dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012). The decision to use existing results instead of prescribing new simulations was based largely on the need to have a "first look" synthesis of existing, mostly independent TBM results. Thus, the NACP RCIS provided a comprehensive assessment of the range of estimates of landatmosphere carbon exchange, and by extension the uncertainty associated with such estimates, including uncertainties resulting not only from model formulation and assumptions, but also from the choice of environmental driver data and spin up procedure. However, the lack of a detailed simulation protocol and consistent forcing data precluded the attribution of observed across-model variability to differences in modeling approaches. While the NACP RCIS and Site syntheses efforts provided a unique forum for summarizing the status of terrestrial carbon modeling, they also reinforced the need for a consistent and unified model evaluation framework in order to isolate, interpret, understand, and better address differences -primarily structural or process representations -among stateof-the-art TBMs simulating land-atmosphere exchange over continental to global extents.
This manuscript provides an overview of, and describes the experimental protocol for, the Multi-Scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The MsTMIP activity was created to build off of and complement recent and ongoing synthesis efforts such as the NACP RCIS and site interim synthesis activities described above, as well as the Trends in Net Land-Atmosphere Carbon Exchange (TRENDY 1 ), the regionally focused Large Scale Biosphere Atmosphere-Data Model Intercomparison Project (LBA-MIP 2 ), and the International Land-Atmosphere Benchmarking Project (ILAMB 3 ; a framework confronting coupled carbon-climate models with data). The goal of MsTMIP is to quantify, within a unified intercomparison framework (Fig. 1 ), the contribution of model structural differences to across-model variability in estimates of landatmosphere carbon exchange, thus providing the critical synthesis, benchmarking, evaluation, and feedback needed to improve the current state of the art in carbon cycle modeling.
, 2013)  in order to test the influence of both spatial resolution and changing driver data on model estimates. Building off of lessons learned from the NACP interim synthesis activities, one of the primary goals of the MsTMIP activity is to quantify and assess the impact of TBM structural variability, and therefore uncertainty, by examining how inter-model differences influence variability among model results. Structural uncertainty results from differences across models in their representation (or lack of representation) of biogeochemical and biophysical processes. Although structural uncertainty can be quantified, in part, for a given model through a series of sensitivity simulations, it is best quantified through a MIP. To do so, however, a large ensemble of models is needed to span the range of biogeochemical and biophysical process representations, and the simulation protocol must isolate structure, at least to the extent possible, by holding constant as many aspects of simulating the terrestrial carbon cycle as feasible except for the models themselves.
For example, models in the "tree" to the left include an explicit nitrogen cycle, while models in the "tree" to the right do not. Models are further separated or clustered by their treatment of soil carbon pools (e. g. , SiB3-JPL does not include live, soil, or litter carbon pools) and their treatment of radiation and canopy heat storage (e. g. , Biome-BGC, DLEM, TEM6, and MC1 do not account for canopy heat storage, nor partition radiation into latent and sensible heat). Refer to the Supplement for the binary data used to create this diagram. cycle, including how they simulate canopy and stomatal conductance and partition net radiation between latent and sensible heat (Fig. 4a ). All models account for energy fluxes, but do so in somewhat different ways. Conversely, not all models explicitly consider nitrogen cycling.
, 2011). Although more work is needed in structural development and parameterization of TBMs for incorporating multiple disturbances, the limitation is probably more due to the lack of globally consistent and comprehensive data sets on historical disturbances and their future projections. Because of the lack of robust data sets on disturbance history, disturbance is not explicitly accounted for in the MsTMIP simulation protocol. Instead, models participating in MsTMIP deal with disturbances in their simulations as they normally would, and report their treatment of disturbance in the model surveys discussed in Sect. 2. 2. For example, some of the models participating in MsTMIP (e. g. , CLM, CLM4VIC, MC1) include disturbances such as fire prognostically (i. e. , predictively), while others require a diagnostic forcing data set (e.
