dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of data and materials All sequencing data have been deposited in the GEO as a super-series (accession number GSE98233). The images included in the figures are available on Figshare (Fig. 1 , doi:10. 6084/m9. figshare. 5198161; Fig. 2 , doi:10. 6084/m9. figshare. 5198242; Fig. 3 , doi:10. 6084/m9. figshare. 5198326; Fig. 4 , doi:10. 6084/m9. figshare. 5198359; Additional file 1: Figure  S3 , doi:10. 6084/m9. figshare. 5198386).
Determining whether the terrestrial biosphere will be a source or sink of carbon (C) under a future climate of elevated CO 2 (eCO 2 ) and warming requires accurate quantification of gross primary production (GPP), the largest flux of C in the global C cycle. We evaluated six years (2007)(2008)(2009)(2010)(2011)(2012) of flux-derived GPP data from the Prairie Heating and CO 2 Enrichment (PHACE) experiment, situated in a grassland in Wyoming, USA. The GPP data were used to calibrate a light response model whose basic formulation has been successfully used in a variety of ecosystems. The model was extended by modeling maximum photosynthetic rate (A max ) and light-use efficiency (Q) as functions of soil water, air temperature, vapor pressure deficit, vegetation greenness and nitrogen at current and antecedent (past) time scales.
Third, while analyses of climate change experiments often report that treatment effects are contingent upon background climate conditions (e. g. ,  Morgan et al. 2011) , the effects of antecedent climate conditions are often not evaluated. To address these knowledge gaps, we measured and analyzed GPP for six years as part of the Prairie Heating and CO 2 Enrichment Experiment (PHACE). The experiment consisted of six treatments, four of which were applied in a full factorial design with CO 2 (ambient vs. elevated) and temperature (ambient vs. warming), and two others involved deep and shallow irrigation applied under ambient CO 2 and temperature. We drew upon this six-year dataset to address three questions: (1) How does GPP respond to the main and interactive effects of eCO 2 and warming in the context of variable precipitation? (2) What environmental and meteorological factors (e.
g. , soil water content, antecedent conditions) govern potential responses of GPP to climate change? Finally, we illustrate how our modeling approach can be applied to generate more realistic data products for informing TBMs, and we ask: (3) How does the inclusion of antecedent conditions affect the magnitude and uncertainty in such GPP data products? Accurate estimation of uncertainty is essential in model evaluation exercises, and we provide a full accounting of uncertainty in our analyses.
, 2010 , Jasoni et al. , 2005). R eco was measured immediately afterwards and in exactly the same way as the NEE one, except that an opaque cover was placed over the chamber to eliminate light. Midday measurements were made on a total of 88 days over six growing seasons (May through September), and measurement days were typically separated by 2 to 4 weeks. Additional measurements of NEE and R eco , and thus GPP, were made every 6 weeks at five measurement times per day in each plot (nominal times = 04:00, 09:00, 12:00, 16:00 and 21:00). More details on the methods can be found in  Bachman et al. (2010)  and  Pendall et al. (2013). See  Ryan et al. (2015)  for descriptions of the environmental data and the gap-filling employed to estimate missing covariate data on certain days and hours.
Data synthesis and modeling We fit a non-linear mixed effects model to the GPP data to quantify how GPP varied among the experimental treatments at the season, annual, and multi-annual scales. The goal of this analysis is two-fold: (1) to quantify the combined effects of the categorical treatment effects and the timevarying concurrent and antecedent environmental effects (addressing questions 1 and 2), and (2) to estimate GPP on non-measurements times, while accounting for different sources of uncertainty, thus allowing us to gap-fill the GPP dataset and produce estimates of cumulative GPP fluxes (addressing question 3). Given the distributional properties of the observed GPP data (GPP obs ), we assumed that GPP obs followed a normal distribution. Thus, observation i (i = 1,…, 2456): 2 ( ) ~( , ) obs i i t i GPP Normal µ σ (1) µ is the mean or predicted GPP value, σ 2 represents the observation variance, and t(i) indicates treatment t (t = 1, 2, …, 6 treatment levels) associated with observation i.
