Work Description

Date: 17 January, 2024

Dataset Title: Effects of pollen on hydrometeors and precipitation in a convective system

Dataset Creators: Y. Zhang, A. Steiner

Dataset Contact: Yingxiao Zhang yingxz@umich.edu

Funding: This work is supported by National Science Foundation grant AGS-1821173 to ALS and NASA FINESST grant 80NSSC22K1434 to ALS and YZ

Key Points:
- We compared the impacts of pollen and ruptured Sub pollen particles (SPPs) on cloud microphysics.
- SPPs enhance cloud microphysical processes more than intact pollen grains.
- SPPs influence the spatial extent and vertical structure of convective systems.
- Simulated SPP impacts are intensified when incorporating laboratory measured pollen rupture rates.

Research Overview:
Anemophilous (wind-driven) pollen is one type of primary biological aerosol particle (PBAP), which can rupture under high humidity conditions and form smaller sub-pollen particles (SPPs). Both pollen and SPPs can reach the upper troposphere under convective conditions, acting as cloud condensation nuclei (CCN) and ice nucleating particles (INPs), thus influencing cloud formation and precipitation. However, the impacts of these biological aerosols on cold cloud formation and local climate remain unclear as there are large uncertainties on their emission flux and ice nucleating abilities. Here, we incorporate pollen emission and rupture processes in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) simulations and update the Morrison microphysics scheme within WRF-Chem using aerosol-aware INP parameterizations to account for pollen in addition to other anthropogenic and biogenic aerosol. INP parameterizations for pollen and SPP are derived from laboratory experiments. When including pollen rupture rates as observed in a series of chamber studies, SPP concentrations increase, leading to an increase of cloud ice and water by up to 50% and potentially extending the duration of the convective system. Among all simulated hydrometeors, graupel and raindrops exhibit the largest enhancements from the inclusion of SPPs, with intensifying precipitation at the backside of the convective system and a greater spatial extent. Sensitivity simulations indicate that SPPs have a greater effect on cloud microphysical processes than whole pollen grains, and further observational evidence is needed to constrain these processes.

Methodology:
In this work, we present new simulations of primary biological aerosol particles and their interaction with clouds using pollen-WRF-Chem coupled modeling framework, including comprehensive pollen emissions (PECMv2.0 ; Wozniak & Steiner, 2017; Y. Zhang & Steiner, 2022) and the transport, rupture, and fate of pollen in the atmosphere (Subba et al., 2023). We also update the Morrison two-moment bulk scheme inside WRF-Chem to include aerosol-aware INP parameterizations, including ice nucleation from anthropogenic and natural aerosol (e.g., dust, soot, sea salt, sulfate) as well as pollen and SPP, where the INP parameterizations for pollen and SPP are obtained from laboratory experiments (Matthews et al., 2023).

The WRF-Chem model simulation domain is centered on the US SGP region bounded by 33.4-39.6ºN and 93.5-101.5ºW. The domain has 224 × 224 grid cells horizontally with 3km spacing and 45 unevenly spaced vertical layers ranging from 1000-50mb, with a meteorological model timestep of 18 seconds and output saved every 2 hours. We select this domain to evaluate a high-impact convective event from 17-18 April 2013.

To test the effects of pollen and SPP on mixed-phase and cold cloud formation, we included 4 sensitivity experiments here. Five ensemble members are conducted for each sensitivity experiment from 11-20 April 2013, with a 6-hr difference in the start time to alter initial conditions. All results presented below represent the multi-member average to reduce noise common in cloud-aerosol simulations. Experiments include:

‘Old’: WRF-Chem simulation with original MMS (Morrison et al., 2005), where pollen and other aerosol are not included in the heterogeneous ice nucleation scheme.
‘Control’: updates MMS as described in Section 2.0, where the heterogeneous ice nucleation includes all anthropogenic and natural aerosol except pollen and SPPs.
‘POLp’: adds primary pollen only (no pollen rupture) to the anthropogenic and natural aerosol in the D10 scheme to simulate immersion and contact freezing from pollen in mixed-phase clouds. Additionally, we also utilize the deposition freezing scheme for dust (U17) to simulate primary pollen heterogeneous freezing in cirrus clouds.
‘M23’: Includes primary pollen and SPPs with contact and immersion freezing from pollen and immersion freezing for SPPs using the newly developed pollen INP parametrization (M23; Matthews et al., 2023) in mixed-phase clouds. Contact freezing of SPPs is simulated by the D10 scheme as lab-based data is not available. In cirrus clouds, the heterogeneous freezing of primary pollen is simulated using the lab-developed contact freezing parameterization (M23).
‘M23+rup’: Includes primary pollen and SPPs with the M23 scheme to simulate pollen and SPP ice nucleation. Additionally, this experiment increases pollen rupture rate from 1000 SPPs grain–1 to 1.25 × 105 SPPs grain–1 based on Matthews et al. (2023).

Instrument and/or Software specifications: NA

Files contained here:
There are 10 netcdf files in the folder. Five files contain selected variables from WRF-Chem output for different experiments ("old", "Control," "Polp," "M23," "M23+rup"), with the name "XX_wrf.nc". Each file includes 14 variables, namely: 'polp' (whole pollen grain), 'pols' (sub-pollen particles), 'QICE' (ice mass), 'QSNOW' (snow mass), 'QGRAUP' (graupel mass), 'QRAIN' (rain mass), 'QCLOUD' (cloud droplet mass), 'QNICE' (ice number), 'QNSNOW' (snow number), 'QNGRAUPEL' (graupel number), 'QNRAIN' (raindrop number), 'QNDROP' (cloud droplet number), 'W' (vertical velocity). The other five files are for simulated radar reflectivity, including the variable 'rflec,' with the name "Simulated_radar_reflectivity_XX.nc". These files are calculated using precipitating hydrometeors and water vapor.

Related publication(s):
Zhang et al. (2024). Effects of pollen on hydrometeors and precipitation in a convective system

Use and Access:
This data set is made available under an Attribution 4.0 International (CC BY 4.0).

To Cite Data:
Zhang, Y. M. Effects of pollen on hydrometeors and precipitation in a convective system [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/hd7s-8t07