dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2007; 25 Veihelmann et al. , 2007)  and POLDER on PARASOL (Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar), because only ultraviolet (UV) and shortwave visible channels and polarimetric measurements are sensitive to aerosol absorption. Despite their ability to provide a high-degree of spatial coverage, satellite measurements alone are not sufficient for answering question regarding the distributions, magnitudes, and fates of aerosols in the atmosphere. These aspects can be 30 studied using Chemical Transport Models (CTMs), which incorporate meteorological data from external databases with atmospheric physics, considering the physical and chemical processes in the atmosphere, and allow modelling of detailed Atmos. Chem. Phys. Discuss.
, 2001). The daily biomass burning sources are calculating from version 3 of Global Fire Emissions Database (GFED) inventory  (van der Werf et al. , 2006 (van der Werf et al. , , 2010;; Randerson et al. , 2013). The monthly anthropogenic fossil fuel and biofuel BC and OC emissions are adopted from Bond inventory with base year 2000  (Bond et al. , 2007). The sulphate (SU) and sea salt (SS) aerosol simulation in GEOS-Chem is described in  (Park et al. , 2004; Jaeglé et al. , 2011). The standard aerosol dry deposition in GEOS-Chem is described in  (Wang et al. , 1998; Wesely, 1989) , and accounting for gravitational settling and turbulent mixing of particles to the surface  (Pye et al. , 2009; Zhang et al. , 2001). Aerosol wet deposition is through wet scavenging in convective updrafts as well as in-and below-cloud scavenging from 5 convective and large scale precipitation  (Liu et al.
Results In this section, we discuss retrieval of DD, BC and OC emissions simultaneously from the actual PARASOL/GRASP spectral AOD and AAOD data from December 2007 to November 2008. The SU and SS aerosol simulations are kept as the 25 prior model. PARASOL/GRASP retrievals were aggregated to the same horizontal resolution as the GEOS-Chem model (  2° x 2. 5°) and averaged within the grid cells prior to assimilation. When iteratively minimizing Eq. (  3 ), the maximum iteration number was chosen to be 40, which takes about 60 days to complete on a computer workstation with 32x3. 3 GHz CPUs.
m. Figure  11d  and 11e display the grid-to-grid comparison between PARASOL/GRASP spectral AOD and prior and posterior GEOS-Chem simulation during one year, color-coded with the PARASOL Ångström exponent 𝛼 !!"!!"# = ln (𝜏 !!" /𝜏 !"# ) ln (865/443 ). The Ångström exponent α is often used as a qualitative indicator of aerosol particle size; the smaller the α, the larger the particle size. For example, the α values for "pure" dust aerosols are usually near zero, whereas that for smoke or pollution aerosols are generally greater than 1  (Eck et al. , 1999; Schuster et al. , 2006). 10 One of the major discrepancies between the prior GEOS-Chem simulation and PARASOL/GRASP observation is that the model produces the highest annual average AOD values over the major dust source region of Northern Africa; however, satellite data show the maxima AOD in Central and the Southern Africa, where carbonaceous aerosols usually dominate (although Central Africa may also be influenced by dust events).
Hence, compared to PARASOL/GRASP observations, the prior GEOS-Chem AOD is overestimated in Northern Africa, while it is underestimated in the Southern Africa biomass 15 burning and Arabian Peninsula regions. Some recent studies by  Ridley et al. (2012 Ridley et al. ( , 2016) )  and  Zhang et al. (2015)  also indicate that the GEOS-Chem model overestimates dust AOD in Northern Africa. Meanwhile,  Ridley et al. (2012)  and  Zhang et al. (2013)  propose a new and realistic dust particle size distribution according to the measurements from  Highwood et al. (2003) , which can partially adjust the misrepresentation of dust near the source and over transport areas. This new particle size distribution has been adopted in our prior and posterior GEOS-Chem simulation. In addition, the 20 underestimation of model simulated AOD in biomass burning regions with the GFED emission database was also shown in other modeling studies  (Chin et al.
