New energy-budget-derived estimates of climate sensitivity and transient response in Nature Geoscience
Guest post by Nic Lewis
Readers may recall that last December I published an informal climate sensitivity study at WUWT, here. The study adopted a heat-balance (energy budget) approach and used recent data, including satellite-observation-derived aerosol forcing estimates. I would like now to draw attention to a new peer-reviewed climate sensitivity study published as a Letter in Nature Geoscience, “Energy budget constraints on climate response”, here. This study uses the same approach as mine, based on changes in global mean temperature, forcing and heat uptake over 100+ year periods, with aerosol forcing adjusted to reflect satellite observations. Headline best estimates of 2.0°C for equilibrium climate sensitivity (ECS) and 1.3°C for the – arguably more policy-relevant – transient climate response (TCR) are obtained, based on changes to the decade 2000–09, which provide the best constrained, and probably most reliable, estimates.
The 5–95% uncertainty ranges are 1.2–3.9°C for ECS and 0.9–2.0°C for TCR. I should declare an interest in this study: you will find my name included in the extensive list of authors: Alexander Otto, Friederike E. L. Otto, Olivier Boucher, John Church, Gabi Hegerl, Piers M. Forster, Nathan P. Gillett, Jonathan Gregory, Gregory C. Johnson, Reto Knutti, Nicholas Lewis, Ulrike Lohmann, Jochem Marotzke, Gunnar Myhre, Drew Shindell, Bjorn Stevens, and Myles R. Allen. I am writing this article in my personal capacity, not as a representative of the author team.
The Nature Geoscience paper, although short, is in my view significant for two particular reasons.
First, using what is probably the most robust method available, it establishes a well-constrained best estimate for TCR that is nearly 30% below the CMIP5 multimodel mean TCR of 1.8°C (per Forster et al. (2013), here). The 95% confidence bound for the Nature Geoscience paper’s 1.3°C TCR best estimate indicates some of the highest-response general circulation models (GCMs) have TCRs that are inconsistent with recent observed changes. Some two-thirds of the CMIP5 models analysed in Forster et. al (2013) have TCRs that lie above the top of the ‘likely’ range for that best estimate, and all the CMIP5 models analysed have an ECS that exceeds the Nature Geoscience paper’s 2.0°C best estimate of ECS. The CMIP5 GCM with the highest TCR, per the Forster et. al (2013) analysis, is the UK Met. Office’s flagship HadGEM2-ES model. It has a TCR of 2.5°C, nearly double the Nature Geoscience paper’s best estimate of 1.3°C and 0.5°C beyond the top of the 5–95% uncertainty range. The paper obtains similar, albeit less well constrained, best estimates using data for earlier periods than 2000–09.
Secondly, the authors include fourteen climate scientists, well known in their fields, who are lead or coordinating lead authors of IPCC AR5 WG1 chapters that are relevant to estimating climate sensitivity. Two of them, professors Myles Allen and Gabi Hegerl, are lead authors for Chapter 10, which deals with estimates of ECS and TCR constrained by observational evidence. The study was principally carried out by a researcher, Alex Otto, who works in Myles Allen’s group.
Very helpfully, Nature’s editors have agreed to make the paper’s main text freely available for a limited period. I would encourage people to read the paper, which is quite short. The details given in the supplementary information (SI) enable the study to be fully understood, and its results replicated. The method used is essentially the same as that employed in my December study, being a more sophisticated version of that used in the Gregory et al. (2002) heat-balance-based climate sensitivity study, here. The approach is to draw sets of samples from the estimated probability distributions applicable to the radiative forcing produced by a doubling of CO2-equivalent greenhouse gas atmospheric concentrations (F2×) and those applicable to the changes in mean global temperature, radiative forcing and Earth system heat uptake (ΔT, ΔF and ΔQ), taking into account that ΔF is closely correlated with F2×. Gaussian (normal) error and internal climate variability distributions are assumed. ECS and TCR values are computed from each set of samples using the equations:
(1) ECS = F2× ΔT / (ΔF − ΔQ) and (2) TCR = F2× ΔT / ΔF .
With sufficient sets of samples, probability density functions (PDFs) for ECS and TCR can then be obtained from narrow-bin histograms, by counting the number of times the computed ECS and TCR values fall in each bin. Care is needed in dealing with samples where any of the factors in the equations are negative, to ensure that each is correctly included at the low or high end when calculating confidence intervals (CIs). Negative factors occur in a modest, but significant, proportion of samples when estimating ECS using data from the 1970s or the 1980s.
