1 Seasonal and Interannual Variability of Mid-tropospheric CO2 from 2 Atmospheric Infrared Sounder 3 4 5 6 Xun Jiang1*, Moustafa T. Chahine2, Edward T. Olsen2, Luke L. Chen2, 7 and Yuk L. Yung3 8 9 10 11 12 13 14 15 16 17 18 19 20 1 Department of Earth & Atmospheric Sciences, University of Houston, USA 21 2 Science Division, Jet Propulsion Laboratory, California Institute of Technology, USA 22 3 Division of Geological and Planetary Sciences, California Institute of Technology, 23 Pasadena, USA. 24 25 26 * To whom all correspondence should be addressed. E-mail: xjiang4@mail.uh.edu 27 28 To be Submitted to GRL, Oct 26 2009 29 1 1 Abstract: 2 3 [1] The Atmospheric Infrared Sounder offers an opportunity to investigate the 4 variability of middle tropospheric CO2 over the entire globe. In this paper, we use AIRS 5 data to examine the annual and interannual variability of CO2 and show that the annual 6 cycle of AIRS CO2 agrees well with Matsueda aircraft measurements at 25N and 25S. 7 We also find significant correlations between AIRS middle tropospheric CO2 8 concentrations and large-scale atmospheric dynamics. During El Niño events, middle 9 tropospheric CO2 over the central Pacific Ocean is enhanced whereas it is reduced over 10 the western Pacific Ocean as a result of the change in the circulation. The variation of 11 AIRS CO2 in the high latitudes of northern hemisphere is closely related to the strength 12 of the northern hemispheric annular mode. These results will help us to better understand 13 the large-scale dynamics and its influence on tracer in the polar region. 14 2 1 1. Introduction 2 3 [2] The increasing level of atmospheric CO2 has a significant influence on the global 4 climate changes [Dickinson and Cicerone, 1986]. Observations show a global trend of 5 CO2 of approximately 2 ppm/year [Keeling et al., 1995]. Superimposed upon this trend 6 is an annual cycle resulting from the uptake and release of CO2 by vegetation whose 7 amplitude is greatest in the northern hemisphere (NH). In addition to the trend and annual 8 cycle, atmospheric CO2 also shows interannual variabilities. In this paper, we will 9 investigate the impact of the transport and large-scale dynamics on the variability of CO2. 10 11 [3] El Niño and Southern Oscillation (ENSO) is the most important large-scale climate 12 inter-annual variability in the tropics. The atmospheric CO2 is influenced by the ENSO at 13 the surface [Bacastow 1976; Bacastow et al., 1980]. During El Niño events, the 14 atmospheric CO2 growth rate increases at surface stations [Keeling et al., 1995; Jones et 15 al., 2001]. During La Niña events, the atmospheric CO2 growth rates decrease at the 16 surface stations. 17 18 [4] During El Niño events, ocean is a sink for CO2 [Feely et al., 1987; Inoue and 19 Sugimuray 1992; Wong et al., 2003; Feely et al., 1997; Feely et al., 2006]. The upwelling 20 of the cold nutrient-rich waters off the South American coast ceases during El Niño, and 21 the cessation of upwelling will cause surface pCO2 concentrations decrease along the 22 equator [Feely et al., 1987; 1997]. Meanwhile, tropical land becomes dryer and warmer 23 during El Niño. As a result, the gross primary productivity decreases and the respiration 24 increases over the land [Jones et al., 2001]. Thus terrestrial biosphere becomes a greater 25 source of CO2 to the atmosphere during El Niño [Keeling et al., 1995; Francey et al., 26 1995]. The net effect from land and ocean is that the surface CO2 growth rate increases 27 during El Niño events. Conversely, the surface CO2 growth rate decreases during La Niña 28 events. 29 30 [5] The focus of these previous studies was on the influence of climate variability on the 31 CO2 concentration at a few stations near the surface. However, there are no previous 3 1 investigation on understanding the influence of El Niño on the middle tropospheric CO2 2 over a global domain. In this paper, we analyze the influence of ENSO on the middle 3 tropospheric CO2 retrieved by the Atmospheric Infrared Sounder (AIRS). This global 4 data set provides new insight on how ENSO influence the carbon interannual variations 5 in the middle atmosphere and promotes our understanding of vertical transport and future 6 climate change. 