We employ a semiempirical model for the mean GPP, µ, based on the rectangular hyperbola light-response model  (Desai et al. , 2008 , Falge et al. , 2001 , Thornley, 1976 , Landsberg and Waring, 1997) , which we adapted to include the effect of atmospheric CO 2 concentration  (Acock et al. , 1976). We lack sufficient data to parameterize more complex or mechanistic models (E. g. Farquhar et al. , 1980). However, the light-response or radiation-use efficiency type model has been frequently applied, in various formulations, to ecosystem level GPP and NPP flux data (see above references), and thus there is good precedence for using it here. The model for µ is: i i i i i i i i i Q PAR Amax C Q PAR + Amax C µ = (2) PAR i is the measured PAR (µmol m -2 s -1 ); Q i (µmol CO 2 µmol -1 quanta) is the quantum yield or canopy light-use efficiency (i.
Estimates of seasonal, annual and six-year GPP Our Bayesian approach to analyzing the GPP data also provides a framework for predicting GPP for time periods for which it was not measured. Each of the fitted models (main and alternative) was subsequently applied on an hourly time-step during the March-October period (we assumed GPP = 0 during other months due to the lack of vegetation during these winter months) for 2007-2012, and for every plot using each of the 3000 parameter sets sampled from the posterior distribution. The model simulations were implemented using equations (  2 )-(  5.
Comparisons to GPP simulated from 12 terrestrial biosphere models The data-driven predicted GPP values could serve as important 'data-products' for informing and evaluating terrestrial biosphere models (TBMs). Importantly, the Bayesian procedure explicitly quantifies uncertainty in such data products. To exemplify the importance of quantification of data product uncertainty, we considered two different types of data products: (1) six-year cumulative GPP from the main and alternative models as described in the previous subsection, and (2) the percent change in the six-year GPP under warming (cT) and eCO 2 (Ct) relative to the control (ct). As with the first, the second data product was computed using Monte Carlo simulations based on the 3000 posterior estimates of the six-year GPP (see Appendix S3 for description of how both data products were computed).
The six-year GPP and GPP responses predicted from 12 TBMs were compared against the corresponding data products. The TBMs included: six land surface models (CABLE, CLM4. 0, CLM4. 5, ISAM, OCN, and ORCHIDEE); three global dynamic vegetation models (JULES, LPJ-GUESS and SDGVM); and three ecosystem models (DAYCENT, GDAY, and TECO); see Table  S1  in the supplementary material for a description of the TBMs. The TBMs were not calibrated to the site using response data, but they were provided optional data or parameter values (e. g. , Vcmax, specific leaf area, rooting depth, soil texture) representative of the site. Models were also forced with site meteorological data covering the six years of the experiment (see Appendix S4 for details). As a result of the TBMs not being rigorously calibrated against the PHACE data, there was no expectation that the TBM responses would match or be close to the expected PHACE responses.
5a , grey cross and error bar). Under the eCO 2 scenario, there was greater similarity in the number of TBM predictions agreeing with the data product if antecedent versus no antecedent conditions were included for determining the data product (Fig. 5b ). The TBMs also need to accurately predict the relative change in GPP under scenarios of environmental change (e. g. , eCO 2 , warming, or some combination). We used our GPP analysis framework to produce a data product of the percent difference in GPP under treatment conditions relative to control conditions. In contrast to the cumulative GPP estimates, these percent differences were associated with high uncertainty, sometimes spanning both decreases and increases (e. g. , Fig. 5c, d ). This resulted in the majority of TBM simulations that are consistent with this data product (i.
Since the early 1990s, global change experiments, such as Free Air CO 2 Enrichment (FACE) studies, have generated data on responses of key biogeochemical processes to future environmental conditions. Such experiments have become invaluable for informing model forecasts  (Piao et al. , 2013 , Zaehle et al. , 2014 , De Kauwe et al. , 2014 , Walker et al. , 2014, De Kauwe et al. , in review). One of the challenges associated with applying terrestrial biosphere models (TBMs) to understand climate change impacts on GPP and the C cycle is limited access to accurate data products for informing and evaluating the models. Since many data products are derived from simpler models that are fit to observational data, it is prudent to account for uncertainty in such data products since they are not perfect representations of the real system.
