Climate model forecast biases assessed with a perturbed physics ensemble View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-09

AUTHORS

David P. Mulholland, Keith Haines, Sarah N. Sparrow, David Wallom

ABSTRACT

Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the ‘equivalent parameter perturbations’, which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies. More... »

PAGES

1729-1746

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-016-3407-x

DOI

http://dx.doi.org/10.1007/s00382-016-3407-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024790364


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/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Reading", 
          "id": "https://www.grid.ac/institutes/grid.9435.b", 
          "name": [
            "Department of Meteorology, University of Reading, Reading, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mulholland", 
        "givenName": "David P.", 
        "id": "sg:person.012654071341.30", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012654071341.30"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Reading", 
          "id": "https://www.grid.ac/institutes/grid.9435.b", 
          "name": [
            "Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Haines", 
        "givenName": "Keith", 
        "id": "sg:person.01052227550.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052227550.65"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oxford", 
          "id": "https://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Oxford e-Research Centre, University of Oxford, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sparrow", 
        "givenName": "Sarah N.", 
        "id": "sg:person.013147101037.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013147101037.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oxford", 
          "id": "https://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Oxford e-Research Centre, University of Oxford, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wallom", 
        "givenName": "David", 
        "id": "sg:person.015131374022.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015131374022.11"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1175/jcli-d-10-05003.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001897303"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jcli-d-13-00474.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004938855"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1256/qj.04.93", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005443225"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1256/qj.04.93", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005443225"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/gmd-7-1961-2014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005619212"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-010-0808-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008516124", 
          "https://doi.org/10.1007/s00382-010-0808-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/2008jcli1869.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009683013"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1256/qj.04.176", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010291660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1256/qj.04.176", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010291660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2005gl024452", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010906059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2005gl024452", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010906059"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0485(1997)027<0381:soetci>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011709070"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr-d-11-00335.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011785390"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003820050010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015242699", 
          "https://doi.org/10.1007/s003820050010"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2005jc003172", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016948619"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-012-1599-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019039763", 
          "https://doi.org/10.1007/s00382-012-1599-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-012-1599-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019039763", 
          "https://doi.org/10.1007/s00382-012-1599-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jgrd.50304", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019363697"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jcli3430.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023017918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jcli3430.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023017918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/mwr2826.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025114950"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/qj.2172", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025150355"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/jcli-d-12-00429.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026294392"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/qj.2396", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027539724"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/2010mwr3178.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031153622"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-013-1683-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033076144", 
          "https://doi.org/10.1007/s00382-013-1683-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2015gl064799", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034885794"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature02771", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036499414", 
          "https://doi.org/10.1038/nature02771"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature02771", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036499414", 
          "https://doi.org/10.1038/nature02771"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-008-0486-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041631164", 
          "https://doi.org/10.1007/s00382-008-0486-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-008-0486-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041631164", 
          "https://doi.org/10.1007/s00382-008-0486-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0608144104", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042574479"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/wcc.217", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043013835"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/96jc03454", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043523080"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/qj.23", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046604438"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ocemod.2003.12.004", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047427870"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/425242a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049643987", 
          "https://doi.org/10.1038/425242a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/425242a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049643987", 
          "https://doi.org/10.1038/425242a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/pl00013733", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049849175", 
          "https://doi.org/10.1007/pl00013733"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-014-2378-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051952766", 
          "https://doi.org/10.1007/s00382-014-2378-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-012-1429-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053368690", 
          "https://doi.org/10.1007/s00382-012-1429-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.1139540", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062455299"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/os-11-839-2015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1072678656"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-09", 
    "datePublishedReg": "2017-09-01", 
    "description": "Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the \u2018equivalent parameter perturbations\u2019, which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00382-016-3407-x", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2773818", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2761096", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5-6", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "49"
      }
    ], 
    "name": "Climate model forecast biases assessed with a perturbed physics ensemble", 
    "pagination": "1729-1746", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "761a12c25c87f5bbc40074f8732d5dc698240a3448b97e0807a6b3a14e82125c"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00382-016-3407-x"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1024790364"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00382-016-3407-x", 
      "https://app.dimensions.ai/details/publication/pub.1024790364"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T12:22", 
    "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/v90/0000000362_0000000362/records_87083_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00382-016-3407-x"
  }
]
 

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/s00382-016-3407-x'

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/s00382-016-3407-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00382-016-3407-x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00382-016-3407-x'


 

