Global evaluation of SCIAMACHY and MOPITT carbon monoxide column differences for

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1 Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115,, doi: /2009jd012698, 2010 Global evaluation of SCIAMACHY and MOPITT carbon monoxide column differences for A. T. J. de Laat, 1,2 A. M. S. Gloudemans, 1 I. Aben, 1 and H. Schrijver 1 Received 19 June 2009; revised 6 November 2009; accepted 16 November 2009; published 27 March [1] This paper presents a detailed global comparison of Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) and Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) column measurements for the years 2004 and Qualitatively, spatiotemporal variations of SCIAMACHY and MOPITT are similar. Quantitative comparisons have been performed taking the effects of instrument noise errors, vertical sensitivities via the averaging kernel and a priori, different spatiotemporal sampling and clouds into account using simulated CO profiles from the TM4 model. SCIAMACHY and MOPITT CO columns are similar over tropical, subtropical, and Northern Hemisphere oceans as well as over boreal regions where SCIAMACHY and MOPITT agree to within 10% or molecules/cm 2. The short wave infrared SCIAMACHY observations also provide information about lower tropospheric CO in Arctic and subarctic regions north of 60 N, where the MOPITT sensitivity is strongly reduced. South of 45 S, SCIAMACHY CO columns are molecules/cm 2 smaller than MOPITT CO columns. Approximately molecules/cm 2 ( 10%) of this difference is attributed to a bias in the SCIAMACHY CO columns, which is currently under investigation. The remaining difference is possibly related to MOPITT biases in this region. In the transition from oceans to dry desert regions, MOPITT CO total columns show a rapid increase of approximately molecules/cm 2 ( 15%). While MOPITT and SCIAMACHY agree over oceans, MOPITT is approximately molecules/cm 2 ( 25%) larger than SCIAMACHY results over dry land regions. The origin of this bias needs further investigation. Citation: de Laat, A. T. J., A. M. S. Gloudemans, I. Aben, and H. Schrijver (2010), Global evaluation of SCIAMACHY and MOPITT carbon monoxide column differences for , J. Geophys. Res., 115,, doi: /2009jd Introduction [2] Carbon monoxide (CO) is a key trace gas for tropospheric photochemical processes. CO is removed from the troposphere mainly by reaction with the OH radical, and this reaction in turn controls the tropospheric OH amount [Crutzen and Zimmermann, 1991]. The OH radical is the major cleansing agent of the troposphere [Lelieveld et al., 2004]. Large scale changes in CO thus affect the selfcleansing capacity of the troposphere. Furthermore, CO is also a precursor of tropospheric O 3, a greenhouse gas, in the presence of sufficient nitrogen oxides and sunlight. [3] Space based continuous monitoring of tropospheric CO started with the MOPITT (Measurement of Pollution in the Troposphere) remote sensing instrument. MOPITT has provided global tropospheric CO at several tropospheric altitude levels from March 2000 onward [Deeter et al., 2003; Rodgers and Connor, 2003; Deeter et al., 2004a], 1 Netherlands Institute for Space Research, Utrecht, Netherlands. 2 Royal Netherlands Meteorology Institute, De Bilt, Netherlands. Copyright 2010 by the American Geophysical Union /10/2009JD significantly adding to the knowledge of understanding tropospheric CO variability and sources. MOPITT measurements of CO have been extensively evaluated and validated [Barret et al., 2003; Heald et al., 2003; Deeter et al., 2004b; Emmons et al., 2004; Crawford et al., 2004; Deeter et al., 2007a, 2007b; Emmons et al., 2007, 2009]. [4] MOPITT obtains CO measurements from the thermal infrared (TIR) part of the spectrum (around 4.7 mm). The advantage of measuring in this part of the spectrum is that the absorption lines are much stronger than at short wave infrared (SWIR) wavelengths around 2.3 mm. A disadvantage is that the sensitivity to the lower troposphere is limited, although some information can be obtained about the lower troposphere over (dry) land areas where the temperature contrast between surface and free troposphere can be large, especially during daytime [Deeter et al., 2007b; Warner et al., 2007; Clerbaux et al., 2008; Kar et al., 2008]. [5] The Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument (launched March 2002) onboard the ENVISAT satellite [Bovensmann et al., 1999] has provided the first measurements of CO based on reflected sunlight, measuring in the short wave infrared (SWIR) around 2.3 mm. The SCIAMACHY CO 1of15

2 measurements are close to uniformly sensitive down to the Earth s surface [Buchwitz et al., 2004; Sussmann and Buchwitz, 2005; Gloudemans et al., 2008]. SCIAMACHY CO observations have been validated against ground based measurements [Sussmann and Buchwitz, 2005; Warneke et al., 2005; Dils et al., 2006]. [6] Up until now only limited comparisons of SCIA- MACHY and MOPITT observations have been made [Buchwitz et al., 2004, 2007; Turquety et al., 2008]. SCIAMACHY provides column observations, whereas MOPITT provides CO profiles albeit with a limited amount of vertical information; typical degree of freedom (DOF) values range between 1 and 1.5 [Deeter et al., 2004b]. The measured MOPITT profile supplemented with a priori information can be summed to provide a total column. However, the limited sensitivity of MOPITT TIR observations to the lower troposphere, the low sea surface reflectivity in the SWIR, and the different footprints, typically km for MOPITT compared to km for SCIAMACHY, complicate the comparison of SCIAMACHY SWIR and MOPITT TIR CO columns. [7] Some of these issues can be resolved by using chemistry transport model simulations of CO as an intermediate between SCIAMACHY and MOPITT. The model results are used to test whether differences between both observational datasets are related to the factors mentioned above or require a different explanation. Once the recently published MOPITT SWIR CO columns [Deeter et al., 2009] become available these observations may also help to solve some of these issues. [8] This paper presents an in depth evaluation of SCIAMACHY SWIR CO observations against MOPITT TIR CO observations and is organized as follows: section 2 describes the SCIAMACHY, MOPITT and model CO data and the use of model results as intermediate. Section 3 discusses the effect of the SCIAMACHY and MOPITT a priori and averaging kernels, different spatiotemporal sampling, clouded versus cloud free observations, and instrument noise error weighting required for averaging SCIAMACHY CO column observations. In section 4 observed and modeled global distributions are analyzed and differences between SCIAMACHY and MOPITT observations are discussed. Section 5 discusses an important bias found in the comparison. Section 6 ends the paper with conclusions. 