Archive for biogeochemical models

Water clarity impacts temperature and biogeochemistry in Chesapeake Bay

Posted by mmaheigan 
· Thursday, December 3rd, 2020 

Estuarine water clarity is determined by suspended materials in the water, including colored dissolved organic matter, phytoplankton, sediment, and detritus. These constituents directly affect temperature because when water is opaque, sunlight heats only the shallowest layers near the surface, but when water is clear, sunlight can penetrate deeper, warming the waters below the surface. Despite the importance of accurately predicting temperature variability, many numerical modeling studies do not adequately parameterize this fundamental relationship between water clarity and temperature.

In a recent study published in Estuaries and Coasts, the authors quantified the impact of a more realistic representation of water clarity in a hydrodynamic-biogeochemical model of the Chesapeake Bay by comparing two simulations: (1) water clarity is constant in space and time for the calculation of solar heating vs. (2) water clarity varies with modeled concentrations of light-attenuating materials. In the variable water clarity simulation (2), the water is more opaque, particularly in the northern region of the Bay. During the spring and summer months, the lower water clarity in the northern Bay is associated with warmer surface temperatures and colder bottom temperatures. Warmer surface temperatures encourage phytoplankton growth and nutrient uptake near the head of the Bay, thus fewer nutrients are transported downstream. These conditions are exacerbated during high-river flow years, when differences in temperature, nutrients, phytoplankton, and zooplankton extend further seaward.

Figure 1: Top row: Difference in the light attenuation coefficient for shortwave heating, kh[m-1] (variable minus constant light attenuation simulation). June, July, and August average for (A) 2001, (B) average of 2001-2005, and (C) 2003; difference in bottom temperatures [oC] (variable minus constant). Bottom row: Difference in June, July, and August average bottom temperature for (D) 2001, (E) average of 2001-2005, and (F) 2003. Data for 2001 are representative of low river discharge, and 2003 are representative high river discharge years.

This work demonstrates that a constant light attenuation scheme for heating calculations in coupled hydrodynamic-biogeochemical models underestimates temperature variability, both temporally and spatially. This is an important finding for researchers who use models to predict future temperature variability and associated impacts on biogeochemistry and species habitability.

 

Authors:
Grace E. Kim (NASA, Goddard Space Flight Center)
Pierre St-Laurent (VIMS, William & Mary)
Marjorie A.M. Friedrichs (VIMS, William & Mary)
Antonio Mannino (NASA, Goddard Space Flight Center)

Predicting marine ecosystem futures

Posted by mmaheigan 
· Wednesday, September 4th, 2019 

Earth System Models (ESMs) are powerful and effective tools for exploring and predicting marine ecosystem response to environmental change, including biogeochemical processes that underlie threats to ocean health such as ocean acidification, deoxygenation, and changes in productivity. Seasonal to interannual marine biogeochemical predictions with ESMs hold great promise for exploring links between climate and marine resources such as fisheries but have thus far been challenged by limitations associated with observational initialization, model structure, and computational availability. In a recent study published in Science, authors integrated the Geophysical Fluid Dynamics Laboratory’s (GFDL) COBALT (Carbon, Ocean Biogeochemistry and Lower Trophics) marine biogeochemical model with seasonal to multi-annual climate predictions from GFDL’s CM2.1 climate model to examine marine ecosystem futures on these shorter time scales. The global biogeochemical forecasts were initialized on the first of each month between 1991 and 2017 with 12 ensemble members in each prediction, creating a database of nearly 4000 forecasts and 8000 simulation years. The model skillfully predicted seasonal to multi-annual chlorophyll fluctuations in many ocean regions (Figure 1).

 

Figure 1: Prediction skill in reproducing observed variations of monthly chlorophyll anomaly. (Top) Correlation coefficient between predicted and observed chlorophyll at 1-3 month lead time during the period 1997-2017. Stippled areas indicate that the correlation is significantly greater than 0 with 95% confidence. Areas with less than 80% satellite chlorophyll coverage are masked in grey. (Lower panels) Correlation coefficient between predicted and observed chlorophyll as a function of forecast initialization month (x-axis) and lead time (y-axis) in tropical Pacific, Indian, North Atlantic, North Pacific, and South Pacific oceans. In all panels, the darker the red, the higher the correlation up to a perfect correlation of 1.0. White indicates no correlation, while blue indicates negative correlation.

These results suggest that annual fish catches in selected large marine ecosystems can be predicted from chlorophyll and sea surface temperature anomalies up to 2-3 years in advance. Given that fisheries predictions sometimes failed to the point of commercial stock collapse in the past, this prediction capacity could be vital for fisheries managers. Biogeochemical prediction systems can extend beyond sea surface temperature and chlorophyll to include other potential drivers (e.g., oxygen, acidity, net primary production, zooplankton, etc.) as highly valuable tools for marine resource managers of dynamic and changing ecosystems.

Authors:
Jong-Yeon Park (Princeton Univ, NOAA GDFL, Chonbuk National Univ., Korea)
Charles A. Stock, John P. Dunne, Xiaosong Yang, and Anthony Rosati (NOAA GFDL)

Dust-borne iron in the Southern Ocean was more bioavailable during glacial periods

Posted by mmaheigan 
· Wednesday, January 23rd, 2019 

The Southern Ocean is iron (Fe)-limited, and increased fluxes of dust-borne Fe to the Southern Ocean during the Last Glacial Maximum (LGM) have been associated with phytoplankton growth and CO2 drawdown. Dust contains different mixes of Fe-bearing minerals, depending on the source region. Fe(II) silicate minerals from physical weathering are more bioavailable than Fe(III) oxyhydroxide minerals from chemical weathering. The Fe(II) silicates are dominant in dust sources that have been weathered from bedrock by glaciers in Patagonia, but the impact of glacial activity on dust-borne Fe speciation (Fe oxidation state and mineral composition) and bioavailability over the last glacial cycle has not previously been quantified.

Figure 1. The fraction of Fe(II) in dust (Fe(II)/Fetotal, dominated by Fe(II) silicates, shown as blue dots connected with dotted lines on blue axes) in marine sediment cores from (A) the South Atlantic and (B) the South Pacific plotted with the total dust flux (grey lines on grey axes).

A recent study in PNAS reconstructs the speciation of dust-borne Fe over the last glacial cycle in South Atlantic and South Pacific marine sediment cores using Fe K-edge X-ray absorption spectroscopy. The authors observed that the highly bioavailable Fe(II) silicate content of dust-borne Fe is higher in both regions during cold glacial periods, suggesting that a given flux of Fe is more bioavailable in glacial versus interglacial periods (Figure 1). Therefore, all Fe cannot be considered equal in biogeochemical models working on glacial-interglacial timescales. The bioavailability of a given flux of Fe at the LGM was likely a dominant driver of phytoplankton growth, with more bioavailable Fe driving increased phytoplankton activity and associated atmospheric CO2 drawdown and subsequent cooling. The observed association between glacial periods and increased Fe bioavailability in the Southern Ocean may indicate an important positive feedback mechanism between glacial activity and cold glacial temperatures through Fe speciation and the efficiency of the biological pump.

Paper link: https://doi.org/10.1073/pnas.1809755115

Authors:
Elizabeth M. Shoenfelt (Lamont-Doherty Earth Observatory, Columbia University)
Gisela Winckler (Lamont-Doherty Earth Observatory, Columbia University)
Frank Lamy (Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research)
Robert F. Anderson (Lamont-Doherty Earth Observatory, Columbia University)
Benjamin C. Bostick (Lamont-Doherty Earth Observatory, Columbia University)

 

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