This dissertation attempts to improve knowledge of the complex interactions of vegetation and climate by addressing two vegetation-atmosphere mass exchanges that are or might be critical to climate: photosynthetic carbon uptake, and biological aerosol emissions. First, a prognostic model of Pollen Emissions for Climate Models (PECM) for use within regional and global climate models is developed to simulate pollen counts over the seasonal cycle based on geography, vegetation type and meteorological parameters. An observation-based parameterization of pollen season phenology is determined using pollen count data to estimate the linear relationship between start and end dates and prior-year annual average temperature. This regression model explains as much as 57% of the variance in pollen phenological dates for many taxa common to the U.S., 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 simulated surface pollen concentrations from coupling PECM with a regional climate model are evaluated against observations, and simulated pollen counts are within an order of magnitude of the observed concentrations. Second, the first model of atmospheric pollen grain rupture is developed and implemented 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) is compared and found to depend on region and sometimes season, due to the distribution of relative humidity and rain. It is shown that upper-bound estimated SPP concentrations can suppress average seasonal precipitation over the continental U.S.by 32% in clean background aerosol conditions, though this effect is smaller (~2%) for polluted air.Finally, the impact of a vertically complex canopy and its microenvironment on carbon uptake is assessed for deciduous broadleaved forests using different model representations of the canopy. Simulations of a multilayer version of Community Land Model (CLM-ml) are compared with CLM4.5 “big-leaf” simulations for the month of July (peak growing season), and evaluated with observed eddy covariance tower fluxes at five FLUXNET deciduous broadleaf forest sites. Four abiotic environmental drivers –air temperature, relative humidity, incoming shortwave radiation and soil moisture –are correlated with GPP inter-annual variations at each site to determine the strength of their influence and the overall sensitivity of GPP to local climate variability. It is found that, even though the models all underestimate GPP and its inter-annual variability, the patterns of GPP IAV and its drivers loosely resemble observed patterns. When using Ball-Berry stomatal conductance and a uniformly applied soil moisture stress factor in CLM-ml, vertical variation in the sensitivity of leaf-level carbon assimilation rate to climate variability is small, and therefore the canopy-scale GPP and its response to climate variability at all five sites are similar between CLM-ml and CLM4.5 simulations. However, using CLM-ml with plant hydraulics with non-uniform water stress, the sensitivity of carbon assimilation rate to climate variability varies with height and canopy-scale GPP is dampened from CLM4.5. Both reduced dependence on soil moisture and opposing climatic forcings on different leaf layers cause such dampening.This research highlights several unknowns in the climate system stemming from vegetation-climate interactions, as well as the importance of model-data integration for solving these unknowns.The results can be used to further the development of more accurate climate prediction to prepare society for the impacts of climate change.