Well, the word "significant" is a loaded one in this discussion -- in this context, it typically means "statistically significant," which has a very narrow (and sometimes questionable[1]) meaning.
You're right that leaving Katrina in the dataset (assuming the same techniques were used -- there are other ways to deal with outliers other than dropping them) would bias the result further towards indicating that female-named hurricanes are more dangerous. But the persistence of a significant finding in that regard in the absence of that data point does not prove a causal link, much less the specific one the author suggests.
You're right that leaving Katrina in the dataset (assuming the same techniques were used -- there are other ways to deal with outliers other than dropping them) would bias the result further towards indicating that female-named hurricanes are more dangerous. But the persistence of a significant finding in that regard in the absence of that data point does not prove a causal link, much less the specific one the author suggests.
[1] For some criticisms of how statistical significance is currently being used, read this: http://www.deirdremccloskey.com/docs/jsm.pdf