TY - JOUR
T1 - Back to the future
T2 - using long-term observational and paleo-proxy reconstructions to improve model projections of Antarctic climate
AU - Bracegirdle, Thomas J.
AU - Colleoni, Florence
AU - Abram, Nerilie J.
AU - Bertler, Nancy A. N.
AU - Dixon, Daniel A.
AU - England, Mark
AU - Favier, Vincent
AU - Fogwill, Chris J.
AU - Fyfe, John C.
AU - Goodwin, Ian
AU - Goosse, Hugues
AU - Hobbs, Will
AU - Jones, Julie M.
AU - Keller, Elizabeth D.
AU - Khan, Alia L.
AU - Phipps, Steven J.
AU - Raphael, Marilyn N.
AU - Russell, Joellen
AU - Sime, Louise
AU - Thomas, Elizabeth R.
AU - van den Broeke, Michiel R.
AU - Wainer, Ilana
N1 - Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2019/6
Y1 - 2019/6
N2 - Quantitative estimates of future Antarctic climate change are derived from numerical global climate models. Evaluation of the reliability of climate model projections involves many lines of evidence on past performance combined with knowledge of the processes that need to be represented. Routine model evaluation is mainly based on the modern observational period, which started with the establishment of a network of Antarctic weather stations in 1957/58. This period is too short to evaluate many fundamental aspects of the Antarctic and Southern Ocean climate system, such as decadal-to-century time-scale climate variability and trends. To help address this gap, we present a new evaluation of potential ways in which long-term observational and paleo-proxy reconstructions may be used, with a particular focus on improving projections. A wide range of data sources and time periods is included, ranging from ship observations of the early 20th century to ice core records spanning hundreds to hundreds of thousands of years to sediment records dating back 34 million years. We conclude that paleo-proxy records and long-term observational datasets are an underused resource in terms of strategies for improving Antarctic climate projections for the 21st century and beyond. We identify priorities and suggest next steps to addressing this.
AB - Quantitative estimates of future Antarctic climate change are derived from numerical global climate models. Evaluation of the reliability of climate model projections involves many lines of evidence on past performance combined with knowledge of the processes that need to be represented. Routine model evaluation is mainly based on the modern observational period, which started with the establishment of a network of Antarctic weather stations in 1957/58. This period is too short to evaluate many fundamental aspects of the Antarctic and Southern Ocean climate system, such as decadal-to-century time-scale climate variability and trends. To help address this gap, we present a new evaluation of potential ways in which long-term observational and paleo-proxy reconstructions may be used, with a particular focus on improving projections. A wide range of data sources and time periods is included, ranging from ship observations of the early 20th century to ice core records spanning hundreds to hundreds of thousands of years to sediment records dating back 34 million years. We conclude that paleo-proxy records and long-term observational datasets are an underused resource in terms of strategies for improving Antarctic climate projections for the 21st century and beyond. We identify priorities and suggest next steps to addressing this.
KW - Antarctic
KW - Southern Ocean
KW - climate
KW - paleoclimate
KW - CMIP
KW - PMIP
KW - projections
UR - http://www.scopus.com/inward/record.url?scp=85067925592&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/FT160100029
UR - http://purl.org/au-research/grants/arc/CE170100023
UR - http://purl.org/au-research/grants/arc/SR140300001
U2 - 10.3390/geosciences9060255
DO - 10.3390/geosciences9060255
M3 - Review article
AN - SCOPUS:85067925592
SN - 2076-3263
VL - 9
SP - 1
EP - 29
JO - Geosciences
JF - Geosciences
IS - 6
M1 - 255
ER -