Climate Impacts on Density Altitude and Aviation Operations

Christopher J. Goodman The Climate Corporation, St. Louis, Missouri

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Jennifer D. Small Griswold Atmospheric Sciences Department, University of Hawai‘i at Mānoa, Honolulu, Hawaii

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Abstract

A critical determinant of aircraft performance is density altitude, or the density given as a height above mean sea level, which is dependent on air temperature, pressure, and humidity. These meteorological variables change on various time scales (e.g., hourly, seasonal, and decadal) and are regionally impacted by large-scale climate variability as the result of phenomena such as El Niño–Southern Oscillation or the Arctic Oscillation. Here a statistical analysis is performed to determine the impacts of climate variability on seasonally averaged density altitude, a key metric used by pilots to determine aircraft performance and efficiency, as a function of El Niño–Southern Oscillation and the Arctic Oscillation using NCEP–NCAR reanalysis data and historical aviation meteorological records. Regressions show regional dependencies and impacts to density altitudes that vary as a function of season for both El Niño–Southern Oscillation and Arctic Oscillation cases. The results highlight the importance of understanding the regional nature of the impact of climate variability on density altitude and the potential impacts on aviation operations.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jennifer D. S. Griswold, smalljen@hawaii.edu

Abstract

A critical determinant of aircraft performance is density altitude, or the density given as a height above mean sea level, which is dependent on air temperature, pressure, and humidity. These meteorological variables change on various time scales (e.g., hourly, seasonal, and decadal) and are regionally impacted by large-scale climate variability as the result of phenomena such as El Niño–Southern Oscillation or the Arctic Oscillation. Here a statistical analysis is performed to determine the impacts of climate variability on seasonally averaged density altitude, a key metric used by pilots to determine aircraft performance and efficiency, as a function of El Niño–Southern Oscillation and the Arctic Oscillation using NCEP–NCAR reanalysis data and historical aviation meteorological records. Regressions show regional dependencies and impacts to density altitudes that vary as a function of season for both El Niño–Southern Oscillation and Arctic Oscillation cases. The results highlight the importance of understanding the regional nature of the impact of climate variability on density altitude and the potential impacts on aviation operations.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jennifer D. S. Griswold, smalljen@hawaii.edu

1. Introduction

a. Density altitude

Density altitude (DA), the vertical distance above sea level in the standard atmosphere at which a given density is to be found, is an indicator of what altitude an aircraft is operating at given the atmospheric conditions [Federal Aviation Administration (FAA) 2010]. Lower air densities result in higher DAs and negatively impact the performance of the engines and lift produced by the wings. This has an impact of reducing the maximum weight of the aircraft and increasing the distance required to achieve safe takeoff (Coffel and Horton 2015). Increasing air temperature, increasing moisture content, and decreasing atmospheric pressure, or airport locations at high elevations, all result in higher DA. Using a simplified equation, the DA can easily be estimated for an airport, and the impacts of atmospheric pressure and temperature on DA can easily be demonstrated. The simplified DA equation is provided below:
e1a
e1b
where PA is pressure altitude, Po is standard pressure in inches of Hg (1 in. = 25.4 mm), PAltSet is the altimeter setting in inches of Hg, HAirport is the elevation of the airport in feet (1 ft = 30.5 cm), T is the observed air temperature in degrees Celsius, and Ts = To − 0.002 × HAirport. Given a temperature of 25°C and an altimeter setting of 30.01 in. Hg for Utah's Salt Lake City International Airport (which has an elevation of 4226 ft), the estimated DA using the above equations is roughly 6350 ft. Even though an aircraft is physically located at 4226 ft, it would perform as if it were located at 6350 ft. This simplified equation demonstrates how changes in temperature and pressure can result in changes to aircraft performance and is often used as a “rule of thumb” for pilots. For this work, a more rigorous method for calculation DA was applied using virtual temperature Tυ. First the vapor pressure e in hectopascals is determined (Bolton 1980):
e2
where Td is the dewpoint in degrees Celsius. Next, the station pressure Psta is determined in units of inches of Hg:
e3
where Hsta is the station elevation in meters. Note that this equation is simply the result of integrating the hydrostatic equation while assuming a constant lapse rate (International Standard Atmosphere tropospheric) from mean sea level to the elevation of the airport of interest. Next, Tυ, in kelvins, is calculated to incorporate the effects of atmospheric moisture on DA. Station pressure is first converted from inches of Hg to hectopascals (PhPa), and T is in kelvins:
e4
Finally, the DA is calculated by converting Tυ from kelvins to degrees Rankine (TυR) and applying Eq. (5):
e5

