dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1995). In view of the above-mentioned problem of the atmospheric CO 2 inversions, most of these studies use the sea-air flux climatology by  Takahashi et al. (2009)  (or earlier versions) as a Bayesian prior (in some cases, the long-term fluxes from ocean-interior inversions are used in the prior as well, e. g. Rödenbeck et al. (2003) , or formalized as joint inversion by  Jacobson et al. (2007) ). Out of this context, the databased sea-air CO 2 fluxes presented here have been developed aiming to (1) provide not only a monthly seasonal cycle but variability also on short-term (daily) and interannual timescales, (2) use an assimilation scheme that can easily and self-consistently compare or even combine the observational information of atmospheric and surface-ocean data, and (3) use a framework that can be extended to also incorporate constraints from other tracers, such as oxygen.
The presented results are based on the newly available Surface Ocean CO 2 Atlas (SOCAT) database (version 1. 5) of CO 2 fugacity measurements  (Pfeil et al. , 2012 , http://www. socat. info/). We describe the estimation method and test its performance, also using independent data. As a first step of analysis, this study mainly focuses on the mean seasonal cycle, because on this timescale (1) we can compare to the widely used monthly p CO 2 climatology by  Takahashi et al. (2009) , (2) we can investigate mutual consistency between the surface-ocean p CO 2 constraint and that by atmospheric CO 2 data as the signal/noise ratio is large, and (3) we can test the plausibility of our process representations invoking a seasonal surface-ocean phosphate (PO 4 ) climatology. Estimated interannual variations of the sea-air CO 2 flux are presented and discussed in a companion paper  (Rödenbeck et al.
Right: In case SFC, ocean-internal sources/sinks are adjusted to match the surface-ocean CO2 partial pressure observations. Sea-air fluxes are then calculated from the estimated partial pressure field. 2  for mathematical symbols, and Appendix A1 for the equations and further explanation. the individual processes impose spatial and temporal structure to the sea-air fluxes, based on the spatio-temporal information from the driver data (sea-surface temperature (SST), wind speed, etc. , Fig. 1 ). (  2 ) The explicit representation of oceanic processes involves further quantities, notably seasurface CO 2 partial pressure. Through cost function contributions gauging the match of modelled and measured p CO 2 , these data can be used as an observational constraint replacing the atmospheric data (Fig.
Data constraints -base runs We will present results of two cases that differ in the data set used as main constraint (Fig. 2 ): SFC. In the main case used to create the primary product of this study, the ocean part of the diagnostic scheme is fit to surface p CO 2 data points from the SOCAT v1. 5 database  (Pfeil et al. , 2012 , http://www. socat. info/). Data pre-treatment and further details are given in Table  1. SOCAT data cover about 6 million pixels/time steps globally within the calculation period  (until 2007, see Supplement Figs. S7. 3 and S7. 4  for data distribution). ATM. As a comparison case used to assess the consistency between surface-ocean and atmospheric data, the diagnostic scheme is fit to atmospheric CO 2 mixing ratios measured approximately weekly or hourly at a set of sites by various institutions.
As this a-priori p CO 2 field contradicts the data (black dots), the estimation procedure now adjusts the internal flux f DIC int in such a way that the data are matched as closely as possible (blue). Most prominently, this reduces or -as at the example pixel of Fig. 3  -even reverses the seasonal variations of p CO 2 (reflecting that thermal, biological, and physical effects partially oppose each other). The fit at further locations is given below (Sect. 3. 4). For most pixels, the SOCAT data set only sporadically contains data points (in particular, periods of dense ma ), surface-ocean CO2 partial pressure (p CO 2 m ), mixed-layer DIC concentration (C DIC m ), as well as ocean-internal carbon addition to (removal from) the mixed layer (f DIC int ). Estimates have been obtained by fitting the diagnostic scheme to SOCAT surface-ocean CO2 partial pressure data (SFC, blue).
The SOCAT data points that happen to fall in the example pixel are shown as black dots in the p CO 2 m panel. The Bayesian prior (f DIC int = 0 and corresponding C DIC m , p CO 2 m , and f CO 2 ma , Appendix A2. 2, Sect. 3. 2) is also given (thin dashed gray). Constrained Extrapolated pixel pixel f CO2 ma f CO2 ma GE GE p CO2 obs ⇐⇒ Jp p CO2 p CO2 CC CC C DIC m C DIC m CB correlated CB → f DIC int -→ f DIC int adjustments Fig. 5. Illustration of the information flow in the spatio-temporal extrapolation through a-priori correlations. Where data values exist, pixels are directly constrained (left column, which is identical to Figure  2 ). Due to the a-priori spatial correlations (Appendix A2. 2), adjustments to f DIC int at the constrained pixel also change f DIC int at neighbouring unconstrained pixels.
