As part of the IPCC WG1 SPM (pdf) released last Friday, there was a subtle, but important, change in one of the key figures – the radiative forcing bar-chart (Fig. SPM.4). The concept for this figure has been a mainstay of summaries of climate change science for decades, and the evolution over time is a good example of how thinking and understanding has progressed over the years while the big picture has not shifted much.
The Radiative-Forcing bar chart: AR5 version
The earliest version of a bar-chart that shows radiative forcing is this chart from one of Jim Hansen’s papers (Hansen et al, 1981):
In it, they demonstrate the relative importance – cooling or warming – of a number of relevant changes in radiatively important components (CO2, CH4, the sun, aerosols etc.). While the y-axis is the no-feedback surface temperature response, and the changes aren’t with reference to the pre-industrial, this might qualify as the ‘ur’-figure – the one from which all the others below are derived. (Note, if you know of an earlier version, please let me know and I’ll update the post accordingly).
I can’t find any examples for a decade or so, and in the First Assessment Report (FAR) (1990) there wasn’t such a figure either, even in the main text. (Again, please let me know if I’ve missed one). However, in the early 1990s, the figure appears in a form much closer to what we’ve come to expect. For instance, in Hansen et al (1993), the forcings in 1990 with respect to 1850 are given:
The transition to W/m2 as the unit has now been made, different greenhouse gases are separated, and an acknowledgement of more complicated issues associated with ozone and stratospheric water vapor is included. The main conclusion is that CO2 had been historically the most important forcing (around 1.24 W/m2). Shortly thereafter, the 1995 IPCC Second Assessment Report (pdf) added a couple of innovations:
Namely, an assessment of confidence, and the addition of aerosol forcings, while lumping the well-mixed gases all together. There is also the addition of the non-anthropogenic solar term. The figure was updated in 1998 and 2000 by Hansen and colleagues:
These updates added land use/land cover changes to albedo, decadal trends in volcanoes, and (in 2000) made the subtle point that the greenhouse effect from CFCs was offset a little by the impact CFCs were having on the ozone layer. An analogous diagram was very prominent in the 2001 IPCC Third Assessment report (TAR):
As with the SAR version, the confidence levels are present, there has been a switch from 1850 as a baseline in the SAR version, to 1750 in order to capture the beginning of the industrial rise in the GHGs, and again additional items were included: some aerosol related (sulphates, mineral dust, biomass burning, carbonaceous aerosols (incl. black carbon)), and two associated with aviation (via contrails and enhanced cirrus cloud formation). Concurrently, the Hansen et al (2001) version:
included even more details – the effect of black carbon on snow, nitrate aerosols, and an enhancement of the solar effect via ozone changes.
In the 2007 AR4 SPM, the main innovation was to rotate the axes by 90º and to add a bit more colour:
Though stratospheric water vapour makes a comeback, and the indirect effect of black carbon on snow makes an entrance for IPCC. In the AR5 SPM though, something more interesting happened…
The effects are now grouped by emissions, rather than by concentrations. This too has it’s antecedents, Fig 2.21 in the AR4 full report did the same thing, but was little noticed. In turn, that figure was drawn from work by Shindell et al (2009). This allows many of the indirect effects to be seen clearly. A particular point of interest is that the forcing by emission for CH4 is twice as large than its forcing by concentration, because of the important indirect effects on ozone and aerosols. The inclusion of CO, VOCs and NOx – normally considered as air quality issues – which affect climate via their indirect effects on ozone etc, is a salient reminder that the two issues are very much connected.
Summary
The most obvious change over time is that the visual styling of the graphs has improved over time. The latest version is far more comprehensive – including more effects, more connections, more error bars – and is, arguably, more useful. This follows from the fact that it is emissions that can be potentially moderated, and the latest iteration shows explicitly what the key emissions are (as opposed to what their consequences are after atmospheric chemistry has done it’s thing).
A key change over time is of course the increasing forcing from CO2. In 1993 it was 1.24 W/m2, in 2001, 1.4 W/m2, to today’s 1.7 W/m2.
