the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Aug 2023
Direct Observational Evidence from Space of the Effect of CO2 Increase on Longwave Spectral Radiances: The Unique Role of High Spectral Resolution Measurements
Abstract. We present a direct measurement of the impact of increased atmospheric CO2 on the spectra of Earth's longwave radiation obtained from space. The goal of this study is to experimentally confirm that the direct effects of CO2 increase on the Earth’s outgoing longwave spectra follow theoretical estimates, by developing a methodology that allows for a direct and more precise comparison between theory and observations. In this methodology, a search is performed to find selected ensembles of observed atmospheric vertical profiles of temperature and water vapor that are as close as possible to each other in terms of their values. By analysing the spectral radiances measured from space by the Atmospheric Infrared Sounder (AIRS), corresponding to the selected ensembles of profiles, the effects of increased CO2 on the spectra can be isolated from the temperature and water vapor effects. The results illustrate the impact of the increase of CO2 on the longwave spectra and compare well with theoretical estimates. As far as the authors are aware, this is the first time that the spectral signature of the increase of CO2 (isolated from temperature and water vapor changes) has been directly observed from space.
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RC1: 'Comment on egusphere-2023-924', Anonymous Referee #1, 12 Sep 2023
While past studies have evaluated and interpreted the effects of CO2 on IR spectral changes from observations, none have done so from a purely observational standpoint. Instead, the past studies have relied on modeling or theoretical interpretation to separate the direct effects of CO2 change from the effects of temperature and water vapor changes that also occur at the relevant CO2 abortion bands. This manuscript represents the first successful attempt to perform that isolation solely using observations. To do so, the authors search for profiles over different years with significantly different CO2 concentrations, but with very similar T and WV profiles. They then quantify the difference between the corresponding spectral radiances (between a reference year and more recent year) to demonstrate that the expected isolated effect of CO2 to reduce OLR is evident in the AIRS observations over the tropospheric CO2 absorption band evaluated in this study. The authors also perform radiative transfer calculations solely with changes in CO2 concentration, as further support that they are truly isolating the effects of CO2 in their observational estimates. Their work will certainly be of interest to ACP Letters readers and marks an important milestone in observing the effects of CO2 on the climate. I provide some minor comments below that will hopefully help improve the manuscript.
To identify analogous profiles, the authors use RMS difference thresholds of 1.2 K for temperature and 1.2 g/kg for water vapor to identify analogues to the reference profiles and thresholds of 1.4 K and g/kg for Experiment B. Some evidence should be provided that those thresholds do indeed, represent sufficiently small radiative effects from T and WV changes. One could suspect a 1.2 K temperature change, even if just locally, could have a significant radiative response relative to the influence of CO2 (for instance, thinking in the context of a climate radiative feedback). One option is to run both the reference and analogue profiles through kCarta, with the same CO2 concentrations, and show the radiative effects from any T and WV are small compared to the direct CO2 effects
Line 70-73: The authors should explain why it is important to stay as close to nadir as possible. Although they explain in the appendix that doing so leads to smaller biases relative to the theoretical calculations, it would be helpful to mention why that is the case.
It’s not clear why the authors chose to publish both experiment A and B. Experiment A seems like a light test of the methodology before performing the more robust Experiment B. I can understand performing A while putting this study together, but its not clear why the authors have chosen to feature the results of A so prominently in the manuscript (and have given it a figure). I suspect the authors have good reason for doing so, but it does not come across clearly in the text. I worry someone who skims this Letter won’t realize Figure 2 is the more robust, important figure.
For Figure 1, experiment A, the observed radiance difference has a clear negative bias relative to theoretical for both experiments. The authors should explore the source of this bias further. They correctly mention that the bias increases towards the higher wavenumbers where H2O is a stronger absorber. Does this suggest the 2006-2015 analogue profiles have systematically more WV than the 2005 references (albeit still within the threshold)? And that this could be leading to the systematic bias in the difference calculation? One can imagine that due to the small sample size, this could be possible.
Line 223-225: It is not clear why the authors are using just three CO2 concentrations for three different years and then using a curve fit to identify the corresponding spectral radiances for years in between. Doesn’t the Maona Loa data have CO2 concentrations for all months and years within the studied timeframe? Some clarification would be helpful.
I view this work as an important proof of concept that AIRS is able to detect the influence of CO2 on radiances in isolation. That alone, is worthy of publication. I wonder if this methodology can be applied longer-term to isolate and track trends in how CO2 is influencing the climate (e.g. in the context of radiative forcing). Using analogues with similar T and WV would seem to be the only way to isolate CO2 effects purely from observations, but radiative forcing itself is sensitive to the underlying climate state (e.g. Y. Huang et al. 2016). So on one hand, by trying to keep T and WV fixed, the method is not capturing the true direct effects of CO2 on the climate. Additionally, one could imagine that, if this method is applied over a wider range of years, thus covering more climate change, it would become more difficult over time to find analogues within a reasonably small threshold and T and WV undergoes more changes. For the sake of appealing to a broader audience, I encourage to authors to add some discussion along these lines, about the broader implications of their work. Maybe in their conclusion section.
Huang, Y., Tan, X., and Xia, Y. (2016), Inhomogeneous radiative forcing of homogeneous greenhouse gases, J. Geophys. Res. Atmos., 121, 2780–2789, doi:10.1002/2015JD024569.
