three-dimensional variation of atmospheric co 2: a

Three-dimensional variation of atmospheric CO2: A comparison of aircraft measurements with inverse model

simulations

Eugene D. Cody

Academic Affiliation, Fall 2014: Senior, Haskell Indian Nations University

SOARS® Summer 2014

Science Research Mentor: Abhishek Chatterjee Writing and Communication Mentor: Nan Rosenbloom

Coach: Jeff Weber Peer Mentor: Jonathan Martinez

ABSTRACT

Accumulation of CO2 impacts climate resulting in an increase of global temperatures. It is vital to know the underlying processes driving the uptake of CO2 emissions by the biosphere and oceans to infer the rate at which CO2 concentrations will increase in the atmosphere. In this paper, we compare vertical profiles of carbon dioxide concentrations from aircraft measurements to simulated CO2 concentrations from an atmospheric inverse model simulation. The inverse model simulations are based on assimilation of atmospheric CO2 observations from: (a) the Greenhouse gases Observing SATellite (GOSAT) instrument, (b) the NOAA/ESRL surface flask network, and (c) the Total Carbon Column Observing Network. Since the inverse model simulations generate CO2 fluxes at ~1 degree, these are then fed into an atmospheric transport model to simulate the atmospheric CO2 concentrations. The independent set of aircraft measurements are obtained from a suite of NOAA/ESRL and the HIAPER pole-to-pole flight campaigns (HIPPO-3 and HIPPO-5 field phases). Both qualitative and quantitative analyses are used to evaluate the quality of the simulated CO2 concentrations from the inverse modeling approach. Results show: (a) a greater difference between the aircraft and the inverse modeling simulated CO2 concentrations over land regions relative to over ocean basins, and (b) the inverse simulations under-estimate in the winter and over-estimate in the summer. Both of these differences can be attributed to the greater variability and heterogeneity in the CO2 signal near the land surface, which does not get simulated well by the inverse modeling approach. Future work will examine possible ways to improve the inverse model simulations in order to obtain better agreement with the aircraft data. This work was performed under the auspices of the Significant Opportunities in Atmospheric Research and Science Program. SOARS is managed by the University Corporation for Atmospheric Research and is funded by the National Science Foundation, the National Oceanic and Atmospheric Administration, the National Center for Atmospheric Research, the University of Colorado at Boulder, Woods Hole Oceanographic Institution and by the Center for Multiscale Modeling of Atmospheric Processes.

1. Introduction

Carbon dioxide (CO2) is a greenhouse gas that plays a significant role in climate change. Accumulation of carbon dioxide greatly impacts climate change by affecting the earth’s carbon cycle resulting in an increase of global temperatures (Chatterjee [2012]; Ciais et al. [2010]). Carbon fluxes occur all over the world in a large, unknown quantity (Ciais et al. [2010]) and it is vital to know the underlying processes driving the uptake of CO2 emissions by the biosphere and oceans to infer the rate at which CO2 concentrations will increase in the atmosphere (Chatterjee [2012]; Niwa et al. [2011]; Pickett-Heaps et al. [2011]; Stephens et al. [2007]). Strides have been made in understanding the global carbon cycle, for example, the exchange of CO2 between the atmosphere, biosphere, and oceans and the identification of variables associated with terrestrial sinks (Chatterjee [2012]). Assessing the global carbon cycle to analyze the interactions of carbon sources and sinks would better predict future CO2 concentrations (Chatterjee [2012]; Niwa et al. [2011]; Pickett-Heaps et al. [2011]). This paper analyzes the performance of different measurement systems (ground-based observations versus satellite system observations) and our capability to infer CO2 emissions from them using inverse model simulations (IMS). We plan to determine which of these measurement systems may be better suited towards CO2 flux estimation purposes.