Estimates are made for ECS and TCR using ΔT, ΔF and ΔQ derived from data for the 1970s, 1980s, 1990s, 2000s and 1970–2009, relative to that for 1860–79. The estimates from the 2000s data are probably the most reliable, since that decade had the strongest forcing and, unlike the 1990s, was not affected by any major volcanic eruptions. However, although the method used makes allowance for internal climate system variability, the extent to which confidence should be placed in the results from a single decade depends on how well they are corroborated by results from a longer period. It is therefore reassuring that, although somewhat less well constrained, the best estimates of ECS and TCR using data for 1970–2009 are closely in line with those using data for the 2000s. Note that the validity of the TCR estimate depends on the historical evolution of forcing approximating the 70-year linear ramp that the TCR definition involves. Since from the mid-twentieth century onwards greenhouse gas levels rose much faster than previously, that appears to be a reasonable approximation, particularly for changes to the 2000s.
I have modified the R-code I used for my December study so that it computes and plots PDFs for each of the five periods used in the Nature Geoscience study for estimating ECS and TCR. The resulting ECS and TCR graphs, below, are not as elegant as the confidence region graphs in the Nature Geoscience paper, but are in a more familiar form. For presentation purposes, the PDFs (but not the accompanying box-and-whisker plots) have been truncated at zero and the upper limit of the graph and then normalised to unit total probability. Obviously, these charts do not come from the Nature Geoscience paper and are not to be regarded as associated with it. Any errors in them are entirely my own.
The box-and-whisker plots near the bottom of the charts are perhaps more important than the PDF curves. The vertical whisker-end bars and box-ends show (providing they are within the plot boundaries) respectively 5–95% and 17–83% CIs – ‘very likely’ and ‘likely’ uncertainty ranges in IPCC terminology – whilst the vertical bars inside the boxes show the median (50% probability point). For ECS and TCR, whose PDFs are skewed, the median is arguably in general a better central estimate than the mode of the PDF (the location of its peak), which varies according to how skewed and badly-constrained the PDF is. The TCR PDFs (note the halved x-axis scaling), which are unaffected by ΔQ and uncertainty therein, are all better constrained than the ECS PDFs.
The Nature Geoscience ECS estimate based on the most recent data (best estimate 2.0°C, with a 5–95% CI of 1.2–3.9°C) is a little different from that per my very similar December study (best estimate 1.6°C, with a 5–95% CI of 1.0–2.9°C, rounding outwards). The (unstated) TCR estimate implicit in my study, using Equation (2), was 1.3°C, with a 5–95% range of 0.9–2.0°C, precisely in line with the Nature Geoscience paper. In the light of these comparisons, I should perhaps explain the main differences in the data and methodology used in the two studies:
1) The main difference of principle is that the Nature Geoscience study uses GCM-derived estimates of ΔF and F2×. Multimodel means from CMIP5 runs per Forster et al. (2013) can thus be used as a peer-reviewed source of forcings data. ΔF is accordingly based on simulations reflecting the modelled effects of RCP 4.5 scenario greenhouse gas concentrations, aerosol abundances, etc. My study instead used the RCP 4.5 forcings dataset and the F2× figure of 3.71°C reflected in that dataset; I adjusted the projected post-2006 solar and volcanic forcings to conform them with estimated actuals. Use of CMIP5-based forcing data results in modestly lower estimates for both ΔF and F2× (3.44°C for F2×). Since CO2 is the dominant forcing agent, and its concentration is accurately known, the value of ΔF is closely related to the value of F2×. The overall effect of the difference in F2× on the estimates of ECS and TCR is therefore small. As set out in the SI, an adjustment of +0.3 Wm−2 to 2010 forcing was made in the Nature Geoscience study in the light of recent satellite-observation constrained estimates of aerosol forcing. On the face of it, the resulting aerosol forcing is slightly more negative than that used in my December study.
2) The Nature Geoscience study derives ΔQ using the change in estimated 0–2000 m ocean heat content (OHC) – which accounts for most of the Earth system heat uptake – from the start to the end of the relevant decade (or 1970–2009), whereas I computed a linear regression slope estimate using data for all years in the period I took (2002–11). Whilst I used the NODC/NOAA OHC data, which corresponds to Levitus et al. (2012), here, for the entire 0–2000 m ocean layer, the Nature Geoscience study splits that layer between 0–700 m and 700–2000 m. It retains the NODC/NOAA Levitus OHC data for the 700–2000 m layer but uses a different dataset for 0–700 m OHC – an update from Domingues et al. (2008), here.
