Abstract
The main challenge of this paper is to demonstrate that one of the most frequently conducted analyses in the climate change field could be affected by significant errors, due to the use of rainfall data characterized by coarse time-resolution. In fact, in the scientific literature, there are many studies to verify the possible impacts of climate change on extreme rainfall, and particularly on annual maximum rainfall depths, Hd, characterized by duration d equal to 24 h, due to the significant length of the corresponding series. Typically, these studies do not specify the temporal aggregation, ta, of the rainfall data on which maxima rely, although it is well known that the use of rainfall data with coarse ta can lead to significant underestimates of Hd. The effect of ta on the estimation of trends in annual maximum depths with d = 24 h, Hd=24 h, over the last 100 years is examined. We have used a published series of Hd=24 h derived by long-term historical rainfall observations with various temporal aggregations, due to the progress of recording systems through time, at 39 representative meteorological stations located in an inland region of Central Italy. Then, by using a recently developed mathematical relation between average underestimation error and the ratio ta/d, each Hd=24 h value has been corrected. Successively, commonly used climatic trend tests based on different approaches, including least-squares linear trend analysis, Mann–Kendall, and Sen’s method, have been applied to the “uncorrected” and “corrected” series. The results show that the underestimation of Hd=24 h values with coarse ta plays a significant role in the analysis of the effects of climatic change on extreme rainfalls. Specifically, the correction of the Hd=24 h values can change the sign of the trend from positive to negative. Furthermore, it has been observed that the innovative Sen’s method (based on a graphical approach) is less sensitive to corrections of the Hd values than the least-squares linear trend and the Mann–Kendall method. In any case, the analysis of Hd series containing potentially underestimated values, especially when d = 24 h, can lead to misleading results. Therefore, before conducting any trend analysis, Hd values determined from rainfall data characterized by coarse temporal resolution should always be corrected.
Highlights
Mean global surface temperatures showed an increase of approximately 1.1 ◦ C [6] and, based on the Clausius–Clapeyron relation, for each 1 ◦ C increase in global temperature, the precipitable water increases by ~7% [7,8], even though relative humidity appears to decrease at high temperatures [1,8,9,10]
By using some of the more common climatic trend tests characterized by very different approaches the main objective of this paper is to evaluate the effect of time-resolution of rainfall data on trend estimation for annual maximum depths with duration 24 h
It is possible to find many analyses conducted to assess whether climate change has influenced the trends of the annual maximum rainfall depths with a duration of 24 h
Summary
Mean global surface temperatures showed an increase of approximately 1.1 ◦ C [6] and, based on the Clausius–Clapeyron relation, for each 1 ◦ C increase in global temperature, the precipitable water increases by ~7% [7,8], even though relative humidity appears to decrease at high temperatures [1,8,9,10]. It is expected that temperature will increase near to the surface and will decrease in the upper troposphere, favoring atmospheric instability [11]. Considering that atmospheric warming and water vapor trends have local non-uniformity, the associated variation in average rainfall is typically mutable over the planet [12]
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