Interannual variability and regional climate simulations View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1996-12

AUTHORS

D. Lüthi, A. Cress, H. C. Davies, C. Frei, C. Schär

ABSTRACT

An assessment is made of a regional climate model's skill in simulating the mean climatology and the interannual variability experienced in a specific region. To this end two ensembles comprising three realizations of month-long January and July simulations are undertaken with a limited are a operational NWP model. The modelling suite is driven at its lateral boundaries by analysed meteorological fields and the computational domain covers Europe and the North-western Atlantic with a horizontal resolution of 56 km.Validation is performed against both operational ECMWF analyses and objectively analysed precipitation fields from a network of ~ 1400 SYNOP rain gauge stations. Analysis of the simulated ensemble-mean climatology indicates that the model successfully reproduces both the winter and summer distributions of the primary dynamical and thermodynamical field, and also provides a reasonable representation of the measured precipitation over most of Europe. Typically the domain averaged model-biases are below 0.5 K for temperature and 0.1 g/kg for specific humidity. Analysis of the interannual variability reveals that the model captures the wintertime changes including that of the precipitation distribution, but in contrast the summertime precipitation totals for the individual years is not simulated satisfactorily and only partially reproduces the observed regional interannual variability.The latter shortcomings are related to the following factors. Firstly the model bias in the dynamical fields is somewhat larger for summer than winter, while at the same time summertime interannual variability is associated with weaker effects in the dynamical fields. Secondly the summertime precipitation distribution is more substantially affected by small-scale moist convection and surface hydrological processes. Together these two factors suggest that summertime precipitation over continental extratropical land masses might be intrinsically less predictable than wintertime synoptic scale precipitation. More... »

