dataesr/scientific-paragraphs-categorization huggingface dataset:
The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003-2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO 2 ; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System.
The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.
While the modelling components for aerosols were included directly in the IFS from the beginning of GEMS  (Morcrette et al. , 2009) , the initial approach for the reactive gases was to build a "coupled system" where the chemical transport model (CTM) Model for OZone And Related chemical Tracers (MOZART), version 3. 5  (Kinnison et al. , 2007) , was coupled to the IFS using the Ocean Atmosphere Sea Ice Soil (OASIS 4) coupling software  (Flemming et al. , 2009). Later, in the MACC projects, a modified version of the Carbon Bond 2005 chemistry scheme (CB05;  Huijnen et al. , 2010)  derived from the CTM Transport Model 5 (TM5) was integrated in the IFS (referred to as IFS(CB05);  Flemming et al. , 2015) , making the system more computationally efficient and improving the system's ability to represent interactions between meteorology and chemistry.
In parallel with the model system, the use of observations in the data assimilation system also evolved with time as new datasets became available and satellite retrievals were improved. The initial forecasts and analyses in 2007-2008 had a horizontal resolution of T159  (∼ 110 km). This was increased to T255 (∼ 80 km) in July 2012 and to T511 (∼ 40 km) in June 2016. The vertical resolution has so far always consisted of 60 model levels in the vertical, with the top level at 0. 1 hPa. Details about the different model versions used over time are given in Table  A1  in the Appendix. The continuing upgrades of the CAMS model and data assimilation system and the changes they brought with them made it difficult if not impossible to compare data from a recent period with earlier data in a meaningful way.
For example, it was not possible to calculate seasonal anomalies or trends with a system that had changed so much over time. Therefore, so-called reanalyses were produced with the CAMS system, where a long time period was rerun with a single version of the model and data assimilation system, taking care to minimise changes in the versions of the used emissions or assimilated satellite retrievals. Such a system gives the temporal consistency needed to deduce trends (e. g. Flemming et al. , 2017b)  or to provide maps of annual or seasonal anomalies (e. g. Flemming and Inness, 2018). Producing long reanalyses with a single model version is a well-known procedure in numerical weather prediction (NWP), and several weather centres have produced meteorological reanalysis datasets. It has long been an important activity at ECMWF (ERA-40,  Uppala et al.
The aim of this paper is to document the new CAMSRA dataset for future reference. The paper gives information about the model and data assimilation setup used to produce CAMSRA, presents initial validation results, and intercompares CAMSRA with CIRA and MACCRA. Additional validation of CAMSRA can be found in reanalysis validation reports  (Eskes et al. , 2018)  and in a validation paper that is in preparation  (Wagner et al. , 2019). This paper is structured in the following way. Section 2 describes the CAMS model, the data assimilation system and the emission datasets used to produce the CAMS reanalysis. Section 3 lists the assimilated AC observations and bias correction procedure. Section 4 gives validation results for some of the reactive gases and aerosols, and Sect. 5 presents the conclusions.
CAMS model system The IFS aerosol and chemistry modules applied in CAMSRA were similar to those in CIRA, and more details about the modules are given in  Flemming et al. (2015)  and references therein. Major updates relative to CIRA are described in the Sect. 2. 1. 1 and 2. 1. 2 below. The meteorological modelling part of the IFS changed from cycle 40R1 used for CIRA to cycle 42R1 used for CAMSRA (see also Table  1 ).
Chemistry module updates The chemical mechanism of the IFS is a modified and extended version of the CB05  (Yarwood et al. , 2005)  chemical mechanism for the troposphere, as implemented in the CTM TM5  (Huijnen et al. , 2010). CB05 describes tropospheric chemistry with 55 species and 126 reactions. Stratospheric ozone chemistry in IFS(CB05) is parameterised by a "Cariolle-scheme"  (Cariolle and Déqué, 1986; Cariolle and Teyssèdre, 2007). Wet deposition is modelled following  Jacob (2000) , and monthly mean gridded dry deposition veloc-ities calculated by the SUMO model of  Météo-France (Michou et al. , 2004)  were used to calculate dry deposition. The chemistry module of the IFS is documented in more detail in  Flemming et al. (2015)  and  Flemming et al. (2017b). The following updates of the chemistry scheme from the configuration used in CIRA were applied to produce CAMSRA: update of heterogeneous rate coefficients for N 2 O 5 and HO 2 based on prognostic clouds and aerosol, modification of photolysis rates by prognostic aerosol, dynamic tropopause definition based on the temperature profile for coupling to the Cariolle scheme in the stratosphere and for tropospheric mass diagnostics, bug fixes, in particular for the diurnal cycle of dry deposition whose correction has decreased the ozone dry deposition flux by about 15 %-20 %.
