QA4ECV D3.8 v1.0 web · 2!/22!!!! ! & ! Work(package&...

Grant agreement n°607405

Date: 24 March 2016 Lead Beneficiary: IASB-‐BIRA (#2) Nature: Report Dissemination level: PU

QA4ECV Report / Deliverable n° D3.8

Historical record of independent reference data for NO2, HCHO, and CO

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Work-‐package WP3 (QA of independent reference data and in-‐situ protocols) Deliverable Deliverable 3.8 – Draft version 1.0 Title Historical record of independent reference data for NO2, HCHO

and CO. Nature R Dissemination PU Lead Beneficiary IASB-‐BIRA (#2) Date 24 March 2016 Status Preliminary Authors F. Hendrick (IASB-‐BIRA), B. Dils (IASB-‐BIRA), C. Gielen (IASB-‐BIRA),

B. Langerock (IASB-‐BIRA), G. Pinardi (IASB-‐BIRA), M. De Mazière (IASB-‐BIRA), M. Van Roozendael (IASB-‐BIRA), E. Peters (IUP-‐UB), A. Richter (IUP-‐UB), A. Piters (KNMI), S. Beirle (MPG), T. Wagner (MPG), T. Drosoglou (AUTH), A. Bais (AUTH), S. Wang (CSIC), C. Cuevas (CSIC), A. Saiz-‐Lopez (CSIC)

Editors F. Hendrick (IASB-‐BIRA) Reviewers Name (Affiliation), Name (Affiliation) Contact F. Hendrick, IASB-‐BIRA ([email protected]) URL http://www.qa4ecv.eu/

This document has been produced in the context of the QA4ECV project (Quality Assurance for Essential Climate Variables). The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7 THEME [SPA.2013.1.1-‐03]) under grant agreement n° 607405. All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission has no liability in respect of this document, which is merely representing the authors’ view.

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Executive Summary / Abstract This reports describes the work undertaken in QA4ECV to build consistent historical record of independent reference data for NO2, HCHO, and CO columns. For NO2 and HCHO, a systematic approach has been adopted to harmonise MAX-‐DOAS retrievals of these species at 12 sites selected for inclusion in the project database. This harmonisation effort has consisted in three steps: (1) an intercomparison of NO2 and HCHO slant column densities (SCDs) retrievals from a common set of measured spectra in order to assess the consistency of DOAS retrieval tools in use in the community and to derive recommendations for standardised analysis settings, (2) the development of generic look-‐up tables of air mass factors (AMFs) generally applicable within the network to convert SCDs into vertical column densities (VCDs), and (3) the systematic use of the Generic Earth Observation Metadata Standard (GEOMS) hdf format for data reporting. In addition, best practices for instrument characterisation, calibration, and operation, as well as harmonised approaches for cloud flagging and for the characterization of the horizontal representativeness of the measurements have been proposed and documented. The corresponding data sets will be used as independent reference for the quality assessment of the QA4ECV satellite-‐based ECVs. In the second part of the report, FTIR measurements of total column CO currently undertaken by the Network for the Detection of Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON) are described. NDACC and TCCON differ in terms of both retrieval methods (optimal estimation and profile scaling approach, respectively) and microwindows used of the spectral fit. Within NDACC, an harmonisation effort on the CO inversion is currently under progress through the comparisons of retrieval strategy and software packages (PROFFIT and SFIT), and uncertainty characterization. A best-‐practice document based on this exercise will be distributed within the NDACC FTIR Working Group (IRWG) and presented at the upcoming IRWG workshop. All PI’s will be then invited to reprocess their time series using these harmonized settings. Historical CO total column time-‐series at Lauder, Jungfraujoch, Izana, Kiruna and Zugspitze currently available on the NDACC database are shown here as example. Regarding TCCON, the current retrieval strategy and error characterization are described. Unlike NDACC, they are both uniform throughout the network. A comparison study between selected NDACC and TCCON sites was part of the EU FP7 NORS project. The general conclusion is that both NDACC and TCCON agree well, but not perfectly: the seasonal cycle of the relative difference is in-‐phase with the seasonal cycle of the total columns itself, indicating that the NDACC profile retrieval is more sensitive to concentration changes than the TCCON retrieval.

