Greenland surface melt falls over a kilometer to the ice sheet base. Potential -> Kinetic > than 10 powerstations combined

abu afak said:
1. Forgetting any mod 'requirement' a Clever poster can find it.
That was the gist of my first reply.
Gameover.

2. The mod an "esp" addition.
Again, a mod can't know if a post is unsourced/plagiarized without finding the source.

3! So you should have removed all unsourced graphs/graphics/etc if that source is not provided.
I am FOR what you rightly call the "posters job."
And I am FOR doing so WITH LINK, not just something in the graphic itself that is ostensibly the the source but is home made.

`
Click to expand...

Forum Rules you are IGNORING:

  • When starting a new Thread, please first check and confirm that there are not Current Threads, on the Same Topic, This will Avoid Merges. Please select the forum that best relates to the subject matter of your topic. Opening Posts require more than a Copy and Paste with a Link, You need to include relevant, on topic material of your own. When posting a new topic do not use the CAPS lock.
and,

  • Copyright. Link Each "Copy & Paste" to It's Source. Only paste a small to medium section of the material.
LINK

red bolding mine

=====

You are wrong constantly!
 
Crick said:
Go to Google. Do an image search for "radiative forcing factors". You will find many versions of these data. All of them show a very small but distinctly NEGATIVE value for Land Use"
Click to expand...

I asked YOU at post 34 for the link you ignored it and later in the thread posted the chart again without the link thus I reported it to the Mods.

Forum rule states:

  • Copyright. Link Each "Copy & Paste" to It's Source. Only paste a small to medium section of the material.
bolding mine
 
Crick said:
Go to Google. Do an image search for "radiative forcing factors". You will find many versions of these data. All of them show a very small but distinctly NEGATIVE value for Land Use"
Click to expand...
That almost sounds like you don't believe there is an urban heat island effect.

That's probably because they lump those temperature readings in to blame CO2 and skew the data to match their narrative, right?
 
Crick said:
Go to Google. Do an image search for "radiative forcing factors". You will find many versions of these data. All of them show a very small but distinctly NEGATIVE value for Land Use"
Click to expand...

Not my job.

"Copyright. Link Each "Copy & Paste" to It's Source. Only paste a small to medium section of the material."
 
ding said:
Crick , Wouldn't stupid be saying orbital cycles were responsible for the 50 to 60 temperature swings of the past 10,000 years? Orbital cycles have really really long cycle times - 26,000 to 100,000 years. This looks more like 5 to 6 cycles per 1000 years.

View attachment 605840
Click to expand...
Where is the link to this graph?
 

1645741034709.png


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1645741223753.png

1645741253564.png

1645741287602.png

1645741343779.png


And so forth and so forth and so forth...
 
So, Antarctica's and Greenland's land-based ice sheets are melting faster than previously known. All the models up to now have been underestimating their contribution to sea level rise. That is the topic of this thread.
 
Crick said:
So, Antarctica's and Greenland's land-based ice sheets are melting faster than previously known. All the models up to now have been underestimating their contribution to sea level rise. That is the topic of this thread.
Click to expand...
But not necessarily faster than has previously occurred, right? It's just that you don't know one way or another, right?

I mean there's been like 33 cycles in the last 3 million years. And even within those cycles there were numerous cycles. It very well could be it's no different today than it has been in the past, right?
 
Crick said:
All the models up to now have been underestimating their contribution to sea level rise. That is the topic of this thread.
Click to expand...
I'd say there's still a long way to go in proving that. Especially since you have made some lofty projections on how fast the sea will rise. We aren't seeing anything like that yet. And if we did it still isn't clear that man is the cause of that as temperature trends up and down are hallmarks of our bipolar glaciated world. Something you seem unwilling to acknowledge. So given the fluctuations that we know have occurred before the industrial revolution it's not as cut and dried as you would like to believe. Especially since they are lumping the urban heat island effect into their models for CO2 and using low variability solar output datasets. ;)
 
Last edited:
Crick said:

View attachment 606323

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View attachment 606334

And so forth and so forth and so forth...
Click to expand...
Scientists come to opposite conclusions about the causes of recent climate change depending on which datasets they consider. For instance, the panels on the left lead to the conclusion that global temperature changes since the mid-19th century have been mostly due to human-caused emissions, especially carbon dioxide (CO2), i.e., the conclusion reached by the UN IPCC reports. In contrast, the panels on the right lead to the exact opposite conclusion, i.e., that the global temperature changes since the mid-19th century have been mostly due to natural cycles, chiefly long-term changes in the energy emitted by the Sun.



