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Research Article
Ana C. Cebrián Guajardo
Universidad de Zaragoza
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9052-9674
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The analysis of non stationary behaviours and trends in the extremes of a series is an important problem in global warming. This work develops statistical tools to analyse that behaviour, using the properties of the occurrence of records in i.i.d. series. The main difficulty of this problem is the scarcity of information in the tails, so that it is important to obtain all the possible evidences from the data available. To that end, first, different statistics based on upper records are proposed and the most powerful is selected. Then, using that statistic, several approaches to join the information of four types of records, upper and lower records of forward and backward series, are suggested. It is found than these joint tests are clearly more powerful.
The suggested tests are specifically useful in the analysis of the effect of global warming in the extremes, for example of daily temperature. They have a high power to detect weak trends and they can be widely applied, since they are non parametric. The proposed statistics join the information of M independent series, what is useful given the necessary split of the series to arrange the data. This arrangement solves usual problems of climate series (seasonality and serial correlation) and provides more series to find evidences. These tools are used to analyse the effect of global warming in the extremes of daily temperature in Madrid.
Keywords
Non parametric tests, Records, Extremes, Non stationary tails, Trend, Record-breaking temperatures, Climate change
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Received 01 Apr, 2021
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This preprint is under consideration at Stochastic Environmental Research and Risk Assessment. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Ana C. Cebrián Guajardo
Universidad de Zaragoza
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9052-9674
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 22 Feb, 2021
No community comments so far
Reviews received
Received 01 Apr, 2021
Reviewers invited
Invitations sent on 10 Feb, 2021
Editor assigned
On 10 Feb, 2021
Editor invited
On 10 Feb, 2021
First submitted
On 05 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
The analysis of non stationary behaviours and trends in the extremes of a series is an important problem in global warming. This work develops statistical tools to analyse that behaviour, using the properties of the occurrence of records in i.i.d. series. The main difficulty of this problem is the scarcity of information in the tails, so that it is important to obtain all the possible evidences from the data available. To that end, first, different statistics based on upper records are proposed and the most powerful is selected. Then, using that statistic, several approaches to join the information of four types of records, upper and lower records of forward and backward series, are suggested. It is found than these joint tests are clearly more powerful.
The suggested tests are specifically useful in the analysis of the effect of global warming in the extremes, for example of daily temperature. They have a high power to detect weak trends and they can be widely applied, since they are non parametric. The proposed statistics join the information of M independent series, what is useful given the necessary split of the series to arrange the data. This arrangement solves usual problems of climate series (seasonality and serial correlation) and provides more series to find evidences. These tools are used to analyse the effect of global warming in the extremes of daily temperature in Madrid.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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