Grant agreement n°283576

MACC-II Deliverable D_16.4

General recommendations for use of observational satellite data and future missions

Date: 07/2014

Lead Beneficiary: NILU (25)

Nature: R

Dissemination level: PU

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Work-package 16 (OBS: Feedback to data providers) Deliverable D_16.4 Title General Recommendations for use of observational

satellite data and future missions Nature R Dissemination PU Lead Beneficiary NILU (25) Date 07/2014 Status Draft version Authors William Lahoz (NILU) Approved by Leonor Tarrasón Contact info@gmes-atmosphere.eu

This report provides general recommendations for the use of observational satellite data in as input to the data assimilation system pre-operational in MACC_II. It considers experiences from MACC-II; new satellites missions planned; and recommendations for future missions, with the focus being on monitoring air quality and greenhouse gases.

This document has been produced in the context of the MACC-II project (Monitoring Atmospheric Composition and Climate - Interim Implementation). The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7 THEME [SPA.2011.1.5-02]) under grant agreement n° 283576. 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 report provides an overview of the satellite data used by MACC-II, and of potential future satellite data which could potentially benefit developments within the Copernicus Atmospheric Service. It considers experiences from MACC-II; new satellites missions planned; and recommendations for future missions, with the focus being on monitoring air quality and greenhouse gases. The methodology of Observing System Simulation Experiments (OSSEs) is introduced as a way of quantifying the added value of future satellite missions and some recommendations are provided as to how to MACC modelling systems can contribute to OSSEs. There is a strong link between the quality and nature of the Global Observing System (GOS) as applies to atmospheric observations, and the benefits provided by the Copernicus Atmosphere Service. Therefore, MACC and related activities should be aware of and be able to, influence efforts to design, modify and extend the GOS.

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Table of Contents

1. Overview of satellite data used in MACC-II ........................................................................... 5

2. Summary of experiences in MACC-II ..................................................................................... 8

2.1. Greenhouse gases data ........................................................................................... 8

2.2. Aerosol data .......................................................................................................... 10

2.3 Reactive gases data ................................................................................................ 11

3. Additional satellite data not presently used in MACC-II that can be valuable for the

Copernicus Atmospheric Service ................................................................................. 17

4. Outlook of new missions and their potential relevance for the Copernicus

Atmospheric Service .................................................................................................... 18

5. Recommendations for future missions to secure their use in the Copernicus

Atmospheric Service .................................................................................................... 28

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1. Overview of satellite data used in MACC-II Introduction. The Earth System includes the atmosphere, the ocean, the land surface and the cryosphere. The methods used to observe the Earth System using instrumentation include (Lahoz, 2010;Thépaut and Andersson, 2010): in situ observations from ground-based stations, buoys and aircraft; and satellite observations from low Earth orbit satellites (LEOs) and geostationary satellites (GEOs) — satellites in highly elliptic orbits (HEOs) are also being considered to observe the Arctic (Masutani et al., 2013). Collectively, these observational platforms are termed the Global Observing System (GOS). The in situ and satellite observational platforms are complementary (USGEO, 2010): in situ platforms have relatively high spatio-temporal resolution, but do not have global coverage; satellite platforms have substantial coverage over the globe, for LEOs this coverage being quasi-global, but have relatively poor spatio-temporal resolution. In situ data and ground-based remote sensing data, are often used to evaluate and calibrate (using these data as unbiased, anchor data) satellite data for the various elements of the Earth System. Satellite observations come from operational and research satellites. The main use for operational satellites is weather forecasting, and for research satellites research of the Earth System. However, the distinction between operational and research satellites is becoming blurred, as more research satellites are used operationally for weather forecasting. Currently, nadir-viewing satellites dominate satellite observations used operationally by the weather centres; limb-viewing satellites are also used (Thépaut and Andersson, 2010). Many operational satellite instruments measure infrared or microwave radiation from the atmosphere and the Earth’s surface. These data provide information on the temperature and humidity of the atmosphere, the temperature and emissivity of the surface, as well as clouds and precipitation, which all affect the measured radiances. Research satellite instruments measure radiation that provides information on the various elements of the Earth System, including the atmosphere (dynamical variables, atmospheric composition); the ocean (sea surface temperature and salinity); the land surface (soil moisture, snow); and the cryosphere (marine ice thickness).

Although observations are essential to estimate the state of the Earth System, they have two key limitations. The first one is that they contain errors — these can be systematic (also called bias), random, and of representativeness (see Cohn, 1997; Lahoz, 2010; Ménard, 2010). The sum of these errors can be termed the accuracy. Averaging random errors reduces them, but does not reduce systematic errors; if known, systematic errors are subtracted from an observation. The representativeness error is associated with differences between the resolution of observational information, and the resolution to interpret this information. The second limitation is that they have spatio-temporal gaps (Lahoz et al., 2010a; Lahoz and Schneider, 2014) — see Figure 1. It is necessary to fill in the gaps in the information provided by observations: (i) to make this information more complete, and more useful; and (ii), to provide information at a regular scale to allow easier quantification of physical processes, e.g., calculation of fluxes between the land and the atmosphere.

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Fig. 1. Plot representing typical data in satellite observations of tropospheric composition, illustrated using nighttime total column carbon monoxide (CO) (units of molecules cm-2) retrieved over Asia using data from the Measurements of Pollution in the Troposphere (MOPITT) instrument on 17 January 2014. The figure shows both gaps between the swaths of the satellite platform as well as missing data points due to clouds and/or other measurement issues within each swath. Red colours indicate relatively high CO total column, blue colours indicate relatively low CO total column. From Lahoz and Schneider (2014). To fill in the gaps in the observations a model is needed (Lahoz et al., 2010a). This model can be simple, e.g., linear interpolation or geostatistical approaches based on the spatial and temporal autocorrelation of the observations, or take account of the system’s behaviour. Various examples of the model used are as follows. (i) A chemistry-transport model (CTM), incorporating a suite of chemical equations and heterogeneous chemistry (Errera et al., 2008). (ii) A general circulation model (GCM), incorporating the discretized Navier–Stokes equations (Salby, 1996). And (iii) A land surface model (LSM) incorporating the transports of energy between the land surface and the atmosphere (Lahoz and De Lannoy, 2014). The model extends the observations, fills in the observational gaps and organizes, summarizes, and propagates the information from observations. The model, like the observations, also exhibits gaps in space and time. It is desirable to find intelligent (e.g., objective) methods for the interpolation, i.e., filling in of the observational information gaps using a model. For example, by finding the state that minimizes a “penalty function” calculated from observational information and prior information (e.g., from a model forecast). We can think of the model used for the forecast as an intelligent interpolator of the observational information: intelligent because it embodies our understanding of the system; intelligent because the combination of the observational

