Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations

Abstract The 0.6 °C warming observed in global temperature datasets from 1940 to 1960 to 2000–2020 can be partially due to urban heat island (UHI) and other non-climatic biases in the underlying data, although several previous studies have argued to the contrary. Here we identify land regions where...

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Published in:Climate Dynamics
Main Author: Scafetta, Nicola
Other Authors: Università degli Studi di Napoli Federico II
Format: Article in Journal/Newspaper
Language:English
Published: Springer Science and Business Media LLC 2021
Subjects:
Online Access:http://dx.doi.org/10.1007/s00382-021-05626-x
https://link.springer.com/content/pdf/10.1007/s00382-021-05626-x.pdf
https://link.springer.com/article/10.1007/s00382-021-05626-x/fulltext.html
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spelling crspringernat:10.1007/s00382-021-05626-x 2023-05-15T16:30:42+02:00 Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations Scafetta, Nicola Università degli Studi di Napoli Federico II 2021 http://dx.doi.org/10.1007/s00382-021-05626-x https://link.springer.com/content/pdf/10.1007/s00382-021-05626-x.pdf https://link.springer.com/article/10.1007/s00382-021-05626-x/fulltext.html en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Climate Dynamics volume 56, issue 9-10, page 2959-2982 ISSN 0930-7575 1432-0894 Atmospheric Science journal-article 2021 crspringernat https://doi.org/10.1007/s00382-021-05626-x 2022-01-04T08:33:44Z Abstract The 0.6 °C warming observed in global temperature datasets from 1940 to 1960 to 2000–2020 can be partially due to urban heat island (UHI) and other non-climatic biases in the underlying data, although several previous studies have argued to the contrary. Here we identify land regions where such biases could be present by locally evaluating their diurnal temperature range (DTR = T Max − T Min trends between the decades 1945–1954 and 2005–2014 and between the decades 1951–1960 and 1991–2000 versus their synthetic hindcasts produced by the CMIP5 models. Vast regions of Asia (in particular Russia and China) and North America, a significant part of Europe, part of Oceania, and relatively small parts of South America (in particular Colombia and Venezuela) and Africa show DTR reductions up to 0.5–1.5 °C larger than the hindcasted ones, mostly where fast urbanization has occurred, such as in central-east China. Besides, it is found: (1) from May to October, T Max globally warmed 40% less than the hindcast; (2) in Greenland, which appears nearly free of any non-climatic contamination, T Mean warmed about 50% less than the hindcast; (3) the world macro-regions with, on average, the lowest DTR reductions and with low urbanization (60S-30N:120 W–90 E and 60 S–10 N:90 E–180 E: Central and South America, Africa, and Oceania) warmed about 20–30% less than the models’ hindcast. Yet, the world macro-region with, on average, the largest DTR reductions and with high urbanization (30 N–80 N:180 W–180 E: most of North America, Europe, and Central Asia) warmed just a little bit more (5%) than the hindcast, which indicates that the models well agree only with potentially problematic temperature records. Indeed, also tree-based proxy temperature reconstructions covering the 30°N–70°N land area produce significantly less warming than the correspondent instrumentally-based temperature record since 1980. Finally, we compare land and sea surface temperature data versus their CMIP5 simulations and find that 25–45% of the 1 °C land warming from 1940–1960 to 2000–2020 could be due to non-climatic biases. By merging the sea surface temperature record (assumed to be correct) and an adjusted land temperature record based on the model prediction, the global warming during the same period is found to be 15–25% lower than reported. The corrected warming is compatible with that shown by the satellite UAH MSU v6.0 low troposphere global temperature record since 1979. Implications for climate model evaluation and future global warming estimates are briefly addressed. Article in Journal/Newspaper Greenland Springer Nature (via Crossref) Greenland Climate Dynamics 56 9-10 2959 2982
institution Open Polar
collection Springer Nature (via Crossref)
op_collection_id crspringernat
language English
topic Atmospheric Science
spellingShingle Atmospheric Science
Scafetta, Nicola
Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations
topic_facet Atmospheric Science
description Abstract The 0.6 °C warming observed in global temperature datasets from 1940 to 1960 to 2000–2020 can be partially due to urban heat island (UHI) and other non-climatic biases in the underlying data, although several previous studies have argued to the contrary. Here we identify land regions where such biases could be present by locally evaluating their diurnal temperature range (DTR = T Max − T Min trends between the decades 1945–1954 and 2005–2014 and between the decades 1951–1960 and 1991–2000 versus their synthetic hindcasts produced by the CMIP5 models. Vast regions of Asia (in particular Russia and China) and North America, a significant part of Europe, part of Oceania, and relatively small parts of South America (in particular Colombia and Venezuela) and Africa show DTR reductions up to 0.5–1.5 °C larger than the hindcasted ones, mostly where fast urbanization has occurred, such as in central-east China. Besides, it is found: (1) from May to October, T Max globally warmed 40% less than the hindcast; (2) in Greenland, which appears nearly free of any non-climatic contamination, T Mean warmed about 50% less than the hindcast; (3) the world macro-regions with, on average, the lowest DTR reductions and with low urbanization (60S-30N:120 W–90 E and 60 S–10 N:90 E–180 E: Central and South America, Africa, and Oceania) warmed about 20–30% less than the models’ hindcast. Yet, the world macro-region with, on average, the largest DTR reductions and with high urbanization (30 N–80 N:180 W–180 E: most of North America, Europe, and Central Asia) warmed just a little bit more (5%) than the hindcast, which indicates that the models well agree only with potentially problematic temperature records. Indeed, also tree-based proxy temperature reconstructions covering the 30°N–70°N land area produce significantly less warming than the correspondent instrumentally-based temperature record since 1980. Finally, we compare land and sea surface temperature data versus their CMIP5 simulations and find that 25–45% of the 1 °C land warming from 1940–1960 to 2000–2020 could be due to non-climatic biases. By merging the sea surface temperature record (assumed to be correct) and an adjusted land temperature record based on the model prediction, the global warming during the same period is found to be 15–25% lower than reported. The corrected warming is compatible with that shown by the satellite UAH MSU v6.0 low troposphere global temperature record since 1979. Implications for climate model evaluation and future global warming estimates are briefly addressed.
author2 Università degli Studi di Napoli Federico II
format Article in Journal/Newspaper
author Scafetta, Nicola
author_facet Scafetta, Nicola
author_sort Scafetta, Nicola
title Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations
title_short Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations
title_full Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations
title_fullStr Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations
title_full_unstemmed Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations
title_sort detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations
publisher Springer Science and Business Media LLC
publishDate 2021
url http://dx.doi.org/10.1007/s00382-021-05626-x
https://link.springer.com/content/pdf/10.1007/s00382-021-05626-x.pdf
https://link.springer.com/article/10.1007/s00382-021-05626-x/fulltext.html
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op_source Climate Dynamics
volume 56, issue 9-10, page 2959-2982
ISSN 0930-7575 1432-0894
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