Emissions Great care has been taken to ensure that the emission datasets are consistent in time and that consistent anthropogenic, biogenic and biomass burning emissions were used for the aerosol and chemistry fields. The emission datasets are listed in Table  1. The emissions are injected at the surface and distributed over the boundary layer by the model's convection and vertical diffusion scheme. The only exception is the aerosol SO 2 emissions, of which 20 % are emitted at the surface as part of the diffusion scheme and 80 % are emitted in the two lowest model levels (see Sect. 2. 1. 1). The emissions datasets used in CAMSRA include emissions from anthropogenic, biogenic, natural and biomass burning sources. Anthropogenic emissions were from the MACCity inventory  (Granier et al. , 2011) , with modifications to increase wintertime road traffic emissions over North America and Europe following the correction of  Stein et al.
g. Ding et al. , 2017). Anthropogenic emissions of black carbon, organic carbon and SO 2 were also taken from MACCity. Emissions of anthropogenic SOA were estimated by applying a scaling factor of 0. 2 to the MACCity (i. e. nonbiomass burning) CO emissions, as suggested in  Spracklen et al. (2011). Monthly mean biogenic emissions of the chemical species were calculated offline by the MEGAN2. 1 model  (Guenther et al. , 2006)  that used meteorological fields from the MERRA-2 reanalysis following  Sindelarova et al. (2014)  for the full period of CAMSRA. Natural emissions from soils and oceans for NO 2 , dimethyl sulfate (DMS) and SO 2 were taken from the Precursors of ozone and their effects in the Troposphere (POET) database for 2000  (Granier et al. , 2005; Olivier et al. , 2003). Daily global biomass burning emissions of reactive gases and aerosols were provided by the Global Fire Assimilation System, version 1.
2 (GFASv1. 2), based on satellite retrievals of fire radiative power  (Kaiser et al. , 2012). The archive of GFASv1. 2 data covers the period 2003-present and was also used in CIRA. In MACCRA early versions of the Global Fire Emissions  Database (GFEDv3. 1; van der Werf et al. , 2010)  were used from 2003 to the end of 2008, and daily GFASv1. 0 data were used from 2009 to 2012. GFEDv3. 1 is on average 20 % lower than GFASv1. 2 (Inness et al. , 2013). Figure  4  shows the GFASv1. 2 time series of monthly mean total carbon wildfire emissions for each of the main continental regions, excluding Antarctica, between 2003 and 2016. The emissions from GFEDv3. 1 and GFASv1. 0 are also shown for comparison. The CO biomass burning emissions do not show a significant trend but considerable inter-annual variability.
Where possible, new reprocessed datasets were used in CAMSRA. However, due to time constraints it was not possible to acquire and process new observations for all the instruments, and for NO 2 from SCIAMACHY and OMI, the data that were already available had to be used. The SCIAMACHY NO 2 retrievals used in CAMSRA were the same data version assimilated in MACCRA  (Koninklijk Nederlands Meteorologisch Instituut -KNMI; V1p from 2003 V1p from to 2011 V1p from , V2 2011 V1p from -April 2012)). The OMI NO 2 data were also produced by KNMI  (Boersma et al. , 2007 (Boersma et al. , , 2011) )  and consisted of offline DOMINO (version 1. 0. 2) data from October 2004 to 2010, the offline DOMINO (version 2) retrieval for 2011-2012 and NRT DOMINO retrievals (version 2) from 2013 onwards. The GOME-2 data were the offline GDP4.