All model submission files are CF-1. x compliant netCDF (version 3), using the standard variable names and units as listed in Table  S6. The CF (Climate and Forecast) standards for writing netCDF files are described in extensive online documentation  5. In past efforts (e. g. , NACP RCIS and Site Synthesis), reformatting model output to a common format required significant time and effort. To avoid this problem, a library of output subroutines written in Fortran90 was created that makes it possible to write model output directly in the required submission file format. These subroutines can be inserted directly in the model code or used in a separate postprocessing program. The submission file subroutines with complete user's guide and documentation are available from the MsTMIP SVN server 6.
Preliminary results This section presents preliminary results from a subset of models participating in MsTMIP, with all results based on the RG1 simulations. Finalized MsTMIP data products will be archived at the ORNL DAAC (http://daac. ornl. gov). In order to evaluate the effectiveness of the MsTMIP experimental design in isolating model structural differences while controlling for other sources of variability (i. e. , driver data, simulation protocol), we compare the some of the initial model results from the MsTMIP standardized protocol (Fig. 5b ) to results from the NACP RCIS (Fig. 5a ), an "unconstrained" protocol (varying environmental driver data, initial conditions, and spatial and temporal resolutions). Steady-state conditions for the NACP RCIS are not available, and we therefore compared the latitudinal gradients of multiyear mean gross primary productivity (GPP) from 2000 to 2005 for the five models (CLM, DLEM, LPJ, ORCHIDEE-LSCE, and VEGAS) common to both synthesis activities.
LPJ-wsl: This work was conducted at LSCE, France, using a modified version of the LPJ version 3. 1 model, originally made available by the Potsdam Institute for Climate Impact Research. ORCHIDEE-LSCE: ORCHIDEE is a global land surface model developed at the IPSL institute in France. The simulations were performed with the support of the GhG Europe FP7 grant with computing facilities provided by "LSCE" or "TGCC". TRIPLEX-GHG: TRIPLEX-GHG developed at University of Quebec at Montreal (Canada) and Northwest A&F University (China) has been supported by the National Basic Research Program of China (2013CB956602) and the National Science and Engineering Research Council of Canada (NSERC) Discover Grant. VISIT: VISIT was developed at the National Institute for Environmental Studies, Japan. This work was mostly conducted during a visiting stay at Oak Ridge National Laboratory.
Statistical analyses Principal component analysis (PCA) was performed using Simca-P + software (Umetrics). Air mass categories were determined with a hierarchical classification from PCA results. To highlight significant differences between data groups, the Kruskal-Wallis non parametric test was carried out with Past software  (Hammer et al. , 2001). Two air mass categories were declared different when the probability for the groups to have identical data distribution was lower than 5 % (p value < 0. 05). This test was chosen due to the lack of normality of the data according to the Shapiro-Wilk normality test.
Seventy We decided in this study to classify our cloud samples according to a statistical analysis that considers physico-chemical parameters of the cloud samples. This approach is more robust than looking only at the back-trajectories of the air masses that reach the puy de D ôme station. Nevertheless, the back-trajectories are drawn for each cloud samples to confirm the statistical classification. Cloud events corresponding to 4 individual samples (No. 19 and 27, Table  S1 ) with air masses originating from the Sahara according to the corresponding back-trajectory plots were excluded due to their lack of representativeness in the dataset.
Back Close Full Southwest/South sector were dominated by a pronounced marine character (64 %) and a continental influence from Spain (36 %). During the past 10 yr, in total 13 % of the air masses sampled at the pdD station were under highly marine influence, 52 % under marine influence, 26 % under continental influence and 9 % under anthropogenic influence. In the next part, we will focus on the variability of the chemical composition of cloud water as a function of air mass categories determined by our statistical analysis. Table S2 presents the physico-chemical parameters (pH, redox potential, conductivity, liquid water content) and concentrations of measured chemical compounds (average, minimum, maximum) as a function of air mass origins together with data from recent studies describing cloud chemical composition.
