Assessing Significance of Global Climate Change in Local Climate Time Series

Friday, 19 December 2014
Marina M Livezey1, Andrea Bair2, Robert Livezey3, Annette Hollingshead1, Fiona M.C. Horsfall1 and Jenna C Meyers1, (1)NOAA Washington DC, Washington, DC, United States, (2)National Weather Service Salt Lake City, Salt Lake City, UT, United States, (3)retired, Bethesda, MD, United States
A common question by users to NOAA National Weather Service (NWS) local offices is how significant is global climate change in their local area. The scientific community provides copious information on global climate change, including assessments, for large regions. However, most decisions are made at the local level, where little or no information typically exists.

To address this need, NOAA NWS released operationally the Local Climate Analysis Tool (LCAT) in 2013 and specifically incorporated a capability into the tool to determine the local Rate of Change (ROC). Although ROC provides answers to some questions, we have seen an additional need for clarification on the significance of the ROC, such as whether or not it differentiates natural variability from a real signal of longer-term climate change. This question becomes very important for decision makers in consideration of their long term planning efforts to build local resilience to changes in climate.

LCAT uses three trend adjustment methods in computing ROC: Hinge, Optimal Climate Normals (OCN), and Exponentially Weighted Moving Average (EWMA). The Hinge tracks changes in climate time series, and OCN and EWMS track changes in climate normals. ROC is the slope of the straight line fit of the trend. Standard statistical methodology in use provides guidance for confidence intervals of the slope parameter (von Storch and Zwiers, 1999), which works well for a linear regression fit and can be used for ROCs of OCN and EWMA. However the Hinge, which is a linear fit anchored on one end, needs some additional adjustments and most likely will have smaller confidence intervals than those estimated by the statistical method.

An additional way to look at the problem is to assess how the climate change signal compares to climate variability in the local time series. Livezey et al. (2007) suggested the use of the signal to noise ratio to estimate the significance of the rate of climate change. The signal to noise ratio of 0.05 means that in 20 years, the local climate change signal will be as large as the standard measure of the noise, computed as the variance of the departure of the trend from the climate time series. This intuitively allows assessment of whether or not the local climate change is within the range of natural variability.