Billion-Dollar Weather and Climate Disasters: Time Series

The graphic below helps to visualize how the different types of identified U.S. Billion-dollar disaster events have changed over time. Caution should be used in interpreting any trends based on this graphic for a variety of reasons. For example, inflation has affected our ability to compare costs over time. To reflect this, the graphic also shows events with less than $1 billion in damage at the time of the event, but after adjusting for Consumer Price Index (inflation), now exceed $1 billion in damages. Continued assessment of these data are in process, as there are other factors as well that affect any rate of change interpretation. Comparison of events in most recent years is most reliable.

Recent milestones to improve the data analysis include the following:

NCDC hosted a workshop in May 2012 including academic, federal, and private sector experts to discuss best practices in evaluating disaster costs. A research article (Smith and Katz, 2013) regarding the loss data we use, our methods and any potential bias was published in 2013. This research article found the net effect of all biases appears to be an underestimation of average loss. In particular, it is shown that the factor approach can result in an underestimation of average loss of roughly 10–15%. This bias was corrected during a reanalysis of the loss data to reflect new loss totals.

It is also known that the uncertainty of loss estimates differ by disaster event type reflecting the quality and completeness of the data sources used in our loss estimation. In 2014, two of the eight billion-dollar events (i.e., the drought and flooding event) have higher potential uncertainty values around the loss estimates due to less coverage of insured assets. The remaining 6 events (i.e., 5 severe local storms and a winter storm) have lower potential uncertainty surrounding their estimate due to more complete insurance coverage. To that end, we have temporarily rounded our loss estimates to the nearest billion dollars while developing research to define uncertainty and confidence intervals surrounding these estimates.