2. The mechanism is illustrated in Fig. 5. In particular, as seasonal variations in f DIC int are allowed to be adjusted much more freely than non-seasonal variations (equivalent to a-priori correlations between adjustments to f DIC int at any given day of year in each year, Appendix A2. 2), the mean seasonal cycle is extrapolated throughout the time period of the calculation. This also means that data points in any year can contribute to constrain this mean seasonality. Thus, though there are regions or periods too far away from data points for extrapolation, larger-scale seasonal variations are constrained almost everywhere in the ocean. This has been confirmed by assessing how well a given p CO 2 field can be retrieved by the scheme from pseudo data sampled from this field at the times and locations of the SOCAT data (Appendix B).
Example locations STM and -to a lesser degree -BTM show close seasonal fit (Fig. 8 ), and agreement also in some high-frequency features (see below). A less successful fit is found at Stratus (South Pacific) in an area of low data density (note that the independent data at this location are mainly from after the end of the SOCAT v1. 5 data period; thus the agreement relies on the extrapolating effect of the dominating seasonal degrees of freedom (Appendix A2. but misses any interannual anomalies). See the Supplement (Sect. S5, Figs. S7. 5 and S7. 6) for more comparisons and statistics. In order to validate the high-frequency variations, we compare them to mooring observations independent of the SO-CAT v1. 5 data set (Fig. 10 , upper panels). In the extra-tropics (example sites BSO or MOSEAN+WHOTS), many shortterm features are reproduced in run SFC, though mostly with reduced amplitude.
Comparison to the Takahashi climatology The monthly mean seasonal cycle calculated from our spatiotemporal p CO 2  field has been compared to the widely used observation-based climatology by  Takahashi et al. (2009) , which differs from our method in the p CO 2 data set used (Lamont-Doherty Earth Observatory (LDEO) database,  Takahashi et al. , 2010)  and in the way of spatio-temporal extrapolation of the point data into pixels without data: while the diagnostic scheme extrapolates the internal flux field f DIC int and then infers p CO 2 from it via the process representations (Fig. 5 ),  Takahashi et al. (2009)  extrapolates the p CO 2 field itself, using a 2-dimensional advection-diffusion equa-tion 1. In data-dense areas, results should not depend much on the extrapolation method. In most regions, the seasonal cycle estimated from SO-CAT by the diagnostic scheme is similar in phase to the p CO 2 climatology by  Takahashi et al.
(2009)  clima-tology using its open-ocean values. For example, this contributes to the differences in the temperate North Atlantic due to the Mediterranean (see Supplement, Fig. S7. 5). Partly, the differences in Fig. 7  can be traced to the fact that the two estimates are based on different p CO 2 data sets, though there is considerable overlap between the SOCAT and LDEO databases (this influence has explicitly been tested in the Supplement Sect. S6). Sensitivity to data selection partially arises from interannual variations; in particular, differences in the tropics may be related to the exclusion of El Niño data in  Takahashi et al. (2009). Moreover, the synthetic data test (Appendix B) reveals slightly too high seasonality in our results, though most of this mismatch is confined to certain marginal areas (that do not contribute much to the sea-air flux due to low gas exchange).
2010 )). Comparison between the measurements (black dots) and the p CO 2 field estimated by fitting to the SOCAT data (blue). The estimated field has been picked at the times where measurements exist; one year as been selected for each site. Bottom: Sea surface temperature measured simultaneously with p CO 2 (black dots) compared to our SST driving field at this pixel (light grey). Sabine et al. , 2010). Comparison between the measurements (black dots) and the p CO 2 field estimated by fitting to the SOCAT data (blue). The estimated field has been picked at the times where measurements exist; one year has been selected for each site. Bottom: sea surface temperature measured simultaneously with p CO 2 (black dots) compared to our SST driving field at this pixel (light grey).
