dataesr/scientific-paragraphs-categorization huggingface dataset:
In the non-linear assimilation procedure, the analysis is constrained to be in the range of values defined by the historical ensemble. As a result, the transformation back to the original space may induce by itself a truncation of the analyzed values. To assess whether the truncations play an important role in the non-linear procedure, it is relevant to check how every assimilated observation compares to the maximum of the historical ensemble used to compute the anamorphosis transformation for the phytoplankton variable (assimilated variable in the CPBM). However, computing strictly how many observations are above the ensemble maximum does not account for observation errors, as the observations are characterized by a probability distribution rather than by an absolute value. We therefore computed the number of assimilated data for which the 95 % confident interval of the observation PDF does not overlap the concerned local ensemble.
This cycle is reproduced by the free run, but with maximum chlorophyll values that remain much below the measurements. Nevertheless, an overestimation of the chlorophyll content by the SeaWiFS data set cannot be excluded in these coastal waters, notwithstanding the rather poor performance of the CPBM for such coastal areas as discussed previously. The non-linear run slightly increases the chlorophyll concentrations toward the measured values. This is not the case for the linear run, probably because the temporal dynamics are completely modified as a result of strong increments on nitrate concentrations (see Fig. 3c , third and fourth row). Considering region 4 in the open ocean, it appears that the bloom in the free run starts slightly too late, does not last as long as it should when compared to the data, and reaches values that overestimate the observed peak.
Comparison with independent in situ nitrate measurements In this section, we investigate how the assimilation is able to propagate the observed information to non-observed quantities (surface and sub-surface nutrients). The nitrate model compartment is chosen here because in situ nitrate observations have been collected with a good coverage in the North Atlantic during the period of reanalysis. These diagnostics will provide further indications that the multivariate scheme is well suited for combining ocean color observations with CPBM predictions. The WOA09 data set was used to objectively evaluate the reanalysis of nutrient distributions. For comparison, only data measured in deep sea waters (i. e. , bottom model topography deeper than 500 meters) were kept in the process. This selection was made to ensure an objective determination of the method efficiency since the CPBM used here is not well designed for shallow waters.
Acknowledgements. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme   FP7/2007-2013  under grant agreement  n°218812  (MyOcean), and from the  LEFE/GMMC Green Mercator  project. We are thankful to  NASA  for providing us ocean color data through the website http://oceancolor. gsfc. nasa. gov. The calculations were performed using NEC-SX8 from  GENCI-IDRIS  resources (Grant  2010-011279 ). Edited by:  M. Hoppema Ocean Sci. , 9,  37-56 , 2013 www. ocean-sci. net/9/37/2013/.
Modern stromatolites/microbialites form in marine as well as lacustrine environments. Marine stromatolites such as Bahamas and Shark Bay stromatolites have been studied in details (e. g. ,  Goh et al. , 2009; Planavsky and Ginsburg, 2009; Reid et al. , 2000; Visscher et al. , 1998)  and have been considered as good analogues to ancient stromatolites since they exhibit the same laminated macrofabric (see for instance R Pamela  Reid et al. , 2003). However, some Archean stromatolites have been shown to form in lakes e. g. , those from the Tumbiana formation (2. 7 Ga)  (Awramik and Buchheim, 2009). Therefore, the study of lacustrine microbialites is also relevant and is needed to complete the available reference database used to interpret ancient microbialites. In that framework, Lake Alchichica is a hyperalkaline lake (pH ∼ 8.
Bulk x-ray diffraction (XRD) A non-treated fragment of microbialite was ground in 100 % ethanol. XRD patterns were recorded with a Panalytical X'Pert Pro MPDH mounted in the Bragg Brentano configuration. Data were recorded with a monochromatic CoKα beam (λ = 0. 17889 nm) in continuous scan mode within a (3-100 • ) 2θ range with steps of 0. 017 • and a counting time of 813. 98 s per step.