Bias correction A variational bias correction (VarBC) scheme  (Dee and Uppala, 2009)  where biases are estimated during the analysis by including bias parameters in the control vector was used for several of the AC datasets. In this scheme, the bias corrections are continuously adjusted to optimise the consistency with all information used in the analysis. VarBC was applied to the TCO3 retrievals from OMI, SCIAMACHY and GOME-2, with a global constant and solar elevation as predictors, while the partial column SBUV/2 and profile MLS and MIPAS data were used to anchor the bias correction, i. e. were assimilated without correction. Experience from MACCRA had shown that it was important to have an anchor for the O 3 bias correction to avoid drifts in the fields  (Inness et al. , 2013). The SBUV/2 data were chosen as an anchor because they are a high-quality reprocessed dataset that covers the whole period of CAMSRA.
The bias correction helps to ensure good time consistency when blending various datasets and adapts to changing biases of the data. An example is shown in Fig. 6 , which shows time series of monthly mean analysis departures (i. e. observations minus analysis fields) and first-guess departures (i. e. observations minus model first guess) for the four TCO3 retrievals (SCIAMACHY, OMI, GOME-2A and GOME-2B) as well as for the applied bias correction. For all four TCO3 datasets the analysis draws to the observations, and the standard deviations of the analysis departures are reduced compared to those of the first-guess departures. The plots show that the bias correction (black lines) is different for all instruments, successfully adapts to changes in the data, and removes the biases between total column data and the model.
OMI data (Fig. 6b ) between 2009 and 2011, for example, show different behaviour than during the rest of the time series, with larger departures (due to larger observation values, not shown) and the need for larger bias correction. However, the bias correction successfully accounts for this, and the bias corrected departures are small and stable. The reason for this change is a deterioration in the OMI row anomalies (Torres et al. , 2018, see their Fig. 1 ;  Schenkeveld et al. , 2017). More information about this can be found in the Supplement. Thanks to the bias correction removing such biases, the bias corrected departures (dotted lines) are small and stable for all four instruments. Monitoring time series for all the atmospheric composition datasets assimilated in CAMSRA are shown in the Supple-.
Ozone We start by looking at TCO3, which is dominated by stratospheric O 3 , and then look at tropospheric and surface O 3 , which are more relevant for air quality users. Figure  7  shows the seasonally averaged TCO3 from CAMSRA and the differences between this dataset and CIRA and MACCRA. The TCO3 differences between CAMSRA and CIRA are very small (below 2DU, <1 %), with slightly larger differences (5 DU, < 3 %) in June, July and August (JJA) over Antarctica. CAMSRA TCO3 is slightly higher than CIRA over the Tropical Atlantic in all seasons and in NH midlatitudes in JJA; it is lower over NH midlatitudes during December, January and February (DJF) and March, April and May (MAM); and it is lower over SH midlatitudes in MAM and JJA. The differences between CAMSRA and MACCRA are larger, with CAMSRA lower than MACCRA everywhere (up to -10 DU, < 5 %).
Differences between the reanalyses are larger in the troposphere, where the impact of the assimilation is smaller  (Inness et al. , 2015)  and differences in the chemistry schemes, emissions and transport become more important. CAMSRA and CIRA agree well above about 200-100 hPa, while MACCRA overestimates O 3 in all areas above about 15 hPa. While this overestimation of upper stratospheric and mesospheric O 3 in MACCRA will not affect the TCO3 bias, ozone in this region is important for radiative transfer and the associated heating rates. A smaller bias in this region will make CAMSRA a better dataset to be used as climatology in, for example, radiation schemes or radiance observation operators. CAMSRA has larger O 3 values than CIRA in the troposphere which leads to an increased bias with respect to the sondes in the tropics but smaller biases in the other areas.
10  looks at time series of the modified normalised mean bias (MNMB) of reanalysis O 3 minus ozone-sondes in the free troposphere (layer between 750-300 hPa) to assess these differences in more detail. Figure  10  confirms that MACCRA has the largest bias with respect to the sondes and shows different behaviour between mid-2004 and the end of 2007 than during the other years, particularly noticeable in the Arctic, NH midlatitudes and Antarctic. This was documented in  Inness et al. (2013)  and was the result of using VarBC for MLS data in MACCRA during the period August 2004-December 2007. CAMSRA is a much improved and temporally more consistent dataset than MACCRA. CAMSRA also has a smaller bias than CIRA in all areas, apart from the NH midlatitudes during 2005-2009. CAMSRA has larger O 3 values than CIRA in the free troposphere so that CAMSRA shows a small positive bias and CIRA a small negative bias, which was also seen in the O 3  profile plots (Fig.
