On the role of statistics in climate research

Abstract

We review the role of statistical analysis in the climate sciences. Special emphasis is given to attempts to construct dynamical knowledge from limited observational evidence, and to the ongoing task of drawing detailed and reliable information on the state, and change, of climate that is needed, for example, for short-term and seasonal forecasting. We conclude with recommendations of how to improve the practice of statistical analysis in the climate sciences by drawing more efficiently on relevant developments in statistical mathematics.

Predictability of the Barents Sea Ice in Early Winter: Remote Effects of Oceanic and Atmospheric Thermal Conditions from the North Atlantic

“…In climate research, CCA is used to identify coupled patterns in climate data (Bretherton et al 1992). A variant of CCA, developed by Barnett and Preisendorfer (1987), is the most reliable and commonly used statistical seasonal forecasting technique (Zwiers and von Storch 2004). The CCA technique has also been used to examine the seasonal forecast skill for July SIC variability in Hudson Bay (Tivy et al 2011).…”

The challenge to detect and attribute effects of climate change on human and natural systems

“…In practice, modelling setups will almost always use a combination of mechanistic and empirical components (e.g. Rodó et al 2013;Zwiers and von Storch 2004): mechanistic hydrological models nevertheless represent small-scale processes through largely empirical means, for instance, while the selection of the institutions and beliefs to consider in an empirical study of the effects of climate change on societies will have involved some mechanistic understanding of what might be worthy of investigation (Ruddell et al 2012;Howe et al 2012).…”

Rainfall Prediction Using Teleconnection Patterns Through the Application of Artificial Neural Networks

“…The probable reasons for the difficulties in conducting long-term rainfall prediction are the complexity of atmosphere-ocean interactions and the uncertainty of the relationship between rainfall and hydro meteorological variables. So far, long-term climate prediction using numerical models has not demonstrated useful performance, and statistical models have shown better performance than numerical models (Zwiers & Von Storch 2004). Consequently, in this study Artificial Neural Networks and linear regression models have been applied to nonlinear and linear statistical prediction.…”