Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results? View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-04

AUTHORS

Keith W. Dixon, John R. Lanzante, Mary Jo Nath, Katharine Hayhoe, Anne Stoner, Aparna Radhakrishnan, V. Balaji, Carlos F. Gaitán

ABSTRACT

Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods – namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications. More... »

PAGES

395-408

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10584-016-1598-0

DOI

http://dx.doi.org/10.1007/s10584-016-1598-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018344062


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Geophysical Fluid Dynamics Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.482795.5", 
          "name": [
            "NOAA Geophysical Fluid Dynamics Laboratory, 201 Forrestal Road, 08540, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dixon", 
        "givenName": "Keith W.", 
        "id": "sg:person.010171616627.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010171616627.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Geophysical Fluid Dynamics Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.482795.5", 
          "name": [
            "NOAA Geophysical Fluid Dynamics Laboratory, 201 Forrestal Road, 08540, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lanzante", 
        "givenName": "John R.", 
        "id": "sg:person.0610623062.47", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610623062.47"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Geophysical Fluid Dynamics Laboratory", 
          "id": "https://www.grid.ac/institutes/grid.482795.5", 
          "name": [
            "NOAA Geophysical Fluid Dynamics Laboratory, 201 Forrestal Road, 08540, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nath", 
        "givenName": "Mary Jo", 
        "id": "sg:person.010353433753.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010353433753.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Texas Tech University", 
          "id": "https://www.grid.ac/institutes/grid.264784.b", 
          "name": [
            "Climate Science Center, Texas Tech University, 79409, Lubbock, TX, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hayhoe", 
        "givenName": "Katharine", 
        "id": "sg:person.010176531565.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010176531565.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Texas Tech University", 
          "id": "https://www.grid.ac/institutes/grid.264784.b", 
          "name": [
            "Climate Science Center, Texas Tech University, 79409, Lubbock, TX, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stoner", 
        "givenName": "Anne", 
        "id": "sg:person.014361046531.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014361046531.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Engility (United States)", 
          "id": "https://www.grid.ac/institutes/grid.438582.0", 
          "name": [
            "Engility, 20151, Chantilly, VA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Radhakrishnan", 
        "givenName": "Aparna", 
        "id": "sg:person.014312030714.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014312030714.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Princeton University", 
          "id": "https://www.grid.ac/institutes/grid.16750.35", 
          "name": [
            "Princeton University, 08544, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Balaji", 
        "givenName": "V.", 
        "id": "sg:person.01147224470.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01147224470.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oklahoma", 
          "id": "https://www.grid.ac/institutes/grid.266900.b", 
          "name": [
            "University of Oklahoma, 73072, Norman, OK, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gait\u00e1n", 
        "givenName": "Carlos F.", 
        "id": "sg:person.010555355624.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010555355624.74"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/jgrd.50112", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001156105"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2006gl027453", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008816505"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2012gl051210", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011883298"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1005633925903", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014339846", 
          "https://doi.org/10.1023/a:1005633925903"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0022-1694(86)90199-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020155219"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.agrformet.2012.04.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024150991"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.1597", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025515283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/hess-17-5061-2013", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030614493"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10584-013-1021-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032152931", 
          "https://doi.org/10.1007/s10584-013-1021-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10584-013-1021-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032152931", 
          "https://doi.org/10.1007/s10584-013-1021-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.3603", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033194090"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/jd095id02p01943", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034992723"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/cobi.12163", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039398041"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2013eo460005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039713077"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jcli-d-11-00687.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045154867"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-014-2098-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047251797", 
          "https://doi.org/10.1007/s00382-014-2098-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/90rg02636", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048473948"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.renene.2012.10.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052030282"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2007gl030295", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053009863"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.aaa0629", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062665062"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/030913339702100403", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063817371"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/030913339702100403", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063817371"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/6908", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1098905428"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-04", 
    "datePublishedReg": "2016-04-01", 
    "description": "Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method\u2019s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a \u201cperfect model\u201d experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods \u2013 namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s10584-016-1598-0", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4049485", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1028211", 
        "issn": [
          "0165-0009", 
          "1573-1480"
        ], 
        "name": "Climatic Change", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3-4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "135"
      }
    ], 
    "name": "Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?", 
    "pagination": "395-408", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "5766d59031ebf1d7aa9c1ad984618918ebac9ed2885cb216146af6e92d9eb576"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10584-016-1598-0"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1018344062"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10584-016-1598-0", 
      "https://app.dimensions.ai/details/publication/pub.1018344062"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:10", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000367_0000000367/records_88247_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s10584-016-1598-0"
  }
]
 

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/s10584-016-1598-0'

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/s10584-016-1598-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10584-016-1598-0'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10584-016-1598-0'


 