Current methods such as the “bottom-up” and “top-down” approaches use estimated flux measurements, inventory datasets, and atmospheric transport models to infer carbon fluxes (Chatterjee [2012]; Niwa et al. [2011]; Wofsy [2011]). The atmospheric “top-down” or inverse model simulation (IMS) method utilizes atmospheric transport models to infer carbon flux distribution of sources and sinks which would lead to a better understanding of the global carbon budget compounded by anthropogenic sources of CO2 (Ciais et al. [2010]; Niwa et al. [2011]; Wofsy [2011]). The IMS problem relies on the distribution of carbon emissions modeled by the atmospheric transport model. IMS statistically optimizes data from surface and satellite measurements to find a set of fluxes that match observed measurements (Chatterjee [2012]; Ciais et al. [2010]). It consists of inputting the optimized fluxes to model optimized CO2 concentration estimates as the output to minimize the distance of errors which results in reduced uncertainties due to information contained in surface and satellite measurements (Ciais et al. [2010]). The IMS problem is highly ill-posed given the large number of fluxes occurring globally and the number of observed sites available for measurements (Chatterjee [2012]; Niwa et al. [2011]; Pickett-Heaps et al. [2011]; Ciais et al. [2010]).

Observed CO2 concentration measurements are taken continuously in situ or as weekly surface flask samples (Sawa et al. [2012]; Ciais et al. [2010]). Measurements from oceanic sites are more attractive due to their large-scale mechanisms that minimize “local noise” (Ciais et al. [2010]). Sampling CO2 concentrations over land primarily occurs with aircraft profiles, which can be used as independent data for verifying models (Graven et al. [2013]; Sawa et al. [2012]; Niwa et al. [2011]; Pickett-Heaps et al. [2011]; Wofsy [2011]; Ciais et al. [2010]; Stephens et al. [2007]). The low number of observation sites and amount of samples taken are a major factor in obtaining adequate CO2 concentration measurements within acceptable certainty in determining carbon sources and sinks (Chatterjee [2012]; Sawa et al. [2012]; Pickett-Heaps et al. [2011]). A way to

SOARS® 2014, Eugene D. Cody, 2

obtain and supplement samples is to measure carbon concentrations using space-based satellites (Sawa et al. [2012]) such as the Greenhouse Gases Observation SATellite (GOSAT) or the other ground-based networks like the Total Column Carbon Observing Network (TCCON) (Chatterjee [2012]). Large errors due to the nature of retrieval algorithms makes it difficult to obtain viable surface flux estimates of CO2 despite providing numerous observation samples. Research efforts are still underway using GOSAT measurements combined with other known techniques that may provide useful results (Chatterjee [2012]). The inverse models used to infer carbon fluxes still contain a significant amount of uncertainty (Chatterjee [2012]; Niwa et al. [2011]; Pickett-Heaps et al. [2011]; Wofsy [2011]).

Assessment of three-dimensional variation of CO2 concentrations has been limited as most observations occur over the earth’s surface (Sawa et al. [2012]). Instruments such as GOSAT have made vertical assessments of CO2 concentrations possible and provide more insight to global carbon fluxes. Previous studies (see Stephens et al. [2007], Ciais et al. [2010], Niwa et al. [2011], Pickett-Heaps et al. [2011], Sawa et al. [2012], and Graven et al. [2013]) have investigated and comparatively analyzed three dimensional vertical gradients of CO2 concentrations against independent aircraft observations. In this study, the IMS are based on assimilation of atmospheric CO2 observations from: (a) the GOSAT instrument, (b) the National Oceanic and Atmospheric Administration – Earth Research Laboratory (NOAA – ESRL) surface flask network, and (c) TCCON. The IMS generates CO2 fluxes at ~1 degree, which are then fed into an atmospheric transport model to simulate the atmospheric CO2 concentrations. The simulations are compared to independent aircraft observations from the HIAPER Pole-to-Pole Observation (HIPPO) flight campaign and the NOAA-ESRL network.