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and the model information is done in an objective way. A methodology that allows this intelligent interpolation is data assimilation (Kalnay, 2003; Lahoz et al., 2010b). The data assimilation methodology adds value to the observations by filling in the spatio-temporal gaps (see Fig. 1), and to the model by constraining it with the observations (see Fig. 2 in Lahoz and Schneider, 2014, and the discussion, for more details). Data assimilation. Data assimilation has several applications, the most advanced being weather forecasting. Applications to other areas of the Earth System benefit from the insights gained from the work of the weather centres. Examples of applications include the design of the Global Observing System (GOS) using observing system simulation experiments, OSSEs; chemical data assimilation; air quality forecasting; land surface data assimilation; ocean data assimilation; and the production of reanalyses for studying the Earth Climate System (Lahoz and Schneider, 2014). Concerning challenges in data assimilation, applications in one area can benefit from issues already known in other areas; in this way, developments at the weather centres provide strong guidance to developments in other areas where data assimilation is applied. Nowadays, with the availability of atmospheric composition measurements from various satellite platforms, e.g., ESA’s Envisat (launched in 2022), NASA’s EOS Aura (launched in 2004), and JAXA’s GOSAT (launched in 2007), it has become possible to replicate results from weather forecasting by providing forecasts and analyses of atmospheric constituents based on chemical modelling and data assimilation techniques. The MACC-II project and its predecessors have led the way in these activities toward implementing the operational atmospheric service of Copernicus. A recent example of the work of MACC is the assimilation of methane data from the satellite platforms SCIAMACHY, IASI and TANSO (Massart et al., 2014). MACC activities. The ECMWF (European Centre for Medium-Range Weather Forecasts) has produced within the MACC and MACC-II projects a reanalysis that includes atmospheric composition data. The data assimilation method used in the MACC reanalysis is incremental 4D-Var (Inness et al., 2013). The MACC reanalysis covers a period of 10 years (January 2003-December 2012) and covers aerosols, reactive gases and greenhouse gases. It combines state-of-art atmospheric modelling with Earth Observation data, providing a fully consistent meteorological and atmospheric composition dataset. One of the MACC-II deliverables that concerns satellite atmospheric composition data for assimilation is Deliverable D16.2 (“Overview reports with summary of experience with the satellite data and different retrievals used”; Philipp Schneider). It discusses the satellite instruments used or considered within the global and regional data assimilation groups at MACC-II. These instruments mainly include the following. (i) IASI (Infrared Sounding Interferometer; Simeoni and Singer, 1997). (ii) OMI (Ozone Monitoring Instrument; Levelt et al., 2006). (iii) GOME-2 (Global Ozone Mapping Experiment-2; Callies et al., 2000). (iv) MOPITT (Measurements of Pollution in the Troposphere; Pan et al., 1998; Emmons et al., 2009). And (v) SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography; Bovensmann et al., 1999; Gottwald, 2006). Other satellite instruments used in the MACC-II project include MODIS (Moderate Resolution Imaging Spectroradiometer; Remer et al., 2005); SEVIRI (Spinning Enhanced Visible and Infrared Imager; Aminou, 2002); and TANSO

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(Thermal And Near-infrared Sensor for carbon Observation; Kuze et al., 2009). The main species used by MACC from these instruments are ozone and CO. The work associated with Deliverable D16.2 identified a number of issues regarding data assimilation in the MACC project. These included the importance of near-real-time observations for analyses, also used as the initial conditions for short-term forecasts (typically, one needs data latencies of less than 3 hours). Furthermore, it was pointed out it is important to have quantitative estimates of the observational errors, including access to metadata. Finally, information such as averaging kernels is helpful in the data assimilation method, e.g., when the vertical resolution of the observations and the models used in the data assimilation system are not comparable. These issues need consideration when assessing the benefit of satellite measurements for the Copernicus Atmospheric Service. Outlook for the report. In the following sections, we discuss the summary of experiences in MACC-II, focusing on atmospheric composition information (observational, model and data assimilation). In Sect. 2 we focus on greenhouse gases, aerosols and reactive gases. In Sect. 3 we focus on additional satellite data not presently used in MACC-II but that could be valuable for the Copernicus Atmospheric Service. In Sect. 4 we focus on the outlook of new satellite missions for atmospheric composition and their potential relevance for the Copernicus Atmospheric Service. Finally, in Sect. 5 we focus on recommendations for future missions to secure their use in the Copernicus Atmospheric Service.

2. Summary of experiences in MACC-II In this section, we discuss the use in MACC-II of the following atmospheric data: (i) greenhouse gases (Sect. 2.1); (ii) aerosols (Sect. 2.2); and (iii) reactive gases (Sect. 2.3).

2.1. Greenhouse gases data Satellite missions. Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, e.g., the Global Carbon Project (GCP); http://www.globalcarbonproject.org/). The greenhouse gases ESA CCI (GHG-CCI) project (http://www.esa-ghg-cci.org/) focuses on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG-CCI project aims at delivering the high quality satellite retrievals needed for this application. It is one of several projects of ESA’s Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs). The GHG-CCI core ECV data products, generated with ECV Core Algorithms (ECAs), are column-averaged mole fractions of CO2 and CH4 retrieved from SCIAMACHY on ESA’s Envisat satellite (2002-2012) and TANSO on JAXA’s GOSAT satellite (launched in 2009). Both sensors enable the retrieval of near-surface sensitive column-averaged dry air mole fractions of CO2 and CH4, i.e., XCO2 (in ppmv) and XCH4 (in ppbv). The GHG-CCI project will use other satellite instruments (e.g., IASI, MIPAS, ACE-FTS) to provide constraints for upper layers in the CO2 and

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CH4 column. The corresponding retrieval algorithms are Additional Constraints Algorithms (ACAs) within the GHG-CCI project. Important aspects also covered by GHG-CCI are calibration, validation and user assessments of the satellite data. Using measurements from the first years of SCIAMACHY it had already been demonstrated prior to the start of the GHG-CCI project that regional methane emissions can be well constrained via inverse modelling approaches. The GHG-CCI project has extended this methane time series. For SCIAMACHY XCO2, the first surface flux inversions have been carried out within GHG-CCI. Note that the XCO2 user requirements are more stringent compared to XCH4. The GHG-CCI project is improving the SCIAMACHY XCH4 and XCO2 retrieval algorithms; this will improve the quality of SCIAMACHY greenhouse gas data products. Launch of the GOSAT satellite occurred shortly before the GHG-CCI project started, and only initial data products were available at that time. JAXA and NIES in Japan generate operational GOSAT data products. GOSAT data products are also generated outside Japan, e.g., in the USA by NASA (the ACOS team). The work at GHG-CCI has significantly strengthened European GOSAT retrieval efforts and generated the first high-quality global multi-year data sets from GOSAT. Improving the quality of these XCO2 and XCH4 time series and extending this time series is a major focus of the GHG-CCI. The data provided by the GHG-CCI is of interest to the Copernicus Atmospheric Service, for example in the areas of environmental monitoring and climatology. In July NASA’s OCO-2 satellite was launched. Section 4, which considers the outlook of new missions, and their relevance to the Copernicus Atmospheric Service, provides details of this satellite. The MACC-II project. Deliverable D_GHG.43.8 (“Inverted CO2 surface fluxes for the 1-year MACC-II reanalysis using available ECV data”; Frederic Chevallier) discusses aspects of the use of greenhouse gas data in the MACC-II project, and their potential benefit when included in the Copernicus Atmospheric Service. This deliverable discusses the MACC-II CO2 flux inversion system. This is a variational atmospheric inversion scheme, developed at LSCE (Chevallier et al., 2005). This system estimates 8-day grid-point day- and night-time CO2 fluxes and the grid point total columns of CO2 at the initial time step of the inversion window. It assimilates surface air sample measurements, but most of these data only start becoming available after about a year from the time the sample is taken. In principle, satellite measurements of the CO2 column are available to the inversion system in much shorter times; their assimilation for flux inversion, initially planned within the MACC-II project, has undergone extensive tests in the GEMS/MACC/MACC-II projects. Tests with simulated data or real column measurements of CO2 made at the surface by the Total Carbon Column Observing Network (TCCON) are very encouraging for providing flux inversion estimates (Chevallier et al., 2007, 2009b, 2011). An exception to these results concerns simulations using different transport models for the generation of the observations and for the inversion (Chevallier et al., 2010). However, owing to relatively large model and observational errors, the realism of the fluxes inverted from the real satellite measurements has been poor up to now (Chevallier et al., 2005, 2009a, 2004). This is a subject of further study at LSCE and elsewhere, including partners in the MACC and MACC-II projects.