PAGES

185-209

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00871736

DOI

http://dx.doi.org/10.1007/bf00871736

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033214691


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/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Atmospheric Physics ETH, Z\u00fcrich, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.5801.c", 
          "name": [
            "Atmospheric Physics ETH, Z\u00fcrich, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "L\u00fcthi", 
        "givenName": "D.", 
        "id": "sg:person.07536521741.86", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07536521741.86"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atmospheric Physics ETH, Z\u00fcrich, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.5801.c", 
          "name": [
            "Atmospheric Physics ETH, Z\u00fcrich, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cress", 
        "givenName": "A.", 
        "id": "sg:person.013613613600.48", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013613613600.48"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atmospheric Physics ETH, Z\u00fcrich, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.5801.c", 
          "name": [
            "Atmospheric Physics ETH, Z\u00fcrich, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Davies", 
        "givenName": "H. C.", 
        "id": "sg:person.0657436457.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0657436457.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atmospheric Physics ETH, Z\u00fcrich, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.5801.c", 
          "name": [
            "Atmospheric Physics ETH, Z\u00fcrich, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Frei", 
        "givenName": "C.", 
        "id": "sg:person.016646760605.09", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016646760605.09"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atmospheric Physics ETH, Z\u00fcrich, Switzerland", 
          "id": "http://www.grid.ac/institutes/grid.5801.c", 
          "name": [
            "Atmospheric Physics ETH, Z\u00fcrich, Switzerland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sch\u00e4r", 
        "givenName": "C.", 
        "id": "sg:person.0635043627.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0635043627.22"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00215735", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027783868", 
          "https://doi.org/10.1007/bf00215735"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00117978", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013264681", 
          "https://doi.org/10.1007/bf00117978"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-94-017-3048-8_7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050457929", 
          "https://doi.org/10.1007/978-94-017-3048-8_7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00240465", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049995677", 
          "https://doi.org/10.1007/bf00240465"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1996-12", 
    "datePublishedReg": "1996-12-01", 
    "description": "An assessment is made of a regional climate model's skill in simulating the mean climatology and the interannual variability experienced in a specific region. To this end two ensembles comprising three realizations of month-long January and July simulations are undertaken with a limited are a operational NWP model. The modelling suite is driven at its lateral boundaries by analysed meteorological fields and the computational domain covers Europe and the North-western Atlantic with a horizontal resolution of 56 km.Validation is performed against both operational ECMWF analyses and objectively analysed precipitation fields from a network of ~ 1400 SYNOP rain gauge stations. Analysis of the simulated ensemble-mean climatology indicates that the model successfully reproduces both the winter and summer distributions of the primary dynamical and thermodynamical field, and also provides a reasonable representation of the measured precipitation over most of Europe. Typically the domain averaged model-biases are below 0.5 K for temperature and 0.1 g/kg for specific humidity. Analysis of the interannual variability reveals that the model captures the wintertime changes including that of the precipitation distribution, but in contrast the summertime precipitation totals for the individual years is not simulated satisfactorily and only partially reproduces the observed regional interannual variability.The latter shortcomings are related to the following factors. Firstly the model bias in the dynamical fields is somewhat larger for summer than winter, while at the same time summertime interannual variability is associated with weaker effects in the dynamical fields. Secondly the summertime precipitation distribution is more substantially affected by small-scale moist convection and surface hydrological processes. Together these two factors suggest that summertime precipitation over continental extratropical land masses might be intrinsically less predictable than wintertime synoptic scale precipitation.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/bf00871736", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1086664", 
        "issn": [
          "0177-798X", 
          "1434-4483"
        ], 
        "name": "Theoretical and Applied Climatology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "53"
      }
    ], 
    "keywords": [
      "interannual variability", 
      "precipitation distribution", 
      "dynamical fields", 
      "synoptic-scale precipitation", 
      "operational ECMWF analyses", 
      "regional interannual variability", 
      "regional climate simulations", 
      "climate model skill", 
      "operational NWP models", 
      "rain gauge stations", 
      "north-western Atlantic", 
      "summertime precipitation", 
      "climate simulations", 
      "mean climatology", 
      "ECMWF analyses", 
      "precipitation fields", 
      "model skill", 
      "July simulations", 
      "NWP models", 
      "hydrological processes", 
      "meteorological fields", 
      "specific humidity", 
      "horizontal resolution", 
      "gauge stations", 
      "precipitation totals", 
      "moist convection", 
      "measured precipitation", 
      "wintertime changes", 
      "model bias", 
      "thermodynamical fields", 
      "land mass", 
      "modelling suite", 
      "scale precipitation", 
      "summer distribution", 
      "lateral boundaries", 
      "individual years", 
      "precipitation", 
      "reasonable representation", 
      "climatology", 
      "variability", 
      "winter", 
      "latter shortcoming", 
      "Atlantic", 
      "summer", 
      "suite", 
      "convection", 
      "distribution", 
      "stations", 
      "Europe", 
      "specific regions", 
      "humidity", 
      "ensemble", 
      "boundaries", 
      "computational domain", 
      "region", 
      "simulations", 
      "model", 
      "field", 
      "resolution", 
      "temperature", 
      "changes", 
      "mass", 
      "analysis", 
      "bias", 
      "contrast", 
      "years", 
      "weak effect", 
      "process", 
      "validation", 
      "assessment", 
      "domain", 
      "factors", 
      "skills", 
      "representation", 
      "network", 
      "effect", 
      "shortcomings", 
      "realization", 
      "total"
    ], 
    "name": "Interannual variability and regional climate simulations", 
    "pagination": "185-209", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1033214691"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf00871736"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf00871736", 
      "https://app.dimensions.ai/details/publication/pub.1033214691"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-08-04T16:52", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220804/entities/gbq_results/article/article_285.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/bf00871736"
  }
]
 

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/bf00871736'

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/bf00871736'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00871736'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00871736'


 