Scanning electron microscopy and FIB milling The section of the microbialite sample collected at a 4 m depth and analyzed by CLSM was coated with goldpalladium and observed by scanning electron microscopy (SEM). Images were collected in backscattered and secondary electron modes using a Zeiss Ultra 55 FEG-SEM operating at 10 kV with a 30 µm aperture and a working distance of 8 mm. Elemental compositions were determined by energy dispersive x-ray spectrometry (EDXS) using an EDS QUANTAX detector and the software ESPRIT. EDXS analyses were operated using a 20 keV acceleration voltage, a 60 µm aperture and a working distance of 7. 5 mm. Two ultrathin foils transparent to electrons (< 200 nm) were prepared by FIB milling with a Zeiss dual FIB-NEON 40EsB using the FIB "lift-out" technique (e. g.
Scanning transmission x-ray microscopy (STXM) Scanning transmission x-ray microscopy (STXM) and nearedge x-ray absorption fine structure (NEXAFS) spectroscopy measurements were carried out on molecular environmental science 11. 0. 2. 2 beamline at the Advanced Light Source (ALS, Berkeley, USA). The rationale for STXM data acquisition and analysis and examples of applications can be found in  Bluhm et al. (2006)  and  Moffet et al. (2010). For STXM imaging, the x-ray beam is focused on an x-ray transparent sample using a zone plate, and a 2-D image is collected by scanning the sample at a fixed photon energy. The achieved spatial resolution is dependent on the zone plate (∼ 25 nm in the present study) and the scanning step (which varies from one image to another). The image contrast results from differential absorption of xrays, which partly depends on the chemical composition of the sample.
In addition to imaging, it is possible to perform at the same spatial resolution, near-edge x-ray absorption fine structure (NEXAFS) spectroscopy at the carbon K-edge (and other absorption edges in the 80-2000 eV energy range) which gives information on the speciation (i. e. , type of functional group and bonding) of carbon (and other elements). Measurements were performed at the C, O and N K-edges and at the Ca L 2,3 -edges. ALS storage ring was operated at 1. 9 GeV and 500 mA current. Energy calibration was done using the well-resolved 3p Rydberg peak at 294. 96 eV of gaseous CO 2 and the L 3 most intense peak of calcite at 349. 3 eV  (Benzerara et al. , 2004). Methods used for STXM data acquisition and analysis and examples of STXM applications can be found, for example, in  Benzerara et al.
Saturation index calculation The saturation indices of the Lake Alchichica solution with respect to aragonite and hydromagnesite were calculated for different pH (between 8 and 12) using the software Visual Minteq 3. 0 and the Minteq thermodynamic database. Concentrations of major ion concentrations, total alkalinity and temperature were measured during collection of the samples and were reported in  Kaźmierczak et al. , 2011 (i. e. , in meq L -1 : Cl -87. 3 / SO 2- 4 16. 73 / Br -0. 1 / F -0. 008 / Na + 100. 5 / Mg 2+ 35. 61 / K + 5. 32 / Ca 2+ 0. 735 / Li + 0. 26 and al-kalinity 30. 9. Calculations were performed considering a temperature of 15 • C).
Acknowledgements. We wish to thank especially  J. Kazmierczak  and  B. Kramer  for organizing the sampling expedition to the Alchichica Lake in 2007 and providing invaluable help during sampling to  P. López-García  and  D. Moreira. This project was financed by the  French Interdisciplinary program  " Environnements planétaires et origines de la vie" (PID OPV-EPOV ). The  SEM/FIB  facility of the  Institut de Minéralogie et de Physique des Milieux Condensés  is supported by  Région Ile de France  grant  SESAME 2006 I-07-593/R ,  INSU-CNRS, INP-CNRS, University Pierre et Marie Curie, Paris. The JEOL JEM-2100F at IMPMC was supported by  Region Ile-de-France  grant  SESAME 2000 E 1435 ,  INSU-CNRS ,  INP-CNRS  and  University Pierre et Marie Curie -Paris 6. ALS-MES beamline 11. 0. 2 is supported by the Director,  Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences , and  Biosciences and Materials Sciences Division of the US Department of Energy  at the  Lawrence Berkeley National Laboratory.
Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semiarid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from the Sea-viewing Wide Field-ofview Sensor (SeaWiFS). Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO 2 uptake.
The mean uncertainty reduction (relative to the prior) over all parameters is 14. 9 % for the simultaneous assimilation of LHF and FAPAR, 8. 5 % for assimilating LHF only, and 6. 1 % for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying maximum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.
6 m high tower in the middle of a homogeneous tall mopane tree stand with a maximum canopy height of about 8 m  (Veenendaal et al. , 2004). Three-dimensional wind speed, humidity, and CO 2 concentration were logged with a frequency of 20 Hz, and fluxes are integrated into half hour means with the EdiSol software  (Moncrieff et al. , 1997). Eddy flux measurements are influenced by contributions from a basal source area, whose size and position is varying depending on the aerodynamic conditions: wind direction, friction velocity, atmospheric stability, etc. At this site,  Veenendaal et al. (2004)  estimated the mean 90 % fetch distance of the installed eddy correlation measurement system to be 520 m for daytime data in March 2000 by a footprint analysis according to  Schuepp et al. (1990)  and  Kolle and Rebmann (2002).
Assuming that a fetch radius of 520 m can be enlarged occasionally depending on aerodynamic conditions and that different directions are averaged over time, the time-integrated fetch area approaches that of the footprint of the FAPAR measurements (a rectangle with length and width of 4500 m centred on the tower site, see details on FAPAR data below). At this spatial scale, the terrain is still generally homogeneous, but exhibits patches of the size of a few hundreds of metres (see Google Earth images, November 2012). Air temperature, shortwave radiation, and precipitation are also measured at the same tower, and are used to calibrate the climate input data, which is extracted from a global data set as described in the following paragraph. Data from 2000 and 2001 are used for assimilation. Due to missing halfhourly data, only 223 points of daily averaged LHF data out of 731 days for two years would have been be available for assimilation if we had restricted ourselves to complete diurnal measurement cycles.
To get both a sufficient number of data points and avoid biases in daily averaged LHF values, we include days with up to four of the 48 half-hourly values missing, where the gaps were filled using an appropriate gap-filling scheme (see Appendix A in the Supplement). This procedure yields a total of 464 daily data points for the two selected years 2000 and 2001. Input data of daily precipitation, daily minimum and maximum temperatures, and incoming solar radiation at the site are derived from a global gridded climate data set, generated through a combination of available monthly gridded and daily station data (R. Schnur, personal communication, 2010) by a method by  Nijssen et al. (2001) , using gridded data from the Summary of the Day Observations (Global CEAS), National Climatic Data Center and the latest updates of gridded data by  Jones et al.
FAPAR is calculated as the vertical integral of absorption of photosynthetically active radiation by healthy green leaves divided by the difference between the incoming and outgoing radiation flux at the top and bottom of the canopy  (Knorr, 1997). This integration is carried out by a two-flux scheme  (Sellers, 1985) , which takes into account soil reflectance, solar angle and amount of diffuse radiation. Equating satellite and model FAPAR means that given the same illumination conditions, the same number of photons enter the photosynthetic mechanism of the vegetation, even if some of the assumptions differ between BETHY and the model used to derive FAPAR  (Gobron et al. , 2000). Differences between simulated LHF and FAPAR values and the observed data are minimized by optimizing process parameters of BETHY.
Here, we only briefly summarise the main methodological aspects. There are two modes in our data assimilation process: a calibration mode and a diagnostic mode. In calibration mode, the optimal parameter set is derived from FAPAR and LHF observations by propagating the observational information in an inverse sense through a chain of models. The mismatch of modeled values to observations is defined as a cost function as explained in the following Sect. 2. 3, and model parameters are then calibrated through iterative parameter adjustment (using the gradient information provided by the adjoint model) until the cost function reaches a minimum. In diagnostic mode, the quantities of interest (i. e. LHF and FAPAR) and their uncertainties can be calculated from the optimized parameter vector and its uncertainty as derived in the calibration mode.
