# Predicting The Relative Climate Pressure Index of The Contiguous United States for 2050 and 2100¶

Thomas P. Braun

Geospatial Sciences and Geography Undergraduate, Oregon State University

## Introduction¶

Issue:

The greatest threat that every human being in the US truly faces is the effects of climate change upon their land. With rising sea levels, increasing extreme temperatures, longer droughts, wells and lakes running dry, unhealthy pollution levels (e.g., in Los Angeles), humans that live in pressured regions are in unforeseen levels of danger.

Knowns:

Currently, farms in key locations within the contiguous United states are starving for water, all while hundreds of thousands of acres of forest and vegetation have burned in the past year alone from absolutely record-shattering fires. For each year that passes, the effects of climate change are affecting the lives of increasingly more people. Furthermore, with an increasing global population, the pressure upon humanity will inevitably increase due to finite resources.

Unknowns:

Since this reality is part order and part chaos, the models which we use to predict the future of our planet rely heavily upon extrapolation. Therefore, we do not know with great certainty which regions of the planet are most likely to be condusive to human existence. In truth, while the public lives in a day-to-day postmodern-hypnotic-trance assuming the system they exist within will be stable for their existence tomorrow, tomorrow is always uncertain especially as we traverse further into the 21st century. We do not know the potential human impact upon mortality rates due to dynamism of human behavior under extreme circumstances and limiting environmental variables.

Resolution:

I compiled 122 years worth of mean temperature, mean dew point, and mean rainfall for each month of each year in order to calculate the RCPI for each 4km^2 partition of the contiguous United States. The lower the RCPI, the higher of an impact climate change will have on that particular partition of the United States. There are four maps: one map has an extrapolation for 2050 and another has an extrapolation for 2100; each has the RCPI extrapolated from the past 30 years of data and the past 122. Finally, the variable $\Delta$-RCPI was calculated as the difference of the 122-year extrapolative models between the 2100 and 2050 RCPI values. Overall, I discovered that RCPI implies various regions in the contiguous United States will experience changes of climate pressure such that some regions are more inhabitable than other regions. With this knowledge, humans can prepare for the future while the US government can begin to strategise the logisitics for areas which RCPI predicts the greatest impact.

## Methods¶

### Process¶

Recursively obtain the raw climate data from the official PRISM FTP server and split the obtained 122 year series into three raster arrays (one for each variable: mean temperature, mean dew point, and mean precipitation). Due to computational restrictions, each raster array was converted into a 1-dimensional array for greatly increased speeds (from ~2 months per variable to ~2 hours). Next, for each separate variable, a blank raster with equal geospatial extent, coordinate system, and spatial resolution was created. Then, for each variable and for each raster cell, either 30 or 122 years of data was linearly regressed in order to extrapolate a predicted value for the year 2050 and 2100 which was stored in the variable's corresponding blank raster. After each blank raster was filled with information, the raster was normalized. Here are examples of the final rasters for the 122-years of data extrapolated into 2100:

2100 Extrapolation from mean temperature, mean dew point, and mean precipitation (respectively) 122-year arrays

Next, these rasters were combined onto a blank raster (once again with equal geospatial extent, coordinate system, and spatial resolution) such that for each cell, the RCPI was calculated as follows:

$RCPI = w_{0}\bar{p} - w_{1}\bar{d} - w_{2}\bar{t}$

In this particular case, the values of the weights $w_0$, $w_1$, and $w_2$ add up to 1 and equal $\frac{4}{10}$, $\frac{1}{10}$, and $\frac{5}{10}$, respectively. The logic for assigning the weights and the polarities above is as follows: water is the most beneficial for human survival, a high mean temperature is most negative for human survival, while a high dew point is relatively marginally negative for human survival. Thus, the higher the RCPI, the greater the potential for human survival.

After the RCPI was calculated for each cell, the raster was processed through an aggregation algorithm such that the average value from every 10x10 cell subset was used to replace each value equally in the respective 10x10 cell subset. Aggregating the raster allowed the data to be more visibly presentable for the final output. Finally, the aggregated raster was input into a clipping algorithm such that only values that existed within a polygon of the contiguous United States remained. Finally, the raster was colorized using a standard distribution.

## Discussion¶

Although the 30 year extrapolations have some clearly visible differences, the trend from the lower to higher latitudes follows a shared trend: the higher North from the equator, the greater the RCPI therefore the more beneficial for humans. Climate predictions from other sources confirm this trend, particularly simulations from the National Aeronautics and Space Administration (NASA) executed on a supercomputer:

source: https://www.nasa.gov/content/nasa-supercomputer-generates-closer-look-at-future-climate-conditions-in-us/

In comparison to the 122-year RCPI extrapolations of 2050 and 2100, the 2090 NASA model bares particularly concerning resemblance. Although the RCPI and the "average springtime temperature" (AST) are different, both extrapolations necessarily considered temperature of either the present, past, or both. Therefore, a correlation can be drawn between RCPI and AST extrapolations because both are functions of temperature. As we see in Florida and Southern Texas, the RCPI and AST extrapolations show areas which are maximally impacted. The RCPI and AST Extrapolations also correlate between the United States and Mexican border. Additionally, we see heavy correlation between the northern latitudes, particularly the states within New England (i.e., the Northeast United States).

Some other regions also show particularly interesting correlations between RCPI and AST. In comparison to the 122-year extrapolation of RCPI for 2100 and AST, the State of Oregon shows a similar fate: regions East of the Cascades experience relatively greater pressure, whereas regions West of the cascades experience relatively average pressure compared to the nation as a whole.

Finally, we can consider the map of $\Delta$-RCPI, which is simply the difference between the 122-year extrapolations of 2100 and 2050. What $\Delta$-RCPI shows is that between 2050 and 2100, the relative amount of pressure shifts; whereas the Southern Latitudes experienced a great amount of pressure in 2050, by 2100, not much more pressure could be added into the system. As a result, the northern latitiudes begin to carry an increasingly heavier burden as time approaches 2100. Although the northern latitiudes carry an increasingly greater burden, the initial RCPI in the northern latitudes is sufficiently low such that this increase in pressure marginally effects the 2100 RCPI. However, due to the dynamism of nature, $\Delta$-RCPI also shows us the volatiliity of a region. The greater the volatility, the less the certainty.

Considering these concepts, the lesson for humans is clear: if you insist on living in the United States, the best places to live are those regions which contain a high RCPI and a low $\Delta$-RCPI. Regions which fall in both of these categories have the lowest relative pressures and lowest volatility. These regions are expected to be the most stable locations to live in.