dataesr/scientific-paragraphs-categorization huggingface dataset:
The availability of a high resolution DEM (digital elevation model), namely HydroSHEDS  (Lehner et al. , 2008) , has offered the opportunity to revise this routing scheme, with the ability of constructing more adequate HTU in each ORCHIDEE grid box. This new RRS with a higher resolution river graph can operate at a fine spatial resolution (e. g. finer than 10 km) and is expected to better represent the complexity of river basins and respond better to the inhomogeneity of precipitation patterns. The next sub-section presents briefly the HydroSHEDS dataset, then the following ones elaborate on the modifications brought to the original routing scheme of ORCHIDEE to create this new version.
HydroSHEDS data As a seamless near-global hydrological data set, HydroSHEDS is a suitable database for improving the river routing scheme in the ORCHIDEE model. It is available at resolutions from 3 to 30 arc-second, and provides all required information including hydrologically-conditioned elevation, drainage directions and watershed boundaries. Derived from Shuttle Radar Topography Mission, the quality of HydroSHEDS has the limitations of this radar product, returning a complex mix of terrain elevation and vegetation height, and only covering the land areas from 56 • S to 60 • N because of the shuttle's orbit. Despite tremendous efforts for void-filling and properly conditioning the drainage directions, some errors remain such as spurious inland sinks, and the assumption of single flow direction prevents from properly describing river bifurcations including deltas  (Lehner et al.
, 2008). Nevertheless, this database is widely considered as the best available DEM for hydrological applications, and has shown its advantages for large-scale high-resolution river routing  (Gong et al. , 2011; Yamazaki et al. , 2011). The stream network in the HydroSHEDS is comparable with other global hydrographic data such as HYDRO1k, ArcWorld, and the Digital Chart of the World  (Lehner et al. , 2006). In this study, we use a resolution of 30 arc-second (1/120 • with a pixel size of 0. 86 km 2 ca. 1 km at the Equator). It is believed sufficient for large-scale applications, and offers the possibility to complete the region north of 60 • N with the HYDRO1k database at 1 km to achieve full global information  (Lehner et al. , 2008; Wu et al. , 2012; Marthews et al. , 2015). A preliminary quality control of HydroSHEDS has been performed to ensure that all pixels drain to ocean or a recognized endorheic basin.
In addition, the old RRS can not represent four small river basins (i. e. the Adige (9), the Shkumbinit (10), the Devollit (11) and the Var River (  12 )). Figure  3b  shows the description of the Tiber River basin (in Italy, with an outlet to the Tyrrhenian Sea at Rome) with the old RRS and a regular latitude-longitude grid at a resolution of 1/2 •. This river basin covers only 4 grid boxes with maximum upstream area about 9100 km 2 , whereas the upstream area of station Rome is about 16545 km 2 according to the GRDC database. In this case, the old RRS misses the southern part of the Tiber river basin. The simulated outlet point (shown by orange circle) of the Tiber River is located about 70 km from the coastline. At the same resolution of 1/2 • the new RRS depicts more satisfactorily the southern part of the Tiber River (Figure  3c ).
Experiment design The goal of this section is to evaluate the performance of full ORCHIDEE simulations against river discharge observations. To this end, the new RRS is fully coupled to the ORCHIDEE LSM, which is driven by the three atmospheric forcing datasets presented in section 2: the WFDEI_CRU and WFDEI_GPCC with a spatial resolution of 1/2 • and the WFDEI_MSWEP with a spatial resolution of 1/4  For Beaucaire, the new RRS not only captures the high peak in January but also the gradual decrease to low flow in August, except with the WFDEI_CRU data. Most simulations also display a positive bias, which can be attributed to excessive runoff fluxes (since the RRS is conservative and does not change the long-term mean discharge), and explained by systematic errors in the water budget parametrization of ORCHIDEE or biases in the three forcing data sets.
