A prognostic pollen emissions model for climate models (PECM1.0)

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Abstract

We develop a prognostic model called Pollen Emissions for Climate Models (PECM) for use within regional and global climate models to simulate pollen counts over the seasonal cycle based on geography, vegetation type, and meteorological parameters. Using modern surface pollen count data, empirical relationships between prior-year annual average temperature and pollen season start dates and end dates are developed for deciduous broadleaf trees ( Acer, Alnus, Betula, Fraxinus, Morus, Platanus, Populus, Quercus, Ulmus), evergreen needleleaf trees (Cupressaceae, Pinaceae), grasses (Poaceae; C3, C4), and ragweed ( Ambrosia). This regression model explains as much as 57ĝ€% of the variance in pollen phenological dates, and it is used to create a climate-flexible phenology that can be used to study the response of wind-driven pollen emissions to climate change. The emissions model is evaluated in the Regional Climate Model version 4 (RegCM4) over the continental United States by prescribing an emission potential from PECM and transporting pollen as aerosol tracers. We evaluate two different pollen emissions scenarios in the model using (1) a taxa-specific land cover database, phenology, and emission potential, and (2) a plant functional type (PFT) land cover, phenology, and emission potential. The simulated surface pollen concentrations for both simulations are evaluated against observed surface pollen counts in five climatic subregions. Given prescribed pollen emissions, the RegCM4 simulates observed concentrations within an order of magnitude, although the performance of the simulations in any subregion is strongly related to the land cover representation and the number of observation sites used to create the empirical phenological relationship. The taxa-based model provides a better representation of the phenology of tree-based pollen counts than the PFT-based model; however, we note that the PFT-based version provides a useful and climate-flexible emissions model for the general representation of the pollen phenology over the United States.

Figures

  • Figure 1. Locations of American Academy of Allergy, Asthma and Immunology (AAAAI) station and geographic regions used in this study: Northeast (NE; 38–48◦ N, 70–100◦W) in blue, Southeast (SE; 25–38◦ N, 70–100◦W) in green, Mountain (MT; 25–48◦ N, 100–116◦W) in red, California (CA; 25–40◦ N, 116–125◦W) in orange, and Pacific Northwest (PNW; 40–48◦ N, 116–125◦W) in dark grey.
  • Figure 2. Daily observed average time series of daily pollen count data (2003–2010) for the four representative plant functional types (deciduous broadleaf forest, DBF; evergreen needleleaf forest, ENF; grasses, GRA; and ragweed, RAG) averaged over the five regions in Fig. 1: (a) Northeast, (b) Southeast, (c) Mountain, (d) California, and (e) Pacific Northwest.
  • Figure 3. Land cover fraction (% coverage) for 11 tree taxa from the Biogenic Emissions Landuse Database version 3 (BELD) regridded to a 25 km resolution grid, including (a) Acer (maple), (b) Alnus (alder), (c) Betula (birch), (d) Cupressaceae (cedar/juniper), (e) Fraxinus (ash), (f) Morus (mulberry), (g) Pinaceae (pine), (h) Platanus (sycamore), (i) Populus (poplar/aspen), (j) Quercus (oak), and (k) Ulmus (elm).
  • Table 1. Production factors (P ) and phenological regression coefficients for the start day of year (sDOY) and end day of year (eDOY) as a function of temperature for the 13 individual pollen-producing taxa. Individual taxa and families are organized into the four PFTs, with the two aggregated tree PFTs denoted as DBF and ENF. Regression slope (days ◦C−1) and coefficient of determination are provided for both sDOY and eDOY (slope/R2).
  • Table 2. Total spatial coverage (km2) of tree taxa and PFTs from BELD and CLM4 land cover datasets in the five US subregions (Northeast, NE; Southeast, SE; Mountain, MT; California, CA; Pacific Northwest, PNW). All individual tree taxa are from the BELD database. BELD DBF and ENF land cover are the sums of the land cover of the taxa belonging to each PFT.
  • Figure 4. BELD (a, c) and CLM4 (b, d, e, f) land cover for the four PFT categories that produce pollen emissions, including (1) deciduous broadleaf forest for (a) BELD and (b) CLM4, (2) evergreen needleleaf forest for (c) BELD and (d) CLM4, (3) grasses, including (e) C3 grasses and (f) C4 grasses, and (g) ragweed, represented by crop and urban CLM4 categories.
  • Figure 5. Phenological regressions for Betula (birch) pollen for (a) sDOY and (b) eDOY versus previous-year annual average temperature (PYAAT; ◦C). Each point signifies one station per year for pollen count data from 2003 to 2010 (total denoted as N).
  • Figure 6. Monthly average emissions potential (E; Eq. 1) for BELD model DBF (2003–2010) in grains m−2 day−1. (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November, and (l) December.

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APA

Wozniak, M. C., & Steiner, A. L. (2017). A prognostic pollen emissions model for climate models (PECM1.0). Geoscientific Model Development, 10(11), 4105–4127. https://doi.org/10.5194/gmd-10-4105-2017

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