The treatment of aerosols – and particularly the difference between absorbing (i.e. black carbon) and scattering (sulphates, nitrates) – has varied a lot. This is partly because of new information (on sources, concentrations, effects), but also because the aerosol issue has been reframed many times. The situation of black carbon is the most complicated. BC on it’s own is strongly warming, and it’s additional indirect effects on snow albedo amplify that. However, BC is almost always emitted in combination with organic carbonaceous aerosols (and/or secondary organic aerosol precursors), and so with respect to the emission-producing activity, the net effect on temperature is partially compensated (see the TAR version for instance). BC is chiefly associated with incomplete combustion of fossil fuel, or alternatively with biomass burning (through deforestation, land clearance or naturally occurring forest fires), and these two classes of sources have sometimes been grouped (2007), and sometimes separated (2001). The AR5 version groups all the aerosol factors into one bar with each of the separate constituents delineated. A further breakdown of this into contributions by activity would be useful, but as I understand it, this was considered not within the scope of WG1.
One final example is also worth noting. In all of the pre-AR5 figures (except Hansen in 2000), tropospheric and stratospheric ozone were considered separately. But while there are two separate effects going on (ozone precursors increasing in the lower atmosphere, and ozone depletion due to CFCs above), there is not a clean separation between changes in the troposphere and stratosphere. Thus the AR5 version correctly shows the ozone changes as indirect effects of the different emissions without delineating where the changes in ozone are occurring. This is a definite conceptual improvement among many.
References
- J. Hansen, M. Sato, R. Ruedy, A. Lacis, and V. Oinas, "Global warming in the twenty-first century: An alternative scenario", Proceedings of the National Academy of Sciences, vol. 97, pp. 9875-9880, 2000. http://dx.doi.org/10.1073/pnas.170278997
- D.T. Shindell, G. Faluvegi, D.M. Koch, G.A. Schmidt, N. Unger, and S.E. Bauer, "Improved Attribution of Climate Forcing to Emissions", Science, vol. 326, pp. 716-718, 2009. http://dx.doi.org/10.1126/science.1174760
The cloud aerosol effects seems to have huge uncertainty ranges (also aerosol effects in general). Is there any hope of narrowing these down in current ongoing research?
[Response: Some. But these are difficult and one of the key uncertainties is that we don’t have a good global source of information for what kinds of aerosol are in the air. This would have been one of the key datasets from the unsuccessful GLORY mission. – gavin]
The new AR5 table would be more readable if the second column had shown not just “resulting A drivers” but “net change (+ or -) in A drivers”. For example:
NOX emissions – +Nitrate+, -CH4, +O3tro
Then I would understand that CH4 negative radiative forcing in that row is actually due to -CH4 change.
As the table stands, I just glance at that row I scratch my head: methane has cooling effect, what’s going on? I must go back and figure out how aircraft NOX emissions react with atmosphere; then I finally realise it results in net decrease of CH4, that’s why negative RF.
Other wordings in the table could be changed accordingly, e.g. “Albedo Change due to Land Use” could just be “Albedo Increase due to Land Use”.
That simple change would greatly increase the table readability.
[Response: Good points. – gavin]
Thanks for this very useful explanation of the evolution of forcing diagrams.
There are some pretty strong feelings about the validity of the AR5 reduction in aerosol forcing.
Is it possible that people are overlooking the larger role BC has (in terms of reducing the net aerosol cooling), or is it your sense that it may indeed be the case that the AR5 has taken too narrow (versus e.g. doi:10.1029/2012GL051870) a view of the plausible range of aerosol dimming?
Someone may want to check the math. Unless there are substanial rounding errors (highly unlikely), the total is a little off.
Not just the aerosol forcing, but the high level of confidence seems a bit awry. What happened to the albedo change due to glacial melt? Is that including in the land change?
[Response: Errors are not symmetric and so the net effect is not a simple addition. This was also the case in AR4. I have no idea why you think albedo change due to glacier melt is an important factor over the last century – for that to be true, you’d need to have a really large fraction of current glacier area disappear. – gavin]
> glacial melt
Perhaps (we can only guess) what DanH refers to is albedo difference when there’s melted water on top of the ice, rather than to the difference between glacier/no glacier at all.
