Work Description

Date: 25 January, 2022

Dataset Title: Simulated historical and future pollen emission data using Pollen Emission model for Climate Models (PECM)

Dataset Creators: Y. Zhang, A. Steiner

Dataset Contact: Yingxiao Zhang yingxz@umich.edu

Funding: National Science Foundation (NSF) grant AGS-182117

Key Points:
- We simulate the pollen emission in the historical and future.
- Future pollen emissions are impacted by climate change, we considered temperature, precipitation, CO2, and land cover change in our study.
- In the future, pollen season would become longer and pollen emission magnitude would become higher.

Research Overview:
Atmospheric aerosols are emitted from both nature and anthropogenic sources, and they play an important role in climate, impacting solar radiation and cloud formation. Compared to other types of aerosol particles, primary biological aerosol particles (PBAP, e.g., fungal spores, bacteria, pollen, virus, etc.) are relatively understudies. However, they are linked to the adverse health effects and have the potential to influence ice nucleation at higher temperatures. Anemophilous (or wind-driven) pollen is one of the important PBAP, impacts cloud properties under some conditions and triggers allergic diseases such as allergic rhinitis (also known as hay fever) and asthma. Because pollen emission is closely associated with environmental drivers, climatic change could influence pollen emission and consequently the incidence of allergic disease. Using CMIP6 model data, our research projects continental-scale changes in pollen emissions at the end of the century, considering the effects of temperature, precipitation, CO2, and future vegetation distribution change. While prior studies have evaluated single types of pollen, we use a mechanistic model to comprehensively simulate total pollen across the United States from all sources. Similar to previous single-source pollen studies, our simulations suggest that pollen season duration will lengthen, and pollen emission will increase in the future, but in addition, we identify new synergies between different pollen types that can influence the maximum daily pollen. Our work highlights that the changes of overlap between pollen seasons of different vegetation taxa can magnify or mitigate the impacts of climate change, which addresses the importance to study all pollen emissions comprehensively. Given pollen is one of the most common triggers of seasonal allergies, our findings also provide information to evaluate global health conditions in the future.

Methodology:
We project the change of pollen emissions at the end of the century (2081-2100) compared to the historical period (1995-2014) over the United States for 13 of the most prevalent airborne pollen taxa (including Acer, Alnus, Ambrosia, Betula, Cupressaceae, Fraxinus, Poaceae, Morus, Pinaceae, Platanus, Populus, Quercus, and Ulmus) using the updated Pollen Emissions model for Climate Models (PECM 2.0; https://doi.org/10.5281/zenodo.5874177) based on Wozniak et al. (2017). The model is driven by the meteorology data from 15 Coupled Model Intercomparison Project version 6 (CMIP6) (https://esgf-node.llnl.gov/search/cmip6/) model ensembles. In the future, the Shared Socioeconomic Pathways (SSP) 585 scenario (Meinshausen, M. et al. 2019) is used to project future temperature/precipitation changes. In addition, we also have tested how future rising CO2 and vegetation species range shifts may influence the emissions of pollen.

PECM (Wozniak et al., 2017) is a prognostic model developed from historical pollen count data from the National Allergy Bureau (NAB) of the American Academy of Allergy, Asthma and Immunology (AAAAI). It predicts pollen emission for a broad range of taxa at a large geographic scale (25 km resolution in this study), and it is able to capture up to 57% of the variance of pollen season.

These data can be reproduced using the PECM2.0 code we provided above, the detailed information of model input data model design can be seen in the paper Methods section.

Wozniak, M. C. & Steiner, A. L. A prognostic pollen emissions model for climate models (PECM1.0). Geosci. Model Dev 10, 4105–4127 (2017).
Meinshausen, M. et al. The SSP greenhouse gas concentrations and their extensions to 2500. Geosci. Model Sev. Discuss 2019, 1–77 (2019).

Instrument and/or Software specifications: NA

Files contained here:
There are 10 netcdf files in the folder, 4 for historical period and 6 for future period under scenario SSP 585, showing 10-year averaged daily pollen emission flux. Pollen emissions are simulated using the meteorology data input from each CMIP6 model and then an evenly weighted multi-model average from 15 PECM simulations is calculated for analysis. The data resolution is 25 km over the United States, and we use LambertConformal map projection. Each folder includes 15 taxa, the orders of different taxa: ACER,ALDR,BETU,CUPR,FRAX,MORU,PINU,PLAT,POPU,QUER,ULNU,ULN2,GRC3,GRC4,AMBR

The pollen emissions are simulated both in the historical(1995-2014) and future(2081-2100), considering different impacting factors (temperature, precipitation, CO2):

_ef_v3: Pollen simulation including both temperature and precipitation effects

_ef_T_v3: Simulations with temperature effects only

_ef_co2_v3: Sensitive test includes CO2 effects at the end of century with Scenario 585

Related publication(s):
Zhang & Steiner (2021). Projected climate-driven changes in pollen emission season length and magnitude over the continental United States. Forthcoming.

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

To Cite Data:
Zhang, Y., Steiner, A. Simulated pollen emission using PECM-averaged data [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/628t-r416