THE EFFECT OF AVIATION
INDUCED CONTRAIL CIRRUS ON
GLOBAL RADIATION
21-05-2021 Amsterdam
Image: https://www.metoffice.gov.uk/weather/learn-about/weather/types-of-weather/clouds/other-clouds/contrails By Jelmer Dexter Kooij 12064610 Primary assessor: Dr. In. J. H. van Boxel Secondary assessor Dr. K. F. Rijsdijk Primary supervisor: Dr. In. J. H. van Boxel Secondary supervisor: D. C. Danesh
Abstract
The increase of radiative forcing is a main driver of global warming. Aviation is known to affect the radiative forcing on Earth significantly, thus being responsible for a share of global warming. In order to gain a broader understanding of this system, this research aims to assess how aviation affects the amount of global radiation. Based on a review of existing literature concerning the effect of cirrus clouds on global radiation and the contribution of aviation on cirrus clouds, a quantitative study was conducted comparing observed global radiation data for hours without cloud cover, to modelled global radiation data under clear skies. This data was grouped by year, with the 2010-2019 data later to be pooled. By dividing the observed global radiation data by the modelled data, the global radiation proportions were calculated. The median global radiation proportions of 2010-2019 and 2020 were then compared using a one sided one sample T-test. The results of the one sided one sample T-test comparing the medians of the 2010-2019 period and 2020 show that the global radiation proportion of 2020 does not significantly differ from the 2010-2019 (T-statistic = -0.366). However, literature suggests that air traffic leads to an increase in cirrus clouds. These (contrail) cirrus clouds reflect a share of the radiation, which thus decreases the amount of global radiation on Earth. Therefore it is concluded that less air traffic does increase the amount of global radiation, but that this effect is practically insignificant when compared to other factors.
Table of content
Title page ... Fout! Bladwijzer niet gedefinieerd.
Abstract ... 0
Table of content ... 2
Introduction ... 3
Methods and Data ... 4
Results ... 6
Conclusion ... 11
Acknowledgements ... 12
Bibliography ... 13
Introduction
The climate on Earth is changing constantly in a very slow pace. This natural climate change is caused by shifting concentrations of greenhouse gases in the atmosphere. These greenhouse gases increase the radiative forcing which affects the radiation budget. This is the balance of short-wave radiation that Earth absorbs and reflects, as well as the amount of long-wave radiation that is emitted (Forster et al, 2007). On average, of the 340 W/m2, 100 W/m2 is
reflected back into space by clouds (Hartmann et al, 2013). Due to a shift in the radiative budget of the Earth’s atmosphere, the climate is slowly changing.
On Earth the amount of greenhouse gases in the atmosphere has increased significantly during the Anthropocene, largely due to the use of fossil fuels (Solomon et al., 2009). This causes a rapid change in temperature which affects many natural weather processes and could
potentially disrupt entire ecosystems (Hoegh-Guldberg, 2009). In order to slow down climate
change and prevent Earth’s global average temperature from rinsing more than 1.5℃ it is important to reduce the amount of greenhouse gases emitted drastically (Eickhout et al., 2004). In the past some studies have been conducted in order to try to explain and predict the effect of aviation induced contrail cirrus on the Earth’s radiation budget (Bock and Burkhardt, 2019; Meerkötter et al, 1999). However, due to the Covid-19 pandemic a rare opportunity has presented itself to study the direct effect of a large reduction in air traffic on the global radiation
Research has shown that aviation is a relatively large contributor to the amount of radiative forcing in the atmosphere. Aviation contributes to approximately 1.6% of the total
radiative forcing caused by carbon dioxide (CO2). However, the radiative forcing caused by
contrail cirrus, which results from the emission of water following the combustion of jet fuel at 10-12 kilometre has a far greater effect. In total, aviation contributes to approximately 5% of the total amount of the anthropogenic radiative forcing as of 2005 (Lee et al, 2009). However, due to the fact that air traffic is expected to increase over the next few decades, this contribution is expected to increase as well (ICAO, 2021).