Section 2 discusses the data sets, an overview of the inverse model, and the qualitative and quantitative analytical methods for comparing aircraft measurements to IMS flux estimates. The results from qualitative and quantitative analyses are discussed in section 3. We conclude with a summary of our findings from this study in section 4.

2. Methods

Vertical profiles of atmospheric carbon dioxide concentrations from independent aircraft observations were compared to IMS flux estimations and qualitatively and quantitatively analyzed.

a. Atmospheric CO2 Concentration Data

1.) NOAA – EARTH SYSTEMS RESEARCH LABORATORY (NOAA-ESRL) SURFACE FLASK

The NOAA-ESRL Global Monitoring Division cooperative air sampling network provides sources of surface and airborne flask samples for measuring carbon dioxide concentrations (http://www.esrl.noaa.gov/gmd/obop/). Flask samples are taken at weekly intervals (Chatterjee [2012]); Washenfelder et al. [2006]) that provide airborne profiles


ranging between 0.5 km to 4 km in altitude (Washenfelder et al. [2006]). Table 1 lists the NOAA-ESRL sites chosen for aircraft vertical profile data sets.

Table 1. List of NOAA-ESRL sites and location (see Figure 1). Site Date Location

CAR 3/17/10 Briggsdale, CO 8/16/11

ESP 3/31/10 Estevan Point, BC 8/14/11

LEF 3/29/10 Park Falls, WI 8/16/11

2.) TOTAL COLUMN CARBON OBSERVING NETWORK (TCCON) TCCON is “a network of ground-based, sun-viewing, near-IR, Fourier transform”

spectrometers that that takes precise column measurements of greenhouse gases (Toon [2009]). TCCON is operated at over a dozen low altitude tower sites that take vertical in-situ measurements. The network utilizes Bruker 120HR or 125HR spectrometers that cover the 3900 to 15500 cm-1 spectrum to yield column averages of CO2 (Toon [2009]; Washenfilder et al. [2006]).

3.) GREENHOUSE GASES OBSERVATION SATELLITE (GOSAT)

GOSAT, a remote-sensing mission launched in January of 2009, globally measures and column-averages dry air mole fractions of atmospheric gases (i.e. CO2) from space, with increased sensitivity in the lower troposphere (Chatterjee [2012]). The instrument provides global coverage in which CO2 concentrations were averaged to give a temporal resolution of 1° x 1.25° longitude/latitude with measurements at an altitude of a 666 km orbit, which is significantly higher than in-situ monitoring locations (Chatterjee [2012]).

4.) HIAPER POLE-TO-POLE OBSERVATIONS (HIPPO)

A 3-year flight campaign, HIPPO measured atmospheric trace gas concentrations over a period of different seasons from the North Pole to the South Pole (Wofsy [2011]; https://www.eol.ucar.edu/node/3402). The flight campaign consisted of a pole-to-pole destination with varying flight paths for a total of 5 phases spanned over 3 years that provided a robust data set of atmospheric chemical profiles (Wofsy [2011]). Two HIPPO phases were selected based on the dates of March 2010 (HIPPO-3) and August 2011 (HIPPO-5) (see Table 2 and Figure 1). The dates selected within those months measured CO2 concentrations (among others) over both land mass and ocean basins at a high-resolution.

Table 2. List of HIPPO flight campaign sites and location. HIPPO-3 occurred over the East Pacific Ocean basin and HIPPO-5 occurred

over North American land mass (https://www.eol.ucar.edu/node/3402). Site Date Location

