Predictable skill and its association to sea-surface temperature variations in an ensemble climate simulation

C. Adam Schlosser, Benjamin Kirtman

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Simulated near-surface air temperature (Ta) and precipitation in an ensemble climate simulation is assessed. The ensemble climate-simulation was constructed with the Center for Ocean-Land-Atmosphere (COLA) atmospheric general circulation model (AGCM) in conjunction with the Atmospheric Model Intercomparison Project Phase II (AMIP II). To diagnose the ensemble simulation, a measure of "predictable skill" is formalized. This diagnostic is based upon the statistical significance of spatial correlation over any given region (for this analysis, North America) between the ensemble mean and observed anomalies and the inter-member scatter of the spatial anomaly correlation. Using this measure as a function of time, periods of predictable skill within the COLA AMIP II ensemble simulation are identified, and through a point-wise, multiple correlation technique, spatial patterns of contemporaneous sea-suiface temperature (SST) variability are also constructed. This diagnosis can essentially define which skillfully simulated climate signals over North America (given by predictable skill) can be attributed to specific SST anomalies. Spatially coherent patterns of SST variability are found to be associated with predictable skill. These patterns are, not surprisingly, primarily related to the El Niño Southern Oscillation (ENSO). One of the more prevalent ENSO associations to predictable skill (for the COLA AGCM) is found in sub-tropical regions of the western Pacific. Moreover, a skillful episode of simulated North American precipitation is seen to be associated with SST variability over the east Indian Ocean. In addition, a strong contemporaneous association of Ta predictable skill with tropical Atlantic SST variability is found. Complementary simulations are performed with the COLA AGCM to further assess the degree of attribution of predictable skill to these regions of SST variability. The results of the experimental simulations suggest that, for the AMIP II simulation period, the COLA AGCM can be skillfully attributed to SST variability primarily associated with ENSO, warm tropical Atlantic anomalies, and an abrupt, cooling event over the east Indian Ocean.

Original languageEnglish (US)
Article numberD19107
Pages (from-to)1-21
Number of pages21
JournalJournal of Geophysical Research C: Oceans
Volume110
Issue number19
DOIs
StatePublished - Oct 10 2005
Externally publishedYes

Fingerprint

sea surface temperature
climate
Atmospheric General Circulation Models
atmospheric general circulation model
Southern Oscillation
oceans
simulation
atmosphere
atmospheric models
ocean
temperature
atmospheres
Temperature
anomalies
anomaly
Indian Ocean
climate signal
subtropical regions
subtropical region
sea

ASJC Scopus subject areas

  • Earth and Planetary Sciences (miscellaneous)
  • Atmospheric Science
  • Geochemistry and Petrology
  • Geophysics
  • Oceanography
  • Space and Planetary Science
  • Astronomy and Astrophysics

Cite this

Predictable skill and its association to sea-surface temperature variations in an ensemble climate simulation. / Schlosser, C. Adam; Kirtman, Benjamin.

In: Journal of Geophysical Research C: Oceans, Vol. 110, No. 19, D19107, 10.10.2005, p. 1-21.

Research output: Contribution to journalArticle

@article{1f1777c4902d4ed69a90bdeae28bf919,
title = "Predictable skill and its association to sea-surface temperature variations in an ensemble climate simulation",
abstract = "Simulated near-surface air temperature (Ta) and precipitation in an ensemble climate simulation is assessed. The ensemble climate-simulation was constructed with the Center for Ocean-Land-Atmosphere (COLA) atmospheric general circulation model (AGCM) in conjunction with the Atmospheric Model Intercomparison Project Phase II (AMIP II). To diagnose the ensemble simulation, a measure of {"}predictable skill{"} is formalized. This diagnostic is based upon the statistical significance of spatial correlation over any given region (for this analysis, North America) between the ensemble mean and observed anomalies and the inter-member scatter of the spatial anomaly correlation. Using this measure as a function of time, periods of predictable skill within the COLA AMIP II ensemble simulation are identified, and through a point-wise, multiple correlation technique, spatial patterns of contemporaneous sea-suiface temperature (SST) variability are also constructed. This diagnosis can essentially define which skillfully simulated climate signals over North America (given by predictable skill) can be attributed to specific SST anomalies. Spatially coherent patterns of SST variability are found to be associated with predictable skill. These patterns are, not surprisingly, primarily related to the El Ni{\~n}o Southern Oscillation (ENSO). One of the more prevalent ENSO associations to predictable skill (for the COLA AGCM) is found in sub-tropical regions of the western Pacific. Moreover, a skillful episode of simulated North American precipitation is seen to be associated with SST variability over the east Indian Ocean. In addition, a strong contemporaneous association of Ta predictable skill with tropical Atlantic SST variability is found. Complementary simulations are performed with the COLA AGCM to further assess the degree of attribution of predictable skill to these regions of SST variability. The results of the experimental simulations suggest that, for the AMIP II simulation period, the COLA AGCM can be skillfully attributed to SST variability primarily associated with ENSO, warm tropical Atlantic anomalies, and an abrupt, cooling event over the east Indian Ocean.",
author = "Schlosser, {C. Adam} and Benjamin Kirtman",
year = "2005",
month = "10",
day = "10",
doi = "10.1029/2005JD005835",
language = "English (US)",
volume = "110",
pages = "1--21",
journal = "Journal of Geophysical Research: Oceans",
issn = "2169-9275",
publisher = "Wiley-Blackwell",
number = "19",