The worse overall results are found at stations Boara Pisani (9), Dar El Caid (6), Roma (8), Sidi Belatar (4), and Meric Koep (5). For Boara Pisani (Adige River, Northern Italy), NSD values of the experiment WFDEI_CRU and WFDEI_MSWEP are both higher than 2. 2, which is linked to the high positive bias with these forcing datasets (i. e. the estimated discharge is about twice higher than observation, as shown in Table  2 ). As the overestimation in experiment WFDEI_GPCC is smaller (i. e. the PCBIAS is about 22%), NSD value stays around 1. 10. It should be noted, however, that CC values for this station range from 0. 77 to 0. 94, so the monthly variability is quite well captured. In particular, the simulated monthly series reproduce well the observed hydrological regime with rather weak seasonal variations and two moderate peaks although good CC (about 0.
9) are still found at Beaucaire (1), Pontelagoscuro (3), Yakapinar Misis (7). The errors at station Boara Pisani (9), Sidi Belatar (4) and Dar El Caid (  6 ) are more clearly demonstrated. Regarding the effect of different forcing data, no clear hierarchy is found, although the above findings confirm that the biases of surface and sub-surface runoff by the ORCHIDEE LSM, at least partly due to the biases of the forcing datasets, have a strong impact on the simulated streamflow. This is probably the main reason for the low values of NS and high absolute values of PCBIAS (shown in Table  2 ). It is important to note that the impact of human regulation on natural streamflow is neglected in the current version of the new RRS. As a result, irrigation is not represented at all in these simulations while it is known to play an important role in the Mediterranean region  (Margat and Treyer, 2004).
According to  Montanari (2012) , the annual water withdrawal for irrigation in the Po River basin is about 17 km 3 , i. e. a third of the mean annual discharge (47 km 3 ). Not all withdrawals are transformed into evaporation and thus a part of these abstractions will return to the river. Nevertheless, one can assume that the observations for Pontelagoscuro displayed in Figure  7  probably underestimate the natural river discharge. Snoussi et al. (2002)  also underline that the water discharge at the Moulouya River (station Dar El Caid) has been reduced by almost 50% due to the construction of Mohamed-V reservoir in 1967, which could explain the large positive biases of our simulations. Figure  9a  shows that, in the Ebro basin (Spain), the discharge peaks in May and June are largely overestimated by the simulations, whichever the forcing dataset.
10b ), explaining the good results presented in Table  3. At Yakapinar Misis (Fig. 10c ), in contrast, the RRS captures well the low flows under 500 m 3. s -  , but the the highest flows which happen less than 10% of time are strongly underestimated. The poor performances at station Sidi Belatar are confirmed by Fig. 10d. At this station, stream flow exceeds 100 m 3. s -  occur only 5% of time and more than 50% of times, the flow is close to zero. It is difficult to capture the daily discharge at this station, and the large agricultural water demand over the Chelif basin  (Mahe et al. , 2013)  probably contributes to the errors. But the main error source seems to be the forcing datasets, since the observations fall in their wide uncertainty range. Stream flow fluctuations possesses alternative frequencies in addition to the daily and monthly oscillation.
Conclusions This study presents an attempt to revise the river routing scheme in the ORCHIDEE LSM, in order to benefit from the accurate hydrography from the HydroSHEDS database at 1 km resolution. This high resolution information is aggregated in Hydrological Transfer Units which are constructed inside each ORCHIDEE grid box. River networks which are depicted by connecting these HTUs provide precise flow pathways. The results show a wide range of river catchment size can be delineated precisely by the new RRS. The routing technique which is still based on a simple the linear reservoirs which requires information on the orography and slopes. This information is improved by the accurate altitude provided by the HydroSHEDS data. In addition to satisfactory simulations at monthly timescale, the new RRS promises the ability to capture well other frequencies in the surface freshwater flows.
in June and October). But most of the poor performances are found in the driest part of the Mediterranean basin. The negative 12 Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2018-57 Manuscript under review for journal Geosci. Model Dev. Discussion started: 12 April 2018 c Author(s) 2018. CC BY 4. 0 License. bias at Roma can be attributed to the underestimated runoff during summer time (July to September) or water management practices. The underestimation of discharge is even worse at Sidi Belatar, which belongs to the longest river in Algeria and rises from the Saharan Atlas. The observed annual mean discharge is lower than 20 m 3 s -1 , with very low values in summer time (close to zero), and none of the simulations can reproduce this characteristic, whichever the forcing dataset.
