Climate Impacts

Getting Climate Crisis Monkey Off Public Health Services

Posted on by Ron Clutz

Advances in medical science and public health have  benefited billions of people with longer and higher quality lives.  Yet this crucial social asset has joined the list of those fields corrupted by the dash for climate cash. Increasingly, medical talent and resources are diverted into inventing bogeymen and studying imaginary public health crises.

Thus it is welcome news that confirmed Secretary of Health and Human Services (HHS) RFK Jr. has stopped funding of climate medicine at National Institutes of Health (NIH). Mother Jones reported its disapproval RFK Jr., Onetime Environmentalist, Kills NIH Climate Change Programs.
Subtitled: He pulled HHS support from projects that aim to protect Americans’ health.  fight climate change. (my correction of MJ subtitle).

On February 14 of this year, his second day as secretary of the Department of Health and Human Services, he ended HHS funding for climate change and health programs at the National Institutes of Health, a move that will likely terminate this work.

That day, Ken Callahan, a senior adviser for policy and implementation in the Immediate Office of the Secretary for HHS, sent an email to Dr. Matthew Memoli, the acting director of NIH, noting that HHS would no longer support three programs run by the agency:  the Climate Change and Health Initiative, the Climate Change and Health Research Coordinating Center, and the Climate and Health Scholars Program.

In the email, a copy of which was obtained by Mother Jones, Callahan cited Executive Order 14154, titled “Unleashing American Energy,” which President Donald Trump signed on his first day in office last month to revoke executive orders President Joe Biden had previously issued to implement actions to address climate change.

As Richard Lindzen predicted, everyone wants on the climate bandwagon, because that is where the money is. Medical scientists have pushed for their share of the pie, as evidenced by the Met office gathering on Assessing the Global Impacts of Climate and Extreme Weather on Health and Well-Being (following Paris COP). Not coincidentally, the 2nd Global Conference on Health and Climate was held July 7-8, 2016 in Paris. Following that the American Public Health Association declared: 2017 is the Year of Climate Change and Health.

NIH: Why Climate Change Is a Health Threat

The NIH Climate Change and Health Initiative Strategic Framework claims:

For some time, international scientific consensus has been that climate change poses an existential threat to human beings. A report of the Intergovernmental Panel on Climate Change (IPCC), the United Nation’s body for assessing the science related to climate change, concluded in a recent report: “Any increase in global warming is projected to affect human health, with primarily negative consequences (high confidence).” The report further concludes that, “Compared to current conditions, 1.5°C of global warming would nonetheless pose heightened risks to eradicating poverty, reducing inequalities, and ensuring human and ecosystem well-being (medium evidence, high agreement)

and they conclude:

A mounting number of assessments and reports provide undeniable evidence that climate change is resulting in increasingly profound changes to the global environment with direct and indirect consequences for human health and well-being. Closely intertwined with this threat are the more tangible and proximal risks of natural disasters, a global pandemic, societal unrest, and the ever-familiar menaces of poverty and inequity. The need for NIH to lead this science-based initiative, in partnership with communities throughout the world, is now warranted and vitally necessary to address the imminent threat that climate change poses to our health, humanity, and our planet.

Comment: 

There are numerous posts here why the IPCC alarmist narrative is speculative and exaggerated, for example:

Climatists Make Their Case by Omitting Facts

Thus it is high time to uncouple the globalist push to fuse health care with CO2 hysteria.

Two Sides of the Same Coin

Background:

Climate Health Crisis Meme Goes Viral

 

 

 

 

 

Solar Activity Linked to Ocean Cycles

Posted on by Ron Clutz

Solar energy accumulates massively in the ocean and is variably released during circulation events.

Thanks to Franklin Isaac Ormaza-González alerting me to this paper Did Schwabe cycles 19–24 influence the ENSO events, PDO, and AMO indexes in the Pacific and Atlantic Oceans? by Ormaza-González, Espinoza-Celi and Roa-López, all from ESPOL Polytechnic University, Ecuador.  Why is this important? Because warming in the modern era is closely tied to El Niño and La Niña events (ENSO).  For example,

The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4.  This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C.  Previously the 1997-98 El Nino produced a plateau increase of 0.4C.  Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

As shown in the synopsis below, the paper analyzes multiple oceanic oscillations during the years 1954 to 2019 in order to compare with solar cycles of sunspots 19 through 24 occurring during that time frame.  The title is stated as a question, and the conclusion provides this answer (in italics with my bolds).

Finally, did Schwabe cycles 19–24 influence the ENSO events, PDO, and AMO indexes in the Pacific and Atlantic Oceans? Yes, it has been found a wide range correlation coefficient from 0.100 to about 0.500 statistically significant (p < 0.05) with lag times from few months to over 2 years between the Schwabe cycles and the ocean indices chosen here. These results could be a potential source to improve predictive skills for the understanding of ENSO, PDO and AMO interannual and decadal fluctuations. Better predictive models are imperative given that El Niño or La Niña has vast impacts on lives, property, and economic activity around the globe, especially when dramatic peaks of El Niño occur. The new cycle 25 has started and could have a major oceanic swing follow suit, and the next El Niño would be in around 2023–2024 according to historical events and results presented here.

Given that the paper was drafted before submitting in February 2022, and publication in October that year, the forecast of a 2023-24 El Nino was confirmed in a remarkable way.

To enlarge, open image in new tab.

The cyan line represents SST anomalies in the Tropics and shows the major El Ninos, 2015-16, 2019-20 and 2023-24.  Note all three events included pairs of major NH summer warming peaks. The synopsis below consists of excerpts in italics with my bolds to present the broad strokes of the analyses and findings. (Note: The paper includes detailed analyses and many references to supporting studies, and interested readers can access them by linking there.)

Context

The surface-subsurface layers of the ocean that interact with the lower atmosphere alternately release and absorb heat energy. The work of Zhou and Tung (2010) reported the impact of the TSI on global SST over 150 years, finding signals of cooling and warming SSTs at the valley and peak of the SS cycles. Schlesinger and Ramankutty (1994) report a global cycle of 65–70 years for SST that is affected by greenhouse anthropogenic gases, sulphate aerosols and/or El Niño events, but they did not imply any external forcing such as the SS. There have been other studies on how solar radiation variability could affect temperature; recently, Cheke et al. (2021) have studied those solar cycles of SS that would affect the El Nino Southern Oscillation (ENSO) indexes.

