Dear colleagues,
during these days of COP24 in Katowice, it may be an interesting option
to study a recent review paper entitled "Trend analysis of climate time
series", which includes sentences such as:
"Despite its discussion in the public and the searches for the impact of
the warming in climate science, it is statistical science that puts
numbers to the warming."
"Statistics also reveals that there is no basis to infer a global
warming hiatus after the year 1998."
"The application of statistical methods to data has also a moral aspect."
As author, I am ready to answer your questions on this theme.
Best wishes
Manfred
Dr. Manfred Mudelsee, CEO
=== BIBLIOGRAPHICAL DETAILS ===
Earth-Science Reviews
Trend analysis of climate time series: A review of methods
Manfred Mudelsee
In Press, Accepted Manuscript, Available online 11 December 2018
=== OPEN ACCESS FULL ARTICLE
https://www.sciencedirect.com/science/article/pii/S0012825218303726
=== FULL ABSTRACT ===
The increasing trend curve of global surface temperature against time
since the 19th century is the icon for the considerable influence humans
have on the climate since the industrialization. The discourse about the
curve has spread from climate science to the public and political arenas
in the 1990s and may be characterized by terms such as “hockey stick” or
“global warming hiatus”. Despite its discussion in the public and the
searches for the impact of the warming in climate science, it is
statistical science that puts numbers to the warming. Statistics has
developed methods to quantify the warming trend and detect change
points. Statistics serves to place error bars and other measures of
uncertainty to the estimated trend parameters. Uncertainties are
ubiquitous in all natural and life sciences, and error bars are an
indispensable guide for the interpretation of any estimated curve—to
assess, for example, whether global temperature really made a pause
after the year 1998.
Statistical trend estimation methods are well developed and include not
only linear curves, but also change-points, accelerated increases, other
nonlinear behavior, and nonparametric descriptions. State-of-the-art,
computing-intensive simulation algorithms take into account the peculiar
aspects of climate data, namely non-Gaussian distributional shape and
autocorrelation. The reliability of such computer age statistical
methods has been testified by Monte Carlo simulation methods using
artificial data.
The application of the state-of-the-art statistical methods to the
GISTEMP time series of global surface temperature reveals an accelerated
warming since the year 1974. It shows that a relative peak in warming
for the years around World War II may not be a real feature but a
product of inferior data quality for that time interval. Statistics also
reveals that there is no basis to infer a global warming hiatus after
the year 1998. The post-1998 hiatus only seems to exist, hidden behind
large error bars, when considering data up to the year 2013. If the fit
interval is extended to the year 2017, there is no significant hiatus.
The researcher has the power to select the fit interval, which allows
her or him to suppress certain fit solutions and favor other solutions.
Power necessitates responsibility. The recommendation therefore is that
interval selection should be objective and oriented on general
principles. The application of statistical methods to data has also a
moral aspect.
===
Dr. Manfred Mudelsee
Chief Executive Officer
Climate Risk Analysis
Kreuzstrasse 27
Heckenbeck
37581 Bad Gandersheim
Germany
Telephone: +49 5563 9998140
Email: mudel...@climate-risk-analysis.com
URL: http://www.climate-risk-analysis.com
Skype: mudelsee1
LinkedIn: https://de.linkedin.com/in/mudelsee
Twitter: @MMudelsee
Climate Time Series and Risk Analyses
Book: http://www.manfredmudelsee.com/book/
Courses: http://www.climate-risk-analysis.com/courses/
===
*Short Course in Climate Time Series Analysis for Beginners*
Bad Gandersheim, Germany, 25 to 27 September 2018
http://www.climate-risk-analysis.com/courses/time-series/25th-Training-Course-for-Beginners-in-Climate-Time-Series-Analysis-2019.html
