Tentative subjects and descriptions

1. Data set development
Faculty: L. Alexander, M. Donat
Participants: F.B. Avila, K.P. Menang, J.W. Rajczak, S. Dong, M. Renom Molina

Uncertainty in observed datasets has many forms, from the quality and/or consistency of the underlying data to the choices made within a chosen gridding/interpolation method (parametric uncertainty), to the network selection and analytical framework (structural uncertainty). Of these structural uncertainty generally has the largest influence on the resulting gridded product, particularly in the representation of extremes and their trend estimates. However rarely are datasets produced with uncertainty estimates and users are often unaware that the choice of observational product can substantially affect results. This project will assess how changing station networks or parameter settings within interpolation methods affect trends in temperature extremes and in turn whether this could affect detection and attribution analysis. The objective of this research problem is to test the sensitivity of gridded output to changing parameters and input station networks and to discuss in detail how and why the results vary when changing input parameters, what is important/less important when considering the climate of the region. Students will decide what parameters settings to test and how the station networks are set up. Data from the ETCCDI temperature indices e.g. annual maxima Tmax (TXx), annual minima Tmin (TNn) contained in the HadEX2 observational extremes indicators dataset (Donat et al., 2013) will be supplied for different regions. Ultimately the results from this project could feed into Research Problem 3.