7 8 [6] The global coverage of AIRS CO2 retrievals also allows us to investigate the 9 influence of large-scale dynamics on the polar CO2 concentration. The polar region has 10 profound significance for climate. The current general circulation models (GCMs) do not 11 work well for the large-scale dynamics in the polar region, particularly in simulating the 12 exchange between the stratosphere and troposphere in that region [Meloen et al. 2003; 13 Jiang et al., 2009]. Therefore, observations are essential for understanding the large-scale 14 dynamics in the polar region. However, most observations and measurements in the polar 15 region suffer from limited coverage both in time and space. The global distributions of 16 CO2 retrieved from AIRS offer a unique opportunity to study the large-scale dynamics in 17 the polar region. 18 19 [7] The annular modes are the most important inter-annual variability in the polar 20 region. Jiang et al. [2008a, 2008b] applied principal component analysis to the 21 extratropical total column ozone from the combined Merged Ozone Data product and the 22 European Center for Medium-Range Weather Forecasts assimilated ozone from Jan 1979 23 to Aug 2002. In both hemispheres, the first two leading modes are nearly zonally 24 symmetric and represent the connections to the annular modes. When the polar vortex is 25 stronger (positive annular mode phase), there is less ozone transported to the polar region 26 due to a weaker Brew-Dobson circulation. Similar results in extratropical column ozone 27 are found in the Goddard Earth Observation System Chemistry-Climate Model (GEOS- 28 CCM) [Jiang et al., 2008a; 2008b]. Since there is no complex chemistry for CO2 as that 29 for O3, it is easier to use CO2 for investigating the large-scale dynamics in the polar 30 region. In this paper, we will investigate the influence of the annular modes on AIRS 31 middle tropospheric CO2. 4 1 2 2. Data 3 [8] AIRS is a cross-track scanning grating spectrometer with 2378 channels from 3.7 to 4 15.4 m with a 13.5 km field of view at nadir [Aumann et al., 2003]. Chahine et al. 5 [2005] found that the range 690-725 cm-1 is best for selecting the main channel set to 6 retrieve the middle tropospheric CO2 mixing ratio. The mixing ratios of middle 7 tropospheric CO2 are retrieved using the Vanish Partial Derivative Method (VPD) 8 [Chahine et al., 2005; Chahine et al., 2008; Olsen et al., 2009]. The sensitivity function 9 of AIRS middle tropospheric CO2 peaks between 500 hPa to 300 hPa. AIRS middle 10 tropospheric CO2 is retrieved globally in the middle troposphere day and night under 11 clear and cloudy conditions. Validation by comparison to in situ aircraft measurements 12 and retrievals by land-based upward looking Fourier Transform Interferometers 13 demonstrates that AIRS CO2 is accurate to 1-2 ppm between latitudes 30°S and 80°N 14 [Chahine et al., 2008; Olsen et al., 2009]. The middle tropospheric CO2 retrieved via the 15 VPD method captures the correct seasonal cycle compared with those from Matsueda 16 [Chahine et al., 2005; Olsen et al., 2009]. 17 18 [9] In Section 3 of this paper, we compare the seasonal cycle for the AIRS CO2 with 19 independent biweekly aircraft measurements over the western Pacific between Australia 20 and Japan [Matsueda et al., 2002]. The latitudinal range of the Comprehensive 21 Observation Network for Trace gases by AIrLiner (CONTRAIL) measurements at 22 cruising altitude (above 10 km) is approximately from 35S to 35N and the longitudinal 23 range is from 135E to 150E. 24 25 3. Results and Discussions 26 3.1 Annual Cycle of AIRS CO2 27 [10] We compared the NH and southern hemisphere (SH) annual cycles of detrended 28 AIRS retrieved CO2 and detrended CONTRAIL measurements over the period spanning 29 September 2002 and December 2007. A 2 ppm/year trend has been removed from both 30 data sets. The CONTRAIL CO2 data above 8 km altitude are averaged over 10° latitude 31 ranges centered at 25°S and 25°N. AIRS data for the same