1632186412722.png



Both sets of panels are based on published scientific data, but each uses different datasets and assumptions. On the left, it is assumed that the available temperature records are unaffected by the urban heat island problem, and so all stations are used, whether urban or rural. On the right, only rural stations are used. Meanwhile, on the left, solar output is modeled using the low variability dataset that has been chosen for the IPCC’s upcoming (in 2021/2022) 6th Assessment Reports. This implies zero contribution from natural factors to the long-term warming. On the right, solar output is modeled using a high variability dataset used by the team in charge of NASA’s ACRIM sun-monitoring satellites. This implies that most, if not all, of the long-term temperature changes are due to natural factors.

Here is the link to the full paper.
ShieldSquare Captcha
 
ding said:
Clouds, water vapor, urban heat island effect and high variability solar output datasets confuse them, so they just ignore it or add it into CO2.
Click to expand...
It confuses "them" but not you. Again, you're claiming to be smarter than several thousand, published, PhD scientists actively researching in this field. My apologies, but I don't buy it.
 
Crick said:
It confuses "them" but not you. Again, you're claiming to be smarter than several thousand, published, PhD scientists actively researching in this field. My apologies, but I don't buy it.
Click to expand...
The whole water vapor / cloud thing is pretty damn complex. Whether it is a positive or negative feedback is complicated because it's probably both. So the question is what's the net and that answer may not always hold true for all landmass configurations throughout geologic time. That's how complicated I think it is. But what I will say is given the wave like function of temperature fluctuations it sure appears that there is a self compensating feature at work that we do not fully understand and if there is it's probably water vapor and clouds.

I'm not claiming anything. I am telling you it's not as simple as YOU want to make it.

When you can tell me what caused climate fluctuations during THIS interglacial cycle prior to industrialization and what the thresholds are for each polar region for extensive continental glaciation, then maybe we can have an intelligent conversation. But until then, you are just a partisan blindly rooting for your party, a football fan blindly rooting for his team, a person with zero interest in the truth.

And I'm going to keep beating you over the head with it until you actually address it.
 
Last edited:
ding said:
Scientists come to opposite conclusions about the causes of recent climate change depending on which datasets they consider. For instance, the panels on the left lead to the conclusion that global temperature changes since the mid-19th century have been mostly due to human-caused emissions, especially carbon dioxide (CO2), i.e., the conclusion reached by the UN IPCC reports. In contrast, the panels on the right lead to the exact opposite conclusion, i.e., that the global temperature changes since the mid-19th century have been mostly due to natural cycles, chiefly long-term changes in the energy emitted by the Sun.



1632186412722.png



Both sets of panels are based on published scientific data, but each uses different datasets and assumptions. On the left, it is assumed that the available temperature records are unaffected by the urban heat island problem, and so all stations are used, whether urban or rural. On the right, only rural stations are used. Meanwhile, on the left, solar output is modeled using the low variability dataset that has been chosen for the IPCC’s upcoming (in 2021/2022) 6th Assessment Reports. This implies zero contribution from natural factors to the long-term warming. On the right, solar output is modeled using a high variability dataset used by the team in charge of NASA’s ACRIM sun-monitoring satellites. This implies that most, if not all, of the long-term temperature changes are due to natural factors.

Here is the link to the full paper.
ShieldSquare Captcha
Click to expand...
Here is the conclusion to the paper from which your datasets are drawn:

Conclusion. In the title of this paper, we asked “How much has the Sun influenced Northern Hemisphere temperature trends?” However, it should now be apparent that, despite the confidence with which many studies R. Connolly et al.: How Much has the Sun Influenced Northern Hemisphere Temperature Trends? 131–59 claim to have answered this question, it has not yet been satisfactorily answered. Given the many valid dissenting scientific opinions that remain on these issues, we argue that recent attempts to force an apparent scientific consensus (including the IPCC reports) on these scientific debates are premature and ultimately unhelpful for scientific progress. We hope that the analysis in this paper will encourage and stimulate further analysis and discussion. In the meantime, the debate is ongoing.

