dataesr/scientific-paragraphs-categorization huggingface dataset:
The tiling structure of LM3 and ESM2 was designed to work with the CMIP5 LUH data set  (Hurtt et al. , 2011). Changes in the area and type of tiles occur annually based on gross transitions from the LUH data set. Similarly to the scenario design, secondary or agricultural lands are never allowed to return to primary lands. The physical and ecological states and properties of each of the tiles are different, and the physical and biogeochemical fluxes between land and the atmosphere are calculated separately for every tile. Each cropland, pasture, and secondary tile has three anthropogenic pools with three different residence times (1, 10, and 100 years). For LUMIP sub-grid tile reporting, all secondary and natural tiles will be aggregated into the primary and secondary tiles (PSL). For each requested land-use tile, the three different residencetime anthropogenic pools will be aggregated into one.
Data availability As with all CMIP6-endorsed MIPs, the model output from the LUMIP simulations described in this paper will be distributed through the Earth System Grid Federation (ESGF). The natural and anthropogenic forcing data sets required for the simulations will be described in separate invited contributions to this Special Issue and made available through the ESGF with version control and digital object identifiers (DOIs) assigned. Links to all forcings data sets will be made available via the CMIP Panel website. Author contributions. David M. Lawrence and George C. Hurtt are co-leads of LUMIP. David M. Lawrence wrote the document with contributions from all other authors.
Figure6. Global time series of land cover (a), land use (b, c, e), and land management (d, f) for the future simulations. Lines indicate SSP-RCP scenarios chosen for ScenarioMIP, with colored lines representing scenarios with specific LUMIP experiments. Data is provided by the IAM community. Data will be harmonized to ensure consistency between the end of the historical period and the beginning of the projection period for each of the scenarios. Note that not all IAMs predict all the LUH2 land management quantities (e. g. , wood harvest is missing for SSP5-8. 5). The missing land management variables will be generated during the harmonization process in a manner that is consistent with the underlying scenario.
Acknowledgements. We would like to thank  Andy Pitman ,  Paul Dirmeyer ,  Alan DiVittorio , and  Ron Stouffer  for their thoughtful and constructive reviews that led to considerable improvements to the document. David M. Lawrence  is supported by the  US Department of Energy  grants  DE-FC03-97ER62402/A010  and  DE-SC0012972  and  US Department of Agriculture  grant  2015-67003-23489. Julia Pongratz  is supported by the  German Research Foundation 's  Emmy Noether Program. Sonia I. Seneviratne  acknowledges support from the  European Research Council  ( ERC   DROUGHT-HEAT  project). Almut Arneth  acknowledges support by the  EC   FP7  project  LUC4C  (grant no. 603542 ) and the  Helmholtz Association  through its  ATMO programme. Nathalie de Noblet-Ducoudré  acknowledges support by the  EC   FP7  project  LUC4C  (grant no.
Global changes in gene expression upon treatment with melanoma conditioned medium To characterize the molecular mechanisms underlying the survival effect, we performed a whole genome gene expression study and analyzed the changes in relative mRNA abundance induced by treating ECs with melanoma CM or basal medium for 12 h under hypoxic and normoxic conditions. The 12 h time point was chosen, as basal medium-treated ECs under hypoxia maintain substantial morphological integrity and cell viability; prolonging the treatment results in extensive cell death. Based on the confidence level of their expression measurements (Affymetrix presence calls), 27,410 probesets passed the set filtering threshold and were used for further statistical analysis. Unsupervised multidimensional scaling (MDS) analysis identifies hypoxic/normoxic growth conditions and melanoma CM treatment as the main sources of variability in our dataset.
A table reporting all the genes passing the test along with p values, fold change, and per gene FDR estimates is posted as Table  S1. Probesets passing the test were clustered and displayed as a Heatmap using the clustering tool in BRB ArrayTools. Additionally, we visualized the expression values of the same probesets in ECs treated for 12 h under normoxia (Fig. S4 ) and observed a similar pattern of gene expression modulation. The gene expression changes identified appear consistent with the observed survival activity of melanoma CM. Among the genes more differentially expressed, we observed increased expression of transcripts encoding cytokines and other gene products involved in cytokine signaling (CXCL2, CCL2, IL32, A2M, JAK3, STAT6, CXCR7, CASP1), cell metabolism, and survival (INSR, IGF1R, AKT3, MAP2K5, JUNB).