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

180 TRIPLES      21 PREDICATES      108 URIs      96 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf00871736 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author Nb2ecf34b59434449b2f2c990034833c4
4 schema:citation sg:pub.10.1007/978-94-017-3048-8_7
5 sg:pub.10.1007/bf00117978
6 sg:pub.10.1007/bf00215735
7 sg:pub.10.1007/bf00240465
8 schema:datePublished 1996-12
9 schema:datePublishedReg 1996-12-01
10 schema:description An assessment is made of a regional climate model's skill in simulating the mean climatology and the interannual variability experienced in a specific region. To this end two ensembles comprising three realizations of month-long January and July simulations are undertaken with a limited are a operational NWP model. The modelling suite is driven at its lateral boundaries by analysed meteorological fields and the computational domain covers Europe and the North-western Atlantic with a horizontal resolution of 56 km.Validation is performed against both operational ECMWF analyses and objectively analysed precipitation fields from a network of ~ 1400 SYNOP rain gauge stations. Analysis of the simulated ensemble-mean climatology indicates that the model successfully reproduces both the winter and summer distributions of the primary dynamical and thermodynamical field, and also provides a reasonable representation of the measured precipitation over most of Europe. Typically the domain averaged model-biases are below 0.5 K for temperature and 0.1 g/kg for specific humidity. Analysis of the interannual variability reveals that the model captures the wintertime changes including that of the precipitation distribution, but in contrast the summertime precipitation totals for the individual years is not simulated satisfactorily and only partially reproduces the observed regional interannual variability.The latter shortcomings are related to the following factors. Firstly the model bias in the dynamical fields is somewhat larger for summer than winter, while at the same time summertime interannual variability is associated with weaker effects in the dynamical fields. Secondly the summertime precipitation distribution is more substantially affected by small-scale moist convection and surface hydrological processes. Together these two factors suggest that summertime precipitation over continental extratropical land masses might be intrinsically less predictable than wintertime synoptic scale precipitation.
11 schema:genre article
12 schema:isAccessibleForFree false
13 schema:isPartOf N48ee0cf63ceb4b7abeb26579daf46267
14 N8453afe7b8204795a5483d0fe3b1800c
15 sg:journal.1086664
16 schema:keywords Atlantic
17 ECMWF analyses
18 Europe
19 July simulations
20 NWP models
21 analysis
22 assessment
23 bias
24 boundaries
25 changes
26 climate model skill
27 climate simulations
28 climatology
29 computational domain
30 contrast
31 convection
32 distribution
33 domain
34 dynamical fields
35 effect
36 ensemble
37 factors
38 field
39 gauge stations
40 horizontal resolution
41 humidity
42 hydrological processes
43 individual years
44 interannual variability
45 land mass
46 lateral boundaries
47 latter shortcoming
48 mass
49 mean climatology
50 measured precipitation
51 meteorological fields
52 model
53 model bias
54 model skill
55 modelling suite
56 moist convection
57 network
58 north-western Atlantic
59 operational ECMWF analyses
60 operational NWP models
61 precipitation
62 precipitation distribution
63 precipitation fields
64 precipitation totals
65 process
66 rain gauge stations
67 realization
68 reasonable representation
69 region
70 regional climate simulations
71 regional interannual variability
72 representation
73 resolution
74 scale precipitation
75 shortcomings
76 simulations
77 skills
78 specific humidity
79 specific regions
80 stations
81 suite
82 summer
83 summer distribution
84 summertime precipitation
85 synoptic-scale precipitation
86 temperature
87 thermodynamical fields
88 total
89 validation
90 variability
91 weak effect
92 winter
93 wintertime changes
94 years
95 schema:name Interannual variability and regional climate simulations
