Master's Research - Loblolly Pine: How will annual number of freezing days affect the Loblolly Pine?

Introduction:

I have found the research to be utterly fascinating. I sincerely wish I had more time to work on it in order to test more variables. To begin, the USDA is interested in planting the Loblolly Pine tree in the Southeastern United States. Considering the climate has been changing and will continue to change, the USDA would like to understand how the forest may be affected. The Loblolly's natural range goes as far west as eastern Texas and as far north as southern New Jersey. Extreme winter temperatures and ice storms are a factor in lack of northward extension. However, with the projected increase in temperature, the natural Loblolly range may extend northward. It grows relatively quickly, compared to other trees, and does not have any long-term health problems. However, like most living things, it is susceptible to diseases, pests, and damage. For my study, I chose to focus on one pest, the Southern Pine Beetle (SPB).

It is well known that climate has an influence on the life cycle of the Loblolly. However, does climate influence the SPB? Well, numerous studies have found climate (i.e. temperature and precipitation) impact SPB outbreaks (Beal 1929, Duehl et al. 2011, Gan 2004, Mcclelland and Hain 1979, Michaels 1984). Over the years, studies found that severely cold winters resulted in broad morality rates of the SPB (Beal 1929, Mcclelland and Hain 1979, and Michaels 1984). Those same studies found that mild winters kill less SPBs and lead to outbreaks the following spring and summer months. It is important to note that all these studies found extremely cold temperatures (i.e. -5F) lead to these mortality rates. As a consequence, I thought I would determine if there was a relationship between the number of freezing days and the number of outbreaks. I will also study how the annual number of freezing days may change with the future. I will determine the likely hood of temperatures occurring by calculating probability density functions (PDF). Lastly, to determine a level of confidence in the models, I will conduct a statistical bias test. 

Data and Methods: 

For this study, I used outbreak data from the USDA and temperature data calculated from one downscaled method, the Multivariate Adaptive Constructed Analogs (MACA). The outbreak data lists the number of outbreaks per county from 1960-2004. For this study, I chose to use two variables that determined outbreaks. The first is the total number of spots (tspot) in a county and the second is a measure of outbreak level determined by the size of the county (disc). "Tspot" ranges from 0 to about 1000. However, the tspot data is unreliable with missing data. Tspot data is reliable from 1992-2004. "Disc" is 5 different values. 0 equals less than 0.1 spots per thousand acres, 1 equals 0.1 to less than 1 spot per thousand acres, 2 equals 1 to less than 3 spots per thousand acres, 3 equals 3 or more spots per thousand acres, and 9 equals more than 1 spot per thousand acres. The disc data was consistent with no missing data, therefore disc data was used from 1986 to 2004. The MACA data is 6 by 6 km resolution gridded data over the entire continental United States. I used the historical observations of the annual number of freezing days from 1986-2005. So, the historical observations are an averaged value from 1986-2005. I used two models for future projections (from 2020-2099). The RCP 4.5 and RCP 8.5, which is run under different two scenarios. The RCP 4.5 is the stabilization scenario in which the total radiative forcing stabilized shortly after 2100. The RCP 8.5 increases the greenhouse has emissions over time which leads to high amounts of CO2 levels. I calculated PDFs for the historical observations (1986-2005) and 20 climate models (1950-2005) over 15 climate regions. Lastly, to determine a bias, I compared the 20 climate model baseline periods (1950-2005) to that of the historical observation period (1986-2005) over the entire U.S. 

Considering the outbreak data is per county, I needed to get the averaged number of freezing data per county, too. I imported the gridded data as a raster file and applied zonal statistics in ArcMap to receive the average per county (Fig. 1). For example, the average number of days below freezing in wake county is 70 days per year or about 20 per cent of the year. 

Figure 1. The historical averaged number of freezing days per county. 

I used the same method to conduct the future projections for each county over the U.S. The projections are separated into 4 ranges: 2020-2039, 2040-2059, 2060-2079, 2080-2099. So, in total there are 4 projections for each RCP scenario. 

I use the programming language R to compute the average number of tspots per county from 1992-2004 (because data is missing) and the average disc per county from 1986-2004 to correlate with the historical average (1986-2005). From here, I used R to conduct a correlation test between the mean tspot and mean number of freezing days and mean disc and mean number of freezing days.  

I also used R to produce PDFs for the historical observations and 20 climate models. I made PDFs for 15 climate regions over the Southeastern U.S. Overall, each climate zone has a historical PDF from 1986-2005 and 20 historical model runs from 1950-2005. 

R was also used to compute the statistical bias of the climate models. The mean of all 20 models was subtracted from the historical observation. This will inform me of how the climate models over and under predicted the annual number of freezing days. 

Results:

The correlation test between mean tspot and mean annual number of freezing days resulted with weak negative correlation. Additionally, the correlation test between mean disc and mean number of freezing days resulted with weak positive correlation. This was a bit surprising. It was assumed that if a year was above average in days below freezing, it may be colder than normal as well. However, the extreme temperature data does not give the temperature value. Therefore, there's no way to know how far below freezing it was. Previous studies found a correlation between severely cold temperatures (i.e. -5F) and less outbreaks. 

