the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Can pollen affect precipitation?
Abstract. Large primary bioparticles such as pollen can be abundant in the atmosphere, for example near surface pollen concentrations above 10 000 particles per cubic meter can occur during intense pollination periods. On one hand, due to their large size (10–100 micrometres), pollens can act as giant cloud condensation nuclei and enhance the collision-coalescence process in clouds that leads to drizzle formation. On the other hand, in humid conditions pollens are known to rupture and release many fine particles that can increase the cloud stability by reducing the droplet size. Additionally, both whole pollen grains and the sub-pollen particles released by pollen rupture are known to act as ice-nucleating particles (INPs). Due to these complex interactions, the role of pollen in modulating the cloud cover and precipitation remains uncertain.
We used the UCLALES-SALSA large eddy simulator for simulating birch pollen effects on liquid and mixed-phase clouds. Our simulations show that the pollen concentrations observed during the most intense pollination seasons can locally enhance precipitation from both liquid and mixed phase clouds, while more commonly encountered pollen concentrations are unlikely to cause a noticeable change. The liquid precipitation enhancement depended linearly on the emitted pollen flux in both liquid and mixed phase clouds, however, the slope of this relationship was case dependent. Ice nucleation happened at relevant degree only if the process of rupturing pollens producing large number of fine ice nucleating particles was included in the simulations. The resulting precipitation saturated for the highest INP concentrations. Secondary ice formation by rime splintering had only minor effect in the considered one-day timescale.
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RC1: 'Comment on egusphere-2024-876', Anonymous Referee #1, 06 May 2024
Summary
The study investigates the impacts of pollen and sub pollen particles (SPPs) on liquid cloud and mixed-phase cloud using a high-resolution large eddy model. The study simulates two cases for 24 hours each and investigates two pollen species: birch and pine. It examines the impact of varying pollen concentration, pollen rupture properties, and the presence or absence of secondary ice production with a series of sensitivity tests. The results indicate that whole pollen grains increase liquid precipitation, while SPPs reduce total precipitation when acting as CCN. However, SPPs increase ice nucleation in mixed-phase cloud as INPs, which increases precipitation and counteracts their impacts as CCNs. Overall, this study contributes to the current field by comparing the different roles of pollen and SPPs on both liquid and mixed-phase clouds. The advantage of this study is that it uses high resolution model simulation, which benefits pollen–cloud interaction simulations. The paper is suitable for publication after addressing several issues detailed below, including several details about the simulations and minor comments about the figures to improve clarity.
Major Comments:
- The temperature range for the mixed-phase cloud used in this study is from -7 to -3 degrees Celsius, which is much higher than the temperature range of experiments in Augustin et al., 2013 (-23 to -19 degrees Celsius). The heterogeneous ice nucleation rate from Augustin et al. (2013)’s scheme decreases exponentially at warmer temperatures. Thus, the observed small effects of pollen on cloud ice in this study could be because of the relatively warmer temperatures in the simulation. Given that some studies, including Augustin et al. (2013), suggest pollen/SPP ice nucleation does not occur until lower than -10 degrees Celsius (e.g., Gute & Abbatt 2020; Matthews er al., 2023), the select cases in this study may be too warm to investigate the impact of pollen or sub-pollen particles (SPPs) as ice nucleating particles (INPs). The authors should discuss this potential limitation further in the discussion section.
- The authors should include details about the modeling of pollen ice nucleation. It is mentioned that one SPP has one active ice site. Does this mean that regardless of the T04 or B21 size distribution, their ice nucleation activity will be the same since both distributions have the same rupture rate? If so, does the observed difference between the two experiences mainly resulting from SPP’s varying impacts as CCNs? Additionally, how is the ice nucleation activity for whole pollen grains modeled? How many ice active sites does each pollen grain have? After rupture, will their ice active sites change?
- For the first case, Figures 2 and 4-7 show the model domain average. Given the relatively small cloud fraction over the model domain (shown in Figure 3), I would suggest to show the impacts in these figures averaged over a subset of the model domain with high cloud liquid water path. This would provide a more accurate picture of how pollen is influencing the cloud microphysical processes. (This seems less important for the second ice-cloud case, as the authors state that cloud cover is 100% in this case, line 269).
- The authors make a minor mention of “background aerosol” on line 244, but this should be explained in the general simulation description. Do the “no emissions” simulations include background aerosol, and if so, what is the value used?
- In general, the discussion of the model simulation results is rather sparse – some more discussion would be helpful. Specifically, more description of the process figures is needed (Figure 5-6 for the first case and 12-13 for the second case) – if there is no discussion to accompany some of the panels, maybe they number of panels should be reduced.