A number of transcripts encoding proteins involved in apoptosis and inhibition of transcription (ID2, EID3, FAS) were among the most repressed ones. To gain further insight into the biological functions altered as a consequence of the gene expression changes induced by CM treatment, we performed pathway analysis and interrogated different databases using the pathway analysis tool in BRB ArrayTools. The threshold of determining significant gene sets was set at p < 0. 005. Different pathways were significant under the test conditions used including KEGG "proteasome" (hsa03050) showing coordinated downregulation of multiple proteasome subunits, and GO "regulation of glycolysis" (GO:0006110) showing augmented expression of transcripts involved in glucose metabolism and energy production. Heatmaps displaying expression of genes in relevant pathways are shown in Fig.
6b. Furthermore, we clustered and imaged the expression values of genes constituting the "Apoptosis," "MAPK kinase signaling," "Insulin signaling," and "Cytokine-cytokine receptor signaling" pathways of the KEGG database under hypoxic and normoxic conditions. We could clearly identify two main clusters of genes showing consistent modulation upon melanoma CM treatment. Consistent with the observed survival effect induced by melanoma CM, genes involved in the proapoptotic signaling cluster were downregulated in CM-treated group, whereas genes involved in the pro-survival signaling cluster were upregulated (Fig. 6c ). The gene expression changes observed in the microarray experiments were validated by real-time PCR. For all the transcripts tested, the results of the real-time PCR validation experiments were in good agreement with the microarray analysis.
Cell survival assay ECs were seeded at a density of 6 £ 10 3 cells per well in 96-well cluster plates and grown to 60% confluence. After 24 h in endothelial culture medium, tumor CM was added to cultured cells, and incubations continued for 24-72 h under normoxic or hypoxic conditions. After 24-72 h, cells were fixed with 10% trichloroacetic acid, washed under tap water, and stained with SRB. After washing with 1% acetic acid, plates were dried at 50 C and the dye was solubilized in 10 mM Tris buffer. The absorbance was measured using a microplate reader (Victor 1420, Wallac, Turku, Finland) at 510 nm. All reported EC survival data have been normalized to EC survival with basal medium treatment at 24 h under normoxic conditions and are denoted as normalized cell viability (%).
Cell morphology ECs were seeded at a density of 3 £ 10 4 cells per well in 24-well cluster plates and grown to 60% confluence. After 24 h in endothelial culture medium, CM was added to the ECs, and incubations continued for 24-72 h under normoxic or hypoxic conditions. After 24-72 h, cells were analyzed microscopically using an Axiovert 100 M inverted microscope with a 10X/0. 30 Plan-Neofluar objective (Carl Zeiss) and images were captured with an Axiocam MRC digital camera using AxioVision 4. 5 software (Carl Zeiss B. V. , Sliedrecht, NL).
Flow cytometry ECs cultured under hypoxic conditions in the presence of melanoma CM or basal medium were stained with FITC Annexin V/Dead Cell Apoptosis Kit (Molecular Probes, Carlsbad, CA), as per the manufacturer's instructions, and analyzed by flow cytometry (FACScan, Becton Dickinson, Palo Alto, CA). Data analysis was performed using the FlowJo software (TreeStar Inc. , Ashland, OR).
Affymetrix GeneChips In this study, we used the Affymetrix HG-U133-plus 2. 0 Gene-Chip (Affymetrix Inc. , Santa Clara, CA). Target synthesis was performed using 5 mg total RNA as template, as described in the Affymetrix Gene Expression Manual. GeneChips were washed and stained using the Affymetrix fluidic station 430 and analyzed using Affymetrix 3000 7 G GeneChip scanner. Gene expression values were summarized from probesets using RMA as implemented in Affymetrix Gene Expression Console. The same software was used to quality control (QC) the Gene-Chips. Chips not meeting QC criteria were excluded from further analysis. RMA expression summaries were merged with the Affymetrix MAS 5. 0 Presence calls into a single matrix, filtered in Excel and imported into BRB ArrayTools for further analysis.
Real-time RT-PCR Microarray gene expression data were validated by measuring differential gene regulation in treated ECs, from three separate donors, using real-time PCR. Briefly, total RNA extraction was performed with TRIzol Reagent (Invitrogen), according to the manufacturer's instructions. Reverse transcription was performed with 2 mg total RNA using the First-Strand cDNA Synthesis Kit (Invitrogen). 50 ng of cDNA was used for the qPCR reaction. Quantitative PCR was performed in duplicates, using the iCycler (Bio-Rad Laboratories, Munich, Germany) with specific TaqMan Gene Expression assays (Applied Biosystems). The reactions were incubated in a 96-well optical plate at 50 C for 2 min (UDG incubation) and 95 C for 10 min, followed by 40 cycles of 95 C for 15 sec and 60 C for 60 sec.