Re-evaluation of MODIS MCD43 Greenland albedo accuracy and trends
Remote Sensing of Environment
Volume 138, November 2013, Pages 199–214
The change for 2012 against baseline is significant (detected with confidence) for Greenland — but not large, from what the abstract says.
No DOI there yet, sorry.
DanH, don’t tell us what source/cite you’re talking about. You get more attention when we have to guess.
It seems that you missed a very important radiative forcing chart:
[Response: Ha! – gavin]
I hope this doesn’t sound to lazy- I was hoping for a similar chart of “feedbacks” to compare with forcings. So increased water, clouds, that kind of thing. Is it in another section?
[Response: Chapter 9 – page 67 onwards. – gavin]
Gavin,
Are you saying that more than one factor overlap, such that the RF from different sources are co-dependent? That would answer my question, as opposed to the claims of simple addition.
[Response: No. The error bar for aerosol indirect effects is skewed, therefore the most likely value of the GHG+aerosol is a little less than the sum of the means. – gavin]
Regarding the albedo, I was thinking on the fly. The AR4 report (to take out the guesswork) attributed albedo changes to carbon black affecting the albedo, not melt.
[Response: The albedo changes associated with black carbon are included in the aerosol bar (I think). See section 8.3.4.4 and figure 8.17. – gavin]
Thanks, it is always good to see concise histories of progress in science via graphical representations, and the latest one uses colors better and really offers much information in one chart. However, Joe’s chart was simpler… :-)
Gavin- thanks- I didn’t have that chapter yet.
Thanks for the great overview (and ahead of time, more thanks to anyone willing to bother answering my questions).
So stratospheric water vapor, that popped up in AR4, disappeared again in AR5?
Was that because it was considered negligible? Or because it didn’t fit into their emissions schema neatly?
How important is it now considered to be, and what are its main sources?
[Response: Strat water vapour as a forcing is an indirect effect from CH4 increases and so is included there. – gavin]
The non-peer reviewed references cited on denier websites come from the group working on both actual and potential effects and comprehensive mitigation strategies, Working Group II. It’s hardly surprising that they include some non-peer reviewed references among the peer reviewed ones. This is a topic which blends science, economics, and politics.
For those still on the global warming denial train, let’s see your list of non peer reviewed references for Working Group I, Chapter 2, the part of the report that says global warming is real, and mostly caused by us. I’ll quite concede that some of the potential effects and mitigation strategies are speculative, will others concede those two points?
To give people a more objective look at what Working Group I, Chapter 2 actually considered, here are the first five references, unselected for rhetorical purposes. And the website that lists all of the hundreds of references, overwhelmingly (entirely?) peer reviewed. I invite people click the link, and get an idea of the MOUNTAIN of peer reviewed data that’s behind the statement of the scientific community that global warming IS real, and mostly caused by us.
Abdul-Razzak, H., and S.J. Ghan, 2002: A parametrization of aerosol activation: 3. Sectional representation. J. Geophys. Res., 107(D3), 4026, doi:10.1029/2001JD000483.
Abel, S.J., E.J. Highwood, J.M. Haywood, and M.A. Stringer, 2005: The direct radiative effect of biomass burning aerosol over southern Africa. Atmos. Chem. Phys. Discuss., 5, 1165–1211.
Abel, S.J., et al., 2003: Evolution of biomass burning aerosol properties from an agricultural fire in southern Africa. Geophys. Res. Lett., 30(15), 1783, doi:10.1029/2003GL017342.
Ackerman, A.S, M.P. Kirkpatrick, D.E. Stevens, and O.B. Toon, 2004: The impact of humidity above stratiform clouds on indirect aerosol climate forcing. Nature, 432, 1014–1017.