No where does this paper even suggest that the primary cause of the observed global warming is due anything other than the greenhouse effect operating on human GHG emissions. No where does this paper even suggest that the primary or even a major cause of the observed warming is changes in TSI. This paper, as it states repeatedly, deals solely with arriving at a better estimate of the input of the TSI changes on solar warming in the Northern Hemisphere. They fully admit in their concluding recommendations that the impact of including or excluding UHI-affected data is under robust debate. And no where in this paper does it suggest that UHI-affected data should be excluded in the calculation of the Earth's actual temperature. You have misunderstood this paper in hopes that it would provide evidence for your flat-Earth level suppositions about global warming. It simply does not.

And, since the topic of this thread is NOT TSI, global warming, UHI or any of the other strawman arguments you've attempted to bring up here, your entire input to this conversation is off topic. If you have no comments to make concerning kinetic heating of meltwater in Greenland and Antarctica, I suggest you find another thread.
 
Crick said:
Here is the conclusion to the paper from which your datasets are drawn:

Conclusion. In the title of this paper, we asked “How much has the Sun influenced Northern Hemisphere temperature trends?” However, it should now be apparent that, despite the confidence with which many studies R. Connolly et al.: How Much has the Sun Influenced Northern Hemisphere Temperature Trends? 131–59 claim to have answered this question, it has not yet been satisfactorily answered. Given the many valid dissenting scientific opinions that remain on these issues, we argue that recent attempts to force an apparent scientific consensus (including the IPCC reports) on these scientific debates are premature and ultimately unhelpful for scientific progress. We hope that the analysis in this paper will encourage and stimulate further analysis and discussion. In the meantime, the debate is ongoing.

No where does this paper even suggest that the primary cause of the observed global warming is due anything other than the greenhouse effect operating on human GHG emissions. No where does this paper even suggest that the primary or even a major cause of the observed warming is changes in TSI. This paper, as it states repeatedly, deals solely with arriving at a better estimate of the input of the TSI changes on solar warming in the Northern Hemisphere. They fully admit in their concluding recommendations that the impact of including or excluding UHI-affected data is under robust debate. And no where in this paper does it suggest that UHI-affected data should be excluded in the calculation of the Earth's actual temperature. You have misunderstood this paper in hopes that it would provide evidence for your flat-Earth level suppositions about global warming. It simply does not.

And, since the topic of this thread is NOT TSI, global warming, UHI or any of the other strawman arguments you've attempted to bring up here, your entire input to this conversation is off topic. If you have no comments to make concerning kinetic heating of meltwater in Greenland and Antarctica, I suggest you find another thread.
Click to expand...
Then you didn't read the paper....

In Section 3, we will compile and generate several different estimates of Northern Hemisphere temperature trends. We will show that the standard estimates used by IPCC (2013b), which include urban as well as rural stations, imply a much greater long-term warming than most other estimates. This suggests that the standard estimates have not adequately corrected for urbanization bias (McKitrick & Nierenberg 2010; Soon et al. 2015; Soon et al. 2018, 2019b; Scafetta & Ouyang 2019; Scafetta 2021; Zhang et al. 2021).

Our main analysis involves estimating the maximum solar contribution to Northern Hemisphere temperature trends assuming a linear relationship between TSI and temperature. However, since IPCC (2013) concluded that the most important factor in recent temperature trends is “anthropogenic forcings” (chiefly from greenhouse gas emissions), a useful secondary question we will consider is how much of the trends unexplained by this assumed linear solar relationship can be explained in terms of anthropogenic forcings. Therefore, a second step of our analysis will involve fitting the statistical residuals from the first step using the anthropogenic forcings recommended by IPCC (2013a). In Section 4, we will describe the IPCC’s anthropogenic forcings datasets. In Section 5, we will calculate the best fits (using linear least-squares fitting) for each of the TSI and Northern Hemisphere temperature reconstructions and then estimate the implied Sun/climate relationship from each combination, along with the implied role of anthropogenic (i.e., human-caused) factors.