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Table of Contents

Table of Contents 1. Part 1 – Historical record for independent reference data for NO2, HCHO ............ 5 1.1. Introduction .................................................................................................................................... 5 1.2. Recommended settings for NO2 and HCHO DOAS analyses ........................................................... 5 1.3. Development of NO2 and HCHO AMF LUTs .................................................................................... 7 1.4. Generation of GEOMS hdf files ..................................................................................................... 10 1.5. MAX-‐DOAS instrument best practice ........................................................................................... 13 1.6. MAX-‐DOAS spatial representativeness characterisation .............................................................. 13 1.7. MAX-‐DOAS cloud screening .......................................................................................................... 14 1.8. Schedule of the remaining tasks ................................................................................................... 15 1.9. References .................................................................................................................................... 15

2. Part 2 – Historical record for independent reference data for CO ......................... 17 2.1 Introduction ................................................................................................................................... 17 2.2 Historical NDACC CO reference time series ................................................................................... 17 2.2 Harmonization strategy and best practice document. .................................................................. 19 2.3 Historical TCCON CO reference series ........................................................................................... 21 2.4 References ..................................................................................................................................... 22

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1. Part 1 – Historical record for independent reference data for NO2, HCHO

1.1. Introduction Part 1 of this document describes the harmonization effort on NO2 and HCHO column retrievals at the QA4ECV reference MAXDOAS sites. Those are listed in Table 1:

Table 1: List of QA4ECV MAXDOAS reference stations.

This harmonisation effort has consisted in three main steps:

1. Intercomparison of NO2 and HCHO slant column densities (SCDs) retrieved from common spectra in order to assess the overall consistency of the retrievals performed within the MAXDOAS community and to derive recommendations for standardized analysis settings.

2. Development of NO2 and HCHO airmass factor (AMF) look-‐up tables in order to harmonize the conversion of SCDs into vertical column densities (VCDs)

3. Generation of MAXDOAS data files using the Generic Earth Observation Metadata Standard (GEOMS) hdf format. The implementation of this format by all QA4ECV MAXDOAS groups has been successfully realised leading to the creation of a first version – not yet fully harmonised on the content – of the MAX-‐DOAS database.

These three steps are described in Sections 1.2, 1.3, and 1.4, respectively. Best practices for instrument characterisation, calibration, and operation are discussed in Section 1.5. Standard approaches developed for the characterization of spatial representativeness and cloud conditions are given in Sections 1.6 and 1.7. The schedule for the remaining main tasks is given in Section 1.8.

1.2. Recommended settings for NO2 and HCHO DOAS analyses In order to derive standard settings for the NO2 and HCHO DOAS analyses, NO2 and HCHO SCDs have been retrieved by the different groups from common absorption spectra

Station Lat, Long Class Data Source De Bilt/Cabauw (NL) 52°N, 5°E Sub-‐urban KNMI Uccle (BE) 50°N, 4°E Urban IASB-‐BIRA Beijing (CHN) 40°N, 116°E Urban IASB-‐BIRA, MPG Xianghe (CHN) 39°N, 117°E Sub-‐urban IASB-‐BIRA Bujumbura (BU) 3°S, 29°E Sub-‐urban IASB-‐BIRA Bremen (DE) 53°N, 9°E Urban IUP-‐UB Nairobi (KEN) 1°S, 37°E Rural / Urban IUP-‐UB Athens (GR) 38°N, 23°E Urban IUP-‐UB Mainz (DE) 50°N, 8°E Urban MPG Greater Noida (IND) 28°N, 77°E Urban MPG Thessaloniki (GR) 41°N, 23°E Urban AUTH Madrid (ESP) 40°N, 3°W Urban CSIC

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recorded by the IUP-‐UB MAXDOAS spectrometer on 18/06/2013 during the MADCAT intercomparison campaign held in Mainz in June-‐July 2013 (http://joseba.mpch-‐mainz.mpg.de/mad_cat.htm). Tests on synthetic spectra were also included in both exercises led by IUP-‐UB (NO2) and IASB-‐BIRA (HCHO). It should be noted that the selection of settings to be tested by the different groups has been based on previous SCD comparison studies carried out within the framework of the CINDI and MADCAT campaigns (see Pinardi et al., 2013 and http://joseba.mpch-‐mainz.mpg.de/mad_analysis.htm, respectively). The preliminary recommendations on DOAS settings derived from the SCD comparison results are described in Tables 2 (NO2) and 3 (HCHO) and have been sent to the different groups for discussion. A firm decision on these settings will be taken by end of March 2016 and they will be then used for the generation of the QA4ECV MAX-‐DOAS reference data sets.

Table 2: Recommended DOAS settings for NO2.

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Table 3: Recommended DOAS settings for HCHO.

