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Reality of global warming

sayak83

Veteran Member
Staff member
Premium Member
Science is not about huff and puff and appeal to authority, it is about the scientific method. Unfortunately quoting a paper from the discredited Michael Mann does not help your cause. So if you do not want to accept the evidence provided in the post, here is one based on IPCC AR5...
fig-nearterm_all_UPDATE_2017-1024x509.png


Comparing CMIP5 & observations | Climate Lab Book
Again going directly through center line.
Michael Mann is one of the best global warming researchers. Far from discredited, he is one of the leading reliable scientists. All those dishonest accusations have proven totally baseless by 9 independent inquiries all stating that his research is unimpeachable. Judith Curry has been discredited and has resigned as her research was going nowhere. After all one cannot do science on a false set of beliefs.
 

sayak83

Veteran Member
Staff member
Premium Member
RELIABILITY OF CLIMATE MODELS

Climate models are even more accurate than you thought | Dana Nuccitelli

Corresponding paper

Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures

Till now there has been a discrepancy between what was being measured as data and model that caused some mismatch between model and data.


In previous evaluations like the one done by the IPCC, climate model simulations of global surface air temperature were compared to global surface temperature observational records like HadCRUT4. However, over the oceans, HadCRUT4 uses sea surface temperatures rather than air temperatures.

Thus looking at modeled air temperatures and HadCRUT4 observations isn’t quite an apples-to-apples comparison for the oceans. As it turns out, sea surface temperatures haven’t been warming fast as marine air temperatures, so this comparison introduces a bias that makes the observations look cooler than the model simulations.

This difference has now been corrected.


The new study addresses this problem by instead blending the modeled air temperatures over land with the modeled sea surface temperatures to allow for an apples-to-apples comparison.

But this is not all. The increasingly open ocean on the Arctic also changes which temperature is being measured there.

The authors also identified another challenging issue for these model-data comparisons in the Arctic. Over sea ice, surface air temperature measurements are used, but for open ocean, sea surface temperatures are used. As co-author Michael Mann notes, as Arctic sea ice continues to melt away, this is another factor that accurate model-data comparisons must account for.


The models had projected a 0.226°C per decade global surface air warming trend for 1975–2014 (and 0.212°C per decade over the geographic area covered by the HadCRUT4 record). However, when matching the HadCRUT4 methods for measuring sea surface temperatures, the modeled trend is reduced to 0.196°C per decade. The observed HadCRUT4 trend is 0.170°C per decade.So when doing an apples-to-apples comparison, the difference between modeled global temperature simulations and observations is 38% smaller than previous estimates.

The result far better match of data and models now that both have the same inputs

a8de4360-fca6-4b5a-92f7-819699ab4fe8-620x485.png


Note that this is without considering the new 2015 and 2016 data that moves the black line above the model mean. Here is how it looks

srep19831-f1.jpg


CMIP5 is the most reliable model based upon an international collaboration of 20 climate modeling labs going on since 2008. It is based at Lawrence Livermore National laboratory. The fidelity of the model is all to obvious. Here is more about CMIP5

CMIP5 - Overview



CMIP5 - Coupled Model Intercomparison Project Phase 5 - Overview


At a September 2008 meeting involving 20 climate modeling groups from around the world, the WCRP's Working Group on Coupled Modelling (WGCM), with input from the IGBP AIMES project, agreed to promote a new set of coordinated climate model experiments. These experiments comprise the fifth phase of the Coupled Model Intercomparison Project (CMIP5). CMIP5 will notably provide a multi-model context for 1) assessing the mechanisms responsible for model differences in poorly understood feedbacks associated with the carbon cycle and with clouds, 2) examining climate “predictability” and exploring the ability of models to predict climate on decadal time scales, and, more generally, 3) determining why similarly forced models produce a range of responses.


It is expected that some of the scientific questions that arose during preparation of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) will through CMIP5 be addressed in time for evaluation in the Fifth Assessment Report (AR5, scheduled for publication in late 2013). The IPCC/CMIP5 schedule (pdf ) is now available and the three key dates are as follows:

  • Februrary 2011: First model output is expected to be available for analysis,
  • July 31, 2012: By this date papers must be submitted for publication to be eligible for assesment by WG1,
  • March 15, 2013: By this date papers cited by WG1 must be published or accepted.
The IPCC’s AR5 is scheduled to be published in September 2013. Future timeline information can be found on IPCC WG1 website.


CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. CMIP5 is not, however, meant to be comprehensive; it cannot possibly include all the different model intercomparison activities that might be of value, and it is expected that various groups and interested parties will develop additional experiments that might build on and augment the experiments described here.