dates as the CONTRAIL 5 1 measurements have also been averaged over these latitude ranges. The annual cycle was 2 calculated for each week from the detrended CO2 data. The annual cycles for AIRS and 3 CONTRAIL CO2 at 25N and 25S are shown in Figure 1. Error bars are the standard 4 deviations for CO2 in each month. The CONTRAIL annual cycle is depicted by the red 5 line; the AIRS CO2 retrieval sampled in the same locations is depicted by the black line. 6 The annual cycle amplitude and phase of AIRS CO2 agree very well with those of the in- 7 situ aircraft data. The annual cycle amplitude is greater in the NH than in the SH due to 8 the larger seasonal cycle from surface biosphere contribution in the NH. 9 10 3.2 Interannual Variability of Tropical AIRS CO2 11 [11] We will explore the interannual variability of tropical CO2 and the influence of 12 ENSO on CO2 in this section. ENSO is the most important large-scale climate variability 13 in the tropics, The Southern Oscillation Index (SOI), shown in Fig 2a, is an index for 14 ENSO. It is defined as the monthly mean sea level pressure difference between Tahiti and 15 Darwin. Negative SOI index corresponds to an El Niño event and positive SOI index 16 corresponds to a La Niña event. We have derived the spatial distributions of AIRS CO2 in 17 Feb 2005 (El Niño event; Negative SOI Index) and in Feb 2008 (La Niña event; Positive 18 SOI index). Figs 2b and 2c are the spatial patterns of the detrended AIRS CO 2 for these 19 two events. Detrending was accomplished by removing a 2 ppm/year trend from the data. 20 21 [12] During an El Niño event, warm sea surface temperature (SST) anomalies appear in 22 the central and eastern Pacific and cold anomalies appear in the western Pacific, and the 23 convection will move eastward to the central Pacific [Gage and Reid, 1987]. As a result, 24 the convection in the central Pacific will bring surface high concentration CO2 into 25 middle troposphere. Fig. 2b shows that AIRS CO2 retrievals indicate that surface layer 26 CO2 has been lifted into the middle troposphere over the central Pacific region during the 27 El Niño event. Low concentration of middle tropospheric CO2 is seen in the western and 28 eastern Pacific Ocean which is a result of sinking motions over these regions transporting 29 air from high altitude to the middle troposphere. 30 6 1 [13] In contrast, during the La Niña event (Positive SOI index) the SST is warmer in the 2 western Pacific and its associated convection is stronger. Fig. 2c shows that AIRS CO2 3 retrievals indicate that CO2 has been lifted into the middle troposphere over the western 4 Pacific during the La Niña event. Lower middle tropospheric CO2 is seen in the eastern 5 Pacific Ocean during the La Niña event as a result of transport of low CO2 from high 6 altitude to the middle troposphere. Fig. 2d is a map of the difference between El Niño and 7 La Niña middle tropospheric CO2. The difference, (El Niño - La Niña), of AIRS middle 8 tropospheric CO2 is about 1 2 ppm over the central Pacific and -1 to -2 ppm over the 9 western Pacific. 10 11 3.3 Impact of Polar Vortex on AIRS CO2 12 [14] The annular modes are the most important climate variability in the high latitudes. 13 To investigate the influence of the polar vortex on AIRS middle tropospheric CO2, we 14 averaged the detrended AIRS polar CO2 north of 60°N from November to April, for the 15 years 2003 through 2007. The result is shown as solid line in Fig 3a. The strength of the 16 polar vortex is characterized by the Arctic Oscillation (AO) index. The AO index is 17 defined as the leading time series for the sea-level pressure anomalies within November 18 to April from 20N to 90N [Thompson and Wallace, 1998]. The detrended AO index is 19 shown as dashed line in Fig 3a. Pearson’s correlation coefficient for the detrended AIRS 20 polar CO2 and detrended AO index is -0.74, and the corresponding significance level is 21 7%. The significance statistic for correlations was generated by a Monte Carlo method 22 [Press et al., 1992; Jiang et al., 2004]. 