer.”

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McKinley et al., 2017

“The current inability to accurately quantify the mean CO2 sink regionally or locally also suggests that present-day observational constraints are inadequate to support a detailed, quantitative, and mechanistic understanding of how the ocean carbon sink works and how it is responding to intensifying climate change. This lack of mechanistic understanding implies that our ability to model (Roy et al. 2011, Ciais et al. 2013, Frolicher et al. 2015, Randerson et al. ¨ 2015), and thus to project the future ocean carbon sink, including feedbacks caused by warming and other climate change, is seriously limited. … [I]t is not yet possible to directly confirm from surface observations that long-term growth in the oceanic sink is occurring. … [T]his CESM-LE analysis further illustrates that variability in CO2 flux is large and sufficient to prevent detection of anthropogenic trends in ocean carbon uptake on decadal timescales.”