Figures  12d  and 12e  show the comparisons of PARASOL/GRASP observed AAOD at 6 wavelengths with the corresponding GEOS-Chem simulated quantities using prior or posterior emissions. The very low linear regression slope 5 between the model simulated AAOD using prior emissions with observations (less than 0. 11 over all six wavelengths) indicates that the prior simulations significantly underestimate the AAOD. In contrast, model simulations with the posterior simulations improve the slope to 1. 01 at 443 nm and 0. 70 at 1020 nm. Similar to the case of AOD, the agreements between the PARASOL/GRASP AAOD data and the model simulations are much better using the posterior emissions than using the prior emissions, with the correlation coefficients increased from 0. 14-0. 33 to 0. 90-0. 92 and the root mean square error 10.
, 2017). Meanwhile, the retrieval increases the model annual carbonaceous aerosol emission by about 2. 5 times. This value is close to the recommendation given in  Bond et al. (2013)  that increasing global BC absorption by a factor of 3 to fit the observation of columnar aerosol absorption. In addition, there are many other efforts to improve the simulation of AAOD, e. g. treating hydrophilic BC as an internal aerosol core with other soluble hygroscopic aerosol species  (Wang et al. , 2016) ; including the 25 light absorbing brown carbon in the simulation  (Wang et al. , 2014b). These studies are all crucial to improve current CTMs aerosol simulation, which should be adopted in our aerosol emission inversion framework in the future. AERONET 5  In order to objectively evaluate our retrieved aerosol emissions based on PARASOL/GRASP spectral AOD and AAOD, we made a series of evaluations using independent datasets and models not used by our inversion.
For example, in Mongu, the prior simulation underestimates AOD and AAOD significantly. In September, the underestimations are about 3 times (a bias of -0. 56 for monthly average) for AOD and 4 times for AAOD (a bias -0. 09). Such bias is significantly reduced to -0. 22 for AOD and +0. 01 for AAOD in posterior simulation with retrieved emissions. In 20   model? To investigate this, we implement our PARASOL/GRASP based aerosol emission database into the GEOS-5/GOCART model  (Chin et al. , 2002 (Chin et al. , , 2009 (Chin et al. , , 2014;; Colarco et al. , 2010). The prior and posterior GEOS-5/GOCART model simulated seasonal AOD are compared with MODIS observations in Figure  20. GEOS-5/GOCART uses similar meteorological fields as GEOS-Chem, with the prior anthropogenic emissions from the Hemispheric Transport of Atmospheric Pollution (HTAP) Phase 2, biomass burning emissions from the Fire Energetics and Emission Research (FEER) 5 database  (Ichoku and Ellison, 2014) , dust emission calculated as a function of 10-m winds and surface characteristics  (Ginoux et al.
With the posterior emissions, the GEOS-5/GOCART simulation shows improvements compared with AERONET and MODIS observations, with higher correlation coefficient and lower root mean square error in all 4 seasons than the prior GEOS-5/GOCART simulation. The posterior GEOS-5/GOCART simulated AOD is a little lower than MODIS on average 13% (Normalized Mean Bias, NMB=-13%, NMB= ( ! - ! )/  ! , where sums 20 are over the ensemble of all data , and  ! and  ! are the modeled and observed values), likely associated with that the MODIS AOD is observed at noon, however the GEOS-5/GOCART AOD is average over 24 hours during a day. Because only ultraviolet and shortwave visible channels and polarimeter measurements are sensitive to aerosol absorption properties, long-term records of AAOD are limited to AERONET, PARASOL/GRASP and OMI.
We use the 10 latest OMI aerosol products (OMAERUV version 1. 7. 4)  (Torres et al. , 2007 (Torres et al. , , 2013) )  to evaluate the GEOS-5/GOCART model simulated AAOD from prior aerosol emission inventories and our retrieved aerosol emission database. Meanwhile, collocated AERONET data over the study area are also employed to the evaluation. Detailed assessments of OMI aerosol products are described in other studies  (Torres et al. , 2013; Ahn et al. , 2014; Jethva et al. , 2014). Figure  21  shows the validation results. Figure  21a   resolution 0. 5° x 0. 5°. We plot grid-to-grid comparison between OMI and GEOS-5/GOCART AAOD in Figure  21d -e, here OMI AAOD are re-scaled to the same resolution with model simulation 2. 0° x 2. 5°. Any 2. 0° x 2. 5° grid box with less than 10 OMI original AAODs (~50% coverage) for averaging is abandoned.