Acknowledgements. The authors acknowledge helpful advice from two anonymous reviewers. We thank  Dr. Luca Brocca  ( CNR-IRPI, Italy)  for providing us the daily discharge of station Pontelagoscuro and  Roma. We also gratefully acknowledge the  GRDC (Global Runoff Data Centre ) for providing valuable data. This work was supported by computing resource of the  IPSL ClimServ cluster at École Polytechnique, France. 19  Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2018-57Manuscript under review for journal  Geosci. Model Dev. Discussion started: 12 April 2018 c Author(s) 2018. CC BY 4. 0 License.
( in June and October). But most of the poor performances are found in the driest part of the Mediterranean basin. The negative 12 Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2018-57 Manuscript under review for journal Geosci. Model Dev. Discussion started: 12 April 2018 c Author(s) 2018. CC BY 4. 0 License. bias at Roma can be attributed to the underestimated runoff during summer time (July to September) or water management practices. The underestimation of discharge is even worse at Sidi Belatar, which belongs to the longest river in Algeria and rises from the Saharan Atlas. The observed annual mean discharge is lower than 20 m 3 s -1 , with very low values in summer time (close to zero), and none of the simulations can reproduce this characteristic, whichever the forcing dataset.
• Ṫhe three corresponding simulations are performed with practical average HTU sizes identified above, viz. 22 km 2 at 1/2 • and 11 km 2 at 1/4 • Ḟor comparison, similar simulations are also performed with different average HTU sizes (from 0. 3% to 50% of the grid box area), and also with the old RRS. In this case, the ORCHIDEE simulations are only run with the two 1/2 • forcing datasets, owing to the limitations of the OLD RRS at finer resolutions. All the simulations extend from 1979 to 2013 after a 10-year spin-up. 11 Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2018-57 Manuscript under review for journal Geosci. Model Dev. Discussion started: 12 April 2018 c Author(s) 2018. CC BY 4. 0 License.
These studies have focused on high latitude northern peatlands and have shown that in warmer climates increases in plant productivity overcome increases in respiration and that these peatlands will likely become a more efficient sink if soil moisture is maintained  11, 12, 13. Here we use 294 profiles from globally distributed peatlands to build a dataset of global carbon accumulation over the last millennium (850-1850 AD) (Figure  1a ). We improve the coverage of northern high latitudes and expand the dataset to low latitudes and southern high latitudes by including over 200 new profiles compared to previous data compilations  12. There are areas of the world where extensive peatlands exist where data are still lacking (e. g. East Siberia, Congo Basin  14  ), but our data pr comprehensive coverage of peatland carbon accumulation records over this time period.
Methods Carbon accumulation estimates. Mean annual carbon accumulation over the last millennium was estimated for 294 peatland sites (Table  SIT1 ). In line with climate modelling studies, we use the term 'last millennium' to refer to the pre-industrial millennium between AD 850-1850). The total carbon accumulated over this period was calculated for all sites in Table  SI1  by using a flexible Bayesian approach that incorporated estimates of age and minimum and maximum accumulation rates  12. A number of sites were previously published (Reference 12 and references therein), but we added over 200 sites to the database from new field coring, as well as additional analysis for bulk density, carbon and radiocarbon dating from a range of existing samples held in laboratories around the world to bring the data to comparable standards.
Acknowledgements The work presented in this article was funded by the  Natural Environment Research Council  ( NERC  standard grant number  NE/I012915/1 ) to D. J. C. , A. G. S. , I. C. P. , S. P. and P. F. , supported by  NERC  Radiocarbon Allocation 1681. 1012. The work and ideas in this article have also been supported by PAGES funding, as part of C-PEAT. CDJ was supported by the  Joint UK DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). This research is also a contribution to the  AXA Chair Programme in Biosphere and Climate Impacts  and the  Imperial College initiative on Grand Challenges in Ecosystems  and the Environment. This research was also supported by a grant from the  National Science Centre ,  Poland 2015/17/B/ST10/01656. We wish to thank  Dale Vitt ,  Jukka Alm ,  Ilka E.
