David Medvigy - About Me
David Medvigy joined the Department of Geosciences in 2009. Previously, he was a postdoctoral fellow at Duke University. He received his Ph.D. from Harvard University in 2006.
Medvigy’s research focuses on understanding local-regional scale variability in climate and terrestrial biosphere, with a focus on the processes linking these two components of the Earth system. This research includes study of the relationships between the atmospheric circulation, terrestrial ecology, and biogeochemical fluxes, and how all of these are responding (and are projected to respond) to anthropogenic forcings. His lab addresses these issues through numerical models, including variable-resolution general circulation models, mesoscale meteorological models, and mechanistic models of ecosystem composition, structure, and functioning.
Medvigy is particularly interested in high-frequency climate variability and its implications for terrestrial ecosystems. Strategies for adaptation to climate change hinge on the expected changes in the distribution functions of climate variables. Although contemporary climate studies have overwhelmingly focused on two properties of the distribution functions, the mean and the tails (i.e., extreme events), other statistics are also important. In particular, any process that depends nonlinearly on a climate variable will be sensitive the variance of that climate variable. To illustrate the range of processes satisfying this requirement, note that photosynthesis and the properties of solar cells depend nonlinearly on solar radiation, and that runoff and soil moisture, mosquito populations, and microbial respiration all depend nonlinearly on precipitation. Thus, changes in climate variances can impact energy production, the global carbon cycle, and disease outbreaks.
In recent and ongoing work, Medvigy has been using the Ecosystem Demography model 2 (ED2; Fig. 1) to investigate the sensitivity of North American and tropical ecosystems to the high-frequency variability of environmental parameters (sunlight, precipitation, temperature). Daily fluctuations in these parameters are strongly coupled to ecosystem function, with effects that accumulate through annual and decadal time scales. Based on Intergovernmental Panel on Climate Change model estimates for changes in high-frequency meteorological variability over the next 100 years, terrestrial ecosystems may be affected by changes in variability almost as much as by changes in mean climate.
Medvigy is also actively researching questions related to vegetation phenology. Phenology is the study of recurrent biological events, including leaf formation in the springtime and leaf drop in the autumn. Important two-way feedbacks exist between spatiotemporal patterns of phenological variability and patterns of temperature and precipitation variability. Phenological variability also impacts terrestrial ecosystem photosynthesis, respiration, and carbon budgets. Despite the importance of phenology for climate, carbon budgets, and ecosystems, the representations of phenology in dynamic vegetation models are poorly constrained and large mismatches have been found between simulated and observed phenology. Medvigy is developing new phenology models to address this challenge.
Medvigy is also interested in South American climate. Over the next 40 years, 40% of the Amazon rainforest is projected to become deforested. The consequences for climate are numerous and varied: changes in the amount of absorbed sunlight will change the surface energy balance; changes in ecosystem composition will change the amount of water transpired to the atmosphere; changes in the surface roughness will change regional wind patterns. These local-regional changes may impact the large-scale Hadley and Walker circulations, and thus have global consequences. To complicate matters, the net response to deforestation may strongly depend on both the total area deforested and the spatial pattern of deforestation. Medvigy is currently using variable-resolution general circulation models (Fig. 2) to address these problems.
FIGURE 1: ED2 model structure and processes: (a) Each grid cell is subdivided into a series of tiles. The relative area of each tile is determined by the proportion of canopy-gap sized areas within the grid cell having a similar canopy structure as a result of a common disturbance history. (b) ED2 computes the multilayer canopy fluxes of water, internal energy, and carbon within each subgrid scale tile. (c) Summary of the long-term vegetation dynamics within each tile arising from the integration of short-term fluxes shown in Panel (b).
Figure 2: Ocean-Land-Atmosphere Model (OLAM) grid with 25 km resolution inside the Amazon basin and 200 km resolution away from South America. Two transition grids (each doubling the resolution length scale) are needed for the transition. Colors indicate topography.