Our hierarchical Bayesian approach to analyzing the GPP data in the context of a fairly simple light-response model provides a mechanism for predicting GPP at non-measurement timeperiods, while accounting for uncertainty in these predictions. However, we wish to emphasize that the purpose of the comparison between TBMs and our "data product" (Fig. 5 ) was not to validate the TBMs, but rather to evaluate the utility of the data products. We are confident in our seasonal, annual, and six-year cumulative GPP predictions given their relatively narrow 95% CIs (e. g. , Fig. 5a  and 3b ). The width of the intervals, however, did vary among global change treatments, with the widest intervals (and weakest model fits [lowest R 2 s]) occurring for treatments involving eCO 2 (Ct and CT). This suggests that additional information or improved model structure is required to obtain more accurate GPP estimates under eCO 2.
In general, the tight estimates for cumulative GPP at different time scales suggest that this would be a valuable (semi-)independent data stream that TBMs can be compared against. The importance of antecedent environmental conditions on grassland GPP has been highlighted by the Bayesian model selection procedure used in this study. Antecedent conditions were key predictors of GPP, in particular air temperature and vapor pressure deficit of the past week, and research into the mechanism by which antecedent Tair and VPD affect GPP would be an interesting and useful contribution to understanding the carbon cycle in these grassland ecosystems. Including antecedent conditions substantially improved the fit of the Bayesian model and led to a consistent reduction in the computed multi-year GPP in this grassland ecosystem, across the vast majority of treatments and years.
Acknowledgements This material is based upon work supported by the  US Department of Agriculture, Agricultural Research Service Climate Change ,  Soils & Emissions Program, USDA-CSREES Soil Processes Program  (# 2008-35107-18655 ),  US Department of Energy Office of Science (BER) , through the  Terrestrial Ecosystem Science program  (# DE-SC0006973 ) and the  Western Regional Center  of the  National Institute for Climatic Change Research , and by the  National Science Foundation  (DEB# 1021559 ). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We thank  D. LeCain ,  J. A. Morgan ,  J. Heisler-White ,  A. Brennan , S. Bachman,  Y. Sorokin ,  T. J. Zelikova ,  D.
g. ,  McConnell et al. , 2010). On the other hand, very few studies have been performed on natural aerosol samples. They include the estimates obtained with the KBr pellet technique by  Volz (1972 Volz ( , 1973)) ,  Fouquart (1987 ), and, more recently, by Di Biagio et al. (2014a) , on dust samples collected at a few geographical locations (Germany, Barbados, Niger, and Algeria). Besides hardly representing global dust sources, these datasets are also difficult to extrapolate to atmospheric conditions as (i) they mostly refer to unknown dust mineralogical composition and size distribution, and also (ii) are obtained from analyses of field samples that might have experienced unknown physico-chemical transformations. In addition, they have a rather coarse spectral resolution, which is sometimes insufficient to resolve the main dust spectral features.
, 2004). The mineralogical composition and size distribution of the particles were measured along with the optical data, thus providing a link between particle physico-chemical and optical properties. In this study, we review, optimize, and extend the approach of DB14 to investigate the LW optical properties of mineral dust aerosols from 19 soils from major source regions worldwide, in order to (i) characterize the dependence of the dust LW refractive index on the particle origin and different mineralogical compositions, and (ii) investigate the variability of the refractive index as a function of the change in size distribution that may occur during medium-and long-range transport. The paper is organized as follows: in Sect. 2 we describe the experimental set-up, instrumentation and data analysis, while in Sect.
Particle loss was found to be negligible at D g < 1 µm, reaching 50 % at D g ∼ 5 µm, 75 % at D g ∼ 6. 3 µm, and 95 % at D g ∼ 8 µm for the WELAS, the only instrument considered in the supermicron range. Data for the WELAS were then corrected as follows: dN/d log D g Corr,WELAS = dN/d log D g WELAS / × 1 -L WELAS D g , (3) where [dN/d log D g ] WELAS is the size measured by the WE-LAS and L WELAS (D g ) is the calculated particle loss as a function of the particle diameter. Data at D g > 8 µm, for which the loss is higher than 95 %, were excluded from the dataset due to their large uncertainty. The uncertainty on L WELAS (D g ) was estimated with a sensitivity study by varying the PLC software values of the input parameters within their uncertainties. The L WELAS (D g ) uncertainty varies between ∼ 50 % at 2 µm and ∼ 10 % at 8 µm.
