Correcting Observational Biases in Sea Surface Temperature Observations Removes Anomalous Warmth during World War II
Correcting Observational Biases in Sea Surface Temperature Observations Removes Anomalous Warmth during World War II
复制标题
纠正海面温度观测中的观测偏差消除了二战期间的异常温暖
作者:
Duo Chan;P. Huybers
Most historical sea surface temperature (SST) estimates indicate warmer World War II SSTs than expected from forcing and internal climate variability. If real, this World War II warm anomaly (WW2WA) has important implications for decadal variability, but the WW2WA may also arise from incomplete corrections of biases associated with bucket and engine room intake (ERI) measurements. To better assess the origins of the WW2WA, we develop five different historical SST estimates (reconstructions R1–R5). Using uncorrected SST measurements from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) version 3.0 (R1) gives a WW2WA of 0.41°C. In contrast, using only buckets (R2) or ERI observations (R3) gives WW2WAs of 0.18° and 0.08°C, respectively, implying that uncorrected biases are the primary source of the WW2WA. We then use an extended linear-mixed-effect method to quantify systematic differences between subsets of SSTs and develop groupwise SST adjustments based on differences between pairs of nearby SST measurements. Using all measurements after applying groupwise adjustments (R4) gives a WW2WA of 0.13°C [95% confidence interval (c.i.): 0.01°–0.26°C] and indicates that U.S. and U.K. naval observations are the primary cause of the WW2WA. Finally, nighttime bucket SSTs are found to be warmer than their daytime counterparts during WW2, prompting a daytime-only reconstruction using groupwise adjustments (R5) that has a WW2WA of 0.09°C (95% c.i.: −0.01° to 0.18°C). R5 is consistent with the range of internal variability found in either the CMIP5 (95% c.i.: −0.10° to 0.10°C) or CMIP6 ensembles (95% c.i.: −0.11° to 0.10°C). These results support the hypothesis that the WW2WA is an artifact of observational biases, although further data and metadata analyses will be important for confirmation.
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影响因子:
4.6
作者:
Pfeiffer M;Zinke J;Dullo WC;Garbe-Schönberg D;Latif M;Weber ME
通讯作者:
Weber ME
DOI:
10.1002/joc.4775
发表时间:
2017-04-01
期刊:
INTERNATIONAL JOURNAL OF CLIMATOLOGY
影响因子:
--
作者:
Freeman, Eric;Woodruff, Scott D.;Smith, Shawn R.
通讯作者:
Smith, Shawn R.
DOI:
10.1002/joc.4492
发表时间:
2015
期刊:
International Journal of Climatology
影响因子:
--
作者:
Carella G
通讯作者:
Carella G
影响因子:
64.8
作者:
Chan, Duo;Kent, Elizabeth C.;Huybers, Peter
通讯作者:
Huybers, Peter
影响因子:
8
作者:
Kent, Elizabeth C.;Kennedy, John J.;Zhang, Huai-Min
通讯作者:
Zhang, Huai-Min