the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On the differences in the vertical distribution of modeled aerosol optical depth over the southeast Atlantic
- 1School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA
- 2NASA Ames Research Center, Moffett Field, California, USA
- 3Bay Area Environmental Research Institute, Moffett Field, California, USA
- 4Cooperative Institute for Climate, Ocean and Ecosystem Studies, University of Washington, Seattle, Washington, USA
- 5Department of Atmospheric Science, University of Washington, Seattle, WA, USA
- 6National Institute for Space Research, São José dos Campos, Brazil
- 7Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA
- 8NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- 9Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California, USA
- 10Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California, USA
- 11Department of Atmospheric and Earth Science, University of Alabama in Huntsville, Huntsville, Alabama, USA
- 12Centre National de Recherches Météorologiques, UMR3589, Météo-France-CNRS, Toulouse, France
- 13Science Systems and Applications, Inc., Greenbelt, Maryland, USA
- 14Institute for Environmental and Climate Research, Jinan University, 510632 Guangzhou, China
- 15Minerva Research Group, Max Planck Institute for Chemistry, 55128 Mainz, Germany
- 16Environmental Science Division, Argonne National Laboratory, Argonne, Illinois, USA
- 17NASA Langley Research Center, Hampton, Virginia, USA
- 18Department of Geophysics, Porter School of the Environment and Earth Sciences, Tel-Aviv University, Israel
- 19Science and Technology Corporation (STC), Moffett Field, CA, USA
- 20School of Natural Sciences, University of California, Merced, Merced, California, USA
- 21Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, Florida, USA
- 22Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama, USA
- anow at: NOAA Chemical Sciences Laboratory (CSL), Boulder, Colorado, USA
- bnow at: Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, USA
- 1School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA
- 2NASA Ames Research Center, Moffett Field, California, USA
- 3Bay Area Environmental Research Institute, Moffett Field, California, USA
- 4Cooperative Institute for Climate, Ocean and Ecosystem Studies, University of Washington, Seattle, Washington, USA
- 5Department of Atmospheric Science, University of Washington, Seattle, WA, USA
- 6National Institute for Space Research, São José dos Campos, Brazil
- 7Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa, USA
- 8NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- 9Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California, USA
- 10Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California, USA
- 11Department of Atmospheric and Earth Science, University of Alabama in Huntsville, Huntsville, Alabama, USA
- 12Centre National de Recherches Météorologiques, UMR3589, Météo-France-CNRS, Toulouse, France
- 13Science Systems and Applications, Inc., Greenbelt, Maryland, USA
- 14Institute for Environmental and Climate Research, Jinan University, 510632 Guangzhou, China
- 15Minerva Research Group, Max Planck Institute for Chemistry, 55128 Mainz, Germany
- 16Environmental Science Division, Argonne National Laboratory, Argonne, Illinois, USA
- 17NASA Langley Research Center, Hampton, Virginia, USA
- 18Department of Geophysics, Porter School of the Environment and Earth Sciences, Tel-Aviv University, Israel
- 19Science and Technology Corporation (STC), Moffett Field, CA, USA
- 20School of Natural Sciences, University of California, Merced, Merced, California, USA
- 21Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, Miami, Florida, USA
- 22Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama, USA
- anow at: NOAA Chemical Sciences Laboratory (CSL), Boulder, Colorado, USA
- bnow at: Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, USA
Abstract. The southeast Atlantic is home to an expansive smoke aerosol plume overlying a large cloud deck for approximately a third of the year. The aerosol plume is mainly attributed to the extensive biomass burning activity that occurs in southern Africa. Current Earth system models (ESMs) reveal significant differences in their estimates of regional aerosol radiative effects over this region. Such large differences partially stem from uncertainties in the vertical distribution of aerosols in the troposphere. These uncertainties translate into different aerosol optical depths (AOD) in the planetary boundary layer (PBL) and the free troposphere (FT). This study examines differences of AOD fraction in the FT and AOD differences among ESMs (WRF-CAM5, WRF-FINN, GEOS-Chem, EAM-E3SM, ALADIN, GEOS-FP, and MERRA-2) and aircraft-based measurements from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign. Models frequently define the PBL as the well-mixed surface-based layer, but this definition misses the upper parts of decoupled PBLs, in which