23 24 [15] Fig 3a indicates that there are strong polar vortices in 2005 and 2007, i.e., for those 25 years the AO index is positive. When the polar vortex is strong, there is less horizontal 26 mixing of air between the mid-latitudes and high-latitudes. As a result, the transport of air 27 containing a high concentration of CO2 from the mid-latitudes into the polar region is 28 weakened. During those years the concentration of AIRS CO2 in the polar region should 29 therefore be relatively low. Fig 3b shows that the average of AIRS polar CO2 from 30 November to April in 2005 and 2007 was reduced. 31 7 1 [16] On the other hand, Fig 3a indicates that the polar vortices are weak in 2006 and 2 2008, for the AO index is negative in those years. For the case of a weak polar vortex, the 3 horizontal mixing is strong and mid-latitude air containing a high concentration of CO2 4 can be transported into the polar region. Fig. 3c shows that the AIRS polar CO2 was 5 enhanced from November to April in 2006 and 2008. The difference of AIRS polar CO2 6 between the strong polar vortex and weak polar vortex years is shown in Fig. 3d. There is 7 less CO2 at the polar region for the strong polar vortex years. The minimum value for the 8 difference can be 2-3 ppm. Student-t test is used to calculate the statistical significance of 9 the difference for CO2 concentration in the strong and weak vortex years. The result is 10 statistically significant when t is larger than a certain value t0. The number of degrees of 11 freedom for the CO2 difference between two groups is equal to 2. Given the number of 12 degrees of freedom, t0 can be found from the t distribution table. t0 with a 10% 13 significance level is 2.9. When the t-value in Fig. 3e is larger than 2.9, the results is 14 within 10% significance level. 15 16 8 1 4. Conclusions 2 3 [17] AIRS mid-tropospheric CO2 retrievals have been used to investigate the annual 4 cycle and interannual variability of CO2 in the middle troposphere for the first time. The 5 comparison between 6 demonstrates that the AIRS CO2 measurements in the middle troposphere capture correct 7 seasonal cycle of CO2 in the tropics. Globally distributed CO2 retrievals offer a unique 8 opportunity to explore the relationships between the middle tropospheric CO2 9 concentration and large-scale atmospheric processes. Our analysis suggests that the 10 influences of El Niño events and polar vortex on the CO2 concentration are apparent in 11 the AIRS data. During El Niño, middle tropospheric CO2 is enhanced in central Pacific 12 Ocean and diminished in the western Pacific Ocean. In the polar region, middle 13 tropospheric CO2 is diminished if the polar vortex is strong and enhanced if it is weak. the AIRS retrieved CO2 and the in-situ observations 14 15 16 Acknowledgement 17 This work was carried out at the Jet Propulsion Laboratory, California Institute of 18 Technology, under contract with the National Aeronautics and Space Administration. 19 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 References: Aumann, H. H., et al. (2003), AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems, IEEE Trans. Geosci. Remote Sens., 41, 253. Bacastow, R. (1976), Modulation of atmospheric carbon dioxide by the Southern Oscillation, Nature, 261, 116-118. Bacastow, R., J. Adams, C. Keeling, D. Moss, T. Whorf, and C. Wong (1980), Atmospheric carbon dioxide, the southern oscillation, and the weak 1975 El Niño, Science, 210, 66-68. Chahine, M., C. Barnet, E. T. Olsen, L. Chen, and E. Maddy (2005), On the determination of atmospheric minor gases by the method of vanishing partial derivatives with application to CO2, Geophys. Res. Lett., 32, doi:10.1029/2005GL024165. Chahine, M., et al. (2008), Satellite remote sounding of mid-tropospheric CO2, Geophys. Res. Lett., 35, doi:10.1029/2008GL035022. Dickinson, R. E., and R. J. Cicerone (1986), Future global warming from atmospheric trace gases, Nature, 319, 109-115. Feely, R. (1987), Distribution of chemical tracers in the eastern equatorial Pacific during and after the 1982-1983 El Niño/Southern Oscillation event, J. Geophys. Res., 92, 6545-6558. Feely, R., et al. (1997), Variability of CO2 distributions