CAMSRA has the smallest global mean bias against GAW data of the three reanalyses averaged over the period 2003-2012 (0. 51 ± 6. 95 ppb for CAMSRA, -2. 7 ± 8. 7 ppb for CIRA and -2. 3 ± 9. 4 ppb for MACCRA) and also has smaller biases than CIRA for the period 2003-2016 (see Table  4 ). The mean bias for Europe  (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  is -1. 9 ± 9. 3 ppb for MACCRA, -4. 4 ± 8. 7 ppb for CIRA and -2. 3 ± 7. 7 ppb for CAMSRA. In summary, it can be said that for O 3 CAMSRA is temporally more consistent than the older reanalyses and has smaller biases compared with independent observations (see Tables  3  and 4 ). The comparisons also show that it is not advisable to concatenate the older reanalyses with more recent years from CAMSRA because the datasets are too different and that users should use only data from CAMSRA if they are interested in the complete period from 2003 to 2016.
Carbon monoxide Next, we look at CO fields from the reanalyses and compare them with independent observations. Figure  12  shows the seasonally averaged TCCO fields from CAMSRA and the differences between this dataset and CIRA and MACCRA. The TCCO differences between CAMSRA and CIRA are small (below 0. 1 × 10 18 molec cm -2 , < 5 %) with CAMSRA generally lower than CIRA, apart from African biomass burning areas south of the Equator in JJA and parts of Southeast Asia in DJF and MAM. The largest relative differences (of up to 15 %, not shown) are found over the tropical oceans where background values are small. The differences between CAMSRA and MACCRA are larger. CAMSRA is lower than MACCRA over the oceans (0. 1-0. 2 × 10 18 molec cm -2 , relative differences mainly < 15 %) and is much higher over biomass burning areas, e.
The differences between CAMSRA and MACCRA are mainly negative over land in the tropics and positive over areas of anthropogenic emissions apart from the eastern US and parts of China in DJF. These differences are a result of the different chemistry schemes and biomass burning emissions used in CAMSRA and MACCRA as well as the assimilation of NO 2 retrievals from different instruments (only SCIAMACHY assimilated in MACCRA). It is difficult to find independent NO 2 data for validation which are representative for the grid box size of the CAMSRA global reanalysis. We use the following two datasets for validation: (1) a satellite-based tropospheric column NO 2 dataset and (2) surface NO 2 measurements from selected GAW stations in Europe. GAW stations aim to have an uncertainty of about 3 % for monthly mean data  (Penkett et al.
, 2011). As the number of GAW stations measuring NO 2 is small and drops considerably with time during the period of interest, it is not meaningful to look at time series of area means. We therefore restrict our validation to four European GAW stations that have observations for most of the period from 2003 to 2016. The dataset (1) is produced by the University of Bremen based on SCIAMACHY-Envisat NO 2 satellite retrievals (IUP-UB v0. 7, before April 2012;  Richter et al. , 2005)  and GOME-2 and Metop-A NO 2 satellite retrievals (IUP-UB v1. 0, from April 2012 to the end of 2016;  Richter et al. , 2011). The retrieval product used for validation is different to the SCIAMACHY and GOME-2 NO 2 retrievals that are assimilated in CAMSRA (which are produced by the ACSAF; see Table  2 ). Despite the retrievals being based on the same Level 1 spectral irradiance data, the retrieval procedures are completely independent, from the spectral fit to the assumptions made on the a priori data used for the air mass factor calculations.
As a rough estimate, systematic uncertainties in regions with significant pollution are on the order of 20 %-30 %. Figure  18  shows time series of tropospheric column NO 2 from the Bremen satellite dataset (0. 5 • × 0. 5 • ), CAMSRA, CIRA and MACCRA averaged over Europe and East Asia for the period from 2003 to 2016. The figure illustrates that, while the seasonality of NO 2 (with low values during summer and high values during winter) is captured in both areas, there is generally an underestimation of the seasonal cycle, mainly due to an underestimation of the wintertime maximum. This underestimation could be related to an underestimation of anthropogenic emissions or uncertainties in the photochemistry of the models and is particularly pronounced over East Asia. Over Europe the differences between the three reanalyses are small; over East Asia they are larger.
