the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Aug 2024
Causal Analysis of Aerosol Impacts on Isolated Deep Convection: Findings from TRACER
Abstract. This study employs a novel application of causal machine learning, specifically g-computation, to quantify aerosol effects on deep convective clouds (DCCs). Focusing on isolated DCCs in the Houston-Galveston region, we leverage comprehensive ground-based observations from the TRacking Aerosol Convection interactions ExpeRiment (TRACER) to estimate aerosol influences on convective core depth, intensity, and area. Our results reveal that greater aerosol number concentrations generally have a limited impact on convective core echo top height (ETH), with an increase of about 1 km (13 % of average ETH). This effect is observed under specific conditions, particularly when ultrafine particles are activated in updraft regions. Additionally, greater aerosol levels correspond to increased convective core intensity and area, though these changes remain within radar measurement uncertainties. In DCCs associated with sea breezes, aerosol effects are more pronounced, resulting in a 1.4 km deepening of ETH. However, this heightened effect could be attributed to the exclusion of key confounders such as boundary layer updrafts in the causal model. This study pioneers the application of causal machine learning to explore aerosol-convection interactions, shedding light on unraveling complex interplay between aerosols and meteorological variables.
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RC1: 'Comment on egusphere-2024-2436', Toshi Matsui, 30 Aug 2024
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Summary.
This study used novel causal machine learning techniques to statistically quantify the impact of aerosols on tracked isolated deep convection observed by the NEXRAD. The authors argue that the new machine-learning technique can separate the various meteorological parameters to isolate the relationship between aerosols and deep convection. The results indicate that increases in background aerosols are associated with a 1.4km deepening of the echo-top height of deep convection. Overall, this paper is well-written, and the approach seems appropriate. However, I suggest some modifications in writing and explanation, which can improve the readability. These are described in the major comments, but not so critical. Hence, my recommendation is a "minor revision".
Major Comments.
1) Readability: The new statistical approach is meticulously written, but it is often hard to read due to various statistical jargon that is unfamiliar to atmospheric scientists like me. Can you clearly define these terms at the beginning? For example, define like this in the table.
Confounder: a variable that affects both the dependent and independent variables in a study, causing an association that may not be accurate. (parameters include ….)
Exposures: Any factor that may be associated with an outcome of interest. (parameters include ….)
Probably these terms are common in epidemiology, but not in atmospheric science.
2) New and traditional approach: At the end of the manuscript, authors mentioned quite significant statements “Nevertheless, this study pioneers the use……….. scientistifc questions”. To be honest, I still wonder why this new method is so novel compared to the previous old approach because there’s no comparison between the new and traditional statistical approaches. For example, here is one of the earliest aerosol-deep convection manuscript.
Lin, J. C., Matsui, T., Pielke, R. A., & Kummerow, C. (2006). Effects of biomass-burning-derived aerosols on precipitation and clouds in the Amazon Basin: A satellite-based empirical study. Journal of Geophysical Research: Atmospheres, 111(D19). https://doi.org/10.1029/2005JD006884
In this paper, DCC properties (precipitation, cloud top height, and cloud fraction) are related to aerosol optical depth for a given meteorological parameter (cloud work function in that study). Can you compare your novel approach with this traditional approach (simple statistics stratified by meteorological parameters)? Do you think the old approach leads to significant biases in understanding the aerosol-DCC relationship? Can you prove or briefly explain?
3) Potential biases in radar-based approach: Authors use threshold NEXRAD radar parameters to define DCC. However, if DCC has a much smaller amount of raindrops due to a large number of background aerosols, this cell may not be counted as DCC due to larger concentrations of small-size droplets, which won’t increase S-band reflectivity. Alternatively, if you use cloud optical depths and top height, the DCC sampling can include such cells. This is a NEXRAD-based cell tracking approach, so you cannot change your approach. However, it is important to discuss potential sampling biases using the NEXRAD radar.
Minor Comments.
Line 87: Please remove parenthesis “(either invi….. )”.
Line 120-121: “exclude the presence of shallow convection” sounds like removing the sampling during shallow stages. So I suggest just re-write as “exclude the shallow convection cells”.
Line 179: Please define the threshold of diameters of “ultrafine aerosols”.
Line 274: “buoyancy-driven DCCs”. Well, all DCCs are driven by the buoyancy over the flat terrain. So you may re-write this as “locally driven DCCs”.
Line 294: “30-dBZ ETH/15-dBZ ETH” should be “30-dBZ ETH and15-dBZ ETH”.
Line 303-306: We won’t be able to measure supersaturation directly within the convective storms. however, you can infer the required supersaturation in order to active all aerosols (including ultrafine). For this case, can you describe roughly how much supersaturation is required to support your argument?
Fig. 4: Why is there no correlation between thermodynamics and Nccn? It seems to be more important?
Line 547: “30-dBZ ETH/15-dBZ ETH is 1.1 km/1.0 km,” should be “30-dBZ ETH and15-dBZ ETH is 1.1 km and 1.0 km, respectively.”
Citation: https://doi.org/10.5194/egusphere-2024-2436-RC1
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