( ) T and ( ) -1 denote the transpose and inverse of matrices. J is minimized iteratively using derivative information calculated by the adjoint model. The Hessian matrix, the second derivative of J with respect to the parameters, is used to estimate posterior parameter uncertainties, using the mathematical property that the inverse of the Hessian matrix at the cost function minimum approximates the posterior parameter error covariance matrix. All derivative code is derived efficiently from the model's source code (see  Kaminiski et al. , 2003)  by applying the automatic differentiation tool TAF (Transformation of Algorithms in Fortran;  Giering and Kaminski, 1998). The uncertainty of observational LHF is taken as the maximum of 10. 0 W m -2 and 23 % of measured LHF, which is in analogy to the approach taken by  Knorr and Kattge (2005).
Experimental set-up To investigate the impact of multiple data streams on the assimilation results, we perform three assimilation experiments: (1) assimilating only LHF, (2) assimilating only FA-PAR data, and (3) assimilating LHF and FAPAR data simultaneously (denoted combined assimilation). In addition, we run a prior simulation of the model with prior parameter values and no data assimilation. The assimilation experiment with only LHF data considers the first and second terms in Eq. (  1 ), the one with only FAPAR considers the first and third terms, and combined assimilation all three terms.
Optimization and parameter uncertainty The optimizations took 38, 43, and 41 iterations to converge to a minimum for LHF, FAPAR and combined assimilation, respectively. The total value of the cost function was reduced by a factor of between 1. 1 and 5. 7, while the norm of the gradient of the cost function was reduced by many orders of magnitude to a final value close to zero for all experiments (see Table  2 ). The main metric to measure the impact of the observational constraint provided by the assimilated data stream is the relative uncertainty reduction. It is defined as 1-σ posterior /σ prior , where σ is one standard deviation of the respective parameter uncertainty before or after assimilation. It is shown in Table  1  and Fig. 2  along with prior parameter values and uncertainties.
Prior Which parameters show high uncertainty reduction differs only slightly among the experiments (Table  1  and Fig. 2 ). However, the largest uncertainty reductions always occur for the combined assimilation where we simultaneously assimilate both data streams. While the water balance-related parameters W max and τ W are strongly constrained even by one data stream alone, V max 25 for PFT2 and ξ require the combination of both to be constrained relative to their prior. For V max 25 (PFT10) and f Ci C3 , we find that LHF delivers some constraint, but the addition of FAPAR increases the constraint considerably, even though FAPAR assimilation alone delivers almost no uncertainty reduction. Notably, parameter 24 (W max ) changes substantially from a prior value of 104 mm upward to 323 mm for LHF assimilation, and downward to 58 mm for FAPAR assimilation, while for combined assimilation the posterior value of 129 mm is close to the prior.
These are shown in Table  3  as R p i, j. A positive uncertainty correlation of two parameters means that if we underestimate one parameter, we are likely to underestimate the other parameter, too, and vice versa. This means we have a strong constraint on the difference of the two parameters but not on their sum. Negative uncertainty correlation is associated with a strong constraint on their sum but a weak constraint on their difference. A positive uncertainty correlation (> 0. 25) is found for W max against V max 25 PFT10 for LHF, and against V max 25 PFT2 for FAPAR assimilation, but a negative one (< 0. 25) against V max 25 PFT10 for the combined assimilation. In the combined case, W max is also strongly uncertainty correlated with τ W PFT10, while for the assimilation of single data stream, it is more strongly correlated to τ W PFT2 than to τ W PFT10.
Simulation of carbon fluxes A comparison with data of gross primary production (GPP) derived from the eddy covariance data is shown in Fig. 5. The LHF assimilation case gives the highest GPP among the three experiments, which is mainly an effect of the simulated large LAI values. FAPAR assimilation results in much lower simulated GPP than the observations, while matching satellitederived FAPAR rather well (Fig. 4 ). Apart from a good fit of LHF for the combined case, the simulated GPP also shows a moderately good fit in seasonality, which is reflected in a lower RMSE of 22 Wm -2 compared to the FAPAR assimilation case (27 Wm -2 ). All in all, the LHF assimilation case gives the best agreement with eddy covariance-derived GPP. We must note, however, that LHF.
