Over the last 10 years, Hudson Bay polar bears have morphed from being the “most at risk” across the Arctic to the “least at risk.” Who would have thought?
That’s probably because the experts now have to admit that polar bear numbers have not declined since 2004 and bears have been in good body condition since at least 2016. Southern Hudson Bay bears have apparently increased in number since 2016. How ironic is it that the photo above, taken in Hudson Bay — the only Arctic region where trees grow — was used to illustrate a recent Mother Jonesarticle promoting a new prediction of future Arctic summer sea ice loss that’s said to pose a threat to polar bear survival.
Here is a brief retrospective of predictions for survival of Western Hudson Bay polar bears (based on predictions of future sea ice loss), my emphasis throughout:
2013
In 2013, Andrew Derocher told The Guardian (27 November):
“All indications are that this population could collapse in the space of a year or two if conditions got bad enough,” said Andrew Derocher, a polar bear scientist at the University of Alberta.
“In 2020, I think it is still an open bet that we are going to have polar bears in western Hudson Bay.”
Contrary to this prediction, sea ice conditions over Western Hudson Bay haven’t changed since about 1998: most years, the ice-free season has been about 3 weeks longer than it was in the 1980s. Summer sea ice conditions are not getting worse.
2016
In 2016, seal biologist Steve Ferguson told the Winnipeg Free Press (6 December):
Hudson Bay could experience its first ice-free winter within five to 10 years, Ferguson said.
“I don’t think polar bears and seals will be able to adapt. I think they’ll just die out in places like Hudson Bay. There’s little to stop the trend in loss of sea ice, even if we stop producing greenhouse gasses,” he said.
Contrary to this dire prediction, Hudson Bay has been no where near to ice-free in winter, see the sea ice development chart below showing ice thickness for the week of 4 March 2024:
2024
Steven Amstrup told the Winnipeg Free Press in 2024 (28 February) the following about Western Hudson Bay polar bears, seemingly in direct contradiction to a paper he co-authored last year:
Even so, the population seems to be faring better than the rest of the world, Amstrup said during his presentation.
“If there’s a likelihood of being able to save bears anywhere, it’s probably in (the) Hudson Bay,” he said.
The rate of decline in sea ice is lower in the western and southern portions of the Hudson Bay than anywhere else globally, Amstrup relayed.
This prediction seems like a clear admission that previous predictions were wrong. This means that any of the models that formerly used WH bears as a proxy to predict the survival of all other subpopulations, including the one published last year, are not worth the paper they were printed on. What a surprise!
In part 4 the impact of convection and atmospheric circulation on climate was discussed. When circulation patterns change, they change the speed and efficiency of the transport of excess heat from the tropics to the poles. When this heat transport is faster, the world becomes cooler since the poles have a weak greenhouse effect and are more efficient at radiating heat to space. The tropics are humid and have a very strong greenhouse effect because water vapor is the most powerful greenhouse gas. Thus, the thermal energy (heat) transported to the low humidity poles is more easily sent to space than it would be in the tropics.
The temperature gradient formed between the tropics and the poles helps drive this heat transfer, but it is modulated by atmospheric and oceanic circulation and convection patterns. A very steep gradient, such as we have today, increases storminess.
The temperature gradient powers meridional transport and weather
It is uncontroversial that temperature gradients, at least in part, power our weather. Heat wants to flow from warmer areas to colder areas as it seeks an equilibrium temperature. On a larger scale, meridional transport is also, in part, a function of the temperature difference between the tropics and the poles. Leon Barry and colleagues write:
“The atmospheric heat transport on Earth from the Equator to the poles is largely carried out by mid-latitude storms.”
Leon Barry, et al., Nature, 2002
Strong mid-latitude storms, cyclones, and other storms carry most excess tropical heat poleward, and the more they carry the colder Earth becomes. The temperature gradient is not the only factor controlling meridional transport, there are many other modulators, but it is an important factor, perhaps the most important one.
The atmosphere is a huge heat engine powered from the surface (except some limited areas in the polar regions) that expels unused heat to outer space. Just like in a train engine, as the heat moves through the atmosphere, it can produce work, the work is our weather, mainly storms. As discussed in part 4, tropical temperatures do not vary much, so the critical factor is the temperature at the poles. The necessary corollary to this concept is that weather is more extreme when the planet is colder, meridional transport is strong, and the temperature gradient is steep. It is less extreme when the planet is warmer, meridional transport is weak, and the temperature gradient is smaller. This is the opposite of the conclusions reached by the IPCC, who believe warmer weather brings more storms based on the CMIP models (AR6 WGI, page 1526-1527). But that is not what is observed. Each hemisphere has more severe weather during its winter months than during its summer months.
