Climate Update

Source; Sent from an internet friend...

Climate models, the supposed science for claiming there's a climate crises,  routinely run too hot, both hindcasting and forecasting far more warming than is actually measured by surface stations, weather balloons, and global satellites.

Research from the Earth System Science Center at the University of Alabama, published in Theoretical and Applied Climatology, concludes the Earth is not as sensitive to additions of CO2 as has been assumed, because of flawed assumptions about feedback loops, because almost no models properly conserve energy, because various other factors mitigating CO2 and impacting temperatures are ignored or inadequately modeled.

Despite 30 years of refinements and revisions, and multiple iterations and versions of Coupled Model Intercomparison Project models, climate simulations have been unable to significantly close the projected gap or settle on a unified number. As a result, 80% of climate models project larger, steeper global warming trends, since 1970, than actual observations record and trend data reveals.

2 scientists have worked for over a decade to develop a single-dimension climate model which incorporates time-dependent forcing-feedbacks of temperature departures from energy equilibrium to match measured ranges of global-average surface and sub-surface land and ocean temperature trends during 1970–2021. Their model produced a climate sensitivity estimate of 1.9℃ in response to a doubling of CO2. If warming is partly natural, it would further reduce climate sensitivity.

The science of climate change knows very little about the factors which impact climate sensitivity(1). Climate models have been specifically developed and designed to produce 1 output: average global temperature, a made-up metric. If there is no certainty for climate sensitivity across climate models, then there is certainly no reason to trust or enact public policies in response to any of the ancillary extreme weather outputs and projections that climate models forecast in response to different emission concentration pathways.

In the end, science hasn’t produced a solid measure of climate sensitivity and what drives it. Science hasn’t produced and modeled concentration pathways that reflect actual emissions. Scientists can’t agree on how various forcing factors, like solar activity, clouds, large-scale ocean currents, and aerosols actually impact temperatures, much less how to incorporate them into climate models. Scientists disagree about how various ecosystems and component parts of them might respond to warmer temperatures and what feedback loops they might produce, contributing to or detracting from general warming. And scientists don’t know what features and physical mechanisms might remain unaccounted for, rather than just difficult to model—forcing factors or features that impact temperatures and long-term weather patterns on local, regional, or global scales that remain unknown at present.

With all this in mind, climate science, rather than speaking with confidence of an impending climate crisis absent the cessation of fossil fuel use, adopt the humility of Socrates, who understood how little he actually knew, or, per Einstein, “The more I learn, the more I realize how much I don’t know.” The public would certainly be better served if rather than proclaiming the science is settled, admit there are a lot of unknowns and because the stakes are so high, advised policy makers to proceed with caution, adopting policies(2) that are flexible and allow adaptation in the face of an unknowable future.

1. A Statistics Norway report claims that climate research operates with too short time intervals to be able to determine whether the influence of CO2 on temperatures has a statistical correlation. Other factors such as cloud formation, solar activity, and ocean currents have a significant impact, the researchers claim. Key properties of global climate models and statistical analyses conducted by others on the ability of the global climate models to track historical temperatures show that standard climate models are rejected by time series data on global temperatures.

Statistics Norway points out huge gaps in the climate models, like their inability to account for forcing factors beyond CO2, like water vapor, solar activity, internal natural variation based on large-scale periodic shifts in oceanic and atmospheric currents and activities, and other stochastic, seemingly chaotic and unpredictable occurrences, which have historically affected temperatures across different time scales.

The statistical methods and analysis used by Statistics Norway strongly suggests these factors likely play a much more significant role in present temperatures and temperature trends than is assumed in climate models or understood by the Intergovernmental Panel on Climate Change. Analysis shows that temperature variations over various time scales, especially longer time scales, are neither accurately represented by climate models nor explained by assuming CO2 and other trace anthropogenic greenhouse-gas emissions are the sole or even primary factor driving present temperatures.

Others have long pointed out that climate models run too hot and consistently fail to accurately reflect past temperatures when hindcast without significantly forcing (aka, lying) them to match actual measured temperatures. Statistics Norway’s statistical analysis is providing independent confirmation of what previous research has indicated about the limits of models. Its analysis goes further by pointing toward specific alternative factors which could be driving temperature changes—factors which honest researchers and scientific bodies say merit further research to determine if better accounting for them could provide an improved understanding of climate change.

This study provides a concise analysis of various factors which have historically impacted temperature variations. It also provides an original statistical time-series comparison of temperature trends—as understood from temperature reconstructions of historical temperatures based on proxy data and, more recently, of temperature data from various modern measurement technologies—and climate model reconstructions which assume carbon dioxide concentrations drive most temperature changes. SN states: "Recent work on statistical analyses on the ability of the GCMs to track historical temperature data … raise serious doubts about whether the GCMs are able to distinguish natural variations in temperatures from variations caused by man-made emissions of CO2...we find that the effect of man-made CO2 emissions does not appear to be strong enough to cause systematic changes in the temperature fluctuations during the last 200 years.

This study does not some come from organization that can be portrayed as fringe or funded by interested parties, or a narrow group of climate researchers who can be dismissed (wrongly, of course) as climate deniers, the IPCC and other scientific bodies will have to contend with and take account of this report’s findings going forward.

Andrew Montford recently produced a new paper for Net Zero Watch which demonstrates that it is almost an impossibility for new renewable power construction to decrease consumers’ electric power prices.

2. Net Zero Watch makes transparent the economic, political, and normative implications of climate change policies. It details a series of effects any new windfarm construction imposes on the grid and the types of costs it adds to people’s power bills, which make it virtually impossible that adding any new windfarm to the grid would ever reduce consumer prices.

In order to reduce bills, a new generator generally has to force an old one to leave the electricity market—otherwise there are 2 sets of costs. But with wind power, you can’t let anything leave the market, because there might be no wind.

Renewables need subsidies, they cause inefficiency, they require new grid balancing services that need to be paid for; the list of all the different effects is surprisingly long. There is only 1 way a windfarm will push your power bills, and that’s upwards.

6 distinct ways that adding a new wind farm to an electric grid will add new costs that are almost impossible to offset:
1.The inefficiency effect. The added cost related to the need to switch fuels and operate plants less efficiently, meaning higher costs per unit of energy produced, as new generation, especially subsidized intermittent generation is added to the grid.
2. The capacity market effect. The cost added to get now non-competitive generating units to continue operating rather than shut down to ensure sufficient power is available during emergencies or peak demand
3. The levy effect. The actual cost of the subsidies needed to get expensive wind farms built and approved--their capital, legal, and regulatory costs.
4. Constraints payments
5. Artificial inertia
6. The transmission effect

The wind may free, but harnessing wind power is far from it.

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