At the high altitude that (contrail) cirrus cloud formations occur, the temperature is well below freezing and frequently as low as -50℃. Due to the cold air, cloud formations at this altitude do not exist out of water droplets, but rather ice crystals (Matus and L’Ecuyer, 2017). During overcast conditions when only cirrus clouds are present, approximately 83% of the radiation is still transmitted, depending on the airmass (Haurwitz, 1946). However, approximately 25% of this effect is caused by the atmosphere which scatters and absorbs a share of the radiation (Van Boxel, 2002; Kasten and Czeplak, 1980).
Due to the low temperature at the cruising altitude of aeroplanes (10-12km), the hot, humid exhaust quickly supersaturates. Following this, water in the exhaust condensates and subsequently freezes into ice crystals forming contrail cirrus clouds (Schumann, 2005). These contrails affect the amount of radiation that reaches Earth’s surface in the same manner that regular cirrus clouds do (Minnis et al, 2013). Thereby, small soot particles in the exhaust may trigger nucleation of ice crystals in humid air (Schröder et al, 2000). Cirrus cloud cover that
can be attributed to aviation has increased by 1% to 2% between 1984 and 1999 and due to an annual increase in jet fuel consumption is expected to keep increasing (Stordal et al, 2005).
In the days following the 9/11 attacks in the United States of America, average temperatures in the USA rose by 1.1℃, this increase in temperature was to be attributed to the decrease in contrail cirrus following all commercial aircraft to be grounded for multiple days (Travis et al, 2002). However other studies, while acknowledging the fact that contrails do effect the weather system, suggest that this effect might be overestimated (Kalkstein and Balling, 2004). This shows that the effect that air traffic has on this system is yet poorly understood. During the COVID-19 pandemic, global air traffic decreased as well in order to stop the spread of the virus. This makes for an unique opportunity in order to study the effects of aviation induced contrail cirrus on global radiation. This knowledge attributes to a better understanding of the effects of contrail cirrus on the radiation budget, temperatures and possibly global warming.
The aim of this research is to gain insight in how aviation induced contrail cirrus affects the amount of global radiation on Earth. Therefore, the research question of this project is; “How does aviation induced contrail cirrus affect global radiation?”. In order to answer this question two sub-questions will be answered. The first being “How do cirrus clouds affect global radiation?”. The second “How does aviation affect the amount of cirrus clouds?”. These sub-questions are answered by studying existing scientific literature and by comparing observed global radiation under unclouded skies during 2020, a year which had little air traffic due to the COVID-19 pandemic, to that in normal years.
Taking into account the fact that cirrus clouds have a negative effect on the transmittance of short-wave radiation of the atmosphere, as well as the fact that air traffic increases the amount of cirrus clouds in several ways, it seems obvious that higher air traffic leads to lower global radiation. Therefore the hypothesis of this research is that aviation induced contrail cirrus decreases the amount of global radiation and that this effect is significant.
Methods and Data
This research focusses on gaining a deeper understanding in how aviation induced contrail cirrus affects global radiation on Earth. Firstly, this research uses existing literature on the effects of cirrus clouds on the amount of global radiation. This helps to gain insight in how cirrus clouds in general affects the amount of global radiation. Thereby, existing literature is used in order to asses to what share aviation is responsible for the total amount of cirrus clouds. Finally, a quantitative research is conducted in order to establish whether global radiation is significantly different during periods with lower air traffic compared to periods with average numbers of air traffic.
Due to the COVID-19 pandemic, the amount of air traffic in 2020 has been much lower than the years before (Eurocontrol, 2021a). The quantitative research focusses on the extend to which the global radiation on days with a clear sky in the Netherlands in 2020 has varied from what would be expected under normal circumstances. For this research the KNMI weather station at Schiphol airport is selected due to its position near high-traffic waypoints, SPY and PAM, and thus the contrail cirrus is expected to be as highly concentrated as possible (Eurocontrol, 2021b).
The majority of this research, the statistical analysis concerning global radiation data on days from 2010-2020 relies on hourly global radiation data of the Royal Dutch Institute of Meteorology (KNMI, 2021) and the global radiation model constructed by Van Boxel (2002). The model has been adapted to the same location as the weather station. For this, the longitude and the real solar time in the model are adjusted to the values applicable for this weather station. The model is written in R-studio and copied to an excel spreadsheet. This spreadsheet is subsequently transferred back into a R-studio data frame..
