Distinct seasonal climate drivers revealed in a network of tree-ring records from Labrador, Canada View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2020-01-02

AUTHORS

R. Parfitt, C. C. Ummenhofer, B. M. Buckley, K. G. Hansen, R. D. D’Arrigo

ABSTRACT

Traditionally, high-latitude dendroclimatic studies have focused on measurements of total ring width (RW), with the maximum density of the latewood (MXD) serving as a complementary variable. Whilst MXD has typically improved the strength of the growing season climate connection over that of RW, its measurements are costly and time-consuming. Recently, a less costly and more time-efficient technique to extract density measurements has emerged, based on lignin’s propensity to absorb blue light. This Blue Intensity (BI) methodology is based on image analyses of finely-sanded core samples, and the relative ease with which density measurements can be extracted allows for significant increases in spatio-temporal sample depth. While some studies have attempted to combine RW and MXD as predictors for summer temperature reconstructions, here we evaluate a systematic comparison of the climate signal for RW and latewood BI (LWBI) separately, using a recently updated and expanded tree ring database for Labrador, Canada. We demonstrate that while RW responds primarily to climatic drivers earlier in the growing season (January–April), LWBI is more responsive to climate conditions during late spring and summer (May–August). Furthermore, RW appears to be driven primarily by large-scale atmospheric dynamics associated with the Pacific North American pattern, whilst LWBI is more closely associated with local climate conditions, themselves linked to the behaviour of the Atlantic Multidecadal Oscillation. Lastly, we demonstrate that anomalously wide or narrow growth rings consistently respond to the same climate drivers as average growth years, whereas the sensitivity of LWBI to extreme climate conditions appears to be enhanced. More... »

PAGES

1897-1911

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-019-05092-6

DOI

http://dx.doi.org/10.1007/s00382-019-05092-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1123774843


