Fingerprints of ‘natural’ climate variability.

I have collected more than 30 fingerprints now. They were initially in a blog, but it makes more sense to turn them into a page and classify them into similar groups.

  1. Solar cycles significantly affect seismic activity.

    Volcanoes and earthquakes happen at seismic fault lines where tectonic plates overlap.

    "In this paper, we analyze 20 years of proton density and velocity data, as recorded by the SOHO satellite, and the worldwide seismicity in the corresponding period, as reported by the ISC-GEM catalogue. We found clear correlation between proton density and the occurrence of large earthquakes (M> 5.6), with a time shift of one day. The significance of such correlation is very high, with a probability to be wrong lower than 10-5 " [meaning one in 10000] ... In this paper, we demonstrate that it [the correlation] can likely be due to the effect of solar wind, modulating the proton density and hence the electrical potential between the ionosphere and the Earth ... our hypothesis only implies that the proton density would act as a further, small trigger to cause the fracture on already critically charged faults, thus producing the observed large scale earthquake correlation."

    Marchitelli et al only claim a fingerprint between seismicity and solar wind proton density. Increased seismicity leads to both volcanoes and earthquakes. Only earthquakes were used in the data because there are tens of thousands per year; leading to excellent statistical certainty. In contrast, there are about 25+ new volcanoes each year; too few to give good statistical certainty.

    Links: Salon | Nature (paper, open access)

  2. Correlation between earthquakes and cosmic radiation, 2023, phy.org

    A clear 6-sigma statistical correlation emerged between changes in the intensity of secondary cosmic radiation (recorded at the surface of earth) and the summed magnitude of all earthquakes with magnitudes greater-than or equal to 4. This correlation only becomes apparent when the cosmic ray data are shifted 15 days forward relative to the seismic data. This was discovered by the CREDO project, initiated in 2016 by the Institute of Nuclear Physics of the Polish Academy of Sciences (IFJ PAN) in Krakow.

  3. Over the pacific: Outgoing longwave radiation to space (cooling) correlates with ocean cycles:
  4. Natural CO₂ follows temperature, usually a bit father behind.. Hat-tip: True Science @socratesccost.
  5. Hurricane (frequency) is inversely proportional to sunspot frequency. Hat-tip: Ryan Maue and Roger J. Pielke.
  6. La Nina is a delayed response to the minimum of an 11 year sunspot cycle; occurring about 2 years after the minimum.
    Hat-tip to John Shewchuk, @_ClimateCraze
  7. Hurricanes increase after cosmic ray count drops
  8. Sunspots and hurricanes are inversely proportional
    (hat-tip Tony Heller)
  9. More clouds reflect away sunlight, leading to lower surface temperatures.
  10. Cosmic rays control cloud cover. From 1996, Henrik Svensmark / Eigil Friis Christensen first show how.

  11. Low Cloud cover is determined by cosmic rays entering from space. The chart below is from a presentation in March 2018 by Henrik & Jacob Svensmark. It shows the strong connection over a 30 year period between cosmic ray variations, as modulated by the non-radiant features of solar activity [NRSA], and their effect on the level of cover from low clouds. This is able to explain NRSA's link with temperature on timescales of days to decades.

    This controlling role of the NRSA is crucial to the theory of the solar factor because major variation in total solar radiant heat output - RHO - is slow and therefore only able to account for climate changes on timescales from about 50 years upwards.

  12. Forbush decreases: are clinching evidence for the link between cosmic ray flux and the level of cloudiness Forbush decreases are sudden and short term decreases in this cosmic ray flux, and its apparent effect on cloudiness within days, as shown by in the diagrams below. [see note below]

    Note on Forbush decreases. These arise from sudden and short term changes to the magnetic field of the solar plasma wind, which follow from ejections of material by the sun known as coronal mass ejections [CME's] - see Wikipedia for details