There are well known oceanic events that show periodicity with low or high frequencies: 25–30 and 3–7 years, respectively. These include the Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO),  and Interdecadal Pacific Oscillation (IPO), as well as El Niño or La Niña.  During El Niño events, the surface and subsurface lose energy to the atmosphere and the opposite occurs during La Niña; these events have a periodicity of 3–7 years. The Interdecadal oscillations have a series of impacts; e.g., the PDO gives rise to teleconnections between the tropic and mid-latitudes, and the effects include:

1) ocean heat content,
2) the lower and higher levels of the trophic chain including small pelagic fisheries (tuna and sardines);
3) biogeochemical air-sea CO2 fluxes;
4) the frequency of La Niña/El Niño.

The interactions between decadal oscillations PDO/IPO and AMO may also affect ocean heat content. All these low and high frequency oceanographic events have a direct impact on local, regional, and global climate patterns, and there is growing evidence from many studies that the driving source of energy is the sun.

Thus, whatever affects the solar irradiation falling on the surface of the oceans, including volcanic eruptions (Fang et al., 2020), and cloudiness for example, it would affect the gain or loss of heat content of the oceans. The cited works tried to find the physical reasons for these connections, but they remained unknown or difficult to explain.

The work reported here investigates how fluctuations of sunspots over time (1954–2019) may cross-correlate with low and high frequency oceanic events such as the sea surface temperature (SST), anomalies (SSTA), Oceanographic El Niño Index (ONI), Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI) in the central and east equatorial Pacific Ocean; and PDO, as well as on the AMO in the North Pacific and Atlantic basins. The hypothesis is that even small variations of the TSI can be reflected in these tele-connected indexes.

Discussion

Fig. 1. Behaviour of monthly counts of SS, ONI, MEI, PDO and AMO. The Indexes start at t = 0, 12, 24 and 36 months (panels a, b, c, and d respectively). The SS series starts at t = 0 in the four panels. The left vertical axis gives the values for the Indexes, and SS counts at the right vertical scale. The end of each Schwabe cycle is marked by vertical dashed lines.

Maxima in the PDO, AMO, ONI, and MEI series were offset by 0, 12, 24 and 36 months (Fig. 1, panels a, b, c, and d respectively), with the SS series starts at t = 0 in the four panels. It has been reported that the lag times for responses of some Indexes to SS cycles (SS) are around 12–36 months (see fig. 1 of Hassan et al., 2016), and Fang et al. (2020) have reported that ENSO responds with a 2–3 years of lag time after a major volcanic eruption. From 1954 to the present time, each sunspot cycle from 19 to 24 has occurred with a period of around 11 years (Hathaway, 2015), which is slightly less than the 11.2 years reported by Dicke (1978). The highest SS activity is seen in cycle 19 with around 250 SS/month, followed by <150, and at cycle 21 around 200, before decreasing steadily over cycles 22 to 24 to just over 100 SS/month. Cycle 24 is the lowest contemporary value of SS activity that is comparable only to cycles 12–15 (around 1880–1930) and is the lowest in the last 200 years (Clette et al., 2014).

Fig. 12. Sunspots monthly counts curves per cycle. Red and blue lines represent El Niño and La Niña events. Note that Cycle 24 finished on December 2019 (National Weather Service, 2020).

The SSTA in El Niño 1 + 2 region cross-correlated with SS many times, especially during descending phases of all cycles except SS 22 with cc-ρ up 0.389 (SS 24) and main lag times from 5 to 13 months. The SS cycles (20 and 24) during cold phase PDO showed alternate cross-correlation reaching a maximum 0.389 and negative −0.314 (p < 0.05). During the ascending phase in El Niño 1 + 2 region (blue bars, Fig. 5a) the cc-ρ peaked at 0.393 (p < 0.05). In the cycles 19 and 24 the highest cc-ρ were found, −0.460 and 0.394 (p < 0.05) respectively. These coefficients coincided with the largest (over 2 years) and most intense (<−1.5C) La Niña during 1954–1955, and 2010–2012 (Fig. 12).

It must be noticed that during cycle 21 two big events El Niño (1983–1985) and La Niña (1984–1985) were registered as well as in cycles 23 and 24 with coefficients just around 0.2. The highest coefficients would mean an influence up to 21.2% and 15.5% of the SS on the SSTAs in El Niño 3.4 region. These results would suggest the cross-correlations are stronger in El Niño 3.4 region due to the less dispersing oceanographic-meteorological conditions than in El Niño 1 + 2 region. Also, these findings would suggest that during the cold phase of PDOs (see NOAA, 2016), the cc-ρ in El Niño 3.4 region tends to be higher, as the solar energy reaching the ocean surface increases as the cloudiness tends to decrease significantly during prolonged periods around or over in El Niño 3.4 region (Porch et al., 2006).

The sun cycle 19 is the most intense since the last 100 years, the contrary is the cycle 24 (NWS, 2021). In general, the ascending phase of the SS cycles takes a shorter time than descending phase, therefore the slope of the curve is steeper (Fig. 12); then the increasing change of the TSI influences in a clearer way the studied indexes. It seems that during the ascending phases, El Niño events are prone to develop as TSI increases (as well as UV radiation does, NWS, 2021), while during plunging SS phases, when the TSI tends to diminish (see Formula (1)), could lead to La Niña events, like the 2020–2022 occurrence (Ormaza-González, 2021).

Most of the La Niña events occur during the descending phase or just when approaching or leaving the valley or minimum SS counts (Fig. 12) when the TSI decreases and reaches the minimum (Scafetta et al., 2019). La Niña 2020–2022 is a good example, the lowest SS counts (<2 counts/months) occurred during extended periods when reaching the valley of the SS 24. The valley of SS 24 has had an extended period of close to 3 years, during which there have been weeks and months without sunspots, before the SS 25 started in December 2020.

The weakest sunspot cycle (SS 24) over the last 100 years (NWS, 2021) has had four La Niña events: 2007–2009, 2010–2012, 2016–2017, and 2020–2022 (Fig. 12), it is the only cycle with that number of La Niña events.

