Because it succinctly explains why the models the IPCC relies upon are flawed and has yet to be refuted.I'm curious why you've obsessively linked to a 69 page paper with dozens of data graphics but only present one of those graphics and speak as if the entire work is contained in two paragraphs. Your endlessly repeated assertion, that climate scientists come to different conclusions based on which dataset they are shown, is not the conclusion of this paper but part of its premise. The paper's title is "How much has the Sun influenced Northern Hemisphere temperature trends? An ongoing debate".
Among its list of authors may be found a 'veritable who's who' of AGW deniers: Ronan Connolly, Willie Soon, Michael Connolly, Sallie Baliunas, Johan Berglund, C. John Butler, Rodolfo Gustavo Cionco, Ana G. Elias, Valery M. Fedorov, Hermann Harde, Gregory W. Henry, Douglas V. Hoyt, Ole Humlum, David R. Legates, Sebastian Luning, Nicola Scafetta, Jan-Erik Solheim, Laszlo Szarka, Harry van Loon, Vıctor M. Velasco Herrera, Richard C. Willson, Hong Yan and Weijia Zhang. The inclusion of Legates, who has no climatological, astronomical or statistical background at all and whose most famous paper should have gotten him thrown out of whatever professional or academic institutions were blind enough to have admitted him, is simply an insult to the reader. Legates and Connolly have worked together on several other papers
Abstract In order to evaluate how much Total Solar Irradiance (TSI) has influenced Northern Hemisphere surface air temperature trends, it is important to have reliable estimates of both quantities. Sixteen different estimates of the changes in TSI since at least the 19th century were compiled from the literature. Half of these estimates are “low variability” and half are “high variability”. Meanwhile, five largely-independent methods for estimating Northern Hemisphere temperature trends were evaluated using: 1) only rural weather stations; 2) all available stations whether urban or rural (the standard approach); 3) only sea surface temperatures; 4) tree-ring widths as temperature proxies; 5) glacier length records as temperature proxies. The standard estimates which use urban as well as rural stations were somewhat anomalous as they implied a much greater warming in recent decades than the other estimates, suggesting that urbanization bias might still be a problem in current global temperature datasets – despite the conclusions of some earlier studies. Nonetheless, all five estimates confirm that it is currently warmer than the late 19th century, i.e., there has been some “global warming” since the 19th century. For each of the five estimates of Northern Hemisphere temperatures, the contribution from direct solar forcing for all sixteen estimates of TSI was evaluated using simple linear least-squares fitting. The role of human activity on recent warming was then calculated by fitting the residuals to the UN IPCC’s recommended “anthropogenic forcings” time series. For all five Northern Hemisphere temperature series, different TSI estimates suggest everything from no role for the Sun in recent decades (implying that recent global warming is mostly human-caused) to most of the recent global warming being due to changes in solar activity (that is, that recent global warming is mostly natural). It appears that previous studies (including the most recent IPCC reports) which had prematurely concluded the former, had done so because they failed to adequately consider all the relevant estimates of TSI and/or to satisfactorily address the uncertainties still associated with Northern Hemisphere temperature trend estimates. Therefore, several recommendations on how the scientific community can more satisfactorily resolve these issues are provided.
I'm no climate scientist, but drawing the potential conclusion that ubanization bias might still be present because rural and urban weather stations show more warming than SST, glacier length and tree-ring widths seems a clear case of apples and oranges. The instantaneous nature of the former vs the severe lags of the latter are a blatant conflict they don't mention. Several of these authors: Connolly, Soon, Bailunas (who have put out numerous papers together) and others have argued for years that global warming is not anthropogenic but due to increases in TSI not reflected in its most widely accepted measures. Soon and Bailunas, in particular, are famous for the oil industry funding for their work. I'm not foolish enough to think that any scientist begins a study tabula rasa, but most of this crew had formed these conclusions long before this study was even imagined. A strong majority of climate scientists disagree with this contention that previous studies and the IPCC had "prematurely concluded" that global warming is mostly human-caused.
I would guess that this paper is where your preference for Northern Hemisphere temperature data begins. If so, you failed to note that what drove the author's choice was simply the relative paucity of data from the Southern Hemisphere, not any superiority in representation. I also failed to see any comments supporting your claim that it was best to restrict your analysis to temperature outliers.
This paper finshes by concluding it has no conclusion at all and that its authors could not agree on a singular answer to the title's question. Your contention all along, has been that "climate scientists" come to different conclusions about global warming when looking at different datasets. What this paper actually shows is that the authors of this paper, with differing preferences for TSI and temperature data, come to different conclusions. You have no survey of climate scientists looking at different datasets. And since you never seem to venture beyond that initial claim but have linked to this study at least eleven times, I will have to state what it appears to me that you are attempting to say: that the resolution of TSI centered on Matthes 2017 is unwarranted, that distinct possibilities exist that TSI has been far greater than commonly held, particularly during the ACRIM gap, that actual global warming trends have not been accurately determined, that urbanization bias may still be present in commonly used temperature data and thus that common conclusions about the reality of AGW are unjustified. If you disagree, please explain what it is you are actually attempting to take from this paper.
From the study
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.
Acknowledgements The main analysis and first draft of the manuscript were carried out by the first three authors (RC, WS and MC). All other co-authors are listed in alphabetical order. As explained in the Introduction, the approach we have taken in this review is to explicitly acknowledge the many dissenting scientific perspectives on a lot of the issues reviewed. As a result, the co-authors have not reached a mutual consensus on all issues. Rather, we have endeavored to present all competing scientific perspectives as fairly and open-mindedly as possible. With that in mind, all co-authors have approved of the text, even though most of us have definite opinions on many of the debates which have been described, and those opinions vary between co-authors.
1. They include temperatures from urban temperature stations which blames the UHI effect on CO2.
2. They use the low variability solar output dataset instead of the high variability dataset.
Garbage in equals garbage out. And don't even get me started on how they routinely tune out natural variations.