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0406", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Geography and Environmental Geoscience", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.56466.37", 
          "name": [
            "Department of Earth Ocean and Atmospheric Science, Florida State University, Tallahassee, FL, USA", 
            "Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Parfitt", 
        "givenName": "R.", 
        "id": "sg:person.012534064056.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012534064056.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA", 
          "id": "http://www.grid.ac/institutes/grid.56466.37", 
          "name": [
            "Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ummenhofer", 
        "givenName": "C. C.", 
        "id": "sg:person.0723530564.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723530564.41"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tree-Ring Laboratory, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA", 
          "id": "http://www.grid.ac/institutes/grid.473157.3", 
          "name": [
            "Tree-Ring Laboratory, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Buckley", 
        "givenName": "B. M.", 
        "id": "sg:person.01360445530.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01360445530.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tree-Ring Laboratory, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA", 
          "id": "http://www.grid.ac/institutes/grid.473157.3", 
          "name": [
            "Tree-Ring Laboratory, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hansen", 
        "givenName": "K. G.", 
        "id": "sg:person.015274566154.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015274566154.69"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tree-Ring Laboratory, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA", 
          "id": "http://www.grid.ac/institutes/grid.473157.3", 
          "name": [
            "Tree-Ring Laboratory, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "D\u2019Arrigo", 
        "givenName": "R. D.", 
        "id": "sg:person.01250635254.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01250635254.19"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/978-94-015-7879-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017783681", 
          "https://doi.org/10.1007/978-94-015-7879-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-018-4513-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107848907", 
          "https://doi.org/10.1007/s00382-018-4513-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-002-0275-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1086138775", 
          "https://doi.org/10.1007/s00382-002-0275-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms4836", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013461581", 
          "https://doi.org/10.1038/ncomms4836"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4020-5725-0_5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035764884", 
          "https://doi.org/10.1007/978-1-4020-5725-0_5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00704-014-1248-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018906354", 
          "https://doi.org/10.1007/s00704-014-1248-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-74065-7_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040001898", 
          "https://doi.org/10.1007/978-3-642-74065-7_8"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2020-01-02", 
    "datePublishedReg": "2020-01-02", 
    "description": "Traditionally, high-latitude dendroclimatic studies have focused on measurements of total ring width (RW), with the maximum density of the latewood (MXD) serving as a complementary variable. Whilst MXD has typically improved the strength of the growing season climate connection over that of RW, its measurements are costly and time-consuming. Recently, a less costly and more time-efficient technique to extract density measurements has emerged, based on lignin\u2019s propensity to absorb blue light. This Blue Intensity (BI) methodology is based on image analyses of finely-sanded core samples, and the relative ease with which density measurements can be extracted allows for significant increases in spatio-temporal sample depth. While some studies have attempted to combine RW and MXD as predictors for summer temperature reconstructions, here we evaluate a systematic comparison of the climate signal for RW and latewood BI (LWBI) separately, using a recently updated and expanded tree ring database for Labrador, Canada. We demonstrate that while RW responds primarily to climatic drivers earlier in the growing season (January\u2013April), LWBI is more responsive to climate conditions during late spring and summer (May\u2013August). Furthermore, RW appears to be driven primarily by large-scale atmospheric dynamics associated with the Pacific North American pattern, whilst LWBI is more closely associated with local climate conditions, themselves linked to the behaviour of the Atlantic Multidecadal Oscillation. Lastly, we demonstrate that anomalously wide or narrow growth rings consistently respond to the same climate drivers as average growth years, whereas the sensitivity of LWBI to extreme climate conditions appears to be enhanced.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00382-019-05092-6", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.5300864", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5301189", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3-4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "54"
      }
    ], 
    "keywords": [
      "latewood BI", 
      "ring width", 
      "climate drivers", 
      "climate conditions", 
      "Pacific\u2013North American pattern", 
      "large-scale atmospheric dynamics", 
      "Atlantic Multidecadal Oscillation", 
      "summer temperature reconstruction", 
      "tree-ring records", 
      "North American pattern", 
      "more time-efficient technique", 
      "total ring width", 
      "extreme climate conditions", 
      "local climate conditions", 
      "climate connection", 
      "climate signals", 
      "Multidecadal Oscillation", 
      "temperature reconstructions", 
      "atmospheric dynamics", 
      "American pattern", 
      "dendroclimatic studies", 
      "climatic drivers", 
      "core samples", 
      "narrow growth rings", 
      "late spring", 
      "sample depth", 
      "growth rings", 
      "growth years", 
      "Labrador", 
      "MXD", 
      "density measurements", 
      "time-efficient technique", 
      "Canada", 
      "maximum density", 
      "summer", 
      "drivers", 
      "depth", 
      "spring", 
      "complementary variables", 
      "latewood", 
      "season", 
      "records", 
      "measurements", 
      "oscillations", 
      "conditions", 
      "reconstruction", 
      "systematic comparison", 
      "dynamics", 
      "patterns", 
      "samples", 
      "years", 
      "Bi", 
      "width", 
      "comparison", 
      "increase", 
      "study", 
      "allows", 
      "image analysis", 
      "relative ease", 
      "significant increase", 
      "signals", 
      "connection", 
      "analysis", 
      "density", 
      "variables", 
      "sensitivity", 
      "methodology", 
      "strength", 
      "network", 
      "technique", 
      "light", 
      "behavior", 
      "database", 
      "ring", 
      "predictors", 
      "propensity", 
      "ease", 
      "blue light"
    ], 
    "name": "Distinct seasonal climate drivers revealed in a network of tree-ring records from Labrador, Canada", 
    "pagination": "1897-1911", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1123774843"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00382-019-05092-6"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00382-019-05092-6", 
      "https://app.dimensions.ai/details/publication/pub.1123774843"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-11-24T21:05", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221124/entities/gbq_results/article/article_834.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00382-019-05092-6"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00382-019-05092-6'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00382-019-05092-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00382-019-05092-6'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00382-019-05092-6'


 

This table displays all metadata directly associated to this object as RDF triples.