Conclusions

Over the studied period 1954–2019, sunspot numbers decreased from a monthly maximum between 225 (SS 21) to a minimum around 20–25 (SS 24). The SS 24 had 913 days without SS counts until December 2019 (Burud et al., 2021), being this cycle the weakest since 1755; and the SS 25 will probably be weaker than or like SS 24 (Ineson et al., 2014; Chowdhury et al., 2021; NASA, 2021a, NASA, 2021b). Thus, the Earth has been receiving slightly decreasing solar energy over this almost 7-decade period.

On the ocean surface the influence of sunspots could chiefly be due to UV energy fluctuation (Ineson et al., 2014) as this radiation penetrates down to 75–100 m depth in the water column (Smyth, 2011). van Loon et al. (2007) suggested that even though SS cycles produce weak changes on the Total Solar Irradiation (TSI) of about 0.07% (Gray et al., 2010), these can still produce decadal and millennial impacts on global thermohaline circulation (Bond et al., 2001; Gray et al., 2016).

The ONI Index showed to be poorly cross-correlated with cc-ρ values <0.100, only twice approached to −0.200. On the other hand, the MEI registered around ±0.200 through all cycles and predominant lag times within 12 months. The SOI showed cross-correlations with SS cycles (19–21, and) averaging a coefficient of 0.200 with lags times range of 9–34 months. The SOI temporal behaviour has also been associated with SS and it could enhance or affect the oceanographic Indexes of the equatorial Pacific (Higginson et al., 2004). [The Multivariate ENSO Index does not only consider the SST Anomaly but also sea-level pressure and other variables.]

The MEI index could have been influenced from 7.3% up to 23%. The MEI correlated in all ascending and descending phases of SS cycles. The SOI had similar cross-correlation coherence to those oceanographic indexes during ascending and descending phases. These results would provide evidence on how SS affects the studied Indexes during the ascending/descending phases of their cycles. In some cycles, the impact will be stronger and in other weaker depending on intensity and behaviour in time of the cycle.

Finally, did Schwabe cycles 19–24 influence the ENSO events, PDO, and AMO indexes in the Pacific and Atlantic Oceans? Yes, it has been found a wide range correlation coefficient from 0.100 to about 0.500 statistically significant (p < 0.05) with lag times from few months to over 2 years between the Schwabe cycles and the ocean indices chosen here. These results could be a potential source to improve predictive skills for the understanding of ENSO, PDO and AMO interannual and decadal fluctuations. Better predictive models are imperative given that El Niño or La Niña has vast impacts on lives, property, and economic activity around the globe, especially when dramatic peaks of El Niño occur. The new cycle 25 has started and could have a major oceanic swing follow suit, and the next El Niño would be in around 2023–2024 according to historical events and results presented here.

It Must Be Climate Change

Posted on by Ron Clutz

Prager U video can be seen at this link: https://www.prageru.com/video/it-must-be-climate-change

Transcript is below in italics with my bolds and some added images.

Have you noticed that every extreme weather event is blamed on climate change formerly known as global warming?

Every. . . Single. . .One. 

Can you think of an exception?

Too hot — climate change.    Too cold – climate change. 

Previously, cold spells were termed “Weather” in contrast to “Global Warming.” Now it’s all “Climate Change.”

Drought – climate change.   Too much rainfall – climate change. 

And there’s always a climate scientist at some university who’s willing to make a statement blaming the current catastrophe on our profligate use of fossil fuel. 

Some years ago, on The Late Show with David Letterman, MSNBC host Rachel Maddow made the definitive statement on this issue. She said, “I think global warming probably means extreme weather events of all kinds.” Naturally, Dave agreed.

What’s behind all these confident assertions? 

As a PhD in geochemistry, former member of the University of Alabama Department of Geological Sciences and someone who has written and lectured widely on the subject of climate and geology, I can tell you that it comes down to two things: 

Obscure metrics and highly speculative models.

Mix these ingredients together and voila! You can get any result you want. The scarier, of course, the better. “Torrential rain” makes a much better headline than “heavy rain.” 

To show you how this works, let’s look at a recent example. 

Here’s the assertion:   Climate change is making air turbulence more volatile and thus air travel more dangerous. 

Scary, right?   But is it true?  No. Not if we look at the observable data; that is, hard data we can easily verify. 

Here’s a chart of the number of turbulence-related accidents in the US from 1989 to 2018. 

Despite the rise in annual US airline passengers from about 400 million in 1989 to nearly one billion by 2018, turbulence-related accidents have remained constant. If climate change were indeed making turbulence significantly worse, we would expect to see a corresponding increase in these accidents.

Yet, the data does not support this assertion. Instead, it suggests that the relationship between turbulence and climate change is either negligible or nonexistent.

In fact, the co-author of the original study cited in a BBC article admitted as much.

“When we add [data back to 2002] to the previous results, the statistical significance assigned to the…North Atlantic winter jet stream…disappears.”

This was conveniently left out of the BBC article.

This disconnect between obscure metrics and highly speculative models, and observable data is not limited to turbulence. 

The broader climate crisis narrative is built on similar shaky foundations. 

Let’s look at three more examples. 

No Increase in Extreme Weather: The number of hydrological, meteorological, and climatological disasters has remained relatively flat since 2000. If climate change were causing more extreme weather events, we would expect to see a clear increase in these numbers. Instead, the data, again, reflects no such increase.

No Increase in Loss of Life: Deaths from meteorological, hydrological, and climatological disasters have not increased. This is a critical metric because it directly reflects the human impact of these events. Despite frequent claims that climate change is making weather more deadly, the data does not bear this out.

No Increase in Costs: Global weather losses as a percent of global GDP have not risen significantly. This is another crucial metric because it accounts for the economic impact of climate-related disasters. If climate change were truly making these events more severe, we would expect to see a rising trend in economic losses relative to global GDP.

We are left with this conclusion:

The reliance on obscure metrics and highly speculative models
to support the climate crisis narrative often serves
to cloud the truth rather than illuminate it. 

By focusing on projections and models rather than observable data, environmental activists, climate scientists, attention-seeking politicians and click bait media make claims that are difficult to verify and easy to manipulate. 

