Seminar and Events Schedule

Transit time of carbon and the climate benefit of sequestration in terrestrial ecosystems

Ecosystems play a fundamental role in climate change mitigation by photosynthetically fixing carbon from the atmosphere and storing it for a period of time in organic matter. Although climate impacts of carbon emissions by sources can be quantified by global warming potentials, the appropriate formal metrics to assess climate benefits of carbon removals by sinks are unclear. We introduce here the climate benefit of sequestration (CBS), a metric that quantifies the radiative effect of fixing carbon dioxide from the atmosphere and retaining it for a period of time in an ecosystem before releasing it back as the result of respiratory processes and disturbances. In order to quantify CBS, we present a formal definition of carbon sequestration (CS) as the integral of an amount of carbon removed from the atmosphere stored over the time horizon it remains within an ecosystem. Both metrics incorporate the separate effects of (i) inputs (amount of atmospheric carbon removal) and (ii) transit time (time of carbon retention) on carbon sinks, which can vary largely for different ecosystems or forms of management. These metrics can be useful for comparing the climate impacts of carbon removals by different sinks over specific time horizons, to assess the climate impacts of ecosystem management, and to obtain direct quantifications of climate impacts as the net effect of carbon emissions by sources versus removals by sinks.

From cycles to networks: biogeochemical and cultural transformations

In this talk I’ll discuss findings from research in the U.S. Midwest, Central Great Plains and the Caribbean to explore how a geographic perspective can provide insights into biogeochemical cycling of carbon, with a focus on above and belowground ecosystem response to disturbance. I will also discuss the role of disturbance in changing behaviors and organizational climate and policies and the power of social networks for advancing equity in the practice of science.

A U.S. History of Federal Collaborations around Carbon – Sustaining and Assessing Impactful Climate Change Research

The U.S. Carbon Cycle Science Program, established in 1999, has a mission to coordinate and facilitate federally funded carbon cycle research, and provide leadership to the U.S. Global Change Research Program (USGCRP)  on carbon cycle science priorities. The USGCRP, established in 1990, is mandated by Congress to ‘assist the Nation and the world to understand, assess, predict, and respond to human-induced and natural processes of global change’. This talk will provide a historical overview of both programs, focusing on how we are facilitating interagency scientific endeavors toward seeking and enabling climate change solutions via interdisciplinary carbon cycle research. The Second State of the Carbon Cycle Report (SOCCR2), a multiagency decadal science assessment will be highlighted. An overview of the federal carbon cycle and climate change research grants, fellowship and engagement opportunities will also be provided, in relation to the activities of the U.S. Carbon Cycle Science Program.

Adapting Efficient Reservoir Neural Methods for Statistical Long-Lead Forecasting of Ecological Processes

If it is so difficult to forecast the weather a few days from now, how can we forecast the state of weather-sensitive ecological systems one year out? The atmosphere is a chaotic dynamical system, and because of that, skillful weather forecasts are only possible out to about 7 to 10 days.  However, dynamical processes in the ocean operate on a much longer time scales, and many atmospheric processes depend crucially on the ocean as a forcing mechanism.  Some of these processes can be forced remotely, leading to so-called teleconnections.  This coupling between the slowly varying ocean and the faster varying atmosphere and associated processes, allows for the skillful prediction of some general properties of such systems many months in advance.  These long-lead forecasts are important for activities such as agricultural and wildlife management, energy production, and disaster planning, to name a few.  It has consistently been established that statistical models can provide some of the most skillful long-lead forecasts. However, it can be particularly challenging to specify parameterizations for nonlinear dynamical spatio-temporal forecast models that are simultaneously useful scientifically and efficient computationally, and that allow for proper uncertainty quantification.  In some cases, when such information is available, one can embed mechanistic information into multi-level (deep/hierarchical) models to facilitate parameter reduction and interpretability.  When such information is not available (such as with animal behavior) then alternative learning strategies such as deep neural models can be applied.  One challenge with such methods can be uncertainty quantification and the necessity of having very large data sets for model training.  Here I present an approach where we have integrated an alternative efficient “AI” spatio-temporal dynamical model, a deep echo state network, in a statistical framework to accommodate uncertainty quantification. This is applied to long-lead (one-year ahead) forecasting of settling patterns of Northern Pintails (Anas acuta) in the Prairie Pothole region of North America.

Interdisciplinary Career Trajectory in Urban Sustainability.

Anu Ramaswami, Ph.D., is a professor at Princeton University in the departments of India studies, civil and environmental engineering, and at the High Meadows Environmental Institute. She is an interdisciplinary environmental engineer recognized as a pioneer and leader on the topic of sustainable urban infrastructure systems. Her work explores how eight key sectors – that provide water, energy, food, buildings, mobility, connectivity, waste management and green/public spaces – shape human and environmental wellbeing, from local to global scales. Ramaswami’s work integrates environmental science and engineering, industrial ecology, public health and public affairs, with a human-centered and systems focus. She is the inaugural director of the M.S. Chadha Center for Global India at Princeton University, the lead principal investigator and director of the National Science Foundation (NSF)-supported Sustainable Healthy Cities Network, and serves on the United Nations Environment’s International Resource Panel and the US NSF’s Advisory Committee for Environmental Research and Education. She received her BS in chemical engineering from the Indian Institute of Technology Madras in Chennai and her PhD in civil and environmental engineering from Carnegie Mellon University.

You can find more about Dr. Ramaswami’s work and her collaborations via the following resources:

Websites

https://ramaswami.princeton.edu/

http://www.sustainablehealthycities.org/

Social Media

Twitter – @AnuRamaswami

@RamaswamiSUSLab – https://twitter.com/RamaswamiSUSLab

@PrincetonEnviro (High Meadows Environmental Institute)

@Eprinceton – Princeton engineering

@PrincetonCGI – Chadha Center for Global India

@Princeton (Princeton University)

Sustainable Healthy Cities (NSF-funded research group for which Prof. Ramaswami is the director)

Twitter – @SRNcities https://twitter.com/SRNcities

Facebook – @sustainablehealthycities https://www.facebook.com/sustainablehealthycities/

LinkedIn – https://www.linkedin.com/company/sustainable-healthy-cities-network/

Ramaswami Sustainable Urban Systems Lab

Twitter – @RamaswamiSUSLab

https://twitter.com/RamaswamiSUSLab

LinkedIn – @RamaswamiSUSLab – https://www.linkedin.com/company/RamaswamiSUSLab

Facebook – @RamaswamiSUSLab – https://www.linkedin.com/company/RamaswamiSUSLab

Instagram – @RamaswamiSUSLab – https://www.instagram.com/RamaswamiSUSLab/