The observed hourly data for weather station Schiphol are downloaded from the
database of the KNMI (KNMI, 2021). The KNMI data is converted from J/cm2/s to J/m2/h to
make it line up with the model The amount of measurements over the course of the two datasets used are 87.648 for the 2010-2019 period and 8.784 during 2020. Therefore, these measurements are firstly grouped into datasets containing just one year. Of these datasets only the amount of global radiation and the cloud cover for that hour are selected. In order to make an observed dataset containing all the data for global radiation on cloudless days, since this eliminates the effect of lower- and middle-altitude clouds, a variable is made that is 1 in case there is no cloud coverage and 0 in case there is any. By multiplying this layer with all the observed global radiation data every observation made under a clouded sky will have a value of 0. Thereafter, the observed data is divided by the modelled global radiation of that hour. Finally, all data that has a value of 0 or is not available is removed, resulting in the proportion of observed global radiation compared to the modelled global radiation on hours without any cloud coverage or Relative Global Radiation (RGR). This results in eleven datasets, one for each year with the RGR centred approximately around 1, since assuming the model is accurate, the observed and modelled global radiation should be near identical.
Data containing measurements from the period 2010-2019 are grouped together to form the control group. After this dataset has been constructed, a statistical analysis is conducted comparing multiple properties of the data sets. the global radiation proportions of 2020 to the data of the grouped 2010-2019 data. This one sided one sample T-test inspects whether the medians of the two groups (RGR in 2010-2019 and RGR in 2020) differ significantly or not. Comparing the medians of the RGR data is chosen instead of mean since the latter is sensitive to outliers. For this one sided one sample T-test the mean of the median RGR data and the standard deviation for 2010-2019 are calculated. Then the T-statistic is calculated by subtracting the median of 2020 from the mean median of 2010-2019, divided by the standard deviation, divided by the square root of the sample size (in this case 10).
This methodology was chosen since it is relatively simple and can be applied for any measurement location that at least registers global radiation and cloud cover, as well as at what time these measurements were taken. For this research, the at Schiphol located weather station was the most logical since it has much air traffic flying over. Therefore it is expected to see the most significant variation in contrail cirrus depending on air traffic. Previous research has shown that especially near these kind of flight corridors, the amount of aviation induced contrail cirrus can be significantly higher compared to areas with less air traffic (Yang et al, 2010). Thereby, the model (Van Boxel, 2002) is constructed for an altitude at sea level, since Schiphol is situated nearly at sea level, the atmosphere’s turbidity does not vary as much as it would for locations located high above sea level. Thereby, by choosing hourly data over daily data, which is also provided by the KNMI, the amount of observations suitable for the analysis is increased resulting in a more reliable outcome.
Results
The literature study conducted in this research found that water vapour in the hot air that is emitted during the combustion of jet fuel may lead to supersaturation of the air. Due to the supersaturation the water vapour condenses and, due to the low temperature at cruising altitude (10-12km), subsequently freezes forming ice crystals (Schumann, 2005). These ice crystals then form contrail cirrus.
The properties of cirrus clouds are different compared to other cloud types which occur at lower altitudes (Matus and L’Ecuyer, 2017). During cirrus overcast conditions approximately 83% of the short-wave radiation eventually reaches the Earth’s surface, without being absorbed, reflected or scattered. Of the 17% that is either absorbed, reflected or scattered, about 75% can be attributed to cirrus clouds, the latter 25% to the atmosphere (Van Boxel 2002). In total, approximately 14% of the radiation is absorbed or scattered by cirrus clouds and does not reach the Earth’s surface (Kasten and Czeplak, 1980).