The fear that fuels the “climate crisis” is simply not justified by the data. That’s why — over and over again — end-of-the-world predictions don’t pan out. 

This does not mean that we should ignore environmental issues. We live on the same planet. We all want clean air and water. 

However, it does mean that we should approach claims of climate catastrophe with a healthy dose of skepticism and demand that assertions be backed up by observable, measurable data. Given that politicians and government agencies are spending tens of billions of our tax dollars every year to “save the planet” that would seem to be the least they could do: give us some hard facts, rather than unproven assertions. 

And the hard facts are, turbulence-related accidents have not increased despite a massive rise in airline passengers. Extreme weather events, loss of life, and economic costs have not shown the dramatic increases that alarmists would have us believe. 

By focusing on observable data we can have a more grounded, rational discussion about our environmental challenges and how best to address them.

That’s the way to practical, real-world solutions.  The blame game — “it’s climate change” — gets us nowhere. 

I’m Matthew Wielicki for Prager University.

EPA Priorities Announced

Posted on by Ron Clutz

During Trump 1.0 the appointed EPA Director summarized the false dichotomy long plaguing the agency: “If you are for the Environment, you must be against Development; and if you are for Development, you must be against the Environment.” In reality, a balance must be struck, and a new administration intends to find it.  There has been much gnashing of teeth in the legacy media over this month’s dismissal of scientists from EPA advisory boards, without mentioning the same housecleaning happened in 2021 when Biden regime took over.  Now we have an official announcement about the new EPA direction and priorities.  Text in italics with my bolds and added images.

WASHINGTON – On February 4, 2025, U.S. Environmental Protection Agency (EPA) Administrator Lee Zeldin announced the agency’s Powering the Great American Comeback Initiative, to achieve the agency’s mission while energizing the greatness of the American economy. This plan outlines the agency’s priorities under the leadership of President Trump and Administrator Zeldin. The newly announced Powering the Great American Comeback initiative consists of five pillars that will guide the EPA’s work over the first 100 days and beyond:

Pillar 1: Clean Air, Land, and Water for Every American

“Every American should have access to clean air, land, and water. I will ensure the EPA is fulfilling its mission to protect human health and the environment. In his first term, President Trump advanced conservation, reduced toxic emissions in the air, and cleaned up hazardous sites, while fostering economic growth for families across the country. We remain committed to these priorities in this administration, as well as ensuring emergency response efforts are helping Americans get back on their feet in the quickest and safest way possible. We will do so while remaining good stewards of tax dollars and ensuring that every penny spent is going towards advancing this mission,” said Administrator Zeldin.

Pillar 2: Restore American Energy Dominance

“Pursuing energy independence and energy dominance will cut energy costs for everyday Americans who are simply trying to heat their homes and put gas in their cars. This will also allow our nation to stop relying on energy sources from adversaries, while lowering costs for hardworking middle-income families, farmers, and small business owners. I look forward to working with the greatest minds driving American innovation, to ensure we are producing and developing the cleanest energy on the planet,” said Administrator Zeldin.

Pillar 3: Permitting Reform, Cooperative Federalism, and Cross-Agency Partnership

“Any business that wants to invest in America should be able to do so without having to face years-long, uncertain, and costly permitting processes that deter them from doing business in our country in the first place. It will be important for the EPA to work with our partners at the state and federal levels to ensure projects are being approved and companies can invest billions of dollars into our nation. Streamlining these processes, while partnering with businesses to follow the necessary steps to safeguard our environment, will incentivize investment into our economy and create American jobs,” said Administrator Zeldin.

Pillar 4: Make the United States the Artificial Intelligence Capital of the World

“As we rapidly advance into this new age of AI, it is important that the United States lead the world in this field. Those looking to invest in and develop AI should be able to do so in the U.S., while we work to ensure data centers and related facilities can be powered and operated in a clean manner with American-made energy. Under President Trump’s leadership, I have no doubt that we will become the AI capital of the world,” said Administrator Zeldin.

Pillar 5: Protecting and Bringing Back American Auto Jobs

“Our American auto industry is hurting because of the burdensome policies of the past.

Under President Trump, we will bring back American auto jobs and invest in domestic manufacturing to revitalize a quintessential American industry. We will partner with leaders to streamline and develop smart regulations that will allow for American workers to lead the great comeback of the auto industry,” said Administrator Zeldin.

Footnote:

The Trump Administration not only cut “environmental justice” programs at the Environmental Protection Agency, they put nearly 200 staffers on leave.

According to reports, the staffers were called into a meeting on Thursday afternoon where they were informed that they were being placed on leave.

“Effective immediately, you are being placed on administrative leave with full pay and benefits. This administrative leave is not being done for any disciplinary purpose,” the email stated, according to Politico.

“Career staff made determinations on which Office of Environmental Justice employees had statutory duties or core mission functions,” EPA spokesperson Molly Vaseliou said in a statement. “As such, 168 staffers were placed on administrative leave as their function did not relate to the agency’s statutory duties or grant work. EPA is in the process of evaluating new structure and organization to ensure we are meeting our mission of protecting human health and the environment for all Americans.” Source.

Carnage: Trump Cuts ‘Environmental Justice’ Programs, Puts Nearly 200 EPA Staffers on Leave

No, Grist, MSN, et al: CO2 Is Not Making Oceans Boil

Posted on by Ron Clutz

 

The Climate Crisis media network is announcing a new claim that rising CO2 is causing recent ocean warming, proving it’s dangerous and must be curtailed.  Examples in the last few days include these:

Finally, an answer to why Earth’s oceans have been on a record hot streak Grist

Ocean warming 4 times faster than in 1980s — and likely to accelerate in coming decades MSN

News spotlight: Fossil fuels behind extreme ocean temperatures, study says. Conservation International

Ocean temperature rise accelerating as greenhouse gas levels keep rising UK Natural History Museum

The surface of our oceans is now warming four times faster than it was in the late 1980s The Independent UK

Oceans Are Warming Four Times Faster as Earth Traps More Energy Bloomberg Law News

All this hype deriving from one study,
and ignoring the facts falsifying that narrative.

Fact:  Historically, ocean natural oscillations drive observed global warming.

The long record of previous warmings prior to the satellite record shows that the entire rise of 0.8C since 1947 is due to oceanic, not human activity.

