{"id":{"repo_id":"east-anglia","oai_identifier":"oai:ueaeprints.uea.ac.uk:42328"},"canonical_url":"https://search.dev.ndltd.org/etd/east-anglia/oai:ueaeprints.uea.ac.uk:42328","repository":{"repo_id":"east-anglia","name":"University of East Anglia","base_url":"https://ueaeprints.uea.ac.uk/cgi/oai2"},"display":{"title":"Spatio-temporal Variability in Surface Ocean pCO2 Inferred from Observations","abstract":"The variability of surface ocean pCO2 is examined on multiple spatial and temporal scales. Temporal autocorrelation analysis is used to examine pCO2 variability over multiple years. Spatial autocorrelation analysis describes pCO2 variability over multiple spatial scales. Spatial autocorrelation lengths range between <50 km in coastal regions and other areas of physical turbulence up to 3,000 km along major currents. Analysis of the drivers of pCO2 shows that ocean currents are the primary driver of spatial variability. Autocorrelation lengths of air-sea CO2 fluxes are approximately half as long as for pCO2 due to the effects of highly variable wind speeds. The influence of modes of climate variability on ocean pCO2 and related air-sea CO2 fluxes is examined through correlations of climate indices with interannual pCO2 anomalies separated from the long-term trend and mean seasonal cycle. Changes in the El Ni˜no Southern Oscillation alter pCO2 levels by -6.6 � 1.0 �atm per index unit (�atm iu","abstract_html":"The variability of surface ocean pCO2 is examined on multiple spatial and temporal scales. Temporal autocorrelation analysis is used to examine pCO2 variability over multiple years. Spatial autocorrelation analysis describes pCO2 variability over multiple spatial scales. Spatial autocorrelation lengths range between &lt;50 km in coastal regions and other areas of physical turbulence up to 3,000 km along major currents. Analysis of the drivers of pCO2 shows that ocean currents are the primary driver of spatial variability. Autocorrelation lengths of air-sea CO2 fluxes are approximately half as long as for pCO2 due to the effects of highly variable wind speeds. The influence of modes of climate variability on ocean pCO2 and related air-sea CO2 fluxes is examined through correlations of climate indices with interannual pCO2 anomalies separated from the long-term trend and mean seasonal cycle. Changes in the El Ni˜no Southern Oscillation alter pCO2 levels by -6.6 � 1.0 �atm per index unit (�atm iu","abstract_has_math":false,"creators":["Jones, Steve"],"institution":"University of East Anglia","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-11","date_published":"2012-11","updated_at":"2026-07-24T02:11:51Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Jones, Steve"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012-11"]},{"key":"dc:date.issued","label":"Date","values":["2012-11"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Schoolof Environmental Sciences"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of East Anglia"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://ueaeprints.uea.ac.uk/id/eprint/42328/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://ueaeprints.uea.ac.uk/id/eprint/42328/1/2012JonesPhD.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The variability of surface ocean pCO2 is examined on multiple spatial and temporal scales. Temporal autocorrelation analysis is used to examine pCO2 variability over multiple years. Spatial autocorrelation analysis describes pCO2 variability over multiple spatial scales. Spatial autocorrelation lengths range between <50 km in coastal regions and other areas of physical turbulence up to 3,000 km along major currents. Analysis of the drivers of pCO2 shows that ocean currents are the primary driver of spatial variability. Autocorrelation lengths of air-sea CO2 fluxes are approximately half as long as for pCO2 due to the effects of highly variable wind speeds. The influence of modes of climate variability on ocean pCO2 and related air-sea CO2 fluxes is examined through correlations of climate indices with interannual pCO2 anomalies separated from the long-term trend and mean seasonal cycle. Changes in the El Ni˜no Southern Oscillation alter pCO2 levels by -6.6 � 1.0 �atm per index unit (�atm iu"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Spatio-temporal Variability in Surface Ocean pCO2 Inferred from Observations"]}]}],"canonical_facts":{"dc:creator":["Jones, Steve"],"dc:date":["2012-11"],"dc:date.issued":["2012-11"],"dc:description.abstract":["The variability of surface ocean pCO2 is examined on multiple spatial and temporal scales. Temporal autocorrelation analysis is used to examine pCO2 variability over multiple years. Spatial autocorrelation analysis describes pCO2 variability over multiple spatial scales. Spatial autocorrelation lengths range between <50 km in coastal regions and other areas of physical turbulence up to 3,000 km along major currents. Analysis of the drivers of pCO2 shows that ocean currents are the primary driver of spatial variability. Autocorrelation lengths of air-sea CO2 fluxes are approximately half as long as for pCO2 due to the effects of highly variable wind speeds. The influence of modes of climate variability on ocean pCO2 and related air-sea CO2 fluxes is examined through correlations of climate indices with interannual pCO2 anomalies separated from the long-term trend and mean seasonal cycle. Changes in the El Ni˜no Southern Oscillation alter pCO2 levels by -6.6 � 1.0 �atm per index unit (�atm iu"],"dc:format":["application/pdf"],"dc:identifier.uri":["https://ueaeprints.uea.ac.uk/id/eprint/42328/1/2012JonesPhD.pdf"],"dc:language":["en"],"dc:publisher.department":["Schoolof Environmental Sciences"],"dc:publisher.institution":["University of East Anglia"],"dc:relation.isreferencedby":["https://ueaeprints.uea.ac.uk/id/eprint/42328/"],"dc:title":["Spatio-temporal Variability in Surface Ocean pCO2 Inferred from Observations"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T02:11:51Z"}