Here is the actual article: https://www.pnas.org/content/118/7/e2013284118 https://www.pnas.org/content/118/7/e2013284118 > We tested eight annual and seasonal

Here is the actual article: https://www.pnas.org/content/118/7/e2013284118 https://www.pnas.org/content/118/7/e2013284118 > We tested eight annual and seasonal climate variables, including temperature, precipitation, frost days, and atmospheric CO2 concentrations in a mixed-effects model framework to account for city-to-city variation. We found that mean annual temperature was the strongest predictor of these four pollen metrics (P < 0.0001 for all metrics; Fig. 2 and SI Appendix, Table S4). The full mixed-effects models explained 51–90% of the variance (i.e., conditional R^2) in pollen metrics, and mean annual temperature alone (i.e., marginal R^2) explained 14–37% of the variance in pollen metrics (SI Appendix, Figs. S4 and S5 and Table S4). Notably, while atmospheric CO2 concentrations were sometimes included in the group of the most parsimonious models, the variation explained was often quite low (e.g., annual integral R^2 marginal = 0.01). I'm not sure what I expected, but temperature fluctuations seem like a sensible primary driver of pollen count. Other variables that I think might be interesting include wind speed and relative humidity.