Between 2011 and 2013, spectra acquisition was more frequent, and more than 4500 spectra from 117 measurement days were recorded and analysed. The absorption lines of each atmospheric species observed in the solar spectra are used to retrieve its abundance in the atmosphere by appropriate radiative transfer and inversion algorithms  (Pougatchev and Rinsland, 1995; Zhao et al. , 1997; Hase et al. , 2006). We have used the PROFFIT (profile fit) algorithm developed by F. Hase to analyse the Paris data using the HITRAN 2008 spectral database  (Rothman et al. , 2009). PROFFIT is a code especially adapted for the analysis of solar absorption spectra from the ground and it has been widely applied and tested  (Hase et al. , 2004; Duchatelet et al. , 2010; Schneider et al. , 2010; Té et al. , 2012; Viatte et al.
Instrumentation and measurements at Jungfraujoch, Switzerland The Jungfraujoch station in Switzerland is part of NDACC and the instrumental set-up is similar to the one in Paris, cf. The Jungfraujoch data set corresponds to an update of the CO time series described by  Dils et al. (2011). It covers the January 2009 to December 2013 time period and includes 1733 individual spectra recorded on 539 different days. Mean signal-to-noise ratio (S/N) is 2930, with the 2nd percentile still being above 1000. The  SFIT-2 (v3. 91) algorithm (Rinsland et al. , 1998)  based on the semi-empirical implementation of the optimal estimation method (OEM) of  Rodgers (1990)  is used, allowing retrieval of information on the vertical profile of most FTIR target gases. The standard NDACC approach for the CO retrieval is adopted to simultaneously fit three microwindows spanning the 2057.
IASI observations provide an important contribution to the monitoring of atmospheric composition over time  (Clerbaux et al. , 2009). The IASI-MetOp is a Fourier transform spectrometer with a medium spectral resolution of 0. 5 cm -1 and a radiometric noise of about 0. 2 K at 280 K using nadir viewing and working in the thermal infrared (TIR) range extending from 645 to 2760 cm -1 with no gaps. The CO products (L2) are downloaded from the ETHER database for the period from 1 January 2009 to 31 December 2013  (Clerbeaux, 2014). The total column data were generated from the IASI radiance spectra in the 4. 7 µm spectral range and from meteorological data (surface and vertical profile of temperature, humidity vertical profile and cloud cover)  (August et al. , 2012) , using the Fast Optimal Retrievals on Layers for IASI (FORLI) code  (Hurtmans et al.
, 2006). The CO total columns were compared to other CO satellite data  (George et al. , 2009) , from which a DOFS value of about 2 was provided and a relative uncertainty between 4 and 10 % could be estimated. The total columns are calculated from the ground altitude to 60 km height. For this paper, we also provide additional vertical volume mixing ratio (VMR) profiles and partial columns in the PBL and in the troposphere layers around Île-de-France, as well as the partial columns above 4 km height around the Jungfraujoch site. The MOPITT instrument  (Drummond and Mand, 1996; Deeter et al. , 2004)  is on board NASA's Terra spacecraft in a sun-synchronous polar orbit at an altitude of 705 km. The satellite was launched on 18 December 1999. MOPITT has been in operation since March 2000. The instrument uses the technique of gas-filter correlation radiometry based on the IR absorption bands of CO to retrieve the vertical profiles of CO.
The horizontal footprint of each MOPITT retrieval is 22 km by 22 km. The MOPITT data were downloaded from the NASA website  (Deeter et al. , 2014). We are using version 6 retrievals of CO vertical profiles and total columns, for the period from the beginning of 2009 to the end of 2013. Since version 5 of the MOPITT retrieval algorithm, TIR (4. 7 µm) radiances are combined with the near IR (2. 3 µm) daily radiances to improve the sensitivity to lower tropospheric CO over land. The DOFS value is about 2  (Worden et al. , 2010). The retrieved vertical VMR profile is reported at 10 pressure levels (at the surface and every hundred hPa between 900 and 100 hPa). The retrieved CO total columns are obtained by integrating the retrieved VMR profile. In this paper, we are using the level 2 TIR/NIR products.
