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Study Shows Coastal Wetlands Aid in Carbon Sequestration

data collection in marsh

PNNL scientist, Amy Borde collects data in a marsh on the Columbia River estuary.

Photo: Heida Diefenderfer

Sea-level rise impacts will likely decrease ecosystem carbon stocks

August 13, 2020
August 13, 2020
Highlight

Tidal marshes, seagrass beds, and tidal forests are exceptional at absorbing and storing carbon. They are referred to as total ecosystem carbon stocks, yet little data exists quantifying how much carbon is absorbed and stored by tidal wetlands in the Pacific Northwest (PNW). Knowing this information is valuable, particularly in the context of sea level rise and with the associated need for Earth system modeling to predict changes at the coast.

The Science

Researchers found that the average total ecosystem carbon stock in the PNW is higher than in other areas of the U.S. and other parts of the world. Marsh carbon stocks, in particular, are twice the global average. Researchers found progressive increases in total ecosystem carbon stocks along the elevation gradient of coastal wetland types common in the PNW: seagrass, low marshes, high marshes, and tidal forests. Total carbon also increased along the salinity gradient, with more carbon occurring in lower salinity areas.

Additionally, this research showed that common methods used to estimate soil carbon actually underestimate soil carbon stocks in coastal wetlands. Soil carbon storage below the depth of 100 centimeters proved to be an important carbon pool in PNW tidal wetlands.

The Impact

The results suggest that long-term sea-level rise impacts, such as tidal inundation and increased soil salinity, will likely decrease ecosystem carbon stocks. This is a concern if wetlands can’t migrate with increased sea level due to being bound by topography and human development.  

Summary

This research arose from the Pacific Northwest Blue Carbon Working Group, of which Amy Borde and Heida Diefenderfer of Pacific Northwest National Laboratory’s Coastal Sciences Division are members. The team studied 28 tidal ecosystems across the PNW coast, from Humboldt Bay, California, to Padilla Bay, Washington. They sampled common coastal wetland types that occur along broad gradients of elevation, salinity, and tidal influences, collecting the data necessary to calculate total carbon stocks in both above ground biomass and the soil profile.

In three years of study, the researchers found that most carbon is in the wetland soils not aboveground, and much of it is deeper than one meter—a typical lower limit of sampling. Total ecosystem carbon stocks progressively increased along the terrestrial-aquatic gradient of coastal wetland ecosystems common in the temperate zone including seagrass, low marshes, high marshes, and tidal forests. The findings were reported in “Total Ecosystem Carbon Stocks at the Marine-Terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, USA,” published in the August 2020 online edition of Global Change Biology (DOI: 10.1111/gcb.15248).

Research Team: PNNL’s Amy Borde and Heida Diefenderfer, along with J. Boone Kauffman, Leila Giovanonni, James Kelly, Nicholas Dunstan, and Christopher Janousek (Oregon State University); Craig Cornu and Laura Brophy (Institute for Applied Ecology/Estuary Technical Group); and Jude Apple (Padilla Bay National Estuarine Research Reserve).

Funding

The grant award was administered by the Institute of Applied Ecology, and other partners included Oregon State University and the Padilla Bay National Estuarine Research Reserve. This research was supported by the National Oceanic and Atmospheric Administration, through a cooperative agreement with the University of Michigan. 

10.1111/gcb.15248

Kauffman, J Boone, Leila Giovanonni, James Kelly, Nicholas Dunstan, Amy Borde, Heida Diefenderfer, Craig Cornu, Christopher Janousek, Jude Apple, and Laura Brophy. “Total Ecosystem Carbon Stocks at the Marine‐terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, United States.” Global change biology, no. 0 (August 11, 2020). DOI: 10.1111/GCB.15248

August 11, 2020

Making Sense of the 2018 National Biodefense Strategy

July 23, 2020
July 23, 2020
Journal Article

Following the release of the 2018 National Biodefense Strategy, PNNL released a second-generation, publicly available tool—free for use at https://bplat.pnnl.gov—that maps out current biodefense responsibilities and brings clarity to the tangle of laws, directives, and agencies that together protect US citizens. The Biodefense Policy Landscape Analysis Tool, or B-PLAT, is affectionately called the “spaghetti monster,” because it visualizes information using spaghetti-like strands to demonstrate relationships between agencies, their specific responsibilities, and the degree of complexity and interconnectedness of the biodefense policy domain.

RA Bartholomew and KM Omberg.  “Making Sense of the 2018 National Biodefense Strategy.” Bulletin of the Atomic Scientists.  January 2019.  https://thebulletin.org/2019/01/making-sense-of-the-2018-national-biodefense-strategy/ 

January 18, 2019

A Publicly Available Landscape Analysis Tool for Biodefense Policy

July 23, 2020
July 23, 2020
Journal Article

In 2017, Pacific Northwest National Laboratory chartered an internal effort to capture relevant federal biodefense policy directives and laws in a format conducive to visualization and to better understanding the current state of the US biodefense enterprise.The resulting Biodefense Policy Landscape Analysis Tool (B-PLAT) is publicly available and captures more than 200 enduring biodefense responsibilities assigned by the following directives and laws.