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2.2. Aerosol data Currently, aerosol data assimilation in MACC-II makes use of MODIS satellite sensor data. Mainly due to their good temporal and spatial coverage (globally at least two samples, clouds permitting) and due to their near-real time availability MODIS aerosol retrievals have been the preferred choice in global assimilations. It is important that the quality of the aerosol retrieval algorithm is high. To quantify recent MODIS retrieval improvements and to investigate alternative options for satellite sensor data, MACC-II deliverable D_64.6 (“Final Assessment of suitability of satellite datasets for assimilation”; Stefan Kinne) considered available aerosol products from satellite retrievals from the year 2008, and compared them against each other, including retrievals by MODIS, MISR, ATSR, MERIS, POLDER and IASI. In addition, Kinne studied the performance of two different MACC-II assimilation algorithms with MODIS data. To determine the skill of the assimilation algorithms, comparison was made between local (1ox1o latitude/longitude) daily and monthly averages against highly accurate data or statistics by sun-photometry of ground-based measurement networks (mainly from AERONET over the land and the Marine Ocean Network over the ocean). Computation of the total scores for AOD (aerosol optical depth, a measure of aerosol amount) and the Ångstrom coefficient (a measure of aerosol size), came from a concatenation of regional sub-scores, which consisted of detailed scores for bias, spatial and temporal correlation. Among all the retrievals tested, for the mid-visible AOD, the combination of the new MODIS collection 6 with the MODIS deep blue (now also covering data over bright surfaces) yields the overall highest score (although some way from being perfect). The version MODIS 6 improves on the version MODIS 5 in almost every aspect, but characteristic weaknesses in some regions remain (e.g., over the USA). Over the continents, MISR AOD retrievals score near the top and over the oceans, POLDER has near-top scores. The skill of the AOD retrieval from European sensors is mixed. While the ATSR retrieval skill improved over recent years to reach scores comparable with those from MODIS and MISR in some regions, the retrieval skills of MERIS (providing aerosol correction) products continues to disappoint. AOD scores are typically better over oceans than over continents, as surface contributions over land are much more difficult to quantify. To derive the Ångstrom parameter with good accuracy, at least two good AOD retrievals at two different solar wavelengths are required. Thus, Ångstrom scores are usually lower than AOD scores. Data from MODIS collection 6, and ATSR from Swansea University, offer better Ångstrom scores and demonstrate their superior skill in capturing the characteristic aerosol size and the aerosol type. Overall, a comparison of scores from the two MACC-II assimilations schemes discussed above, with a more complex two-parameter (AOD, Ångstrom coefficient over the oceans) assimilation scheme, yielded lower scores. However, the simpler schemes showed improvement for the scores for the Ångstrom coefficient over the oceans. Although there is

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evidence of benefit to the Copernicus Atmospheric Service from aerosol satellite data, further work is required at MACC, and elsewhere, to optimize use of these data in assimilation algorithms that provide aerosol analyses.

2.3 Reactive gases data The reactive gases used in MACC considered for validation of free model and assimilation results in this section cover the troposphere and the stratosphere. For the troposphere these reactive gases are ozone (profiles and tropospheric column), NO2 (tropospheric column), CO (profiles), and formaldehyde (tropospheric column). For the stratosphere these reactive gases are ozone (profiles and total column) and NO2 (total column). The deliverable D_82.12 (“Validation report of the MACC near-real-time global atmospheric composition service: System evolution and performance statistics, status up to 1 March 2014”; T. Antonakaki et al.) provides further details. Table 1 below provides an overview of the quick-look validation websites of the MACC system.

Below we provide a summary of the validation of the various MACC model-generated products of reactive gases. Troposphere. The validation of model and analysed ozone is with respect to surface and free troposphere ozone observations from the GAW and ESRL networks, IAGOS airborne data and ozonesondes. Results for the free troposphere ozone are as follows:

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Modified normalized mean biases (MNMBs) for model ozone are generally between 20% and -20% for three MACC model-generated products, (MACC_osuite, MACC_CIFS_TM5, and MACC_fcnrt_MOZ). MACC_osuite is the pre-operational coupled IFS-MOZART modelling system with data assimilation, with aerosol analyses and forecasts based on the MACC prognostic aerosol module in the IFS at ECMWF – the other two MACC products do not include data assimilation, and thus represent free model runs;

Larger deviations occur for the Tropics (up to 40%), Antarctica (between -28% and 46%) and in the MACC_osuite for the Arctic (up to -38%);

In the Northern Hemisphere (NH) and Tropics, MACC_fcnrt_MOZ has larger positive MNMBs than MACC_osuite. This difference between the analysis and free model run illustrates the important positive impact of the assimilation of satellite ozone measurements;

IAGOS flights show that at mid-latitudes in the NH, the vertical gradient of ozone of c. 100 ppbb/km is realistic in the three models, but there are underestimates of the altitude of the UTLS.

Results for surface ozone are as follows:

MACC_CIFS_TM5 and MACC_fcnrt_MOZ models tend to overestimate observations of ozone concentrations in the period Dec 2013 –Mar 2014, except for Arctic and Antarctic stations. MACC_osuite shows negative deviations also for the USA, Asia and Europe. The MNMBs range between -24% and 23%, with the exception of the large negative bias of MACC_CIFS_TM5 over southern latitudes (by c. -66% at Ushuaia station). This may be related to a corresponding negative bias in stratospheric ozone at these latitude bands. Note the large negative bias observed for the MOZART based runs at two stations in the Arctic (MNMB up to -100%);

The correlations between observations and models are high in all regions (with some exceptions excluded, these are between 0.3 and 0.9), with MACC_CIFS_TMS showing best results.

Results for NO2 are as follows:

Model validation with respect to SCIAMACHY NO2 data before April 2012 and GOME-2/MetOp-A NO2 data afterwards, shows that tropospheric NO2 columns are well-reproduced by all three NRT model runs, indicating that emission patterns and NO2 photochemistry are generally well represented;

Some differences are apparent with larger shipping signals in an all models and an underestimation of NO2 columns over the continents in general and China in particular, which points to an underestimate of anthropogenic NO2 emissions in the inventories;

Compared to satellite data, all model runs underestimate tropospheric background values over Africa, South America, Eurasia and Australia;

The seasonality in regions affected by biomass burning is reasonably well represented by all model runs after Oct 2011.

Results for CO are as follows:

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Model validation with respect to GAW network surface observations and IAGOS airborne data (until Feb 2014), and IASI and MOPITT satellite retrieval data (until May 2013) shows that the seasonality of CO can be reproduced well by all models;

However, there is a systematic underestimation of CO surface mixing ratios by all model runs in the NH (MNMBs between -1% and -28% in comparison with GAW observations). The biases are largest during winter and early spring and during fires season over Siberia, Alaska and Africa;

IAGOS flight observations show that the models generally reproduce CO profiles but with offsets in the amount of CO at elevated levels;

Maximum concentrations of CO show better comparison with IASI data than with MOPITT data - the surface concentrations are either underestimated or overestimated with differences about 50-100 ppb;

MACC_osuite shows a better performance than MACC_fcnrt_MOZ with similar seasonality and lower biases over most regions, especially during summer – this better performance comes from the assimilation of CO satellite data. This indicates that data assimilation is most effective in summer and less effective during the winter season. Due to the globally constant (but time varying) bias correction applied to MOPITT data during the assimilation, MACC_osuite agrees generally better with MOPITT data, with average relative biases of the models (free model and assimilation) ranging between -10% and -25% against MOPITT v5 data;

MACC_CIFS_TM5 shows lower biases than the other model runs between Dec 2013 and Feb 2014 for Europe – this is confirmed by the better performance of this run with respect to MOPITT v5_TN at mid-latitudes;

An assessment of the 96-hr forecasts showed that MACC_osuite provides stable forecasts with increasing forecast time for most NH stations, except for single stations where a trend is seen with increasing forecast time.

Results for formaldehyde are as follows:

Model validation with respect to SCIAMACHY HCHO data before April 2012 and GOME-2/MetOp-A HCHO data afterwards, shows significant differences between satellite data and models, particularly over South America, Africa, Northern Australia and Indonesia/Borneo;

MACC_CIFS_TM5 generally simulates lower values over the oceans than the other two MOZART-based model runs and higher values over the continents where the satellites show a maximum;

There is good continuity between MACC_CIFS_TM5 and the previous coupled TM5-IFS system;

For time series over East Asia and the Eastern USA (both regions where HCHO columns are likely dominated by biogenic emissions), models and satellite retrievals are of similar magnitude and seasonality. In the African regions dominated by biogenic and biomass burning HCHO (precursor) emissions, satellite values are lower than those from the models up to the year 2012 and there is little similarity in seasonality, indicating that the emissions used in the model are not accurate. However, model performance improved for these regions since 2012;

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No assimilation of formaldehyde observations took place - these results thus reflect the performance of the unconstrained models.