In the northern hemisphere, contrail cirrus clouds cover between 0.40% to 0.07%, with a mean coverage of 0.13%. However, in higher traffic areas this can be higher than 1.00% (Duda et al, 2013). Due to the increasing trend of the amount of air traffic, cirrus cloud cover is increasing by 1% to 2% per decade (Stordal et al, 2005). However, this increase seems to be more evident in higher latitude areas (Yang et al, 2010). This increase in aviation induced contrail cirrus decreases the amount of radiation that reaches the Earth’s surface (Minnis et al, 2013). While the amount of global radiation decreases due to radiation being reflected into space, on the other hand these clouds transmit and absorb the long wave radiation coming from Earth’s surface. This greenhouse effect of cirrus cloud coverage add up to approximately +1.3 W/m2 of radiative forcing (Chen et al, 2000).
The statistical study in the research compares the hourly data of the amount of global radiation on hours without any cloud cover. It uses observation data from 2010 until 2020, these observations are conducted at the weather station at Schiphol airport near Amsterdam.
The relative global radiation (RGR) was defined as the quotient of the observed global radiation and the modelled global radiation, as can be seen in the methods section. Figure 1 shows the RGR of each year ranging from 2010 to 2020. The boxplots show that the median RGR value for each year is well nigh a value of 1. A small variation can be noted per year for the median RGR, as well as the location of the median RGR compared to the quantiles. The plot also shows that there are quite some outliers for every year, both above and below the whiskers. In figure 2 just the median values are plotted, here a more clear difference can be noted between the years. This plot clearly shows that the RGR value was higher than the median RGR in 2014 and 2020, compared to the other years. Yet, neither of these two median values are higher than 1.
Figure 3 shows the mean RGR value per year. In this plot it can be seen that not only 2014 and 2020 have a relatively high mean RGR value compared to the average, but also both 2010 and 2012 have a high mean RGR. Thereby, a trend line is plotted which starts at the 11 year mean and increases by 2% per decade.
Figure 4 shows a boxplot of the global radiation proportions compared to the model of the data of the period 2010-2019 and the year 2020. What stands out in this boxplot is that the whiskers, both quantiles and the median of the proportion is higher for 2020 than for the period
2010-2019. Furthermore, the year 2002 has only very little outliers on the lower side compared to the period of 2010-2019.
A one sided one sample T-test was used in order to determine whether the pooled RGR values of 2010-2019 were smaller than the RGR value for the year 2020. By conducting this test it is possible to tell whether the 2020 RGR value is significantly higher than the value of the ten years before the COVID-19 pandemic. With 9 degrees of freedom and a significance level of α=0.05, the minimal T-statistic in order to reject the null hypothesis that the RGR value for 2020 is the same as in the ten year period before is 1.833 or -1.833.
Figure 1 showing boxplots for the distributions of the proportion of the observed global radiation compared to the modelled data at Schiphol for each year.
Figure 2 showing the median proportion of the observed global radiation compared to the modelled data at Schiphol for each year.
Figure 3 showing the mean proportion of the observed global radiation compared to the modelled data at Schiphol for each year (black), as well as a trend of 2% increase per decade starting at the mean value of the period 2010-2020 (red).
Figure 4 showing boxplots for the distributions of the proportion of the observed global radiation compared to the modelled data at Schiphol for the pooled data of the period 2010-2019 as well as 2020 on its own.
Discussion
Both the literature as the statistical analysis which are conducted in this study do indicate that a lower amount of air traffic immediately decreases the amount of contrail cirrus. This in turn increases the amount of global radiation observed. The result indicates that a higher amount of air traffic decreases global radiation.
The results of this study show that cirrus cloud formations in general, have high reflection and absorption properties. Approximately 62% of the short-wave radiation does not pass through cirrus cloud formations under overcast circumstances. Thereby, 25% of the total short-wave radiation is either absorbed or scattered by the atmosphere. This results in a 17% decrease in global radiation compared to a situation in which all the short-wave radiation would reach Earth’s surface. In all, this contributes to a slight positive net radiative forcing of 1.3
W/m2 (Chen et al, 2000). This would mean the Earth’s atmosphere and surface could slightly
warm up (Campbell et al, 2016).
