Predictability of temperature and precipitation on interannual to decadal time scales in perfect-model experiments
This thesis examines the potentially achievable prediction skill of temperature and precipitation on interannual to decadal time scales by analyzing predictability in perfect-model experiments using coupled climate models. I develop a framework which (1) compares perfect‐model prediction experiments...
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Format: | Doctoral or Postdoctoral Thesis |
Language: | English |
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UNSW, Sydney
2021
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Online Access: | http://hdl.handle.net/1959.4/71052 https://unsworks.unsw.edu.au/bitstreams/b979a49e-049d-42a8-b7dc-644a65ce3362/download https://doi.org/10.26190/unsworks/2334 |
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ftunswworks:oai:unsworks.library.unsw.edu.au:1959.4/71052 2023-05-15T17:32:04+02:00 Predictability of temperature and precipitation on interannual to decadal time scales in perfect-model experiments Liu, Yiling 2021 application/pdf http://hdl.handle.net/1959.4/71052 https://unsworks.unsw.edu.au/bitstreams/b979a49e-049d-42a8-b7dc-644a65ce3362/download https://doi.org/10.26190/unsworks/2334 EN eng UNSW, Sydney http://hdl.handle.net/1959.4/71052 https://unsworks.unsw.edu.au/bitstreams/b979a49e-049d-42a8-b7dc-644a65ce3362/download https://doi.org/10.26190/unsworks/2334 open access https://purl.org/coar/access_right/c_abf2 CC BY-NC-ND 3.0 https://creativecommons.org/licenses/by-nc-nd/3.0/au/ free_to_read CC-BY-NC-ND CSIRO-Mk3-6-0 model Climate science Predictability Decadal prediction CESM model doctoral thesis http://purl.org/coar/resource_type/c_db06 2021 ftunswworks https://doi.org/10.26190/unsworks/2334 2022-08-29T22:31:09Z This thesis examines the potentially achievable prediction skill of temperature and precipitation on interannual to decadal time scales by analyzing predictability in perfect-model experiments using coupled climate models. I develop a framework which (1) compares perfect‐model prediction experiments with predictions of the real world, and (2) assesses the added value from capturing the initial state in the climate system in decadal predictions. I find that for the annual average near‐surface air temperature, ideal initialization may substantially improve the predictions during the first two forecast years particularly in parts of the Southern Ocean, the Indian Ocean, the tropical Pacific and North Atlantic, and some surrounding land areas. On longer time scales, the predictions rely more on model performance in simulating low‐frequency variability and long‐term changes due to external forcing. This thesis also investigates conditional predictability dependent on initial states, in particular multi-year predictability conditional on the state of the El Niño–Southern Oscillation (ENSO) at the time of prediction initialization. I find that predictions starting with El Niño or La Niña conditions exhibit higher skill in predicting near-surface air temperature and precipitation multiple years ahead, compared to predictions initialized with neutral ENSO conditions. This thesis also compares the predictability for mean and extreme temperature and precipitation on interannual to decadal time scales. The results show that both the mean and likelihood of near-surface air temperature extremes are predictable in many regions in the first lead year, while the areas exhibiting precipitation predictability tend to be mostly located in low-latitude regions. On decadal time scales, significant potential prediction skill for mean and extreme temperatures is found over the North Atlantic and the Southern Ocean and also over some land areas. Indices of moderate temperature extremes tend to show a higher predictability than the mean. ... Doctoral or Postdoctoral Thesis North Atlantic Southern Ocean UNSW Sydney (The University of New South Wales): UNSWorks Southern Ocean Pacific Indian |
institution |
Open Polar |
collection |
UNSW Sydney (The University of New South Wales): UNSWorks |
op_collection_id |
ftunswworks |
language |
English |
topic |
CSIRO-Mk3-6-0 model Climate science Predictability Decadal prediction CESM model |
spellingShingle |
CSIRO-Mk3-6-0 model Climate science Predictability Decadal prediction CESM model Liu, Yiling Predictability of temperature and precipitation on interannual to decadal time scales in perfect-model experiments |
topic_facet |
CSIRO-Mk3-6-0 model Climate science Predictability Decadal prediction CESM model |
description |
This thesis examines the potentially achievable prediction skill of temperature and precipitation on interannual to decadal time scales by analyzing predictability in perfect-model experiments using coupled climate models. I develop a framework which (1) compares perfect‐model prediction experiments with predictions of the real world, and (2) assesses the added value from capturing the initial state in the climate system in decadal predictions. I find that for the annual average near‐surface air temperature, ideal initialization may substantially improve the predictions during the first two forecast years particularly in parts of the Southern Ocean, the Indian Ocean, the tropical Pacific and North Atlantic, and some surrounding land areas. On longer time scales, the predictions rely more on model performance in simulating low‐frequency variability and long‐term changes due to external forcing. This thesis also investigates conditional predictability dependent on initial states, in particular multi-year predictability conditional on the state of the El Niño–Southern Oscillation (ENSO) at the time of prediction initialization. I find that predictions starting with El Niño or La Niña conditions exhibit higher skill in predicting near-surface air temperature and precipitation multiple years ahead, compared to predictions initialized with neutral ENSO conditions. This thesis also compares the predictability for mean and extreme temperature and precipitation on interannual to decadal time scales. The results show that both the mean and likelihood of near-surface air temperature extremes are predictable in many regions in the first lead year, while the areas exhibiting precipitation predictability tend to be mostly located in low-latitude regions. On decadal time scales, significant potential prediction skill for mean and extreme temperatures is found over the North Atlantic and the Southern Ocean and also over some land areas. Indices of moderate temperature extremes tend to show a higher predictability than the mean. ... |
format |
Doctoral or Postdoctoral Thesis |
author |
Liu, Yiling |
author_facet |
Liu, Yiling |
author_sort |
Liu, Yiling |
title |
Predictability of temperature and precipitation on interannual to decadal time scales in perfect-model experiments |
title_short |
Predictability of temperature and precipitation on interannual to decadal time scales in perfect-model experiments |
title_full |
Predictability of temperature and precipitation on interannual to decadal time scales in perfect-model experiments |
title_fullStr |
Predictability of temperature and precipitation on interannual to decadal time scales in perfect-model experiments |
title_full_unstemmed |
Predictability of temperature and precipitation on interannual to decadal time scales in perfect-model experiments |
title_sort |
predictability of temperature and precipitation on interannual to decadal time scales in perfect-model experiments |
publisher |
UNSW, Sydney |
publishDate |
2021 |
url |
http://hdl.handle.net/1959.4/71052 https://unsworks.unsw.edu.au/bitstreams/b979a49e-049d-42a8-b7dc-644a65ce3362/download https://doi.org/10.26190/unsworks/2334 |
geographic |
Southern Ocean Pacific Indian |
geographic_facet |
Southern Ocean Pacific Indian |
genre |
North Atlantic Southern Ocean |
genre_facet |
North Atlantic Southern Ocean |
op_relation |
http://hdl.handle.net/1959.4/71052 https://unsworks.unsw.edu.au/bitstreams/b979a49e-049d-42a8-b7dc-644a65ce3362/download https://doi.org/10.26190/unsworks/2334 |
op_rights |
open access https://purl.org/coar/access_right/c_abf2 CC BY-NC-ND 3.0 https://creativecommons.org/licenses/by-nc-nd/3.0/au/ free_to_read |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.26190/unsworks/2334 |
_version_ |
1766130015021301760 |