1.3. Development of NO2 and HCHO AMF LUTs The MAXDOAS groups involved in QA4ECV use different methods for the conversion of SCDs into VCDs: an AMF LUT approach for KNMI (Vlemmix et al., 2011), a full OEM-‐based profiling scheme for IASB-‐BIRA and IUP-‐UB, and the so-‐called geometrical approximation for MPG, AUTH, and CSIC. The latter consists in assuming the trace gas layer to be located below the scattering altitude at 30° elevation, so that tropospheric NO2 or HCHO VCDs can be derived by applying a geometrical AMF to measured DSCDs at 30° elevation (Hönninger et al., 2004; Brinksma et al., 2008). Potentially, using such different methods for the derivation of VCD can be at the origin of discrepancies/bias between retrieval results. To overcome this problem, it has been decided to use an AMF LUT approach for all QA4ECV MAXDOAS stations. The approach consists in dividing the differential SCDs with respect to the zenith (90°elevation) of the scan by the corresponding differential AMFs: where α an off-‐axis elevation angle of the scan, generally chosen between 15 and 30° in order to minimize the impact of aerosols on the retrieval. NO2 and HCHO AMF LUTs have been generated by IASB-‐BIRA using the bePRO/LIDORT radiative transfer suite (Clémer et al., 2010; Spurr, 2008) for the following parameter values:

°

°

−−

==90

90

AMFAMFSCDSCD

DAMFDSCDVCD

α

α

α

αα

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Table 4: Parameter values for which the LUTs were calculated. In order to extract appropriate AMF values for each QA4ECV station, an extraction/interpolation tool has been developed and is described in Figure 1.

Figure 1: Flow chart of the NO2 and HCHO AMF extraction tool.

It consists in a Fortran executable fed by the following parameters in addition to the AMF LUTs:

• Geometry parameters (solar zenith, relative azimuth, and elevation angles) • Boundary layer height (BLH; which is used in a first approximation to initialise SH; see

Vlemmix et al., 2015), aerosol optical depth (AOD), and surface albedo values • Profile shapes used for the AMF LUT calculation • NO2 and HCHO column averaging kernels (AVKs) LUTs

Parameter Value(s)

Wavelength NO2: 360, 427, 477nm; HCHO: 343 nm Altitude grid 0-‐2km/step 0.1 km ; 2-‐20km/step 1km p,T,O3 profiles AFGL 1976 Viewing elevation angle 10, 15, 20, 25, 30, 35o (+zenith) Relative azimuth angle 0, 30, 60, 90, 120, 150, 180o Solar zenith angle 0, 20, 30, 40, 50, 60, 65, 70, 75, 80, 82, 84, 86, 88o Surface albedo 0, 0.5, 1 NO2 and HCHO vertical Profiles

Exponentially-‐decreasing profile: Scaling Height (SH)= 0.2, 0.5, 1, 1.5, 3 km; Tropospheric NO2 and HCHO VCDs: 2e16 molec/cm2

Aerosol scenarios Exponentially-‐decreasing profile: Scaling Height (SH)= 0.2, 0.5, 1, 1.5, 3 km; AOD: 0, 0.2, 0.5, 1, 2, 4; single scattering albedo: 0.90; asymmetry factor: 0.72; Henyey-‐Greenstein phase function

Cloud scenarios Clear-‐sky only

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For each station, BLH (SH), AOD, and surface albedo values can be defined by the user or extracted in a transparent way from coupled climatologies based on collocated ECMWF, AERONET, and GOME (Koelemeijer et al., 2008) data, respectively. As an output of the extraction tool, interpolated AMFs and corresponding profile shapes and column averaging kernels are generated. This information is needed to properly take into account the differences in vertical sensitivity when validating satellite nadir measurements using MAXDOAS observations: MAXDOAS shows a maximum of sensitivity close to the ground (0-‐1.5km altitude range) while for the satellite measurements, the maximum of sensitivity is located at higher altitudes (see Figure 2). Column AVK LUTs have been calculated based on the Eskes and Boersma (2003)’s approach, using the bePRO/LIDORT RTM initialized with similar parameter values as for the AMF LUTs calculation (see Table 2). The profile shapes are those used for the calculation of the AMF LUTs and are interpolated at the BLH (SH) values provided by the user or from a pre-‐determined ECMWF-‐based climatology.

Figure 2: Typical examples of MAXDOAS and satellite nadir (OMI) HCHO column AVKs.

The LUT approach has been successfully tested for NO2 and HCHO at IASB-‐BIRA stations (Xianghe, Bujumbura, Uccle). As an example, Figure 3 shows the comparison between AMF-‐based HCHO VCDs at 15 and 30° elevation and those retrieved by the bePRO/LIDORT profiling tool and the geometrical approximation for the Xianghe station (year 2010).

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Figure 3: Comparison of HCHO VCDs retrieved by the AMF-‐based approach at 15 and 30° elevation, the bePRO/LIDORT profiling tool, and the geometrical approximation at the Xianghe station (year 2010). As can be seen, for the present case (HCHO in Xianghe), the AMF LUT approach significantly improves the agreement with the profiling method compared to the simple geometrical approximation, in terms of both mean bias and seasonality of the relative differences (the latter is significantly reduced when using the AMF approach). For the particular conditions of the Xianghe site (high HCHO content and high aerosol load), optimal results are obtained at 30° elevation because aerosol-‐related uncertainties are minimised at high elevation. Different conclusions might eventually be reached at cleaner sites.