}

TY - JOUR

T1 - Predictable skill and its association to sea-surface temperature variations in an ensemble climate simulation

AU - Schlosser, C. Adam

AU - Kirtman, Benjamin

PY - 2005/10/10

Y1 - 2005/10/10

N2 - Simulated near-surface air temperature (Ta) and precipitation in an ensemble climate simulation is assessed. The ensemble climate-simulation was constructed with the Center for Ocean-Land-Atmosphere (COLA) atmospheric general circulation model (AGCM) in conjunction with the Atmospheric Model Intercomparison Project Phase II (AMIP II). To diagnose the ensemble simulation, a measure of "predictable skill" is formalized. This diagnostic is based upon the statistical significance of spatial correlation over any given region (for this analysis, North America) between the ensemble mean and observed anomalies and the inter-member scatter of the spatial anomaly correlation. Using this measure as a function of time, periods of predictable skill within the COLA AMIP II ensemble simulation are identified, and through a point-wise, multiple correlation technique, spatial patterns of contemporaneous sea-suiface temperature (SST) variability are also constructed. This diagnosis can essentially define which skillfully simulated climate signals over North America (given by predictable skill) can be attributed to specific SST anomalies. Spatially coherent patterns of SST variability are found to be associated with predictable skill. These patterns are, not surprisingly, primarily related to the El Niño Southern Oscillation (ENSO). One of the more prevalent ENSO associations to predictable skill (for the COLA AGCM) is found in sub-tropical regions of the western Pacific. Moreover, a skillful episode of simulated North American precipitation is seen to be associated with SST variability over the east Indian Ocean. In addition, a strong contemporaneous association of Ta predictable skill with tropical Atlantic SST variability is found. Complementary simulations are performed with the COLA AGCM to further assess the degree of attribution of predictable skill to these regions of SST variability. The results of the experimental simulations suggest that, for the AMIP II simulation period, the COLA AGCM can be skillfully attributed to SST variability primarily associated with ENSO, warm tropical Atlantic anomalies, and an abrupt, cooling event over the east Indian Ocean.

AB - Simulated near-surface air temperature (Ta) and precipitation in an ensemble climate simulation is assessed. The ensemble climate-simulation was constructed with the Center for Ocean-Land-Atmosphere (COLA) atmospheric general circulation model (AGCM) in conjunction with the Atmospheric Model Intercomparison Project Phase II (AMIP II). To diagnose the ensemble simulation, a measure of "predictable skill" is formalized. This diagnostic is based upon the statistical significance of spatial correlation over any given region (for this analysis, North America) between the ensemble mean and observed anomalies and the inter-member scatter of the spatial anomaly correlation. Using this measure as a function of time, periods of predictable skill within the COLA AMIP II ensemble simulation are identified, and through a point-wise, multiple correlation technique, spatial patterns of contemporaneous sea-suiface temperature (SST) variability are also constructed. This diagnosis can essentially define which skillfully simulated climate signals over North America (given by predictable skill) can be attributed to specific SST anomalies. Spatially coherent patterns of SST variability are found to be associated with predictable skill. These patterns are, not surprisingly, primarily related to the El Niño Southern Oscillation (ENSO). One of the more prevalent ENSO associations to predictable skill (for the COLA AGCM) is found in sub-tropical regions of the western Pacific. Moreover, a skillful episode of simulated North American precipitation is seen to be associated with SST variability over the east Indian Ocean. In addition, a strong contemporaneous association of Ta predictable skill with tropical Atlantic SST variability is found. Complementary simulations are performed with the COLA AGCM to further assess the degree of attribution of predictable skill to these regions of SST variability. The results of the experimental simulations suggest that, for the AMIP II simulation period, the COLA AGCM can be skillfully attributed to SST variability primarily associated with ENSO, warm tropical Atlantic anomalies, and an abrupt, cooling event over the east Indian Ocean.

UR - http://www.scopus.com/inward/record.url?scp=28044466932&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=28044466932&partnerID=8YFLogxK

U2 - 10.1029/2005JD005835

DO - 10.1029/2005JD005835

M3 - Article

VL - 110

SP - 1

EP - 21

JO - Journal of Geophysical Research: Oceans

JF - Journal of Geophysical Research: Oceans

SN - 2169-9275

IS - 19

M1 - D19107

ER -