Due to the possible supersaturation of emitted air following the combustion of jet fuel at cruising altitude, the water vapour condenses, freezes and forms cirrus cloud formations. On average, contrail cirrus clouds cover 0.40% to 0.07%, with a mean of 0.13% of Earth’s atmosphere being covered, with the cover at higher latitudes being higher (Duda et al, 2013) (Yang et al, 2010). However, in areas with high air traffic, such as near Schiphol airport where the weather station that is used in the statistical analysis is based, this can be over 1.00%. This would mean that in areas with a high amount of air traffic, there would be more cirrus cloud coverage due to the contribution of aviation induced contrail cirrus. This increase in cirrus cloud coverage, due to its reflecting and absorbing properties, global radiation would be lower compared to a situation with little to no air traffic.
The plots have shown that the median RGR for 2020, are higher compared to the ten year period between 2010 and 2019, for the exception of the median RGR of 2014. When comparing the mean RGR, we see that besides 2020, 2013 and 2014 are also relatively high compared to the other observed years. However, this might be due to the high number of outliers that these years have. Firstly, the 2013 and 2014 data result in a boxplot which has a relatively large difference between the RGR and the quantiles. They also show whiskers which are relatively further apart than those of the other years. Thereby they also show some outliers which are higher compared to these of the other years. All these factors might have affected the mean to become a little higher. When looking at the median RGR value, it can be observed that 2020 again is relatively large compared to many of the other years in this period. However, the RGR value of 2014 again is relatively high as well. The proportion value of 2013 is quite low compared to its mean value, due to the outliers. This gives reason to believe that the mean was indeed higher than expected due to its high outliers. The same goes for many of the other years, albeit in smaller proportions, such as the RGR of 2010. What stands out as well is that none of the median observed global radiation proportions exceed a value of 1. This result is as expected since it would not make sense that many of the global radiation measurements would be higher than the potential global radiation.
The results also show that every data set has a relatively high amount of outliers. These outliers mostly occur where the RGR values are high. However, also a smaller amount of outliers on the low side can be observed. The high RGR outliers nearly always occur at either dawn or sunset. Due to the amount of global radiation rising and falling very quickly at these times of the day, if the model is only slightly off, the relative difference between the model and the observations by the weather station can be quite large. This is however largely filtered out by removing all data where either the modelled data or the observed global radiation is lower than 50. The same goes for the data with a low RGR value. However, there are several more reasons which may also explain these low values. Firstly, the cloud cover data can be off. When the cloud cover is larger, this data will have a lower amount of global radiation than under cloudless conditions. This would result in lower RGR values. Thereby, it is also possible that the measurement instruments have been temporarily blocked casting a shadow on the instrument. This does also decrease the amount of global radiation that is registered, resulting in a lower RGR value.
Due to the relatively large amount of outliers which do occur mostly on the higher RGR values, the distribution of the RGR values is slightly skewed. Thereby, the means are also affected by the outliers. As a result, instead of the means, the median for each year is selected to test the data for 2010-2019 to the 2020 data.
However, in order to give a quantitative answer to determine whether the difference is significant, a one sided one sample T-test was used in order to determine whether the pooled RGR values of 2010-2019 were smaller than the RGR value for the year 2020. By conducting this test it is possible to tell whether the 2020 RGR value is significantly higher than the value of the ten years before the COVID-19 pandemic. With 9 degrees of freedom and a significance level of α=0.05, the boundary T-statistics in order to reject the null hypothesis that the RGR value for 2020 is the same as in the ten year period before is 1.833 or -1.833. The result of the one sample test calculates a statistic of -0.366. This value lies within the minimal T-statistics. This leads us to not reject the null-hypothesis. This means that the RGR value for 2020 is not significantly higher than that of the period 2010-2019 and based on the data used in
this research, it is not possible to conclude that the reduction in air traffic during 2020 has lead to a higher amount of global radiation.
By failing to reject the null-hypothesis of the T-test, the initial research hypothesis is rejected. Since the results found in this statistical analysis do not correspond to the results found in earlier studies, instead it can be concluded that even though a reduction in air traffic leads to an increase in global radiation, the effects are insignificant when they are compared to other factors such as for instance the turbidity of the atmosphere.