1.4. Generation of GEOMS hdf files The GEOMS UVVIS.DOAS hdf format has been developed during the FP7 NORS project by the NDACC/NORS UV-‐vis Working Group. It consists of four templates for reporting UV-‐vis data in off-‐axis (trace gas + aerosols), zenith, and direct-‐sun viewing geometries. They are described on the AVDC website (http://avdc.gsfc.nasa.gov/index.php?site=1876901039). An example GEOMS hdf file is shown in Figure 4.

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Figure 4: Example of GEOMS hdf file for tropospheric NO2 off-‐axis measurements.

The added value of such file format is the possibility to include ancillary metadata which can be useful for the interpretation of comparisons between MAX-‐DOAS and satellite or model data, like averaging kernels, cloud conditions, location (latitude, longitude) of the effective air masses, AOD, in addition to the trace gas data. The QA4ECV MAX-‐DOAS groups have generated a first version of the NO2 and HCHO GEOMS hdf files for their stations based on VCDs retrieved using their own algorithms and settings for DOAS spectral fitting and SCDs to VCDs conversion. These data files constitute the first version of the QA4ECV MAX-‐DOAS database and are made available at ftp-‐ae.oma.be (for username and password, please contact the PI’s of the instruments). The structure of this database is shown in Figure 5. Although this data base is not yet fully harmonised, the main motivations to create it are:

(1) to ensure the effective implementation of the GEOMS hdf format by all QA4ECV MAX-‐DOAS groups, and

(2) to start in WP5 the development of the satellite validation tools which will use the MAX-‐DOAS GEOMS reference files as input.

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Figure 5: Structure of the QA4ECV MAX-‐DOAS database (v1).

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1.5. MAX-‐DOAS instrument best practice

A best practice report on instrument characterisation, calibration, and operation has been drafted by IUP-‐UB. In the first part, the main components of the different MAX-‐DOAS instrument types are described, with focus on telescopes (pointing or imaging), optical components (mirrors, lenses, prisms, fiber bundles and mixers, filters), and temperature stabilisation of both spectrometers and detectors. Supporting measurements, like video camera (for monitoring the sky conditions and pointing direction), meteorological observations (needed for an optimal interpretation of MAX-‐DOAS data), and GPS (for accurate time synchronisation), are also discussed. The second part of the document is dedicated to the characterisation of spectral (spectral range and sampling, slit function, stray light) and pointing (field of view, possible viewing directions) properties. Calibration and characterisation procedures for different instrument parameters (field of view, spectrometer straylight, polarisation, dark signal, detector linearity and pixel-‐to-‐pixel variability, and temperature dependence) are discussed in the third part. Finally, the operation parameters (e.g. viewing geometries, automatic procedures for calibration, data acquisition and QA, and DOAS spectral fitting) are listed and described in the last part of the document. A preliminary version of this best practice report is made available at ftp-‐ae.oma.be/qa4ecv_gb (user: qa4ecv_gb; password: ecv!MXDOAS), in folder ‘documents’.

1.6. MAX-‐DOAS spatial representativeness characterisation

As for all remote sensing techniques, MAX-‐DOAS observations average over space and time providing integrated values representative of a certain atmospheric volume. The vertical and horizontal extents of this volume depend strongly on the wavelength, viewing geometry (elevation and relative azimuth angles, SZA), and sky conditions (presence or not of clouds and/or aerosols). Within QA4ECV, MPG has extended its work done in the FP7 project NORS on the horizontal representativeness of MAX-‐DOAS observations. A universal 2-‐D polynomial function of the SZA and relative azimuth angle has been developed, which describes the ratio between the measured O4 DAMF and the horizontal distance (in km). This 2D function is determined separately for the four wavelengths, at which important O4 absorption bands are located (360nm, 477nm, 577nm, and 630nm). It is valid for elevation angles <6°; the same function can be applied to O4 DAMFs (differential AMFs with respect to the zenith) derived from any elevation angle between 1° and 6°. As illustration, Figure 5 shows the relationships between O4 DAMF and horizontal extent at 1° elevation at 360 and 630 nm.

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Figure 6: Relationships between the retrieved O4 differential AMF (1° elevation – 90° elevation) and the horizontal sensitivity range for 1° elevation angle and 360 nm (left) and 630 nm (right) (SZA: 60°: relative azimuth angles: 0°, 90°, 180°). The different colours represent results for different aerosol extinction (box) profiles. The 2D polynomial functions including the corresponding coefficients are described in a dedicated report available at ftp-‐ae.oma.be/qa4ecv_gb (user: qa4ecv_gb; password: ecv!MXDOAS), in folder ‘documents’.

