the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Record-breaking statistics detect islands of cooling in a sea of warming
- 1Multidisciplinary Department, Federal University of São Paulo, São Paulo, Brazil
- 2Department of Earth and Planetary Sciences, Weizmann Institute of Sciences, Rehovot, Israel
- 3Department of Physics, Michigan Technological University, Houghton, USA
- 1Multidisciplinary Department, Federal University of São Paulo, São Paulo, Brazil
- 2Department of Earth and Planetary Sciences, Weizmann Institute of Sciences, Rehovot, Israel
- 3Department of Physics, Michigan Technological University, Houghton, USA
Abstract. Record-breaking statistics are combined here with geographic mode of exploration to construct a new object: a record-breaking map. Such maps are shown here to reveal surprisingly robust statistical results and spatial content. Specifically, we examine a time series of sea surface temperature (SST) values and show that high SST records have been broken far more frequently than the expected rate for a trend-free random variable (TFRV) over the vast majority of oceans (83 % of the grid cells). This, together with the asymmetry between high and low records and their deviation from a TRFV, indicate SST warming over most oceans, obtained by using a general and simple-to-use method. The spatial patterns of this warming are coherent and reveal islands of cooling, such as the "cold blob" in the North Atlantic and a surprising elliptical area in the Southern Ocean, near the Ross sea gyre, not previously reported.
Elisa T. Sena et al.
Status: open (until 14 Jul 2022)
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RC1: 'Comment on acp-2022-316', Anonymous Referee #1, 28 Jun 2022
reply
In this study, the authors investigate the observed record-breaking SST events by comparing with the expected rate for a trend-free random variable (TFRV). The authors find the asymmetric nature of the high and low records and reveal islands of cooling in the North Atlantic and Southern Ocean. The record-breaking theory is interesting; however, the assumption of the theory may not be enough reliable and the results may be sensitive to the length of a time series. Given these issues, I would recommend that this paper is not suitable for publication in this high-rank journal. More specific comments are listed as below.
Major comments:
1. The results of this study depend mainly on the comparison with the TFRV model. However, the climate system is clearly not a TFRV. Significant trends in SST can be detected even without the influence of human-induced greenhouse gases. As Deser et al.(2013) and Wallace et al. (2015), the internal variability is important for multi-decadal trends in climate variables, which are independent of human activities.
The authors use only a trend-free model for comparison, which is more of a hypothesis testing tool to test observed trends. and is of little implication for the climate community to understand the observations and climate change.It is suggested that considering internal trends of the climate variables, such as add the trend distribution of Pre-industrial experiments from CMIP5/6 into the record-breaking statistics and comparing it with observed data, may yield more valuable results.
2. The results may depend to a large extent on the sample size. As shown in Equation 2, the broken k-record varies with the length of the time series. In other words, the results may be sensitive to the length of the time series and not robust.
Elisa T. Sena et al.
Elisa T. Sena et al.
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