As expected, both RCP scenarios projected a decrease in annual freezing days across the study area. The most dramatic change occurring by the final years 2080-2099. RCP 8.5. projected more of a decrease than the RCP 4.5 This was expected because the RCP 8.5 scenario incorporated more CO2 emissions than the RCP 4.5. More specifically, both mean RCP scenario values of Eastern and Western NC decreased significantly. To compare, the mean historical amount of freezing days across Eastern and Western NC is 65 and 102 days respectively. The RCP 4.5 scenario projected the mean amount of freezing days to decrease by 25 days across Eastern NC and 31 across Western NC by 2080-2099. The RCP 8.5 scenario projected the mean amount of freezing days to decrease by 40 days across Eastern NC and 52 days across Western NC by 2080-2099. The decrease in the annual number of freezing days could allow for the Loblolly to extend northwards.  

In general, the PDF plots showed model consensus among the 20 climate models. One model consistently deviated from the norm across all evaluated domains. There was less model consensus over areas of varied elevation (Fig. 2). 

Figure 2. The probability distribution function for the annual number of freezing days over the Southern Appalachians. 

All 20 models differed from the observed historical data and did possess a bias. Most of the bias was over prediction across the mountainous region (Fig. 3). Overall, the models prediction of the past resembled that of the observations. Due to this, we can have more confidence in the future projections. Also, the areas the models struggled the most with does not include the natural range of the Loblolly Pine. 

Figure 3. The mean annual number of freezing days bias map for all 20 climate models. 

Conclusions: 

Overall, this was an interesting study. I was a bit surprised to see such a weak correlation between outbreaks and freezing days. For future studies, I would like to incorporate other climate variables into the study. As well as seeing if there is any inter-seasonal variability. In conclusion, this study further solidified my confidence in the climate future projections. As a consequence, I deem it necessary that the forestry sector utilizes this information to help with future impacts.  

Does the El Nino-Southern Oscillation affect spring storms?

The El Nino-Southern Oscillation (ENSO) alters the climate all over the world. The 1997-1998 El Nino was one of the strongest on record. Consequently, 1998 is one of the warmest years on record globally. ENSO is known to impact the United States winter weather, but what does it do during the spring?

New research suggests that ENSO affects severe storms during the spring time. In general, the authors found that an ENSO warm phase (El Nino) acts to suppress the frequency of tornadoes and hail in the southern central US, while an ENSO cool phase (La Nina) increases the frequency of tornadoes and hail. 

Figure 1. El Nino influence is on the left and La Nina is on the right. Orange colors indicate less frequent, while purple colors indicate more frequent. Source: http://www.climate.gov/news-features/featured-images/el-ni%C3%B1o-and-la-ni%C3%B1a-affect-spring-tornadoes-and-hailstorms

When the sea surface temperatures over the Equatorial Pacific are above or below normal conditions, the general circulation temporarily changes. This anomaly causes the Jet Stream, over the US, to change. El Nino weakens the surface winds that cause warm, moist air to advect northwards. La Nina increases the winds. It's interesting to note that the 2010/2011 winter season was influence by a La Nina. Also, the spring 2011 severe weather seasons was one of the worst on record.

Unfortunately, the understanding of ENSO's infleunce on spring climate is less certain. Considering the severe weather season peaks in the spring for the United States, more research should be conducted to better understand ENSO's influence. 

Cheers, 

Source: http://www.climate.gov/news-features/featured-images/el-ni%C3%B1o-and-la-ni%C3%B1a-affect-spring-tornadoes-and-hailstorms

May Summary

If May gives any indication of how the summer will be, it will be a hot and dry one.

Just kidding. It does not.

So, this past May in Raleigh was a hot and dry month. The average temperature was 72.48F (3.85F above the norm), which was the 9th warmest May on record! The average maximum temperature was 83.56F (3.41F above normal) and the average minimum temperature was 61.41F (4.3F above normal).

Figure 1. The blue line is the observed daily max temps and the orange is the "normal" daily max temps

Figure 2. The blue line is the observed daily min temps and the orange is the "normal" daily min temps

Although is was a warm May, Raleigh only reached the 90F temp twice. Raleigh reached the upper 80s quite frequently, which caused the abnormal heat. 

As for the precipitation, Raleigh only received 3.04" of rain. Luckily, Tropical Storms Ana brought us precipitation. However, that's not the case for western N.C. Just one county to our west (and most of NC for that matter) is under a slight drought. Click this link for more information. 

Figure 3. The yellow color indicates areas of abnormally dry conditions. 

Like I stated in the previous post, El Nino is expected to play a role in our climate this year, so it will be interesting to see how it unfolds. 

Cheers, 

ENSO Discussion and Summer Outlook

The El Nino-Southern Oscillation (ENSO) is one of many natural climate oscillations that can alter our Earth's climate. Normally, the ocean upwells cold water off the western coast of South America, therefore causing a consistent band of thunderstorms around the equator. The thunderstorms usually propagate westward across the equatorial pacific. Sometimes, warmer than average Sea Surface Temperatures (SST) occur across the equatorial pacific causing the atmospheric circulation to temporarily switch (Fig 1).