Because most of the energy that keeps the Earth warmer than space comes from incoming solar radiation, i.e., TSI, it stands to reason that a multidecadal increase in TSI should cause global warming (all else being equal). Similarly, a multidecadal decrease in TSI should cause global cooling. For this reason, for centuries (and longer), researchers have speculated that changes in solar activity could be a major driver of climate change (Laut 2003; Gray et al. 2010; Lockwood 2012; Hoyt & Schatten 1997; Singer & Avery 2008; Soon et al. 2015; Maunder & Maunder 1908; Soon & Yaskell 2003; Scafetta 2010, 2014a). However, a challenging question associated with this theory is “How exactly has TSI changed over time?” One indirect metric on which much research has focused is the examination of historical records of the numbers and types/sizes of “sunspots” that are observed on the Sun’s surface over time (Beck et al. 2014; Hoppe & Rodder ¨ 2019; Sarewitz 2011; Hulme 2013; Bateman et al. 2005; Kahneman & Klein 2009; Rakow et al. 2015; Matthes et al. 2017). Sunspots are intermittent magnetic phenomena associated with the Sun’s photosphere, that appear as dark blotches or blemishes on the Sun’s surface when the light from the Sun is shone on a card with a telescope (to avoid the observer directly looking at the Sun). These have been observed since the earliest telescopes were invented, and Galileo Galilei and others were recording sunspots as far back as 1610 (Soon & Yaskell 2003; Hoyt & Schatten 1998; Svalgaard & Schatten 2016; Vaquero et al. 2016; Schove 1955; Usoskin et al. 2015). The Chinese even have intermittent written records since 165 B.C. of sunspots that were large enough to be seen by the naked eye (Wang & Li 2019, 2020) Moreover, an examination of the sunspot records reveals significant changes on sub-decadal to multidecadal timescales. In particular, a pronounced “sunspot cycle” exists over which the number of sunspots rises from zero during the Sunspot Minimum to a Sunspot Maximum where many sunspots occur, before decreasing again to the next Sunspot Minimum. The length of this “sunspot cycle” or “solar cycle” is typically about 11 years, but it can vary between 8 and 14 years. This 11-year cycle in sunspot behavior is part of a 22-year cycle in magnetic behavior known as the Hale Cycle. Additionally, multidecadal and even centennial trends are observed in the sunspot numbers. During the period from 1645 to 1715, known as the “Maunder Minimum” (Soon & Yaskell 2003; Hoyt & Schatten 1998; Svalgaard & Schatten 2016; Vaquero et al. 2016; Usoskin et al. 2015), sunspots were very rarely observed at all.

