Work Description

Title: Data for "Pollen rupture and its impact on precipitation in clean continental conditions" Open Access Deposited

http://creativecommons.org/licenses/by-nc/4.0/
Attribute Value
Methodology
  • We use the Regional Climate Model version 4 (RegCM4), which is based on the hydrostatic version of the Penn State/NCAR mesoscale model MM5 and includes a coupled aerosol tracer module with an option for transporting pollen tracers. For the following simulations, we use the Subgrid Explicit Moisture Scheme (SUBEX) for large-scale precipitation and a cumulus convection scheme based on the parameterization presented by Tiedtke. The effect of CCN on autoconversion rates from cloud to rain water (second aerosol indirect effect) is explicitly accounted through modification of a Kessler-type formulation of the critical cloud water content, which depends on CCN number concentration . Additionally, we use the Community Land Model version 4.5 for the land surface scheme and the rapid radiative transfer model for radiation. To study the efficacy of SPPs in precipitation processes, we run RegCM over the continental United States for the spring pollen season in 2002 (March-June). We select the year 2002 as monthly average precipitation for this year has the minimum anomaly with respect to a 2001-2010 reference period. The horizontal resolution of the domain is 25-km with 144x243 grid cells on a Lambert Conformal Projection centered on 39ºN, 100ºW with parallels at 30ºN and 60ºN (Figure S3). The vertical resolution includes 18 vertical sigma levels. Boundary conditions are provided by the ERA-Interim Reanalysis (Dee et al., 2011) with sea surface temperatures prescribed from NOAA Optimum Interpolation SSTs (Smith et al. 2008). We conduct an ensemble of sensitivity tests that evaluate the impact of SPPs on regional precipitation for three cases using a background CCN of 100 cm-3: i) background CCN only (100 CCN cm-3; BASE), ii) online CCN-active SPPs added to the background with nspg = 1,000 SPPs grain-1 (literature-derived value; SPP_LOW), and iii) online simulated CCN-active SPPs added to the background with nspg = 1,000,000 SPPs grain-1 (SPP_HIGH).
Description
  • Pollen grains emitted from vegetation can rupture, releasing subpollen particles (SPPs) as fine atmospheric particulates. Previous laboratory research demonstrates potential for SPPs as efficient cloud condensation nuclei (CCN). We develop the first model of atmospheric pollen grain rupture, and implement the mechanism in regional climate model simulations over spring pollen season in the United States with a CCN-dependent moisture scheme. The source of SPPs (surface or in-atmosphere) depends on region and sometimes season, due to the distribution of relative humidity and rain. Simulated concentrations of SPPs are approximately 1-10 or 1-1,000 cm-3, depending on the number of SPPs produced per pollen grain (nspg). Lower nspg (103) produces a negligible effect on precipitation, but high nspg (106) in clean continental CCN background concentrations (100 CCN cm-3) shows SPPs suppress average seasonal precipitation by 32% and shift rates from heavy to light while increasing dry days. This effect is likely smaller for polluted air. pollen_rupture_precipitation_BASE_ensemble_daily.nc - data for BASE ensemble average pollen_rupture_precipitation_SPPHIGH_ensemble_daily.nc - data for SPPHIGH ensemble average pollen_rupture_precipitation_SPPLIT_ensemble_daily.nc - data for SPPLIT ensemble average
Creator
Depositor
  • mcwoz@umich.edu
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
ORSP grant number
  • AGS 0952659
Citations to related material
  • Wozniak, M. C., Solmon, F., Steiner, A. L. (2018). Pollen Rupture and Its Impact on Precipitation in Clean Continental Conditions. Geophysical Research Letters, 45(14), 7156-7164. https://doi.org/10.1029/2018GL077692
Resource type
Last modified
  • 11/18/2019
Published
  • 11/18/2019
DOI
License
To Cite this Work:
Wozniak, M., Steiner, A., Solmon, F. (2019). Data for "Pollen rupture and its impact on precipitation in clean continental conditions" [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/cn2t-2346

Relationships

Files (Count: 3; Size: 2.98 GB)

Download All Files (To download individual files, select them in the “Files” panel above)

Total work file size of 2.98 GB may be too large to download directly. Consider using Globus (see below).



Best for data sets > 3 GB. Globus is the platform Deep Blue Data uses to make large data sets available.   More about Globus