199 TRIPLES      21 PREDICATES      109 URIs      94 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00382-019-05092-6 schema:about anzsrc-for:04
2 anzsrc-for:0406
3 schema:author Ne5e8cd69c023492db913f3f837675781
4 schema:citation sg:pub.10.1007/978-1-4020-5725-0_5
5 sg:pub.10.1007/978-3-642-74065-7_8
6 sg:pub.10.1007/978-94-015-7879-0
7 sg:pub.10.1007/s00382-002-0275-3
8 sg:pub.10.1007/s00382-018-4513-8
9 sg:pub.10.1007/s00704-014-1248-2
10 sg:pub.10.1038/ncomms4836
11 schema:datePublished 2020-01-02
12 schema:datePublishedReg 2020-01-02
13 schema:description Traditionally, high-latitude dendroclimatic studies have focused on measurements of total ring width (RW), with the maximum density of the latewood (MXD) serving as a complementary variable. Whilst MXD has typically improved the strength of the growing season climate connection over that of RW, its measurements are costly and time-consuming. Recently, a less costly and more time-efficient technique to extract density measurements has emerged, based on lignin’s propensity to absorb blue light. This Blue Intensity (BI) methodology is based on image analyses of finely-sanded core samples, and the relative ease with which density measurements can be extracted allows for significant increases in spatio-temporal sample depth. While some studies have attempted to combine RW and MXD as predictors for summer temperature reconstructions, here we evaluate a systematic comparison of the climate signal for RW and latewood BI (LWBI) separately, using a recently updated and expanded tree ring database for Labrador, Canada. We demonstrate that while RW responds primarily to climatic drivers earlier in the growing season (January–April), LWBI is more responsive to climate conditions during late spring and summer (May–August). Furthermore, RW appears to be driven primarily by large-scale atmospheric dynamics associated with the Pacific North American pattern, whilst LWBI is more closely associated with local climate conditions, themselves linked to the behaviour of the Atlantic Multidecadal Oscillation. Lastly, we demonstrate that anomalously wide or narrow growth rings consistently respond to the same climate drivers as average growth years, whereas the sensitivity of LWBI to extreme climate conditions appears to be enhanced.
14 schema:genre article
15 schema:isAccessibleForFree false
16 schema:isPartOf N4be88bbc77bb43e088cdc6c7cb770ad7
17 N6726927f83c041a29d38ccdf00e12e73
18 sg:journal.1049631
19 schema:keywords American pattern
20 Atlantic Multidecadal Oscillation
21 Bi
22 Canada
23 Labrador
24 MXD
25 Multidecadal Oscillation
26 North American pattern
27 Pacific–North American pattern
28 allows
29 analysis
30 atmospheric dynamics
31 behavior
32 blue light
33 climate conditions
34 climate connection
35 climate drivers
36 climate signals
37 climatic drivers
38 comparison
39 complementary variables
40 conditions
41 connection
42 core samples
43 database
44 dendroclimatic studies
45 density
46 density measurements
47 depth
48 drivers
49 dynamics
50 ease
51 extreme climate conditions
52 growth rings
53 growth years
54 image analysis
55 increase
56 large-scale atmospheric dynamics
57 late spring
58 latewood
59 latewood BI
60 light
61 local climate conditions
62 maximum density
63 measurements
64 methodology
65 more time-efficient technique
66 narrow growth rings
67 network
68 oscillations
69 patterns
70 predictors
71 propensity
72 reconstruction
73 records
74 relative ease
75 ring
76 ring width
77 sample depth
78 samples
79 season
80 sensitivity
81 signals
82 significant increase
83 spring
84 strength
85 study
86 summer
87 summer temperature reconstruction
88 systematic comparison
89 technique
90 temperature reconstructions
91 time-efficient technique
92 total ring width
93 tree-ring records
94 variables
95 width
96 years
97 schema:name Distinct seasonal climate drivers revealed in a network of tree-ring records from Labrador, Canada
98 schema:pagination 1897-1911
99 schema:productId N2266ddabfc2f4b3a8adef20de86e6ee9
100 Nddd6c52c53b44f02869c4b62888f203c
101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1123774843
102 https://doi.org/10.1007/s00382-019-05092-6
103 schema:sdDatePublished 2022-11-24T21:05
104 schema:sdLicense https://scigraph.springernature.com/explorer/license/
105 schema:sdPublisher Nbaa904aef4624fcdb7a440c3deff305d
106 schema:url https://doi.org/10.1007/s00382-019-05092-6
107 sgo:license sg:explorer/license/
108 sgo:sdDataset articles
109 rdf:type schema:ScholarlyArticle
110 N1b8eea18737740f0b55965fa212122df rdf:first sg:person.015274566154.69
111 rdf:rest N7e08baff11a148fbb8994983644004ca
112 N2266ddabfc2f4b3a8adef20de86e6ee9 schema:name dimensions_id