Ackerman, A.S., et al., 2000a: Reduction of tropical cloudiness by soot. Science, 288, 1042–1047
http://www.ipcc.ch/publications_and_data…
Gavin
Thanks for bringing out this shift in the way that radiative forcing by gas is being treated in the WG1-AR5. But I think that it should be noted that this is also starting to make the analysis quite a bit more model dependent because for some gases it folds in a lot of atmospheric chemistry. In particular, bringing in the indirect feedback effects on OH caused by CH4, CO, etc goes into an area where models still differ and the extent to which they can reproduce observations is pretty debatable.
For example, when CH4 suddenly started going up in 2006, particularly across the southern hemisphere where it had been remarkably stable for six years, the first explanation was that this was due to a reduction in OH. But why had CH4 also suddenly stopped increasing in 2000? Was that an increase in OH? Multi-decadal increases in CH4, HCFCs and HFCs have occurred along with a downward trend in CO across most of the clean air sites in the southern hemisphere, which suggests that the reduced species are not decreasing OH, as is being assumed in the indirect effect for the CH4 global warming potential.
At the AGU meeting in San Francisco in December there will be a Union session on atmospheric methane and some of us are putting together a summary of why we think changes in the chemistry are still not well understood. We may get to include something on new evidence from 14CO data which now seems to show that past fluctuations in CH4 growth rate were related to changes in the spatial distribution of OH rather than in its global average, and while some had anticipated that this might happen about ten years ago, it is not yet coming out of the chemistry models.
So I think that this radiative forcing figure in the AR5 has a subtle form of model based attribution folded in, and for which the level of confidence is definitely lower than it is for the direct effects of each gas. Also while it’s a great figure for scientists to consider, I don’t think that many policymakers will understand this level of detail.
[Response: Hi Martin, Thanks for your comments. It is inevitable that moving closer to the source of any change requires more modelling, and so the uncertainties will have to increasingly include the multi-model spread – as constrained by observations as possible. However, instead of a reason not to do it, I see that as the price that needs to be paid in becoming more relevant. Moving from RF to ERF also has a model component in it. But even in previous incarnations the CH4 effect on OH was implicitly included in the tropospheric ozone and sulphate column. The switch to an emission-based presentation is not a change in physics, just a change in accounting. As to whether policy-makers appreciate this more or less than previous presentations, we would have to ask them. My interactions suggest that they are actually hungry for even more policy-specific, emission-based analyses (a la UNEP report on black carbon and tropospheric ozone), but perhaps your experience is different. It might be worth making a distinction here between policy-analysts/policy-makers and politicians. The former, not the latter, are the target here. – gavin]
[Response: AR4 adopted a hybrid scheme in its discussion of present-day radiative forcing and its discussion of feedbacks on emissions. In the radiative forcing bar chart, things like ozone and stratospheric water were given their own separate RF category wherever they came from. This makes sense if you’re trying to describe the present situation, since you can actually measure the constituents, and simply stating their effects without saying what causes the ozone or stratospheric WV concentration to be what it is avoids some model uncertainty. However, in the discussion of emissions effects (notably global warming potential) the feedbacks are included in the effective radiative efficiency. That also makes sense, since you need to relate RF to emissions in order to predict the future. (I’d ignore GWP, but the radiative efficiency is still a relevant quantity). There is an argument for switching to a more emissions-based approach in AR5, but as Gavin and Martin note, this incurs model uncertainty. I think AR5 made a bit of a mishmash of the transition to emissions-based accounting, though, because they included aggregate effects of methane, but they did not present the black carbon results in a way that shows the aggregate effect of all emissions from BC rich sources, which (according to Bond et al’s JGR assessment) is likely to be a small net cooling when co-emissins are taken into account. The intent of the new AR5 RF diagram is to make abatement priorities easier to read off the graph, but it doesn’t really achieve that. For that matter, it’s not possible to infer abatement priorities without taking into account lifetime; GWP was meant to do that, but as we now know it’s a hopelessly broken concept. –raypierre]
Martin Manning @13:
Yes, that’s an important point to emphasize. Allocation of RF to the emissions rather than to the atmospheric concentrations of individual substances brings the relationship between emissions and concentrations into the calculation, and this relationship is based partly on models (e.g. of the carbon cycle, of OH chemistry, etc.) with inherent uncertainties. This is presumably why the uncertainty range of the CO2 forcing is wider in AR5 than in the AR4 figure.