Therefore, we used PARASOL/GRASP retrieved spectral AOD and AAOD to optimize BC, OC and DD aerosol emissions in a year (December 2007 to November 2008) over the study area with horizontal resolution of 2. 0° x 2. 5° in order to match the adjoint GEOS-Chem spatial resolution. The retrieved emissions are publically available at http://csuchencheng. wixsite. com/chencheng/research-blog; this dataset will be available soon at the GEOS-Chem inventory findings website (http://wiki. seas. harvard. edu/geos-chem/index. php/Inventory_Findings). 10 Our analysis of the retrieved aerosol emissions indicates that the prior GEOS-Chem model overestimates annual desert dust aerosol emissions by a factor of about 1. 8 (with the DEAD scheme) over the study area, similar to other previous modeling studies  (Huneeus et al.
, 2003; Zhang et al. , 2015). However, we cannot rule-20 out the possibility that differences between model and observations could also be attributed to the errors in removal processes and aerosol microphysical properties, in addition to the deficiencies in emissions  (Bond et al. , 2013). Nevertheless, the fidelity of our results is confirmed by comparison of posterior simulations with measurements from AERONET that are completely independent from and more temporally frequent than PARASOL observations. Specifically, to analyze the PARASOL/GRASP based aerosol emission database further, we implemented these emissions in the GEOS-5/GOCART 25 model and compared the resulting simulations of AOD and AAOD with independent MODIS and OMI observations. The comparisons show better agreements between model and observations with the posterior GEOS-5/GOCART results (lower biases and higher correlation coefficients) than prior simulations.
Acknowledgments This work is supported by the  Laboratory of Excellence CaPPA -Chemical and Physical Properties of the Atmosphere Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-35Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 7 March 2018 c Author(s) 2018. CC BY 4. 0 License.
Importantly, demonstration of the sporulation ability in the AA strain makes its phenotype unrelated to the degeneration phenomenon, which suggests that its genome contains solventogenic-specific mutations. A point mutation in the sigL gene is predicted to impede solvent formation and sugar consumption. We next adopted a forward genetic approach to identify the gene(s) responsible for the AA phenotype. Whole-genome sequencing followed by read mapping on the wild-type genome allowed the identification of 13 SNPs (Table  1 ) along the chromosome of the strain. Among the affected genes, CIBE_0767 particularly retained our attention as P-BLAST analysis against Swissprot database revealed high similarities with putative RNA polymerase σ 54 factors (encoded by the sigL gene) and notably the one from Clostridium kluyveri  15.
This enzyme can be involved in glycerol metabolism, or directly in the central carbon pathway when associated to 6P-fructose aldolases  26, 27. Notably, two 6P-fructose aldolases (CIBE_0334 and CIBE_0411), one of which being well expressed in the available RNA-seq dataset  8  , are annotated in the DSM 6423 genome. Lastly, parts of the pentose phosphate pathway also seem to be regulated by σ 54 , with two conserved similar operons encoding transketolases, ribose/ribulose phosphate epimerases and isomerases (Supplementary File S2). Importantly, some of these putative σ 54 -dependent transcriptional units appeared to be well conserved in the NCIMB 8052 and NRRL-B598 strains (Fig. 2d ). In summary, analysis of the AA genotype revealed an amino acid substitution potentially inactivating a protein predicted not only to regulate solventogenesis but also other central metabolic pathways such as sugar consumption in several C.
We therefore kept on investigating its impact on other predicted metabolic pathways. Plate assays on minimal medium were used to analyze carbon sources usage. In our conditions, no effect after 24 or 48 hours was seen on bacterial growth on fructose, mannose, xylose, arabinose, sucrose, glycerol, inulin, starch or mannitol (data not shown). In contrast, growth was impeded with cellobiose and lactose in the AA control strain (empty vector), and restored when complemented with wild-type σ 54 (Fig. 3b ). However, quantification of this inhibition in liquid medium revealed unexpected results as growth was not altered on 2YT 20 g/L cellobiose medium (data not shown) whereas lactose did exert a strong effect on bacterial growth. After 24 h of fermentation the AA pFW01 strain had grown similarly on 2YT 20 g/L lactose than on the 2YT control condition containing no supplementary carbon source (Fig.
