Natural Ecosystems

How important is diversity for capturing environmental-change responses in ecosystem models?

Marine ecosystem models used to investigate how global change affects ocean ecosystems and their functioning typically omit pelagic plankton diversity. Diversity, however, may affect functions such as primary production and their sensitivity to environmental changes. Here we use a global ocean ecosystem model that explicitly resolves phytoplankton diversity by defining subtypes within four phytoplankton functional types (PFTs). We investigate the model's ability to capture diversity effects on primary production under environmental change. An idealized scenario with a sudden reduction in vertical mixing causes diversity and primary-production changes that turn out to be largely independent of the number of coexisting phytoplankton subtypes. The way diversity is represented in the model provides a small number of niches with respect to nutrient use in accordance with the PFTs defined in the model. Increasing the number of phytoplankton subtypes increases the resolution within the niches. Diversity effects such as niche complementarity operate between, but not within PFTs, and are constrained by the variety of traits and trade-offs resolved in the model. The number and nature of the niches formulated in the model, for example via trade-offs or different PFTs, thus determines the diversity effects on ecosystem functioning captured in ocean ecosystem models.

© 2014 Prowe et al.

Modeling Regional Carbon Dioxide Flux over California using the WRF-ACASA Coupled Model

Many processes and interactions in the atmosphere and the biosphere influence the rate of carbon dioxide exchange between these two systems. However, it is difficult to estimate the carbon dioxide flux over regions with diverse ecosystems and complex terrains, such as California. Traditional carbon dioxide measurements are sparse and limited to specific ecosystems. Therefore, accurately estimating carbon dioxide flux on a regional scale remains a major challenge.

In this study, we couple the Weather Research and Forecasting Model (WRF) with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF lack the capability to simulate carbon dioxide. ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically. Carbon dioxide, sensible heat, water vapor, and momentum fluxes between the atmosphere and land surface are estimated in the ACASA model through turbulence equations with a third order closure scheme. It therefore permits counter-gradient transports that low-order turbulence closure models are unable to simulate.

A new CO2 tracer module is introduced into the model framework to allow the atmospheric carbon dioxide concentration to vary according to terrestrial responses. In addition to the carbon dioxide simulation, the coupled WRF-ACASA model is also used to investigate the interactions of neighboring ecosystems in their response to atmospheric carbon dioxide concentration. The model simulations with and without the CO2 tracer for WRF-ACASA are compared with surface observations from the AmeriFlux network.

This study aims to quantify the climate-warming feedback potential from emitted trace gases, as well as landscape changes within Arctic ecosystems. Analyzing these areas, we will test the hypothesis that there exists a warming threshold beyond which permafrost degradation and lake/wetland expansion will stimulate increases in methane and carbon dioxide emissions. This proposed research further improves our earth-system model by enhancing our representation of permafrost and dynamic wetland and lake systems to explore their effects on hydrological and carbon dynamics.

Quantifying Climate Feedbacks of the Terrestrial Biosphere under Thawing Permafrost Conditions in the Arctic

This project aims to quantify trade-offs between production of biofuels, loss of forests, and production of food. The project involves the comparison of scenarios without expansion in biofuel production in the U.S. and in Europe to a baseline where biofuel expansion proceeds according to recent policies. Effects on tropical forests, greenhouse gas emissions, and on food prices are being explored.

Biofuel Trade-Offs: Fuel, Forests, and Food

Despite nitrous oxide (N2O) being a major ozone depleting species and greenhouse gas, its sources and emission rates  are not  well  understood.  There are large uncertainties in soil emissions, both from natural processes and fertilizer use, as well as discrepancies in ocean emissions.  Improved understanding of N2O emission processes over the surface of the globe is needed to accurately quantify past, present, and future impacts on stratospheric O3  and climate.  We address these issues by using an interdisciplinary approach that integrates observations (from NOAA, the Advanced Global Atm

Assessing the Terrestrial and Atmospheric Nitrogen Cycle

Phytoplankton form the base of the marine food web and are a crucial component in the global carbon cycle. They are also extremely diverse, with different species ranging widely in size, biochemical functions, and light and temperature requirements. How phytoplankton establish communities (mixtures of the different species living in the same place) and how these vary between regions and with time is poorly known. Community structure is important for the type of food webs they can support and the amount of carbon they sequester in the ocean.

Size Structure and Function of Phytoplankton Communities in a Changing Ocean

This study aims to identify regions where the resiliency to withstand extreme weather and climate events is at risk, and therefore degrade the regions' ability to resist any changes. This will aid stakeholders and decision-makers as they prepare for and adapt to environmental change. By employing a variety of models, including MIT's Integrated System Model (IGSM), we will evaluate how a set of environmental stresses affects specific regions. This work will also develop a heuristic model to serve as more efficient and powerful predictive tool to help guide adaptation strategies.

Future of Ecosystems and Extremes: Using Diverse Environmental Data Sets in Support of Regional to Global Earth-System Models and Predictions

System-based modeling is now widely used to quantify ecosystem and environmental elemental cycling (e.g., C and N), hydrological dynamics, and energy fluxes. In this context, deterministic differential equations link state variables and fluxes of ecosystems or environmental entities (e.g., lakes, forests, or areas of coastal ocean). Traditionally, these models are parameterized with limited observational data and then applied over extended temporal and spatial scales.

A Paradigm Shift in Ecosystem and Environmental Modeling: An Integrated Stochastic, Deterministic, and Machine Learning Approach

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