The following precautions have been taken in order to improve comparability between satellite and ground-based measurements. -Only clear-sky data from ground-based FTIR and satellites has been used. -The CO abundance is retrieved from the same spectral domain (4. 7 µm for both the ground-based FTIR and the satellite instruments), allowing minimisation of possible biases related to spectroscopic parameters and interfering atmospheric constituents (gaseous species, aerosols). -Satellite data were selected within a range of ±0. 15 • for the latitude and ±0. 23 • for the longitude, corresponding to a 33 km × 33 km square centred at the Paris site. 3 In situ analyser measurements and GEOS-Chem model simulation data.
Surface in situ measurements at Wollongong Surface CO at Wollongong is measured using two highprecision in situ FTIR trace gas analysers  (Griffith et al. , 2012). The analysers use an IR source, modulated through a Michelson interferometer with a CaF 2 beam splitter. The modulated IR beam is passed through a dried atmospheric sample within a White cell in a 24 m folded path and subsequently detected by thermoelectrically cooled MCT (Mercury Cadmium Telluride) detector. Ambient air is measured daily over 23. 5 h, with 30 min reserved for calibration using constant composition air. Ambient air is flushed through an inlet line at 5 L min -1 and sample air is continuously drawn from this line through the instrument at 1 L min -1. The Spec-tronusTM software (Ecotech P/L, Knoxfield, VIC, Australia) is used to automate internal valve control and stabilise parameters, such as flow, pressure and temperature.
Data from the GEOS-Chem model The describe the atmosphere from the surface up to 0. 01 hPa with 72 hybrid pressure-σ levels, at a 6 h temporal frequency (3 h for surface properties and mixing depths). In this study, we use the degraded GEOS-5 meteorological fields as model input to a 2 • × 2. 5 • horizontal resolution and 47 vertical levels, lumping together levels above ∼ 80 hPa. We apply here the standard full chemistry GEOS-Chem simulation, including detailed O 3 -NO x -volatile organic compound (VOC)aerosol coupled chemistry  (Bey et al. , 2001; Park et al. , 2004;  with updates by  Mao et al. , 2010). Tropospheric CO is sourced from anthropogenic, biomass burning and biofuel burning emissions, as well as from the degradation of many VOCs. The emission inventory of the Emissions Database for Global Atmospheric Research (EDGAR; http://edgar.
All these global and regional inventories are scaled to the years of interest according to the method described by  van Donkelaar et al. (2008). Anthropogenic sources of ethane and propane are derived from an offline simulation  (Xiao et al. , 2008). The global biomass burning emissions are provided by the Global Fire Emissions Database (GFED) v3 (van der  Werf et al. , 2010)  and the global biogenic emissions are obtained with the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2. 1  (Guenther et al. , 2006). Methane concentrations in GEOS-Chem are based on measurements from the NOAA Global Monitoring Division flask measurements. The GEOS-Chem data set employed in the present work covers the period from January 2009 to May 2013 and is derived from a July 2005 to May 2013 simulation, for which the GEOS-5 meteorological fields are available.
Remote sensing observations Figure  1  shows the CO total columns of the three groundbased FTIR sites from 2009 to the end of 2013. The data from Paris are less numerous than from the other two sites, because measurements are not fully automated and spectral acquisitions are only launched when clear sky is expected for more than half of the daytime. Moreover, from 2009 to 2010, Paris CO spectra were recorded only during intensive measurement campaigns, and not on a regular basis. As expected, the CO abundance is higher in the Northern Hemisphere. The CO column mean value is about 2. 1 × 10 18 molecules cm -2 at the Paris station which is almost twice as high as the value at Wollongong (1. 3×10 18 molecules cm -2 ). The CO column mean value of 1. 1×10 18 molecules cm -2 at the Jungfraujoch station is quite low and is attributed to the site's height.
For the Northern Hemisphere (Paris and Jungfraujoch) stations, the maximum is observed around March-April and the minimum around September-October. The average amplitude of the seasonal variability is about (13±3) % of the column average and the average half cycle is about 188±4 days for both Northern Hemispheric sites. For Paris, the value w = 191 ± 3 days is slightly, but not significantly, higher than at Jungfraujoch, probably due to the lack of data before 2011. This seasonal variability is also observed by  Rinsland et al. (2007)  at Kitt Peak, which is the US National Table  2. Parameters obtained from the sinusoidal fit of the seasonal variability to the CO total columns from ground-based, satellite and GEOS-Chem modelling data.