Stratosphere. The main constituent validated for the stratosphere is ozone, both for vertical profiles and total columns. Comparison of ozone profiles is against the following independent observations and model analyses. (i) Vertical profiles from balloon-borne ozonesondes. (ii) Ozone profile retrievals from the OMPS and OSIRIS satellites. (iii) Ground-based remote sensing observations from the NDACC (Network for the Detection of Atmospheric Composition Change; http://www.ndacc.org) collected by the network NORS (http://nors.aeronomie.be), including microwave observations for Ny Ålesund and Bern, FTIR observations for Jungfraujoch and Izaña; and Lidar observations from the Haute Provence stations. And (iv) Analyses from the BIRA-IASB BASCOE assimilation system. The results for the ozone vertical profiles are as follows:

Relative monthly mean biases of the MACC_osuite typically have values below 5-10% compared with vertical profiles from balloon-borne ozonesondes. Stratospheric ozone is generally slightly overestimated – except for the Arctic and for the forecast run based ion MACC_CIFS_TM5 in the Tropics;

Reproduction of the Antarctic ozone hole of 2013 by the MACC_osuite was realistic with relative biases less than 10%. Considering that the corresponding model run without data assimilation cannot reproduce low stratospheric ozone values (biases up to 25%), data assimilation has been shown to be essential for a correct representation of this ozone hole event;

Ozone fields at 50 hPa from MACC_osuite and the BASCOE assimilation system are very similar (differences less than 8%). Confirmation of the MACC_osuite good performance comes from comparison with OMPS satellite retrievals during the season Sep-Oct-Nov (SON). However, a positive bias of c. 15% occurs at mid-latitudes between 30 km and 40 km, where ozone is more abundant. Similar discrepancies are observed in a comparison with OSIRIS data for Dec-Jan-Feb (DJF) at the same altitudes, but now also in the Tropics;

The MACC_osuite agrees very well with observed stratospheric columns at Ny Ålesund, but the bias increases toward the end of the period considered, reaching a maximum of c. 40% in Nov 2013. This strong increase in bias is also seen in MACC_CIFS_TM5, which overestimates upper (50 km and higher) and lower (40 km and lower) stratospheric ozone columns by almost 100%, and to a lesser extent by MACC_fcnrt_MOZ, which overestimates these partial columns by c. 20% during the last 3 months of 2013;

A bias of c. 10% until Nov and decreasing to c. 2% in Dec occurs above Bern. This bias is mostly due to the assimilation as it is lower in the corresponding forecast experiment MACC_fcnrt_MOZ until Nov, but becomes negative from Dec;

At Haute Provence the mean biases of the three models show an increase from Sep reaching differences of c. 7% (MACC_osuite), 12% (MACC_fcnrt_MOZ) and 20% (MACC-CIFS_TM5);

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Detailed profile comparisons show more variability in the performance of the model for the Arctic station than for the mid-latitude stations, due to the movement of the Arctic vortex during the winter months;

At the Tropics (Izaña), the MACC_osuite shows an annual bias of -2.5% with small variability, whereas MACC_fcnrt_MOZ shows an annual bias of 1% and deviations up to c. 12% during spring, accentuated at altitudes lower than 50 hPa. MACC_CIFS_TM5 has biases of c. -12% between July and Oct;

The poor performance of MACC_CIFS_TM5 in the stratosphere comes from the use of the Cariolle scheme for stratospheric ozone, and that this model runs without constraint from observations.

Comparison of the total ozone columns is against the three independent assimilation models TM3DAM, BASCOE and SACADA. The reference for the ground truth is TM3DAM as it applies a bias correction to GOME-2 data based on the surface Brewer-Dobson measurements. The results for total ozone column are as follows:

BASCOE results approach those of TM3DAM after the BASCOE upgrade in early Jan 2013;

SACADA shows much improved results for the ozone analyses once it starts to assimilate in Aug 2013 MLS ozone profiles instead of GOME-2 total ozone columns;

Even though underestimation and overestimation alternate in the vertical distribution, the total ozone columns provided by the IFS-based models compare generally very well with those from TM3DAM;

MACC_CIFS_TM5 systematically overestimates total ozone in the NH extra-tropical region (by up to 15%) and underestimates total ozone in the Tropics;

In the Southern Hemisphere (SH), MACC_CIFS_TM5 underestimates total ozone from Sep 2013. During autumn and winter (May-Aug 2013) it shows an overestimation.

The other stratospheric species validated is NO2. Results are as follows:

Compared to NO2 columns from SCIAMACHY and GOME-2/MetOp-A satellite retrievals, the overall representation of the amplitude and seasonality of stratospheric NO2 by MOZART chemistry runs is good, being very good from 30oS to 90oN at least until May 2013;

From June onwards, only the MOZART without data assimilation reproduces satellite values very well, while the MACC_osuite shows a systematic low bias relative to the satellite observations since the update of MACC-osuite in July 2012. Furthermore, this bias appears to have increased since Oct 2013 when a new cycle (CY38R2) was introduce to MACC-osuite;

While the seasonality of the old MACC_fcnrt_TM5 (which is nudged to a climatology) stratospheric columns was very similar to the observations, their values were too low. This improved on MACC_CIFS_TM5, but the values are now significantly larger than the satellite observations in extratropical regions.

MACC_CIFS_TM5 simulates an unexpected increase in NO2 from April 2013 to July 2013 in the Antarctic and the SH mid.latitudes which is not seen in any other model run nor in the GOME-2 data. However, its values decreased to the magnitude of

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satellite retrievals from Oct 2013 to Feb 2014 over the Antarctic. Note that MACC_CIFS_TM5 only contains explicit reactions for tropospheric NOx/NOy chemistry.

General comments. The studies discussed in Sections 2.1-2.3 identify a number of general issues, which need addressing within the MACC community. These issues, or challenges, are the focus of activities in the data assimilation community (Lahoz and Schneider, 2014). These issues include:

Improved representation of observational and model errors, including development of hybrid variational/ensemble methods in data assimilation methods;

Extension to include and couple various elements of the Earth System, for example the land and the atmosphere to improve representation of fluxes between them;

A reduction in spatial scales simulated and forecast, thus getting closer to the needs of users.

These developments will improve the products made available by the Copernicus Atmosphere Service, with benefit to users. Furthermore, it is expected that fully coupled, higher-resolution and more accurate reanalyses of the whole Earth System will lead to a better understanding of climate variability and the predictability of weather events, with benefit to society.