and sea-air fluxes in the central and eastern equatorial Pacific during the 1991-1994 El Niño, Deep Sea Res., Part II, 44, 1851-1867. Feely, R., et al. (2006), Decadal variability of the air-sea CO2 fluxes in the equatorial Pacific Ocean, J. Geophys. Res., 111, doi:10.1029/2005JC003129. Francey, R., et al. (1995), Changes in oceanic and terrestrial carbon uptake since 1982, Nature, 373, 326-330. Gage, K. S., and G. C. Reid (1987), Longitudinal variations in tropical tropopause properties in relation to tropical convection and El Niño-Southern oscillation events, J. Geophys. Res., 92, 14197-14203. Inoue, H. Y., and Y. Sugimura (1992), Variations and distributions of CO2 in and over the equatorial Pacific during the period from the 1986/88 El Niño event to the 1988/89 La Nina event, Tellus Series B, 44, 1-22. Keeling, C. D., T. P. Whorf, M. Wahlen, and J. Vanderplicht (1995), Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980, Nature, 375, 666-670. Jiang, X., C. D. Camp, R. Shia, D. Noone, C. Walker, and Y. Yung (2004), Quasibiennial oscillation and quasi-biennial oscillation-annual beat in the tropical total column ozone: A two-dimensional model simulation, J. Geophys. Res., 109, doi:10.1029/2003JD004377. Jiang, X., S. Pawson, C. D. Camp, E. Nielsen, R. Shia, T. Liao, V. Limpasuvan, and Y. L. Yung (2008a), Interannual variability and trends in extratropical ozone. Part I: Northern hemisphere, Journal of the Atmospheric Sciences, 65, 3013-3029. Jiang, X., S. Pawson, C. D. Camp, E. Nielsen, R. Shia, T. Liao, V. Limpasuvan, and Y. L. Yung (2008b), Interannual variability and trends in extratropical ozone. Part II: Southern hemisphere, Journal of the Atmospheric Sciences, 65, 3030-3041. 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Jiang, X., M. T. Chahine, E. T. Olsen, Q. Li, L. Chen, M. Liang, and Y. L. Yung (2009), A study of polar stratosphere-troposphere exchange using AIRS CO2, Submitted to Journal of Atmospheric Sciences, 2009. Jones, C. D., M. Collins, P. M. Cox, and S. A. Spall (2001), The carbon cycle response to ENSO: A coupled climate-carbon cycle model study, Journal of Climate, 14, 4113-4129. Matsueda, H., H. Y. Inoue, and M. Ishii (2002), Aircraft observation of carbon dioxide at 8-13 km altitude over the western Pacific from 1993 to 1999, Tellus, 54, 1-21. The data are available at http://gaw.kishou.go.jp/wdcgg.html. Meloen, J., P. Siegmund, P. van Velthoven, H. Kelder, M. Sprenger, H. Wernli, A. Kentarchos, G. Roelofs, J. Feichter, C. Land, C. Forster, P. James, A. Stohl, W. Collins, and P. Cristofanelli (2003), Stratosphere-troposphere exchange: A model and method intercomparison, J. Geophys. Res., 108, doi:10.1029/2002JD002274. Olsen, E. T., M. T. Chahine, L. L. Chen, X. Jiang, T. S. Pagano, and Y. Yung (2009), Validation of six years of mid-tropospheric CO2 data from the Atmospheric Infrared Sounder, Submitted to J. Geophys. Res.. Press, W., S. Teukolsky, W. Vetterling, and B. Flannery (1992), Numerical Recipes in Fortran 77: The Art of Scientific Computing, 2nd ed., 933 pp., Cambridge Univ. Press, New York. Thompson, D. W. J., and J. M. Wallace (1998), The Arctic Oscillation signature in the wintertime geopotential height and temperature fields, Geophys. Res. Lett., 25, 1297-1300. Wong, C. S., et al. (1993), Changes in equatorial CO2 flux and new production estimated from CO2 and nutrient levels in Pacific surface waters during the 1986/87 El Niño, 45, 64-79. 28 29 11 1 Figure 1: CO2 annual cycle at 25 N and 25 S. Red line is Matsueda CO2. Black line is 2 AIRS CO2. 3 4 12 1 Figure 2: (a) Southern Oscillation Index, (b) AIRS CO2 in Feb 2005, (c) AIRS CO2 in 2 Feb 2008, and (d) AIRS CO2 difference between Feb 2005 and Feb 2008. Units are ppm. 3 4 5 13 1 Figure 3: (a) Detrended AIRS CO2 (solid line) averaged from 60N to 90N and 2 detrended Arctic Oscillation index (dash line). (b) AIRS CO2 averaged in 2005 and 2007, 3 which are two strong polar vortex years. (c) AIRS CO2 averaged in 2006 and 2008, 4 which are two weak polar vortex years. (d) Difference of AIRS CO2 for strong and weak 5 polar vortex years. (e) t-value for the CO2 difference. 6 7 14