CMIP5 promotes a standard set of model simulations in order to:

  • evaluate how realistic the models are in simulating the recent past,
  • provide projections of future climate change on two time scales, near term (out to about 2035) and long term (out to 2100 and beyond), and
  • understand some of the factors responsible for differences in model projections, including quantifying some key feedbacks such as those involving clouds and the carbon cycle
 

Ben Dhyan

Veteran Member
You should get and eye surgeon. I see the temperature to be nicely progressing bang along the centerline of the projections. The figure confirms how well the projections are predicting the temperature rise.
No, the actual temperature only correlates with the low end of the projected models, no alarm is looming. The reason for the recent high temperatures was the El Nino which is giving way to a La Nina so temperatures are falling...you need to catch up. :)
 

Ben Dhyan

Veteran Member
I am seeing the black dots going directly through the center. You are not looking at the last 4 dots. Magnify and see. The last black dot is actually above the centerline. Bottom line, predictions matching actual data brilliantly.
The recent highs are due to El Nino effect, they are falling back... But even so, given that even with the El Nino, half or more of the models over estimate the human contribution, so the relevant scientists have it wrong.
 
Last edited:

Ben Dhyan

Veteran Member
Again going directly through center line.
Michael Mann is one of the best global warming researchers. Far from discredited, he is one of the leading reliable scientists. All those dishonest accusations have proven totally baseless by 9 independent inquiries all stating that his research is unimpeachable. Judith Curry has been discredited and has resigned as her research was going nowhere. After all one cannot do science on a false set of beliefs.

Michael Mann is a fraud... Global Warming Bombshell
Where is your evidence that Judith Curry has been discredited, she was recently called to provide evidence to the house congressional hearings into climate science. And here is a clip that explains her position..

 

Ben Dhyan

Veteran Member
RELIABILITY OF CLIMATE MODELS

Climate models are even more accurate than you thought | Dana Nuccitelli

Corresponding paper

Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures

Till now there has been a discrepancy between what was being measured as data and model that caused some mismatch between model and data.


In previous evaluations like the one done by the IPCC, climate model simulations of global surface air temperature were compared to global surface temperature observational records like HadCRUT4. However, over the oceans, HadCRUT4 uses sea surface temperatures rather than air temperatures.

Thus looking at modeled air temperatures and HadCRUT4 observations isn’t quite an apples-to-apples comparison for the oceans. As it turns out, sea surface temperatures haven’t been warming fast as marine air temperatures, so this comparison introduces a bias that makes the observations look cooler than the model simulations.

This difference has now been corrected.


The new study addresses this problem by instead blending the modeled air temperatures over land with the modeled sea surface temperatures to allow for an apples-to-apples comparison.

But this is not all. The increasingly open ocean on the Arctic also changes which temperature is being measured there.

The authors also identified another challenging issue for these model-data comparisons in the Arctic. Over sea ice, surface air temperature measurements are used, but for open ocean, sea surface temperatures are used. As co-author Michael Mann notes, as Arctic sea ice continues to melt away, this is another factor that accurate model-data comparisons must account for.


The models had projected a 0.226°C per decade global surface air warming trend for 1975–2014 (and 0.212°C per decade over the geographic area covered by the HadCRUT4 record). However, when matching the HadCRUT4 methods for measuring sea surface temperatures, the modeled trend is reduced to 0.196°C per decade. The observed HadCRUT4 trend is 0.170°C per decade.So when doing an apples-to-apples comparison, the difference between modeled global temperature simulations and observations is 38% smaller than previous estimates.

The result far better match of data and models now that both have the same inputs

a8de4360-fca6-4b5a-92f7-819699ab4fe8-620x485.png


Note that this is without considering the new 2015 and 2016 data that moves the black line above the model mean. Here is how it looks

srep19831-f1.jpg


CMIP5 is the most reliable model based upon an international collaboration of 20 climate modeling labs going on since 2008. It is based at Lawrence Livermore National laboratory. The fidelity of the model is all to obvious. Here is more about CMIP5

CMIP5 - Overview



CMIP5 - Coupled Model Intercomparison Project Phase 5 - Overview


At a September 2008 meeting involving 20 climate modeling groups from around the world, the WCRP's Working Group on Coupled Modelling (WGCM), with input from the IGBP AIMES project, agreed to promote a new set of coordinated climate model experiments. These experiments comprise the fifth phase of the Coupled Model Intercomparison Project (CMIP5). CMIP5 will notably provide a multi-model context for 1) assessing the mechanisms responsible for model differences in poorly understood feedbacks associated with the carbon cycle and with clouds, 2) examining climate “predictability” and exploring the ability of models to predict climate on decadal time scales, and, more generally, 3) determining why similarly forced models produce a range of responses.


It is expected that some of the scientific questions that arose during preparation of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) will through CMIP5 be addressed in time for evaluation in the Fifth Assessment Report (AR5, scheduled for publication in late 2013). The IPCC/CMIP5 schedule (pdf ) is now available and the three key dates are as follows:

  • Februrary 2011: First model output is expected to be available for analysis,
  • July 31, 2012: By this date papers must be submitted for publication to be eligible for assesment by WG1,
  • March 15, 2013: By this date papers cited by WG1 must be published or accepted.
The IPCC’s AR5 is scheduled to be published in September 2013. Future timeline information can be found on IPCC WG1 website.


CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. CMIP5 is not, however, meant to be comprehensive; it cannot possibly include all the different model intercomparison activities that might be of value, and it is expected that various groups and interested parties will develop additional experiments that might build on and augment the experiments described here.



CMIP5 promotes a standard set of model simulations in order to:

  • evaluate how realistic the models are in simulating the recent past,
  • provide projections of future climate change on two time scales, near term (out to about 2035) and long term (out to 2100 and beyond), and
  • understand some of the factors responsible for differences in model projections, including quantifying some key feedbacks such as those involving clouds and the carbon cycle
Dana Nuccitelli is a alarmist, and not a credible climate scientist, and to post stuff from the alarmist Skeptical Science Blog is an insult to real science. The fact remains, the correlation between IPCC models and reality is not sufficient to show human contribution is the predominate cause, that's the state of play.

Even if half the models are in correlation, then what about the other half? The science is not settled or else they would all be in correlation. Are those models now dismissed? There obviously is still much research to be done to understand planetary climate.
 
Last edited:

sayak83

Veteran Member
Staff member
Premium Member
Michael Mann is a fraud... Global Warming Bombshell
Where is your evidence that Judith Curry has been discredited, she was recently called to provide evidence to the house congressional hearings into climate science. And here is a clip that explains her position..


It's a total MYTH that the so called hockey stick curve is inaccurate. Not only was it not a fraud, it was a complete and correct representation of the data of its time, honestly reported and subsequently added with other data without changing its nature and form. Another ridiculous conspiracy theory busted.

Climate myths: The ‘hockey stick’ graph has been proven wrong

Not only that, all current data continue to support the hockey stick. Here is the latest from 2013. First two plots show the hockey stick. Note also figure g that gives the contributors to warming, greenhouse gas vs natural forces.
ngeo1797-f4.jpg


a, Previously published Northern Hemisphere 30-year-mean temperature reconstructions relative to the 1961–1990 reference period5, 43, 44, 45. b, Standardized 30-year-mean temperatures averaged across all seven continental-scale regions. Blue symbols are area-weighted averages using domain areas listed in Table 1, and bars show twenty-fifth and seventy-fifth unweighted percentiles to illustrate the variability among regions; open black boxes are unweighted medians. The red line is the 30-year-average annual global temperature from the HadCRUT4 (ref. 29) instrumental time series relative to 1961–1990, and scaled visually to match the standardized values over the instrumental period. c, Running count of the number of regional reconstructions. d, For each 30-year period since AD 1500, the proportion of individual proxy records within each region that indicate the highest temperature during that 30-year period. e, For each century since AD 500, the proportion of individual proxy records within each region that indicate the highest temperature during that century. f, Long-term volcanic forcing from ref. 16 (black curve; spikes beyond −8 Wm−2 are truncated), and solar forcing from ref. 17 (red curve). g, Radiative forcings relative to AD 2000 smoothed using 30-year averages from ref. 31, including: two estimates of volcanic forcing46, 47; two estimates of solar forcing that span the range from strong48 to weak49; and well-mixed greenhouse gases relative to AD 850. h, Change in summer (July and January) insolation at 65° N/S and 15°N/S latitudes relative to AD 2000 from ref. 50. Vertical red bands indicate volcanic-solar downturns as defined in Methods.


Source paper is from Nature

Continental-scale temperature variability during the past two millennia : Nature Geoscience : Nature Research


Continental-scale temperature variability during the past two millennia


Just so you know. This paper is the Consensus position of about a 100 scientists who co-workers it. Here are the most important lead researchers from the paper