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3. Additional satellite data not presently used in MACC-II that can be valuable for the Copernicus Atmospheric Service Several satellite datasets used in MACC-II (e.g., for validation, for assimilation) have been discussed in Section 2. Additional satellite datasets not presently used in MACC-II that could be valuable for the Copernicus Atmospheric Services fall into two categories; (i) additional satellite platforms (these include atmospheric species already used in MACC-II); and (ii) additional atmospheric species. Additional platforms. Future (or recently launched) satellite platforms (planned or proposed) with potential, for future use in the various atmospheric ECV ESA CCI projects include (the list is not exhaustive) – see also Sect. 4:

Greenhouse gases: OCO-2 (NASA), CarbonSat (ESA);

Air quality: Sentinels 4, 5 and 5-Precursor (ESA); TEMPO, GEO-CAPE (NASA). Additional atmospheric species. We focus on N2O and SO2. N2O: Nitrous oxide (N2O) mixing ratios have been increasing steadily in the atmosphere over the past few decades at an average rate of c. 0.3% per year, reaching 323 nmol mol−1 (equivalently parts per billion, ppb) in recent years (WMO, 2011) compared with c. 270 ppb before the industrial revolution (Forster et al., 2007). The growth rate of N2O is a direct consequence of the imbalance between the emissions and the sinks of N2O. The sinks, that is, photolysis and oxidation by O(1D) in the stratosphere, are thought to have increased at a slower rate than that of the emissions, which have been increasing since the mid-19th century largely due to human activities. There are strong links between N2O emissions and the amount of reactive nitrogen (ammonium, nitrate and organic forms) in the environment. The global demand for food, and more recently bio-fuels, has led to an increasing production of reactive nitrogen, used in fertilizers, especially in the latter half of the 20th century (Galloway et al., 2008). The increase in N2O is a major concern because it is a greenhouse gas and has the third largest contribution to net radiative forcing after CO2 and CH4, and currently has radiative forcing of 0.17Wm−2 (Myhre et al., 2013). Furthermore, N2O plays an important role in stratospheric ozone loss and currently the ozone-depleting-potential weighted emissions of N2O are the highest of any ozone depleting substance (Ravishankara et al., 2009). The atmospheric mole fraction of N2O has increased significantly since the mid-20th century largely owing to agricultural activities and, in particular, the use of nitrogen fertilizers (Park et al., 2012). Thompson et al. (2014a, b) discusses the influence of transport and fluxes on N2O variability, and the use of atmospheric inversion to estimate N2O emissions, respectively. SO2: Sulphur dioxide (SO2) is an atmospheric pollutant (White Book, 2013), and like other pollutants such as ozone, particulate matter (PM) and NO2 it impacts human society, ecosystems and materials. High concentrations of these pollutants, including SO2, near the Earth’s surface cause health problems, in particular pulmonary and cardiovascular diseases, and recognition is growing of the combined health effects of multiple pollutants. SO2 (as well

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as ozone and NO2) is an irritant, which can affect severely the respiratory tract, particularly in asthmatics, children and the elderly. During the period 1960s-1980s acid rain, resulting from reaction between SO2 and NOx and water molecules in the atmosphere, was responsible for degradation of many lakes and forests in Europe and North America. One impact of acid rain was to modify chemical equilibria in lakes, thereby enhancing solubility of heavy metals toxic for fauna and flora. Soil acidification due to acid rain can promote loss of minerals useful to plants, and adversely affect soil biology. Photochemical pollution from NOx, SO2 and particulate matter can also damage materials such as plastic, glass and stone. Main effects include alteration of the aesthetic appearance of buildings, increased cleaning costs, and irreversible degradation and loss of cultural heritage such as historical monuments and sculptures. The European legislation regulating atmospheric pollution requires EU Member States to set up and maintain in situ observation systems. Thousands of stations all over Europe continuously survey the ambient levels of atmospheric pollutants to which one might be exposed, chiefly ozone, NO2, SO2 and PM. A substantial fraction of these stations is located in urban areas where most people live or are close to industrial and traffic sources. Key target species for MACC/MACC-II are ozone, NO/NO2, PM10 and PM2.5 and, for some “hot spots”, SO2. Typical resolution of models covering Europe is currently 0.2o to 0.5o (20 to 50 km) and will likely reach 0.05o-0.1o (5-10 km) within the next five years. The regional forecast products in MACC-II use satellite products to a limited extent as current regional data assimilation activities rely mostly on surface data. ). Inclusion of satellite data into these air quality operational systems would be of benefit to society. The Copernicus Atmospheric Service can realize this benefit. A notable development towards improving the Copernicus Atmosphere Service is likely to be the exploitation of synergies of information from various elements of the Earth System, including the atmosphere and the land surface. For example, the expectation is that information on temperature and humidity in the lowermost atmosphere, together with information on land surface temperature and soil moisture, will provide better information (better coverage, higher accuracy) of land-atmosphere fluxes. Furthermore, the expectation is that better information on the land surface will also provide better information on emissions of pollutants and greenhouse gases.

4. Outlook of new missions and their potential relevance for the Copernicus Atmospheric Service Introduction. In this Section, we discuss a number of areas of societal interest where work is ongoing to make best use of atmospheric composition data, focusing on data from satellite platforms. To illustrate the requirements for this work we first focus on the key area of air quality, making connections to other key areas (e.g., greenhouse gases). Air quality. We define air quality (AQ) by the atmospheric composition of gases and particulates near the Earth’s surface. This composition depends on local contributions

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(emissions of pollutants), chemistry, and transport processes; it is highly variable in space and time (McNair et al., 1996). Key lower-tropospheric pollutants include ozone (O3), aerosols (e.g., PM, particulate matter), and the O3 precursors NOx (= NO + NO2) and VOCs, volatile organic compounds (Brasseur et al., 2003). Tropospheric O3 controls the oxidation of many tropospheric species through reactions involving the OH (Brasseur et al., 2003). Owing to its relatively longer lifetime (in comparison with other easily observed species), tropospheric CO observations provide information on sources of pollution and transport processes affecting AQ. C2H2O2 is virtually unaffected by road traffic emissions and thus is a good indicator of photochemical smog (Volkamer et al., 2005). Air quality impacts human society: high concentrations of O3, PM, and NO2 near the Earth’s surface cause health problems, in particular, pulmonary and cardiovascular diseases (Brunekreef and Holgate, 2002), and recognition is growing of the combined health effects of multiple pollutants (Dominici et al., 2010). Air quality satellite missions. We now discuss current, planned and proposed AQ satellite missions in Europe, USA and Asia. We first discuss the various observing satellite geometries, LEOs and GEOs, and their usefulness for monitoring air quality. LEO satellites observe partial or full columns of several species relevant to air quality, AQ (O3, CO, NO2, HCHO, and SO2) and make important contributions to pollution sources, transport, and characterization of air pollution variability at global, continental, and regional scales (NRC, 2008). The last decade has seen major advances in observations of tropospheric species from LEO satellites (Fishman et al., 2008). LEO satellites provide a long-term global view of atmospheric composition, but they have limitations with respect to sampling atmospheric constituents in the lower troposphere at the spatio-temporal resolution necessary to monitor AQ. Their main advantage concerning AQ is their global coverage (this is possible from a single LEO satellite), typically covering most of the Earth, including regions close to the poles (in contrast, GEO satellites are limited to about a quarter of Earth’s surface, and do not normally make observations poleward of 60°N or 60°S). Thus, the main drawback for LEO satellites is the difficulty of achieving full temporal coverage, because for this purpose a constellation of more than 10 LEO satellites is required to give long-term hourly coverage at continental scales (Lahoz et al., 2012). A GEO platform has much better sampling of diurnal variability than a LEO platform (see Fig. 2), and an improved likelihood of cloud-free observations (this, however, depends on pixel size) with continuous observations of a particular location during at least part of the day. This “stare” capability from a GEO provides significantly greater measurement integration times compared to those from a LEO satellite. This feature of the GEO satellite platform makes it very effective for the retrieval of the lowermost troposphere information for capturing the diurnal cycle in pollutants and emissions, and the import/export of pollutants or proxies for pollutants. Realistically (given technical, scientific, and cost considerations), a GEO satellite is the only satellite platform that can provide this information at the spatio-temporal scales that are associated with variability of tropospheric pollutants (temporal frequencies less than 1 hr; spatial scales less than ~10 km). A single GEO satellite provides AQ information with complete temporal coverage over a key continental area of the globe (e.g., Europe, East Asia, or North America), whereas many LEO satellites would be required to provide the same information.