Also important to emphasize that the total anthropogenic RF has not become more uncertain because of this change. It is only the allocation to individual emitted substances that is affected, not the overall total.
Given that emissions-based breakdown is needed for policy purposes, it is far better that IPCC WGI do this and include an assessment of the additional uncertainties, than leave it to others to do after the fact.
For AR4, in addition to Fig. 2.21 that Gavin pointed to, it’s worth looking at footnotes to AR4 Table 2.13 to see the numbers and uncertainties in AR4, compared with the new AR5 estimates/uncertainties.
thingsbreak,
Black carbon is mainly a factor in the direct effect which hasn’t changed much in the final reckoning: -0.5W/m2 for DirectRF in AR4 and -0.45 for DirectERF/ERFari in AR5. Estimated Black carbon positive forcing is larger but that’s also the case for some reflective aerosol species. The real difference from AR4 comes from aerosol-cloud interactions.
It looks to me that their total aerosol forcing best estimate has essentially been determined by their assessment of the six satellite obs. constrained studies listed in Table 7.4. It seems to me the treatment of the six papers is somewhat questionable, with choices and interpretations tending to push the median towards less negative values. Also questionable is the imposition of a +0.2W/m2 longwave forcing on the four papers which only estimated shortwave changes. No reference is provided to support this figure. They talk about it as the low-end of a range from models but don’t cite which models. Seems a little ad-hoc and puntish.
Ultimately though, a total aerosol forcing of -0.9W/m2 is probably a plausible expert judgement (not at all to say it’s the only plausible expert judgement) estimate given the full balance of evidence accrued post-AR4. However, this is far from the final word and I wouldn’t be at all suprised to see future assessments go back down to -1.2 or even more negative.
Paul S,
I concur with your comment on the imposed longwave forcing “correction”. While I would have preferred a percentage-based reduction (as function of the actual forcing in each respective study/model), I consider the missing reference for their statement (“[…] modeled longwave effects which varied from +0.2 to +0.6 W/m2 in the assessed models”) a bit unfortunate. Any idea which paper they are referring to? For the same reason (missing reference) I had some trouble to make sense of the -0.85 W/m2 for Bellouin et al. 2013. Although I now think I know what they’ve done, I hope Nicolas can help (I’m waiting for his answer). Otherwise, Drew Shindell might be the right person to ask anyway.
As you said, ultimately, total aerosol ERF of -0.9 W/m2 is certainly more than plausible, so my bet wouldn’t have been much different (probably -1 W/m2). However, it’s important to note that this is only true for the current forcing, while I tend to think that it might have been (globally) higher when sulphate emissions peaked (1970s). Little discussion on the temporal evolution of the aerosol forcing in AR5 either.
Karsten,
My guess was that they were referring to LW estimates given in the papers listed in Table 7.4 since they were mentioned earlier in the section and included mixed-phase/ice clouds and convective schemes. However, when looking at the papers I found a range of -0.3 to +0.6 and average about zero.
Also, Wang et al. 2011 discussed the source of their +0.26W/m2 LW estimate and noted that it was mostly found in clear sky areas. It was basically a response to aerosol-induced land-surface cooling in the fixed-SST simulations used to diagnose model forcing.
To me that seems like a confusion of forcing and response.
Paul S,
while there is, indeed, a range of values extractable from the reference list in Table 7.4, it’s not even close to their suggested range, as you’ve pointed out. As far as I can see, the only model exercise which reports on aerosol LW effects (!) is Quaas et al. 2009. So not even a forcing estimate. In any case, “scaling” to some degree can be seen there, i.e. higher neg. SW forcing is on average counterbalanced by higher pos. LW effect. Regardless of the fact that the effective LW forcing might be stronger, I’m still having trouble to follow their argument with respect to LW.