Summary and conclusions We presented a temporally and spatially resolved estimate of the global surface-ocean CO 2 partial pressure field and the sea-air CO 2 flux, obtained on the basis of CO 2 partial pressure data from the SOCAT database. In order to interpolate the point data into a spatio-temporal field and to add short-term variations not constrained from the data, a simple model of surface-ocean biogeochemistry, including representations of sea-air gas exchange, solubility, carbonate chemistry, mixed-layer DIC budget, and seasonal re-entrainment ("history"), was fitted to the observations. The inverse procedure was similar to atmospheric inversion calculations, using spatial and temporal a-priori correlations to extrapolate the data information. Focusing first on seasonal variations, the following conclusions were drawn: -The diagnostic scheme can be robustly fit to SOCAT p CO 2 data, to estimate the spatio-temporal p CO 2 field and sea-air CO 2 flux (run SFC).
5 in latitude direction). f fos : fossil fuel burning. Only a fixed (prior) term, as in the Jena inversion v3. 2 (yearly emissions from the EDGAR v4. 0 database, update of  Olivier and Berdowski, 2001). f ini : purely technical component. Flux pulse at beginning of inversion period to adjust initial atmospheric mixing ratio field (see  Rödenbeck, 2005). The numerical minimization of the cost function is done by Conjugate Gradient descent with re-orthonormalization  (Rödenbeck, 2005).
A2. 3 Extension: the p CO 2 constraint Analogously to the observed atmospheric mixing ratios c obs , all selected partial pressure values from the SOCAT database (Table  1 ) are collected into a vector p obs. A corresponding vector p mod of modelled partial pressures is formed by sampling the gridded p CO 2 m field of the model at the locations and times of the measurements (i. e. the modelled value is that found in the gridcell/timestep which encloses the location/time of the corresponding observation). If several SO-CAT data points fall into the same gridcell and timestep, they are averaged together and only form a single element in p obs and p mod. The vector p mod is a function of the adjustable variable f int , as is p mod (except that p mod is also a function of further adjustable variables, f nee and f ini , which are irrelevant now).
We can define an analogous cost function contribution: J p = 1 2 p obs -p mod T Q -1 p p obs -p mod. ( A30 ) The covariance matrix Q p is chosen diagonal as well; the uncertainty for every individual pixel with two or more SOCAT data points is set to 10 µatm (for pixels with a single data point to √ 2 × 10 µatm). The fit to the SOCAT data is then done in the same way as to the atmospheric data, just with J c replaced by J p (those degrees of freedom that do not depend on p CO 2 m -land fluxes and atmospheric initial condition -are unconstrained in this inversion and just remain at their a-priori values). For the case combining atmospheric and oceanic constraints (Sect. 3. 6), both cost function contributions are added (J = J c + J p + 1 2 x T x).
(2009). This combined field then contains variations similar to reality on all relevant timescales (though variations on the fastest (weekly to daily) timescales are smaller than observed). (2) This synthetic p CO 2 field is sampled at the locations and times of the SOCAT measurements, in the same way as the modelled p CO 2 field (Sect. A2. 3). This gives a set of pseudo data representing the same amount of information (in terms of data density available to the scheme) as the actual data (note that SOCAT also contains along-track variability on scales much shorter than the size of our grid cells, which the scheme cannot make use of in any case). (3) We then fit the diagnostic scheme to the pseudo data, and compare the result with the synthetic truth as the known correct answer. In terms of mean seasonality considered here, the retrieval (blue) fits the "known truth" (violet) closely (Fig.
The p CO 2 fields have been averaged over the regions as in Figure  7. sub-sampled at locations/times of the SOCAT measurements (blue), compared to the simulated p CO 2 field itself ("known truth", violet). For further comparison, a fit of the scheme to the simulation at every grid cell and time step ("complete information", green) and the Bayesian prior ("no data information", thin dashed grey, partially off-scale) is given. The p CO 2 fields have been averaged over the regions as in Fig. 7. -We also did the retrieval based on maximum information about the p CO 2 field available, by constraining the scheme at every grid point and time step, not only where actual SOCAT data exist. The result (green) fits the known truth almost perfectly, showing that there are sufficient degrees of freedom in the scheme to reproduce the mean seasonality.
Acknowledgements. We would like to thank all contributors to the SOCAT database, as well as all providers of atmospheric CO 2 data, which are the basis of this work. We gratefully acknowledge helpful discussions with  Nicolas Cassar ,  Andrew Dickson ,  Niki Gruber ,  Roberta Hamme ,  Steve Jones ,  Bob Key ,  Armin Köhl ,  Wolfgang Koeve ,  Iris Kriest ,  Corinne Le Quéré ,  Miguel Mahecha ,  Bill Munger ,  Andreas Oschlies ,  Taro Takahashi ,  Yasunori Tohjima , and  Lisan Yu. Sara Mikaloff Fletcher  and  Rik Wanninkhof  made very helpful comments as referees. This study was also supported through  EU   FP7  project  CARBOCHANGE  ( 264879 ) and is a contribution to the international IMBER/SOLAS projects. The service charges for this open access publication have been covered by the  Max Planck Society.
