the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantifying the tropospheric ozone radiative effect and its temporal evolution in the satellite-era
Richard J. Pope
Alexandru Rap
Matilda A. Pimlott
Brice Barret
Eric Le Flochmoen
Brian J. Kerridge
Richard Siddans
Barry G. Latter
Lucy J. Ventress
Anne Boynard
Christian Retscher
Wuhu Feng
Richard Rigby
Sandip S. Dhomse
Catherine Wespes
Martyn P. Chipperfield
Abstract. Using state-of-the-art satellite ozone profile products, and chemical transport model, we provide an updated estimate of the tropospheric ozone radiative effect (TO3RE) and observational constraint on its variability over the decade 2008–2017. Previous studies have shown the short-term (i.e. a few years) globally weighted average TO3RE to be 1.17±0.03 W/m2, while our analysis suggests that the long-term (2008–2017) average TO3RE to be 1.21–1.28 W/m2. Over this decade, the modelled/observational TO3RE linear trends show negligible change (i.e. ±0.1 %/year), so the tropospheric ozone radiative contribution to climate has remained stable with time. Two model sensitivity experiments fixing emissions and meteorology to one year (i.e. start year – 2008) show that ozone precursor emissions (meteorological factors) have had limited (substantial) impacts on the long-term tendency of globally weighted average TO3RE. Here, the meteorological variability in the tropical/sub-tropical upper troposphere is dampening any tendency in TO3RE from other factors (e.g. emissions, atmospheric chemistry).
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Richard J. Pope et al.
Status: open (until 20 Oct 2023)
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RC1: 'Comment on egusphere-2023-1513', William Collins, 20 Sep 2023
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This study quantifies the tropospheric ozone radiative effect (TO3RE) from the IASI instrument of 1.21-1.28 W/2 with negligible trend over the 2008-2017 period. It uses a chemistry transport model to show that the lack of trend is due to compensating effects of increasing ozone precursor emissions and meteorology.
The authors need to make it clearer what the major advances are over previous studies, such as Rap et al. 2015. Is it simply that they have now been able to calculate TO3RE for a longer timespan? Is there a significant advantage of IASI over TES as used by Rap et al.? Would the same timespan for TES give a similar result? Or is the bias correction using sondes the key improvement?
Many of the statements made seem to conflate decadal trends with interannual variability. Since the lack of trend (0%/yr) is due to the compensation of increases due to emissions by decreases due to meteorology, it must be that emissions (0.2%/yr) and meteorology (-0.3% /yr) make comparable magnitude contributions to the decadal trend. However it does seem clear (figure 3) that the year-to-year variability is dominated by meteorology.
Given that the main advance in this study seems to be the long time series available, more explanation is needed for the science reasons behind the trend and variability. For instance, some quantification of the emission trends over this period (i.e. the drivers behind fig 4(e)), and some explanation of the meteorological variability is needed. The text mentions “changes in global circulation”. Do the authors literally mean circulation as in upwards/downwards transport of ozone? Or do they mean more generally changes in meteorological patterns including water vapour and temperature? The dip in the TC-Ems timeseries seems to coincide with the 2015-2016 El-Nino which presumably would affect tropical water vapour and humidity?
I guess the only influence of meteorology included here is on the ozone concentrations. Meteorology will also affect TO3RE through the radiative transfer, particularly through cloud cover and through the surface and atmospheric temperatures. Presumably the Rap radiative kernels are based on a fixed climatology? This should be explained.
As the authors discuss, the TO3RE is very sensitive to the vertical distribution of ozone. So it seems surprising that their headline numbers are derived from the (effectively) single tropospheric point from the satellite retrievals. This contrast with the Rap et al. paper who used the modelled profiles to derive the TO3RE and only used the satellite columns to constrain this. The authors should provide in the supplement examples of the ozone vertical profiles derived from the satellite retrievals and from TOMCAT before and after applying averaging kernels. I couldn’t spot what the TO3RE was from the non-AK TOMCAT data (tables of values are needed). To me it would make most sense to combine the satellite (+sonde) derived kernel-averaged TCO3 with the vertical distribution from TOMCAT to generate a blended ozone dataset.
The TOMCAT+AK values are not useful in their own right, since they throw away vertical information. It needs to be made clearer that these data are only useful for comparison with the satellites, and are not estimates of the “true” TO3RE. This is evident from the strong dependence of TO3RE on the AK. Some explanation needs to be given of why TOMCAT+IMS is so different. This doesn’t seem related to the TCO3.
Numbers need to be presented in tables. It is difficult looking through all the figure panels to find numbers and compare them.
