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Climate forcing of the spring bloom in Chesapeake Bay.
Open accessAbstract
Interannual variability of the spring phytoplankton bloom is strongly expressed in estuarine ecosystems such as Chesapeake Bay.Quantifying this variability is essential to resolve ecosystem responses to eutrophication from variability imposed by climate.We developed a 'synoptic climatology' from surface sea-level pressure (SLP) maps to categorize and quantify atmospheric circulation patterns and address climate forcing of phytoplankton dynamics in the Bay.The 10 patterns we identified had unique frequencies-of-occurrence and associated meteorological conditions (i.e.precipitation, temperature, wind speed and direction).Four measures of phytoplankton biomass, surface chlorophyll a (B), euphotic layer chlorophyll a (B eu ), water column chlorophyll a (B wc ), and total biomass (B tot ), were obtained from remotely sensed ocean color data spanning 16 yr (1989 to 2004) combined with concurrent shipboard data.Years with more frequent warm/wet weather patterns had spring blooms that reached peak biomass farther seaward in the estuary, were greater in magnitude, occurred later in the spring, and covered a larger area than years with a predominance of cool/dry weather patterns.Frequencies of winter weather patterns were used to forecast spring B, B eu , B wc , and B tot , explaining between 23 and 89% of the variance in the regional time-series.Residuals from these models did not show time-trends attributable to either accelerating eutrophication or management actions intended to decrease nutrient loadings.These findings extend our understanding of climatic influences on phytoplankton dynamics in the Bay by quantifying the effects of synoptic climate variability on spring bloom intensity, thereby supporting forecasts of seasonal phytoplankton biomass based on sub-continental scale weather patterns in this mid-Atlantic estuary.
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Bloom patterns respond to regional climatic variations.
“Bloom patterns also respond to regional climatic variations (Miller and Harding, 2007;Morse et al., 2014).”
From Current and Future Remote Sensing of Harmful Algal Blooms in the Chesapeake Bay to Support the Shellfish Industry (2020) · cites this paper as Miller and Harding, 2007
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Metadata
- Authors
- Miller, W., & Harding Jr, L.
- Venue
- Marine Ecology Progress Series
- Publisher
- Inter-Research Science Center
- Published
- 2007-02-16
- Volume
- 331
- Pages
- 11-22
- ISSN
- 0171-8630 (ISSN-L)
- 1616-1599
- OpenAlex
- W2064050501
- Cited by
- 70
- References
- 39
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1 · 2023: 2 · 2022: 4 · 2021: 4 · 2020: 6 · 2019: 4 · 2018: 2 · 2017: 3 · 2016: 3 · 2015: 5 · 2014: 2 · 2013: 7 · 2012: 3
- FWCI
- 4.51
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Marine Ecology Progress Series (publishedVersion)
- Funders
- National Aeronautics and Space Administration
- National Oceanic and Atmospheric Administration
- Maryland Sea Grant, University of Maryland
- NASA Headquarters
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Marine Plankton and Biogeochemistry
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Keywords
- spring bloom, radiative forcing, Chesapeake Bay, synoptic climatology, atmospheric circulation patterns, sea level pressure, chlorophyll a, euphotic layer, eutrophication, interannual variability, bloom prediction, weather patterns
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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