Zhongwei Yan and colleagues, which include Philip Jones and Anders Moberg, found that extreme weather has decreased, especially in the winter months since the 19th century, as the world has warmed. They note that the relationship between warmth and weaker weather is most pronounced in Europe and China in the critical winter months. In contrast to Yan, et al., AR6 reports:
“… both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. Anthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall increases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify heatwaves, droughts and heavy precipitation events when they occur (high confidence). Dynamic processes are often indirect responses to thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is low confidence in how dynamic changes affect the location and magnitude of extreme events in a warming climate.”
AR6 WGI, page 1527
Thus, in spite of the evidence reported by Yan, et al. and many others (see Chapter 11 in The Frozen Climate Views of the IPCC), AR6 has concluded that anthropogenic global warming is causing and has caused increases in extreme weather. This is another example of bias.
On longer time scales storminess also increases during historically cold periods. It reached a peak in Europe during the very cold Little Ice Age period, labeled “LIA” in figure 1.
Figure 1. Periods of storminess over the past 6,500 years in Europe. After (Vinós, 2022).
Figure 1 shows periods of storminess identified from sedimentological features in Europe from 18 studies. The most concentrated periods of extreme storminess occurred during the cold Little Ice Age, which the IPCC calls the “pre-industrial” period. It is logical that extreme weather and storminess increases in cold times, and that is what is observed. Tropical temperatures do not change much, global temperature change happens mostly in the higher latitudes. As higher latitude temperatures grow colder, the temperature gradient steepens, powering stronger winds, and strong winds are the hallmark of extreme weather.
IPCC AR6 WG1 Model Bias, Summary
The weakest IPCC assumption is that the Sun has remained constant in its effect on climate since 1750. As we showed in part 3, there is a lot of evidence that the Sun has varied in that timeframe and contributed to global warming. The IPCC’s implicit assumption, as shown in figure 2 in part 3, that a change in solar forcing is equivalent to the same change (in W/m2) in greenhouse forcing is simply not true, basic physics tells us that. Solar forcing has a longer residence time, at least in the oceans, and causes more warming Watt-for-Watt due to the higher frequencies in sunlight. The higher frequencies penetrate the ocean surface and the lower frequency greenhouse gas emissions do not.
The CMIP6 models do try and account for ocean heat content, ocean heat buffering, and inertia, but as far as I can see they do not account for the difference in the climatic effects of sunlight and greenhouse gas emissions. If they do, it is buried deeply in AR6 WG1. While they admit there is a difference in ocean uptake in the model documentation, they seem to just ignore the fact. They treat them the same or nearly the same and they are not.
This is important because, according to AR6, increasing ocean heat content is 91% of the total global energy inventory change from 1971-2006 to 2006-2018, two periods of fast warming. Coincidentally or not, this is also the last half of the Modern Solar Maximum, when the oceans would be expected to be expelling the excess heat they accumulated during the first half of the maximum. Oceans expel heat primarily with El Ninos, tropical cyclones (hurricanes), monsoons, and mid-latitude storms. How long it takes for the world’s atmosphere to begin cooling after a solar maximum is unknown, maybe we will find out soon.
While we have many measurements of heat content to 2000 meters ocean depth now, prior to 2004 these measurements are quite rare, thus the specific model used to compute heat content in the ocean, prior to 2000, is very important. It is unfortunate that our measurements of deep ocean heat content improved so dramatically just as the Modern Solar Maximum was ending, we need this data as the heat content was accumulating as well. Satellite measurements of outgoing radiation from Earth’s surface are very sensitive to surface temperature, but not necessarily to below the ocean surface.
There are three processes that work together to keep Earth’s climate stable, the greenhouse effect, clouds, and meridional heat transport. These factors also help determine the residence time of energy received from the Sun. The IPCC has focused exclusively only on the first by developing their CO2“control knob” climate hypothesis.
The effect of clouds and their variability on climate change is still largely unknown and remains the largest source of uncertainty in calculating the effect of greenhouse gases on our climate. The IPCC cannot tell if the net effect of increasing cloud cover warms or cools the Earth. Because cloud formation and destruction cannot be modeled, their effect is ignored or simply “parameterized” by the IPCC.