3) The periods used for the headline results differ slightly. I used changes from 1871–80 to 2002–11, whilst the Nature Geoscience study uses changes from 1860–79 to 2000–09. The effects are very small if the CMIP5 GCM-derived forcing estimates are used, but when employing the RCP 4.5 forcings, switching to using changes from 1860–79 to 2000–09 increases the ECS and TCR estimates by around 0.05°C.
Since the Nature Geoscience study and my December study give identical estimates of TCR, which are unaffected by ΔQ, the difference in their estimates of ECS must come primarily from use of different ΔQ figures. The difference between the ECS uncertainty ranges of the two studies likewise almost entirely reflects the different central estimates for ΔQ they use. The ECS central estimate and 5–95% uncertainty range per my December heat-balance/energy budget study were closely in line with the preferred main results estimate for ECS, allowing for additional forcing etc. uncertainties, per my recent Journal of Climate paper, of 1.6°C with a 5–95% uncertainty range of 1.0–3.0°C. That paper used a more complex method which, although less robust, avoided reliance on external estimates of aerosol forcing.
The take-home message from this study, like several other recent ones, is that the ‘very likely’ 5–95% ranges for ECS and TCR in Chapter 12 of the leaked IPCC AR5 second draft scientific report, of 1.5–6/7°C for ECS and 1–3°C for TCR, and the most likely values of near 3°C for ECS and near 1.8°C for TCR, are out of line with instrumental-period observational evidence.
===============================================================
Here’s a figure of interest from from the SI file – Anthony
Fig. S3| Sensitivity of 95th percentile of TCR to the best estimate and standard error of the change in forcing from the 2000s to the 1860-1879 reference period. The shaded contours show the 95th percentile boundary of the TCR confidence interval, the triangles show cases (black and blue) from the sensitivity Table S2, and a smaller adjustment to aerosol forcing for comparison (red).
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Allan MacRae: It is interesting to note, however, that the natural seasonal variation in atmospheric CO2 ranges up to ~16ppm in the far North, whereas the annual increase in atmospheric CO2 is only ~2ppm.
Another reason to suggest it is drive by out-gassing in colder waters. Tropical SST is more stable and contains less CO2 that colder polar waters.
There is a striking similarity between AO index and CO2 at MLO in the middle of the Pacific.
http://climategrog.wordpress.com/?attachment_id=231
This also suggests a dominant polar influence of atmospheric CO2
I have a question about the report: in Table S2 the total system heat uptake for the 2000’s is 0.65 (±0.27) W/m2, a tripling of the values for the previous decades. That is not in the upper 700 m which don’t show much heat uptake over the past decade, thus all in the deeper oceans, where measurements are scarce and unreliable.
The deep oceans thus are responsible for the offset of the 1.95 W/m2 extra radiation from the increase in GHGs, without affecting the upper oceans layer?
How is that possible?
Greg Goodman says:
May 20, 2013 at 3:04 am
Another reason to suggest it is drive by out-gassing in colder waters. Tropical SST is more stable and contains less CO2 that colder polar waters.
The seasonal changes are definitely linked to the NH mid to high latitude growth and wane of forests and crops. The 13C/12C seasonal ratio changes are opposite to the CO2 level changes, thus vegetation related, not ocean related. The Ferrel cells bring CO2 from the mid-latitudes to the high North stations, which makes that the change is mostly visible there, but measurements at 1000 m height over the Black Forest (Schauinsland, Germany) did show larger seasonal variations than Barrow, AK, USA.
Joel Shore says:
May 19, 2013 at 6:48 pm
“Congratulations, Nic, on your work on this paper. If the central estimates for TCR and ECS from these papers turn out to be correct, it looks like we have a better chance of being able to avoid the 2 deg C of warming that most scientists agree to be roughly the major danger threshhold, assuming that we do work pretty diligently to gradually wean ourselves off of fossil fuels over the next several decades (or implement sequestration, etc.)
That is certainly a more optimistic assessment than one would get if the middle- or high-end IPCC numbers were to turn out to be correct…in which case we are pretty screwed…and would need to be much more aggressive in stopping, or even reversing, the rise in CO2.”