Affiliations
  1. Department of Botany, Federal Urdu University of Arts, Science and Technology, Karachi, 75300, Pakistan
    • Moinuddin Ahmed
  2. Lamont Doherty Earth Observatory, Columbia University, Palisades, New York 10964, USA
    • Kevin J. Anchukaitis,
    • Brendan M. Buckley,
    • Edward R. Cook &
    • Jason E. Smerdon
  3. Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 2543, USA
    • Kevin J. Anchukaitis
  4. School of Earth Sciences, Addis Ababa University, Addis Ababa, Ethiopia
    • Asfawossen Asrat &
    • Mohammed Umer
  5. Indian Institute of Tropical Meteorology, Pune, 411008, India
    • Hemant P. Borgaonkar
  6. Dipartimento di Matematica e Geoscienze, University of Trieste, 34128, Italy
    • Martina Braida &
    • Barbara Stenni
  7. Swiss Federal Research Institute WSL, Birmensdorf, 8903, Switzerland
    • Ulf Büntgen &
    • Raphael Neukom
  8. Département Paléoenvironnements et Paléoclimats (PAL), Université Montpellier, Montpellier, 34095, France
    • Brian M. Chase
  9. Department of Archaeology, History, Cultural Studies and Religion, University of Bergen, Bergen, 5020, Norway
    • Brian M. Chase
  10. Laboratorio de Dendrocronología y Cambio Global, Universidad Austral de Chile, Casilla 567, Valdivia, Chile
    • Duncan A. Christie &
    • Antonio Lara
  11. Center for Climate and Resilience Research, Universidad de Chile, Casilla 2777, Santiago, Chile
    • Duncan A. Christie &
    • Antonio Lara
  12. Australian Antarctic Division, Kingston, Tasmania 7050, Australia
    • Mark A. J. Curran,
    • Andrew D. Moy &
    • Tas van Ommen
  13. Antarctic Climate & Ecosystems Cooperative Research Centre, University of Tasmania, Sandy Bay, Tasmania 7005, Australia
    • Mark A. J. Curran,
    • Andrew D. Moy &
    • Tas van Ommen
  14. Cooperative Institute for Research in Environmental Sciences, National Oceanic and Atmospheric Administration, Boulder, Colorado 80305, USA
    • Henry F. Diaz
  15. Department of Geography, Johannes Gutenberg University, Mainz, 55099, Germany
    • Jan Esper
  16. Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Yunnan, 666303, China
    • Ze-Xin Fan
  17. Faculty of Science, Nepal Academy of Science and Technology, Khumaltar, GPO Box 3323, Lalitpur, Nepal
    • Narayan P. Gaire
  18. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
    • Quansheng Ge &
    • Xuemei Shao
  19. School of Earth Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia
    • Joëlle Gergis
  20. Departamento Astrofísica y CC de la Atmósfera, Universidad Complutense de Madrid, Madrid, 28040, Spain
    • J Fidel González-Rouco
  21. Lemaitre Center for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, 1348, Belgium
    • Hugues Goosse
  22. School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Wits, 2050, South Africa
    • Stefan W. Grab &
    • David J. Nash
  23. Hydrologic Research Center, San Diego, California 92130, USA
    • Nicholas Graham &
    • Rochelle Graham
  24. Oeschger Centre for Climate Change Research & Institute of Geography, University of Bern, Bern, 3012, Switzerland
    • Martin Grosjean &
    • Heinz Wanner
  25. Department of Environmental Sciences, University of Helsinki, Helsinki, 00014, Finland
    • Sami T. Hanhijärvi &
    • Atte A. Korhola
  26. School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, Arizona 86011, USA
    • Darrell S. Kaufman &
    • Nicholas P. McKay
  27. International Project Office, Past Global Changes (PAGES), Bern, 3012, Switzerland
    • Thorsten Kiefer &
    • Lucien von Gunten
  28. Department of Symbiotic System Science, Fukushima University, Fukushima, 960-1248, Japan
    • Katsuhiko Kimura
  29. Department of Physical Geography and Quaternary Geology, Stockholm University, Stockholm, 106 91, Sweden
    • Paul J. Krusic
  30. Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), Université Pierre et Marie Curie, Paris cedex, 575252, France
    • Anne-Marie Lézine
  31. Department of History, Stockholm University, Stockholm, 106 91, Sweden
    • Fredrik C. Ljungqvist
  32. National Institute of Water and Atmospheric Research Ltd., National Climate Centre Auckland, 1011, Zealand
    • Andrew M. Lorrey
  33. Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University, Giessen, 35390, Germany
    • Jürg Luterbacher &
    • Johannes P. Werner
  34. Laboratoire des Science du Climat et de l'Environnement, Gif-sur-Yvette, 91 191, France
    • Valérie Masson-Delmotte
  35. Department of Geography, Swansea University, Swansea, SA2 8PP, UK
    • Danny McCarroll &
    • Maria R. Prieto
  36. Desert Research Institute, Nevada System of Higher Education, Reno, Nevada 89512, USA
    • Joseph R. McConnell &
    • Michael Sigl
  37. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA), CCT-CONICET-Mendoza, Mendoza, 5500, Argentina
    • Mariano S. Morales,
    • Ignacio A. Mundo &
    • Ricardo Villalb