Citation: https://doi.org/10.5194/egusphere-2023-924-RC1 -
AC1: 'Reply on RC1', Joao Teixeira, 06 Dec 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-924/egusphere-2023-924-AC1-supplement.pdf
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AC1: 'Reply on RC1', Joao Teixeira, 06 Dec 2023
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RC2: 'Comment on egusphere-2023-924', Anonymous Referee #2, 04 Oct 2023
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AC2: 'Reply on RC2', Joao Teixeira, 06 Dec 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-924/egusphere-2023-924-AC2-supplement.pdf
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AC2: 'Reply on RC2', Joao Teixeira, 06 Dec 2023
Status: closed
-
RC1: 'Comment on egusphere-2023-924', Anonymous Referee #1, 12 Sep 2023
While past studies have evaluated and interpreted the effects of CO2 on IR spectral changes from observations, none have done so from a purely observational standpoint. Instead, the past studies have relied on modeling or theoretical interpretation to separate the direct effects of CO2 change from the effects of temperature and water vapor changes that also occur at the relevant CO2 abortion bands. This manuscript represents the first successful attempt to perform that isolation solely using observations. To do so, the authors search for profiles over different years with significantly different CO2 concentrations, but with very similar T and WV profiles. They then quantify the difference between the corresponding spectral radiances (between a reference year and more recent year) to demonstrate that the expected isolated effect of CO2 to reduce OLR is evident in the AIRS observations over the tropospheric CO2 absorption band evaluated in this study. The authors also perform radiative transfer calculations solely with changes in CO2 concentration, as further support that they are truly isolating the effects of CO2 in their observational estimates. Their work will certainly be of interest to ACP Letters readers and marks an important milestone in observing the effects of CO2 on the climate. I provide some minor comments below that will hopefully help improve the manuscript.
To identify analogous profiles, the authors use RMS difference thresholds of 1.2 K for temperature and 1.2 g/kg for water vapor to identify analogues to the reference profiles and thresholds of 1.4 K and g/kg for Experiment B. Some evidence should be provided that those thresholds do indeed, represent sufficiently small radiative effects from T and WV changes. One could suspect a 1.2 K temperature change, even if just locally, could have a significant radiative response relative to the influence of CO2 (for instance, thinking in the context of a climate radiative feedback). One option is to run both the reference and analogue profiles through kCarta, with the same CO2 concentrations, and show the radiative effects from any T and WV are small compared to the direct CO2 effects
Line 70-73: The authors should explain why it is important to stay as close to nadir as possible. Although they explain in the appendix that doing so leads to smaller biases relative to the theoretical calculations, it would be helpful to mention why that is the case.
It’s not clear why the authors chose to publish both experiment A and B. Experiment A seems like a light test of the methodology before performing the more robust Experiment B. I can understand performing A while putting this study together, but its not clear why the authors have chosen to feature the results of A so prominently in the manuscript (and have given it a figure). I suspect the authors have good reason for doing so, but it does not come across clearly in the text. I worry someone who skims this Letter won’t realize Figure 2 is the more robust, important figure.
For Figure 1, experiment A, the observed radiance difference has a clear negative bias relative to theoretical for both experiments. The authors should explore the source of this bias further. They correctly mention that the bias increases towards the higher wavenumbers where H2O is a stronger absorber. Does this suggest the 2006-2015 analogue profiles have systematically more WV than the 2005 references (albeit still within the threshold)? And that this could be leading to the systematic bias in the difference calculation? One can imagine that due to the small sample size, this could be possible.
Line 223-225: It is not clear why the authors are using just three CO2 concentrations for three different years and then using a curve fit to identify the corresponding spectral radiances for years in between. Doesn’t the Maona Loa data have CO2 concentrations for all months and years within the studied timeframe? Some clarification would be helpful.
I view this work as an important proof of concept that AIRS is able to detect the influence of CO2 on radiances in isolation. That alone, is worthy of publication. I wonder if this methodology can be applied longer-term to isolate and track trends in how CO2 is influencing the climate (e.g. in the context of radiative forcing). Using analogues with similar T and WV would seem to be the only way to isolate CO2 effects purely from observations, but radiative forcing itself is sensitive to the underlying climate state (e.g. Y. Huang et al. 2016). So on one hand, by trying to keep T and WV fixed, the method is not capturing the true direct effects of CO2 on the climate. Additionally, one could imagine that, if this method is applied over a wider range of years, thus covering more climate change, it would become more difficult over time to find analogues within a reasonably small threshold and T and WV undergoes more changes. For the sake of appealing to a broader audience, I encourage to authors to add some discussion along these lines, about the broader implications of their work. Maybe in their conclusion section.
Huang, Y., Tan, X., and Xia, Y. (2016), Inhomogeneous radiative forcing of homogeneous greenhouse gases, J. Geophys. Res. Atmos., 121, 2780–2789, doi:10.1002/2015JD024569.
Citation: https://doi.org/10.5194/egusphere-2023-924-RC1 -
AC1: 'Reply on RC1', Joao Teixeira, 06 Dec 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-924/egusphere-2023-924-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Joao Teixeira, 06 Dec 2023
-
RC2: 'Comment on egusphere-2023-924', Anonymous Referee #2, 04 Oct 2023
-
AC2: 'Reply on RC2', Joao Teixeira, 06 Dec 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-924/egusphere-2023-924-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Joao Teixeira, 06 Dec 2023
Data sets
AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V005 AIRS Project https://doi.org/10.5067/YZEXEVN4JGGJ
AIRS/Aqua L2 Standard Physical Retrieval (AIRS+AMSU) V006 AIRS Science Team https://doi.org/10.5067/Aqua/AIRS/DATA201
Model code and software
kCARTA L. Strow, S. DeSouza-Machado, H. Motteler and S. Hannon https://github.com/sergio66/kcarta_gen
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