| Statistical analyses The total duration of antennation and grooming behaviors (together) was analyzed using a general linear mixed effects model (LMM), in which the explanatory factors were the donors (Queens/Kings), the recipients (Partner/Offspring), the species (R. flavipes/R. grassei), and the developmental stage of the offspring (Eggs/Descendants). To interpret the resulting significant triple interaction involving recipients, species, and offspring developmental stage (see Section 3), the dataset was then split per developmental stage and the two resulting subsets were used to conduct two additional LMMs with the same explanatory factors (without the developmental stage factor). The observation of at least one type of trophallaxis (presence/ absence) and the total duration of trophallaxis (when observed) were then tested using a generalized linear mixed effect model (GLMM) with binomial error distribution and a LMM, respectively.
In these models, the explanatory factors were the donor, the recipient, the species, and the type of trophallaxis (proctodeal/stomodeal). Note that these models were restricted to the dataset where descendants were present, because trophallaxis is not possible toward eggs. Finally, the observation of at least one body-shaking (presence/ absence) and the total number of body-shaking (when observed) were tested using a GLMM with binomial error distribution and a LMM, respectively. In these models, the explanatory factors were the donors, the species, the presence of eggs, and the presence of descendants. In all the above statistical models, the cross ID and the pair ID (nested into the cross ID) were included as random factors to control for the fact that several kings and queens came from the same field colonies.
08). Note that some non-significant interactions are reported in the results to allow direct comparison between analyses, but their removal induces no qualitative changes. When required, we conducted post hoc pairwise comparisons within each model using model contrasts based on estimated marginal means. When appropriate, non-significant factors were pooled in the presented figures. All analyses were performed using the software R v3. 4. 3 (www. r-project. org) loaded with the packages lme4, car, and emmeans.
I G U R E 2 Effects of species (R. flavipes/R. grassei), recipients (Partner/ Offspring), donors (Queens/Kings), and types (Proctodeal/Stomodeal) on the observation (a-c) or total duration of trophallaxis (d-f). When the factors showed no significant interactions (see tables), they were then pooled to better represent the statistical results. Bars represent proportion of movies (a-c) or mean values of the log-transformed total duration ±SEM (d-f); Different letters refer to p < 0. 05. Note that these models were restricted to the dataset where descendants were present, because trophallaxis is not possible toward eggs trophallaxis when present (log, ± SE).
F I G U R E 2 Effects of species (R. flavipes/R. grassei), recipients (Partner/ Offspring), donors (Queens/Kings), and types (Proctodeal/Stomodeal) on the observation (a-c) or total duration of trophallaxis (d-f). When the factors showed no significant interactions (see tables), they were then pooled to better represent the statistical results. Bars represent proportion of movies (a-c) or mean values of the log-transformed total duration ±SEM (d-f); Different letters refer to p < 0. 05. Note that these models were restricted to the dataset where descendants were present, because trophallaxis is not possible toward eggs trophallaxis when present (log, ± SE).
The ocean is the main source of thermal inertia in the climate system. Ocean heat uptake during recent decades has been quantified using ocean temperature measurements. However, these estimates all use the same imperfect ocean dataset and share additional uncertainty due to sparse coverage, especially before 2007. Here, we provide an independent estimate by using measurements of atmospheric oxygen (o 2 ) and carbon dioxide (CO 2 ) -levels of which increase as the ocean warms and releases gases -as a whole ocean thermometer. We show that the ocean gained 1. 29 ± 0. 79 × 10 22 Joules of heat per year between 1991 and 2016, equivalent to a planetary energy imbalance of 0. 80 ± 0. 49 W watts per square metre of Earth's surface. We also find that the ocean-warming effect that led to the outgassing of O 2 and co 2 can be isolated from the direct effects of anthropogenic emissions and CO 2 sinks.
Acknowledgements We thank  Mike Winton  and eight anonymous reviewers for their useful suggestions. L. R. gratefully acknowledges support of the  Climate Program Office of the National Oceanic and Atmospheric Administration  grant  NA13OAR4310219  and the  Princeton Environmental Institute. NCAR is sponsored by the  National Science Foundation. We also thank the people maintaining the APO time-series at Scripps andoping th the groups devele models CESM, GFDL, IPSL and UViC used in this study.