The animation is an update of a previous analysis from Dr. Murry Salby.  These graphs use Hadcrut4 and include the 2016 El Nino warming event.  The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4.  This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C.  Previously the 1997-98 El Nino produced a plateau increase of 0.4C.  Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate.  On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles.

Fact: Recent rise in SST was driven by ENSO and N. Atlantic Anomalies.

And now in 2024 we have seen an amazing episode with a temperature spike driven by ocean warming in all regions, along with rising NH land temperatures, now dropping below its peak.

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through December 2024.  A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024.

To enlarge, open image in new tab.

Note that in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  A small warming was driven by NH summer peaks in 2021-22, but offset by cooling in SH and the tropics, By January 2023 the global anomaly was again below the mean.

Now in 2023-24 came an event resembling 2015-16 with a Tropical spike and two NH spikes alongside, all higher than 2015-16. There was also a coinciding rise in SH, and the Global anomaly was pulled up to 1.1°C last year, ~0.3° higher than the 2015 peak.  Then NH started down autumn 2023, followed by Tropics and SH descending 2024 to the present. After 10 months of cooling in SH and the Tropics, the Global anomaly came back down, led by NH cooling the last 4 months from its peak in August. It’s now about 0.1C higher than the average for this period. Note that the Tropical anomaly has cooled from 1.29C in 2024/01 to 0.66C as of 2024/12.

Fact: Empirical measurements show ocean warms the air, not the other way around.

One can read convoluted explanations about how rising CO2 in the atmosphere can cause land surface heating which is then transported over the ocean and causes higher SST. But the interface between ocean and air is well described and measured. Not surprisingly it is the warmer ocean water sending heat into the atmosphere, and not the other way around.

The graph displays measures of heat flux in the sub-tropics during a 21-day period in November. Shortwave solar energy shown above in green labeled radiative is stored in the upper 200 meters of the ocean. The upper panel shows the rise in SST (Sea Surface Temperature) due to net incoming energy. The yellow shows latent heat cooling the ocean, (lowering SST) and transferring heat upward, driving convection. [From An Investigation of Turbulent Heat Exchange in the Subtropics by James B. Edson]

As we see in the graphs ocean circulations change sea surface temperatures which then cause global land and sea temperatures to change. Thus, oceans make climate by making temperature changes.

Fact: On all time scales, from last month’s observations to ice core datasets spanning millennia, temperature changes first and CO2 changes follow.

Previously I have demonstrated that changes in atmospheric CO2 levels follow changes in Global Mean Temperatures (GMT) as shown by satellite measurements from University of Alabama at Huntsville (UAH). That background post is included in the posting referenced later below.

My curiosity was piqued by the remarkable GMT spike starting in January 2023 and rising to a peak in April 2024, and then declining afterward.  I also became aware that UAH has recalibrated their dataset due to a satellite drift that can no longer be corrected. The values since 2020 have shifted slightly in version 6.1, as shown in my recent report  Ocean Leads Cooling UAH December 2024.

I tested the premise that temperature changes are predictive of changes in atmospheric CO2 concentrations.  The chart above shows the two monthly datasets: CO2 levels in blue reported at Mauna Loa, and Global temperature anomalies in purple reported by UAHv6.1, both through December 2024. Would such a sharp increase in temperature be reflected in rising CO2 levels, according to the successful mathematical forecasting model? Would CO2 levels decline as temperatures dropped following the peak?

The answer is yes: that temperature spike resulted
in a corresponding CO2 spike as expected.
And lower CO2 levels followed the temperature decline.

Above are UAH temperature anomalies compared to CO2 monthly changes year over year.

Changes in monthly CO2 synchronize with temperature fluctuations, which for UAH are anomalies now referenced to the 1991-2020 period. CO2 differentials are calculated for the present month by subtracting the value for the same month in the previous year (for example December 2024 minus December 2023).  Temp anomalies are calculated by comparing the present month with the baseline month. Note the recent CO2 upward spike and drop following the temperature spike and drop.

Summary

Changes in CO2 follow changes in global temperatures on all time scales, from last month’s observations to ice core datasets spanning millennia. Since CO2 is the lagging variable, it cannot logically be the cause of temperature, the leading variable. It is folly to imagine that by reducing human emissions of CO2, we can change global temperatures, which are obviously driven by other factors.

12/2024 Update–As Temperature Changes, CO2 Follows

 

 

 

 

 

 

Arctic Ice Recovery Stalls January 2025

Posted on by Ron Clutz

Arctic ice recovered more slowly than usual in December and January, likely due to polar vortex pulling freezing air from the Arctic down into lower latitudes, replaced by warmer southern air.  A post at Severe Weather Europe is February 2025 Forecast, describing the dynamics this winter.  

After a mild start, a new Polar Express is looming
for the United States and Canada mid-month.

As January is slowly ending, we can look at preliminary surface temperature data for the month so far. Below is the CDAS analysis, and you can see that January was colder than normal across the entire United States, apart from California and parts of Nevada. But these anomalies do not show the full picture of just how cold some days in the month were, breaking records for several years and even decades in the past.

On the other hand, we can see that Canada had warmer than normal temperatures. This is an expected pattern, as while the colder air was transported further south into the United States, it was replaced by high-pressure and a warmer-than-normal airmass.

The movement of the pressure systems drives these temperature patterns and weather changes. Pairs of pressure systems are also known as Rossby Waves. You can see an example of Rossby waves in the image below by NOAA and how they are all connected and function with the jet stream.

The purple line connecting these pressure systems is called the jet stream. This rapid stream of air is found around 9 to 14 kilometers (6 to 9 miles) above sea level.

In late January, the average temperatures in the northern United States and southern Canada are still around or below freezing, so even a strong positive anomaly does not actually mean warm temperatures in that region. But, it is interesting to see the rapid shift in temperature anomalies as the pressure systems reposition.

February 2025 is about to start, with the latest weather forecasts indicating a very dynamic month over the United States and Canada. After the power struggle between the cold and warmth at the start of the month, another Polar Vortex lobe looms for the United States around mid-month.

Below is the surface temperature anomaly, averaged for next week. You can see the large supply of colder air over the northern United States and western Canada. Another cooler area is forecast for eastern Canada and the northeastern United States.