Lines 43-46: The mention of upper troposphere needs to be removed here since all these effects are happening at all altitudes. The longwave effect is most pronounced in the upper troposphere, but it occurs everywhere.
Line 49: “estimate these model TO3RE estimates”. It is not clear what the authors mean here.
Line 59: Suggest to cite AR6 (Forster et al. 2021).
Line 64-65: This needs to be more specific as to what year is used for PI. The model studies used 1850 whereas the IPCC use 1750. This could be updated to IPCC AR6 (Forster et al. 2021, based on Skeie et al. 2020).
Line 74: “TO3RE which is used to derive the TO3RF” is it not obvious how the TO3RE from this study can be used to derive the TO3RF.
Line 169: Does TOMCAT include stratospheric chemistry? If not how is ozone calculated above the tropopause?
Line 175: Hoesy et al. 2018 would be a better reference for the emissions.
Line 177: Be more specific about where the BVOC emissions come from. Presumably UKESM+JULES wasn’t run specifically for this study. Was UKESM+JULES forced by the ERA-interim, or with AMIP SSTs, or free-running CMIP6 historical?
Line 192-194: This sentence isn’t quite correct as I don’t think SOCRATES is used directly at all in this study. Rather the Rap kernels were used.
Line 225: “Appears to have a limited impact …”. This is a strange phrasing for something that is purely a geometric effect. It has a limited impact because the area of a latitude band is zero at the pole. It is little or nothing to do with TO3.
Line 225: “supports this” – again a strange phrasing. If you multiply two numbers by cos(90) you get zero in each case whatever the original numbers were, so this doesn’t support anything.
Line 239-241: This doesn’t seem to quite make sense. If NTO3RE in the S. Pacific is similar to other ozone regions then it is not true that “the South Pacific is more effective”. It must be similarly effective.
Line 245-247: The different ozone profiles need to be shown here (or in the supplement) to explain this point.
Line 247-253: This is a long sentence, but doesn’t seem to be complete.
Line 272: Again, getting zero when multiplying by cos(90) isn’t necessarily consistency.
Line 280: But line 239 says the radiative efficiency of the south pacific is similar.
Page 8: Relevant numbers on this page need to be in a table.
Line 318-319: This squashing of the interannual variability by applying the AKs seems worrying. This suggests that a considerable cause of the variability in TO3RE is due to changes in the vertical distribution which is washed out by the averaging.
Line 328: Are all emissions (including biomass burning and BVOCs) fixed?
Line 334-335: The contributions of emissions (0.2%) and meteorology (-0.3%) seem to have comparable effects on the decadal trend. This is different from the year-to-year variability which does seem to be driven more by meteorology.
Line 337: This discussion (and figure 4) needs to be clearer that it is comparing a single year to a decadal average that includes that year. For the meteorology it might have been more instructive to compare 2008 with 2015 (when the dip in TO3RE is strongest). It would be useful to show some meteorological variables (in the supplement) such as q, T, w to see what is changing. Similarly maps of emission changes should be shown in the supplement too so we could see whether fig 4(e) is due to anthropogenic, biomass burning or BVOC changes.
Line 347: How do the authors know this is a circulation effect rather than due to water vapour and temperature changes?
Line 372-373: This comparison with Rap et al. is presented without comment. Do these numbers supersede Rap (because they are better)? Or are they just representative of different years?
Line 378: The emission changes have had a similar effect (0.2% vs -0.3%) to meteorology on the trend.
Line 381: This phrasing of “stabilising the tropospheric ozone contribution” seems to overstate the case. It is not obvious whether the compensation of the meteorology and emissions is a coincidence for this particular time period (that includes an El Nino near the end) or whether it is a more general compensation due to a climate trend.
Citation: https://doi.org/10.5194/egusphere-2023-1513-RC1 -
RC2: 'Comment on egusphere-2023-1513', Anonymous Referee #2, 29 Sep 2023
reply
The manuscript by Pope et al. presents an analysis of the effect of tropospheric ozone on the radiative balance of the Earth over the period 2008 – 2017. The analysis is based on three different estimates of the radiative effect of tropospheric ozone (TO3RE) using observations from the IASI instrument. The analysis finds small and not-statistically significant trends in TO3RE over the period. The analysis is extended using the TOMCAT model to simulate the changes in ozone and TO3RE over the period, with additional sensitivity experiments holding emissions or meteorology constant – using either emissions or repeating meteorology from 2008 for the entire period. The TOMCAT model also produces small trends in TO3RE over 2008 – 2017 and shows that meteorological variability seems to have had a larger effect on TO3RE trends than emissions.