What the results add to previous knowledge is that the impact that aviation induced contrail cirrus on amount of global radiation is insignificant. This is at least, compared to for instance air pollution and other factors that affect the turbidity of the atmosphere. What the results of this study do not show however is the expected decrease in global radiation due to the trend of a 1% to 2% increase in contrail cirrus per decade. This is partly due to the fact that the variation in the median yearly RGR is much larger than this 1% to 2%. However, this trend might would be more easily observed when studying observations for a larger time period than just 11 years.
It should be noted that in the results however, even though 2020 has a higher mean proportion than the pooled average proportion median of the 2010-2019 period, some others years stand out as well. Most notably the data for 2014. However, since it is beyond the scope of this study to address why this unexpected high median value has occurred, the reason for this phenomenon cannot be answered in this research. This could however be an interesting subject for future research. This future research is required to establish whether there are any irregularities which affect global radiation during 2014 compared to other years, in order to investigate whether the unexpected high median proportion is coincidental or caused by another factor.
In figure 2 it can be concluded that there have been many instances in which the observed global radiation was actually higher than the modelled potential global radiation. This does not make sense since the model is constructed for a situation with a clear sky with neither cirrus- nor lower- and middle altitude cloud cover. This would mean that either the model is slightly off and underestimates the amount of global radiation, or the measurements of the KNMI slightly overestimate the amount of global radiation observed. Therefore in order to determine which of the two reasons can be held accountable for these slight errors, research assessing the measurement equipment’s accuracy by comparing them to other equipment could be conducted. If this proves that the equipment is not slightly off, the global radiation model could be refined adding additional factors which might play a role.
Other factors however, can also contribute to a variation in global radiation. For examples changes is the turbidity of the atmosphere as a result of volcanic eruptions or air pollution. These factors have not been taken into account in this research and thus may have caused a slight alternation of the global radiation proportions compared to the modelled data.
Conclusion
This research aimed to assess how aviation affects the amount of global radiation. The literature review conducted in this study indicated that cirrus cloud formations can, under the right circumstances and taking into account the atmosphere’s turbidity, decrease the amount of global radiation under overcast conditions by over 17%, resulting in a share of 83% of the
short-wave radiation that reaches Earth’s surface (Haurwitz, 1946) . Aviation contributes to the amount of cirrus clouds by oversaturating the air with water vapour as a product of the combustion of jet fuel as well as soot particles triggering nucleation. Aviation induced contrail cirrus accounts for approximately 1.00% cover in high-traffic areas, and is more prevalent in areas with a high latitude. The quantitative research conducted has shown that the proportion of global radiation compared to the modelled global radiation was not significantly higher in 2020, than the proportion of the period 2010-2019. These results do not match with existing literature on the topic.
Based on the qualitative and quantitative analysis that have been conducted in this study, it can be concluded that a higher amount of air traffic, is expected to result in a reduction in global radiation. However, the effects of contrail cirrus on the amount of global radiation are slim and thus do not impact the global radiation significantly compared to other factors. The methodology for the qualitative research has been quite effective since it ended up approximating the proportion of 1 between the observed KNMI global radiation data and the modelled data closely. However, new questions that have emerged while conducting this research. As for example why the median global radiation proportions for the years 2010 and 2014 are as high as they are.
This research shows how aviation induced contrail cirrus effects the amount of global radiation, albeit in an amount that is too little to differentiate its effect while other factors also effect the amount of global radiation. Therefore, it may attribute to a more broad understanding about the way in which aviation induced contrail cirrus affects the entire Earth’s radiation budget. This can help to assess the role of aviation has as a cause of global warming and what the effects may be.
Acknowledgements
I would like to thank miss Donya C. Danesh especially for helping me out to get started on this research. Initially she has helped me with setting up this research a great deal. I would like to give a special thanks to Dr. Ir. John H. van Boxel who has helped me a great deal for the entire duration of the research. Especially during the last few week to help me out with the results. I would also like to thank him for all the time he has invested into me and this research, outside of the planned personal and group meetings. I am convinced without his help this research would not exist in this form.
All data that is used for this project is uploaded on figshare.com and is freely accessible.
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Met opmerkingen [JvB1]: Other journal titles are in title case, so also put this one in title case.