2. CO Observations and Model Data 2.1. SCIAMACHY CO Measurements [9] The CO columns are retrieved from spectra measured by SCIAMACHY between nm using the Iterative Maximum Likelihood Method (IMLM), which simultaneously retrieves columns of CO, methane (CH 4 ) and water vapor (H 2 O). A detailed description of the IMLM algorithm is given by Gloudemans et al. [2008]. [10] In this paper, IMLM version 7.4 results are used which is the same data set as presented by Gloudemans et al. [2009]. The most important improvements in IMLM v7.4 compared to the previous version 6.3 used by de Laat et al. [2007] are an improved treatment of instrument noise errors in the retrieval as well as an update of the spectroscopic parameters for water vapor and CH 4. The increasing number of radiation damaged detector pixels in SCIAMACHY s 2.3 mm channel is accounted for by including a time dependent pixel mask in the retrievals [Gloudemans et al., 2005]. The effects of the ice layer have been taken into account by including an empirical correction based on calibrating the retrieved CH 4 over the Sahara to model values [Gloudemans et al., 2005]. [11] de Laat et al. [2006, 2007] used only land observations with cloud fraction <20% and instrument noise error < molecules/cm 2. Gloudemans et al. [2009] show that SCIAMACHY CO observations over clouded ocean scenes can also be used due to the much higher SWIR cloud reflectance of compared to the SWIR ocean surface reflectance of <0.01. The effective cloud top height is obtained by comparing the simultaneously retrieved partial CH 4 column to the TM4 modeled CH 4 total column. For the remainder of the paper we therefore also use observations over clouded ocean scenes with a cloud top height between the surface and 800 hpa, i.e., boundary layer clouds only, and instrument noise errors smaller than molecules/cm 2 in addition to the SCIAMACHY CO total column measurements over land. [12] For comparison with model results and MOPITT observations, the SCIAMACHY measurements are regridded on either a 1 1 or a 3 2 grid and/or averaged over a certain time period. Since the instrument noise errors of individual SCIAMACHY CO measurements can be large, i.e., %, multiple SCIAMACHY observations falling within the same grid box are averaged over a certain time period in order to obtain a better precision. The weighted averaging procedure as presented by de Laat et al. [2006, 2007] is used, taking the square of the instrument noise error of each measurement as 1/weight. [13] Spatial variations in surface reflectance determine the instrument noise errors and spatial variations in cloud cover determine the number of available observations over the oceans. Hence, the number of observations required to reduce instrument noise errors differs significantly spatially [de Laat et al., 2007; Gloudemans et al., 2008, 2009]. Figure 1 shows the spatial distribution of average instrument noise errors, which show considerable spatial variations both over land and oceans. The former is related to variations in surface reflectance, the latter is related to low altitude cloud statistics: the more persistent the clouds, the smaller the instrument noise error. Over bright dry desert regions individual SCIAMACHY observations can be of sufficient precision. However, for vegetated land regions the instrument noise errors of single SCIAMACHY measurements can be as large as molecules/cm 2. With a SCIAMACHY overpass once every 6 days in combination with frequent cloud cover averaging more than 1 year of SCIAMACHY observations within a 1 1 grid can be required to reduce the instrument noise error to molecules/cm 2, the estimated precision of the SCIAMACHY CO measurements [de Laat et al., 2007]. In order to make sure that this precision is reached globally, we focus on biannual averages in this paper MOPITT CO Measurements [14] Daytime MOPITT level 2 CO profile measurements (version 3; available at ftp://l4ftl01.larc.nasa.gov/) are used in this paper for comparison with SCIAMACHY CO column measurements. This allows accounting for variations in 2of15

3 Figure 1. Average 1 1 gridded SCIAMACHY instrument noise errors (10 18 molecules/cm 2 )for for the SCIAMACHY CO column observations. the vertical sensitivity of MOPITT CO profiles. MOPITT CO total columns and corresponding total column averaging kernels are calculated from the CO profiles using the procedure as described by Emmons et al. [2004]. Only cloudfree MOPITT measurements are available. The error of the MOPITT total column measurements is generally less than 20% and consists of a smoothing error, model parameter error, forward model error and an error due to instrument noise. The MOPITT instrument noise error itself is only a few percent [Pan et al., 1998]. As for SCIAMACHY, the MOPITT measurements have been averaged on either a 1 1 or a 3 2 grid over the same time period as for SCIAMACHY. Emmons et al. [2009] report a global MOPITT CO total column bias of only 3% for the years 2004 and 2005 based on an extensive comparison with aircraft measurements over a wide range of CO total column values. Significant regional differences in the biases were reported, however. For those situations uncertainties are typically several times larger than the bias TM4 Model Results [15] The TM4 model used as intermediate in the SCIAMACHY MOPITT comparisons is exactly the same as used by de Laat et al. [2007]. The horizontal resolution of TM4 is 3 2 longitude latitude, and vertically 25 levels are used. Meteorological ECMWF analysis input fields used in TM4 are preprocessed as described by