With respect to meridional heat transport, energy is only exchanged between the climate system and outer space through the top of the atmosphere, this results in global meridional heat transport necessarily having a net zero value when integrated instantaneously over the entire global climate system. Moving energy from one region to another does not alter the total amount of energy within the system, although it can, and does change the energy residence time. By ignoring the impact of changing convection patterns on energy residence time, the IPCC has concluded that meridional heat transport cannot constitute a significant cause for climate change, their most fundamental mistake.
From both a thermodynamic and dynamic point of view, the defining feature of Earth’s climate system is the transport of energy. Energy transport is the cause of all weather, especially all extreme weather. Most of the solar energy that is not reflected by the planet is stored in the oceans, where 99.9% of the climate system “heat” or thermal energy resides. However, the oceans do not transport energy very well. Differences in water temperature tend to cause vertical movements through altered buoyancy, not lateral movements, so the oceans are temperature stratified. Most of the energy in the climate system is transported by the atmosphere, and what energy is transported laterally by surface ocean currents is driven by winds. The flow of non-solar energy at the atmospheric-ocean boundary (including across sea-ice) is almost always, almost everywhere, from the ocean to the atmosphere.
These first five parts point out the most serious systematic biases in the IPCC AR1 report, Climate Change 2021, The Physical Science Basis. The bias is introduced by ignoring the valid peer-reviewed articles cited in this series of posts and numerous other similar articles, thus it is mostly “reporting” or “confirmation” bias. The evidence that solar variability affects Earth’s climate is huge and well established in the literature as reviewed by Douglas Hoyt and Kenneth Schatten in their excellent book The Role of the Sun in Climate Change, and by Joanna Haigh in her report, Solar Influences on Climate.
Yet, these monumental works, as well as the many papers in the bibliography, are ignored by the IPCC, or brushed off as unimportant. In addition, many of the hundreds of papers cited in Connolly, et al., and in Nicola Scafetta and Fritz Vahrenholt’s chapter in The Frozen Climate Views of the IPCC are ignored in AR6.
It is well documented that the IPCC/CMIP6 climate models run much too hot relative to observations. They overestimate tropical mid-tropospheric temperature, sea surface temperature, and global warming in general. This bias is acknowledged in AR6 itself, where they write:
“Hence, we assess with medium confidence that CMIP5 and CMIP6 models continue to overestimate observed warming in the upper tropical troposphere over the 1979–2014 period by at least 0.1°C per decade, in part because of an overestimate of the tropical SST trend pattern over this period.“
AR6 WGI, page 444
The IPCC/CMIP6 climate models not only predict more warming than we observe, they also “drift” in their calculations with time. In most models the drift is toward more warming, adding to the natural tendency of models to run hot. In addition, the energy and mass budgets do not close properly in individual model runs, violating the physical laws of conservation of mass and energy. In some respects the CMIP6 models do better than CMIP5, but in others they do worse, suggesting that the models are not getting any better with time and may have reached the end of their useful lives.
Model unforced drift contaminates the forced (that is changing CO2 or volcanism) trends. In some models, the drift is a significant proportion of the warming computed. It results from the parameterization of quantities that cannot be modeled, like cloud cover, and from an imbalance between the prescribed initial model state from observations and the physics programmed into the model.
Another culprit can be the “coupling shock” when the various model components, like the ocean and atmospheric components are merged, sometimes called “boundary shock.” Suffice it to say, the fact that mass and energy are not conserved in these models, and they drift, is a real and significant problem. The model drift is simply the model trying to reach a stable state after the start up (or spin-up) shocks occur. Equilibrium, after spin-up, is not reached for over a thousand years, so letting the model run that long just to get rid of the drift is not practical. Usually when the models are corrected for drift, their mass/energy conservation problems are reduced. However, the mismatch between the model physics and the initial model state, which is from observations, is worrying all by itself.
From this modeler’s point of view, the conceptual model or hypothesis that the CMIP6, and its predecessors, were designed to investigate has been shown to be incorrect. Only by introducing increasing amounts of bias can this observation be covered up. But the more bias introduced into the models and in the interpretation of the output, the more obvious it becomes. It is time to quit and develop a new hypothesis and conceptual model to investigate, the “CO2 control knob” idea is dead.