Is there any hope that this plot will ever revert back to units of delta T as opposed to watts per square meter? I frequently refer to this chart in one form or another when I answer questions from readers about how much such and such a forcing has contributed to climate change, or “have scientists thought about forcing X as an explanation for climate change?” More often than not, there is at least one more round of follow-up questions in which they want to know “but what does that mean in terms of temperature?” It would be valuable for communicators to have this chart reproduced using units of temperature change.
Rebecca (#20)
You can multiply the numbers in the charts by a factor of 0.3 to get the no-feedback temperature change in degrees Celsius. Multiply by 0.75 or so to get a ballpark “real temperature change” once you get to equilibrium (though with a bit more uncertainty).
20 Rebecca, I wholeheartedly agree. The IPCC needs to use REAL words (as opposed to sciency ones)
Continued:
When 99% of your audience won’t understand something where a different wording would mean 100% of your audience would understand, then use the 100% language, EVEN IF IT’S NOT THE MOST CONCISE. In this particular case, where (I think) there’s a linear relationship between w/m2 and delta T within the range being considered, simply putting two Y axis labels would have satisfied everyone.
> two Y axis labels
but you’d have to make the numbers rather fuzzy to indicate the uncertainty, wouldn’t you? these axes aren’t sharp yet.
Its not just glacier melt, we have the longer snowfree season in most places with seaonal snowcover. We also have less lake and sea ice.
I was confused by that caption/label rofing relative to 1750 (W/M**2). I was reading 1750 wattsper meter squared, not realizing 1750 was a data. Perhaps that could be made clearer?
Comparing the last two graphs 2007 vs 2013 I see that for CO2 both the RF values and the error bars are more streched in both directions. This indicates less confidence.
Strangely, in the confidence column the confidence level has gone up from H to VH (very high). This does not make sense.
thingsbreak,
I’ve realised I didn’t address your question in the straightforward way it deserved, because it is quite simple: Global satellite observation-constrained estimates of the cloud albedo/first indirect effect have consistently indicated a forcing of ~ -0.4W/m2 compared to the -0.7W/m2 given in AR4.
The AR4 figure was derived from a weighting of pure model results (typically around -1.0W/m2) against a couple of observationally-constrained results. Since AR4 a few more independent estimates have been published still consistent with about -0.4, plus the passage of time has not thrown up clear contradictions to these results. Hence, this time around the observationally-constrained studies have been trusted almost to the exclusion of model results, meaning a smaller (less negative) indirect effect.
There have been a few recent papers which suggest some, if not all, of these estimates may be significantly biased (notably Penner et al. 2011 and McComiskey and Feingold 2012). AR5 mentions these but suggests the sign and extent of any bias is yet to be demonstrated.
AntonyIndia,
I don’t think the ‘LOSU’ and ‘Level of Confidence’ metrics are precisely the same thing. In any case ‘High’ was the maximum available rating for LOSU in AR4.
So is this evolution by natural selection of survivors out of random variation?
Or is this rather an example of improvement through intelligent design?
(-:
I am always struck by the large error bars on the radiative forcing due to CO2. What is that all about?
In the AR5 figure the range is given as 1.68 +/- 0.35 (or +/- 20% of the central estimate).
In AR4 this range was 1.66 +/- 0.17 (or +/- 10% of the central estimate).
Why the large increase in uncertainty?
In AR3 it is hard to tell, because CO2 is bundled with the other well-mixed GHGs, but in the concurrent Hansen paper it is given as 1.4 +/- 0.2 (+/- 14%), which is still lower than the AR5 uncertainty range.
On the one hand this isn’t massively important – even at the lower end of the bound CO2 is still a key GHG, etc, but I would have thought that the radiative physics of CO2 were the simple bit, compared to the aerosol effects, the cloud feedback, the response of ice sheets to warming, etc, and a 20% uncertainty range is really huge, which is not helpful for making specific climate predictions.