Since the chemical makeup of clouds is sufficiently different from that of fog, primarily due to the environments in which they form  (Collett et al. , 2002; Straub et al. , 2012; Weathers et al. , 1988) , data for fog have been excluded here. Although these studies featured a variety of cloud collection devices, different analytical techniques, and often a limited number of samples, some general comparisons can be made with our dataset.
Conclusions and recommendations Clouds incorporate the local pollution when droplets nucleate and then transform pollutants during its lifetime. In this context, the impact of regional and long-range transport Figures of chemical constituents at the puy de D ôme site is apparent and due to the remote (and elevated) geographical character. In this paper, a long-term database of the aqueous phase chemical composition of clouds sampled at the puy de D ôme station between 2001 and 2011 is presented. The abundance of inorganic and organic chemical compounds as well as a series of physico-chemical parameters permitted the identification of various influences corresponding to distinct air-mass histories. A classification was performed by principal component analysis that allows to define discriminating factors (pH and concentrations of Na + , Cl -, NO 3 -, NH 4 + and SO 4 2-) for 4 different categories: polluted, continental, marine and highly marine.
As shown in this study, chemical speciation is a powerful tool in analyzing air mass history. For example, the presence of inorganic compounds such as nitrate and sulfate followed in this study are due to anthropogenic emissions such as automobiles or power production. However, it is difficult to differentiate the various sources of these measured chemicals in cloud water due to the various potential processes (transfer from the gas phase, dissolution of particles, aqueous chemical reactivity). In this frame, some chemical compounds that are molecular markers of emissions (such as levoglucosan, a marker of biomass burning) should be followed in the future to correlate cloud features with emissions. Our results also underline that, although this database is rather complete, the characterization of the organic matter is still incomplete since around 10 % of the dissolved organic matter is characterized.
ACPD  ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD Acknowledgements. We would like to thank the  Auvergne Region  for PhD grants provided to  T. Charbouillot ,  M. Joly  and  M. Parazols. M. Vaïtilingom  acknowledges PhD scholarships from the  French Ministry of Research. This work on the long term analysis of the cloud water chemical composition was supported by the  French Ministry and CNRS-INSU  through the  ORE/SO-BEAM project. Authors acknowledge additional financial support from the council of  Region Auvergne , from the  "Observatoire de Physique du globe de Clermont-Ferrand" (OPGC)  and from the " F éd ération de Recherche en Environnement ". Authors also thank  C. Bernard  for his technical support for the cloud sampling. The publication of this article is financed by  CNRS-INSU. ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD.
Study species and field methods Geese and eiders are partial capital breeders that molt all their flight feathers simultaneously after the breeding season. During incubation, female eiders and geese lose up to 40% of their body mass due to fasting  [33]. After hatching, female eiders must recover and regain body condition before initiating molt, another energetically costly event  [34]. For this study we used data from long-term monitoring studies in which birds were fitted with permanent markers with unique combination to allow resightings in subsequent years  [35, 36]. Captures occurred prior to breeding in spring staging areas for geese and on the breeding colony for eiders. Geese 81u479W) in the East Bay Migratory Bird Sanctuary, Southampton Island, Nunavut, Canada from 2007-2011  [37].
Precise information on reproduction (laying date of the first egg and subsequent nest success) was available through the daily monitoring (via a maximum of seven blinds within the colony) and nest visitation (15 to 78 nests each year) of marked females. Laying date of females was determined by monitoring. 358 nests annually at the colony. We investigated possible links between relative pre-laying biomass for a given year and CORT levels sequestered in new feathers grown during the pre-basic molt of the same year immediately following breeding (though feathers were plucked in the following year). We restricted these analyses to birds captured and weighed during the pre-laying period in order to avoid any change in body mass due to egg laying. To do so and for each year, we only included data from birds caught before the date at which.
2. 5% of the population had started laying. Individuals with known laying dates were subsequently added to the dataset if known laying date was later than capture date (with a buffer of 3 days to account for potential error on laying date estimation). This procedure was applied during all the years of the study, which yielded a sample of 16 eiders with known laying dates and 17 with known body mass. Finally, we investigated the effect of environmental covariates on feather CORT variation with a larger sample size (N = 652) that included all captured individuals from 2007 to 2011 for which we had feather CORT data. For this last analysis, we included body size as an individual covariate because structural body size is a static variable that remains similar over time once eiders reach sexual maturity.