But most of the central and southern half of the United States is forecast to have above-normal temperatures. We often see such a division in the weather patterns, where the colder and warmer air separate along the jet stream.

Going into the weather trend for the second half of February, we will use the extended-range ensemble forecasts. These forecasts serve as trends that show the prevailing idea of where the pressure systems are positioned and how the airmass is expected to move.

The continuous low-pressure systems over Canada helped to initiate large-scale cold air transport from the Arctic into the United States and Canada, also powered by the Polar Vortex in the stratosphere.

We continue to see the presence of the low-pressure area over Canada in the forecast for February. But the forecast now indicates an interesting core movement of the Polar Vortex in the stratosphere, likely to initiate another deep cold event around mid-month over the United States and Canada.

Impact on Arctic Ice Extents

The 19-year average for January shows Arctic ice extents started at 13.13M km2 and ended the month at 14.36M km2.  2024 started somewhat higher and matched average at the end.  Other recent years have been lower, and 2025 started 540k km2 in deficit and 818k km2 below average at month end. The gap had closed to 400k km2 before losing extents at the end.  SII and MASIE tracked closely this month.

The table below shows year-end ice extents in the various Arctic basins compared to the 19-year averages and some recent years.  2007 seven was close to the average, so 2018 is shown for comparison.

Region 2025031 Ave Day 031 2025-Ave. 2018031 2025-2018
 (0) Northern_Hemisphere 13543740 14362137 -818398 13792271 -248532
 (1) Beaufort_Sea 1071001 1070386 614 1070445 556
 (2) Chukchi_Sea 965989 965974 15 965971 18
 (3) East_Siberian_Sea 1087137 1087063 74 1087120 18
 (4) Laptev_Sea 897845 897824 21 897845 0
 (5) Kara_Sea 921520 917381 4139 895363 26157
 (6) Barents_Sea 428814 563859 -135044 481947 -53133
 (7) Greenland_Sea 614789 613370 1418 501411 113378
 (8) Baffin_Bay_Gulf_of_St._Lawrence 1080930 1328380 -247450 1406903 -325972
 (9) Canadian_Archipelago 854878 853510 1368 853109 1769
 (10) Hudson_Bay 1260903 1260778 125 1260838 66
 (11) Central_Arctic 3211379 3210507 872 3184817 26562
 (12) Bering_Sea 534452 648807  -114354 382206 152245
 (13) Baltic_Sea 39334 62876  -23542 41713.99 -2380
 (14) Sea_of_Okhotsk 559692 823877  -264185 704398 -144707

This year’s ice extent is 818k km2 or 5.7% below average.  About half of the deficit comes from the Pacific basins of Bering and Okhotsk sea.  The other two major losses are in Barents Sea and Baffin Bay.  With the annual maximum typically occurring mid-March, it is likely the ice then will also be lower than usual.   

 

 

Illustration by Eleanor Lutz shows Earth’s seasonal climate changes. If played in full screen, the four corners present views from top, bottom and sides. It is a visual representation of scientific datasets measuring Arctic ice extents and NH snow cover.

 

Devious Climate Attribution Studies

Posted on by Ron Clutz

Patrick Brown raises the question Do Climate Attribution Studies Tell the Full Story? Excerpts in italics with my bolds and added images, his analysis concluding thusly:

How a cascade of selection effects bias
the collective output of extreme event attribution studies.

Weather and climate extremes—such as high temperatures, floods, droughts, tropical cyclones, extratropical cyclones, and severe thunderstorms—have always threatened both human and natural systems. Given their significant impacts, there is considerable interest in how human-caused climate change influences these extremes. This is the focus of the relatively new discipline of Extreme Event Attribution (EEA).

Over the past couple of decades, there has been an explosion in EEA studies focusing on (or, “triggered by”) some prior notable weather or climate extreme. Non-peer-reviewed reports from World Weather Attribution (e.g., here, here, and here) represent some of the most notable examples of these kinds of analyses, and many similar studies also populate the peer-reviewed literature. The Bulletin of the American Meteorological Society’s “Explaining Extreme Events From a Climate Perspective” annual series compiles such studies, as does the Sabin Center for Climate Change Law, and they are also synthesized in reports like those from the IPCC (IPCC WG1 AR6 Chapter 11.2.3) and the United States National Climate Assessment.

The collective output of these kinds of studies certainly gives the impression that human-caused climate change is drastically changing the frequency and intensity of all kinds of weather extremes. Indeed, Carbon Brief recently published an extensive summary of the science of EEA studies, which begins with the proclamation, “As global temperatures rise, extreme weather events are becoming more intense and more frequent all around the world.”

However, these numbers cannot be taken as an accurate quantification of the influence of climate change on extreme weather because they are heavily influenced by a cascade of selection biases originating from the physical climate system, as well as researcher and media incentives. Identifying and understanding these biases is a prerequisite for properly interpreting the collective output of EEA studies and, thus, what implications they hold for general scientific understanding, as well as political and legal questions.

The large apparent discrepancy between the size of the influence of human-caused climate change on extreme weather reported in EEA studies (like those compiled by Carbon Brief) compared to more comprehensive systematic analyses (like those compiled by the IPCC) can, in large part, be attributed to the many layers of Selection Biases that influence the EEA literature’s collective output.

Selection Bias is a broad term that refers to any bias that arises from a process that selects data for analysis in a way that fails to ensure that data is representative of the broader population that the study wishes to describe.

Selection biases in the context of EEA studies include those associated with the physical climate system itself, those concerning proclivities and incentives facing researchers/journals, and those concerning the proclivities and incentives facing the media. They include

Occurrence Bias is a bias introduced by the physical climate system. Since EEA studies tend to be triggered by extreme events that have actually occurred, there is reason to believe that these studies will disproportionately sample events that are more likely than average to be exacerbated by climate change because the events occurred in the first place. Essentially, extreme events that are more likely to occur under climate change—and thus more likely to be observed—are going to be overrepresented in EEA studies, and extreme events that are less likely to occur under climate change—and thus less likely to be observed—are going to be underrepresented in EEA studies.