Real-Time PCR (RT-PCR) The quantification of napA transcript was performed by RT-PCR as described previously  (Jian et al. , 2012)  with some modifications. Two pairs of primers, napA1-S/R and napA2-S/R, were used to quantify the relative transcript abundance of napA1 and napA2  (Chen et al. , 2011). Real-time PCR was performed using an Applied Biosystems StepOnePlus TM Real-Time PCR system and Power SYBR Green PCR Master Mix (Applied Biosystems). The RT-PCR was performed using StepOne Software (ABI) in reaction mixtures with total volumes of 20 µl containing 10 µl FastStart Universal SYBR Green Master Mix (Rox) (Roche, Mannheim, Germany), 0. 5 µM of each primer and 1 µl cDNA template. The amount of target was normalized to that of the reference gene swp2079. Every experiment was performed in triplicate for each sample, and a mean value and standard deviation were calculated for the relative RNA expression levels.
in press). Discard practices are believed to be similar between handlines and bottom longlines. Observer data suggest that the catch of individuals smaller than the MLS is the main cause for discarding in this fishery, followed by low market value  (Canha 2013). About 90 species are regularly discarded by this fishery, 40% of which due to low commercial value  (Canha 2013). However, 61% of the discards can be attributed to six species of commercially important fish, such as silver scabbardfish (Lepidopus caudatus), European conger (Conger conger), blackbelly rosefish, splendid alfonsino (B. splendens), blackspot seabream, and thornback ray (Raja clavata). At least ten species of deepwater sharks are occasionally caught by this fishery. Even if limited, this bycatch is of concern since many deepwater sharks such as Deania spp.
Acknowledgment The authors acknowledge the financial support from the  European COST Action "Ocean Governance for Sustainabilitychallenges , options and the role of science" and by the  ICES Science Fund  Project " Social Transformations of Marine Social-Ecological Systems ", and  MINOUW  (Grant Agreement  634495 ). C. Pita acknowledges FCT/MEC national funds and FEDER co-funding, within the  PT2020 partnership Agreement and Compete 2020 , for the financial support to CESAM (Grant no  UID/AMB/50017/2013 ). C. M. Teixeira  had the support of the  Fundação para a Ciência e a Tecnologia (FCT)  ( Pest-OE/MAR/UI0199/2011 ); and  C. M. Teixeira  and  C. Pita  were supported by the  Research Project  " LESSisMORE -LESS discards and LESS fishing effort for BETTER efficiency on the small-scale fisheries" (Ref.
DESIRABILITY AND FEASIBILITY OF A GLOBAL REANALYSIS OF TROPICAL CYCLONES What: Accurate records of historical tropical cyclones are invaluable for scientific research and risk quantification. Yet most tropical cyclone data were collected in aid of operational forecasting with mixed attention to their use as a climate archive. To remedy this, as far as possible, a comprehensive reanalysis of Atlantic tropical cyclones was undertaken and is enjoying widespread use. To explore the feasibility of undertaking a similar effort for the rest of the globe, covering about 88% of all tropical cyclones, a workshop was convened, involving 12 scientists from around the world, including researchers, data analysts, and forecasters.
H istorical observations of tropical cyclones have proven to be of great value to scientific research, to forecasting, and to many industries, such as insurance and reinsurance. But in almost all cases, the observations were made in support of immediate forecasting needs and were not quality controlled with an eye toward the uniformity and consistency that we demand of climatological datasets. Yet the increasing use of such data for risk assessment and in the detection of trends and variability warrants both a careful reanalysis of existing data and the application of uniform standards to future observations. The North Atlantic NHC "best track" hurricane database (HURDAT2; available from www. nhc. noaa. gov/data/#hurdat; Landsea and Franklin 2013) has profited enormously from detailed reanalyses  (Landsea et al.
Yet risk modeling and insurance rates depend on historical data in these regions as much as they do in the North Atlantic. For these reasons, we convened a workshop in Asheville, North Carolina, on 22-23 May 2017 to explore the feasibility of performing a global reanalysis of tropical cyclones and to lay the foundations for a set of standards for future observations. The workshop was funded by the Bermuda Institute of Ocean Sciences' Risk Prediction Initiative (RPI), a nonprofit organization supported by the risk transfer industry and focused on questions surrounding the quantification of catastrophic risk. The meeting consisted of a series of 30-min presentations and discussion about desired outcomes and possible paths forward. Here, we summarize those discussions for the benefit of the broader meteorological community.