Environmental covariates We addressed the influence of environmental conditions on CORT during molt only on eiders because not enough data was available in geese. Molting in females occurs outside the colony in our studied eider population, presumably in East Bay (Southampton Island) but precise information on molting period and molting sites are still lacking  [41]. Based on our knowledge of the study system, we considered a two months molting period  [42]. Climate. We used different climate indices (temperature, precipitation, and North Atlantic Oscillation). At the regional scale, large-scale indices are often better predictors of ecological processes than local climatic variables, likely because features of several weather components are combined  [43]. The North Atlantic Oscillation (NAO) is a major source of atmospheric mass balance measured as the mean deviation in average sea level pressure between the subarctic and subtropical Atlantic  [44].
The methanol was then separated from feather material by vacuum filtration, using a plug of synthetic polyester fibre in the filtration funnel. The methanol extract was placed in a 50uC water bath and subsequently evaporated in a fume hood. Extract residues were reconstituted in a small volume of phosphate-buffered saline (0. 05 M, pH 7. 6) and frozen at 220uC until analysed by radioimmunoassay (RIA). We assessed the efficiency of the methanol extraction by including feather samples spiked with a small amount (approximately 5000 CPM) of 3 H-corticosterone in the extraction. RIA assays were performed on reconstituted methanol extracts, and samples were measured in duplicate. Serial dilutions of feather extracts produced displacement curves parallel to the standard curves. Data values are expressed as pg CORT per mm of feather, which gives a valid estimate of CORT per unit time of feather growth  [6, 47].
Statistical analyses Intra-individual repeatability of feather CORT among years was calculated using the within and between-variance components in a linear mixed effects model, using the restricted maximumlikelihood method (REML) with bird identity as the grouping random factor  [48, 49]. Data were analysed using R studio v2. 1 (R Development Core Team) and the package rptR to calculate repeatability (R) and associated standard errors based on bootstraps (1000 iterations) that generated the distributions of likelihood ratios. To examine variations in feather CORT, we used generalized linear mixed models with climatic and predation indices as explanatory covariates. Year was considered as a random factor to account for multiple samples per year. Our dataset contains yearly covariates (temperature, SST etc.
Acknowledgments This manuscript is dedicated to our co-author,  Dr. Gary Bortolotti , who was actively involved in this study beginning in 2007, and passed away in 2011. Catching geese and eiders involved the efforts of many field workers and volunteers. We are especially grateful to  Gerald Picard ,  Johanne Dussureault ,  Marie-Claude Martin ,  Peter L. F. Fast ,  Mike Janssen ,  Christy MacDonald ,  Isabel Buttler ,  Josanne Verhagen  and  Guylaine Se  ´guin. We were greatly assisted by Colette Trouve,  Charline Parenteau ,  Sonia Cabezas ,  Tracy Marchant ,  Vicky Fachal ,  Hollie Remenda ,  Wendy van Dijk , and  Graham Fairhurst  during laboratory analysis and/or verification of data quality assurance. Loran Dubroca helped us to acquire remote sensing data.
We present a global data set of free tropospheric ozone-CO correlations with 2 • × 2. 5 • spatial resolution from the Ozone Monitoring Instrument (OMI) and Atmospheric Infrared Sounder (AIRS) satellite instruments for each season of 2008. OMI and AIRS have near-daily global coverage of ozone and CO respectively and observe coincident scenes with similar vertical sensitivities. The resulting ozone-CO correlations are highly statistically significant (positive or negative) in most regions of the world, and are less noisy than previous satellite-based studies that used sparser data. Comparison with ozone-CO correlations and regression slopes (dO 3 /dCO) from MOZAIC (Measurements of OZone, water vapour, carbon monoxide and nitrogen oxides by in-service AIrbus airCraft) aircraft profiles shows good general agreement.