Considering the observational uncertainties, we find that both the LHF-assimilated and the combined-assimilated models generally agree with LHF observations, but that LHF is underestimated by the FAPAR-assimilated model. The opposite applies to the simulation of FAPAR: The LHFassimilated model clearly overestimates FAPAR compared to the observations throughout the entire assimilation period, effectively simulating a semi-evergreen ecosystem (Fig. 4 ). At the same time, the model is capable of matching observed FAPAR after assimilating this quantity, but at the cost of reducing LHF and GPP somewhat below observations (Figs. 3  and 4 ). The reduction in these quantities is expected given the reduced LAI required to match a lower FAPAR compared to the LHF assimilation or the prior. We also find that the FAPAR-assimilated model shows a slight delay in the maximum FAPAR against the satellite data and that the combined assimilation still overestimates FAPAR during the wet season.
Since the intermediate state found by the combined assimilation maximizes overall agreement with observations, the assimilation can be considered successful. We have thus demonstrated the capability of CCDAS to assimilate multiple data streams in a technical sense. However, the more interesting question is what can be learned from the way the model is adjusted and from the match or mismatch between model and observations. To match the observed LHF, the model needs to increase its simulated LAI beyond the observational constraint provided by FAPAR. Because CCDAS in calibration mode explores the complete parameter space of BETHY, we know that no combination of parameters is able to fully match both data streams, and that matching the observed LHF is only possible by simultaneously ignoring constraints by observed FAPAR.
For example, negative observation-based GPP from eddy covariance data in November 2000 (Fig. 5 ) indicates inadequate data processing in terms of the estimated daytime ecosystem respiration for calculating GPP by subtracting NEE from it or the gap-filling procedure for missing data. Currently, we can not avoid such systematic errors easily. We have, however, considered the size of the energy disclosure as a prior uncertainty for LHF observations as a pragmatic solution. A further problem may be associated with insufficient cloud screening for the FAPAR product, which would affect observations mainly in the wet season. This needs to be taken into account when interpreting the slight delay between modeled and satellite FAPAR for the FAPAR-assimilation case. A possible wet-season bias, if corrected, would bring the FAPAR-assimilated model closer to the prior or combined case, decreasing the mismatch between observed and model LHF.
We further believe that the relatively coarse spatial resolution of the FAPAR product may not always capture the local phenomena inside the footprint of the flux tower. It is important to note that most of the discrepancies just discussed would not have been detected if either BETHY had been run only with fixed parameters, without the CCDAS calibration step, or only one data stream had been assimilated. Therefore, an important lesson is that the assimilation of multiple data streams in CCDAS, by virtue of exploring the full range of model parameterisations, is a robust method for identifying data biases as well as conceptual limitations of models, and as such clearly superior to the standard approach of model parameterisation and validation. In an earlier study  (Knorr et al. , 2010) , CCDAS was used to explore a range of possible formulations for leaf phenology, with assimilation of FAPAR providing a rigorous test for the combined surface, eco-physiology and phenology model in CCDAS.
Constraint of parameters by eddy covariance LHF and satellite FAPAR data The most important outcome of this data assimilation exercise is that the biggest constraint delivered by the data streams in question is on the parameter W max. This is not only because this parameter is constrained to a large degree by all assimilation experiments, but also because the maximum water holding capacity of the maximum soil volume that can be reached by the vegetation's root system is a fundamental quantity in these water-limited ecosystems. Since it is not possible to measure this quantity at anything but the plot scale, assimilation of LHF and indirect inference of W max via data assimilation has the potential to deliver estimates at the scale of hundreds of metres. In addition, assimilation of FAPAR vastly expands the potential range to cover whole ecoregions and the globe.