[Response: Actually this is quite interesting. The main shift is because of the change from stratospherically-adjusted radiative forcing to ‘effective’ radiative forcing. This shift allows more very fast adjustments into the forcing numbers and (inevitably) leads to slightly more structural uncertainties. This was done for ease of calculation across a whole range of forcings and the larger error bars on the CO2 is a acceptable price. Note that the clear sky forcings for CO2 and most other GHGs are very well known, but once you are looking at the global all-sky conditions, there is some inevitable impact from the water vapour/cloud/temperature distribution and properites and that is where the bulk of the uncertainty comes in. – gavin]
Is it possible to compare the older charts to the newer ones to determine how accurate the old charts were compared to what is known now? Have the additional data led to forcing estimates becoming larger, smaller or about the same? Obviously the CO2 forcing has increased, but are current estimates of the forcing in 1990 similar to estimates of the forcing that were made in 1990? This could give another way to evaluate the accuracy of the older reports.
.
The solar forcing has been downgraded ,
I’m puzzled why such a small forcing has in the past be a climate driver
[Response: At different time periods it could have been larger – this is simply an assessment for the long term trend over the period 1750-2010. – gavin]
from what I understand the way the forcing is calculated
is the total Solar irradiance in W/m2 divided by the apparent disk area ,
should the troposphere layer thickness too be included ,
after all it refract the sun rays quite a lot ,
Hi Gavin,
Uncertainty in aerosol effects (direct and indirect) seems to dominate the overall uncertainty in the AR5 table of forcings. Is there any chance of a replacement satellite for the failed Glory mission?
So far nobody mentioned this so I guess I will have to embarrass myself: why is there no forcing from volcanoes on some charts while the other chars have it? Even if it’s zero it should be present with error bars, I reckon.
[Response: The graphs are now radiative forcings since 1750, and over that period there is no trend to speak of in volcanic forcings. Over shorter periods, there can be important contributions though and a graph focussed on that would include them. – gavin]
Chris (#21)
…and then multiply by 1.8 to get change in degrees F?
Rebecca-
For good enough “government work” in a public lecture multiply the W/m2 forcing by one to get the feedback-included sensitivity in units of degrees F. No thinking involved.
[Response: Hey! No-one’s allowed to be doing any ‘government work’ here today…. – gavin]
It seems the bottom line is without sulphate aerosol cooling we would be on course for 2c warming already. The question is what happens when the emerging industrial counties get serious about air pollution control?
Gavin,
Thanks for putting this together. This is the figure I use to teach non-science policy-makers-to-be about climate change accounting, how the accounting has changed and not changed over the years, and esp. the influence of air quality controls on what the future will bring. Will China and India scrub their coal? How soon?
This concise history of how the science has changed will be invaluable in these efforts.
Best,
Mike Wara
I’m in discussion with a couple denialists swearing the chart is all a big fraud, because the TSI difference since 1750 is more like +0.5 W/m^2, while the chart shows a solar irradiance factor of +0.05 W/m^2.
Now, I understand that the solar irradiance factor on the chart is not the same thing as the straight TSI difference. For one, there’s the geometry factor knocking it down by a factor of 4. But I’d like to be able to explain the rest. Quick and dirty, what else goes into calculating that factor?
Mamoo,
TSI is very difficult to measure from Earth’s Surface. We have a correlation between TSI and Sun Spot # derived from satellite data and we can use that to reconstruct an approximate history. Try here
http://www.mps.mpg.de/dokumente/publikationen/solanki/j96.pdf
and
http://spot.colorado.edu/~koppg/TSI/
Short answer–it tracks really well until you get to about 1970 or so. Gee, what happened then, I wonder?
re 39 mamoo –
(1-albedo)*(TSI difference)/4 = solar forcing TOA
The forcing at the top of the atmosphere (TOA) prior to stratospheric adjustment is equal to 1/4 of the TSI change, multiplied by the fraction absorbed by the Earth (about 0.7), so it’s about 0.175 * TSI change. For TSI change of 0.5 W/m2, that would be ~ .08 W/m2. There have been different estimates for TSI change over the years; it’s also possible some ‘prefer’ the higher estimates.