Bregman et al. [2003]. Actual biomass burning emission estimates for 2004 and 2005 are taken from the Global Fire Emission Database (GFED), version 2 [van der Werf et al., 2006]. Anthropogenic emissions are based on the EDGAR v3 emission database [van Aardenne et al., 2001] and are modified to be representative for the year 2000 with a total of 331 Tg CO/yr for fossil fuels and 194 Tg CO/yr for biogenic emissions [Dentener et al., 2003]. Oceanic and natural emissions are 40 and 75 Tg CO/yr, respectively, as described by Houweling et al. [1998]. [16] de Laat et al. [2007] presented a validation of this model simulation for 1 year of observations using in situ surface measurements. The results clearly showed that in the Southern Hemisphere (SH) average CO surface concentrations agree, whereas in the Northern Hemisphere (NH) the model underestimates surface CO by 10 20% for nearly all stations. This finding is consistent with Shindell et al. [2006] who drew similar conclusions based on a multimodel analysis of CO using both satellite and in situ measurements. They found no clear indication for a TM4 bias in OH and attributed the NH bias to underestimated East Asian emissions. de Laat et al. [2007] also showed that the seasonal variability of CO on both hemispheres is well captured Using TM4 for Comparing Observations [17] The direct comparison of SCIAMACHY and MOPITT CO column observations is complicated by many factors, like differences in vertical sensitivity, differences in spatial and temporal sampling, clouded versus cloud free measurements, and measurement precision. Luo et al. [2007] provide a method for direct comparison of different satellite data sets. However, in the case of SCIAMACHY and MOPITT the number of collocations is limited and would allow comparisons over only few geographical regions. Since there is limited or no other observational data to account for the above mentioned factors on a global scale, we use TM4 chemistry transport model results for the years 2004 and 2005 to quantify their effects. The effect of potential model biases in this procedure will be discussed in section 4.2. [18] Comparing individual SCIAMACHY and MOPITT observations does not provide much information on the agreement between both instruments because of the large SCIAMACHY instrument noise errors. However, the most relevant information about CO and possible biases of both instruments is present in the average spatial distribution as well as in seasonal variations. Here we focus on the biannual global distribution and the possible systematic biases on this scale. Seasonally dependent biases are only evaluated qualitatively. [19] We calculate averages based on all available SCIAMACHY and MOPITT observations within a grid box 3of15

4 Figure 2. The shape of the different total column averaging kernels color coded as function of their respective solar zenith angles as a function of the pressure altitude as presented by Gloudemans et al. [2008]. rather than only truly collocated observations. The latter would restrict the comparison mainly to land regions as over the oceans only SCIAMACHY observations over clouded scenes are used whereas MOPITT only provides cloud free data, strongly limiting the number of true collocations over oceans. [20] We use the model as an intermediate in these comparisons by sampling the model in the same way, both spatially and temporally, as the observations and comparing the satellite data with the model. In this way the model can be used to quantify the effects of averaging kernels, a priori, the presence/absence of clouds, and the different spatial and temporal samplings on the comparisons as described in section 3. This can then be used to correct for these effects in the direct SCIAMACHY MOPITT comparisons (section 4). 3. Factors Complicating SCIAMACHY MOPITT Comparisons 3.1. SCIAMACHY Averaging Kernels and a Priori [21] The SCIAMACHY CO total column averaging kernels are shown in Figure 2. They have been calculated following the method described by Gloudemans et al. [2008], which is analogous to the total column averaging kernel method used for satellite observations of nitrous dioxide (NO 2 ) total columns as described by Eskes and Boersma [2003] and Boersma et al. [2004]. A detailed description of the shape of the total column averaging kernelsisgivenbygloudemans et al. [2008]. Figure 2 shows that the SCIAMACHY CO total column averaging kernels are close to unity for solar zenith angles between 25 and 80 degrees up to 200 hpa altitude. [22] To test the effect of the SCIAMACHY total column averaging kernels on CO total columns they were applied to 1 year (2004) of TM4 simulated daily CO profiles. These profiles provide a range of CO profile shapes from very polluted cases with large amounts of CO in the boundary layer to very clean profiles with low CO concentrations throughout the free troposphere. SCIAMACHY observations have solar zenith angles ranging from in the tropics to at high latitudes. Applying the SCIAMACHY CO total column averaging kernel results in a slightly larger CO total column of 4 ± 4% or 0.04 ± molecules/cm 2 for solar zenith angles of 25 to 0.4 ± 0.1% or ± molecules/cm 2 for solar zenith angles of 80 compared to those without applying the total column averaging kernel. The spread in these values is related to deviations of the shape of the modeled CO profiles from the IMLM a priori CO profile shape which represents the US standard atmospheric profile [Anderson et al., 1986]: the largest differences occur for profiles representing clean air masses in the Southern Hemisphere or equatorial Pacific or highly polluted air masses close to major CO sources. The differences are smaller for larger solar zenith angles since the total column averaging kernels below 500 hpa are closer to unity, in particular for solar zenith angles close to 80 (Figure 2). The effect of the increase in the total column averaging kernel above 200 hpa is small, about 1 2%, as less than 10% of the CO total column is located above 200 hpa. [23] While the effect of the total column averaging kernel is not entirely insignificant, it is considerably smaller than the instrument noise error of a single measurement as well as typical spatiotemporal