1.7. MAX-‐DOAS cloud screening

MAX-‐DOAS trace gas retrievals can be strongly affected by clouds and aerosols due to their impact on the photon light path. This is especially true in the presence of broken clouds, which show strong temporal and spatial variations. To reduce the impact of cloud-‐related errors on MAX-‐DOAS trace gas retrievals, methods have been developed allowing to identify cloudy scans so they can be excluded from subsequent analysis steps. These methods are based on the use of 4 main parameters (Wagner et al., 2014; Gielen et al., 2014): measured radiance (clouds are bright), color index (CI=Iλlow/Iλhigh; ratio of intensities at two different wavelengths) (clouds are white), and O4 absorption and Ring effect (clouds modify the atmospheric light path). Within QA4ECV, a simplified version of the cloud screening approach described in Gielen et al. (2014) has been adopted. This uses only measured CI in the zenith viewing geometry, so that no radiative transfer model simulations are needed. This method can therefore be easily applied at all QA4ECV MAX-‐DOAS sites. Firstly, an histrogram analysis is made on the measured CI values and the region [CImax-‐FWHM, CImax+FWHM] is defined: scans located in this region are flagged as thick clouds/extreme pollution, while scans outside this region are for clear-‐sky/thin clouds conditions. Secondly, the presence of broken clouds is detected based on the fit of a double-‐sine functions on the diurnal variation of the measured CI (zenith geometry) and the outliers, identified as (CI-‐fitted function)/fitted function>0.1, are flagged as being affected by broken clouds.

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These simplified cloud screening methods are described in more details in a dedicated report available at ftp-‐ae.oma.be/qa4ecv_gb (user: qa4ecv_gb; password: ecv!MXDOAS), in folder ‘documents’.

1.8. Schedule of the remaining tasks

The schedule of main remaining tasks on MAX-‐DOAS harmonisation within QA4ECV WP3 are described in Table 5. A final version of the reference data for NO2 and HCHO will be delivered to the consortium by the end of June 2016.

2016

Jan-‐Mar Apr-‐Jun MADCAT intercomparison of NO2 and HCHO SCD + recommendations for standard settings

Final results and recommendations provided in March 2016 by IUP-‐UB (NO2) and IASB-‐BIRA (HCHO)

Implementation by the different groups

LUTs of NO2 and HCHO AMFs LUTs made available in March 2016 by IASB-‐BIRA

Implementation by the different groups

NO2 and HCHO VCD data series in GEOMS format

Version 1 (based on own retrieval tools and settings) made available in February 2016

Version 2 (harmonised data sets) will be made available in June 2016

Document on Instrument characterization and operation protocol

Final (draft version provided in June 2015 by IUP-‐UB)

Document on harmonised horizontal representativeness characterisation

Provided in December 2015 by MPG

Document on harmonised cloud flagging method

Provided in September 2015 by IASB-‐BIRA

Table 5: Schedule of the main tasks remaining for the harmonisation of QA4ECV MAX-‐DOAS data sets.

1.9. References

Brinksma, E. J., Pinardi, G., Volten, H., Braak, R., Richter, A., Schönhardt, A., Van Roozendael, M., Fayt, C., Hermans, C., Dirksen, R. J., Vlemmix, T., Berkhout, A. J. C., Swart, D. P. J., Oetjen, H., Wittrock, F., Wagner, T., Ibrahim, O. W., de Leeuw, G., Moerman, M., Curier, R. L., Celarier, E. A., Cede, A., Knap, W. H., Veefkind, J. P., Eskes, H. J., Allaart, M., Rothe, R., Piters, A. J. M., and Levelt, P. F.: The 2005 and 2006 DANDELIONS NO2 and aerosol intercomparison campaigns, J. Geophys. Res., 113, D16S46, doi:10.1029/2007JD008808, 2008.

Clémer, K., M. Van Roozendael, C. Fayt, F. Hendrick, C. Hermans, G. Pinardi, R. Spurr, P. Wang, and M. De Mazière, Multiple wavelength retrieval of tropospheric aerosol optical properties from MAXDOAS measurements in Beijing, Atmospheric Measurement Techniques, 3, 863-‐878, 2010.

Eskes, H. J. and K. F. Boersma, Averaging kernels for DOAS total-‐column satellite retrievals, Atmos. Chem. Phys., 3, 1285-‐1291, doi:10.5194/acp-‐3-‐1285-‐2003, 2003.

Gielen, C., M. Van Roozendael, F. Hendrick, G. Pinardi, T. Vlemmix, V. De Bock, H. De Backer, C. Fayt, C. Hermans, D. Gillotay, and P. Wang, A simple and versatile cloud-‐screening

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method for MAX-‐DOAS retrievals, Atmos. Meas. Tech., 7, 3509-‐3527, doi: 10.5194/amt-‐7-‐3509-‐2014, 2014.