Figure 1. The far left picture depicts El Nino conditions, the middle depicts normal conditions, and the far right depicts La Nina Conditions. Source: http://www.reefresilience.org/images/Stressors_ENSO3.png

The above average SSTs normally begin around the spring/summer and if they last til the winter, it can alter the Earth's climate (these are what we call teleconnections). The more prevalent teleconnections occur in the winter months. In the United States, during the winter months an El Nino normally (each episode is different) causes:

1. Wetter conditions in Southern California and the Southwest 

2. Warmer conditions in the Pacific Northwest 

3. Wetter and cooler conditions in the Southeast 

The Climate Prediction Center (CPC) just released the current ENSO's evolution earlier this week. CPC has issued an El Nino Advisory. Meaning, there is a 90% chance El Nino conditions will continue through the summer and an 80% chance of it lasting through the winter. Here are the latest SST anomalies: 

As you can see, there is an extensive range of above normal SSTs. They are about 1C above normal, which is pretty strong for this time of year. There is also fairly negative Outgoing Longwave Radiation, which enhances the convection (thunderstorms) and precipitation. Lastly, there are anomalous low level (850 hPa) westerly winds present. Overall, most of the models predict El Nino will persist throughout this year. If it does, it will give some relief to California because it usually causes decent rainstorms there. 

As for the summer outlook, CPC predicts normal temperature and precipitation conditions for most of NC (Fig 2). 

This May has been kind of dry with just 2.46" of rain thus far in Raleigh. A lot of NC is in a slight drought and hopefully it will not worsen too much. However, if El Nino conditions continue, the slight drought should be relieved. I personally like El Nino conditions because it makes for an interesting winter. We shall see. 

Cheers, 

Raleigh, North Carolina April Climate

I realize this is a bit overdue, but I've been extremely busy with finals and work. So, please forgive me. I've decided that after each month has ended, I will discuss the state of climate.

April is usually the start of severe weather season for the United States. I know this is bad, but I look forward to it every year. In my undergraduate years, my friends and I would chase storms all over the Southeast. It was an exciting time. 2011 was a particularly bad year with many, many destructive storms/tornadoes. North Carolina does not typically receive destructive tornadoes, but we are certainly not immune. This April was fairly quiet. There were a few thunderstorms that rolled through. In fact, I saw hail (pea size) for the first time in years. Overall, I do not recall very many severe storms in the Raleigh area.

The average temperatures and precipitation values did not depart from their norm too much. The average maximum temperature was 72.5F, which was right around normal (72.4F).

 The blue line indicates the observed maximum temperatures for each day in April. The orange line shows the "normal" maximum temperature for each day. Again, the maximum temperature for April was right around normal.

The average minimum temperature was 50.6F, which was above normal (48.03F).

The blue line is the observed minimum temperatures for every day in April. The orange line is the "normal" minimum temperature for each day. The daily temperature oscillates around the normal temperature. Keep in mind that the "normal" temperature is constructed after about 30 years of data. We do not expect the temperature to really be at or near normal every day. It simply gives us an idea of how the climate is changing. In this instance, we saw the average high temperature did not exceed the norm, while the average low temperature was above normal. A single months data is not conclusive evidence that a change is occurring, but it's important to consistently record the changes so we may better understand what direction we're headed in. Plus, this data does coincide with hypothesis that the high temperature is not getting higher, only the low temperature is increasing. 

Now, let's have a look at April's precipitation:

Overall, the total monthly precipitation was 5.28" and the normal temperature is 2.92". So, it was a relatively wet month (because the precipitation total was above normal). This is also consistent with the hypothesis that the Southeast will experience an increase in precipitation totals in the future. It would be interesting to research if any natural climate variation played a role. It's also interesting because there was a total of 14 rain producing storms in the month. 

I shall research if any climate oscillation had any influence. That's all for now. 

Cheers, 

10 of the best YouTube videos on Climate Change

10 of the best YouTube videos on Climate change

The link above is the original article. To make things a little more simple, I'll post the 10 best YouTube videos on Climate Change here:

Last Week Tonight with John Oliver: Climate Change Debate (HBO)

NASA | A Year in the Life of Earth's CO2

The History of Climate Change Negotiations in 83 seconds

Climate Change | David Mitchell's Soapbox UPDATE

The Most Terrifying Video You'll Ever See

Gavin Schmidt: The emergent patterns of Climate Change 

I'm a Climate Scientist (Hungry Beast) 

Climate Change -- Those hacked emails

300 Years of Fossil Fuels in 300 seconds

13 Misconceptions about Global Warming

Greenhouse Effect Video

After many, many hours of work and frustrated I managed to produce a video discussing the greenhouse effect. Studies and surveys show that this topic is not as understood as it should be. I hope the video provides answers and clears up any confusion. It's kind of long (9 minutes), but I hope you watch and enjoy the whole thing!

Cheers,

Aurelia