CO column variations (see Figures 6 and 7 in section 4.1). Hence, for the remainder of the paper it is assumed that the SCIAMACHY CO column measurements are true columns MOPITT Averaging Kernels and a Priori [24] MOPITT derives CO profiles from longwave thermal radiation emitted by the Earth s surface and atmosphere and its sensitivity to the lower troposphere directly depends on the temperature differences between the ground and the troposphere. MOPITT observations are predominantly sensitive to the middle troposphere [Deeter et al., 2003]. The sensitivity to the lower troposphere below 700 hpa is strongly reduced in case of cold surface temperatures, for example during night or local winter [Deeter et al., 2007b; Kar et al., 2008]. Figure 3 shows an example of the MOPITT normalized total column averaging kernels for a winter and a summer day in an area over North America between 40 N 60 N and 80 W 100 W. These kernels have been calculated using the method described by Deeter et al. [2007b], and clearly show the reduced sensitivity of MOPITT to the lower troposphere for lower surface temperatures. [25] Figure 4 shows the differences in TM4 modeled CO total columns with and without applying the MOPITT a priori and averaging kernel. For applying the averaging kernel, the TM4 CO mixing ratio profiles are used and modeled concentrations are interpolated onto the seven MOPITT vertical pressure levels. At high southern latitudes modeled columns are considerably larger when the MOPITT averaging kernel and a priori are applied. Due to the reduced sensitivity of MOPITT to the lower troposphere at high latitudes the MOPITT profiles in the lower troposphere mainly consist of a priori information. The MOPITT a priori CO profile is based on predominantly Northern Hemisphere CO profile measurements and represents a slightly polluted profile. At high southern latitudes the true CO profiles are much cleaner, especially in the lower half of the troposphere, than the MOPITT a priori profile. Hence, 4of15

5 Figure 3. Normalized MOPITT CO total column averaging kernels, following the approach presented by Deeter et al. [2007b], for MOPITT CO profiles on 29 January (blue) and 28 July (black) 2004 within a box over North America as a function of the pressure altitude. Figure 4. Spatial distribution of average seasonal relative differences between TM4 CO total columns with and without applying the MOPITT averaging kernels and a priori CO profile for 2004 and 2005 on a 1 1 resolution. Here the absolute differences in the CO total columns are shown (10 18 molecules/cm 2 ). DJF, December January February; MAM, March April May; JJA, June July August; SON, September October November. 5of15

6 Figure 5a. TM4 modeled CO column below low oceanic clouds (800 hpa) averaged over the period in molecules/cm 2. Only TM4 results collocated with SCIAMACHY measurements gridded on 1 1 are used. applying the MOPITT averaging kernel and a priori results in a difference in observed and true total columns at high southern latitudes [Edwards et al., 2006]. [26] At high northern latitudes MOPITT is also less sensitive to the lower troposphere, matching seasonal variations in continental snow cover. However, during local winter and early spring (December to May), lower tropospheric CO concentrations are much larger than those at high southern latitudes due to reduced photochemical destruction [e.g., Edwards et al., 2004] and larger than the MOPITT a priori, resulting in a negative difference after applying the MOPITT averaging kernel and a priori. During local summer and autumn, modeled tropospheric CO concentrations are smaller than the MOPITT a priori due to rapid photochemical CO destruction. Hence, a positive difference is present, but the area of positive difference is confined to the very high Arctic latitudes where surface temperatures remain relatively low, unlike the interior of the continents where high surface temperatures prevail during summer and the MOPITT total columns are more sensitive to lower tropospheric CO. [27] The major emission regions of the world, i.e., industrialized regions in North America, Europe and Asia as well as biomass burning regions of Africa, South America and Indonesia can also be identified in Figure 4. Over emission regions CO concentrations will be large in the lower troposphere, for which MOPITT is less sensitive and the a priori contribution is larger. Because the lower tropospheric a priori CO concentrations over emission areas are considerably smaller than the true concentrations, applying the MOPITT averaging kernel and a priori results in smaller model CO total columns. Similarly, due to the larger sensitivity of MOPITT to the middle troposphere regions with enhanced middle tropospheric CO are also visible, for example, the equatorial Atlantic and Northern Hemisphere oceanic outflow regions of continental pollution. [28] Given the uniform SCIAMACHY vertical sensitivity, the differences between SCIAMACHY and MOPITT can provide information about boundary layer or lower tropospheric CO, as shown by Turquety et al. [2008] Ocean Measurements and Averaging Effects [29] The part of the CO column below the cloud for SCIAMACHY ocean measurements is estimated using TM4 model results and the effective cloud top height as obtained from the simultaneously retrieved CH 4 column (see section 2.1). Figure 5a shows the TM4 modeled partial CO columns below SCIAMACHY oceanic low clouds averaged over the years The largest values are found at high Northern Hemisphere latitudes downwind of continental emission areas. At high southern latitudes values are smaller because less CO is present in the atmosphere due to a lack of large CO sources. The below cloud CO partial columns range on average from molecules/cm 2 at southern latitudes to molecules/ cm 2 close to Northern Hemisphere CO emission sources. For daily column observations these values can be larger, occasionally up to molecules/cm 2 for very polluted outflow episodes. [30] The effect of the different spatiotemporal sampling and averaging procedures for SCIAMACHY and MOPITT is investigated by comparing model columns based on sampling the model results in the same way as either the MOPITT or the SCIAMACHY observations. Figure 5b shows the difference between the average model columns sampled as SCIAMACHY observations and those based on the MOPITT sampling for the period For calculating the average model columns sampled as 6of15