Honninger, G., C. von Friedeburg, and U. Platt, Multi axis differential optical absorption spectroscopy, Atmos. Chem. Phys., 4, 231–254, 2004.

Koelemeijer, R. B. A., J. F. de Haan, and P. Stammes, A database of spectral surface reflectivity in the range 335 – 772 nm derived from 5.5 years of GOME observations, J. Geophys. Res., 108(D2), 4070, doi:10.1029/2002JD002429, 2003.

Pinardi, G., M. Van Roozendael, N. Abuhassan, C. Adams, A. Cede, K. Clémer, C. Fayt, U. Frieß, M. Gil, J. Herman, C. Hermans, F. Hendrick, H. Irie, A. Merlaud, M. Navarro Comas, E. Peters, A. J. M. Piters, O. Puentedura, A. Richter, A. Schönhardt, R. Shaiganfar, E. Spinei, K. Strong, H. Takashima, M. Vrekoussis, T. Wagner, F. Wittrock, and S. Yilmaz, MAXDOAS formaldehyde slant column measurements during CINDI: intercomparison and analysis improvement, Atmos. Meas. Tech., 6, 167-‐185, 2013.

Rodgers, C. D.: Inverse Methods for Atmospheric Sounding, Theory and Practice. World Scientific Publishing, Singapore-‐NewJersey-‐London-‐Hong Kong, 2000.

Spurr, R., LIDORT and VLIDORT: Linearized pseudo-‐spherical scalar and vector discrete ordinate radiative transfer models for use in remote sensing retrieval problems, Light Scattering Reviews, Volume 3, ed. A. Kokhanovsky, Springer, 2008.

Vlemmix, T., A. J. M. Piters, A. J. C. Berkhout, L. F. L. Gast, P. Wang, and P. F. Levelt Atmos. Meas. Tech., 4, 2659-‐2684, doi:10.5194/amt-‐4-‐2659-‐2011, 2011.

Vlemmix, T., F. Hendrick, G. Pinardi, I. De Smedt, C. Fayt, C. Hermans, A. Piters, P. Levelt, and M. Van Roozendael, MAX-‐DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: comparison of two profile retrieval approaches, Atmospheric Measurement Techniques, 8, 941-‐963, 2015.

Wagner, T., A. Apituley, S. Beirle, S. Dörner, U. Friess, J. Remmers, J., and R. Shaiganfar, Cloud detection and classification based on MAX-‐DOAS observations, Atmos. Meas. Tech., 7, 1289–1320, doi:10.5194/amt-‐7-‐1289-‐2014, 2014.

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2. Part 2 – Historical record for independent reference data for CO

2.1 Introduction

Total column measurements of CO, using ground-‐based solar-‐absorption FTIR measurements are currently undertaken by two international networks: the Network for the Detection of Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON). They differ in many areas but primarily in the fact that TCCON measurements are undertaken in another spectral region and that it uses a profile scaling retrieval method, while NDACC adheres to optimal estimation. Details on both networks and corresponding CO data are given below.

2.2 Historical NDACC CO reference time series

Figure 1: Overview of TCCON and NDACC sites. Only NDACC sites submitting data in Generic Earth

Observation Metadata Standard (GEOMS) HDF format are shown.

NDACC is a cross-‐border international research network of remote sounding stations. It is a major contributor to the World Meteorological Organisation (WMO) GAW programme and it works under the auspices of United Nations Environment Programme (UNEP) and the International Ozone Commission (IO3C). Relying on a strong involvement of European partners and efficient collaboration with partners worldwide, the network started operations officially in 1991, but a few data records extend back to the 1970’s and even to the 1950’s. At present time it includes more than 70 high-‐quality, remote-‐sensing research stations/sites distributed worldwide for (i) observing and understanding the physical / chemical state of the stratosphere and troposphere, and (ii) assessing the impact of stratospheric changes on the underlying troposphere and on global climate.

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The Fourier Transform Infrared (FTIR) spectrometer, a Michelson-‐type interferometer is an official NDACC instrument and measures the absorbance spectrum of solar (lunar) light along the line of sight through the atmosphere. For operational measurements the ground-‐based FTIR spectra are measured with a typical resolution of about 0.005 cm−1 (i.e. maximum optical path difference (OPD) of 180cm).

Figure 2: Examples of AVK transformation matrices acting on VMR profiles. Sensitivity decreases above

20km.