For a discussion of how meridional transport is modulated see: (Vinós, Climate of the Past, Present and Future, A Scientific Debate, 2nd Edition, 2022, pp. 157-174) ↑
(Barry, Craig, & Thuburn, 2002) and (Huang, 2004) ↑
(Costas, Naughton, Goble, & Renssen, 2016) and (Sorrel, et al., 2012) ↑
The IPCC does not use the “Little Ice Age” label, they consider it to be poorly defined (IPCC, 2021, p. 295). They define pre-industrial as either before 1850 or before 1750 depending upon context. When referring to the temperature record, 1850-1900 is the reference temperature period for the “pre-industrial.” When referring to human emissions of greenhouse gases, the boundary is 1750. See footnote 13, page 43 of WGI or page 2244 in the AR6 glossary. ↑
(Crok & May, 2023, Ch 6, by Scafetta and Vahrenholt) and (Vinós & May, 2022b) ↑
(Griffies, Harrison, Pacanowski, & Rosati, 2004). The radiation-ocean surface model is parameterized but seems to be limited to the top 10-30 meters of the ocean. The model documentation is unclear. Important point: the average thickness of the ocean mixed layer is more than 50 meters, see here, figure 5 for details. ↑
(Griffies, Harrison, Pacanowski, & Rosati, 2004, p. 137) ↑
(Nakamura, 2018), or see (Taveira, 2019) and (IPCC, 2021, pp. 978-979) ↑
Parametrization in climate modeling is replacing a process in a model with a simplified set of model parameters. See: (Lu, Liu, Niu, Krueger, & Wagner, 2013) ↑
(May, Meridional Transport, the most fundamental climate variable, 2022g), (Vinós & May, The Sun-Climate Effect: The Winter Gatekeeper Hypothesis (III). Meridional transport, the most fundamental climate variable, 2022c), and (Vinós, Climate of the Past, Present and Future, A Scientific Debate, 2nd Edition, 2022, p. 157) ↑
(Yu & Weller, 2007), (Schmitt, 2018)), and (Vinós & May, The Sun-Climate Effect: The Winter Gatekeeper Hypothesis (VI). Meridional transport is the main climate change driver, 2022e) ↑
(McKitrick & Christy, 2020), (McKitrick R. , Biases in climate fingerprinting methods, 2022), (McKitrick & Christy, A Test of the Tropical 200- to 300-hPa Warming Rate in Climate Models, Earth and Space Science, 2018), (McKitrick R. , Checking for model consistency in optimal ingerprinting: a comment, 2021), (Scafetta N. , CMIP6 GCM ensemble members versus global surface temperatures, 2022b), (IPCC, 2021, pp. 443-444), (Scafetta N. , Impacts and risks of “realistic” global warming projections for the 21st century, 2024), (Mauritsen & Stevens, 2015), and (Spencer, 2024). ↑
(McKitrick & Christy, A Test of the Tropical 200- to 300-hPa Warming Rate in Climate Models, Earth and Space Science, 2018), (IPCC, 2021, p. 444) ↑
The silliest climate story of the week must be this from the Independent:
Women in south Asia are becoming silent victims of an alarming side effect of the climate crisis as sources of clean water for drinking and bathing become more and more scarce – the loss of their hair.
In the coastal regions of Bangladesh, where over half of the water is tainted with salt and pollutants, women are walking back-breaking distances to fetch slightly safer water for drinking.
But with clean water being such a precious resource, women are bathing in hard water, which is not just leading to infections but is also causing them to lose their hair, laying bare the deeper wounds inflicted by the climate crisis.
In the Satkhira region in southwestern Bangladesh, where residents rely solely on rice crops and fishing for their livelihood, women say they are losing hair at an alarming rate. Some are worried about premature baldness, while others are selling their hair to vendors in an attempt to make ends meet.
“Women in the Satkhira region are living without the basics of clean water, decent toilets, and good hygiene, and the climate crisis is making it worse,” Anindita Hridita, programme lead on climate resilience at WaterAid Bangladesh, tells The Independent.
“They say continued exposure to contaminated water sources is not only causing dangerous waterborne diseases but also robbing them of their hair, an added layer of injustice in an already dire situation.”
Hridita and her team travelled through the region for days, collecting stories from women in the villages of Satkhira who are suffering through the multifaceted effects of the climate crisis.
“The water I bathe in causes my skin to get very dry, and I often get blisters,” shares Shyamoli Munda, a rice and fish farmer from Bhetkali village in Satkhira. “The blisters sometimes get so dry they bleed.”
In reality, access to safe water has markedly improved in recent years in Bangladesh:
But the idea that Bangladeshi women would ever have bathed in scarce drinking water is absurd – just the sort of thing a dopey Independent reporter would believe! Since time immemorial, most would bathe in rivers or ponds, where such were available.
There is no evidence that climate change has made the slightest difference to any of this, or that similar problems did not occur in the past.
Even Independent readers seem to have realised this is a non-story, as there is only one comment on the article, which probably hits the nail on the head:
Iowa Climate Science Education 