7 Figure 5b. Average differences (10 18 molecules/cm 2 ) in TM4 modeled CO columns for the period according to MOPITT or SCIAMACHY spatiotemporal sampling. For calculating the average SCIAMACHY sampled model columns the instrument error weighting has been applied. For the MOPITT sampling the model CO profiles are regridded onto the seven MOPITT vertical layers while using the MOPITT surface pressure rather than the TM4 surface pressure to obtain the total column. SCIAMACHY observations the instrument error weighting has been applied. No MOPITT or SCIAMACHY averaging kernels and a priori were applied to the model results. The difference pattern is rather scattered although the magnitude of the differences is too large to be neglected and some coherent patterns can be seen. For example, over most Northern Hemisphere midlatitude oceans the average model CO total columns sampled as SCIAMACHY are larger than those sampled according to the MOPITT observations. Over these oceans more SCIAMACHY observations and fewer MOPITT observations are available during spring compared to other seasons due to cloud cover. Springtime observations thus have more weight in the SCIAMACHY mean and less in the MOPITT mean. Combined with the notion that the largest Northern Hemisphere CO total columns occur during early spring (see Figure 6), this explains why the model total columns sampled as SCIAMACHY are larger than those sampled as MOPITT over Northern Hemisphere midlatitude oceans (Figure 5b). 4. SCIAMACHY and MOPITT CO Column Comparison 4.1. SCIAMACHY, MOPITT, and TM4 Means and Seasonality [31] SCIAMACHY and MOPITT CO columns can be compared directly, keeping in mind the effects discussed in section 3. Figure 6 shows the zonal 5 day mean CO columns for SCIAMACHY, MOPITT and TM4 to investigate the seasonal variations in CO. The seasonal cycle of tropospheric CO is an important fingerprint of tropospheric CO variability and related to both photochemical processes and seasonal variations in emissions and transport patterns. The 5 day period is chosen to ensure that for the majority of SCIAMACHY zonal mean CO columns the instrument noise errors are smaller than molecules/cm 2, which is the estimated SCIAMACHY precision [de Laat et al., 2007]. [32] The seasonal cycles are qualitatively similar. At middle and high latitudes, maximum CO columns occur during local winter/early spring, i.e., February April in the Northern Hemisphere, September October in the Southern Hemisphere. CO decreases rapidly in the Northern Hemisphere during the period May July due to photochemical destruction. The Southern Hemisphere shows a distinct latitudinal gradient, which is absent in the Northern Hemisphere, related to the close vicinity of major CO sources in the Northern Hemisphere and the absence of large emission sources in the Southern Hemisphere. Tropical CO increases due to seasonal biomass burning emissions can be distinguished between the equator and 30 S from July to November, as well as just north of the equator throughout the year due to various other biomass burning emission regions. [33] Although seasonal cycles are important, in this paper we aim to identify global systematic biases between SCIAMACHY and MOPITT. Therefore we focus on the global biannual distribution for the years 2004 and Figure 7 shows the geographical patterns of average CO columns for for SCIAMACHY, MOPITT and corresponding TM4 modeled columns. The global patterns of SCIAMACHY, MOPITT and TM4 CO total columns are qualitatively in good agreement. All patterns show equatorial biomass burning emission regions in South America and Africa and industrial emission regions in North America, 7of15

8 Figure 6. Five day zonal mean CO columns for (top left) SCIAMACHY, (top right) MOPITT, and (bottom) corresponding TM4 modeled CO total columns for For the TM4 columns collocated with SCIAMACHY observations over oceans the SCIAMACHY cloud top height is used (see bottom left plot). The TM4 columns collocated with MOPITT take the MOPITT averaging kernel and a priori into account. Zonal means are calculated for 1 zonal latitude bands in molecules/cm 2. Five day MOPITT CO total columns are averages for the same 5 day time period and zonal band, but without collocating the data with SCIAMACHY. Periods with either missing SCIAMACHY or MOPITT data are in grey. Europe and southeastern Asia. Furthermore, oceanic outflow regions with enhanced CO columns downwind of major emission regions can also be identified, like the equatorial Atlantic Ocean, the northern Indian Ocean, the northern Pacific and the North Atlantic. Finally, all patterns show small CO columns over the clean tropical Pacific and Southern oceans. Figure 7 (bottom) show that SCIA- MACHY CO is smaller than TM4 in the Southern Hemisphere, as well as over some tropical emission regions. In the tropics and Northern Hemisphere SCIAMACHY is consistently larger than TM4, with in particular more CO over East Asia. MOPITT is larger than both SCIAMACHY and TM4 for most parts of the world, with the largest differences throughout the Northern Hemisphere. Figure 6 (bottom) and the Figure 7 (middle) indicate that part of this difference is related to the MOPITT averaging kernel and a priori, in particular at higher latitudes. Parts of the Northern Hemisphere differences are related to the model bias found by Shindell et al. [2006] which was discussed in section 2.3. This model bias will be taken into account when using TM4 as an intermediate in the comparison of SCIAMACHY and MOPITT CO columns. [34] The global differences between SCIAMACHY and MOPITT as illustrated in Figure 7 will be evaluated in more detail in sections 4.2 and Reconciling the Differences Between SCIAMACHY and MOPITT [35] Figure 8a shows the global variations of the average difference between MOPITT and SCIAMACHY CO columns without taking the effects discussed in section 3 into account and is equivalent to the difference between the plots in Figure 7 (top). For most locations the MOPITT CO total