The age at event for each subject in each source population was generated from a standard Cox model with time-dependent covariates, using a permutation algorithm described elsewhere and assuming Weibull marginal distribution of age at event  [4, 22, 23]. Three Cox models of interest from an epidemiological point of view were simulated. Model 1 included intensity and duration of exposure only. Model 2 included age at first exposure in addition to intensity and duration. Model 3 was similar to Model 2 but used time since last exposure instead of age at first exposure. The distribution of the exposures variables were chosen to be close to the observed distributions of occupational asbestos exposure variables in our case-control data on PM  [15]  described in the application section. Specifically, the ages at first and at last exposure were generated for all subjects from lognormal distributions.
The exposure intensity at each age was generated from a linear function of age. Parameters for the random intercept and slope were chosen such that either 85% of subjects had a constant intensity, 6% a highly increasing, 6% a moderately decreasing, and 3% a moderately increasing intensity over lifetime (Scenario A); or 50% a highly increasing and 50% a moderately decreasing intensity over lifetime (Scenario B). Scenario A reflects our real case-control data on occupational exposure to asbestos. The exposure intensity at each age was represented in all our models by a variable that equaled the cumulated value of intensity at that age divided by the total duration of exposure at that age. This exposure intensity variable is equivalent to the mean index of exposure (MIE) variable introduced in the application section.
e. 14 years) increase in time since cessation of exposure. Censoring for age at event in the source population was independently generated from a uniform distribution such that the event rate was about 10% in each source population of 1000 subjects, and 2% in each source population of 5 000 subjects. Each subject of the source population who had the event of interest was selected as a case in the case-control dataset. The event rates in the source population thus implied that we had about 100 cases in each case-control data set. For each case, 1, 2, or 4 controls were randomly selected with replacement among subjects at risk at the case's event age, which corresponds to 1:1, 1:2, or 1:4 individual matching on age, respectively. On average, each case-control dataset was therefore made of about 100 cases and 100, 200, or 400 controls.
Statistical criteria used to compare the performance of the different estimators For each of the four regression models WC1, WC2, CLR, and ULR, we calculated the relative bias of the regression parameter estimator β associated with each exposure variable, as compared with the true effect β of that exposure variable, 1 1000 X 1000 i¼1 βi À β β ! , where βi is the parameter estimate of the model based on the i th casecontrol dataset (i = 1,. , 1 000). To evaluate whether the relative bias was not partly due to a bias generated in the population source data, we also derived the relative bias as compared with the estimated effect βCox of the well specified time-dependent Cox model using the full population source data, 1 1000 X 1000 i¼1 βi À βCox;i βCox;i !. We also derived the root mean squared error (RMSE) ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi β À β 2 þ var β q , where β is the mean of the 1 000 parameter estimates βi.
Data source The data came from a large French population-based case-control study described in Lacourt et al. [15]. Cases were selected from a French case-control study conducted in 1987-1993 and the French National Mesothelioma Surveillance Program in 1998-2006. Population controls were frequency matched to cases by sex and year of birth within 5 years group. Occupational asbestos exposure was evaluated for each subject with a job-exposure matrix (JEM) which allowed us to derive the mean index of exposure (MIE) that was used in the regression models to represent intensity of exposure, as in Lacourt et al. [15]. The MIE at age t was given by MIE t ð Þ ¼ X L l¼1 d l Â p l Â f sl Â i sl ð Þþ f al Â i al ð Þ ½ ! = X L l¼1 d l where L is the total number of jobs exposed to asbestos till age t; d l the duration (in years) of job l, p l the probability of asbestos exposure for job l, f sl and i sl the frequency and intensity of asbestos exposure due to specific task of job l, respectively, f al and i al the frequency and intensity of asbestos exposure due to environment work contamination of job l, respectively.