Minor comments on text:
- Better description of the LES simulations is needed (including acronyms). The table is not helpful and it is hard to connect the simulations to later figures (Figure 2 and onwards)
- Line 55-58, the study by Zhang et al., 2024 did not use the long-time or large-scale averages.
- What percentage of whole pollen grains rupture to produce SPPs during the simulation?
- Line 124 – state where/what time of year the RICO campaign is
- Line 163 – “significant” – was this based on statistical significance testing?
- Line 185-6: “The effect is larger in the case of the B21 parameterization…” – I don’t see this in Figure … can you clarify?
- Line 248: “As seen from Figures 9-11…” – it’s very difficult for a reader to assess this conclusion without the description of the individual figures. I would suggest to discuss the figures one by one before making such a statement
- Line 289: “… the fraction of them reaching the ground is much larger than those with smaller core particles.” How can the authors make this conclusion about the size from the figure (which isn’t included?). Perhaps I am missing this point here.
- Line 319: “trice” – do the authors mean 3x? The use of “trice” seems like a typo – while technically it is a word it is not in common usage.
- The final section of the manuscript could frame the caveats more clearly in context with the conclusions. As written, it feels like a laundry list of items without a clear path forward on next steps.
Minor comments on tables and figures:
- Table 1 – use superscripts; is the conversion from flux to concentration based on modeled values?
- Figure 3 – What is the value of the white color, as this is not included on the color bar? Include the instantaneous time displayed in panel A in the caption. Additionally, axes aren’t labeled.
- 4a, “blue – no rupture”, do you mean green- no rupture?
- 6a and Fig. 13a, why does the SPP concentration tendency in aerosol phase show much larger values from evaporation/transportation than from rupture?
- Figure 7 – how are the layer fluxes calculated from the model output?
- Figures 3 & 9 – the many lines (solid/dashed + colors) make it very hard to understand this figure and parse through the lengthy caption. This needs to be more clear in the legends. Perhaps including the solid/dashed legend on the individual panels would make this more accessible to the reader.
- Figure 9d: is the ice fraction really 0.01-0.03%? That is extremely low and likely within the model noise. Or do the authors mean 1-3%?
- 13f, why is the concentration of SPP consistent between 200 to 600 m?
References
Gute, E. and Abbatt, J.P., 2020. Ice nucleating behavior of different tree pollen in the immersion mode. Atmospheric environment, 231, p.117488.
Matthews, B.H., Alsante, A.N. and Brooks, S.D., 2023. Pollen emissions of subpollen particles and ice nucleating particles. ACS Earth and Space Chemistry, 7(6), pp.1207-1218.
Citation: https://doi.org/10.5194/egusphere-2024-876-RC1 -
RC2: 'Comment on egusphere-2024-876', Anonymous Referee #2, 17 Jul 2024
1.The INP from pollen that is formed in Mixed-phase condition case is between 0 to -6 C while the parameterization that is used for pollen INP calculation (Augustin et al., 2013) is for temperatures below -17C
2. There is no discussion about other types of aerosols that contribute to cloud formation and affect precipitation rates. (dust )
3. The size of the pollen can be improved using a more recent approximation from Hoose et 2010.
4. There is no discussion about the simulated updraft velocity which controls all meteorological variables .
5. It is not clear if pollen is emitted insoluble and turn over to soluble. However they set the hygroscopicity parameter to 0.16 for both SPPs and whole pollens.
``` as particles with diameters in the range of tens of micrometres activate easily as cloud droplets as long as they are not hydrophobic, this approximation should have limited impact.``` this is contrary with model's results that found SSPs and pollen above cloud top. Fig 12-13Line 40: After INPs add a reference
Line 118: check Celsius acronymLine 160: theme font is not everywhere the same
Line 267: ‘’ (2500 pollen/m2/s), no rupture’’ to ‘’ ’ (2500 pollen/m2/s) and no rupture’’
Line 273: Figure 11, A not right
Figure 5, ABCD should be capital letters
Figure 5 d should be deleted. the sum gives the budget ?
Figure 8 y axis has no units
Citation: https://doi.org/10.5194/egusphere-2024-876-RC2
Data sets
UCLALES-SALSA model output of the experiments presented in the manuscript Marje Prank, Juha Tonttila, Xiaoxia Shang, Sami Romakkaniemi, and Tomi Raatikainen https://doi.org/10.57707/fmi-b2share.5b37722cc31d4b8c9edfeca6a8dd88f6
Model code and software
UCLALES-SALSA model code used for the presented simulations Marje Prank, Juha Tonttila, Xiaoxia Shang, Sami Romakkaniemi, and Tomi Raatikainen https://doi.org/10.57707/fmi-b2share.5b37722cc31d4b8c9edfeca6a8dd88f6
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