Project Department: Uni Research Climate (group: Climate Dynamics) period: 01.01.15 - 31.12.18

About the project

Water Cycle Extremes across Scales (WaCyEx)

Relate observed extreme precipitation events to synoptic flow patterns and moisture transport. Identify the most prominent synoptic patterns leading to extreme precipitation events in different regions in Norway using clustering analysis.

Extreme daily rainfall intensity and/or frequency has increased over most continents (Donat et al., 2013), and approximately 65% of areas from which reliable data are available exhibit positive trends for annual maximum precipitation extremes over 1951–1999 (Min et al., 2011; Westra et al., 2013). The globally averaged 20th and early 21st century rate of increase in annual maximum daily rainfall intensity was recently estimated to be between 5.9 and 7.7% per °C of globally averaged near-surface atmospheric temperature (Westra et al., 2013). This average consists of greater rates of change in the topics and the high latitudes of the Northern Hemisphere, and lower rates recorded in the drier midlatitudes. Daily or longer time scale outputs from general circulation models (GCMs) also suggest that extreme rainfall intensities will increase with global warming (SREX, 2012)  with a sensitivity of about 6% per °C globally but with very large model variability  (Kharin et al., 2012).

We believe the project proposal will strengthen two of the SKD Strategic Thematic Areas: Regional climate scenarios and extremes and Process understanding and uncertainties.

Specific objectives:

(i): Develop a Norwegian dataset of long-term observations of daily precipitation from 1900 to present 

(ii): Calculate trends and investigate possible decadal to multidecadal variability in extreme precipitation events (intensity, number of events etc.) for different regions in Norway.

(iii): Develop new diagnostic for understanding the role of variability in vertical velocity in explaining the frequency and intensity of extreme events



cp: 2018-07-18 04:17:15