  13. Sea level follows the sun:
  14. Average global temperature tracks water vapor, not CO2. Data from all reporting agencies agree that, for at least as far back as 2002 (Figure 0.2). Hat-tip: Dan Pangburn.
  15. Global temperature anomaly is inversely proportional to specific humidity.
  16. Correlation of specific humidity and Sunspot Numbers within Schwabe cycles (smoothed over 100 months) 1940 to 2010, around 30,000 feet (tropopause). Sun spot count: green. Source Tallbloke blog: solar system dynamics: August 8, 2010. Humidity changes are "totally overwhelmed by a much more powerful forcing, i.e. the Sun. I discovered this years ago looking at NCEP reanalysis data. Sunspots averaged over 8 years in green compared with 300mb specific humidity data in brown."
    -- Rog Tallbloke
  17. Atlantic ocean cycles, AMO, and land surface temperature in the Eastern USA show delayed correlation:

  18. Climate of the North Atlantic has warmed and cooled nine times in the past 12000 years in step with the waxing and waning of the sun. Kerr 2001, Science, 294, 1431-1433.

  19. Correlation of global sea surface temperatures with the solar wind speed; Limin Zhou, Brian Tinsley, Huimin Chu, Ziniu Xiao, JASTP 149, 2016, 232-239, ISSN: 1364-6826, DOI: 10.1016/j.jastp.2016.02.010.

    1. Atlantic Multidecadal Oscillation temperatures are an inverse response to solar wind strength, with very little lag, rather than a direct and lagged response to changes in solar irradiance.
    2. That AMO anomalies remain locked to solar cycles through each AMO envelope, and are always colder around solar cycle minimum during a cold AMO phase, and never colder around solar cycle minimum during a warm AMO phase.
    3. That the very long term mean AMO frequency has to be 55 years, as every other warm AMO phase is during a centennial solar minimum. The last two AMO cycles of 60 and 70 years are due to the previous centennial solar minimum being 130 years before the current centennial minimum.

    Hat Tip to the man who inspired this page: Ulric Lyons

  20. The Great Climate shift matches the PDO. The Great Climate Shift. Something of a mystery. We can certainly say what happened, but can we say what caused what, and how?
  21. Tree ring and isotope determined proxy temperatures agree. 9,400 years of cosmic radiation and solar activity from ice cores and tree rings; Steinhilber, Abreu, Beer, et al. PNAS April 2, 2012; 109 (16) 5967-5971; doi: 10.1073/pnas.1118965109
    Here we combine different 10Be ice core records from Greenland and Antarctica with the global 14C tree ring record using principal component analysis. This approach is only possible due to a new high-resolution 10Be record from Dronning Maud Land obtained within the European Project for Ice Coring in Antarctica in Antarctica. The new cosmic radiation record enables us to derive total solar irradiance, which is then used as a proxy of solar activity to identify the solar imprint in an Asian climate record. Though generally the agreement between solar forcing and Asian climate is good, there are also periods without any coherence, pointing to other forcings [as well].
  22. Precipitation vs. Sunspot numbers. February precipitation in Germany compared to changes in sunspots. Shown is the optimum positive correlation (r = 0.54) with a solar lag of +17 months. Solar cycles are numbered 14–24. The probability that the correlation r = 0.54 is by chance is less than 0.1% (p < 0.001). Source: NoTricksZone Abstract

  23. Rapid AMO warming from the mid 1990’s is covariant with:

    1. A decline in low cloud cover globally, leading to surface warming, and increased upper ocean heat content.
    2. Changes in the vertical distribution of water vapour: Declines in lower stratosphere water vapour leading to cooling. Increases in low-mid troposphere water vapour, at least due to higher SST’s coupled with an increase in surface wind speeds over the oceans, leading to low-mid troposphere warming.
    3. Reduced CO₂ uptake in the warmer North Atlantic and in land regions made drier by the warm AMO phase (and increased El Nino).
      All because ocean phases vary inversely to changes in climate forcing.
  24. Strong coherence between Solar variability and the Monsoon in Oman between 9 and 6 kyr. Neff et al, Nature 411 | 17 May 2001.
    Hat tip: Nir Shaviv
  25. Persistent Solar Influence on North Atlantic Climate during the Holocene