113 schema:value pub.1123774843
114 rdf:type schema:PropertyValue
115 N4be88bbc77bb43e088cdc6c7cb770ad7 schema:issueNumber 3-4
116 rdf:type schema:PublicationIssue
117 N6726927f83c041a29d38ccdf00e12e73 schema:volumeNumber 54
118 rdf:type schema:PublicationVolume
119 N6cdaea397f1d4da78a6d75597e4a50df rdf:first sg:person.0723530564.41
120 rdf:rest Nf331585531434913bf322cf773c54e52
121 N7e08baff11a148fbb8994983644004ca rdf:first sg:person.01250635254.19
122 rdf:rest rdf:nil
123 Nbaa904aef4624fcdb7a440c3deff305d schema:name Springer Nature - SN SciGraph project
124 rdf:type schema:Organization
125 Nddd6c52c53b44f02869c4b62888f203c schema:name doi
126 schema:value 10.1007/s00382-019-05092-6
127 rdf:type schema:PropertyValue
128 Ne5e8cd69c023492db913f3f837675781 rdf:first sg:person.012534064056.16
129 rdf:rest N6cdaea397f1d4da78a6d75597e4a50df
130 Nf331585531434913bf322cf773c54e52 rdf:first sg:person.01360445530.45
131 rdf:rest N1b8eea18737740f0b55965fa212122df
132 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
133 schema:name Earth Sciences
134 rdf:type schema:DefinedTerm
135 anzsrc-for:0406 schema:inDefinedTermSet anzsrc-for:
136 schema:name Physical Geography and Environmental Geoscience
137 rdf:type schema:DefinedTerm
138 sg:grant.5300864 http://pending.schema.org/fundedItem sg:pub.10.1007/s00382-019-05092-6
139 rdf:type schema:MonetaryGrant
140 sg:grant.5301189 http://pending.schema.org/fundedItem sg:pub.10.1007/s00382-019-05092-6
141 rdf:type schema:MonetaryGrant
142 sg:journal.1049631 schema:issn 0930-7575
143 1432-0894
144 schema:name Climate Dynamics
145 schema:publisher Springer Nature
146 rdf:type schema:Periodical
147 sg:person.01250635254.19 schema:affiliation grid-institutes:grid.473157.3
148 schema:familyName D’Arrigo
149 schema:givenName R. D.
150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01250635254.19
151 rdf:type schema:Person
152 sg:person.012534064056.16 schema:affiliation grid-institutes:grid.56466.37
153 schema:familyName Parfitt
154 schema:givenName R.
155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012534064056.16
156 rdf:type schema:Person
157 sg:person.01360445530.45 schema:affiliation grid-institutes:grid.473157.3
158 schema:familyName Buckley
159 schema:givenName B. M.
160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01360445530.45
161 rdf:type schema:Person
162 sg:person.015274566154.69 schema:affiliation grid-institutes:grid.473157.3
163 schema:familyName Hansen
164 schema:givenName K. G.
165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015274566154.69
166 rdf:type schema:Person
167 sg:person.0723530564.41 schema:affiliation grid-institutes:grid.56466.37
168 schema:familyName Ummenhofer
169 schema:givenName C. C.
170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723530564.41
171 rdf:type schema:Person
172 sg:pub.10.1007/978-1-4020-5725-0_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035764884
173 https://doi.org/10.1007/978-1-4020-5725-0_5
174 rdf:type schema:CreativeWork
175 sg:pub.10.1007/978-3-642-74065-7_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040001898
176 https://doi.org/10.1007/978-3-642-74065-7_8
177 rdf:type schema:CreativeWork
178 sg:pub.10.1007/978-94-015-7879-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017783681
179 https://doi.org/10.1007/978-94-015-7879-0
180 rdf:type schema:CreativeWork
181 sg:pub.10.1007/s00382-002-0275-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086138775
182 https://doi.org/10.1007/s00382-002-0275-3
183 rdf:type schema:CreativeWork
184 sg:pub.10.1007/s00382-018-4513-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107848907
185 https://doi.org/10.1007/s00382-018-4513-8
186 rdf:type schema:CreativeWork
187 sg:pub.10.1007/s00704-014-1248-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018906354
188 https://doi.org/10.1007/s00704-014-1248-2
189 rdf:type schema:CreativeWork
190 sg:pub.10.1038/ncomms4836 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013461581
191 https://doi.org/10.1038/ncomms4836
192 rdf:type schema:CreativeWork
193 grid-institutes:grid.473157.3 schema:alternateName Tree-Ring Laboratory, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA
194 schema:name Tree-Ring Laboratory, Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, USA
195 rdf:type schema:Organization
196 grid-institutes:grid.56466.37 schema:alternateName Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA
197 schema:name Department of Earth Ocean and Atmospheric Science, Florida State University, Tallahassee, FL, USA
198 Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA
199 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...