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

190 TRIPLES      21 PREDICATES      48 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10584-016-1598-0 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Nbbfdceefdb544a4986d72ffaf0069c54
4 schema:citation sg:pub.10.1007/s00382-014-2098-4
5 sg:pub.10.1007/s10584-013-1021-z
6 sg:pub.10.1023/a:1005633925903
7 https://doi.org/10.1002/2013eo460005
8 https://doi.org/10.1002/jgrd.50112
9 https://doi.org/10.1002/joc.1597
10 https://doi.org/10.1002/joc.3603
11 https://doi.org/10.1016/0022-1694(86)90199-x
12 https://doi.org/10.1016/j.agrformet.2012.04.007
13 https://doi.org/10.1016/j.renene.2012.10.001
14 https://doi.org/10.1029/2006gl027453
15 https://doi.org/10.1029/2007gl030295
16 https://doi.org/10.1029/2012gl051210
17 https://doi.org/10.1029/90rg02636
18 https://doi.org/10.1029/jd095id02p01943
19 https://doi.org/10.1111/cobi.12163
20 https://doi.org/10.1126/science.aaa0629
21 https://doi.org/10.1142/6908
22 https://doi.org/10.1175/jcli-d-11-00687.1
23 https://doi.org/10.1177/030913339702100403
24 https://doi.org/10.5194/hess-17-5061-2013
25 schema:datePublished 2016-04
26 schema:datePublishedReg 2016-04-01
27 schema:description Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods – namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications.
28 schema:genre research_article
29 schema:inLanguage en
30 schema:isAccessibleForFree true
31 schema:isPartOf Ncc0faf97e52d4c80bf4f2ad489e64d38
32 Nf6b84411f9784123aadb07f23c6453e6
33 sg:journal.1028211
34 schema:name Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?
35 schema:pagination 395-408
36 schema:productId N051ffb4fca1c4b44b2a115b1c4013724
37 N70274be846474e7c97a8afdb45ec4f12
38 N889a00e2fc5f4179a95efc4fb83597ea
39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018344062
40 https://doi.org/10.1007/s10584-016-1598-0
41 schema:sdDatePublished 2019-04-11T13:10
42 schema:sdLicense https://scigraph.springernature.com/explorer/license/
43 schema:sdPublisher Nb14ec56b596445cb8ed5dc1af5299678
44 schema:url http://link.springer.com/10.1007/s10584-016-1598-0
45 sgo:license sg:explorer/license/
46 sgo:sdDataset articles
47 rdf:type schema:ScholarlyArticle
48 N051ffb4fca1c4b44b2a115b1c4013724 schema:name doi
49 schema:value 10.1007/s10584-016-1598-0
50 rdf:type schema:PropertyValue
51 N1e71dbf821334a62ab55049b1fb07d10 rdf:first sg:person.01147224470.27
52 rdf:rest N8552e80e2fd0440483847b9f04e876a9
53 N208f63aff9df4e8ba9ae063f3b24ee1b rdf:first sg:person.014312030714.76
54 rdf:rest N1e71dbf821334a62ab55049b1fb07d10
55 N70274be846474e7c97a8afdb45ec4f12 schema:name dimensions_id
56 schema:value pub.1018344062
57 rdf:type schema:PropertyValue
58 N8552e80e2fd0440483847b9f04e876a9 rdf:first sg:person.010555355624.74
59 rdf:rest rdf:nil
60 N889a00e2fc5f4179a95efc4fb83597ea schema:name readcube_id
61 schema:value 5766d59031ebf1d7aa9c1ad984618918ebac9ed2885cb216146af6e92d9eb576
62 rdf:type schema:PropertyValue
63 N9018f8aa4b4341fd8ce0b38d556a8090 rdf:first sg:person.0610623062.47
64 rdf:rest Na1550b726b364e9c92e48693867a5457
65 Na1550b726b364e9c92e48693867a5457 rdf:first sg:person.010353433753.44
66 rdf:rest Nfd81afe705844db7a72fd8107abe7632
67 Nb14ec56b596445cb8ed5dc1af5299678 schema:name Springer Nature - SN SciGraph project
68 rdf:type schema:Organization
69 Nb45ff276eda7477e999a83b1a07be633 rdf:first sg:person.014361046531.34
70 rdf:rest N208f63aff9df4e8ba9ae063f3b24ee1b
71 Nbbfdceefdb544a4986d72ffaf0069c54 rdf:first sg:person.010171616627.26
72 rdf:rest N9018f8aa4b4341fd8ce0b38d556a8090
73 Ncc0faf97e52d4c80bf4f2ad489e64d38 schema:volumeNumber 135
74 rdf:type schema:PublicationVolume
75 Nf6b84411f9784123aadb07f23c6453e6 schema:issueNumber 3-4
76 rdf:type schema:PublicationIssue
77 Nfd81afe705844db7a72fd8107abe7632 rdf:first sg:person.010176531565.07
78 rdf:rest Nb45ff276eda7477e999a83b1a07be633
79 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
80 schema:name Mathematical Sciences
81 rdf:type schema:DefinedTerm
82 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
83 schema:name Statistics
84 rdf:type schema:DefinedTerm
85 sg:grant.4049485 http://pending.schema.org/fundedItem sg:pub.10.1007/s10584-016-1598-0
86 rdf:type schema:MonetaryGrant
87 sg:journal.1028211 schema:issn 0165-0009
88 1573-1480
89 schema:name Climatic Change
90 rdf:type schema:Periodical
91 sg:person.010171616627.26 schema:affiliation https://www.grid.ac/institutes/grid.482795.5
92 schema:familyName Dixon
93 schema:givenName Keith W.
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010171616627.26
95 rdf:type schema:Person
96 sg:person.010176531565.07 schema:affiliation https://www.grid.ac/institutes/grid.264784.b
97 schema:familyName Hayhoe
98 schema:givenName Katharine
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010176531565.07
100 rdf:type schema:Person
101 sg:person.010353433753.44 schema:affiliation https://www.grid.ac/institutes/grid.482795.5
102 schema:familyName Nath
103 schema:givenName Mary Jo
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010353433753.44
105 rdf:type schema:Person
106 sg:person.010555355624.74 schema:affiliation https://www.grid.ac/institutes/grid.266900.b