HIPPO-3 3/29/10 Alaska to Hawaii 3/31/10 Hawaii to American Samoa

HIPPO-5 8/11/11 CO to TX, LA, AR, OK 8/16/11 Colorado to Alaska


Figure 1. NOAA-ESRL and HIPPO locations

b. Inverse Model Framework The inversion model used for carbon dioxide flux estimations was the

parameterized chemistry and transport model (PCTM) based on the Bayesian synthesis inversion formulation (Chatterjee [2012]). PCTM incorporates meteorological events such as “horizontal winds, surface pressure, vertical diffusion coefficient, and cloud-convective mass flux” (Chatterjee [2012]). The data used in the inverse model was a combination of surface flask, TCCON, and GOSAT (STG) measurements and surface flask and TCCON (ST) measurements to estimate carbon flux concentrations (Chatterjee [2012]). The inverse model data sets have a temporal resolution of 1° x 1.25° longitude/latitude with 55 vertical layers that correspond to March 2010 and August 2011.

c. Aircraft Observations and IMS Flux Estimation Comparison The sampling size of observed and flux estimations depended on the site in question. The data for the NOAA-ESRL sites were sparse compared to the density of the data for the HIPPO sites. We retrieved the IMS data points using latitude/longitudinal coordinates to isolate the NOAA-ESRL and HIPPO sites from the IMS flux estimations. The extracted data was averaged over 55 vertical layers then plotted against the aircraft measurements for the NOAA-ESRL and HIPPO sites. Four altitude bins were defined: (1) 1000 – 0 hPa, (2) 1000 – 800 hPa, (3) 800 – 500 hPa, and (4) < 500 hPa. The 1000 – 0 hPa contained all sample points whereas the other bins were contained a subset of sampling points for both the aircraft and IMS flux estimations. The corresponding data points were then analyzed to determine error estimates based on statistical analysis. The root mean square error (RMSE) was applied to each altitude bin:

𝑅𝑀𝑆𝐸 = �∑ (𝑚𝑖 − 𝑜𝑖)2𝑀𝑖=1

𝑀


m IMS flux estimate; o aircraft observation measurements; M number of aircraft observations in each profile; i ith aircraft observation for each profile (Pickett-Heaps et al. [2011];

Willmott and Matsuura [2005]).

The vertical profiles and RMSE calculations for each NOAA-ESRL and HIPPO site showed the error between the aircraft observations and IMS flux estimates.

3. Results and Discussion

a. NOAA-ESRL Vertical Profiles and RMSE Figures 2, 3, and 4 show the IMS flux estimates compared to aircraft observations

for the NOAA-ESRL sites in March 2010 (winter) on the left and August 2011 (summer) on the right. For each site in the winter months, the inverse model simulations STG and ST under-performed with the ST flux estimates being a more accurate fit than the STG

(a) CAR – Winter (b) CAR – Summer

Figure 2: CAR. Vertical profiles of STG (red) and ST (blue) plotted against aircraft observations of CO2 concentrations for the CAR site with (a) March 2010 on the left and (b) August 2011 on the right. Pressure altitude is hPA and CO2 concentrations in ppm. The profiles show seasonal differences between flux estimates and aircraft observations.

Table 3: CAR. RMSE values for the altitude bins of (1) 1000 – 0 hPa, (2) 1000 – 800 hPa, (3) 800 – 500 hPa, and (4) < 500 hPa at the NOAA-ESRL CAR site. The RMSE values show that the ST flux estimates have a better fit to the aircraft observations than the STG flux estimates for March 2010. However, in August 2011, the STG flux estimates agree better with the aircraft observations than the ST flux estimates for each altitude bin.

Site Date IMS Altitude Bins (hPa)

RMSE 1000-0 N RMSE

1000-800 N RMSE 800-500 N RMSE

< 500 N

CAR 3/17/10 STG 1.90 12 0 0 2.08 7 1.61 5

ST 0.83 12 0 0 0.89 7 0.73 5

8/16/11 STG 1.55 12 0 0 1.91 7 0.83 5 ST 5.91 12 0 0 6.38 7 5.19 5


(a) ESP – Winter (b) ESP – Summer

Figure 3: ESP. Vertical profiles of STG (red) and ST (blue) plotted against aircraft observations of CO2 concentrations for the ESP site with (a) March 2010 on the left and (b) August 2011 on the right. Pressure altitude is hPA and CO2 concentrations ppm. The plots show the seasonal differences between the flux estimations and the aircraft observations.