So 2 degree C is the critical threshold, say climate scientists and Joel Shore. I guess that means 2 deg C of warming. what IPCC climate scientists and Joel Shore are not talking about is what this is in temperature, i.e., what the “normal” “average global temperature” is they refer to.
What is the average temperature of the planet?
Schellnhuber said 15.3 deg C in 2009. He knew the average temperature of the planet to a decimal.
The IPCC said 14.5 deg C in 2007. They also knew the temperature to a decimal.
So, while IPCC climate scientists know for a fact that a threshold of exactly 2.0 deg C warming must not be crossed, they obviously do not agree on what the “average global temperature” is, with deviations of a whopping 0.8 deg C.
http://notrickszone.com/2012/07/31/temperature-charts-reveal-astounding-cluelessness-among-top-scientists-on-real-global-temperature
And that’s the reason why they all talk about climate sensitivity and warming and anomalies without ever specifying at which “average global temperature” problems are expected to occur. It makes their doom-mongering business so much easier.
So, Joel, would you please give us the exact “average global temperature” at which the dangerous threshold is crossed, and not the “2 degrees warming” from an unspecified baseline.
I’m amazed at how people reading this blog, people that should be well familiar with the work of Bob Tisdale, one of its more active contributors, still manage to just blank out completely when reading about ‘climate sensitivity’.
Look, the entire rise in global temperatures since ~1970-75 can be derived from Pacific oceanic processes and their global atmospheric propagation. After the great Pacific climate shift (1976-79) evidently forced the world to switch from a La Niña-favored response regime to an El Niño-favored one, there have only been two (2) global upward shifts in temperatures relative to the NINO3.4 SSTa (representing the part of the ENSO pendulum (the central/eastern one) with the strongest signal), following it slavishly for the rest of the time, no general divergence whatsoever. Furthermore, the sudden global shifts are easily traceable to the western part of the ENSO pendulum (the West-Pacific/East-Indian oceans) and to the North Atlantic (AMO). They are fully explained (extensively and thoroughly by Tisdale) by natural oceanic and atmospheric processes, all known and described in the scientific literature and easily observed and tracked in all kinds of relevant climatic data.
http://i1172.photobucket.com/albums/r565/Keyell/NINO34vsHadCRUT3gl2_zps3d189621.png
http://i1172.photobucket.com/albums/r565/Keyell/EPacvsGlSSTa2_zpsc33ca917.png
http://i1172.photobucket.com/albums/r565/Keyell/EPacvsGlSSTa_zpsbc15e85d.png
There simply is nothing left for a CO2 warming signal. And still people are absolutely convinced that there has to be some GHG warming effect of significance somewhere. If natural processes explain all of the 0.6 C rise in global temperatures from 1970 to 2013 (43+ years) and that entire rise occurred in three (3) sudden shifts (1976-79 (major/phase climate shift), 1988 (mode climate shift) and 1998 (another mode climate shift)) and nowhere else whatsoever, during the period with the most rapid rise and allegedly the highest absolute content of atmospheric CO2 for many hundreds of thousands of years … THEN THERE IS NO CLIMATE SENSITIVITY TO CO2. THE SENSITIVITY IS ZERO! There is no background trend. There is only the very specific and process-related shifts.
And OLR at ToA just follow temps over the last decades of global warming, as temps follow ENSO. No sign anywhere of an ‘enhanced GHE’ as the warming cause. Pure and simple.
This is what observational data from the real Earth system is telling us, showing us. And it’s simply ignored. All for the sake of ‘Oh, but CO2 has to do something, right?’
Snap out of it!
Kristian
Why did SST increase since 1880 while ENSO is flat?
Ferdi: “The seasonal changes are definitely linked to the NH mid to high latitude growth and wane of forests and crops. The 13C/12C seasonal ratio changes are opposite to the CO2 level changes, thus vegetation related, not ocean related. The Ferrel cells bring CO2 from the mid-latitudes to the high North stations, which makes that the change is mostly visible there, but measurements at 1000 m height over the Black Forest (Schauinsland, Germany) did show larger seasonal variations than Barrow, AK, USA.”
Thanks, that’s useful info. What I plotted used a filter to precisely remove the annual cycle from both CO2 and SST, so the what you say is not contradictory to what I said.
Do you know of a source for long term 13C/12C ratio data? I presume they have this at Mauna Loa but I’ve never seen a reference to it.
In reply to:
Mike Jonas says:
May 19, 2013 at 11:13 pm
Howdy.