Continued
 

sayak83

Veteran Member
Staff member
Premium Member
  1. British Antarctic Survey, Cambridge, CB3 0ET, UK
    • Robert Mulvaney
  2. Department of Earth and Environmental Sciences, Nagoya University, Nagoya, 464.8601, Japan
    • Takeshi Nakatsuka &
    • Masaki Sano
  3. School of Environment and Technology, University of Brighton, Brighton, BN2 4GJ, UK
    • David J. Nash
  4. Department of Earth, Ocean and Atmospheric Sciences, Florida State University, Tallahassee, Florida 32308, USA
    • Sharon E. Nicholson
  5. Department of Glaciology, Alfred Wegener Institute for Polar and Marine Research in the Helmholtz Association, Bremerhaven, 27570, Germany
    • Hans Oerter
  6. College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4RJ, UK
    • Jonathan G. Palmer
  7. Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
    • Jonathan G. Palmer,
    • Steven J. Phipps &
    • Chris S.M. Turney
  8. ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, NSW 2052, Australia
    • Steven J. Phipps
  9. Centro de Estudios Cientificos, Valdivia, Chile
    • Andres Rivera
  10. Department of Chemistry 'Ugo Schiff', University of Florence, Sesto Fiorentino, 50019, Italy
    • Mirko Severi
  11. Jackson School of Geosciences, University of Texas at Austin, Austin, Texas 78712, USA
    • Timothy M. Shanahan
  12. LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
    • Feng Shi
  13. Institute of Geography, Russian Academy of Sciences, Moscow, 119017, Russia
    • Olga N. Solomina
  14. Department of Earth and Space Sciences, University of Washington, Seattle, Washington 98195, USA
    • Eric J. Steig
  15. National Centre for Antarctic and Ocean Research, Goa, 403 804, India
    • Meloth Thamban
  16. Laboratory of Tree-Ring Research, University of Arizona, Tucson, Arizona 85721, USA
    • Valerie Trouet
  17. Department of Biology, Ghent University, Ghent, 9000, Belgium
    • Dirk Verschuren
  18. Department of Geography, University of Ottawa, Ottawa, K1N 6N5, Canada
    • Andre E. Viau
  19. Niels Bohr Institute, University of Copenhagen, Copenhagen, 2100, Denmark
    • Bo M. Vinther
  20. Institute for Coastal Research, Helmholtz-Zentrum Geesthacht, Geesthacht, 21502, Germany
    • Sebastian Wagner &
    • Eduardo Zorita
  21. National Climatic Data Center, National Oceanic and Atmospheric Administration, Boulder, Colorado 80305, USA
    • Eugene R. Wahl
  22. Institute of Arctic and Alpine Research, University of Colorado, Boulder, Colorado 80309, USA
    • James W.C. White
  23. Department of Forest Science, Shinshu University, Nagano, 399-4598, Japan
    • Koh Yasue
  24. Deceased
    • Mohammed Umer


Some fraud to have his data and analysis vindicated by all major science labs!


Has Judith Curry published anything debunking the studies and data of global warming? The main position she took was that the "pause" in warming showed the mods were not trustworthy. Now that the pause itself has been debunked and the temperature has moved up alarmingly in the last three years, the models have been vindicated and she lost the debate. It's quite obvious reading her blog that this was why she retired.
 
Last edited:

sayak83

Veteran Member
Staff member
Premium Member
Dana Nuccitelli is a alarmist, and not a credible climate scientist, and to post stuff from the alarmist Skeptical Science Blog is an insult to real science. The fact remains, the correlation between IPCC models and reality is not sufficient to show human contribution is the predominate cause, that's the state of play.

Even if half the models are in correlation, then what about the other half? The science is not settled or else they would all be in correlation. Are those models now dismissed? There obviously is still much research to be done to understand planetary climate.
Dude. The article was reportage on a scientific paper which I linked. I read the paper first, noted that the article is faithfully representing its results, and only then did I quote the article. We are never going to agree on who is reliable. So read the paper and see if it's results are correctly referred by me.

Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures


RESEARCH LETTER

Robust comparison of climate models with observations using
blended land air and ocean sea surface temperatures

Key Points:
Kevin Cowtan1, Zeke Hausfather2, Ed Hawkins3, Peter Jacobs4, Michael E. Mann5, Sonya K. Miller5, •Byron A. Steinman6, Martin B. Stolpe7, and Robert G. Way8



Finally it is not true that half the models were wrong. As was shown in your own post figures, the actual temperature is tracking along the middle of the estimates when data of 2015, 2016 are added. The previous model estimates outputs were changed to match the data being measured, that is predict mean of sea surface and land air temperature instead of mean of land air and sea air temperatures. This leads to a tighter fit and decline of underestimation of ALL the models, not just half. Suppose you have 10 models that predict body temperature. But you are using the model to output temperature under the armpits but measuring temperature under the tongue. Obviously the prediction will be off for All of them. Now if you instead output tongue temperatures, the models will be predicting what one is actually measuring and one gets tighter fit.
 

sayak83

Veteran Member
Staff member
Premium Member
The recent highs are due to El Nino effect, they are falling back... But even so, given that even with the El Nino, half or more of the models over estimate the human contribution, so the relevant scientists have it wrong.
False. If the data track the centerline of the model predictions, then majority are right. And no temperatures are not falling.



