the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Sep 2024
Detectability of forced trends in stratospheric ozone
Abstract. The continued monitoring of the ozone layer and its long-term evolution leans on comparative studies of merged satellite records. Such records present unique challenges due to differences in sampling, coverage, and retrieval algorithms between observing platforms, leading to discrepancies in trend calculations. Here we examine the effects of optimal estimation retrieval algorithms on vertically resolved ozone trends, using one merged record as an example. We find errors as large as 1 % per decade and displacements in trend profile features of as much as 6 km altitude due to the vertical redistribution of information by averaging kernels. Furthermore, we show that averaging kernels tend to increase the length of record needed to determine whether vertically resolved trend estimates are distinguishable from natural variability with good statistical confidence. We conclude that trend uncertainties may be underestimated, in part because averaging kernels misrepresent decadal to multi-decadal internal variability, and in part because the removal of known modes of variability from the observed record can yield residual errors. The study provides a framework to reconcile differences between observing platforms, and highlights the need for caution when using merged satellite records to quantify trends and their uncertainties.
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RC1: 'Comment on egusphere-2024-2627', Anonymous Referee #1, 14 Sep 2024
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The manuscript uses ozone time series from the SBUV satellites along with model simulations from the Earth System Chemistry Climate Model (ESM) and the Chemistry Climate Model Intercomparison (CCMI) to investigate long-term trends in ozone and compare them with ozone variability from the model simulations. There are two major results: 1) wide averaging kernels of observations like SBUV mix information from different vertical levels. This can shift and distort vertical trend profiles. 2) Uncertainty estimates are necessary to determine if a trend is significant compared to natural variability. Again, wide averaging kernels combine information from different altitude levels, and this tends to result in underestimated variability. One example are ozone variations associated with the QBO. These are important for trend estimation in the atmosphere, but are reduced and smeared out in SBUV data. This tends to result in errors when accounting for the QBO, and in incorrect uncertainty estimates. Overall this is important information. The paper is well written and deserves publication in ACP.
There are a number of points that should be improved, though:
Abstract and other places in the text: The authors point out a number of problems with merged satellite records (sampling, calibration, instrumental differences, ..). I find this misleading, because the manuscript does not account for any of these "merging" issues. The only issue addressed here, are the SBUV averaging kernels. So I don't think the merging issues should be mentioned in the abstract. The first two sentences should be dropped. In line 4, "one merged" should be replaced by "the SBUV MOD". In line 11 "merged satellite records" should be replaced by "records from instruments with wide averaging kernels".
Line 16/17: "continued recovery" I think this should be "beginning recovery". This also applies to other places where "recovery" is mentioned. We are just at the beginning of ozone recovery. We are far from "recovered" and, as explained in the paper, we are also far from significant recovery in many regions of the atmosphere.
Lines 20,22: delete "lower" and add "in the tropics" after "abundances". The main branch of the Brewer Dobson circulation transports ozone rich air in the mid- and upper stratosphere from the tropics to the extra-tropics. Enhanced upwelling in the tropics is decreasing ozone in the tropical lower stratosphere.
Line 29: suggest to replace "newly detected" by "recent illegal"
Line 30: "increasingly large" is too strong. I would say "possibly increasing"
Line 32: explicitly add "e.g. Hunga Tonga-Hunga Ha'apai in 2022".
Lines 46-47: Reword. You are not adressing merging challenges, you are only adressing the effects of wide averaging kernels.
Figure 1: Please explain why the power density of the ESM4 historical runs in the 2 to 20 year range is lower in the top panel and larger in the bottom panel. I guess it is due to applying the SBUV averaging kernel in the top panel. I think this needs to be said / clarified in caption and text. I looks like the averaging kernels reduce variability. Also change the text in the legend in the top panel e.g. to ESM4 historical@SBUV resolution. It needs to be different from ESM4 historical in the legend of the lower panel.
Line 98: better to say "pre-industrial simulations" instead of "these simulations"
Line 134: I would start a new paragraph after NOAA. It should also be pointed out here that SBUV-MOD and SBUV from NOAA have wide averaging kernels and use the same nadir-viewing satellite data. On the other hand, GOZCARDS, SWOOSH, and the other data sets use LIMB and occultation instruments, which have much finer altitude resolution.