Table 4: ESP. RMSE values for the altitude bins of (1) 1000 – 0 hPa, (2) 1000 – 800 hPa, (3) 800 – 500 hPa, and (4) < 500 hPa at the NOAA-ESRL ESP site. The RMSE values show that the ST flux estimates have a better fit to the aircraft observations than the STG flux estimates for March 2010. However, in August 2011, the STG flux estimates agree better with the aircraft observations than the ST flux estimates for each altitude bin.


RMSE 1000-0 N RMSE

1000-800 N RMSE 800-500 N RMSE

< 500 N

ESP 3/31/10 STG 3.47 12 3.41 5 3.52 7 0 0

ST 2.77 12 2.48 5 2.96 7 0 0

8/14/11 STG 3.45 12 1.59 5 4.31 7 0 0 ST 10.89 12 9.05 5 12.03 7 0 0

(c) LEF

Figure 4: LEF. Vertical profiles of STG (red) and ST (blue) plotted with aircraft observations of CO2 concentrations for the LEF sites with March 2010 on the left and August 2011 on the right. Pressure altitude is hPA and CO2 concentrations in ppm. The seasonal difference can be observed between the flux estimation and aircraft observations. Table 5: LEF. RMSE values for the altitude bins of (1) 1000 – 0 hPa, (2) 1000 – 800 hPa, (3) 800 – 500 hPa, and (4) < 500 hPa. Similar to the CAR and ESP sites, the RMSE values show that the ST flux estimates have a better fit to the aircraft observations than the STG flux estimates for March 2010. In August 2011, the STG flux estimates agree better with the aircraft observations than the ST flux estimates for each altitude bin.


RMSE 1000-0 N RMSE

1000-800 N RMSE 800-500 N RMSE

< 500 N

LEF 3/29/10 STG 5.30 10 6.43 5 3.62 5 0 0

ST 5.00 10 6.37 5 3.06 5 0 0

8/22/11 STG 6.58 10 7.23 5 5.86 5 0 0 ST 12.35 10 14.52 5 9.69 5 0 0


flux estimate. On the other hand, the inverse model simulations STG and ST over-performed with the STG flux estimate being more accurate than the ST flux estimate during the summer. Tables 3, 4, and 5 reflect the RMSE differences for the four altitude bins defined at each site: (1) 1000 – 0 hPa, (2) 1000 – 800 hPa, (3) 800 – 500 hPa, and (4) < 500 hPa.

b. HIPPO Vertical Profiles and RMSE The HIPPO vertical profiles for March 2010 (winter) (see Figure 5) were from

over the West Pacific Ocean basin and the August 2011 (summer) vertical profiles (see Figure 6) were taken over land mass in North America. On March 29, 2010, the ST flux estimate performed slightly better than the STG flux estimate. The IMS flux estimates under-performed overall, but fit better as it went up into the upper atmosphere. The March 31, 2010 vertical profile shows that the IMS flux estimates performed very well with the ST flux estimate performing slightly better than the STG flux estimate. Although there was a sharp deviation between 600 – 400 hPa, the profile fit was better than any other site including the NOAA-ESRL sites.

The IMS flux estimates over-performed for both days in the summer. The STG flux estimate performed significantly better than the ST flux estimate. The RMSE values (Table 7) show a large difference between both IMS flux estimates and the aircraft observations. There is a larger difference near the surface of the Earth than in the upper atmosphere for the IMS flux estimates compared to the aircraft observations.

(a) HIPPO-3 – Alaska to Hawaii (b) HIPPO-3 – Hawaii to American Samoa

Figure 5: HIPPO-3 – Winter. Vertical profiles of STG (red) and ST (blue) plotted with aircraft observations of CO2 concentrations for the HIPPO-3 flight phase in March 2010. Pressure altitude is hPA and CO2 concentrations in ppm.