William: I think we are in agreement. The $200 billion/year question is how much of the 20th century warming was caused by solar magnetic cycle changes rather than by the increase in atmospheric CO2. I would assume the PDO phases are related to the solar magnetic cycle changes.
The data and analysis I have seen supports the assertion that the majority of the 20th century warming and the past Dansgaard-Oeschger cyclic warming and cooling was caused by solar magnetic cycle changes.
In reply to:
lsvalgaard says:
May 19, 2013 at 10:08 pm
William Astley says:
May 19, 2013 at 9:32 pm
As I said, there is observational evidence that the Northern hemisphere, in particular northern high latitudes regions have started to cool.
So what? The climate warms and cools all the time.
William:
Yes, we agree in agreement that ‘the climate warms and cools’. It does not however warm and cool ‘all the time’, as you state.
The climate warms and cools when there is a specific type of solar magnetic cycle changes. (i.e. One needs to understand the mechanisms and cannot just simplistically and ignorantly compare the number of sunspots on the surface of the sun to validate or invalidate the mechanisms.)
The IPCC is assuming 100% of the 20th century warming was caused by the increase in atmospheric CO2.
As can be seen in the graph in logic point 1, in the last 11,000 years there have been nine cyclic warming and cooling periods on the Greenland Ice sheet. These warming and cooling cycles are called Dansgaard-Oeschger cycles. The paper you quote notes that Be10 and C14 changes correlate with the nine warming and cooling.
The paper you quoted provides no data or logic to challenge the assertion that solar magnetic cycle changes cause the D-O cycles. The paper you quote ignores the data and logic from other papers that rule out internal ice sheet variability as a cause of the D-O cycle in the glacial period. The rush of ice into the ocean after the period of warming comes from multiple ice sheets that are disconnected from each other. The rush of ice into the ocean starts and stops synchronously which is not possible due to internal ice sheet dynamics. The solar magnetic cycle changes affect all of the ice sheets. Also there is synchronous cooling and warming in the Southern Hemisphere. The solar magnetic cycle changes affect both hemispheres.
http://myweb.wwu.edu/dbunny/pdfs/easterbrook-et-al_ch2evidence-for-synchronous-global-climatic-events.pdf
The following are logical points, observational data, and analysis that supports the assertion that the majority of the 20th warming was caused by solar magnetic cycle changes (50% to 75%) rather than the increase in atmospheric CO2.
1) There is in the paleo record, cycles of warming and cooling, that correlate with solar magnetic cycle changes, D-O cycles. The solar magnetic cycle activity in the 20th century was higher for a longer period in the 20th century than any other period in the last 11,000 years. The important point is how long the period of high activity. In fact there is evidence that the mechanism saturates.
Greenland ice temperature, last 11,000 years determined from ice core analysis, Richard Alley’s paper. See the Dansgaard-Oeschger cycles in the data.
http://www.climate4you.com/images/GISP2%20TemperatureSince10700%20BP%20with%20CO2%20from%20EPICA%20DomeC.gif
http://icecap.us/images/uploads/DOUGLASPAPER.pdf
2) The regions that warmed in the past D-O cycles are the same regions that warmed during the 20th century, Northern hemisphere primarily, high latitude Northern Hemisphere. i.e. The D-O cycle is very clearly evident in the Greenland Ice sheet data.
http://www.essc.psu.edu/essc_web/seminars/spring2006/Mar1/Bond%20et%20al%202001.pdf
Persistent Solar Influence on North Atlantic Climate During the Holocene (William: Holocene is the name for this interglacial period)
http://www.wsl.ch/fe/landschaftsdynamik/dendroclimatology/Publikationen/Esper_etal.2012_GPC
Palaeoclimatic evidence revealed synchronous temperature variations among Northern Hemisphere regions over the past millennium. The range of these variations (in degrees Celsius) is, however, largely unknown. We here present a 2000-year summer temperature reconstruction from northern Scandinavia and compare this timeseries with existing proxy records to assess the range of reconstructed temperatures at a regional scale. The new reconstruction is based on 578 maximum latewood density profiles from living and sub-fossil Pinus sylvestris samples from northern Sweden and Finland. The record provides evidence for substantial warmth during Roman and Medieval times, larger in extent and longer in duration than 20th century warmth.