107 schema:familyName Gaitán
108 schema:givenName Carlos F.
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010555355624.74
110 rdf:type schema:Person
111 sg:person.01147224470.27 schema:affiliation https://www.grid.ac/institutes/grid.16750.35
112 schema:familyName Balaji
113 schema:givenName V.
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01147224470.27
115 rdf:type schema:Person
116 sg:person.014312030714.76 schema:affiliation https://www.grid.ac/institutes/grid.438582.0
117 schema:familyName Radhakrishnan
118 schema:givenName Aparna
119 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014312030714.76
120 rdf:type schema:Person
121 sg:person.014361046531.34 schema:affiliation https://www.grid.ac/institutes/grid.264784.b
122 schema:familyName Stoner
123 schema:givenName Anne
124 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014361046531.34
125 rdf:type schema:Person
126 sg:person.0610623062.47 schema:affiliation https://www.grid.ac/institutes/grid.482795.5
127 schema:familyName Lanzante
128 schema:givenName John R.
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0610623062.47
130 rdf:type schema:Person
131 sg:pub.10.1007/s00382-014-2098-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047251797
132 https://doi.org/10.1007/s00382-014-2098-4
133 rdf:type schema:CreativeWork
134 sg:pub.10.1007/s10584-013-1021-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1032152931
135 https://doi.org/10.1007/s10584-013-1021-z
136 rdf:type schema:CreativeWork
137 sg:pub.10.1023/a:1005633925903 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014339846
138 https://doi.org/10.1023/a:1005633925903
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1002/2013eo460005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039713077
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1002/jgrd.50112 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001156105
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1002/joc.1597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025515283
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1002/joc.3603 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033194090
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/0022-1694(86)90199-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1020155219
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/j.agrformet.2012.04.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024150991
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/j.renene.2012.10.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052030282
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1029/2006gl027453 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008816505
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1029/2007gl030295 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053009863
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1029/2012gl051210 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011883298
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1029/90rg02636 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048473948
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1029/jd095id02p01943 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034992723
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1111/cobi.12163 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039398041
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1126/science.aaa0629 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062665062
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1142/6908 schema:sameAs https://app.dimensions.ai/details/publication/pub.1098905428
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1175/jcli-d-11-00687.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045154867
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1177/030913339702100403 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063817371
173 rdf:type schema:CreativeWork
174 https://doi.org/10.5194/hess-17-5061-2013 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030614493
175 rdf:type schema:CreativeWork
176 https://www.grid.ac/institutes/grid.16750.35 schema:alternateName Princeton University
177 schema:name Princeton University, 08544, Princeton, NJ, USA
178 rdf:type schema:Organization
179 https://www.grid.ac/institutes/grid.264784.b schema:alternateName Texas Tech University
180 schema:name Climate Science Center, Texas Tech University, 79409, Lubbock, TX, USA
181 rdf:type schema:Organization
182 https://www.grid.ac/institutes/grid.266900.b schema:alternateName University of Oklahoma
183 schema:name University of Oklahoma, 73072, Norman, OK, USA
184 rdf:type schema:Organization
185 https://www.grid.ac/institutes/grid.438582.0 schema:alternateName Engility (United States)
186 schema:name Engility, 20151, Chantilly, VA, USA
187 rdf:type schema:Organization
188 https://www.grid.ac/institutes/grid.482795.5 schema:alternateName Geophysical Fluid Dynamics Laboratory
189 schema:name NOAA Geophysical Fluid Dynamics Laboratory, 201 Forrestal Road, 08540, Princeton, NJ, USA
190 rdf:type schema:Organization
 




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


...