In contemplating the general idea of a global tropical cyclone (TC) reanalysis, we discussed the problem of addressing quite disparate user needs. On the one hand, there is a demand for the best possible reconstruction of each individual storm, based on all the available historical data and bringing to bear the expertise of a forensic team. This is the philosophy behind the reanalyzed Atlantic dataset  (Landsea and Franklin 2013). But there is also a demand for a more homogeneous reanalysis that treats each event the same way, to aid in, for example, the detection of trends in activity, even at the expense of a degradation of the quality of some individual events. In both cases, there is a strong desire to quantify the uncertainty of the reanalysis of each of the variables. We also discussed performing a third reanalysis that attempts to blend in situ and satellite-based data with an eye toward producing the best-state estimates for each event in a purely objective manner.
This reanalysis would be in parallel to the other reanalysis efforts outlined in tasks 2-4 above. The goal of this effort will be to provide a transparent, fully reproducible database that can be easily and quickly updated when new (or legacy) data become available and/ or when significant advances in scientific knowledge or methodologies occur. The use of objective state estimation methods is particularly amenable to the generation of time-dependent uncertainty bounds, subject to the characteristic uncertainty of each data type. The influence (e. g. , weight) of each input datum will also be quantitatively estimated and recorded by storing the weighting factors for each analysis time. This objective reanalysis will have the added benefit that it can be updated provisionally in real time. This reanalysis can be considered as a metareanalysis in the sense that the parameter estimates of state-of-the-art objective satellite-based techniques will be blended with operational fixes, surface observations (buoys, ships, land based), land-based radar, aircraft data (when available), and certain parameters obtained from dynamical atmospheric reanalysis data.
A preliminary foray into this approach created a new historical database that uses objective state estimates from aircraft data to refine HURDAT2 parameter values and add additional parameters  (Vigh et al. 2016). Participants agreed that all the reanalyzed data should include quantitative estimates of uncertainty for all the variables analyzed and should also include comprehensive metadata. All of the reanalyzed data and metadata should be freely and openly available. Much of the rest of the workshop was spent discussing the details of such issues as what variables should be provided and at what frequency, how to define the beginning and end of each event, and the application of uniform conventions to such matters as conversions between maximum wind speed and central surface pressure, between 10-and 1-min sustained winds, and between gust and sustained wind  (gust factors) , in what units to express the variables and at what precision, consistency in the definitions of, for example, subtropical cyclones and extratropical transitioning systems, and whether and how to include tropical cyclone-like phenomena such as medicanes and polar lows.
We also discussed the possibility of applying numerical weather prediction (NWP) techniques to the reanalysis of tropical cyclones, for example, whether the initial conditions used in NWP models can be brought to bear on tropical cyclone reanalysis. Participants agreed that the National Centers for Environmental Information (NCEI) is the natural home for most and perhaps all reanalyzed datasets, especially with the precedent set by the International Best Track Archive for Climate Stewardship (IBTrACS;  Knapp et al. 2010). We also agreed that with international cooperation, the various data streams we propose to create or enhance could be sustained on an annual basis to avoid the need for future reanalyses looking back before the previous year. That is, we propose to lay out standards and guidelines for archiving tropical cyclone data going forward so as to minimize the need for future reanalyses.
guyannensis from 20 localities within the study area, between northern French Guiana and Brazil, north of the Amazon River (Amapaand Para). The two species were sympatric at 11 sites (Fig  1 ). We also sequenced 800 bp of CYTB in 8 samples collected in Brazil (Amazonas and Roraima states), Venezuela, and Peru (Fig  1 ) for phylogenetic analyses. Only 25 additional sequences were obtained from GenBank. We analyzed a total of 124 sequences of P. cuvieri and P. guyannensis in 50 localities; details on specimens, localities and GenBank accession numbers, are given in S1 Appendix.