most low-level clouds occur. To account for the presence of decoupled boundary layers in the models, the height of maximum vertical gradient of specific humidity profiles from each model is used to define PBL heights. Results indicate that the monthly mean contribution of AOD in the FT to the total-column AOD ranges from 44 % to 74 % in September 2016 and from 54 % to 71 % in August 2017 within the region bounded by 25°S–0° and 15°W–15°E (excluding land) among the ESMs. Using the second-generation High Spectral Resolution Lidar (HSRL-2) to derive an aircraft-based constraint on the AOD and the fractional AOD, we found that WRF-CAM5 produces 40 % less AOD than those from the HSRL-2 measurements, but it performs well at separating AOD fraction between the FT and the PBL. AOD fractions in the FT for GEOS-Chem and EAM-E3SM are, respectively, 10 % and 15 % lower than the AOD fractions from the HSRL-2 and their similarities in the mean AODs are the result of cancellation of high and low AOD biases. GEOS-FP, MERRA-2, and ALADIN produce 24 %–36 % less AOD and tend to misplace more aerosols in the PBL compared to aircraft-based observations. The models generally underestimate AODs for measured AODs that are above 0.8, indicating their limitations at reproducing high AODs. The differences in the absolute AOD, FT AOD, and the vertical apportioning of AOD in different models highlight the need to continue improving the accuracy of modeled AOD distributions. These differences affect the sign and magnitude of the net aerosol radiative forcing, especially when aerosols are in contact with clouds.
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Ian Chang et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2022-496', Anonymous Referee #1, 29 Nov 2022
General comments
This is the first review of the article by Chang et al. “On the differences in the vertical distribution of modeled aerosol optical depth over the southeast Atlantic”. A comparative study of aerosol optical thickness calculated by seven models and measured by various in-situ (airborne) instrumentation is documented in the manuscript.
The manuscript is well written and lets you read it well. So apart from a few stylistic details or minor technical corrections, the exposition is adequate for publication.
Scientifically, the chosen statistical tools are also adequate, although I personally would prefer to read predictive statements about the significance of the differences found between models and measurements, even in the presence of a relatively limited statistical sample, given the time window analyzed. I also think that much of the statistical information in the figures should be reproduced in a separate table for easier reference.
Second, another remark is the role MODIS ACAOD plays in the storytelling: although a separate section is devoted to it in the text, too little consideration is given to this dataset. This is from the abstract, in which MODIS is not even mentioned, throughout the conclusions. The MODIS dataset, which is a nice addition to the suite, should be discussed more because it carries valuable information.
Third (and most important remark so far).
The manuscript appears to be more of an account of a technical project than a scientific article. As it seems to me, this paper goes so far as it does in squeezing out information from the results presented so far. But a paper comes alive not by documenting a particular accomplishments, but by making connections with previous research. If this is not done, then also the interpretation of the results will be less informative.
While the differences between the models and measurements are clearly reported, I found no convincing attempt to go nor to the source of these differences neither to scientifically pinpoint the outcome of these differences. All the more so when the authors themselves in the text indicate possible causes of discrepancy (i.e. PBL calculations, emission inventories and others) and also the possible consequences of these discrepancies (direct, semi-direct, indirect aerosol effects). As such the analysis feels shallow and much more could be inferred from the datasets at hands.
I believe is a reasonable task for such a comprehensive list of co-authors with their respective expertises.Although I believe it is a choice of the authors with what content to populate a scientific article, I would appreciate more effort regarding two issues: (i) going as far as possible - and reasonably - to the source of AOD differences from models (e.g., looking at emission inventories and the treatment of air mass evolution with aerosols); (ii) identification - through measurements or other sources - of those instances where entrainment occurs, that is how much of the differences can be explained by suboptimal representation of aerosols AND clouds togehter? Do the lidar profiles provide additional information on the aerosol and cloud phases (along the vertical) that might be of interest to the ESMs?
