S83 Trends in Record Breaking Temperatures in Germany

Sunday, 6 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Emily Fletcher, NSF, Lincoln, NE; and C. M. Rowe

Climate change affects everyone and everything and should be taken seriously. The more that we know about it, the better we can act to slow it down and prolong our time on this planet. A widespread problem is that many people think climate change is a hoax created by scientists and I hope that research like the investigation proposed below will prove to them that it is no hoax. In the past year many bills and orders that will reverse a lot of the environmentally friendly and helpful laws that had previously been put in place have been proposed or signed. Our country needs a serious wake-up call about climate change and I want to part of the driving force that issues it.

A few years ago, a UCARE study supervised by Professor Clinton Rowe was conducted on trends in record breaking temperatures in the conterminous United States from 1961-2010. I conducted research on trends in record breaking temperatures in Germany from 1967-2016. You can view my findings at this webpage: http://weather.unl.edu/UCARE/efletcher. We are still working on this website, but should have it finished shortly. In the previous study and my current study, a baseline record was established from looking at daily temperatures over a period and keeping it constant rather than establishing a new record every time it was broken, which would make it difficult to see any trends that were due to climate change. We plan to follow this line of study so that the effects of climate change can be observed over the same time frame but different country and later be compared to effects in the U.S.

In this analysis of trends in record-breaking temperatures, I will use the same modified definition of when a record is broken, which is consistent with the previous UCARE study and my own study. Normally, each record-breaking event establishes a new record that must then be broken later in time. Each new record establishes a higher hurdle that must be cleared so that in an unchanging climate, records become increasingly rare as time goes on. This makes it difficult to distinguish any trend due to climate change. For this project, I instead establish daily records from an initial period of 30 years and, for the following 60 years (in my current study I chose 1901-1930 for my initial test period and 1967-2016 for my current period), note when that temperature is exceeded. The difference between my method and the usual method is that I do not adjust the record temperature for that day from its initial value determined in the initial 30 years. Therefore, the probability of breaking any record will remain constant over the entire period of record, making it very easy to see any effects from climate change. The data from the last 60 years can then be analyzed for any trend without the large decrease in normal record statistics (as these records* are not based on the standard definition of what constitutes a record-breaking event, I will denote them with an asterisk, as used in the paper from the prior study). Moreover, as all records* are based on the same 30-year initial period, this method avoids any problems caused by time series of different length, with the inherent differences in record-setting probability, when comparing stations or grouping stations.

Minimum and maximum temperatures series for a specified calendar day are independent as each observation is separated from the preceding and following observations in the series by a full year (meaning that each observation has 365 days between it and the next observation) At any location then, there are 365 separate time series of daily minimum and maximum temperatures, ignoring leap days, one for each calendar day. For any calendar day, two types of records can occur – record lows and record highs – yielding four sets of daily records: record low daily minimum (nTmin), record high daily minimum (xTmin), record low daily maximum (nTmax), and record high daily maximum (xTmax). Utilizing this modified definition, the expected number of records* of any type set in any single year at any station for a daily time series would be approximately 365/30, or 12.2, in the absence of any climate change. If more than this number of records* occurs and, especially, if significant trends exist in the number of records* over the analysis period, this can provide evidence of changing climate.

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