96 schema:pagination 185-209
97 schema:productId N11435dc4b703497bbcc8465316041142
98 N8443b114f0da43a389da9f505da5b33e
99 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033214691
100 https://doi.org/10.1007/bf00871736
101 schema:sdDatePublished 2022-08-04T16:52
102 schema:sdLicense https://scigraph.springernature.com/explorer/license/
103 schema:sdPublisher N7690bad36c90440f90a2e0bf9a01a9c2
104 schema:url https://doi.org/10.1007/bf00871736
105 sgo:license sg:explorer/license/
106 sgo:sdDataset articles
107 rdf:type schema:ScholarlyArticle
108 N11435dc4b703497bbcc8465316041142 schema:name doi
109 schema:value 10.1007/bf00871736
110 rdf:type schema:PropertyValue
111 N29a6b692d6024222966f69f9641b22c7 rdf:first sg:person.016646760605.09
112 rdf:rest N9aa8ff51e5244b239de193dfafc78a9b
113 N48ee0cf63ceb4b7abeb26579daf46267 schema:issueNumber 4
114 rdf:type schema:PublicationIssue
115 N7690bad36c90440f90a2e0bf9a01a9c2 schema:name Springer Nature - SN SciGraph project
116 rdf:type schema:Organization
117 N8443b114f0da43a389da9f505da5b33e schema:name dimensions_id
118 schema:value pub.1033214691
119 rdf:type schema:PropertyValue
120 N8453afe7b8204795a5483d0fe3b1800c schema:volumeNumber 53
121 rdf:type schema:PublicationVolume
122 N9693b085879d473f9be28de4e3242d65 rdf:first sg:person.013613613600.48
123 rdf:rest Nc1aeb199d2c34936a43e76a0839245f4
124 N9aa8ff51e5244b239de193dfafc78a9b rdf:first sg:person.0635043627.22
125 rdf:rest rdf:nil
126 Nb2ecf34b59434449b2f2c990034833c4 rdf:first sg:person.07536521741.86
127 rdf:rest N9693b085879d473f9be28de4e3242d65
128 Nc1aeb199d2c34936a43e76a0839245f4 rdf:first sg:person.0657436457.55
129 rdf:rest N29a6b692d6024222966f69f9641b22c7
130 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
131 schema:name Earth Sciences
132 rdf:type schema:DefinedTerm
133 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
134 schema:name Atmospheric Sciences
135 rdf:type schema:DefinedTerm
136 sg:journal.1086664 schema:issn 0177-798X
137 1434-4483
138 schema:name Theoretical and Applied Climatology
139 schema:publisher Springer Nature
140 rdf:type schema:Periodical
141 sg:person.013613613600.48 schema:affiliation grid-institutes:grid.5801.c
142 schema:familyName Cress
143 schema:givenName A.
144 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013613613600.48
145 rdf:type schema:Person
146 sg:person.016646760605.09 schema:affiliation grid-institutes:grid.5801.c
147 schema:familyName Frei
148 schema:givenName C.
149 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016646760605.09
150 rdf:type schema:Person
151 sg:person.0635043627.22 schema:affiliation grid-institutes:grid.5801.c
152 schema:familyName Schär
153 schema:givenName C.
154 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0635043627.22
155 rdf:type schema:Person
156 sg:person.0657436457.55 schema:affiliation grid-institutes:grid.5801.c
157 schema:familyName Davies
158 schema:givenName H. C.
159 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0657436457.55
160 rdf:type schema:Person
161 sg:person.07536521741.86 schema:affiliation grid-institutes:grid.5801.c
162 schema:familyName Lüthi
163 schema:givenName D.
164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07536521741.86
165 rdf:type schema:Person
166 sg:pub.10.1007/978-94-017-3048-8_7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050457929
167 https://doi.org/10.1007/978-94-017-3048-8_7
168 rdf:type schema:CreativeWork
169 sg:pub.10.1007/bf00117978 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013264681
170 https://doi.org/10.1007/bf00117978
171 rdf:type schema:CreativeWork
172 sg:pub.10.1007/bf00215735 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027783868
173 https://doi.org/10.1007/bf00215735
174 rdf:type schema:CreativeWork
175 sg:pub.10.1007/bf00240465 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049995677
176 https://doi.org/10.1007/bf00240465
177 rdf:type schema:CreativeWork
178 grid-institutes:grid.5801.c schema:alternateName Atmospheric Physics ETH, Zürich, Switzerland
179 schema:name Atmospheric Physics ETH, Zürich, Switzerland
180 rdf:type schema:Organization
 




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


...