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Increasing Trend of Hot Temperature Anomalies by City Tech Blogger Jiehao Huang

This study uses the daily summary data from NOAA’s Integrated Surface Database (ISD) (Smith et al. 2011). We focused on station-based temperature observations from 1979 to 2014 of three airports in the New York City area: John F. Kennedy (JFK), LaGuardia (LGA) and Newark (EWR).

Daily anomaly threshold is defined as two standard deviations away from the daily mean. In this case, the threshold changes daily, which aims to describe more day-to-day variability and details of the weather. The daily anomaly threshold indicates if the strength of the daily maximum is changing over time. Anomalies can also be classified as the weird events or outliers. The magnitude of the temperature anomalies is the greatest temperature in both the positive and negative direction during winter [Fig. 1].

This graph describes both the positive and negative magnitudes of temperature anomalies away from the mean, assuming mean is 0. The Black lines are the thresholds for each of the three different sites.)

In the order words, temperatures tend to deviate most from the mean in winter compared to summer. This plot shows temperatures change from  overall maximal are stronger in the winter. In addition, we further analyzed the frequency of these temperature anomalies monthly and yearly [Fig.2a – Fig3c].

                                                 Fig.2c  

(Bar graphs to show annual frequency of daily temperature anomalies in New York City for each site.)

 

 

Fig. 3a                                     Fig.3b                                Fig.3c

The annual frequency plot shows an increasing trend of hot temperature anomalies, while a decreasing trend for cold temperature anomalies occurred in past 36 years. These plots are related to the climate change and address the issues of global warming. Overall, we observed an increase in the frequency of anomalous daily precipitation and temperature events over the last 36 years (precipitation part are not shown).

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