Interestingly, the relative amplitude is lowest at Jungfraujoch, where the impact of the local surface emissions is small. The seasonal variability of CO is also observed by the IASI-MetOp and MOPITT instruments, cf. Fig. 2  and Table 2. One of the advantages of the satellite measurements is their spatial coverage. In general, the period and amplitude of the seasonal variability obtained from the satellite data agree with the corresponding values from the ground-based FTIR measurements. Ground-based 191 ± 3 14 ± 1 185 ± 1 12 ± 1 178 ± 1 17 ± 1 Satellite data 183 ± 1 14 ± 1 190 ± 2 12 ± 1 182 ± 1 16 ± 1 GEOS-Chem 183 ± 1 19 ± 1 182 ± 1 12 ± 1 180 ± 1 13 ± 1 0 1 / 1 2 / 2 0 0 8 0 1 / 1 2 / 2 0 0 9 0 1 / 1 2 / 2 0 1 0 0 1 / 1 2 / 2 0 1 1 0 1 / 1 2 / 2 0 1 2 0 1 / 1 2 / 2 For the Jungfraujoch station, the satellite data need to be recalculated in order to correspond to the column between the ground altitude and the top of the atmosphere  (Barret et al.
9 8 N = 1 6 5 d a y s R 2 = 0. 5 8 @ J u n g f r a u j o c h S lo p e = 0. 9 which Jungfraujoch belongs. Partial columns above 1100 m were then calculated using the interpolated CO vertical profiles and daily NCEP meteorological pressure and temperature profiles. Data from the ground-based FTIR instruments and satellites are in good agreement. This is demonstrated in Fig. 3 , in which the satellite data are plotted against the ground-based measurements. The agreement is indicated by robust fits yielding slopes of 0. 98 for Paris, 0. 91 for Jungfraujoch and 0. 99 for Wollongong. The robust fit regression is based on a process called iteratively reweighted least squares  (Street et al. , 1988)  and is less sensitive than ordinary least squares to large changes in small parts of the data. GEOS-Chem model outputs are presented in Fig.
The CO averaging kernels for each altitude of the a priori profile indicate high sensitivity of the FTS-Paris instrument to the PBL. Effectively, the left panel of Fig. 5  shows that the retrieval of CO essentially provides two independent measurements of tropospheric CO: the first supplies maximal information in the altitude range between 0 and 1000 m, thus well represents the PBL. The comparability between FTIR retrievals and surface in situ observations is thus assured. The sec- ond one is representative of the upper troposphere, with a maximum around 8-9 km. The FTS-Paris data in the bottom panel of Fig. 4  represent the averaged CO VMR obtained for the altitude range between the ground (60 m a. s. l. ) and the 1000 m level. These remote sensing measurements are consistent with the in situ data, even if they are much less affected by local pollution peaks.
By comparing Figs. 1  and 4 , we notice that the seasonal variability of the total column is shifted by about 2 months compared to the variability at the surface. The free tropospheric columns of CO have been calculated as partial columns between 2 and about 12 km over Paris for both IASI-MetOp and FTS-Paris. The right panel of Fig. 5  shows the July 2010 monthly averaged AVKs of IASI-MetOp. These AVKs show a relatively low surface sensitivity which is more emphasised during winter (we have also checked the January 2010 monthly averaged AVKs not shown here). We thus only exploit the partial columns corresponding to the free troposphere. The seasonal variability in the free troposphere obtained by the three different kinds of data is also shifted by 2 months compared to the surface seasonal variation.
Conclusions This paper investigates the seasonal variability of CO total columns at three NDACC/TCCON sites: Paris and Jungfraujoch in the Northern Hemisphere and Wollongong in the Southern Hemisphere. In the Northern Hemisphere, the variability of CO above the PBL has a seasonal maximum in March-April and a minimum in September-October. In the Southern Hemisphere, this seasonal variation is shifted by 6 months. We have compared the ground-based FTIR data to satellite measurements from IASI-MetOp and MOPITT and to GEOS-Chem model standard outputs, which confirm the observed CO seasonal variability. However with currently implemented inventories, an underestimation of about 20 % by the GEOS-Chem model is observed, which is consistent with previous forward and inverse modelling studies. Interestingly, a time lag of about 2 months between upper altitude and surface CO has been found in the measurements and GEOS-Chem in both Paris and Jungfraujoch.
The presence of global ground-based FTIR networks provides a unique opportunity to obtain these data on a global scale, as the instruments are capable of sampling the surface and the total column data at the same time. The time lag data might also provide an additional benchmark parameter for chemical transport models and emission inventories, taking into account that the modelling of vertical CO gradients in the remote Southern Hemisphere already provides a challenge for chemical transport modelling  (Fisher et al. , 2015).