    Blue = C-14 proxy for solar activity
    Black = sea floor record for N Atlantic proxy for temperature
    Hat tip: Nir Shaviv, via this paper: Persistent Solar Influence on North Atlantic Climate During the Holocene, Bond et al, Science 294 | 7 Dec 2001
  26. Correlation between the cosmic ray flux reconstruction and the geochemically reconstructed tropical temperature
    Shaviv: Correlation between the cosmic ray flux reconstruction (based on the exposure ages of Iron meteorites) and the geochemically reconstructed tropical temperature. The comparison between the two reconstructions reveals the dominant role of cosmic rays and the galactic “geography” as a climate driver over geological time scales. Figure 2 (below)Correlation between the cosmic ray flux reconstruction and the geochemically reconstructed tropical temperature:

    The cosmic ray flux (Φ) and tropical temperature anomaly (ΔT) variations over the Phanerozoic. The upper curves describe the reconstructed CRF using iron meteorite exposure age data (Shaviv, 2002b). The blue line depicts the nominal CRF, while the yellow shading delineates the allowed error range. The two dashed curves are additional CRF reconstructions that fit within the acceptable range (together with the blue line, these three curves denote the three CRF reconstructions used in the model simulations). The red curve describes the nominal CRF reconstruction after its period was fine tuned to best fit the low-latitude temperature anomaly (i.e., it is the “blue” reconstruction, after the exact CRF periodicity was fine tuned, within the CRF reconstruction error). The bottom black curve depicts the 10/50 m.y. (see Fig. 1) smoothed temperature anomaly (ΔT) from Veizer et al. (2000). The red line is the predicted ΔTmodel for the red curve above, taking into account also the secular long-term linear contribution (term B × t in equation 1). The green line is the residual. The largest residual is at 250 m.y. B.P., where only a few measurements of δ18O exist due to the dearth of fossils subsequent to the largest extinction event in Earth history. The top blue bars are as in Figure 1.

    Shaviv & Vezier 2003. "Celestial driver of Phanerozoic climate?" GSA Today 13(7):4-10 (2003). DOI: 10.1130/1052-5173(2003)013<0004:CDOPC>2.0.CO;2

  27. Correlations of global sea surface temperatures with the solar wind speed:
    https://www.sciencedirect.com/science/article/pii/S1364682616300360

    They concluded: Responses of sea-surface temperature to solar wind speed on the seasonal timescale have been found, and in the North Atlantic region in winter they resemble the North Atlantic Oscillation. At the locations of the peak (negative) response in the North Atlantic the SST decreases by approximately 1°C for 100 km s-1 increase in solar wind speed. In the southern hemisphere regions of significant positive and negative correlations with SWS are smaller and less well organized. The analysis is made for 1963-2010 data, and the effect strongest in winter, and greater when the solar wind magnetic field is southward, ensuring greater energy input into the ionosphere-atmosphere system. The response is somewhat stronger in the west phase of the QBO than in the east phase, and stronger than when F10.7 is used for the correlations. These seasonal responses are consistent with cumulative effects of a prompt electrical mechanism affecting cloud microphysics, as found elsewhere. However, on the seasonal timescale there is ambiguity as to which of several inputs to the stratosphere or troposphere is driving the responses. It is even possible that upward propagating waves, resulting from short term responses to the solar wind, are changing the strengths of the northern and southern polar vortices, which on longer timescales cause downward propagating dynamical disturbances that affect the NAO and the southern oceans.


    Fig. 1. Time series analysis of monthly average of 10.7 cm solar radiation flux, solar wind electric field and solar wind speed,
    Fig. 1(a), and the NAO index,
    Fig. 1(b), as standardized monthly indices from 1963 to 2010. In Fig. 1(a) the dashed line is for 10.7 cm solar flux, the solid line for solar wind electric field, and the dotted line for solar wind speed. In
    Fig. 1(c) the corresponding normalized power spectrum results are shown; the dashed lines represent the 95% confidence levels.