...there is considerable ongoing controversy over what exactly the trends in TSI have been (Scafetta 2011; Scafetta & Willson 2014; Soon et al. 2015; Scafetta et al. 2019; Beer et al. 2000; Dudok de Wit et al. 2017; Frohlich ¨ 2012; Gueymard 2018). There are a number of rival composite datasets, each implying different trends in TSI since the late-1970s. All composites agree that TSI exhibits a roughly 11-year cycle that matches well with the sunspot cycle discussed earlier. However, the composites differ in whether additional multidecadal trends are occurring. The composite of the ACRIM group that was in charge of the three ACRIM satellite missions (ACRIM1, ACRIM2 and ACRIM3) suggests that TSI generally increased during the 1980s and 1990s but has slightly declined since then (Scafetta & Willson 2014; Scafetta et al. 2019; Willson 2014; Scafetta & Willson 2019). The Royal Meteorological Institute of Belgium (RMIB)’s composite implies that, aside from the sunspot cycle, TSI has remained fairly constant since at least the 1980s (Dewitte & Nevens 2016). Meanwhile, the PhysikalischMeteorologisches Observatorium Davos (PMOD) composite implies that TSI has been steadily decreasing since at least the late-1970s (Frohlich ¨ 2012, 2009). Additional TSI satellite composites have been produced by Scafetta (2011); Dudok de Wit et al. (2017) and Gueymard (2018). The two main rival TSI satellite composites are ACRIM and PMOD. As we will discuss in Section 3, global temperatures steadily increased during the 1980s and 1990s but seemed to slow down since the end of the 20th century. Therefore, the debate over these three rival TSI datasets for the satellite era is quite important. If the ACRIM dataset is correct, then it suggests that much of the global temperature trends during the satellite era could have been due to changes in TSI (Willson & Mordvinov 2003; Scafetta & West 2008b; Scafetta 2009, 2011; Scafetta & Willson 2014; Scafetta et al. 2019; Willson 2014; Scafetta & Willson 2019). However, if the PMOD dataset is correct, and we assume for simplicity a linear relationship between TSI and global temperatures, then the implied global temperature trends from changes in TSI would exhibit long-term global cooling since at least the late-1970s. Therefore, the PMOD dataset implies that none of the observed warming since the late-1970s could be due to solar variability, and that the warming must be due to other factors, e.g., increasing greenhouse gas concentrations. Moreover, it implies that the changes in TSI have been partially reducing the warming that would have otherwise occurred; if this TSI trend reverses in later decades, it might accelerate “global warming” (Frohlich ¨ 2009; Frohlich & Lean ¨ 2002). The PMOD dataset is more politically advantageous to justify the ongoing considerable political and social efforts to reduce greenhouse gas emissions under the assumption that the observed global warming since the late-19th century is mostly due to greenhouse gases. Indeed, as discussed in Soon et al. (2015), Dr. Judith Lean (of the PMOD group) acknowledged in a 2003 interview that this was one of the motivations for the PMOD group to develop a rival dataset to the ACRIM one by stating, “The fact that some people could use Willson’s [ACRIM dataset] results as an excuse to do nothing about greenhouse gas emissions is one reason we felt we needed to look at the data ourselves” – Dr. Judith Lean, interview for NASA Earth Observatory, August 2003 (Lindsey 2003) Similarly, Zacharias (2014) argued that it was politically important to rule out the possibility of a solar role for any recent global warming, “A conclusive TSI time series is not only desirable from the perspective of the scientific community, but also when considering the rising interest of the public in questions related to climate change issues, thus preventing climate skeptics from taking advantage of these discrepancies within the TSI community by, e.g., putting forth a presumed solar effect as an excuse for inaction on anthropogenic warming.” – Zacharias (2014) We appreciate that some readers may share the sentiments of Lean and Zacharias and others and may be tempted to use these political arguments for helping them to decide their opinion on this ongoing scientific debate. In this context, readers will find plenty of articles to use as apparent scientific justification, e.g., Refs. (Lean 2017; Meftah et al. 2014; Dudok de Wit et al. 2017; Frohlich ¨ 2012; Dewitte & Nevens 2016; Frohlich ¨ 2009; Frohlich ¨ & Lean 2002; Zacharias 2014; Kopp et al. 2016; Lean 2018). It may also be worth noting that the IPCC appears to have taken the side of the PMOD group in their most recent AR5 – see section 8.4.1 of IPCC (2013a) for the key discussions. However, we would encourage all readers to carefully consider the counter-arguments offered by the ACRIM group, e.g., Refs. (Willson & Mordvinov 2003; Scafetta & Willson 2014; Scafetta et al. 2019; Willson 2014; Scafetta & Willson 2019). In our opinion, this was not satisfactorily done by the authors of the relevant section in the influential IPCC reports, i.e., section 8.4.1 of IPCC (2013a). Matthes et al. (2017)’s recommendation that their new estimate (which will be discussed below) should be the only solar activity dataset considered by the CMIP6 modeling groups (Matthes et al. 2017) for the IPCC’s upcoming AR6 is even more unwise due to the substantial differences between various published TSI estimates. This is aside from the fact that Scafetta et al. (2019) have argued that the TSI proxy reconstructions preferred in Matthes et al. (2017) (i.e., NRLTSI2 and SATIRE) contradict important features observed in the ACRIM 1 and ACRIM 2 satellite measurements. We would also encourage readers to carefully read the further discussion of this debate in Soon et al. (2015).

Connolly

Research in Astronomy and Astrophysics
www.raa-journal.org www.raa-journal.org
 
Crick said:
It confuses "them" but not you. Again, you're claiming to be smarter than several thousand, published, PhD scientists actively researching in this field. My apologies, but I don't buy it.
Click to expand...

Let's see the list and include the title of their dissertation and what you think that means ...

And still that leaves ten's of thousands of Atmospheric Scientists who disagree with you ... not that you're wrong, just that most folks with any formal training in these subjects can agree that you have no training what-so-ever, and it's completely useless trying to explain anything to you ... like a house cat ... your lack of understanding of the scientific paper ding posted is a good example ... it's the underlaying physics that trips you up ... and that's why the math says you're wrong; not us, it's the math calling you a liar ...
 
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