columns are larger than SCIAMACHY, although there are regions where SCIAMACHY and MOPITT agree: vegetated land areas and parts of (sub)tropical oceans. [36] In Figure 8b the effects discussed in section 3 are accounted for: the average modeled CO column below the oceanic low clouds (Figure 5a), the differences of modeled CO total columns with and without applying MOPITT averaging kernel (Figure 4), differences due to different spatiotemporal sampling of MOPITT and SCIAMACHY and averaging effects (see section 3.3 and Figure 5b). After accounting for these effects, the MOPITT SCIAMACHY differences are smaller. Using TM4 model results to account for the effects discussed in section 3 also introduces some new features, for example a larger difference over the eastern half of the United States and the Beijing area in northeastern China. [37] It should be noted that transport models like TM4 underestimate Northern Hemisphere CO, likely due to 8of15

9 Figure 7. (top left) SCIAMACHY CO columns (10 18 molecules/cm 2 ) averaged over the period on a 3 2 resolution using the SCIAMACHY weighting procedure. (middle left) Average TM4 modeled CO total columns, collocated with SCIAMACHY CO observations, using the same cloud top height over oceans and the same weighting procedure as the SCIAMACHY measurements in the top left plot. (bottom left) Differences between top left (SCIAMACHY) and middle left (TM4) plots. (top right) Average MOPITT CO total columns, not collocated with SCIAMACHY observations. (middle right) Average TM4 modeled CO total columns, collocated with the MOPITT observations, applying the MOPITT averaging kernel and a priori. (bottom right) Differences between top right (MOPITT) and middle right (TM4) plots. The grey colors indicate areas without observations. underestimated CO emissions in East Asia. Shindell et al. [2006] reported a hemispheric wide difference of about 50 ppbv between the surface and 500 hpa based on both MOPITT and in situ observations. For a layer with a thickness of 200 hpa this equals a column difference of molecules/cm 2. Turquety et al. [2008] reported a difference of about 8% between MOPITT and their model calculations over East Asia. The average difference between SCIAMACHY and TM4 CO total columns for the region 30 N 70 N was 9%. As noted earlier, validation of TM4 with ground based observations also indicated a bias of 10 20% north of 15 N [Shindell et al., 2006; de Laat et al., 2007]. Thus, for SCIAMACHY observations over Northern Hemisphere oceans the estimated part of the column below the clouds based on TM4 model results (Figure 5a) is likely too low. Assuming a 10% TM4 bias and an average Northern Hemisphere CO total column of molecules/cm 2, molecules/cm 2 was added to SCIAMACHY ocean measurements north of 15 N. [38] Figures 8c and 8d show MOPITT SCIAMACHY absolute and relative differences, respectively, including this correction for the TM4 bias over northern hemispheric oceans. Only differences at the 95% confidence level, i.e., larger than 2 times the SCIAMACHY instrument noise error, and in absolute terms larger than molecules/ cm 2 are shown. Many differences over tropical, subtropical and Northern Hemisphere oceans as well Northern Hemisphere boreal regions are small or not statistically significant any more. Note that most differences over high latitude boreal regions like Siberia and Canada in Figure 8c have 9of15

10 Figure 8a. Global distribution of differences between MOPITT and SCIAMACHY CO columns (1018 molecules/cm2) for on a 1 1 grid. The effects of SCIAMACHY below oceanic cloud CO columns, MOPITT averaging kernels, and a priori and spatiotemporal sampling are not accounted for. disappeared because they are not statistically significant (compare also with Figure 1). Furthermore, differences over Northern Hemisphere oceans are now less than 10%, suggesting that using TM4 model results to account for the missing partial column below ocean cloud observations (Figure 8b) has its limitations as the model has a bias in these regions which needs to be corrected for. Furthermore, it is likely that the below ocean cloud partial CO columns are spatially inhomogeneous, so the model bias will likely be larger close to emission sources and smaller remote of emission sources, rather than being constant throughout the Northern Hemisphere as assumed here. [39] Further investigating Figures 8c and 8d, some differences remain. First of all, south of 45 S, MOPITT CO total columns are to molecules/cm2 larger than SCIAMACHY. Comparing SCIAMACHY columns with TM4 total columns and in situ FTIR observa- tions at Lauder, New Zealand (not shown), indicates that SCIAMACHY columns are about molecules/cm2 smaller than the modeled columns but that modeled columns are about molecules/cm2 smaller than independent FTIR observations, which in turn are about molecules/cm2 smaller than MOPITT CO total columns. Figure 4 shows that the latter difference cannot be explained by the MOPITT vertical sensitivity. This suggests that both instruments as well as the model simulation may be biased in this region. [40] Limited validation results for MOPITT indicate that MOPITT total columns in the Southern Hemisphere may be positively biased by 15 20% for 2004 and 2005 [Emmons et al., 2009], which corresponds to a column difference of molecules/cm2 and is sufficient to explain the differences between MOPITT and TM4. These results are consistent with a comparison of MOPITT with ground Figure 8b. As in Figure 8a but subtracting the model estimates of the SCIAMACHY below cloud partial CO columns (Figure 5a), the difference in modeled columns with and without applying the MOPITT averaging kernels (Figure 4), and the difference due to the SCIAMACHY and MOPITT spatiotemporal sampling (Figure 5b) from the MOPITT SCIAMACHY differences. 10 of 15