The map below illustrates this phenomenon. It shows changes in the magnitude of extreme drought under climate change. Specifically, it shows the fractional change in the intensity of once-per-50-year droughts (as quantified by monthly soil moisture) between a preindustrial and 21st-century run (SSP2-4.5 emissions) of the highly-regarded NCAR CESM2 Climate Model. Blue areas represent locations where the model simulates that extreme droughts become less frequent and intense with enhanced greenhouse gas concentrations, and red areas represent locations where the model simulates that extreme droughts become more frequent and intense with enhanced greenhouse gas concentrations. It is notable that overall, this model simulates that warming decreases the frequency and intensity of extreme drought in more locations than it increases it (consistent with soil moistening under warming simulated by other models).

Now, here’s the kicker: The black dots show locations where once-per-50-year droughts actually occurred in the 21st-century simulation and thus represent events that would plausibly trigger EEA studies.

What do you notice about where the dots are compared to where the red is? That’s right; the simulated EEA studies overwhelmingly sample areas where droughts are getting more intense and more frequent by the very nature that those are the types of droughts that are more likely to occur in the warming climate. The result is that the EEA sample is majorly biased: warming decreased the intensity of once-per-50-year droughts by about 1% overall, but it increased their intensity within the EEA sample by 18%!

Thus, if you just relied on the EEA sample, you would come away with an
incorrect impression not only on the magnitude of change in extreme droughts
but also on the sign of the direction of change!

Choice Bias arises when researchers use prior knowledge to choose events for EEA studies that are more likely to have been made more severe by climate change. A clear example of Choice Bias pervading the Carbon Brief database is there have been 3.6 times more studies on extreme heat than there have been on extreme winter weather (205 vs. 57). Another example would be the dearth of EEA studies on extratropical cyclones (the kinds of low-pressure systems with cold and warm fronts that are responsible for most of the dramatic weather outside of the tropics). The IPCC states that the number of extratropical cyclones associated with intense surface wind speeds is expected to decrease strongly in the Northern Hemisphere with warming. Yet, it is relatively rare for EEA attribution studies to be done on these types of systems, which results in an exclusion of this good news from the EEA literature.

Publication Bias could be playing a role, too, where researchers are more likely to submit, and journals are likely to publish studies that report significant effects on salient events compared to studies that find null effects.

From Clark et al., 2023

Finally, the climate reporting media ecosystem is characterized by actors whose explicit mission is to raise awareness of the negative impacts of climate change, and thus, there will be a natural Media Coverage Bias with a tendency to selectively highlight EEA studies where climate change is found to be a larger driver than EEA studies that do not reach such a conclusion. These selection biases are apparent at the aggregate level, but there is also strong evidence of their presence in individual studies.

A more recent specific example suggestive of many of these dynamics is a study, Gilford et al. (2024), titled “Human-caused ocean warming has intensified recent hurricanes”. This study was conducted by three researchers at Climate Central, which summarizes the study’s findings with the following infographic:

From Climate Central press release on Gilford et al. (2024).

Essentially, they claim that climate change is enhancing the intensity of all hurricanes and that the enhancement is quite large: Storms today are calculated to be an entire Category stronger than they would have been in a preindustrial climate.

This is a huge effect, and thus, if it were real, it is reasonable to expect to see clear long-term trends in metrics of tropical cyclone (hurricane) intensity like the accumulated number of major (Category 3+) hurricane days or the accumulated cyclone energy from all tropical cyclones (which is proportional to the square of hurricane windspeed accumulated over their lifetimes). However, any long-term trends in such metrics are subtle at best, both globally and over the North Atlantic.

From Colorado State University Department of Atmospheric Science Tropical Meteorology Project.

So, this is a microcosm of the aforementioned apparent discrepancy between more broad quantifications of changes in extremes and their associated EEA counterparts, and again, I’d argue there are several selection biases at play affecting the production and dissemination of the EEA study.

Let’s start with Choice Bias on methodology. Human-caused warming changes the environment in some ways that work to enhance hurricanes and in other ways that diminish them. The main way that hurricanes are enhanced is via the increase in sea surface temperatures (which provides the fundamental fuel for hurricanes), and the main way that hurricanes are diminished is via changes in atmospheric wind shear and humidity.

The net result of these countervailing factors pulling in opposite directions is that we expect fewer hurricanes overall, but when hurricanes are able to form, they can be stronger than they would otherwise. These factors, though, are small relative to natural random variability, and thus, they are difficult to detect in observations.

However, the Climate Central researchers made the methodological choice
to largely exclude the influence of factors that diminish
hurricane development from the study.

Are these Choice Biases in event type and methodology an accident? There are many reasons to believe they are not.

The research paper itself spells out that the motivation of the study is to “connect the dots” between climate change and hurricanes because “landfalling hurricanes with high intensities—can act as ‘focusing events’ that draw public attention” and that “Increased attention during and in wake of storms creates opportunities for public and private discourse around climate and disaster preparedness.”

Then, there is the extensive media coverage of this study. It was picked up by 134 news outlets and ranked in the 99.95th percentile of research articles (across all journals) of similar age in terms of online attention. Further, it was immediately incorporated into seven Wikipedia articles (likely having high leverage on AI queries, which would make its findings indistinguishable from scientific “fact”). This is affected by the aforementioned Media Coverage Bias, but it is also undoubtedly directly influenced by the efforts of Climate Central, which is explicitly an advocacy organization whose self-described specialty is media placement and dissemination. 

The above sheds light on the reasons for certain choice biases in a particular study, but there is plenty of evidence that these selection biases are pervasive in the EEA field. After all, Dr. Myles Allen essentially founded the field with the motivation of answering the question, “Will it ever be possible to sue anyone for damaging the climate?”. This same motivation seems to animate many of the most high-profile scientists in the field today, like Allen’s protege, Dr. Friederike Otto (co-founder and leader of World Weather Attribution). She and her organization are frequently cited as bringing the necessary intellectual authority to credibly sue fossil fuel companies. She states the motivation of her work explicitly:

“Attributing extreme weather events to climate change, as I do
through my work as a climatologist, means we can hold
countries and companies to account for their inaction.”

Given the explicitly stated motivation of those in the EEA field, it is quite reasonable to suppose that there are major selection biases at play, and thus, it is not at all surprising that the collective output of the EEA field would look so different from more broad comprehensive assessments.