The combined global average temperature over the land and ocean surfaces for March 2017 was 1.05°C (1.89°F) above the 20th century average of 12.7°C (54.9°F). This was the second highest for March since global temperature records began in 1880, behind the record year 2016 by 0.18°C (0.32°F) and ahead of 2015 by +0.15°C (+0.27°F). March 2017 marks the first time since April 2016 that the global land and ocean temperature departure from average was greater than 1.0°C (1.8°F) and the first time the monthly temperature departure from average surpasses 1.0°C (1.8°F) in the absence of an El Niño episode in the tropical Pacific Ocean. Overall, March 2017 tied with January 2016 as the fifth highest monthly global land and ocean temperature departure from average on record (1,647 monthly records). The record monthly temperature departure of 1.23°C (2.21°F) was set in March 2016.

Global Climate Report - March 2017 | State of the Climate | National Centers for Environmental Information (NCEI)

April was also the second warmest on record being 0.9 C above average.

As I said, each El Nino ratchets up the temperature where it stays till the next strong El Nino ratchets up a bit further.
 

Ben Dhyan

Veteran Member
It's a total MYTH that the so called hockey stick curve is inaccurate. Not only was it not a fraud, it was a complete and correct representation of the data of its time, honestly reported and subsequently added with other data without changing its nature and form. Another ridiculous conspiracy theory busted.

Climate myths: The ‘hockey stick’ graph has been proven wrong

Not only that, all current data continue to support the hockey stick. Here is the latest from 2013. First two plots show the hockey stick. Note also figure g that gives the contributors to warming, greenhouse gas vs natural forces.
ngeo1797-f4.jpg


a, Previously published Northern Hemisphere 30-year-mean temperature reconstructions relative to the 1961–1990 reference period5, 43, 44, 45. b, Standardized 30-year-mean temperatures averaged across all seven continental-scale regions. Blue symbols are area-weighted averages using domain areas listed in Table 1, and bars show twenty-fifth and seventy-fifth unweighted percentiles to illustrate the variability among regions; open black boxes are unweighted medians. The red line is the 30-year-average annual global temperature from the HadCRUT4 (ref. 29) instrumental time series relative to 1961–1990, and scaled visually to match the standardized values over the instrumental period. c, Running count of the number of regional reconstructions. d, For each 30-year period since AD 1500, the proportion of individual proxy records within each region that indicate the highest temperature during that 30-year period. e, For each century since AD 500, the proportion of individual proxy records within each region that indicate the highest temperature during that century. f, Long-term volcanic forcing from ref. 16 (black curve; spikes beyond −8 Wm−2 are truncated), and solar forcing from ref. 17 (red curve). g, Radiative forcings relative to AD 2000 smoothed using 30-year averages from ref. 31, including: two estimates of volcanic forcing46, 47; two estimates of solar forcing that span the range from strong48 to weak49; and well-mixed greenhouse gases relative to AD 850. h, Change in summer (July and January) insolation at 65° N/S and 15°N/S latitudes relative to AD 2000 from ref. 50. Vertical red bands indicate volcanic-solar downturns as defined in Methods.


Source paper is from Nature

Continental-scale temperature variability during the past two millennia : Nature Geoscience : Nature Research


Continental-scale temperature variability during the past two millennia


Just so you know. This paper is the Consensus position of about a 100 scientists who co-workers it. Here are the most important lead researchers from the paper