I want to be even clearer on this point: I do not expect the authors to dissect the source code of ESMs and analyze the respective parameterizations. Just as I do not expect them to quantify changes in radiative forcing depending on whether aerosols are detached, in contact, or inside clouds. This would be a topic for a separate study. What I would expect is an analysis - at least a qualitative one, based on both the abundant existing literatures and original reasoning - of the two aspects mentioned above.
And this should then be reported in a section called "Discussion", right before "Summary and conclusions" (which reads just as a repetition of the abstract).
I could request "minor revisions" and the paper might even be fine as it is, although scientifically thin. But I am requesting "major revisions" just to make sure that my comments are considered to augment the interpretation and to warrant the full scientific exploitation of the results obtained so far.
Specific comments
P2 L71: the authors may want to be more precise with the reference about cloud lifetime and add the standard Albrecht's study (https://www.science.org/doi/10.1126/science.245.4923.1227)
P8 L267-269: is it really true that varying the grid cell size affects the standard deviation only? How do you know this is a fact? I would expect that also the mean AOD will be affected. More precisely, finer cells have higher means than coarser cells. As a consequence, this will impact the comparison and the vertical partition of aerosol loading.
P12 L-378-380 (and P7 L225-226): How can MODIS ACAOD report higher AODs while the reported mean biases are negative?
P13 L 417-418: "A deeper analysis of biases in model processes than is possible through the AOD comparisons presented here is essential in order to understand the cause of model biases."
First, check the wording (than). Second, I understand that the results presented here are a first-order assessment of model performances against in-situ and spaceborne observations. As such, the authors suggest the examination of those assumptions and paramterizations leading to the found biases. While the statistics and the presentation of the found biases are sufficiently clear and exhaustive, I miss then the takeaway. Isn't an author's task to reasonably pinpoint the error sources instead of leaving the question open?P14 L 444-458: The short discussion about the nature of aerosol radiative forcing, while correct, feels premature or feels like a natural conclusion of the study for others to be answered.
Except for the wording that could be stylistically improved ("In conditions where ... play roles". Seemingly redundant), I am left with the question of where and how the authors ever touched in their main text upon the presence of layered clouds, the entrainment of aerosols, and ensuing change of thermodynamic phase and change in extinction profile. -
RC2: 'Comment on acp-2022-496', Anonymous Referee #2, 13 Jan 2023
This well written manuscript does a thorough evaluation of the ability of five ESMs to represent the partitioning of the Aerosol Optical Depth (AOD) between the free troposphere and the marine bondary layer over the southeast Atlantic. It takes advantage of the instruments that were deployed to measure aerosol properties during the two campaigns ORACLES 2016 and ORACLES 2017.
The description of both the models under study and of the instruments used to retrieve the AODs at different heights is well conducted but it leaves the reader wandering about what we have learned about the processes that explaining the discrepancies between model and observations that are described in the conclusions. A complete study requires to consider what are the sources of uncertainties in these models and how they differ from one another both in how they parameterize their processes andalso on how they consider the different source inventories for fires that emit these aerosols.A much stronger paperwould emerge if the authors took up the (difficult) task to explain why models either underestimate either overestimate the AODs and what processes the modelers should focus on to improve on the results. This extra work will make up for a much better paper.
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EC1: 'Comment on acp-2022-496', Yves Balkanski, 17 Jan 2023
I suggest significant revisions to this manuscript as both reviewers agree that as it stands this this manuscript does not explain the reason for the different AODs between the different Earth System Models and relate them to their representation of aerosols. Similarly, the authors should discuss how the the different emission inventories could explain part of these differences in aerosol vertical distribution when compared to either HSLR-2 or 4STAR.
To have a strong paper the cause for differences in the vertical distributions should be addressed by explaning both how differences in the way models treat transport and deposition cause differences in the simulated vertical distributions.
The author should focus on analysing how these models differ, and advance their views on how such different parametrisations affect the results shown.
It would also be of interest to the reader to visualize the scatter plot of the free troposphere AODs from HSLR-2 versus the ones from 4STAR.Yves Balkanski
Ian Chang et al.
Ian Chang et al.
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