3) We know there has been no significant cooling for the last 16 years, yet CO2 has been increasing.
http://www.drroyspencer.com/2013/04/global-warming-slowdown-the-view-from-space/
4) There was slight cooling of the Antarctic ice sheet during the 20th century warming. This phenomenon where the Antarctic ice cools when then Greenland Ice sheet warms and vice versa is called the polar see-saw. The polar see-saw occurs when there is a D-O cycle and is explained by this paper by Svensmark.
If it quacks, looks like a duck, and walks like a duck, it is a duck. The 20th century warming was the warming phase of D-O cycle. The D-O warming phase is each and every time followed by a cooling phase.
http://arxiv.org/abs/physics/0612145v1
The Antarctic climate anomaly and galactic cosmic rays
Borehole temperatures in the ice sheets spanning the past 6000 years show Antarctica repeatedly warming when Greenland cooled, and vice versa (Fig. 1) [13, 14]. North-south oscillations of greater amplitude associated with Dansgaard-Oeschger events are evident in oxygenisotope data from the Wurm-Wisconsin glaciation[15]. The phenomenon has been called the polar see-saw[15, 16], but that implies a north-south symmetry that is absent. Greenland is better coupled to global temperatures than Antarctica is, and the fulcrum of the temperature swings is near the Antarctic Circle. A more apt term for the effect is the Antarctic climate anomaly. …. ….Attempts to account for it have included the hypothesis of a south-flowing warm ocean current crossing the Equator[17] with a built-in time lag supposedly intended to match paleoclimatic data. That there is no significant delay in the Antarctic climate anomaly is already apparent at the high-frequency end of Fig. (1). While mechanisms involving ocean currents might help to intensify or reverse the effects of climate changes, they are too slow to explain the almost instantaneous operation of the Antarctic climate anomaly.
Figure (2a) also shows that the polar warming effect of clouds is not symmetrical, being most pronounced beyond 75◦S. In the Arctic it does no more than offset the cooling effect, despite the fact that the Arctic is much cloudier than the Antarctic (Fig. (2b)). The main reason for the difference seems to be the exceptionally high albedo of Antarctica in the absence of clouds.
5) The IPCC’s general circulation models predict the majority of the warming due to CO2 forcing should occur in the tropics as this is the region where the most amount of radiation is emitted to space and there is the largest amount of water to amplify the CO2 warming. The actual warming that occurred was in the high latitude Northern regions which match the warming pattern that occurred in the past during a D-O cycle. Furthermore, the IPCC models predict that there should be warming in tropical troposphere at around 8km above the surface of the planet due to increase water vapor. There is no warming tropical tropospheric warming observed.
http://icecap.us/images/uploads/DOUGLASPAPER.pdf
6) Lindzen and Choi, Idso have found the planet resists forcing changes (negative feedback) rather amplifies forcing changes.
http://www.johnstonanalytics.com/yahoo_site_admin/assets/docs/LindzenChoi2011.235213033.pdf
7) As there is no observed tropical tropospheric warming and planetary cloud cover increases and decreases to resist warming, the majority of 20th century warming was caused by something else besides the rise of CO2 in the atmosphere, changes to the solar magnetic cycle.
http://www.eike-klima-energie.eu/uploads/media/Shaviv.pdf
“We examine the results linking cosmic ray flux (CRF) variations to global climate change. …then proceed to study various periods over which there are estimates for radiative forcing, temperature change and CRF variations relative to today. These include the Phanerozoic as a whole, the Cretaceous, the Eocene, the Last Glacial Maximum, the 20th century, as well as the 11 year cycle… Subject to the above caveats and those described in the text, the CRF/climate link therefore implies that the increased solar luminosity and reduced CRF over the previous century should have contributed a warming of 0.47 +/-0.19C, while the rest should be mainly attributed to anthropogenic causes. Without any effect of cosmic rays, the increase in solar luminosity would correspond to an increased temperature 0.16C +/-C.”
8) Solar cycle 24 is an abrupt slow down of the solar magnetic cycle.
