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:
In May 2012, NOAA's National Centers for Environmental Information -- then known as National Climatic Data Center (NCDC) -- hosted a workshop including academic, federal, and private sector experts to discuss best practices in evaluating disaster costs.
A research article "U.S. Billion-dollar Weather and Climate Disasters: Data Sources, Trends, Accuracy and Biases" (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 2015, three of the ten billion-dollar events (i.e., the drought and flooding events) have higher potential uncertainty values around the loss estimates due to less coverage of insured assets. The remaining seven events (i.e., five severe storm, wildfire and winter storm events) have lower potential uncertainty surrounding their estimate due to more complete insurance coverage. Our newest research defines the cost uncertainty using confidence intervals as discussed in the peer-reviewed article "Quantifying Uncertainty and Variable Sensitivity within the U.S. Billion-dollar Weather and Climate Disaster Cost Estimates" (Smith and Matthews, 2015). This research is a next step to enhance the value and usability of estimated disaster costs given data limitations and inherent complexities.
The most recent analysis offers new graphing options to better visualize event costs over time. These options include: 1) annual U.S. disaster costs for billion-dollar events including 95% confidence interval estimates of cost uncertainty and 2) the 5-year cost mean. The 95% confidence interval (CI) probability is a representation of the uncertainty associated with the disaster cost estimates. Monte Carlo simulations were used to produce the upper and lower bounds (Smith and Matthews, 2015).