Hurricane Patricia - One of the most powerful hurricanes on record.

At 12:00 UTC (8 a.m. EDT) on October 23rd, Hurricane Patricia became the most intense tropical cyclone in recorded history (globally) as it sustained wind speeds at 200 mph. Patricia grew from a tropical storm to a category 5 hurricane in 24 hours. Making Hurricane Patricia the greatest intensity rate (100 mb in 24 hours) in recorded history. When Hurricane Patricia intensified to 879 mb, it became the 5th most intense tropical cyclone in terms of minimum central pressure.

Satellite image of Hurricane Patricia 

Rapid Intensification 

The sea surface temperatures (SST) across the equatorial pacific have been above-normal for most of this year, indicating that El Nino conditions will likely occur during the 2015/2016 winter season. After Patricia organized, it propagated east/northeast over very warm SSTs. The environmental conditions were favorable for Patricia to intensify. There was very little wind shear, no dry air intrusion, and warm SSTs. The SSTs were around 30℃ (86℉). 

Sea surface temperatures across the equatorial pacific. source: http://www.ssd.noaa.gov/PS/TROP/DATA/RT/SST/PAC/20.jpg

Previous studies indicate that El Nino conditions can increase the likelihood of hurricane activity in the east Pacific ocean (Camargo et al. 2007). El Nino normally causes positive SST anomalies across the equatorial pacific as well as decreasing wind shear. However, the presence of El Nino was not the soul contributing factor to Hurricane Patricia's rapid intensification. For the past two years, there has been a consistent "blob" along the west coast of North America. The "blob" is a large area of above-normal SSTs, which might be in association with the Pacific Decadal Oscillation (PDO). Like the El Nino-Southern Oscillation (ENSO), the PDO goes through warm and cool phases. However, the PDO phases are every 20-30 years. Like ENSO, the PDO is in a warm phase, which is manifest with the above-normal SSTs along the pacific coast of North America. The combination of these two climate oscillations created favorable conditions for Hurricane Patricia to intensify rapidly. Additionally, with climate change, the ocean and atmospheric temperatures have increased, too. This combination of natural climate variability and climate change have increased the baseline temperature, which inherently allows the atmosphere to hold more water vapor. Therefore, there's more water available for tropical cyclones.

Satellite Movie Sees Record-Breaking Hurricane Patricia

Landfall
Patricia made landfall at approximately 7:15 p.m. EDT on Friday, October 23 near Cuixmal, Mexico as a category 5 hurricane. Which was only the second Pacific Hurricane on record to make landfall as a category 5. The maximum sustained wind speeds were 165 mph as it made landfall. This is the second most intense tropical cyclone to make landfall on record. Hurricane Patricia is behind Typhoon Haiyan, which made landfall with sustained winds of 174 mph.

What's in store for Patricia now?
Once Hurricane Patricia made landfall, it weakened to a tropical depression in 16 hours and combined with an upper-level low pressure system to cause rain across the southeast. The remnant of Patricia and the upper-level low caused extreme heavy rainfall over Texas, which caused more local flooding. Also, the combined systems are expected to continue across the southeast with the possibility of heavy rainfall and flash flooding.

24 hour total quantitative precipitation forecast. Source: http://www.wpc.ncep.noaa.gov/qpf/fill_94qwbg.gif?1445875070494

References:

Suzana J. Camargo, Kerry A. Emanuel, and Adam H. Sobel, 2007: Use of a Genesis Potential Index to Diagnose ENSO Effects on Tropical Cyclone Genesis. J. Climate, 20, 4819–4834.

doi: http://dx.doi.org/10.1175/JCLI4282.1

November Climate Summary - The Southeast

This is the first in a new climate summary series called Monthly Southeast Climate at a Glance. Aurelia Baca, SERCH Climate Specialist, will review NOAA/NCEI monthly climate summaries and provide temperature and precipitation highlights for the Southeastern region, along with impacts to agricultural and forestry sectors.

November Southeast Climate at a Glance

Temperature Summary

November was another warm and wet month for the entire Southeastern United States. Every Southeastern state observed well above average temperatures (Figure 1) as well as many temperature extremes. In fact, Florida experienced their warmest maximum temperature for the month of November. Alabama, Florida, southern Georgia, and western Virginia experienced the greatest deviations over the region. The monthly temperatures were 5-9 °F above the average.

Fig. 1. Statewide average temperature ranks for November. Source: http://www.ncdc.noaa.gov/sotc/service/national/statewidetavgrank/201511.gif

The warmest weather occurred from the 4th to the 7th. During this time, maximum temperatures exceeded 70°F across most of the southeast with some areas reaching the upper 80s °F and lower 90s °F. The coolest weather of the month occurred a few weeks later on the 23rd and 24th. The minimum temperature reached the 20s °F across much of the region. 

Precipitation Summary

Along with above average temperatures, every Southeastern state observed above normal precipitation totals (Figure 2). Arkansas experienced their wettest November on record. Louisiana, Alabama, Georgia, South Carolina, and North Carolina were well above their respective average with monthly precipitation totals 150 to 300 percent of normal. The northern locations of the southeast and central Florida were the driest areas where precipitation totals were 25 to 50 percent of their normal.