11 Figure 8c. As in Figure 8b but with molecules/cm 2 added to modeled below low cloud column for Northern Hemisphere extratropical latitudes (north of 15 N). Differences smaller than molecules/cm 2 or smaller than 2 times the SCIAMACHY instrument noise error (95% confidence interval) are shown as white. based total column measurements at Lauder, New Zealand, as reported by Edwards et al. [2006]. [41] Preliminary results indicate that the low SCIAMACHY CO columns south of 45S are time dependent with a significantly smaller bias for 2006 and 2007 compared to 2004 and 2005, and CO columns retrieved in a different spectral window show a better qualitative agreement with TM4 in the Southern Hemisphere. However, further investigation is required before a final conclusion can be drawn as to the origin of this bias. [42] Over subarctic and Arctic oceans north of 60 N no significant differences remain, consistent with Shindell et al. [2006] who found the largest model MOPITT differences between 20 N 70 N. The agreement between SCIA- MACHY and MOPITT at subarctic and Arctic latitudes over oceans indicates that SCIAMACHY can measure highlatitude CO variability, if the reflectance is sufficiently high, which is not the case for land areas like Siberia, northern Canada and Alaska. This is a promising result, as the subarctic and Arctic oceans are affected by long range transport of midlatitude air pollution which primarily takes place above the boundary layer [Garrett and Zhao, 2006; Lubin and Vogelmann, 2006; Shindell et al., 2008] and CO is an excellent tracer for monitoring long range transport. Furthermore, the SCIAMACHY observations provide valuable information of lower tropospheric CO above boundary layer clouds in this region which cannot be derived from TIR instruments like MOPITT due to their reduced sensitivity to the lower troposphere in these regions (see section 3.2 and Figure 4). [43] Smaller biases are seen over other land areas, especially over the eastern United States, Europe and East Asia/ Figure 8d. As in Figure 8c but for relative differences (%). 11 of 15

12 northern China, all regions with large anthropogenic CO sources. Figure 4 showed that the reduced MOPITT sensitivity for the lower troposphere should be considered close to large emission regions. The MOPITT vertical sensitivity was taken into account in Figures 8b 8d, but this was based on TM4 modeled CO columns which, as noted before, underestimate Northern Hemisphere CO. However, tests with applying the MOPITT averaging kernel and a priori to model profiles suggest that a 10 20% model bias changes differences between MOPITT and SCIAMACHY CO columns because of the MOPITT vertical sensitivity by about 1 2% for most regions, with the exception of areas with Figure 9 12 of 15

13 very cold surfaces during local winter. Furthermore, Emmons et al. [2009] reported MOPITT biases over the eastern United States and Europe of about 5%. Both biases are insufficient to explain the 10 20% SCIAMACHY MOPITT differences over the eastern United States and Europe. For East Asia and especially northern China the differences might be explained by a combination of underestimated model CO and the MOPITT vertical sensitivity. Note that the results from Emmons et al. [2009] were obtained for aircraft measurements data at limited locations and the comparison shows variations larger than the mean bias, excluding the possibility of a global systematic bias for MOPITT in the SCIAMACHY MOPITT comparisons in Figure 8. [44] Deeter et al. [2007a] noted that using a log normal distribution model constraints rather than a normal distribution for the MOPITT a priori leads to a significant reduction of surface CO mixing ratios (10 25 ppbv). [45] Finally, Figures 8c and 8d shows significant differences over the dry deserts regions of the world which will discussed in more detail in section SCIAMACHY MOPITT Differences Over Dry Land Regions [46] As noted in section 4.2, significant MOPITT SCIAMACHY differences are found over dry desert regions like the Sahara, the Middle East, central Asia and the interiors of Australia and southern Africa. These regions have very small SCIAMACHY instrument noise errors (see Figure 1) and are also regions with high surface temperatures and thus increased sensitivity of MOPITT to the lower troposphere [Deeter et al., 2007b]. [47] In order to investigate the cause of these differences, Figure 9 shows the average CO columns for MOPITT, SCIAMACHY and TM4 along a latitude band right across the Sahara. Over the Pacific Ocean (180 W 100 W; 120 E 180 E), SCIAMACHY and MOPITT CO total columns agree well. However, for the region 60 W to 120 E, significant differences between SCIAMACHY and MOPITT are seen. We therefore focus in more detail on this region, and for analysis purposes we have divided this transect into several subsections, indicated in Figure 9 (middle). [48] Over the Atlantic Ocean (100 W 15 W), SCIAMACHY columns are rather flat while MOPITT columns slowly increase toward the African continent. When accounting for the MOPITT averaging kernels and a priori as well as differences in spatiotemporal sampling, the model results also show this slow increase. [49] At the transition from the Atlantic Ocean (area 1) to the Sahara (area 2), MOPITT clearly shows a discontinuity right at the coastline, which is not present in both SCIAMACHY observations and TM4 model results. Over the Sahara, MOPITT CO total columns are approximately molecules/cm 2 larger than over the adjacent ocean and approximately molecules/cm 2 larger than SCIAMACHY and TM4 model results, which cannot be explained by the MOPITT bias in this region reported by Emmons et al. [2009]. The agreement between SCIAMACHY observations and TM4 modeled columns over the Sahara may be related to the relative isolation of this region and the absence of CO sources in the Sahara itself. All other regions in Figure 9 are directly affected by strong emission regions in equatorial Africa and southern and southeastern Asia. [50] Over the Arabian Peninsula (area 3), SCIAMACHY and MOPITT follow the surface elevation, although a difference remains (area 4). While crossing India (area 5), SCIAMACHY and MOPITT diverge again. Further east (area 6) both SCIAMACHY and MOPITT are more in agreement, although there is a difference in the location of the Southeast Asian peak (area 7 and 8). The latter can at least partly be explained by the MOPITT vertical sensitivity and a priori contribution (see Figure 4). Furthermore, it is possible that the model estimate of the SCIAMACHY below ocean cloud CO partial column over the northern Indian Ocean is underestimated as this