10.1089/hs.2017.0088

KM Omberg, LR Franklin, DR Jackson, KL Taylor, KL Wahl, A Lesperance,  EM Wynkoop, JAS Gray, OP Leiser, SL Frazar, RM Ozanich , and RA Bartholomew. “A Publicly Available Landscape Analysis Tool for Biodefense.” Health Security. February 16(1): 2018.  DOI:  10.1089/hs.2017.0088

February 1, 2018
JULY 14, 2020
Web Feature

Turning the Tides

Their consistency and predictability makes tidal energy attractive, not only as a source of electricity but, potentially, as a mechanism to provide reliability and resilience to regional or local power grids.
MARCH 12, 2020
Web Feature

Tracking Toxics in the Salish Sea

With the help of a diagnostic tool called the Salish Sea Model, researchers found that toxic contaminant hotspots in the Puget Sound are tied to localized lack of water circulation and cumulative effects from multiple sources.

Probabilistic Projections of Sea-Level Rise and Global Mean Temperature using Hector

Sea level at port

Scientists are exploring ways to derive more accurate predictions that can help with preparations for changing sea levels.

Coupled Hector and BRICK models to analyze parametric uncertainties in extreme temperature and sea-level rise projections.

January 22, 2020
January 22, 2020
Highlight

The Science
Simple earth system models are useful tools for quantifying uncertainty, given their flexibility, computational efficiency, and suitability for the largeensemble frameworks necessary for statistical estimation. A team including researchers from Pacific Northwest National Laboratory coupled a new version of the simple model Hector with a 1D diffusive heat and energy balance model (Diffusion Ocean Energy balance CLIMate model) and a sea-level change module (Building blocks for Relevant Ice and Climate Knowledge) that also represents contributions from thermal expansion, glaciers and ice caps, and polar ice sheets. They applied a Bayesian calibration approach to quantify model uncertainties surrounding 39 model parameters, using observational and historical information from global surface temperature, thermal expansion, and other contributors to sea-level change, to analyze the effects of different sources of information on extreme sea-level rise projections.

The Impact
Different observational constraints can yield similar temperatures but drastically different sea-level rise projections, particularly for extreme sea-level rise scenarios. Results pave the way for new research linking global climate uncertainties (e.g., climate sensitivity) with local-scale flood risk analysis.

Summary
Using observational and historical information from global surface temperature, thermal expansion, and other contributors to sea-level change, the research team applied Bayesian calibration to quantify model uncertainties surrounding model parameters and analyzed the effects of different sources of information on extreme sea-level rise projections. They found that the addition of thermal expansion as an observational constraint sharpens inference for the upper tail of equilibrium climate sensitivity estimates (the 97.5 percentile is tightened from 7.1 to 6.6 K), while other contributors to sea-level change play lesser roles. The thermal expansion constraint also has implications for probabilistic projections of global surface temperature (the 97.5 percentile for RCP8.5, year-2100 temperature decreases 0.3 K). Ocean heat data provide a somewhat sharper equilibrium climate sensitivity estimate, while thermal expansion data allow for constrained sea-level projections. Different combinations of observational constraints can yield very similar year-2100 temperatures but drastically different SLR projections. This is particularly important for extreme sea-level projections.

PNNL Contact
Mohamad Hejazi, Pacific Northwest National Laboratory, Mohamad.Hejazi@pnnl.gov  

Funding
This research was supported by the U.S. Department of Energy Office of Science, Biological and Environmental Research through the MultiSector Dynamics, Earth and Environmental System Modeling Program, as well as the Penn State Center for Climate Risk Management

Vega-Westhoff B, RL Sriver, CA Hartin, TE Wong, and K Keller. 2019. “Impacts of observational constraints related to sea level on estimates of climate sensitivity.” Earth’s Future 7(6):667‒690. DOI: 10.1029/2018EF001082

May 7, 2019
May 7, 2019
JANUARY 21, 2020
Web Feature

Forensic Proteomics: Beyond DNA Profiling

A new book by PNNL biochemist Erick Merkley details forensic proteomics, a technique that directly analyzes proteins in unknown samples, in pursuit of making proteomics a widespread forensic method when DNA is missing or ambiguous.
DECEMBER 6, 2019
Web Feature

Converging on Coastal Science

Advancing a more collective understanding of coastal systems dynamics and evolution is a formidable scientific challenge. PNNL is meeting the challenge head on to inform decisions for the future.
AUGUST 14, 2019
Web Feature

Modeling the Future of a Sea

The inner Salish Sea’s future response to climate change, while significant, is predicted to be less severe than that of the open ocean based on parameters like algal blooms, ocean acidification, and annual occurrences of hypoxia.

STAR Workshop: Terrestrial-Aquatic Research in Coastal Systems

August 9, 2019
August 8, 2019
Report

From September 24–26, 2018, Pacific Northwest National Laboratory hosted a System for Terrestrial–Aquatic Research (STAR) workshop to discuss terrestrial–aquatic interface (TAI) research needs. The purpose of this workshop was to continue discussion initiated at the 2016 Department of Energy (DOE)-Biological and Environmental Research (BER) workshop: Research Priorities to Incorporate Terrestrial–Aquatic Interfaces in Earth System Models. Specifically, this workshop focused on terrestrial–aquatic interfaces near the coastline, which have been identified as a major gap in Earth system models (ESMs) and observational networks, important ecosystems that are vulnerable to disturbances from both the land and sea, as well as hubs for human habitation and commerce.

STAR Workshop Report

PNNL – Pacific Northwest National Laboratory. 2019.  STAR Workshop: Terrestrial-Aquatic Research in Coastal Systems. PNNL-28611, Pacific Northwest National Laboratory, Richland, Washington.

April 15, 2019
JULY 17, 2019
Web Feature

Keeping First Responders Safe

When two powerful earthquakes rocked southern California earlier this month, officials’ attention focused, understandably, on safety. How many people were injured? Were buildings up to code? How good are we at predicting earthquakes?