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

205 TRIPLES      21 PREDICATES      62 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00382-016-3407-x schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author N782773e1c46d4613a601fad5ba1e533d
4 schema:citation sg:pub.10.1007/pl00013733
5 sg:pub.10.1007/s00382-008-0486-3
6 sg:pub.10.1007/s00382-010-0808-0
7 sg:pub.10.1007/s00382-012-1429-6
8 sg:pub.10.1007/s00382-012-1599-2
9 sg:pub.10.1007/s00382-013-1683-2
10 sg:pub.10.1007/s00382-014-2378-z
11 sg:pub.10.1007/s003820050010
12 sg:pub.10.1038/425242a
13 sg:pub.10.1038/nature02771
14 https://doi.org/10.1002/2015gl064799
15 https://doi.org/10.1002/jgrd.50304
16 https://doi.org/10.1002/qj.2172
17 https://doi.org/10.1002/qj.23
18 https://doi.org/10.1002/qj.2396
19 https://doi.org/10.1002/wcc.217
20 https://doi.org/10.1016/j.ocemod.2003.12.004
21 https://doi.org/10.1029/2005gl024452
22 https://doi.org/10.1029/2005jc003172
23 https://doi.org/10.1029/96jc03454
24 https://doi.org/10.1073/pnas.0608144104
25 https://doi.org/10.1126/science.1139540
26 https://doi.org/10.1175/1520-0485(1997)027<0381:soetci>2.0.co;2
27 https://doi.org/10.1175/2008jcli1869.1
28 https://doi.org/10.1175/2010mwr3178.1
29 https://doi.org/10.1175/jcli-d-10-05003.1
30 https://doi.org/10.1175/jcli-d-12-00429.1
31 https://doi.org/10.1175/jcli-d-13-00474.1
32 https://doi.org/10.1175/jcli3430.1
33 https://doi.org/10.1175/mwr-d-11-00335.1
34 https://doi.org/10.1175/mwr2826.1
35 https://doi.org/10.1256/qj.04.176
36 https://doi.org/10.1256/qj.04.93
37 https://doi.org/10.5194/gmd-7-1961-2014
38 https://doi.org/10.5194/os-11-839-2015
39 schema:datePublished 2017-09
40 schema:datePublishedReg 2017-09-01
41 schema:description Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the ‘equivalent parameter perturbations’, which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.
42 schema:genre research_article
43 schema:inLanguage en
44 schema:isAccessibleForFree true
45 schema:isPartOf N4b4bdb94874740a5b4183017b4937b28
46 N80b6eddad5af40bd939214bac758e03a
47 sg:journal.1049631
48 schema:name Climate model forecast biases assessed with a perturbed physics ensemble
49 schema:pagination 1729-1746
50 schema:productId N3e0b7d94815c437c83e6703c9811c556
51 N7a22a44e11ad495a9b0c18ed17e281fb
52 Nac79782fb8294c4ab0ba640a67c84079
53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024790364
54 https://doi.org/10.1007/s00382-016-3407-x
55 schema:sdDatePublished 2019-04-11T12:22
56 schema:sdLicense https://scigraph.springernature.com/explorer/license/
57 schema:sdPublisher Nadaa5f5c9b154a67b7dc141df8d710e6
58 schema:url https://link.springer.com/10.1007%2Fs00382-016-3407-x
59 sgo:license sg:explorer/license/
60 sgo:sdDataset articles
61 rdf:type schema:ScholarlyArticle
62 N3e0b7d94815c437c83e6703c9811c556 schema:name dimensions_id
63 schema:value pub.1024790364
64 rdf:type schema:PropertyValue
65 N4b4bdb94874740a5b4183017b4937b28 schema:volumeNumber 49
66 rdf:type schema:PublicationVolume
67 N5bd6c68f2ac4452788a75d77114fb739 rdf:first sg:person.013147101037.33
68 rdf:rest N9028c4a562944a98b2330791eba21eb7
69 N782773e1c46d4613a601fad5ba1e533d rdf:first sg:person.012654071341.30
70 rdf:rest Nac50ba69b54c4324ac6c1970163a399d
71 N7a22a44e11ad495a9b0c18ed17e281fb schema:name readcube_id
72 schema:value 761a12c25c87f5bbc40074f8732d5dc698240a3448b97e0807a6b3a14e82125c
73 rdf:type schema:PropertyValue
74 N80b6eddad5af40bd939214bac758e03a schema:issueNumber 5-6
75 rdf:type schema:PublicationIssue
76 N9028c4a562944a98b2330791eba21eb7 rdf:first sg:person.015131374022.11
77 rdf:rest rdf:nil
78 Nac50ba69b54c4324ac6c1970163a399d rdf:first sg:person.01052227550.65
79 rdf:rest N5bd6c68f2ac4452788a75d77114fb739
80 Nac79782fb8294c4ab0ba640a67c84079 schema:name doi
81 schema:value 10.1007/s00382-016-3407-x
82 rdf:type schema:PropertyValue
83 Nadaa5f5c9b154a67b7dc141df8d710e6 schema:name Springer Nature - SN SciGraph project
84 rdf:type schema:Organization
85 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
86 schema:name Earth Sciences
87 rdf:type schema:DefinedTerm
88 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
89 schema:name Atmospheric Sciences
90 rdf:type schema:DefinedTerm
91 sg:grant.2761096 http://pending.schema.org/fundedItem sg:pub.10.1007/s00382-016-3407-x
92 rdf:type schema:MonetaryGrant
93 sg:grant.2773818 http://pending.schema.org/fundedItem sg:pub.10.1007/s00382-016-3407-x
94 rdf:type schema:MonetaryGrant
95 sg:journal.1049631 schema:issn 0930-7575
96 1432-0894
97 schema:name Climate Dynamics
98 rdf:type schema:Periodical
99 sg:person.01052227550.65 schema:affiliation https://www.grid.ac/institutes/grid.9435.b
100 schema:familyName Haines
101 schema:givenName Keith
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052227550.65
103 rdf:type schema:Person
104 sg:person.012654071341.30 schema:affiliation https://www.grid.ac/institutes/grid.9435.b
105 schema:familyName Mulholland
106 schema:givenName David P.
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012654071341.30
108 rdf:type schema:Person
109 sg:person.013147101037.33 schema:affiliation https://www.grid.ac/institutes/grid.4991.5
110 schema:familyName Sparrow
111 schema:givenName Sarah N.
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013147101037.33
113 rdf:type schema:Person
114 sg:person.015131374022.11 schema:affiliation https://www.grid.ac/institutes/grid.4991.5
115 schema:familyName Wallom
116 schema:givenName David
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015131374022.11
118 rdf:type schema:Person