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Fig. 2. The number of LEO satellites required for a 1-hr revisit time over Europe. (Left) 1ox1o resolution (~100 km) and (Right) 0.4ox0.4o resolution (~40 km). Gray indicates no measurements are possible. In both cases, the least number of LEO satellites required is three (dark blue regions), but this is only for very small regions over Europe. For 1-hr revisit time and ~10 km (or less) resolution, the least number of LEO satellites required over Europe up to 60oN is more than 10; by contrast, only one GEO satellite is required. © 2012 American Meteorological Society (AMS) - From Lahoz et al. (2012). As of 2014, only LEOs make observations of trace gases. By contrast, multi-channel imagers providing information on aerosols and fire-related parameters at high spatio-temporal resolution have been flying on operational (i.e., numerical weather prediction, NWP) satellites in geostationary orbit for the past three decades (e.g., NOAA GOES series and the EUMETSAT Meteosat series). Both qualitative (smoke plume analysis and a dust mask) and quantitative (aerosol optical depth, main fire hot spots, and the size of unexpected fires and burned area) AQ products are currently available from GEO satellites. To address shortcomings in the Global Observing System (GOS), a number of initiatives in Europe are planning GEO satellites capable of monitoring chemical species. The GMES Sentinel-4 UVN platform (ESA, 2007) will measure tropospheric O3, NO2, HCHO, SO2, and aerosol properties (column-averaged optical thickness and aerosol type). The MTG IRS platform (Munro, 2011) will measure tropospheric O3 and CO (although as a NWP sounder, not optimized for these species). Sentinel-4 UVN and MTG IRS instruments are due for launch from 2017/18 onward. The POGEQA/MUSICQA and MAGEAQ initiatives (Peuch et al., 2009, 2010) focus on tropospheric O3 and CO. Claeyman et al. (2011a, b) discusses the general concept behind these initiatives. As part of the Sentinel programme, two LEO satellites are also due for launch by 2017/18 onward: the Sentinel 5 and the Sentinel 5 precursor (ESA, 2007; Lahoz, 2010). A number of projects outside Europe are also developing GEO satellites for monitoring chemical species. These include the NASA GEO-CAPE mission, with a proposed 2020 launch (GEO-CAPE, 2008); the Korea MPGEOSAT mission, with a planned launch in 2017/18 (Lee et al., 2010); and the JAXA AQ-Climate mission, with a 2020 proposed launch (Akimoto et al., 2008; see www.stelab.nagoya-u.ac.jp/ste-www1/div1/taikiken/eisei/eisei2.pdf (in

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Japanese)). These developments in Europe, the USA, and Asia focus on tropospheric aerosols and trace gases such as O3. Because transcontinental transport of pollutants affects AQ, it is a global concern (Lahoz et al., 2012). To address the need of a global societal response to AQ, there are plans for establishing a constellation of GEOs for monitoring AQ in the Northern Hemisphere (CEOS, 2011). More recently, Bowman (2013) discusses the merits of an ozone air quality monitoring system built around a new generation of LEO and GEO satellites, and how it can meet the challenges of air quality and climate. Synergy between GEO and LEO satellite platforms and surface observations would provide further benefits. To extract tropospheric information on AQ from GEO and LEO satellite platforms, it is necessary to separate stratospheric and tropospheric contributions to the signal received by the satellite. The usual method to achieve this is by taking advantage of the different penetration depths into the atmosphere of different spectral regions (ultraviolet, UV; visible, VIS; thermal infrared, TIR; and near infrared, NIR). This approach has been used to measure tropospheric trace gases and aerosols from LEO platforms in the UV, VIS, and infrared (Fishman et al., 2008). For the UV/VIS these include the following measurements. (i) A tropospheric O3 column from GOME with mean biases of up to 3 DU (Dobson Units) and standard deviations within 3–8 DU against ozonesondes (Liu et al., 2005). (ii) O3 profiles from OMI with tropospheric information of up to 1.5 DFS (degrees of freedom of signal), peaking between 500 and 700 hPa and sensitivity down to ~800–900 hPa, and random errors of ~10% (Liu et al., 2010). (iii) Tropospheric columns of NO2, SO2, HCHO, and C2H2O2 from GOME and/or SCIAMACHY (Chance, 2005, 2006, and references therein). And (iv) Aerosol products from biomass burning: aerosol optical depth (AOD) from MODIS and aerosol absorption optical depth and UV aerosol index from OMI (Torres et al. 2010). For the infrared these include the following: (i) Lower troposphere (between the surface and 700 hPa) CO profile retrievals from MOPITT (Deeter et al., 2007). (ii) Near-surface (from the surface to 800 hPa) increased CO information (quantified by the DFS) from TIR + NIR MOPITT retrievals in comparison to TIR-only MOPITT retrievals (Worden et al., 2010). (iii)The partial O3 column (0–6 km) and tropospheric O3 column (0–11 km) from IASI, with a bias of 5% or less against ozonesondes (Eremenko et al., 2008; Keim et al., 2009). And (iv) A capability for height-resolved tropospheric O3 information (DFS of 2.4) from TES and a demonstration of sensitivity to CO concentrations between 5 and 15 km from TES (Worden et al., 2004). As part of this effort, ESA missions have been instrumental in building a picture of pollution over various regions of the globe (Fig. 3).

Fig. 3. NO2 tropospheric densities (representative of the vertical column), averaged for 2009, from the SCIAMACHY instrument on board the ESA Envisat satellite. (Left) USA, (Middle)

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Europe, and (Right) China. Units are 1015 molecules cm-2. (Figure courtesy A. Richter (IUP-IFE, University of Bremen). © 2012 American Meteorological Society (AMS) - from Lahoz et al. (2012). The ability to retrieve trace gas concentrations in the planetary boundary layer (PBL) is important for the characterization of pollutant sources. In addition to source determination, a measure of PBL concentration in conjunction with free troposphere profile information allows separation of local production from transported pollution. However, these retrievals are challenging for both LEO and GEO satellite platforms. Spectral signatures from the UV to NIR are subject to interferences from clouds, aerosols, scattering, and surface reflectivity uncertainties. In the TIR, the general lack of temperature contrast between the atmosphere and surface limits PBL retrieval capability (Orphal et al., 2005). For O3, because the tropospheric column accounts for less than 10% of the total column, it is challenging to separate PBL O3 from stratospheric O3, even for LEO satellites (Fishman et al., 2008). Notwithstanding these challenges, studies show the potential of multi-spectral observations to extract PBL information, with O3 and CO being the best candidate species at present. Retrieval studies for combining OMI and TES O3 measurements (Landgraf and Hasekamp, 2007; Worden et al., 2007) indicate that such combinations are highly promising. This approach requires measurements from at least two O3 bands from among the ultraviolet Hartley–Huggins (200–360 nm), the visible Chappuis (375–650 nm), and the thermal infrared v3 band (9.6 μm). The combination of TIR + VIS (combinations such as UV + VIS, TIR + UV, and TIR + UV + VIS are also being considered) measurements for O3 (in a LEO or GEO platform) is desirable. This is because, for clear skies, the visible Chappuis bands view directly to the ground, overcoming the difficulties from UV O3 measurements (which are limited in near-surface sensitivity by Rayleigh scattering) and infrared O3 measurements (limited by low thermal contrast between the Earth’s surface and the lower atmosphere; note that the VIS and UV do not provide information at night). However, the absorption in the Chappuis band is weak and the potential aerosol contamination of species retrievals and variations in the surface reflectance as a function of wavelength may present challenges. Measurement of PBL O3 concentrations to desired precision levels is the major current technical difficulty for GEO satellite platforms. The major missing components of tropospheric O3 measurements are as follows. (i) The ability to make precise O3 measurements from the nadir (downward looking) geometry using the visible Chappuis band, as has been used for SAGE-II and other measurements of O3 in solar occultation geometry (e.g., McCormick et al., 1989). And (ii) The capability to perform multi-spectral retrievals, which improves the sensitivity to different atmospheric altitudes (Fig. 4). Studies on the feasibility of the multi-spectral approach to make O3 measurements in the PBL from a GEO platform have been performed for GEO-CAPE (Natraj et al., 2011), and for the POGEQA project (Claeyman et al., 2011b). Similarly, for the planned NASA TEMPO mission (Chance et al., 2013).