area is strongly affected by emissions from industrialized Southeast Asian regions. [51] Summarizing these results, SCIAMACHY and MOPITT CO columns show differences that in most cases may be explained by the differing vertical sensitivity of both instruments, different spatiotemporal sampling, the use of clouded ocean scenes for SCIAMACHY observations (areas 1 and 3 to 8). However, the Sahara difference between MOPITT columns on the one hand and SCIAMACHY and TM4 columns on the other hand cannot be explained by the MOPITT vertical sensitivity, CO surface emissions, differences in spatiotemporal sampling, nor the MOPITT bias from comparisons with MOZAIC. George et al. [2009] report for IASI TIR observations that the surface emissivity strongly influences the sensitivity at the surface level. Lack of correct emissivity data over hot/sandy surface causes too large IASI CO concentrations over the Sahara and the Arabic Peninsula. Whether this also plays a role for the MOPITT TIR CO observations and thus could provide a possible explanation for the positive difference between MOPITT and SCIAMACHY over dry desert regions needs to be investigated. 6. Conclusions [52] This paper compares 2 years of SCIAMACHY and MOPITT CO column observations on a global scale. Direct comparison of SCIAMACHY and MOPITT CO columns is Figure 9. (top) Average CO columns for a latitude band from 12 N 18 N based on 3 2 gridded data for The black line indicates the MOPITT CO total columns, while the orange line denotes the MOPITT sampled TM4 modeled CO total columns with the MOPITT averaging kernel and a priori applied. The solid green line denotes the SCIAMACHY CO columns with CO below oceanic low clouds added, and the dashed green line the SCIAMACHY CO columns without these below cloud partial columns. The red line denotes the SCIAMACHY sampled TM4 modeled CO total columns. The blue line indicates the surface elevation for the zonal band in arbitrary units. (middle) A zoom in of the top plot between 60 W and 120 E. (bottom) A surface elevation map (hpa); the red lines indicate the zonal band that is shown in the top and middle plots. The solid vertical lines in the middle and bottom plots indicate the areas defined by the red numbers discussed in the text. The dashed vertical lines in the middle and bottom plots are added for reference. 13 of 15

14 complicated by their different vertical sensitivities and spatiotemporal sampling. An additional process causing differences is the fact that over the oceans SCIAMACHY can only provide useful information about CO above clouds, thus missing the part of the column below the cloud, whereas for MOPITT only cloud free data are available. [53] Considering these effects, average SCIAMACHY and MOPITT CO columns are similar over tropical, subtropical and Northern Hemisphere oceans as well as over boreal regions where SCIAMACHY and MOPITT agree within 10% or molecules/cm 2. However, over Northern Hemisphere oceans a small bias remains over certain areas, possibly related to underestimated modeled Northern Hemisphere CO which is used to estimate the below cloud partial column over oceans for SCIAMACHY. Previous studies show that this model bias is about 10%, which is sufficient to explain most of the SCIAMACHY MOPITT differences over Northern Hemisphere oceans. [54] An interesting result is the correspondence between SCIAMACHY and MOPITT over Arctic and subarctic oceans north of 60 N. Given that SCIAMACHY is close to uniformly sensitive to the troposphere, this suggests that SCIAMACHY observations can be used to study lower tropospheric CO variations in these regions, which is not possible using TIR observations due to their reduced sensitivity to the lower troposphere over cold surfaces. [55] South of 45 S, SCIAMACHY CO columns are molecules/cm 2 smaller than MOPITT CO total columns. One third of this difference may be explained by the MOPITT bias in this region. About molecules/ cm 2 might be caused by too low modeled CO, causing the estimated part of the SCIAMACHY columns below the cloud for ocean measurements to be too low. In addition, the SCIAMACHY CO above low cloud columns in this region are approximately molecules/cm 2 smaller than TM4 modeled CO columns above the cloud. This SCIAMACHY bias is currently being investigated. [56] Over dry desert regions, MOPITT CO total columns are molecules/cm 2 larger than SCIAMACHY. MOPITT CO total columns over dry desert regions are also approximately molecules/cm 2 larger than over adjacent oceans. This jump in MOPITT CO total columns at the coastline could not be explained by differences in MOPITT vertical sensitivity nor by the MOPITT bias found by Emmons et al. [2009]. The agreement between MOPITT and SCIAMACHY over adjacent oceans suggests that the MOPITT increase over dry desert regions may be related to the surface properties of dry land regions [George et al., 2009]. However, without independent verification and more research we cannot draw a definite conclusion. Comparison of SCIAMACHY data with MOZAIC and/or FTIR observations or the recently released MOPITT SWIR data may shed some light on some of the remaining SCIAMACHY MOPITT differences. [57] Acknowledgments. SCIAMACHY is a joint project of the German Space Agency DLR and the Dutch Space Agency NIVR with contribution of the Belgian Space Agency. We thank the Netherlands SCIAMACHY Data Center and ESA for providing data. The work performed is (partly) financed by NIVR. The authors thank Jan Fokke Meirink for providing the TM4 model data, and we thank Guido van der Werf for providing GFEDv2 emissions. The MOPITT team is acknowledged for making their observations publicly available via the NASA Langley Atmospheric Science Data Center. Finally, we thank Dave Edwards for his thoughtful comments on this paper. References Anderson, G. P., S. A. Clough, F. X. Kneizys, J. H. Chetwynd, and E. P. Shettle (1986), AFGL Atmospheric Constituent Profiles (0 120 km), AFGL TR , Air Force Geophys. Lab., Hanscom AFB, Mass. Barret, B., M. De Mazière, and E. Mahieu (2003), Ground based FTIR measurements of CO from the Jungfraujoch: Characterisation and comparison with in situ surface and MOPITT data, Atmos. 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