The forcing at the tropopause level prior to stratospheric adjustment would subtract the fraction absorbed by ozone (and anything higher up, but that’s tiny) – I think that’s a few percent. much of that gets added back after stratospheric adjustment because the stratosphere warms so that it radiates more, by as much as it’s been heated, and some fraction of that is downward.
An earlier IPCC radiative forcing chart was for tropopause-level forcing after stratospheric adjustment; I’m unclear on whether this one is done the same way.
correction: ~ 0.09 W/m2, not 0.08 W/m2.
DP — not just sulfates, if this recent paper has it right; ‘Brucie A’ pointed it out in the unforced variations thread, and I quoted a bit there at 15 Oct 2013 at 12:34 PM
Point is getting serious about air pollution may have several, opposing unintended consequences, depending on how it’s done.
re 39 mamoo – I have read that solar UV is more variable than solar TSI, so presumably fraction of solar forcing (a change) that is absorbed in the stratosphere would be higher than the fraction of total solar heating of the Earth that is absorbed there.
I was able to pick out the TSI contribution from a multivariate linear regression analysis of GISS with respect to 4 noise terms — SOI, Aerosols(volc), LOD, and TSI. This is an interactive view:
http://entroplet.com/context_salt_model/navigate
The TSI temperature response should be about 0.05 C for the dP ~ 1 watt/m^2 coming out of the sun-spot fluctuations at P=1360 watts/m^2, and sure enough that is what the linear regression model picks up.
This is simple differential calculus
P ~ T^4
dT = 1/4*T/P dP = 1/4 * 288/1360 dP = 0.053 dP
P = 5.67 E-8 W/(m2 K4) * T^4 , For the photosphere as a whole, it is not isothermal so this becomes approximate, unless it is used to define an effective temperature.
dP = 3*5.67 E-8 W/(m2 K4) * T^3 * dT
dP at Earth, of sun = 3*TSI/T * dT; for T~=5780 K, dP ~= 3*1360/5780 W/(m2 K) dT ~= 0.7059 W/(m2 K) dT, so about 0.706 W/m2 per K(sun) or 1.42 K(sun) per W/m2.
For Earth’s T, tropopause T ~ 220 K or 200 K, give or take (from memory), sfc is 288 K, effective broadband brightness T ~ 255 K.
dP = 3*OLR/T * dT; for OLR ~= 240 K, that’s
2.5, 2.8, 3.3 W/m2 per K , or 0.40, 0.35, 0.31 K per W/m2, @288,255,220 K (zero non-Planck feedback).
1 W/m2 of TSI -> 0.175 W solar forcing TOA -> 0.070, 0.062, 0.053 K @three Temps (see above), not taking into account stratospheric portion, feedbacks, or time for equilibration.
dP/P = 3*(OLR,TSI)/T * dT /(OLR,TSI) = 3*T/dT
… re evaluation of dT/d(OLR or forcing): (of course, if GHGs were shifted toward one end of the OLR band or the other, the zero-feedback sensivity could be farther reduced or enhanced; At peak wavelength, there is a T^5 proportionality. @220, 255, 288 K, peak wavelength is 13.2, 11.4, 10.1 microns; the last two are in the Atmospheric window, so… whatever the line-by-line results are.)
I think I reversed the order of temps in my 2nd-to-last comment; dT/dP should decrease with increasing T.
Patrick, Hansen said the solar dP fluctuation when averaged at the earth’s surface is 0.25W/m^2, so 1/4 of this is about dT ~ 0.06C.
[1]J. Hansen, M. Sato, P. Kharecha, and K. von Schuckmann, “Earth’s energy imbalance and implications,” Atmospheric Chemistry and Physics, vol. 11, no. 24, pp. 13421–13449, Dec. 2011.
Gavin – “Note that the clear sky forcings for CO2 and most other GHGs are very well known, but once you are looking at the global all-sky conditions, there is some inevitable impact from the water vapour/cloud/temperature distribution and properites and that is where the bulk of the uncertainty comes in.”
So much of the uncertainty in the carbon dioxide forcing is for the same reason that there is so much uncertainty in the climate sensitivity. Damn clouds, eh?
Thanks for the answer!