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Fig. 4. Representative ozone averaging kernels (normalized to 1 km; green and red) for 6-nm sampling. (Left) TIR, middle (VIS), and (right) TIR + VIS. Averaging kernels relate the sensitivity of the retrieval to the true state (Rodgers, 2000), and here identify the location in the vertical where measurements have information. This figure shows that the TIR + VIS multispectral retrieval improves on individual TIR and VIS retrievals in the lowermost troposphere. Improvement is quantified by an increase in the DFS: 1) pressures greater than 800 hPa are 1.04 for TIR + VIS versus 0.55 for TIR and 0.57 for VIS; and 2) pressures greater than 900 hPa are 0.70 for TIR+VIS versus 0.21 for TIR and 0.37 for VIS. Multispectral retrievals are discussed in Natraj et al. (2011). © 2012 American Meteorological Society (AMS) - from Lahoz et al. (2012). Regarding CO measurements in the PBL from a GEO platform, these observations will require a multi-spectral TIR + NIR retrieval (GEO-CAPE, 2008). Regarding tropospheric aerosol measurements, multi-angle observations over the course of a day from a GEO platform should provide information on aerosol properties such as aerosol optical depth (Zhang et al., 2011). The proposed GEO-CAPE spectral coverage in the UV/VIS should provide the capability to measure simultaneously aerosol optical depth, size distribution, and aerosol absorption (GEO-CAPE, 2008). Availability of UV channels in the GEO-CAPE mission should allow identification and characterization of organic aerosols that have a unique spectral signature in the UV but are not easy to differentiate from other aerosol types with visible-only observations. Despite the advantages for monitoring the AQ of a GEO satellite compared to a LEO satellite, a GEO satellite will significantly lose signal and spatial resolution owing to its longer distance from the Earth (compared to a LEO satellite). Thus, data accuracy and spatial resolution are a challenge for a GEO platform. This requires the use of larger telescopes by a GEO satellite. However, the continued observation of a given area by a single pixel by a GEO satellite allows the integration of signals to recover a satisfactory signal-to-noise ratio while still achieving a high temporal resolution (less than 1 hr) and providing an improved likelihood of cloud-free observations (this, however, depends on pixel size). NASA selected the TEMPO mission in 2012 as the first Earth Venture Instrument, for launch c. 2018. It will measure atmospheric pollution for greater North America (Mexico, USA and Canada) from space using ultraviolet and visible (UV + VIS) spectroscopy. TEMPO measures from Mexico City to the Canadian tar sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2 km N/S×4.5 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution

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chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere (see above), substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with European Sentinel-4 and Korean GEMS (see, e.g., CEOS, 2011). Regardless of the eventual instrumentation in GEO platforms to provide the required spatio-temporal information of tropospheric trace gases and aerosols, a substantial challenge is the development of modelling and data assimilation tools to use this information. Work on developing these tools has already started (Lahoz et al., 2010; Elbern et al., 2010; Lahoz and Schneider, 2014), and their application to the study of tropospheric species that define AQ will play a crucial role in the development of a robust capability to monitor, forecast, and manage AQ. Greenhouse gas satellite missions. Other atmospheric composition gases of interest to MACC include the greenhouse gases such as CO2, methane and N2O. The NASA mission OCO-2 (Orbiting Carbon Observatory-2) was launched on 2 July 2014 and will circle the globe, taking an inventory of those places on the planet that absorb carbon from the atmosphere (the sinks) and those places that release it into the atmosphere (the sources). It will measure CO2 in the atmosphere, and these measurements will help quantify the sources and sinks of carbon, and provide a more accurate estimate of the Earth’s carbon budget. To continue the satellite CO2 and CH4 global time series beyond SCIAMACHY, GOSAT and OCO-2 and to deliver important additional information on CO2 and CH4, the new satellite mission Carbon Monitoring Satellite (CarbonSat) was proposed for EE-8. It was selected to undergo further development studies. CarbonSat will measure globally the atmospheric concentrations ("dry-air column-averaged mixing ratios") of CO2 and CH4 with high spatial resolution (the goal being: 2 x 2 km2) and very good spatial coverage (the goal being: 500 km swath width). One can use these atmospheric measurements for inverse modelling of natural and anthropogenic CO2 and CH4 surface fluxes (emissions and uptake, i.e., sources and sinks). CarbonSat will for the first time help to disentangle natural and anthropogenic sources and sinks of CO2 and CH4. Currently, CarbonSat is being optimized and studies are ongoing to quantify how precise and accurate its CO2 and CH4 observations will be under all possible measurement conditions (e.g.,

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Buchwitz et al., 2013). Furthermore, CarbonSat will provide a number of high-quality so-called secondary products such as Vegetation Chlorophyll Fluorescence, also called Solar Induced Fluorescence (Buchwitz et al., 2013). CarbonSat will map for the first time the detailed spatial pattern of the CO2 emissions of moderate to strong localized emission sources such as coal-fired power plants (Bovensmann et al., 2010). CarbonSat also has the capability to monitor CO2 emissions from major cities (e.g., Buchwitz et al., 2013) and from strong geological sources such as volcanoes. Similar capabilities exist also for methane, which has strong localized emission sources such as fossil energy production facilities (oil, gas, coal) and large waste disposal sites. The CarbonSat glint observation mode over oceans/water will allow to track for the first time from space not well-characterized strong marine geological CH4 emission sources, including large seeps, mud volcanoes or methane releases from the destabilization of shallow marine arctic gas hydrates. CarbonSat is an important step towards a future monitoring and verification system for anthropogenic greenhouse gas emissions, needed in the context of international "post-Kyoto/post-Copenhagen" climate agreements (see, e.g., the National Research Council Report: Verifying Greenhouse Gas Emissions: Methods to Support International Climate Agreements, 2010). The need for CarbonSat type of measurements is emphasized in the GEO Carbon Strategy report prepared by the Group on Earth Observations (GEO). Although a single CarbonSat platform should significantly advance our understanding of the sources and sinks of CO2 and CH4, there are some limitations to the CarbonSat concept. Especially for sources with significant time-dependent emissions, it would be advantageous to overfly them more frequently than is possible with a single satellite. One way to address this limitation is to implement a "CarbonSat Constellation", i.e., a series of CarbonSat satellites from, for example, several nations (see, e.g., Velazco et al., 2011). Aerosols. The Aerosol ESA CCI products of interest to the Copernicus Atmosphere Service include the 17-year long-term time-series from ATSR2/AATSR. Going beyond AOD (aerosol optical depth), aerosol parameters of interest include fine-mode AOD, absorbing aerosol, aerosol type and dust. In addition, there is a focus on stratopheric aerosol extinction. Overall observational platforms of interest to the Copernicus Atmosphere Service. These include the following (the list is not exhaustive):

Sensors planned within the EU/EUMETSAT/ESA Copernicus Earth Observation programme: e.g., Sentinel 4, IRS, FDCI, LI on the MTG system;

The Sentinel 5 Precursor; the Sentinel 5, VII, PDF, 3MI, and IAS on the EUMETSAT Polar System second generation (Metop Second Generation);

The SLSTR and OLCI on Sentinel 3 sensors;

Possibly the capability and use to be made of Sentinel 2 information;

Sensors on ESA Explorer missions and Explorer candidate missions: e.g., ADM Aeolus, EarthCARE, and CarbonSat;

Sensors planned for national missions, e.g., the Franco‐German Merlin, a Lidar demonstrator;

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Sensors planned by NOAA, NASA, JAXA, KARI and other space agencies, including VIIRS, CrIS, ATMS, OMPS‐N planned for JPSS;