b. Aviation performance

Recently, the impacts of DA on aircraft performance were discussed in the USA Today editorial “Ask the Captain: How Extreme Heat Hinders Flights” (Cox 2016), bringing public attention to aviation hazards of high elevations and air temperature, which are not discussed as often as phenomena such as turbulence or blizzards. As DA increases, the overall performance of an aircraft decreases. Consider a normally aspirated, fixed-pitch propeller, single-engine aircraft. For every 1000-ft increase in DA, the engine will experience a 3% decrease in power and an overall increase in takeoff distance of 15% (FAA 1999, 2015). Given certain atmospheric conditions, the calculated takeoff or landing length may exceed what is available at an airport. Since lengthening runways or increasing the performance of the aircraft in a short manner of time is highly unlikely, the pilot may then be required to reduce weight or wait until atmospheric conditions are favorable for safe operations, resulting in an economic loss due to decreased revenue load or delay. High DAs not only impact the potential economic profitability of a flight but also the safety of flight as well; between 2003 and 2007, high DAs were cited 120 times as the cause or contributing factor in weather-related accidents in the United States and were the third-most-reported weather factor (FAA 2010). It is important to note that none of 120 accidents occurred on commercial airline flights; however, the prior statistic highlights the potential safety implications of high DAs (FAA 2010).

While it is important to understand the impacts of DA in a present, short-term mindset, for individual flights, understanding how seasonally averaged DA changes as a result of climate variability presents an opportunity for the aviation community to better plan for expected changes in environmental conditions that impact DA. By understanding which airports are likely to see changes in the expected DAs as a result of El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO), scheduling of aircraft, especially those with heavy payloads, could be altered to take off or land during lower DA times, adjust the payload of flights, utilize aircraft with better performance, or plan for increased or decreased delays.

2. Global Impacts of ENSO and AO on density altitude

a. El Niño–Southern Oscillation

The impacts of ENSO on global climates by changing atmospheric circulation patterns have been well documented (Trenberth and Caron 2000; Trenberth et al. 2002; Dai 2006). El Niño events can raise global temperatures in regions above ±30° latitude, with lags of several months, by roughly 0.1°C (Trenberth and Caron 2000; Trenberth et al. 2002), impact the distribution of atmospheric moisture (Trenberth and Caron 2000), and create areas of analogous sea level pressure (Dai 2006). But does ENSO impact these three atmospheric variables in such a way that is relevant to aviation? To answer this question, using DA to understand the combined impacts of ENSO on surface temperature, moisture, and pressure will likely give valuable insights.

To understand the impacts of ENSO on DA, monthly surface NCEP–NCAR reanalysis temperature, relative humidity, and sea level pressure from January 1979 to July 2015 were used to calculate the average seasonal mean DA at each grid point for the December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON) triplets. After detrending the data, the Pearson correlation and linear regression coefficient were calculated against the Southern Oscillation index (SOI) to reveal the SOI–DA correlation patterns (Fig. 1, top panels) and SOI–DA regression patterns (Fig. 1, bottom panels). In Fig. 1 (bottom panels), strong negative correlation values ranging from −0.5 to −0.8 are seen over the eastern central Pacific Ocean (roughly −0.6) for all seasons. As expected, based on Trenberth and Caron (2000), the combined effect of negatively correlated air temperature and positively correlated sea level pressure with the SOI results in lower-than-normal DAs during El Niño periods and higher-than-normal DAs during La Niña periods. Regions in the Pacific warm pool region see high SOI–DA correlation values associated with the known impacts of ENSO on air temperature and sea level pressure (Trenberth and Caron 2000; Dai 2006). Many other regions indicate significant correlation values associated with the SOI, such as the northwest Pacific, Central and South America, and the central Atlantic Ocean.

Fig. 1.
Fig. 1.

DA (top),(top middle) correlation and (bottom middle),(bottom) regression with the SOI for DJF, MAM, JJA, and SON. Shaded regions in the top two rows indicate statistical significance at the 0.05 level. The units for the bottom two rows are feet per SOI.

Citation: Journal of Applied Meteorology and Climatology 57, 3; 10.1175/JAMC-D-17-0126.1