119 sg:pub.10.1007/pl00013733 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049849175
120 https://doi.org/10.1007/pl00013733
121 rdf:type schema:CreativeWork
122 sg:pub.10.1007/s00382-008-0486-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041631164
123 https://doi.org/10.1007/s00382-008-0486-3
124 rdf:type schema:CreativeWork
125 sg:pub.10.1007/s00382-010-0808-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008516124
126 https://doi.org/10.1007/s00382-010-0808-0
127 rdf:type schema:CreativeWork
128 sg:pub.10.1007/s00382-012-1429-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053368690
129 https://doi.org/10.1007/s00382-012-1429-6
130 rdf:type schema:CreativeWork
131 sg:pub.10.1007/s00382-012-1599-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019039763
132 https://doi.org/10.1007/s00382-012-1599-2
133 rdf:type schema:CreativeWork
134 sg:pub.10.1007/s00382-013-1683-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033076144
135 https://doi.org/10.1007/s00382-013-1683-2
136 rdf:type schema:CreativeWork
137 sg:pub.10.1007/s00382-014-2378-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1051952766
138 https://doi.org/10.1007/s00382-014-2378-z
139 rdf:type schema:CreativeWork
140 sg:pub.10.1007/s003820050010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015242699
141 https://doi.org/10.1007/s003820050010
142 rdf:type schema:CreativeWork
143 sg:pub.10.1038/425242a schema:sameAs https://app.dimensions.ai/details/publication/pub.1049643987
144 https://doi.org/10.1038/425242a
145 rdf:type schema:CreativeWork
146 sg:pub.10.1038/nature02771 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036499414
147 https://doi.org/10.1038/nature02771
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1002/2015gl064799 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034885794
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1002/jgrd.50304 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019363697
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1002/qj.2172 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025150355
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1002/qj.23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046604438
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1002/qj.2396 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027539724
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1002/wcc.217 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043013835
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/j.ocemod.2003.12.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047427870
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1029/2005gl024452 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010906059
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1029/2005jc003172 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016948619
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1029/96jc03454 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043523080
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1073/pnas.0608144104 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042574479
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1126/science.1139540 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062455299
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1175/1520-0485(1997)027<0381:soetci>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011709070
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1175/2008jcli1869.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009683013
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1175/2010mwr3178.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031153622
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1175/jcli-d-10-05003.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001897303
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1175/jcli-d-12-00429.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026294392
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1175/jcli-d-13-00474.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004938855
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1175/jcli3430.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023017918
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1175/mwr-d-11-00335.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011785390
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1175/mwr2826.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025114950
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1256/qj.04.176 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010291660
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1256/qj.04.93 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005443225
194 rdf:type schema:CreativeWork
195 https://doi.org/10.5194/gmd-7-1961-2014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005619212
196 rdf:type schema:CreativeWork
197 https://doi.org/10.5194/os-11-839-2015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1072678656
198 rdf:type schema:CreativeWork
199 https://www.grid.ac/institutes/grid.4991.5 schema:alternateName University of Oxford
200 schema:name Oxford e-Research Centre, University of Oxford, Oxford, UK
201 rdf:type schema:Organization
202 https://www.grid.ac/institutes/grid.9435.b schema:alternateName University of Reading
203 schema:name Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading, UK
204 Department of Meteorology, University of Reading, Reading, UK
205 rdf:type schema:Organization
 




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


...