At the above site, the following graph, a comparison of the past solar cycles 21, 22, and 23 to the new cycle 24 is provided. That graph is update every six months or so.
http://www.solen.info/solar/images/comparison_recent_cycles.png
This is a graph, that is also located at the above site, that compares solar cycle 24 to the weakest solar magnetic cycles in the last 150 years.
http://www.solen.info/solar/images/comparison_similar_cycles.png
9) Penn and Livingston have found that the magnetic field strength of newly formed sunspots is for some unknown reason decaying linearly. Furthermore they predict the sun will be spotless by 2017. Solar cycle 25 is predicted to be a Maunder like minimum.
http://arxiv.org/abs/1009.0784v1
Independent of the normal solar cycle, a decrease in the sunspot magnetic field strength has been observed using the Zeeman-split 1564.8nm Fe I spectral line at the NSO Kitt Peak McMath-Pierce telescope. Corresponding changes in sunspot brightness and the strength of molecular absorption lines were also seen. This trend was seen to continue in observations of the first sunspots of the new solar Cycle 24, and extrapolating a linear fit to this trend would lead to only half the number of spots in Cycle 24 compared to Cycle 23, and imply virtually no sunspots in Cycle 25.
10) Based on logical points 1 through 9, the planet will now cool due to the solar down in the solar magnetic cycle. It is observed that when there is a major slow down of the solar magnetic cycle there is a 10 to 12 year delay before there is observed cooling in the high Northern regions. Why this is true is not know. It appears the delay is not due to thermal lag of the oceans.
http://arxiv.org/abs/1112.3256
Solar activity and Svalbard temperatures
The long temperature series at Svalbard (Longyearbyen) show large variations, and a positive trend since its start in 1912. During this period solar activity has increased, as indicated by shorter solar cycles. …. ….The temperature at Svalbard is negatively correlated with the length of the solar cycle. The strongest negative correlation is found with lags 10 to 12 years. These models show that 60 per cent of the annual and winter temperature variations are explained by solar activity. For the spring, summer and fall temperatures autocorrelations in the residuals exists, and additional variables may contribute to the variations. These models can be applied as forecasting models. … ….We predict an annual mean temperature decrease for Svalbard of 3.5 ±2C from solar cycle 23 to solar cycle 24 (2009 to 2020) and a decrease in the winter temperature of ≈6 C.
A systematic study by Solheim, Stordahl and Humlum [15] (called SSH11 in the following) of the correlation between SCL and temperature lags in 11 years intervals, for 16 data sets (William: solar cycles), revealed that the strongest correlation took place 10 to 12 years after the mid-time of a solar cycle, for most of the locations included. In this study the temperature series from Svalbard (Longyearbyen) was included, and a relation between the previous sunspot cycle length (PSCL) and the temperature in the following cycle was determined. This relation was used to predict that the yearly average temperature, which was -4.2 C in sunspot cycle (SC) 23, was estimated to decrease to -7.8 C in SC24, with a 95% confidence interval of -6.0 to -9.6C [15]. SSH11[15] found that stations in the North Atlantic (Torshavn, Akureyri and Svalbard), had the highest correlations.
William: Latitude and longitude of Svalbard (Longyearbyen)
78.2167° N, 15.6333° E Svalbard Longyearbyen, Coordinates
Greg Goodman says:
May 20, 2013 at 4:09 am
http://www.esrl.noaa.gov/gmd/dv/iadv/
You can choose station, carbon cycle gases and then C13/C12 ratio. Not all have these ratio measurements, the main stations have.
Several are trended here:
http://cdiac.ornl.gov/trends/co2/contents.htm
And a combined trend:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/d13c_trends.jpg
http://www.esrl.noaa.gov/gmd/dv/iadv/
many thanks Ferdi, exactly what I was looking for. I now have daily CO2 as well. 😉
I agree with Kristian. Indeed, most commenters, ignore or forget the arguments put forward so convincingly by Bos Tisdale. It is not solar nor CO2, it’s the oceans, stupid.
There does seem to be a positive (Right hand) bias in most of those modelled distribution graphs. Why is this?
Just when I thought I was going to get all that extra transactional sex.
Rosco says:
May 19, 2013 at 5:13 pm
I am amazed.
Do not be amazed, they are deaf and blind to your message. It has been shown before, but they read in to it what they want, like Roy Spencer’s conclusion to DWIR experiment.
Kristian says:
May 20, 2013 at 3:44 am
I’m amazed at how people reading this blog, people that should be well familiar with the work of Bob Tisdale, one of its more active contributors, still manage to just blank out completely when reading about ‘climate sensitivity’.
You should also not be amazed, they have the belief that as CO2 is a greenhouse gas? there must be a sensitivity.
A “fudge factor” can always be adjusted to fit additional data. Try doing your energy balance on the Arctic or the Antarctic where days are a year long. I think you will find that the rates of the processes of freezing and sublimation of water are controlling the rate of OLR, not CO2 concentrations. Global averages do not reflect local rates.