To assess the impacts of ENSO on aviation and DA, it is beneficial to observe not only the correlation patterns but also the regression patterns and the magnitudes of changes. While some regions may experience high, statistically significant correlations of DA and SOI, if the actual change in DA as a result of per unit change of the SOI is minimal, the actual impact may not be relevant in an operational and practical sense. Depending on the situation, a 100-ft increase in DA may mean that an aircraft cannot safely take off or land given the current runway length and aircraft weight, but in other situations a 100-ft increase may not be relevant. The impacts of DA must be assessed for each flight and the given circumstances, and it is therefore inappropriate to define one DA threshold for any given circumstance. Instead, operators must assess the impacts of DA based on the prescribed limitations and procedures that are unique to each individual aircraft. Since high DA is more of a concern during the warm season months, it will be also be more appropriate to focus on DA correlation and regression patterns in the Southern Hemisphere during DJF and DA correlations in the Northern Hemisphere during JJA. Looking at the Southern Hemisphere during DJF, DA is inversely proportional to the SOI in the northern Andes Mountains (−20 ft per unit SOI) and parts of southern Africa (−20 ft per unit SOI). As an example, high-elevation airports in the Andes Mountains will likely experience higher DAs as a result of El Niño. Conversely, parts of Australia, Southeast Asia, and islands in the South Pacific may see beneficial decreases in DA as a result of El Niño (about 20–40 ft per unit SOI). In the Northern Hemisphere in JJA, regions in the northwest Pacific, Central America and Mexico, Hawaii, and northern Africa see increasing DAs (~20 ft per unit SOI) as a result of increasing SOI with potential impacts on airport efficiencies in those areas. Regions of Eurasia see decreases in DAs as a result of increasing SOI.

b. Arctic Oscillation

The impacts of the AO on the Northern Hemisphere seasonal variability of surface air temperature and sea level pressure have been well documented in past research (Rigor et al. 2000; Holland 2003; Polyakov et al. 2003). Again, the question is how does the Arctic Oscillation impact DA through the combined impacts on surface air temperature, surface pressure, and relative humidity? The top panels of Fig. 2 display the AO–DA seasonal correlation patterns where the AO exhibits strong, positive correlations with DA (especially during DJF) in the Arctic regions (0.6), regions in North America (0.4), northern Eurasia (0.5), and Europe (0.5). The AO exhibits strong, negative correlations with DA (especially during DJF) in the North Pacific basin (−0.5), North Atlantic Ocean (−0.4), North Africa (−0.4), southern Europe (−0.4), and southwest Eurasia (−0.4). Other global regions exhibit correlations with DA and AO but are not as strong. Also, correlations in the Southern Hemisphere (especially over Antarctica) are likely due to the relationship of the Arctic Oscillation and the Antarctic Oscillation (Miller et al. 2003).

Fig. 2.
Fig. 2.

As in Fig. 1, but for the AO index. The units for the bottom two rows are feet per AO.

Citation: Journal of Applied Meteorology and Climatology 57, 3; 10.1175/JAMC-D-17-0126.1

The bottom panels of Fig. 2 display the regression patterns for the AO and DA for all four seasons. As the AO becomes more positive, large increases in DA occur in the Arctic (60–100 ft) and northern Eurasia (50–60 ft) regions, whereas decreases occur in the North Pacific (from −40 to −60 ft), North Atlantic (from −20 to −50 ft), southern Europe (from −20 to −40 ft), northern Africa (−20 ft), and southwest Eurasia (from −20 to −30 ft), especially in winter. When focusing on DA changes in the Northern Hemisphere as a result of changing AO in the JJA period, increases in DA occur as the AO increases in central North America (40 ft) and parts of Eurasia (20 ft) and decreases in DA as AO increases in parts of the northwest Pacific (−20 ft), Europe (−10 ft), and Africa (−10 ft). Increasing DAs in mountainous regions (such as in the Rockies), as a result of increasing air temperature or higher humidities, will have a greater impact on aviation operations as DAs are already high.

3. Impacts of ENSO and AO on U.S. airports

Using reanalysis datasets to assess the global impacts of climate teleconnections is useful for observing overall patterns, but assessing individual airports using past aviation weather reports (METAR) may give additional insights into the actual impacts of AO and ENSO on DA. Eleven major airports representing different regions of the United States were selected on the basis of the ease of accessing the METAR, completeness of the records, and length of the records’ availability. These airports are Anchorage International Airport (ANC; Alaska), Atlanta Hartsfield–Jackson International Airport (ATL; Georgia), Dallas Love Field (DAL; Texas), Honolulu International Airport (HNL; Hawaii), Miami International Airport (MIA; Florida), Minneapolis–St. Paul International Airport (MSP; Minnesota), Chicago O'Hare International Airport (ORD; Illinois), Phoenix Sky Harbor International Airport (PHX; Arizona), Seattle–Tacoma International Airport (SEA; Washington), San Francisco International Airport (SFO; California), and Salt Lake City International Airport (SLC). METAR records provide pilots with hourly weather reports (or additional reports when weather conditions change) on pertinent information to assess the safety of operations at different airports, such as temperatures, wind speeds, or the presence of hazardous weather phenomena like thunderstorms. METAR data for 11 airports for the period from 1 January 1950 to 31 July 2015 were obtained from Iowa Environmental Mesonet and were quality controlled and detrended. The seasonal mean DAs were then calculated for DJF, MAM, JJA, and SON. To assess the impacts of ENSO and AO on seasonal DAs, the Wilcoxon–Mann–Whitney nonparametric rank sum significance test was used to determine the significance of the seasonal monthly mean DA changes during El Niño versus non–El Niño periods and positive versus negative AO phases. The results are shown in Table 1.