The paper is well written and logically presented and I have no significant concerns with the analysis or the discussion. My one significant concern is the ability of the IASI observations to constrain the vertical distribution of ozone in the troposphere, particularly given the importance of the vertical distribution for the radiative effect. This issue does come up a couple of times through the manuscript. There is some discussion of the limitations of the IASI observations of tropospheric O3 on lines 242 – 253 in reference to differences in the normalized TO3RE across the three different retrievals. There is also discussion of the differences in TO3RE when the IASI kernels are applied to the TOMCAT model, producing larger values of average TO3RE and, importantly, a marked reduction in the interannual variability as compared to directly using the original ozone distribution of TOMCAT over lines 313-326. The effects of the IASI kernel on the estimates of TO3RE are clearly shown in the figures and discussed where appropriate, but there is no dedicated discussion of the effect, which must surely be well known in the tropospheric ozone satellite observation community. I feel the manuscript would be improved if an overview of the limitations of the IASI observations was presented as part of the introductory material, perhaps as part of Section 2.4 or its own section before the results are presented.
I would also suggest some caution in the presentation of the effect of meteorological variability on trends. As stated in the abstract, ‘the meteorological variability in the tropical/sub-tropical upper troposphere is dampening any tendency in TO3RE from other factors (e.g. emissions, atmospheric chemistry).’ I will note that large differences between the TOMCAT control simulation and the run with repeating meteorology as shown in Figure 3, are only really apparent in the 2014, 2015, 2016 period. At least 2015 – 2016 are years with an exceptionally strong El Nino and since this period falls towards the end of the 2008 – 2017 analysis period it has a significant effect on trends. It is not a significant objection, and the authors do state that the effects of meteorological variability have a strong effect on the trends for the period analysed, but I would urge the authors to be careful about leaving any impression of larger significance to the finding of meteorological variability. In particular, at lines 333 – 336 the authors state ‘On the other hand, meteorological factors, while not dramatically altering the absolute simulated TO3RE values, are more important as fixing the meteorology yields a steady and significant increase (0.3%/year). Thus, without year-to-year variability in meteorology, temporal variability in TO3 would likely have a more substantial impact on the present day climate.’ If most of the differences in TO3RE are due to El Nino in 2015-2016 then large effect found for meteorological variability may be very particular to the exact period being analysed.
In addition to these two rather mild concerns about the overall paper, my minor comments are given below.
Minor Comments:
Line 40: the authors state ‘Here, the meteorological variability in the tropical/sub-tropical upper troposphere is dampening any tendency in TO3RE from other factors (e.g. emissions, atmospheric chemistry).’ I am a bit unclear how to interpret this statement. Is it that meteorological variability is adding noise and making trends less statistically insignificant or is meteorological variability producing trends that are opposite to those imposed by emissions and atmospheric chemistry?
Lines 58 – 65: a number of the references in this paragraph seem dated now. While they are not substantially different, why not refer to more current literature for quantities like the pre-industrial to present-day change in O3 radiative forcing, including the 6th IPCC Assessment Report?
Line 73: unnecessary brackets around the year in ‘Bowman et al., (2013)’
Line 98: I think it should be TOMCAT in ‘with the TOMCT CTM’
Lines 158 – 159: ‘Here, we generated annual-latitude (30° bins) bias correction factors (BCF) which were applied to the gridded satellite records (see SI-2) to harmonise the retrieved TCO3’. Do the authors have any reason to believe the biases in the satellite data would be solely a function of latitude? I understand the sampling limitations of the ozonesonde data, which I assume led to the decision to derive broad latitude-dependent bias corrections, but could the authors present some information on the ozonesonde-IASI differences in TCO3 at individual stations to give the reader some idea of how regionally-dependent the biases are? Perhaps a plot of station locations, similar to Figure S1, but coloured according to the magnitude of the bias for the different IASI retrievals?
Lines 320 – 322: It seems that some parts of the sentence are missing: ‘Interestingly, without the application of the AKs, the TOMCAT TO3RE time-series has similar temporal variability (e.g. peaks in 2008, 2010 and 2017 and troughs in 2009 and 2014.’
Lines 344 – 345: I couldn’t help but notice that one pattern in the differences between the control run and the run with fixed meteorology (Figure 4c) looks like a wave-1 pattern across the tropics with strong positive differences centered over the Pacific and negatives values over the Atlantic and equatorial Africa. A bit of speculation, but I wonder if the pattern of the differences in the tropics is related to the effects of El Nino which would have been an important effect in the 2015 – 2016 period when the largest differences are seen between the control and constant meteorology simulations.Citation: https://doi.org/10.5194/egusphere-2023-1513-RC2
Richard J. Pope et al.
Richard J. Pope et al.
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