Snow fall may decrease sharply, while general precipitation may stay level.

Data from the National Oceanic & Atmospheric Association (NOAA), Global Summary of the Year

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With global temperatures continuing to rise, the behavior of the Earth's weather is drastically affected as a result. Although precipitation in general does not have drastic changes, the decrease in snow is quite significant in comparison.

This chart displays changes in snow and other precipitation side-by-side. The projection model here uses the past 100 years* to predict:

• Projection - the continued climb in temperature on our current trajectory;
• Easing rules - temperatures if an addtional 0.05% CO₂ were allowed each year;
• Stricter rules - temperatures if CO₂ emissions were decreased 0.05% each year.

Hover over the model to view specific values.
Click on values in the legend to add and remove series.

* 100 years of past data were used because values vary so drastically from year to year. More data was utilized to gather the overall trend.

A closer look at snow fall data.

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This chart shows a more detailed visual of the observed snow fall data over the past 130 years.

Although the most drastic decrease in snow happened during the Industrial Revolution, the overall continued decline of snow fall throughout the 21st century shows the effects of global temperature increases. The increased non-frozen water in our ecosystem also has further implications.

More information about the effect of climate change on our oceans can be viewed on our Ocean Impacts page.

Hurricane frequency and intensity are forecasted to increase.

Statistics from the National Hurricane Center (NHC), Hurricanes and Typhoons, 2016

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The data used to analyze hurricane frequency and intensity ranged back to the 1850s, and therefore did not include the customary Saffir-Simpson Hurricane Wind Scale categorization scheme (Category 1, 2, etc.). Therefore, a K-means clustering model was used to classify the hurricanes based on their minimum pressure and maximum wind speed. The model was optimized to group the hurricanes into 4 categories.

Next, linear regression models were used to predict the total number of hurricanes up until 2200, as well as the number of hurricanes in each category. The models used inputs such as historical sea temperature, sea level, and global temperature to create these forecasts.

As can be seen by this chart, the frequency as well as intensity of hurricanes is projected to increase over time based on current historical data.

Tornado frequency and intensity are also forecasted to increase.

Statistics from NOAA, USA - Historical Tornado Tracks, 2020

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Linear regression models were used to project:

• the number of tornadoes we could potentially see up to 2200,
• number of magnitude 0 (F0 on the Fujita scale) tornadoes, and
• magnitude 2 (F2 on the Fujita scale) tornadoes.

These 2 magnitudes were chosen for display as they demonstrated relatively reliable linear regression accuracies of above 70%, likely because there is a slight increase in these tornadoes in the available historical data.

The models used inputs such as historical changes in CO₂ emissions, gas fuel emissions, and global temperature to make these forecasts.

It is worth mentioning that the model shows a greater number of predicted magnitude 0 tornadoes than predicted total tornadoes. As these were created from separate linear regression models, the intent is to display the future trends that could be seen, rather than predict the exact frequency that can be expected.