Gradual and Abrupt Shifts in Ocean Chemistry During Phanerozoic Time

1984 ◽  
pp. 123-143 ◽  
Author(s):  
W. T. Holser
Keyword(s):  
Ocean Chemistry ◽  
Abrupt Shifts

IDENTIFYING LONG-TERM SEAWATER CIRCULATION IN COASTAL AQUIFERS AND ITS POTENTIAL ROLE IN OCEAN CHEMISTRY

2017 ◽  
Author(s):  
Yael Kiro ◽  
◽  
Holly A. Michael ◽  
Carlos Duque ◽  
Yoseph Yechieli
Keyword(s):  
Coastal Aquifers ◽  
Potential Role ◽  
Ocean Chemistry

Paleo-ocean chemistry records in marine opal: Implications for fluxes of trace elements, cosmogenic nuclides (10Be and 26Al), and biological productivity

2006 ◽  
Vol 70 (13) ◽  
pp. 3275-3289 ◽  
Author(s):  
D. Lal ◽  
C. Charles ◽  
L. Vacher ◽  
J.N. Goswami ◽  
A.J.T. Jull ◽  
...  
Keyword(s):  
Trace Elements ◽  
Cosmogenic Nuclides ◽  
Biological Productivity ◽  
Ocean Chemistry

scholarly journals Hydrothermal Flux and Global Ocean Chemistry.

1995 ◽  
Vol 104 (1) ◽  
pp. 16-27 ◽  
Author(s):  
Naotatsu SHIKAZONO
Keyword(s):  
Global Ocean ◽  
Ocean Chemistry

Econometrics for Modelling Climate Change

2021 ◽  
Author(s):  
Jennifer L. Castle ◽  
David F. Hendry
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Econometric Modeling ◽  
Complex Data ◽  
Explanatory Variables ◽  
Stochastic Trends ◽  
Trend Breaks ◽  
Abrupt Shifts ◽  
Climate Time Series ◽  
Do So

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

scholarly journals Ocean Acidification 2.0: Managing our Changing Coastal Ocean Chemistry

BioScience ◽  
2014 ◽  
Vol 64 (7) ◽  
pp. 581-592 ◽  
Author(s):  
Aaron L. Strong ◽  
Kristy J. Kroeker ◽  
Lida T. Teneva ◽  
Lindley A. Mease ◽  
Ryan P. Kelly
Keyword(s):  
Ocean Acidification ◽  
Coastal Ocean ◽  
Ocean Chemistry

Ocean chemistry and deep-sea sediments

1993 ◽  
Vol 110 (1-2) ◽  
pp. 177-178
Author(s):  
R. Chester
Keyword(s):  
Deep Sea ◽  
Deep Sea Sediments ◽  
Ocean Chemistry
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