Figure 2: It would be good to have another panel showing the a-priori ozone profil and the two profiles, in addition to the panel shwing the deviation of the two profiles from the a-priori.
Line 230: for clarification, after "70 hpa", add "from the model simulations"? As shown later the model QBO is quite different from the "real" QBO.
Line 233/234: "see NOAA ... June 2024" Again, I am assuming you are using the ENSO of the model simulations, not the "real" ENSO. So, while the NOAA page is a good reference, it is kind of misleading here. Please move, reword / clarify.
Line 275: It would be helpful to explain skewness and kurtosis a bit more here. What you are saying is that the residuals are often not normally distributed, with distributions leaning to the left (skewness greater than 0.5), and distributions that are narrower than a normal distribution (positive excess kurtosis).
Line 294: would be helpful to add "(e.g. the red curve in Fig. 4c)" after "earlier", and " (the black curve in Fig. 4c)" after "itself".
Figure 5: I find it difficult to see much in panel a.) I think it would be better to show here the ratio (standard deviation)/(average values), i.e. the relative standard deviation, e.g. as percent. The overall ozone distribution (average values) will be well known to the readers. The relative standard deviation (or variability) in percent will be much better to compare, e.g. to trends which are also in percent per decade. If the authors don't want to change panel a.) they should add another panel with the relative standard deviation.
Line 314: add "CCMI" before modeled? I assume you are talking about trends from CCMI here.
Line 353: "sampling and retrieval". The way I understand it from section 2.3 you are using SBUV-MOD monthly zonal mean data. I assume you are also using monthly zonal mean data from the model simulations, but without accounting for the specific times and locations of the individual SBUV measurements. Am I correct? Are you dropping polar night data? My guess would be that your model sampling is the same in both hemispheres / polar caps, so "sampling" differences should not play a role here. You only see differences due to the retrieval / averaging kernels, which mixes and redistributes stuff from different altitudes. But, in my understanding, you do not look at sampling differences, i.e. differences due to the specific times and locations of the SBUV observations. So delete "sampling and".
Section 4.3: What you have done is applied averaging kernels and then done trends (avk -> trend). An interesting question to me is whether doing trends first and then applying the averaging kernels to the trend profile (trend -> avk) would give the same result. For the mean this should be the case, because both averging kernels and trend derivation are linear operations on the underlying data. Not sure what it means for the uncertainties though.
Figure 7: not sure what the difference between these three panels is. Are you just assuming three different trend profiles? What is the difference between the left panel and the middle panel? Please explain.
Figure 10: Please put a label / title on each of the three panels. Top panel is 1.6 to 1 hPa, middle panel 25 to 16 hPa, bottom panel is total column.
Line 438: replace "the total column" by "some ozone column metrics"?
Line 442: "modelled climate" instead of "climate"
Line 443: "large" How large? Give numbers. Overall, the changes in uncertainty / significance don't seem to be very large for SBUV (maybe 0.1 or 0.2 % per decade for trend uncertainty according to Fig. 9, a few years according to Fig. 10). They should be smaller to negligible for the LIMB satellites which have much better altitude resolution. Also in lines 6 to 8 in the abstract, it would be good to give some numbers.
Line 450, and also discussion of Fig. 7: You might want to refer to Fig. 3-10 of WMO 2022, which shows the latitude altitude distribution of ozone trends from various satellite records. SBUV-MOD is shown in the top left panel of that Figure. You can very clearly see that the peak of upper stratospheric trends is shifted downwards to about 10 hPa in the SBUV-MOD record, and that SBUV-MOD trends are reduced in the 2 to 3 hPa region.
Line 457: "which has been large in recent years". I would say "which can be large". Compared to Pinatubo in 1991, or El-Chichon in 1982/83, most recent volcanic eruptions, even Hunga Tonga, have only had a small influence on stratospheric ozone.
Citation: https://doi.org/10.5194/egusphere-2024-2627-RC1
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