Atmospheric CO profile is one of the official FTIR NDACC targets and the retrieval strategy has already been harmonized across the network. The profile retrievals have sensitivity mainly in the troposphere and the degrees of freedom (DOF) are typically between 2 and 3 (see Figure 2). The retrieval microwindows are at 2057.70-‐2058.00, 2069.56-‐2069.76 and 2157.50-‐2159.15 wavenumber, main interfering species are H2O, O3, CO2 and OCS, the a-‐priori profile data is derived from WACCM. At present CO data on NDACC is available as profile data in the Generic Earth Observation Metadata Standard (GEOMS) HDF format, but also column only or data in ASCII format can be found on NDACC. For QA4ECV reference FTIR data sets we will only consider time series provided in GEOMS HDF format and containing profile retrievals. Data provided in GEOMS format ensures that all additional variables are available: a-‐priori data, averaging kernels, surface temperature, pressure, uncertainties are necessary for a proper treatment of the FTIR level 2 datasets. From Table 1, it is clear that, although the FTIR CO retrieval strategy is strongly harmonized across the different sites, the reported uncertainty budgets remain strongly site dependent. Site Date Range Profile data Rand. unc.

[%] Syst. unc.

[%] Altzomoni 24/10/12 29/12/15 X 0.5 2 Arrival Heights 09/02/97 20/02/16 4 -‐ Bremen 08/05/03 25/11/14 X -‐ -‐ Eureka 03/08/06 01/09/14 X 1 3.5 Izana 30/03/99 30/12/15 X .5 2 Jungfraujoch 29/03/92 30/12/15 X 2.4 -‐ Kiruna 27/03/96 20/11/15 X .5 2 Lauder 23/02/94 27/02/16 3.5 -‐ Mauna Loa 25/08/95 30/12/12 X 1 2 Ny Alesund 28/03/93 16/07/14 X .5 .5

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Reunion Maido 05/03/13 15/12/15 X 1 3.5 Reunion StDenis 08/08/04 01/11/12 X 1 1.5 Thule 10/10/99 08/10/13 X 2 2 Toronto 22/05/02 10/12/14 X 2 2 Wollongong 26/05/96 30/12/13 X 2 2 Zugspitze 30/06/95 18/09/14 X -‐ -‐

Table 1. Overview of available data on NDACC and averaged reported uncertainties.

2.2 Harmonization strategy and best practice document.

The harmonization of the uncertainty budgets is ongoing and is done simultaneously for the FTIR NDACC CH4 and O3 targets within the EU GAIA-‐CLIIM project (www.gaia-‐clim.eu). As described in Rodgers (2000), the uncertainty budget on an optimally estimated profile is constructed by propagation of the uncertainties on the different forward model parameters, the measured spectrum and the apriori state (smoothing uncertainty). Up to now, the two main FTIR retrieval software packages (PROFFIT and SFIT) have been compared and this resulted in an update of the uncertainty computation routines for SFIT so that these now work with full 2D covariance matrices instead of standard deviation profiles, a necessary step to get to a harmonized uncertainty budget. The comparison of all leading uncertainty contribution to the target profile is ongoing. NCEP pressure, temperature and water profiles are used as apriori data in the retrieval process. Temperature is a leading term in the uncertainty budget. A detailed comparison of these NCEP profiles against WOUDC radiosonde measurements at Reunion has been performed to have an estimate on the uncertainty of the NCEP temperature and water profile data (see Figure 3). Uncertainty on the spectroscopic data is another leading uncertainty term, and the comparison between different sites is still ongoing. When the comparison exercise is completed, a best-‐practice document will be distributed within the NDACC FTIR working group and presented at the upcoming IRWG workshop. All PI’s will be invited to reprocess their time series with the harmonized strategy and corresponding settings.

Figure 3: Ensemble description of WOUDC sondes used to estimate the NCEP temperature uncertainty. An ensemble of 313 profile differences is drawn representative for continuous measurements throughout the day. From the ensemble of differences, a random (or systematic) covariance uncertainty can be estimated.

FTIR retrieval setup Draft, March 18, 2016

(a) Individual T profile comparison (b) Individual H2O profile comparison

(c) T profile differences (d) H2O profile differences

(e) random T uncertainty (f) systematic T uncertainty

Figure 2.14: NCEP vs WOUDC Sonde profiles for uncertainty estimation

Document generated by BIRA-IASB 41-98

FTIR retrieval setup Draft, March 18, 2016

(a) Individual T profile comparison (b) Individual H2O profile comparison

(c) T profile differences (d) H2O profile differences

(e) random T uncertainty (f) systematic T uncertainty

Figure 2.14: NCEP vs WOUDC Sonde profiles for uncertainty estimation

Document generated by BIRA-IASB 41-98

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Figure 4 shows example time series presently available on NDACC.

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Figure 4: Example long term FTIR CO time series at Lauder, Izana, Jungfraujoch, Kiruna and Zugspitze.