(2012)  and (  2 ) their mineralogy should envelop the largest possible variability of the soil mineralogical composition at the global scale. A large set of soils were available for northern Africa, the Sahel, eastern Africa and the Middle East, eastern Asia, and southern Africa. Here, the selection was performed using as guidance the global database of  Journet et al. (2014) , reporting the composition of the clay (< 2 µm diameter) and silt (< 60 µm diameter) fractions in terms of 12 different minerals. Amongst them, we analyzed the variability of the minerals that are most abundant in dust as well as most optically relevant to LW absorption, namely illite, kaolinite, calcite, and quartz in the clay fraction, and calcite and quartz in the silt fraction. The comparison of the clay and silt compositions of the soils extracted from the Journet database with the available samples resulted in the selection of five samples for northern Sahara, three for the Sahel, three for eastern Africa and the Middle East, and two for eastern Asia and southern Africa, as listed in Table  2.
, 2005). A more direct comparison of our data with field measurements of the dust mineralogical composition is rather complicated due to possible differences linked to the size distribution and representativeness of the specific sources between our data and field measurements  (Perlwitz et al. , 2015). For the Niger sample only, however, a semiquantitative comparison can be performed against field data of the dust mineralogy obtained for aerosols collected at Banwww. atmos-chem-phys. net/17/1901/2017/ Atmos. Chem. Phys. , 17, 1901 Phys. , 17, -1929 Phys. , 17, , 2017  Figure  5. Surface size distributions in the CESAM at the peak of dust injection for all cases analyzed in this study; the total measured dust mass concentration and the percentage of the super-micron to sub-micron number fraction at the peak are also reported in the legend.
, 2009) , and FENNEC  (Ryder et al. , 2013a). The shaded areas for each dataset correspond to the range of variability observed for the campaigns considered. These values are comparable to what has been observed close to sources in proximity to dust storms  (Goudie and Middleton, 2006; Rajot et al. , 2008; Kandler et al. , 2009; Marticorena et al. , 2010). Given that the protocol used for soil preparation and aerosol generation is always the same for the different experiments, the observed differences in both the shape of the size distribution and the mass concentration of the generated dust aerosols are attributable to the specific characteristics of the soils, which may be more or less prone to producing coarse-size particles. The comparison of the chamber data with observations of the dust size distribution from several airborne campaigns in Africa is shown in Fig.
Con-Figure  8. Dust extinction coefficient measured in the LW spectral range for the 19 aerosol samples analyzed in this study. Data for each soil refer to the peak of the dust injection in the chamber. Note that the y scale is different for northern Africa-the Sahara compared to the other cases. Main absorption bands by clays at 9. 6 µm, quartz (Q) at 9. 2 and 12. 5-12. 9 µm, kaolinite (K) at 10. 9 µm, calcite (C) at 7. 0 and 11. 4 µm, and feldspars (F) at 8. 7 µm are also indicated in the spectra. versely, the lowest absorption is measured for the aerosols from Mauritania, Mali, Kuwait, and Gobi, for which the super-micron particle fraction and the mass concentrations are lower. The intensity of the spectral extinction rapidly decreases after injection, following the decrease of the super-micron particle number and mass concentration.
0, 9. 2, 9. 6, 10. 9, and 11. 4 µm for the real part. The correlation between n and the mineral percent mass fraction is found to be statistically significant only for the calcite band at 7. 0 µm, Figure 11. Imaginary part of the complex refractive index (k) versus the mineral content (in % mass) for the bands of calcite (7. 0 and 11. 4 µm), quartz (9. 2 µm), and clays (9. 6 and 10. 9 µm). For the band at 9. 6 µm the plot is drawn separately for total clays, and illite and kaolinite species. The linear fits are also reported for each plot. Linear fits were performed with the FITEXY. PRO Interactive Data Language (IDL) routine taking into account both x and y uncertainties in the data. Same as Fig. 11a  for the real part of the complex refractive index (n). while for all other cases, very poor or no correlation is found.