Affiliations




    • Department of Botany, Federal Urdu University of Arts, Science and Technology, Karachi, 75300, Pakistan
      • Moinuddin Ahmed
    • Lamont Doherty Earth Observatory, Columbia University, Palisades, New York 10964, USA
      • Kevin J. Anchukaitis,
      • Brendan M. Buckley,
      • Edward R. Cook &
      • Jason E. Smerdon
    • Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 2543, USA
      • Kevin J. Anchukaitis
    • School of Earth Sciences, Addis Ababa University, Addis Ababa, Ethiopia
      • Asfawossen Asrat &
      • Mohammed Umer
    • Indian Institute of Tropical Meteorology, Pune, 411008, India
      • Hemant P. Borgaonkar
    • Dipartimento di Matematica e Geoscienze, University of Trieste, 34128, Italy
      • Martina Braida &
      • Barbara Stenni
    • Swiss Federal Research Institute WSL, Birmensdorf, 8903, Switzerland
      • Ulf Büntgen &
      • Raphael Neukom
    • Département Paléoenvironnements et Paléoclimats (PAL), Université Montpellier, Montpellier, 34095, France
      • Brian M. Chase
    • Department of Archaeology, History, Cultural Studies and Religion, University of Bergen, Bergen, 5020, Norway
      • Brian M. Chase
    • Laboratorio de Dendrocronología y Cambio Global, Universidad Austral de Chile, Casilla 567, Valdivia, Chile
      • Duncan A. Christie &
      • Antonio Lara
    • Center for Climate and Resilience Research, Universidad de Chile, Casilla 2777, Santiago, Chile
      • Duncan A. Christie &
      • Antonio Lara
    • Australian Antarctic Division, Kingston, Tasmania 7050, Australia
      • Mark A. J. Curran,
      • Andrew D. Moy &
      • Tas van Ommen
    • Antarctic Climate & Ecosystems Cooperative Research Centre, University of Tasmania, Sandy Bay, Tasmania 7005, Australia
      • Mark A. J. Curran,
      • Andrew D. Moy &
      • Tas van Ommen
    • Cooperative Institute for Research in Environmental Sciences, National Oceanic and Atmospheric Administration, Boulder, Colorado 80305, USA
      • Henry F. Diaz
    • Department of Geography, Johannes Gutenberg University, Mainz, 55099, Germany
      • Jan Esper
    • Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Yunnan, 666303, China
      • Ze-Xin Fan
    • Faculty of Science, Nepal Academy of Science and Technology, Khumaltar, GPO Box 3323, Lalitpur, Nepal
      • Narayan P. Gaire
    • Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
      • Quansheng Ge &
      • Xuemei Shao
    • School of Earth Sciences, University of Melbourne, Melbourne, Victoria 3010, Australia
      • Joëlle Gergis
    • Departamento Astrofísica y CC de la Atmósfera, Universidad Complutense de Madrid, Madrid, 28040, Spain
      • J Fidel González-Rouco
    • Lemaitre Center for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, 1348, Belgium
      • Hugues Goosse
    • School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Wits, 2050, South Africa
      • Stefan W. Grab &
      • David J. Nash
    • Hydrologic Research Center, San Diego, California 92130, USA
      • Nicholas Graham &
      • Rochelle Graham
    • Oeschger Centre for Climate Change Research & Institute of Geography, University of Bern, Bern, 3012, Switzerland
      • Martin Grosjean &
      • Heinz Wanner
    • Department of Environmental Sciences, University of Helsinki, Helsinki, 00014, Finland
      • Sami T. Hanhijärvi &
      • Atte A. Korhola
    • School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, Arizona 86011, USA
      • Darrell S. Kaufman &
      • Nicholas P. McKay
    • International Project Office, Past Global Changes (PAGES), Bern, 3012, Switzerland
      • Thorsten Kiefer &
      • Lucien von Gunten
    • Department of Symbiotic System Science, Fukushima University, Fukushima, 960-1248, Japan
      • Katsuhiko Kimura
    • Department of Physical Geography and Quaternary Geology, Stockholm University, Stockholm, 106 91, Sweden
      • Paul J. Krusic
    • Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), Université Pierre et Marie Curie, Paris cedex, 575252, France
      • Anne-Marie Lézine
    • Department of History, Stockholm University, Stockholm, 106 91, Sweden
      • Fredrik C. Ljungqvist
    • National Institute of Water and Atmospheric Research Ltd., National Climate Centre Auckland, 1011, Zealand
      • Andrew M. Lorrey
    • Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University, Giessen, 35390, Germany
      • Jürg Luterbacher &
      • Johannes P. Werner
    • Laboratoire des Science du Climat et de l'Environnement, Gif-sur-Yvette, 91 191, France
      • Valérie Masson-Delmotte
    • Department of Geography, Swansea University, Swansea, SA2 8PP, UK
      • Danny McCarroll &
      • Maria R. Prieto
    • Desert Research Institute, Nevada System of Higher Education, Reno, Nevada 89512, USA
      • Joseph R. McConnell &
      • Michael Sigl
    • Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA), CCT-CONICET-Mendoza, Mendoza, 5500, Argentina
      • Mariano S. Morales,
      • Ignacio A. Mundo &
      • Ricardo Villalb

Continued
Haha...the temperature now is about where it was 2,000 years ago when there was no agw... no need for panic... :)
 

Ben Dhyan

Veteran Member
Dude. The article was reportage on a scientific paper which I linked. I read the paper first, noted that the article is faithfully representing its results, and only then did I quote the article. We are never going to agree on who is reliable. So read the paper and see if it's results are correctly referred by me.

Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures


RESEARCH LETTER

Robust comparison of climate models with observations using
blended land air and ocean sea surface temperatures

Key Points:
Kevin Cowtan1, Zeke Hausfather2, Ed Hawkins3, Peter Jacobs4, Michael E. Mann5, Sonya K. Miller5, •Byron A. Steinman6, Martin B. Stolpe7, and Robert G. Way8



Finally it is not true that half the models were wrong. As was shown in your own post figures, the actual temperature is tracking along the middle of the estimates when data of 2015, 2016 are added. The previous model estimates outputs were changed to match the data being measured, that is predict mean of sea surface and land air temperature instead of mean of land air and sea air temperatures. This leads to a tighter fit and decline of underestimation of ALL the models, not just half. Suppose you have 10 models that predict body temperature. But you are using the model to output temperature under the armpits but measuring temperature under the tongue. Obviously the prediction will be off for All of them. Now if you instead output tongue temperatures, the models will be predicting what one is actually measuring and one gets tighter fit.
You are avoiding the facts....so long as most of the IPCC models overestimate the warming caused by human CO2, the science is not settled.
 

Ben Dhyan

Veteran Member
False. If the data track the centerline of the model predictions, then majority are right. And no temperatures are not falling.