Table 6: HIPPO-3 – Winter. RMSE values for the altitude bins of (1) 1000 – 0 hPa, (2) 1000 – 800 hPa, (3) 800 – 500 hPa, and (4) < 500 hPa. The RMSE values show that the ST flux estimates have a better fit to the aircraft observations than the STG flux estimates for March 2010. The STG flux estimates agree better with the aircraft observations than the ST flux estimates for March 31, 2010 than in March 29, 2010.


RMSE 1000-0 N RMSE

1000-800 N RMSE 800-500 N RMSE

< 500 N

HIPPO-3 3/29/10 STG 3.07 30 3.61 12 2.69 10 2.62 8

ST 2.58 29 2.81 12 2.12 10 2.73 7

3/31/10 STG 1.12 25 1.04 10 1.11 10 1.31 5 ST 0.66 26 0.26 11 0.73 10 1.03 5


(a) HIPPO-5 – CO to TX, LA, AR, OK (b) HIPPO-5 – CO to AK

Figure 6: HIPPO-5 – Summer. Vertical profiles of STG (red) and ST (blue) plotted with aircraft observations of CO2 concentrations for the HIPPO-3 flight phase in March 2010. Pressure altitude is hPA and CO2 concentrations in ppm. The IMS flux estimates over-performed for both days with the STG flux estimate performing better than the ST flux estimates.

Table 7: HIPPO-5 – Summer. RMSE values for the altitude bins of (1) 1000 – 0 hPa, (2) 1000 – 800 hPa, (3) 800 – 500 hPa, and (4) < 500 hPa. The RMSE values show that the STG flux estimates have a better fit to the aircraft observations than the ST flux estimates for the summer months. The STG flux estimates agree better with the aircraft observations than the ST flux estimates.


RMSE 1000-0 N RMSE

1000-800 N RMSE 800-500 N RMSE

< 500 N

HIPPO-5 8/11/11 STG 2.51 31 1.68 7 2.44 14 2.99 10

ST 6.58 31 7.19 7 7.33 14 5.32 10

8/16/11 STG 2.48 21 0 0 2.92 12 1.74 9 ST 9.12 21 0 0 10.03 12 7.75 9

The results show a seasonal variation of CO2 concentrations which are similar to the findings in Graven et al. [2013], Sawa et al. [2012], Niwa et al. [2011], Ciais et al. [2010], and Stephens et al. [2007]. The inverse model simulations tend to under-perform during the winter and over-perform during the summer. In March of 2010, the ST flux estimates performed slightly better than the STG flux estimates by approximately 1 – 2 ppm based on the RMSE values. The differences between the ST and STG flux estimates during August were significantly higher, which can be attributed to biospheric activity near the earth’s surface resulting in the greater variability the CO2 concentrations, which does not get simulated well by the inverse modeling approach. This is supported by the HIPPO vertical profiles (see Figures 5 and 6) that show a better profile fit over the ocean basins versus the profile fit over land mass.

4. Conclusion A summary of our findings for this study is as follows:

1) The ST flux estimate RMSE values are lower in March than in August, which

again is due to biospheric activity. 2) For the month of August, the STG flux estimates were lower than the ST flux

estimates, which was most likely due to more GOSAT data available in the summer to constrain the fluxes.


The purpose of this study was to analyze the performance of different measurement systems to infer CO2 emissions using inverse model simulations. Which of these measurement systems is better suited towards CO2 flux estimation purposes? Although the ST flux estimation performed better in March 2010, the STG flux estimates performed better overall. The GOSAT measurement system adds valuable information to the ground-based measurement systems and assists in inferring CO2 flux estimations. It is expected that the STG flux estimations would perform much better in the winter if there is more GOSAT data for March 2010. However, more analysis and data acquisition needs to occur in order to test this hypothesis.


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