Fig. 2. Statewide precipitation total ranks for November. Source: http://www.ncdc.noaa.gov/sotc/service/national/statewidepcpnrank/201511.gif

Consistent precipitation alleviated much of the drought conditions over the Southeast. Drought-free conditions (less than D1) increased from 98 to 100 percent by December 1st. The moderate drought (D1) conditions that were present in west-central Alabama, southern Georgia, and the Florida panhandle diminished as consistent precipitation fell across the region. 

Climate Impacts on Agriculture

The above average temperature and precipitation caused some issues among the agricultural sector. The abnormally warm and wet climate caused delayed harvest and reduced the quality of crops across the Southeast. Farmers in Georgia reported their pecan orchards sprouted prematurely. Also, Carolina farmers reported their harvest on cotton, soybeans, and peanuts were poor due to the above average precipitation. 

Conclusions 

The above average precipitation is most likely indicative of El Nino's influence on our weather pattern. Similar conditions are likely to persist throughout the remainder of December causing mild weather conditions for the end of 2015.

References:
NOAA National Centers for Environmental Information, State of the Climate: National Overview for November 2015, published online December 2015, retrieved on December 14, 2015 from http://www.ncdc.noaa.gov/sotc/national/201511 

Raleigh November Climate Summary

The Holiday season is quickly approaching and the weather certainly doesn't agree. This November was much above normal for Raleigh, North Carolina. November was warm and wet. 

The average temperature was 55.43F, which was 3.16F above our norm. For Raleigh, this November was the sixth warmest November on record. In fact, 19 out of 30 days were above normal. Both the maximum and minimum temperature were much above normal, too (1.8F and 4.53F, respectively). See Figure 1 for more details. 

Fig. 1. November average temperature for KRDU. Data source: NCDC

November precipitation was much above normal, too. The total precipitation for Raleigh was 7.14", which is 4.02" above the norm. November was the sixth wettest November on record. See Figure 2 for more details. 

Fig. 2. Daily November precipitation totals for KRDU. Data source: NCDC

December will likely follow a similar trend. Most of the Southeastern U.S. is expected to experience above normal temperatures (figure 3) and above average precipitation (figure 4). 

Fig. 3. One-month temperature outlook. Source: http://www.cpc.ncep.noaa.gov/products/predictions/30day/off15_temp.gif

Fig. 4. One-month precipitation outlook. Source: http://www.cpc.ncep.noaa.gov/products/predictions/30day/off15_prcp.gif

The above average precipitation is most likely in association with El Nino. As stated in the previous post, El Nino tends to enhance the subtropical jet, which causes storms to track across the Southeast. Inherently causing above average precipitation across the Southeast. 

Overall, November was warm and wet. With a little help from El Nino, December will likely be a similar story. 

2015-2016 Winter outlook

El Niño 

The current El Niño is on track to be one of the largest on record (since 1950). This past week's sea surface temperature (SST) anomalies (figure 1) were much above normal with some peaking at 3°C above normal. Although these values are unprecedented, it is not indicative of the events strength. The Climate Prediction Center compares historical El Niño episodes based off of their 3-month SST anomaly mean. The latest Oceanic Niño Index was a 3-month mean over August, September, and October. For this current episode, it was 1.7°C above normal. For comparison, the 1997/1998 El Niño (strongest on record) was 2.0°C above the norm.

FIG 1. Sea Surface Temperatures over the equatorial pacific. source: http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_update/sstweek_c.gif

Every El Niño episode is slightly different. They reach peak intensity at different times and cause various deviations from climatic norms. With that said, the current El Niño has the potential to surpass the 1997/1998 event. Most climate models are in agreement that this episode will peak during the winter and transition to neutral conditions in the spring/summer of 2016.

Southeast Winter Outlook

So, what does this mean for North Carolina? Normally, an El Niño enhances the subtropical jet stream causing storms to track along the southern states of the US. As a consequence, above average precipitation is expected across the Southeast and North Carolina (figure 2).

FIG 2. Climate Prediction Center 3-month precipitation outlook. source: http://www.cpc.ncep.noaa.gov/products/predictions/long_range/lead01/off01_prcp.gif

 As for temperature, that's a little bit more tricky. Sometimes an El Niño can cause above average temperature, while other episodes cause below normal. This winter season was no exception of a tricky outlook. Most of North Carolina could experience either above or below normal temperatures, while the northern tier will likely experience above normal temperatures within the next three months (figure 3).

FIG 3. Climate Prediction Center 3-month temperature outlook. source: http://www.cpc.ncep.noaa.gov/products/predictions/long_range/lead01/off01_temp.gif

My Take:

This El Niño is rivaling the 1997/1998 El Niño in strength. With that said, 1998 was one of the warmest years on record globally and 2015 is on track to be the warmest year on record. I expect December to be above average for both temperature and precipitation. Going into 2016, it is a bit more difficult to say what the temperature deviation will be. As many of you may remember the past two winters, the Arctic Oscillation (AO) plays a strong role for our temperatures. The AO is also difficult to predict well in advance (it switches phases in weeks or sometimes days). If the AO weakens, we will expect to have cold temperatures in our area like the past two winters. Also, because El Niño causes storms to track along the southern states, if the temperatures are in place, we could see a storm or two. However, I do not expect this winter to receive above normal snowfall. For most of the winter, I believe it will be above normal for temperature and precipitation. 

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,