  28. Correlation between sea levels with solar cycles:
  29. Precipitation Climate and Solar modulation of the last 1000 years.Blue: Reconstructed Temperature Storch et al 2004, Red: solar modulation, after Muscheler et al 2007, Black: Reconstruction of precipitation amounts for the edge of the Tibetan Plateau. Sun & Liu (2012).
  30. The sun controls the Mississippi, but there's a 34-year delay!
  31. 300 years of Chinese spruce compared with sunspot counts averaged over the solar cycle length (blue).
    Hat tip Rog Tallbloke
  32. Combined solar cycles (red) correlate with temperatures: 1000frolly
  33. Both the East Asia Summer Monsoon and the Northern Australia Summer Monsoon retreated synchronously during the recent cold Little Ice Age in response to external forcings such as solar irradiance variation and possibly large volcanic eruptions. Fingerprints of the Sun on Asia-Australia Summer Monsoon Rainfalls during the Little Ice Age, Hong Yan, Willie Soon, & Bob Carter:
    Figure 3 | Forcing and modeling results. a,b, Solar forcing representing the solar output (pink) and orbital parameters (blue and green lines are the July and January insolation at 23.5° N and 23.5° S, respectively) during the Holocene (a, orbital changes dominating) and past millennium (b, solar output dominating). c, Total solar forcing at 23.5° N (blue) and 23.5° S (green) calculated by adding up the changes in the solar output and orbital parameters. d, Simulated annual mean precipitation anomaly during the late solar Maunder Minimum phase (AD 1690–1740) of the LIA with reference to the long-term mean (AD 1000–1800) in the MPI-ESM last millennial simulation.
  34. The following graph shows the recorded and predicted temperatures as plotted by Ole Humlum et al from their 2011 paper: “Identifying natural contributions to late Holocene climate change” (Elsevier); O. Humlum et al. / Global and Planetary Change 79 (2011 Central Greenland (GISP2) surface temperature of the past 4,000 years (blue line). Natural cycle modeled and forecasted data are shown by the green line. The coefficient of determination (r2) for the hind-casting period is 0.63.)
    “The causes of long (millennial scale) climate changes are generally poorly understood, and the issue is important for understanding the natural climate variability, as illustrated by ice and ocean cores. Also, the lack of a CO₂ cycle at millennial time scales underscores the independence of such long climate variability from anthropogenic greenhouse enhancement.”
    “The warming following the Little Ice Age is generally perceived as a natural recovery from the previously cold period, and the effect of anthropogenic greenhouse enhancement is assumed to become important only after 1975 (IPCC, 2007). Our simple cyclic model (Fig.) is able to forecast the main features of this recorded warming until 2010, underlining that a significant part of the 20th century warming may be interpreted as the result of natural climatic variations, known to characterize at least the previous 4000 years.”

  35. Spectral coherence between temperature records, solar records and gravitational planetary resonances.

  36. Also see: The next Grand Solar Minimum, Cosmic Rays and Earth Changes (an introduction) BY SACHA DOBLER ON 14. JANUARY 2018

This blog began when I copied a comment by Ulric Lyons (listing 3 fingerprints of natural climate variability) from Judith Curry's blog. | March 4, 2019 at 9:45 am. I expanded it by adding more fingerprints.

A climate fingerprint can be recognized as a relationship (correlation) between two, or more, variables. When these two things change at the same time (or with a lag), on multiple occasions, in the same, or opposite directions then we have an excellent 'fingerprint'.

IMHO, fingerprints and lack of them where they're expected are the best, and simplest, climate evidence. Because fingerprints are simple and obvious evidence. We can all understand them and most likely agree on them when the data and statistics are honest. When found in nature, fingerprints can be used as evidence in both validation and falsification of hypotheses.

I searched for 'fingerprint' at WUWT. Most of the articles here are about fingerprint claims made, or expected fingerprints not found. I think we should balance that with fingerprints actually found. We should put more emphasis on showing what the science IS, rather than just commenting on what other people think the fake science is, and why they are wrong.

No comments:

Post a Comment

Subscribe to: Posts (Atom)

Climate modeling fraud

" The data does not matter... We're not basing our recommendations on the data; we're basing them on the climate models. "...