P. S. Kim et al. : Global ozone-CO correlations from OMI and AIRS greenhouse gas and harmful surface air pollutant, with implications for climate, human health, and agricultural productivity  (IPCC, 2007). Current global models can capture the observed large-scale spatial and seasonal patterns of ozone concentrations but there is large uncertainty in the driving factors, as reflected by the large differences between models in ozone production and loss rates  (Wild, 2007; Wu et al. , 2007)  and in source contributions  (Fiore et al. , 2009). We present here a high-density global satellite database of ozone-CO correlations using data from the Ozone Monitoring Instrument (OMI;  Levelt et al. , 2006)  and the Atmospheric Infrared Sounder (AIRS;  Aumann et al. , 2003) , and explore its value for constraining our understanding of the factors controlling ozone.
A drawback is the limited coverage of TES due to lack of cross-track scanning. Individual satellite retrievals have large random error, compromising correlation analyses unless large ensembles are considered. Correlation analyses using TES have had to aggregate the data over ∼ 1000 km spatial domains and/or over several years. Even so, the random retrieval error is problematic when diagnosing ozone-CO correlations  (Zhang et al. , 2006). In this work, we produce a new database of ozone-CO correlations from space using ozone retrievals from OMI and CO retrievals from AIRS. These two instruments aboard the NASA A-Train observe the same scenes with near coincidence (< 15 min). They use different spectral bands (OMI in the UV, AIRS in the thermal IR) but their vertical sensitivities are similar.
Both provide near-daily global coverage. This allows us to calculate robust statistical relationships between ozone and CO with much less error and finer spatiotemporal resolution than previous satellite studies. We evaluate the resulting product with ozone-CO correlation data from MOZAIC (Measurements of OZone, water vapour, carbon monoxide and nitrogen oxides by in-service AIrbus airCraft) aircraft profiles (www. iagos. fr). Here we present global ozone-CO correlations for each season of 2008 on a 2 • × 2. 5 • grid, and we use the global GEOS (Goddard Earth Observing System)-Chem chemical transport model (CTM) to interpret these correlations. A subsequent paper will examine interannual variability.
GEOS-Chem We use the GEOS-Chem global CTM (version 9-01-02, http: //geos-chem. org), originally described by  Bey at al. (2001)  and  Park et al. (2004) , to interpret the observed satellite ozone-CO relationships. GEOS-Chem is driven by Goddard Earth Observing System (GEOS-5) assimilated meteorological data from the NASA Global Modeling and Assimilation Office (GMAO). The GEOS-5 meteorological data have a native horizontal resolution of 0. 5 • × 0. 67 • with 72 vertical pressure levels and 6 h temporal frequency (3 h for surface variables and mixing depths). We degrade the horizontal resolution to 2 • × 2. 5 • for input to GEOS-Chem. The model results presented here are for 2008 following a 1 yr initialization. Global anthropogenic emissions of CO, NO x , and SO 2 are from the EDGAR 3. 2 monthly inventory for 2000  (Olivier and Berdowski, 2001)  while anthropogenic NMVOC emissions are from the RETRO monthly global inventory for 2000  (van het Bolscher et al.
, 2008). The default inventories are scaled for subsequent years on the basis of economic data  (van Donkelaar et al. , 2008)  and regional overwrites are used where available. These include the US EPA NEI inventory for 2005, Asian emissions from  Zhang et al. (2009)  for 2006, the EMEP inventory for Europe  (Vestreng and Klein, 2002)  for 2005, the CAC emission inventory for Canada for 2005, and the BRAVO emission inventory for Mexico  (Kuhns et al. , 2003)  for 1999. Global shipping emissions are from ICOADS, as implemented in GEOS-Chem by  Lee et al. (2011). Global biofuel emissions are from  Yevich and Logan (2003). Biomass burning emissions are from the Global Fire Emission Database version 3 (GFED3) inventory with monthly resolution  (Giglio et al. , 2010; van der Werf et al. , 2010).