Note that the modelling of the exposure in the WC model could further be improved by incorporating some more complex function of the trajectory of the exposure over time that have recently been proposed  [28] [29] [30]. The application of the WC model requires estimating the age-conditional probabilities in the source population for population-based case-control studies, or in the full cohort for nested case-control studies. In our application to population-based case-control data on PM, these probabilities were estimated from health statistics on the general French male population. Yet, our analyses were restricted to ever exposed males only who have much higher probability to develop PM than the general French male population. Further studies are needed to investigate the impact of biased estimates of the age-conditional probabilities on the WC estimates.
5 at the AirBase sites, maximum 8 h O 3 and PM 10 composition at all sites, column abundance of CO, NO 2 , TOR, and aerosol optical depth (AOD), whereas WRF/Chem-MADRID gives better statistical performance for NH 3 , hourly SO 2 , NO 2 , and O 3 at the AirBase and BDQA (Base de données de la qualité de l'air) sites, maximum 1 h O 3 at the BDQA and EMEP sites, and PM 10 at all sites. WRF/Chem-MADRID generally reproduces well the observed high hourly concentrations of SO 2 and NO 2 at most sites except for extremely high episodes at a few sites, 1 WP -WRF/Polyphemus; the WRF/Chem-MADRID simulations with three BVOC emissions include: WC-S -offline BVOC emissions of Simpson et al. (1999); WC-G -online Guenther et al. (1995); WC-M -onlineGuenther et al. (2006) (i. e. , MEGAN); 2 unit of concentration is µg m -3 ; 3 the best statistics among 4 runs is in green, corr -correlation coefficient, NMB -normalized mean bias, and NME -normalized mean error; 4 the column CO and NO 2 abundance and tropospheric ozone residual (TOR) values are in 1 × 10 18 molec cm -2 , 1 × 10 15 molec cm -2 , and and Dobson unit (DU), respectively.
Y. Zhang et al. : Evaluation of chemical concentrations and sensitivity simulations WRF/Polyphemus than by WRF/Chem-MADRID. For domainwide performance statistics, WRF/Polyphemus underpredicts observed SO 2 concentrations at the AirBase and BDQA (Base de données de la qualité de l'air) sites with normalized mean biases (NMBs) of -30. 4 % and -36. 1 %, respectively, and overpredicts those at the European Monitoring and Evaluation Programme (EMEP) sites with an NMB of 120. 2 %, whereas WRF/Chem-MADRID overpredicts observations at all sites, particularly at the EMEP sites with an NMB of 256. 9 %. WRF/Polyphemus also underpredicts observed NO 2 concentrations, particularly at the Air-Base and BDQA sites, with NMBs of -56. 2 % and -54. 7 %, respectively. WRF/Chem-MADRID performs much better over the AirBase and BDQA sites with NMBs of -15.
At the AirBase sites, larger underpredictions occur in the NH 3 concentrations with NMBs of -38. 0 % for WRF/Polyphemus and -36. 5 % for WRF/Chem-MADRID. Although there are no observational data for HNO 3 from AirBase, moderate to large overpredictions of NH + 4 and NO - 3 concentrations (NMBs of 12. 9 % and 37. 2 % for WRF/Polyphemus and 69. 6 % and 214. 4 % for WRF/Chem-MADRID, respectively) imply an overprediction of HNO 3 concentrations. They also indicate a NH + 4rich environment in which ammonium nitrate (NH 4 NO 3 ) formation is limited by the formation of HNO 3 in the simulation domain, which is consistent with the findings of  Sartelet et al. (2007). Both models predict high concentrations of SOA (thus TOM) over regions with high isoprene and terpene emissions, such as the northeastern portion of the domain, which dominates the concentrations of TOM.
Description of selected sites A total of 16 and 21 sites have been selected from 3 observational databases (AirBase, EMEP, and BDQA) for detailed temporal analyses of chemical predictions of gaseous (e. g. , SO 2 , NO 2 , and O 3 ) and PM (e. g. , PM 2. 5 and PM 10 ) pollutants, respectively. These sites and their characteristics are summarized in Table  3. Among the 16 sites selected for analyses of SO 2 , NO 2 , and O 3 , 8 sites (Melun, Nord-Est Alsace, and Sommet Puy-de-Dôme, France; Deuselbach, Germany; Ispra, Italy; Celje, Slovenia; Harwell, UK; and Avenida Gasteiz, Spain) are in D02, and 8 sites  (Rörvik, Femman, and Södermalm, Sweden; Birkenes, Norway; Topolniky, Slovakia; Beato, Custóias, and Ermesinde, Portugal)  are in D01 but outside D02. Among the 21 sites selected for analysis of PM 2.