Overview of the Large-Scale Biosphere–Atmosphere Experiment in Amazonia Data Model Intercomparison Project (LBA-DMIP)

Abstract

A fundamental question connecting terrestrial ecology and global climate change is the sensitivity of key terrestrial biomes to climatic variability and change. The Amazon region is such a key biome: it contains unparalleled biological diversity, a globally significant store of organic carbon, and it is a potent engine driving global cycles of water and energy. The importance of understanding how land surface dynamics of the Amazon region respond to climatic variability and change is widely appreciated, but despite significant recent advances, large gaps in our understanding remain. Understanding of energy and carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Land surface/ecosystem models have become important tools for extrapolating local observations and understanding to much larger terrestrial regions. They are also valuable tools to test hypothesis on ecosystem functioning. Funded by NASA under the auspices of the LBA (the Large-Scale Biosphere-Atmosphere Experiment in Amazonia), the LBA Data Model Intercomparison Project (LBA-DMIP) uses a comprehensive data set from an observational network of flux towers across the Amazon, and an ecosystem modeling community engaged in ongoing studies using a suite of different land surface and terrestrial ecosystem models to understand Amazon forest function. Here an overview of this project is presented accompanied by a description of the measurement sites, data, models and protocol.

Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado

“…Supply-side analysis of the seasonality of E: coupling with soil moisture measurements All models presented were verified to have balanced the water budget; i.e., the following equation was always satisfied (de Gonç alves et al, 2013) to within 5 mm month −1 .…”

“…The seasonal cycle of P was estimated from the precipitation driver data, which was site-derived (de Gonç alves et al, 2013). We estimated the seasonal cycle of E from hourly eddy covariance turbulent flux measurements by first making daily estimates from daylight hours, followed by monthly E totals, and then averaging across years.…”

“…However, much uncertainty remains surrounding modeling forest response to climate anomalies, due to both to model process differences/parameters or due to uncertainty in climate projections (Huntingford et al, 2008;Sitch et al, 2008;Galbraith et al, 2010;Poulter et al, 2012). This paper seeks to further investigate model process uncertainty by focusing on mechanisms controlling the seasonality and magnitude of E in the Amazon basin using a data-model intercomparison approach (de Gonç alves et al, 2013).…”

Fog and rain in the Amazon

“…In particular, they (i) underestimate the precipitation in the region (10,11), (ii) do not reproduce the seasonality of either precipitation (10,11) or surface fluxes such as evapotranspiration (12), and (iii) produce errors in the diurnal cycle and intensity of precipitation, with a tendency to rain too little and too early in the day (13,14). In the more humid Western part of the basin, surface incoming radiation, evapotranspiration, and photosynthesis all tend to peak in the dry season (15)(16)(17), whereas GCMs simulate peaks of those fluxes in the wet season (10,11).…”

Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation

“…This paper builds upon and extends the approaches of the studies discussed above into ISAM, which has been extensively used in various model inter-comparison studies (Hunzinger et al, 2012;Richardson et al, 2012;Schaeffer et al, 2012;De Goncalves et al, 2013;Kauwe et al, 2013). …”