2.3 Historical TCCON CO reference series

The Total Carbon Column Observing Network (TCCON) is an international network of ground-‐based Fourier transform spectrometers (FTSs) designed to retrieve precise and accurate column abundances of CO2, CH4, N2O and CO from near-‐infrared (NIR) solar absorption spectra. The TCCON became operational in 2004, when the first FTIR measurements using the TCCON protocol were undertaken in Park Falls (USA), shortly followed by Lauder (New Zealand). Since then the number of stations has steadily grown. It is thus a far younger network than NDACC. It is also more geared towards the validation of total column satellite measurements. For instance it foregoes any column profile information and the network itself is extensively validated using in situ aircraft measurements (Wunch et al. 2010), which results in a factor 1.0672 bias correction of the final TCCON XCO data. TCCON CO spectra are taken with 0.02 cm-‐1 spectral resolution using the 4208.7-‐4257.3 cm-‐1 and 4262.0-‐4318.8 cm-‐1 microwindow. The spectra are analysed by the Gfit nonlinear least-‐squares fitting algorithm using the HITRAN2012 line list. Instead of using optimal estimation as done in NDACC, Gfit scales an a-‐priori profile to generate the best spectral fit after which the scaled profile is integrated. The thus retrieved total column abundances are then divided by the column of dry air (using O2 retrieved from the same spectra as proxy) to obtain the total-‐column CO dry-‐air molefraction (XCO): XCO=0.2095 column CO/column O2 The TCCON XCO measurement precision varies from site to site but it is generally <1% (1-‐sigma) for single measurements taken under good conditions (clear or partly cloudy skies (up to 5% fractional variation in solar intensity), and solar zenith angles <82 degrees). Unlike NDACC, both retrieval methodology and error characterization is uniform throughout the network. Detailed information on the network can be found in Wunch at al. (2011) and data can be obtained from http://tccon.ornl.gov/

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Figure 5: Historical CO time series from TCCON as a function of latitude.

A comparison study between selected NDACC and TCCON sites was part of the EU FP7 NORS project, see Petri (2014). The general conclusion is that both NDACC and TCCON agree well, but not perfectly (correlation plots for Reunion, Bremen, Izana and Jungfraujoch are available). The seasonal cycle of the relative difference is in-‐phase with the seasonal cycle of the total columns itself, indicating that the NDACC profile retrieval is more sensitive to concentration changes than the TCCON retrieval. 2.4 References

Petri, C., et al., NORS Carbon monoxide mid-‐ and near infra-‐ red data assessment, EU FP7 NORS project deliverable, 2014. Schneider, M., P. Demoulin, R. Sussmann, and J. Notholt, Fourier Transform Infrared Spectrometry, Chapter 6 in Monitoring Atmospheric Water Vapour, Ground-‐Based Remote Sensing and In-‐situ Methods, ISSI Scientific Report Series, Vol. No. 10 (Editor Niklaus Kämpfer), Springer, DOI 10.1007/978-‐1-‐4614-‐3909-‐7, 2012, ISBN 978-‐1-‐4614-‐3908-‐0, 2013. Rodgers, C. D., Inverse methods for atmospheric sounding, Series on Oceanic and planetary physics – vol. 2, World Scientific, 2000. Wunch, D., Toon, G. C., Wennberg, P. O., Wofsy, S. C., Stephens, B. B., Fischer, M. L., Uchino, O., Abshire, J. B., Bernath, P., Biraud, S. C., Blavier, J.-‐F. L., Boone, C., Bowman, K. P., Browell, E. V., Campos, T., Connor, B. J., Daube, B. C., Deutscher, N. M., Diao, M., Elkins, J. W., Gerbig, C., Gottlieb, E., Griffith, D. W. T., Hurst, D. F., Jiménez, R., Keppel-‐Aleks, G., Kort, E. A., Macatangay, R., Machida, T., Matsueda, H., Moore, F., Morino, I., Park, S., Robinson, J., Roehl, C. M., Sawa, Y., Sherlock, V., Sweeney, C., Tanaka, T., and Zondlo, M. A.: Calibration of the Total Carbon Column Observing Network using aircraft profile data, Atmos. Meas. Tech., 3, 1351-‐1362, doi:10.5194/amt-‐3-‐1351-‐2010, 2010. Wunch, D., G. C. Toon, J.-‐F. L. Blavier, R. A. Washenfelder, J. Notholt, B. J. Connor, D. W. T. Griffith, V. Sherlock, and P. O. Wennberg (2011), The total carbon column observing network, Philosophical Transactions of the Royal Society -‐ Series A: Mathematical, Physical and Engineering Sciences, 369(1943), 2087-‐2112, doi:10.1098/rsta.2010.0240, 2011.

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