Comparison with the literature In Fig. 12 , we compare our results with estimates of the dust refractive index reported in the literature. We consider data by  Volz (1972 Volz ( , 1973) )  for dust collected in Germany and at  Barbados, Fouquart et al. (1987)  for Niger sand, and Di  Biagio et al. (2014a)  for dust from Algeria and Niger. We also report data for dust as assumed in the OPAC database (Optical Properties of Aerosols and Clouds;  Hess et al. , 1998; Koepke et al. , 2015). These literature data, in particular those of  OPAC and Volz (1973) , are the most frequently used references in climate modeling and remote sensing applications. Because of their limited regional span, the literature data clearly cannot do justice to the full range of magnitude and of the spectral variability of the LW complex re-fractive index that is presented in our dataset.
The agreement is even less satisfactory for the real part of the refractive index (upper panel of Fig. 12 ), which is overestimated in OPAC and  Volz (1973)  and underestimated in  Fouquart et al. (1987). As discussed in Di Biagio et al. (2014a), differences for the real part between the various studies come mostly from the different methods used to estimate the dust refractive index. The methods used in the literature most often do not fulfil the Kramers-Kronig relationship for the n-k couples. The only dataset that fulfils the Kramers-Kronig relationship is  Fouquart et al. (1987) , but that has the drawback of underestimating n as a consequence of the low value of n vis (∼ 1) assumed in the retrieval. On average, the differences between our mean refractive index and the values reported in the literature are large enough to have a significant effect on radiative transfer.
The experiments described here were conducted in the realistic and dynamic environment of the 4. 2 m 3 CESAM. Dust aerosols generated in the chamber are characterized by a realistic size distribution, including both the sub-micron and the super-micron fraction, and they have an atmospherically representative mineralogical composition, including the main LW active minerals, such as quartz, clays, and calcite. The complex refractive index of dust at LW wavelengths is obtained following a rigorous approach that permits the determination of n-k couples that satisfy the Kramers-Kronig relation. Refractive index data from the present study are much more reliable than those provided by DB14, given that a better estimate of n vis was used in the retrieval algorithm. The average uncertainty in the obtained n and k is ∼ 20 %.
The main results from this work can be summarized as follows. 1. The imaginary LW refractive index, k, of dust varies strongly both in magnitude and spectral shape as a result of the variability of the particle mineralogy related to the specific emission sources. The value of k is observed to vary both from region to region, as well as within the same region for varying sources. Conversely, for the real part n, values are observed to agree within error for the most part of the spectrum for all dust samples. This implies that while a constant n can be taken for dust from different sources, a varying k should be used both at the global and at the regional scale. The available literature data  (Volz, 1972 (Volz, , 1973;; Fouquart et al. , 1987; OPAC, Hess et al. , 1998; Koepke et al. , 2015)  used nowadays in climate models and satellite retrievals do not adequately represent either the magnitude, or the spectral features and the variability of the LW refractive index of mineral dust observed in our dataset.
This finding supports the common practice in global models to treat the dust LW refractive index as static during transport. This also implies that to represent the dust LW refractive index versus mineralogy, models just have to reproduce the dust composition at the source, without the necessity of following its changes during transport, which could be a challenge. This would considerably simplify the representation of dust mineralogy in models. The unique dataset presented in this study should be particularly useful for improving the dust-climate interactions within regional and global models, and to take into account the geographical variability of the dust LW refractive index, which at present is not represented. This will allow the obtainment of a more realistic representation of the dust LW effect and its radiative forcing upon climate.
To date, as shown in  Boucher et al. (2013) , even the sign of the dust direct effect remains unknown. In this regard, we estimate a lower dust absorption than most of the literature data (see k curves in Fig. 12 ), and in particular than those of  Volz (1973)  and OPAC, which are the reference data most frequently used in climate models. The integral of the Volz and OPAC dust refractive indices (imaginary part) between 3 and 15 µm, for example, is about 15-20 % larger compared to the integral obtained from our max k curve; an up to about 1 order of magnitude overestimate is found when the integral of the Volz and OPAC k over the 3-15 µm range is compared to the integral of our min k curve. As a consequence of this, we can conclude that the use of the Volz and OPAC data may introduce a systematic bias in modeling dust radiative effects at LW wavelengths.