Rosco says:
May 19, 2013 at 3:45 pm
Rosco take two matches and light them. Blue tips are fine. Separate they each are at say 600 F now take and hold the flames together and voila you have a larger flame at 600 F. Do it with torches anything and you cannot add temperature and that is what they are doing. A thing will only get as hot as the hottest thing providing the input heat.
lgl says, May 20, 2013 at 4:01 am:
“Why did SST increase since 1880 while ENSO is flat?”
ENSO is not flat, lgl. Whatever gave you that idea? NINO3.4 is (fairly) flat. NINO3.4 is not ENSO.
http://i1172.photobucket.com/albums/r565/Keyell/ENSOampAMOb_zps2f9f8129.png
And where is that CO2 warming signal on top of the ENSO(->AMO) signal 1970-2013, lgl? Where’s that relentless background trend. Hidden in three specific and sudden jolts in 1979, ’88 and ’98? Mechanism, please.
Read Tisdale at all, lgl?
Mike Jonas says:
May 19, 2013 at 2:14 pm
—
you beat me to it. This is just another piece of guesswork that happens to agree a little better with recent temperature trends than previous pieces of guesswork. As long as there isn’t a comprehensive and quantitatively accurate theory of natural climate variation, it remains impossible to extract the human contribution from analyzing trends. No amount of mathematical voodoo is going to change that.
Another curious aspect: If the work was mainly done by the first author, how come there are as many as 14 authors overall? How exactly did the other authors ensure to split the minor piece of the work to make sure everyone contributed enough to earn coauthorship? Frankly, I suspect that some people were made coauthors even though they did not significantly contribute, simply in order to make reviewers think twice about rejecting this paper.
The can’t. They won’t. And they didn’t. They did, however, spin it like a top. There will still be “Extreme Global Warming!!! Oh NOOEES!!” But it will happen further in the future. Keep sending money.
http://news.yahoo.com/extreme-global-warming-seen-further-away-previously-thought-090821067.html
William Astley says:
May 20, 2013 at 4:11 am
I would assume the PDO phases are related to the solar magnetic cycle changes.
Since when are assumptions science?
The climate warms and cools when there is a specific type of solar magnetic cycle changes.
Again an assumption, no evidence.
The following are logical points, observational data, and analysis that supports the assertion that the majority of the 20th warming was caused by solar magnetic cycle changes (50% to 75%) rather than the increase in atmospheric CO2.
This is not a binary question, solar or CO2. Any complex system has internal cycles, e.g. ocean circulation changes.
The solar magnetic cycle activity in the 20th century was higher for a longer period in the 20th century than any other period in the last 11,000 years.
No, this is simply not true as I have explained.
@- Chris Schoneveld
“I agree with Kristian. Indeed, most commenters, ignore or forget the arguments put forward so convincingly by Bos Tisdale. It is not solar nor CO2, it’s the oceans, stupid.”
97% of scientist are unpersuaded of Bob’s ENSO hypothesis.
Especially as there is no explanation why for several thousand years the ENSO fluctuations have had NO effect on climate trends, but just start to do so when humans start adding massive amounts of fossil CO2 to the atmosphere.
Chris Schoneveld says, May 20, 2013 at 5:02 am:
“It is not solar nor CO2, it’s the oceans, stupid.”
Well, it is solar, the Sun being the ultimate provider of heat after all. Its main influence simply manifests itself indirectly rather than directly, through and across the ocean cycles. But you’re right, either way it’s not CO2.
The BBC look like they are rowing back just a little on AGW doom prediction, saying “Climate slowdown means extreme rates of warming ‘not as likely’ “. However the last line of the piece suggests this is a limited tactical retreat only.
Mike Jonas says:
“I won’t argue with the idea that Nic Lewis’ paper is important, but I wonder why … presumably it is politically important (pity it’s too late for AR5),”
The paper was accepted by the IPCC AR5 WG1 acceptance deadline of 15 March, and will be cited in AR5
Kristian
This gave me that idea, http://climexp.knmi.nl/data/ihadisst1_nino3.4aa.png
There is no trend. Do you have a better proxy for ENSO going back to 1880?
Anyway global warming will also warm the Nino3.4 region.
And here is “that CO2 warming signal on top of the ENSO”, http://virakkraft.com/Hadcrut4-Nino34-detrended.png
It’s 0.26 C between 1880 and 1942 and 0.43 C between 1942 and 2004, and it’s CO2 ++, half of it could be solar.