Sensors on GEMS, and the improved TANSO on GOSAT‐2; Assessment of the Global Observing System for atmospheric composition. There is a strong link between the the quality and nature of the Global Observing System (GOS) as applies to atmospheric observations, and the benefits provided by the Copernicus Atmosphere Service. Therefore, MACC and related activities should be aware of and be able to, influence efforts to design, modify and extend the GOS. Below we provide details of the OSSE tool used to design the GOS. Monitoring atmospheric composition, including air quality, requires an observing system comprised of satellite and in situ observational platforms (Lahoz et al., 2012). Setting up this observational infrastructure requires the capability to design it in an objective way. Particular questions of interest concerning atmospheric composition are the relative contribution of satellite and in situ platforms to their observational information, and the optimum design of the GOS for monitoring atmospheric composition in a cost-effective way. A methodology for addressing these questions is that of observing system simulation experiments (OSSEs) — see Fig. 5. The OSSE is similar to the observing system experiment, OSE. An OSE considers the impact of existing observations, whereas an OSSE considers the impact of future observations. Evaluation of the OSE results is against the experiment incorporating all data; evaluation of the OSSE results is against the Nature Run, i.e., the Truth (see Fig. 5). Owing to the paucity of air quality observations, OSSEs for air quality typically evaluate the benefit of one extra observational type against a model run, i.e., without data assimilation. The italics in Fig. 5 highlight the differences between an OSE and an OSSE. Data denial (associated with OSEs and OSSEs) involves removing observations from the existing GOS and testing the impact of this action. Data adding (associated with OSSEs) involves incorporation of future observations into the existing GOS or a realization of the future GOS, and testing the impact of this action. Implementation of data denial takes place in an OSSE, where both future data are added and existing data removed.

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Fig. 5. Schematic of an OSE (left-hand flow diagram), and an OSSE (right-hand flow diagram). Based on material in Masutani et al. (2013). From Lahoz and Schneider (2014). The OSSE approach was first adopted in the meteorological community to assess the impact of future observations, i.e., not available from current instruments, in order to test potential improvements in weather forecasting (Nitta, 1975; Atlas, 1997; Lord et al., 1997; Atlas et al., 2003). In a review paper, Arnold and Dey (1986) summarized the early history of OSSEs and presented a description of the OSSE methodology, its capabilities and limitations, and considerations for the design of future experiments. The OSSEs also have been performed to assess trade-offs in the design of observing networks and to test new observing systems (Stoffelen et al., 2006). Masutani et al. (2010, 2013) discusses the recent history of OSSEs, several variants of the OSSE method, and issues concerning their set up and interpretation, and their application. Although OSSEs require significant resources in computing power and human resources, the cost is a small fraction of actual observing systems (Masutani et al., 2013). OSEs can be expensive if they use the full data assimilation system. A more affordable approach to OSEs is the recently developed adjoint-based forecast sensitivity to observations (FSO) technique (Lorenc and Marriott, 2014). Although efficient, the FSO method is limited to evaluating observation impacts on forecasts typically no longer than 24-hr due to the necessary approximation of the full forecast model by a simplified linear version. As a result, OSEs still play an important role in evaluating impacts on longer forecasts. Several OSSEs have been performed to assess the benefit of additions to the GOS to measure winds, either tropospheric winds from ESA’s Earth Explorer ADM-Aeolus, the Atmospheric Dynamics Mission (Tan et al., 2007), or stratospheric winds from CSA’s proposed instrument SWIFT, Stratospheric Wind Interferometer For Transport studies (Lahoz et al., 2005). The launch of ADM-Aeolus is expected in 2015. As illustrated by their use in ADM-Aeolus, the space agencies now recognize the value of OSSEs. As a result the OSSEs have become a standard tool to assess

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proposed and planned satellite missions from space agencies in the USA, Europe, and Asia (Tan et al., 2007; CEOS, 2011; Palmer et al., 2011; Lahoz et al., 2012), including those developed for monitoring air quality (Masutani et al., 2013). Several OSSEs have been performed to assess the benefit of additions to the GOS to monitor air quality at the surface and lower troposphere (between the surface and ∼6 km), notably from GEO platforms. These OSSEs have tended to focus on measurements of ozone and CO (Edwards et al., 2009; Claeyman et al., 2011b; Sellitto et al., 2013; Yumimoto, 2013; Hache et al., 2014; Zoogman et al., 2014). Ozone is considered because it is a key lower tropospheric pollutant and CO because it provides information on sources of pollution and transport processes in the lower troposphere. Other OSSEs for air quality have considered measurements of PM, another key tropospheric pollutant (Timmermans et al., 2009a, b). A key aspect of the OSSEs done to assess future observations of ozone and CO to monitor air quality is the recognition of the need for multi-spectral retrievals. Such retrievals typically use combinations including two or more of the thermal infrared (TIR), the visible (VIS) and the ultraviolet (UV) regions of the electromagnetic spectrum (Natraj et al., 2011; Lahoz et al., 2012; Sellitto et al., 2012). Timmermans et al. (2014) discusses use of the OSSE methodology to assess satellite missions for monitoring air quality. The OSSE methodology for quantifying the added value of future AQ satellite missions is applicable to future greenhouse satellite misisons, although work in the latter area is generally less advanced.

5. Recommendations for future missions to secure their use in the Copernicus Atmospheric Service Benefits from data assimilation activities. The benefits of data assimilation for many sectors of the Earth Observation community, and to society in general, have been documented (see Lahoz and Schneider, 2014). The MACC-II activities have contributed to these benefits, notably in the areas of numerical weather prediction and atmospheric composition. MACC-II and its predecessors GEMS and MACC have led the way in activities toward implementing the operational atmospheric service of Copernicus. A recent example of the work in MACC-II in developing this area is the assimilation of methane data (Massart et al., 2014). Recent outlooks for data assimilation, including the weather centres, focus mainly on three areas (Lahoz and Schneider, 2014):

(i) Improved representation of observational and model errors, including development of hybrid variational/ensemble methods.

(ii) Extension to include and couple various elements of the Earth System and (iii) A reduction in spatial scales being simulated and forecast, thus getting closer to

the needs of users—a notable example for weather centres being representation of convective scales.

Fully coupled, higher-resolution and more accurate reanalyses of the whole Earth System should lead to a better understanding of climate variability and the predictability of weather events. These developments feed into the societal requirements for atmospheric composition measurements. It is expected that further developments within the Copernicus Atmospheric Service will follow also along these lines.

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Atmospheric composition requirements. Regarding atmospheric composition and air quality, the importance of taking action and the need for abatement technologies has led to a strong societal response based on monitoring surface and atmospheric concentrations of key species (e.g., atmospheric pollutants, greenhouse gases) and scientific understanding of their impact. This requires the ability to observe the atmosphere at regional, continental and global scales at high spatio-temporal resolution. For air quality (in this case the lowermost troposphere) a geostationary platform is the only realistic space-based solution (given technical, scientific, and cost considerations) providing this information from space, but important technical difficulties concerning instrument design must be overcome. A key part of the effort to improve monitoring and understanding of atmospheric composition will be the use of satellite and in situ data to constrain atmospheric composition forecast models with data assimilation/inverse modelling methods. This will bring model improvement and increased understanding of processes affecting atmospheric composition. Satellite platforms will improve our ability to monitor, forecast, and manage atmospheric composition, benefitting human society. As an example, current plans for the future GOS lack the constellation of dedicated GEO missions needed for monitoring atmospheric composition (particularly for air quality) over Europe, North America, and Asia; this shortcoming needs remedying. Synergies between different elements of the Earth System. A notable development towards improving the Copernicus Atmosphere Service is likely to be the exploitation of synergies of information from various elements of the Earth System, including the atmosphere and the land surface. For example, the expectation is that information on temperature and humidity in the lowermost atmosphere, together with information on land surface temperature and soil moisture, will provide better information (better coverage, higher accuracy) of land-atmosphere fluxes. Furthermore, the expectation is that better information on the land surface will also provide better information on emissions of pollutants and greenhouse gases. Studies to build and exploit these synergies should form a core part of future developments to improve the Copernicus Atmosphere Service. Such studies would be timely, as they would dovetail with the challenges in the area of data assimilation, which will be key in making the best use of satellite observations and ground-based observations of the Earth System

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