The combined global average temperature over the land and ocean surfaces for March 2017 was 1.05°C (1.89°F) above the 20th century average of 12.7°C (54.9°F). This was the second highest for March since global temperature records began in 1880, behind the record year 2016 by 0.18°C (0.32°F) and ahead of 2015 by +0.15°C (+0.27°F). March 2017 marks the first time since April 2016 that the global land and ocean temperature departure from average was greater than 1.0°C (1.8°F) and the first time the monthly temperature departure from average surpasses 1.0°C (1.8°F) in the absence of an El Niño episode in the tropical Pacific Ocean. Overall, March 2017 tied with January 2016 as the fifth highest monthly global land and ocean temperature departure from average on record (1,647 monthly records). The record monthly temperature departure of 1.23°C (2.21°F) was set in March 2016.

Global Climate Report - March 2017 | State of the Climate | National Centers for Environmental Information (NCEI)

April was also the second warmest on record being 0.9 C above average.

As I said, each El Nino ratchets up the temperature where it stays till the next strong El Nino ratchets up a bit further.
It still doesn't change the facts, most of the present IPCC models overestimate the human contribution to the warming, thus the science is not settled.
 

sayak83

Veteran Member
Staff member
Premium Member
It still doesn't change the facts, most of the present IPCC models overestimate the human contribution to the warming, thus the science is not settled.
No they don't. Imagining things do not constitute facts.
 

sayak83

Veteran Member
Staff member
Premium Member
You are avoiding the facts....so long as most of the IPCC models overestimate the warming caused by human CO2, the science is not settled.
You are imagining things. Most climate models make excellent predictions of temperature as seen from the figures.
 

sayak83

Veteran Member
Staff member
Premium Member
Haha...the temperature now is about where it was 2,000 years ago when there was no agw... no need for panic... :)
False. The data, to keep track of such a large time period, has been averaged over 30 year periods and stops at 2000. That is why the mean anomaly is 0.2 between 1970-2000.

Obviously current temperature anomaly is 0.9 which is hotter than it has ever been in the past 100,000 years. Further its rapid and accelerating.

What’s the hottest Earth has been “lately”? | NOAA Climate.gov

Associated paper

A Reconstruction of Regional and Global Temperature for the Past 11,300 Years | Science

A Reconstruction of Regional and Global Temperature for the Past 11,300 Years
  1. Shaun A. Marcott1,
  2. Jeremy D. Shakun2,
  3. Peter U. Clark1,
  4. Alan C. Mix1

+ See all authors and affiliations

Science 08 Mar 2013:


Abstract
Surface temperature reconstructions of the past 1500 years suggest that recent warming is unprecedented in that time. Here we provide a broader perspective by reconstructing regional and global temperature anomalies for the past 11,300 years from 73 globally distributed records. Early Holocene (10,000 to 5000 years ago) warmth is followed by ~0.7°C cooling through the middle to late Holocene (<5000 years ago), culminating in the coolest temperatures of the Holocene during the Little Ice Age, about 200 years ago. This cooling is largely associated with ~2°C change in the North Atlantic. Current global temperatures of the past decade have not yet exceeded peak interglacial values but are warmer than during ~75% of the Holocene temperature history. Intergovernmental Panel on Climate Change model projections for 2100 exceed the full distribution of Holocene temperature under all plausible greenhouse gas emission scenarios.


marcott2-13_11k-graph-610.gif



As is seen, never in the last 10,000 years have temperature gone above 0.6. And it was decreasing slowly in historic times. (interglacial is ending as it usually does). We had utterly and rapidly reversed this trend. The speed of warming is higher than anytime since the dinosaurs went extinct.

http://news.stanford.edu/news/2013/august/climate-change-speed-080113.html

Now we are touching 0.9. More than it has ever been in the last 100,000 years.



Come, I have Linked dozens of papers from scientific publications from Science and Nature with all the plots that demonstrate my points and answer all your questions. So far, you have linked nothing but opinion pieces in blog posts. It's time that you link more than mere opinions.
 

sayak83

Veteran Member
Staff member
Premium Member
So how many models correlate approximately?

fig-nearterm_all_UPDATE_2017-panela-1-1024x525.png
Each model has a spread of 95% confidence interval which has not been shown in this plot to avoid clutter. Given current data, about 80-85% fall within this range and hence is a successful prediction.(Standard practice in all CFD modeling including the plane in which you fly).
Note that this figure (from a blogger) has missed the change where sea surface temperature is used, which moves All the models towards centerline in 2000-2010 period as well.

You do understand that I am a scientist and often to CFD modeling of thermal and fluid systems? If something seems too technical in what I say, feel free to ask. I will clarify. I am happy to go over the entire IPCC physical science basis page by page if you want. The models I use are far more complicated than what climatologists use, so I can understand them if I give some effort (I model fire dynamics and turbulent flow reacting systems in jet planes)
 
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