Data availability Scripps APO data are available at scrippso2. ucsd. edu/apo-data. Model results are available upon reasonable request to R. W. (IPSL anthropogenic aerosol simulations), L. B. (IPSL-CM5A-LR), M. C. L. (CESM-LE), J. P. D. (GFDL-ESM2M) and W. K. (UVic).
Code availability The code used to compute APO Climate (https://doi. org/10. 5281/zenodo. 2571986) is available at https://zenodo. org/record/2571986#. XQ1FYZNKhBw. Earth-system model codes are available online for IPSL-CM5A-LR (cmc. ipsl. fr/ipsl-climate-models), GFDL-ESM2M (mdl-mom5. herokuapp. com/web/docs/project/quickstart), UVic (climate. uvic. ca/model) and CESM (cesm. ucar. edu/models/).
nasa. gov/cgi-bin/above_meas. pl). Synthesis activities across projects, especially, can help integrate datasets within modeling frameworks. However, many of the variables required by models may be absent, non-concurrent with other variables, or lacking the spatial or temporal resolutions and domains from the field campaigns needed to sufficiently refine model performance. For instance, soil carbon dynamics, such as stocks and change trajectories/sensitivities to various forcing variables, were clearly the highest demand by the modelers. At the time of this writing there were 20 projects listed under the Carbon Dynamics category within ABoVE. Nonetheless, most of these projects were not focused on soil carbon (due in part, for example, to available proposals, solicitation wording, and technical difficulty).
Rather, they focus predominantly on carbon fluxes between the land surface and the atmosphere, which while critically important to the modeling community, may overlook some of the key data needs for modelers, presenting a potentially worrisome gap for model-data integration. There is an enormous wealth of complementary data and information existing or in development by programs outside of ABoVE that are relevant to modelers. These include, for example: DOE but, a stronger international cooperative effort is still greatly needed, especially in the face of international politics that may present barriers to scientific collaboration. ABoVE has been coordinating with each of these programs and it may be that some potential gaps in ABoVE's data collection will be filled by these other efforts. However, while such datasets will be useful for model initialization, benchmarking, and evaluation, they may not meet the equally critical demand for variables to be collected concurrently, which is essential for advancing model development and performance.
Moreover, these data are primarily focused in N. America, whereas there is an even greater data dearth in the larger pan-Arctic and Boreal region across the globe. The modeling community additionally needs infrastructure to allow repeatable evaluation of model performance compared to benchmark datasets. The benchmark datasets, constructed from a suite of observations, must thoroughly confront and challenge models against the processes and response functions important to Arctic-Boreal ecosystem dynamics for models to improve. The ABoVE model-data integration framework facilitates the construction, integration, connection, and flow of the valuable data collected by the ABoVE science teams and other data networks to the modeling community  (Stofferahn et al 2016). Through the ABoVE Science Cloud central data repository (daac.
ornl. gov/cgibin/dataset_lister. pl?p=34), the framework provides a back-end database link to a front-end web user interface to access the ABoVE data. These data can and should be used by the modeling community to update and refine model parameterization and structural process representation, especially where data highlight key gaps in process representation in models (e. g. Li et al 2010). In turn, as model versions advance, the framework can be used as a benchmarking system to test improvement in model performance against key ABoVE indicators and science questions related specifically to important Arctic-Boreal Region ecosystem dynamics. Moreover, the integrated framework readily identifies key missing datasets or uncertainties required to test and advance models across the ABoVE indicators-highly useful for feedback to ground campaigns.
The benchmarking system is based on the International Land Model Benchmarking project  (Collier et al 2016 , Hoffman et al 2016 , Luo et al 2012) , and affiliated with the Permafrost Benchmarking System (Schaefer et al 2016) (i. e. some shared datasets and statistical metrics). Additionally, the model-data integration framework provides relatively high-quality and high-resolution model driver data for regional-scale runs, critical for modeling high latitudes  (Guimberteau et al 2017). In sum, this framework helps ease the workload of connecting to all of these disparate but related databases for the modeling community. There is tremendous and well-justified interest, effort, and activity in understanding the Arctic-Boreal Region. Much of the current focus is on identifying what is happening now under a changing climate, but there is particular interest in and concern for what changes may occur in the future.