 

 

 

 

 

Koonin: Reckless Claim of Climate Emergency

Posted on by Ron Clutz

Transcript

Hubris is a Greek word that means dangerously overconfident. Based on my research, hubris fairly describes our current response to the issue of climate change.

Here’s what many people believe:

One: The planet is warming catastrophically because of certain human behaviors.
Two: Thanks to powerful computers we can project what the climate will be like
20, 40, or even 100 years from now.
Three: That if we eliminate just one behavior, the burning of fossil fuels,
we can prevent the climate from changing for as long we like.

Each of these presumptions—together, the basis of our hubris regarding the changing climate—is either untrue or so far off the mark as to be useless.

Yes, it’s true that the globe is warming, and that humans are exerting a warming influence upon it. But beyond that, to paraphrase a line from the classic movie The Princess Bride, “I do not think ‘The Science’ says what you think it says.”

For example, government reports state clearly that heat waves in the US are now no more common than they were in 1900.

Hurricane activity is no different than it was a century ago.

Floods have not increased across the globe over more than seventy years.

Source: Voice of International Affairs

Greenland’s ice sheet isn’t shrinking any more rapidly today than it was 80 years ago.

Why aren’t these reassuring facts better known?

Because the public gets its climate information almost exclusively from the media.

And from a media perspective, fear sells.

“Things aren’t that bad” doesn’t sell.

Very few people, and that includes journalists who report on climate news, read the actual science. I have. And what the data—the hard science—from the US government and UN Climate reports say is that… “things aren’t that bad.”

Nor does the public understand the questionable basis of all catastrophic climate change projections: computer modeling.

Projecting future climate is excruciatingly difficult. Yes, there are human influences, but the climate is complex. Anyone who says that climate models are “just physics” either doesn’t understand them or is being deliberately misleading. I should know: I wrote one of the first textbooks on computer modeling.

While modelers base their assumptions upon both fundamental physical laws and observations of the climate, there is still considerable judgment involved. And since different modelers will make different assumptions, results vary widely among different models.

Let’s just take one simple, but significant assumption modelers must make: the impact of clouds on the climate.

Natural fluctuations in the height and coverage of clouds have at least as much of an impact on the flows of sunlight and heat as do human influences. But how can we possibly know global cloud coverage say 10, let alone 50 years from now? Obviously, we can’t. But to create a climate model, we have to make assumptions. That’s a pretty shaky foundation on which to transform the world’s economy.

By the way, creating more accurate models isn’t getting any easier. In fact, the more we learn about the climate system, the more we realize how complex it is.

Rather than admit this complexity, the media, the politicians, and a good portion of the climate science community attribute every terrible storm, every flood, every major fire to “climate change.” Yes, we’ve always had these weather events in the past, the narrative goes, but somehow “climate change” is making everything “worse.”

Even if that were true, isn’t the relevant question, how much worse? Not to mention that “worse” is not exactly a scientific term.  And how would we make it better?  For the alarmists, that’s easy: we get rid of fossil fuels.

Not only is this impractical—we get over 80% of the world’s energy from fossil fuels—it’s not scientifically possible. That’s because CO2 doesn’t disappear from the atmosphere in a few days like, say, smog. It hangs around for a really long time.

About 60 percent of any CO2 that we emit today will remain in the atmosphere 20 years from now, between 30 and 55 percent will still be there after a century, and between 15 and 30 percent will remain after one thousand years.

In other words, it takes centuries for the excess carbon dioxide to vanish from the atmosphere. So, any partial reductions in CO2 emissions would only slow the increase in human influences—not prevent it, let alone reverse it.

CO2 is not a knob that we can just turn down to fix everything. We don’t have that ability. To think that we do is… hubris.

Hubris leads to bad decisions.  A little humility and
a little knowledge would lead to better ones.

I’m Steve Koonin, former Undersecretary for Science in the Obama Administration, and author of Unsettled: What Climate Science Tells Us, What It Doesn’t, and Why It Matters, for Prager University.

Addendum  Fossil Fuels and Greenhouse Gases (GHGs) Climate Science

Professors Lindzen, Happer and Koonin CO2 Coalition Paper April 2024

Table of Contents

I. THERE WILL BE DISASTROUS CONSEQUENCES FOR THE POOR, PEOPLE WORLDWIDE, FUTURE GENERATIONS AND THE WEST IF FOSSIL FUELS, CO2 AND OTHER GHG EMISSIONS ARE REDUCED TO “NET ZERO”

A. CO2 is Essential to Our Food, and Thus to Life on Earth
B. More CO2, Including CO2 from Fossil Fuels, Produces More Food.
C. More CO2 Increases Food in Drought-Stricken Areas.
D. Greenhouse Gases Prevent Us from Freezing to Death
E. Enormous Social Benefits of Fossil Fuels
F. “Net Zeroing” Fossil Fuels Will Cause Massive Human Starvation by Eliminating Nitrogen Fertilizer

II. THE IPCC IS GOVERNMENT CONTROLLED AND THUS ONLY ISSUES GOVERNMENT OPINIONS, NOT SCIENCE

III. SCIENCE DEMONSTRATES FOSSIL FUELS, CO2 AND OTHER GHGs WILL NOT CAUSE CATASTROPHIC GLOBAL WARMING AND EXTREME WEATHER

A. Reliable Science is Based on Validating Theoretical Predictions With Observations, Not Consensus, Peer Review, Government Opinion or Cherry-Picked or Falsified Data
B. The Models Predicting Catastrophic Warming and Extreme Weather Fail the Key Scientific Test: They Do Not Work, and Would Never Be Used in Science.
C. 600 Million Years of CO2 and Temperature Data Contradict the Theory That High Levels of CO2 Will Cause Catastrophic Global Warming.
D. Atmospheric CO2 Is Now “Heavily Saturated,” Which in Physics Means More CO2 Will Have Little Warming Effect.
E. The Theory Extreme Weather is Caused by Fossil Fuels, CO2 and Other GHGs is Contradicted by the Scientific Method and Thus is Scientifically Invalid

 

 

 

 

 

Climate False Alarms about Agriculture

Posted on by Ron Clutz

Javier Vinós   @JVinos_Climate  Scientist. Molecular neurobiologist and climate researcher

Climate Lemmings