Over East Asia in 2003, the CAMS reanalysis shows a strong variation in values from one month to the next and fails to reproduce the observed seasonality. This is due to assimilating SCIMACHY NO 2 data of degraded quality during 2003 in CAMSRA (see Fig. S5a ). The Bremen dataset shows an increase in the wintertime maximum NO 2 values over East Asia until 2012-2014 and a decrease in the later years. This behaviour is reproduced better in CAMSRA than in CIRA and MACCRA, though the maximum values are still underestimated. This improvement is the result of assimilating more NO 2 satellite data, in particular data from satellites with different overpass times, in CAMSRA. It is not seen in a control run without data assimilation (not shown). However, the magnitude of the positive trend up to 2012 and of the negative trend in the recent years is still underestimated by CAMSRA, and the observed decrease after 2014 is not reproduced by the three reanalyses.
In summary, we find that CAMSRA shows some improvements in the tropospheric NO 2 column (relative to a dataset based on SCIMACHY and GOME-2A data) compared with the older two reanalyses, especially over East Asia, where the assimilation of (more) NO 2 retrievals reduces the bias between the reanalysis and the data. However, the tropospheric NO 2 columns are still underestimated in CAMSRA over East Asia and Europe, particularly the wintertime maxima. This is a long-standing problem of the CAMS system, and it is hoped that work which has started to include an emission inversion capability in the CAMS system will improve this in the future. We find changes in NO 2 compared with CIRA (particularly over the Arabian Peninsula), which are the result of coupling with aerosol in the CAMSRA chemistry scheme that leads to faster NO x removes and a reduced positive bias in those areas.
Aerosols The final validation section looks at aerosol fields from the three reanalyses. Several model changes were included in the version of the IFS used to produce CAMSRA to address issues identified in CIRA, and this has a large impact on the   Bias ± SD Bias ± SD Łeba -0. 2 ± 0. 6 0. 1 ± 0. 6 (0. 2 ± 0. 6) 0. 0 ± 0. 6 (0. 1 ± 0. 6) Jarczew -0. 5 ± 1. 0 0. 6 ± 1. 0 (0. 7 ± 1. 0) -0. 4 ± 1. 0 (-0. 4 ± 1. 0) Hohenpeißenberg -0. 6 ± 0. 7 -1. 1 ± 0. 7 (-1. 1 ± 0. 7) -0. 7 ± 0. 7 (-0. 6 ± 0. 7) Payerne -4. 7 ± 2. 2 -1. 1 ± 1. 6 (-1. 0 ± 1. 7) -1. 2 ± 1. 4 (-1. 2 ± 1. 4) aerosol speciation. Figure  20  shows the mean AOD over the period from 2003 to 2016 from CAMSRA and the differences between this dataset and CIRA and MACCRA (only for  [2003] [2004] [2005] [2006] [2007] [2008] [2009] [2010] [2011] [2012].
Conclusions The Copernicus Atmosphere Monitoring Service (CAMS) has produced a new reanalysis dataset of atmospheric composition, referred to as CAMSRA in this paper. This reanalysis currently covers the years 2003-2016 and will be extended in the future by adding 1 year each year. It was produced by assimilating satellite retrievals of O 3 , CO, NO 2 and AOD from various sensors in ECMWF's Integrated Forecast System (IFS). The new CAMS reanalysis builds on the experience gained during the production of the earlier MACC reanalysis (MACCRA) and CAMS interim reanalysis (CIRA). Great care has been taken to ensure that the emission datasets used in CAMSRA were consistent in time and that consistent anthropogenic, biogenic and biomass burning emissions were used in the aerosol and chemistry modules.
Furthermore, a newer (and therefore better) version of the IFS was used, and new, reprocessed datasets for assimilation were acquired as far as possible. Variational bias correction was applied to some of the O 3 , CO, NO 2 and AOD data to ensure good temporal consistency when blending the various datasets. Known problems from earlier reanalyses were avoided, e. g. issues with the bias correction of MLS data in MACCRA that led to drifts in the ozone field, and a better time consistency in the CO field of CAMSRA than of MACCRA was obtained by assimilating data from only one instrument, i. e. MOPITT. CAMSRA therefore shows a more temporally consistent performance than the previous two reanalyses and has mostly smaller biases with respect to independent observations in most areas for O 3 , CO, NO 2 and AOD.