Table 1.

Seasonal mean differences (ft) for DA at 11 airports. The top half of the table shows the mean difference of DA between El Niño months and non–El Niño months according to the ENSO-3.4 index. The bottom half shows mean difference of DA between positive AO and negative AO. Boldface font indicates values that are statistically significant at the 0.05 level.

Table 1.

Looking at the top half of Table 1 and Fig. 1 together presents a clear view of the impacts of ENSO on DA. Most statistically significant changes occur in DJF, which is expected from Fig. 1. The statistically significant changes that occur primarily during DJF may not negatively impact aircraft performance since DA is low because of cooler temperatures. Also, most changes in DA at the 11 airports follow what would be expected given Fig. 1 given the increasing or decreasing DA average for the region near the airport. From the bottom half of Table 1 and Fig. 2, most changes in the DAs at individual airports seem to follow what is shown in Fig. 2. Increases in DA as a result of El Niño or non–El Niño conditions may indicate that more flights may be impacted by high DAs in those specified seasons. Ultimately, more delays, cancellations, or payload reductions may occur.

While it is important to understand the overall average changes in DA as a result of ENSO or AO, high DAs are the most impactful to the aviation community. Since high DAs are most likely to occur during the summer months, understanding the changes in the occurrence of high DAs can be shown by analyzing the number of hours in which DAs were above a predetermined value. During the JJA period, the mean and standard deviation for DA was determined for the 11 selected airports. Hours that were above the mean by 1.645 times the standard deviation were recorded for each individual month. Assuming that the distribution of DA for the 11 airports is Gaussian, these hourly observations mean that they occur in the top 5% of hourly DA observations. Using the highest 5% of observations can advise airport planners and long-term schedules developers when assessing current limitations or future needs of existing runway lengths or aircraft performance. Using the same method as before, the Wilcoxon–Mann–Whitney nonparametric significance test was used to determine if a statistically significant difference between the mean occurrences of monthly hours of high DAs during El Niño versus non–El Niño months or positive versus negative AO months occurred. The results along with mean and standard deviations of DA are recorded in Table 2.

Table 2.

JJA high DA observations. JJA difference of monthly mean number of occurrences of high DAs for El Niño minus non–El Niño months and positive minus negative AO months, mean JJA DA (ft), and standard deviations of JJA DA (ft). Boldface font indicates values that are statistically significant at the 0.05 level.

Table 2.

Under El Niño conditions, the number of high DA observations statistically increase for HNL and PHX and statistically decrease for ATL. Also all airports, except ANC and SLC, see similar results as those presented in the top half of Table 1 on the overall impacts of ENSO on DA. Statistically significant changes in the number of high DA observations for the AO occur for ATL, HNL, MIA, MSP, PHX, and SLC with only MIA, ORD, and SLC showing disagreement with the bottom half of Table 1. More observations of high DA will likely result in more reductions in payloads or flights departing later when DAs become lower as a result of cooler temperatures. For example, when the AO is positive, more observations of high DA occur at MSP, ORD, and SLC, resulting in more periods when performance impacts to aircraft are likely to occur.

4. Discussion and implications

By understanding how ENSO and AO impact DA, aviation operators can better determine how climate teleconnections impact their operations and allow operators to plan for expected ENSO or AO conditions. It is important to note that most of the variability in meteorological parameters (such as pressure and temperature) occurs on much shorter time scales than ENSO and AO. However, averaging long-term records allows for the statistical analyses used in this current study. For future work it would be interesting to combine this analysis, using METAR and NCEP–NCAR reanalysis, with modeling to estimate how ENSO or AO relationships to DA change as a function of different CO2 emissions scenarios. Future research on the combined impacts of climate change and climate variability on air temperature, pressure, and moisture could provide airlines and other aircraft operators additional insights on what future DA trends will look like and will be useful in future planning and scheduling. Last, this work lays the ground work for future investigations into how impacts of ENSO and AO on DA can be used for seasonal-scale planning of aviation operations.

Acknowledgments

The authors thank NSF for support through NSF CAREERS 1255649.

REFERENCES

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  • Fig. 1.

    DA (top),(top middle) correlation and (bottom middle),(bottom) regression with the SOI for DJF, MAM, JJA, and SON. Shaded regions in the top two rows indicate statistical significance at the 0.05 level. The units for the bottom two rows are feet per SOI.

  • Fig. 2.

    As in Fig. 1, but for the AO index. The units for the bottom two rows are feet per AO.

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