dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors would like to acknowledge the administration of the OHP for hosting the DEMEVAP campaign in 2011, and the technical staff from OAMP ( C. Mollet ,  F. Gomez ,  G. Kazmareck , and  P. Daconceicao ),  Meteo-France  ( J. Barrière ,  P. Chaduteau ,  C. Ciais ,  Q. Kryszack ,  V. Lagorse ,  E. Moulin ,  D. Suquia ), LATMOS ( C. Blaize ,  M. Thetis ) and IGN ( E. Bardière ,  P. Lardeux ,  P. Valentin ) for participating in the field, as well as  E. D'Almeida  ( LATMOS ) for fixing the NDACC-OHP technical problems. DEMEVAP was primarily funded by IGN and  Meteo-France , and benefitted from grants from  IPSL, Allenvi/ROSEA  and  INSU-CNRS/PNTS. INSU  and CNES support the routine operations of the NDACC-OHP lidar. Edited by:  D. Cimini.
Whilst the models were consistent in the simulation of largescale differences in surface air temperature and total precipitation rates, significant variations were noted at regional scales (i. e. in the Arctic). Terrestrial data/model comparison indicated that HadAM3 provided a closer fit to proxy data (biome distributions) in the mid-to high latitudes. However, GCMAM3 performed better than HadAM3 in the tropics. Whilst the scope of the model intercomparison presented in  Haywood et al. (2009)  was limited, it served to encourage the palaeoclimate modelling community to establish a larger model intercomparison project (PlioMIP). Here we present an initial model intercomparison focussed on the large-scale features of mPWP climate derived from the PlioMIP ensemble. PlioMIP established the design for two initial experiments.
Boundary conditions Full details of the boundary conditions used for PlioMIP Experiments 1 and 2 are provided in  Haywood et al. (2010)  and  Haywood et al. (2011) , respectively. In brief both experiments utilised the US Geological Survey PRISM3D boundary condition data set  (Dowsett et al. , 2010). For Experiment 1, this included information on monthly SSTs and sea ice distributions, vegetation cover, sea level, ice sheet extent and topography. For Experiment 2 modelling groups were given the choice of how to initialise their ocean model for the mPWP. They could spin up their model from a standard pre-industrial control run, or specify the PRISM3D data set of ocean temperatures  (Dowsett et al. , 2009). Given the challenging nature of changing the land/sea mask in some atmosphere-ocean climate models, two versions of Pliocene boundary conditions were provided for both Experiment 1 and 2.
Temperature and precipitation anomalies in response to mPWP boundary conditions For Experiment 1, and to a lesser degree Experiment 2, the MMM differences in mPWP climate are closely linked to the specified boundary conditions provided by the PRISM3D data set. Altered SST patterns, sea and land ice volumes are a first order control on the simulated variations of the mPWP climate relative to the pre-industrial. The variations in climate are driven by changes in sensible and latent heat fluxes (SST-driven), and variations in ocean/atmosphere heat exchange caused by differences in sea ice.
Experiment 1 For Experiment 1 the MMM response in annual mean SAT and total precipitation rates are strongly controlled by the imposed boundary conditions from the PRISM3D data set. At high latitudes, reductions in specified land and sea ice generate a significant polar amplification of the SAT anomaly (Fig. 2 ), driven by local altitude changes and also ice/albedo feedbacks. This is augmented on land by a change in vegetation distribution from tundra to forest type biomes, changing surface albedo and evapo-transpiration rates. In the midlatitudes, SAT anomalies are strongly controlled by local vegetation changes and also by elevated SSTs (Fig. 2 ). Increasing total precipitation rates outside the tropics are correlated with SSTs, land and sea ice changes, and where vegetation patterns differ most from modern.
Regional-scale comparison of SSTs and SATs Due to the different spatial scales considered by proxy-data and model outputs it is not surprising to see the kind of discord between proxies and model results seen in Fig. 5. To test the validity of the comparison previously shown, proxy and model simulations at a regional scale are analysed (Fig. 6 ). The globe is subdivided into the seven continents in the  terrestrial realm and the seven major ocean basins in the marine regime. Proxy temperature anomalies pertaining to each marine or terrestrial region were collated and averaged. For marine regions faunal analysis SST calculation methods provide information on cold and warm month SSTs  (Dowsett et al. , 2010 (Dowsett et al. , , 2012)). Available Mg/Ca and alkenone palaeothermometry based estimates available for a sub-section of marine sites provide additional information on mean annual SSTs  (Dowsett et al.
Previous estimates of ESS using the mPWP The mPWP is useful for investigating the concept of ESS because it represents a world in quasi-equilibrium with high CO 2 for a sufficient period that long-term feedbacks are close to equilibrium. Using a combined model-data approach,  Lunt et al. (2010)  estimated ESS to be between 30 % and 50 % greater than CS. They took a climate model (HadCM3), and imposed changes to the CO 2 , orography, ice sheet and vegetation model boundary conditions, which were consistent with changes observed in the mPWP palaeo record. They then evaluated the model simulation relative to mPWP SST records. Finally, they used a series of sensitivity studies to eliminate the orographic forcing effect, arguing that the remaining temperature signal was an approximation to ESS.
Using PlioMIP Experiment 2 to inform estimates of ESS Here, we use the PlioMIP simulations from Experiment 2 to estimate ESS, using a similar approach to  Lunt et al. (2010). Since the PRISM3D orography in the PlioMIP experimental design is similar to modern  (Sohl et al. , 2009) , our approach is actually significantly simpler than  Lunt et al. (2010)  because we argue that in this case, the orographic effect is negligible. In the PRISM2 data set, which was used to provide the boundary conditions for the  Lunt et al. (2010)  estimates, mPWP and modern orographies were less similar. As such, we consider the elevated CO 2 to be the ultimate forcing of the simulated mPWP warmth, and thus our simulations represent the equilibrium state of a world at 405 ppmv of CO 2. To convert this to the usual definition of ESS (i.
In the notation of that study (which also recommends citing values in units of K/W m -2 ), we find here a palaeo sensitivity, S p = S [CO 2 ] = 1. 35 K/W m -2 (assuming a radiative forcing from a doubling of CO 2 of 3. 7 W m -2 ). The subscript [CO 2 ] of S [CO 2 ] indicates that this estimate of sensitivity includes possible carbon cycle feedbacks as a forcing. Also in the notation of that paper, the fast-feedback climate sensitivity, S a = 0. 91 K/W m -2. A first caveat to these calculations of ESS versus CS is that specific models used within the PlioMIP Experiment 2 ensemble have non-negligible top of the atmosphere energy imbalances (TOA). The rate at which models progress towards energy balance varies from model to model, and the specified integration length, whilst being the best that could be pragmatically hoped for, was insufficient to bring all the models into approximate energy balance (Table  2 column  f ).
Outlining a series of potential sensitivity tests, and allowing modelling groups to select which experiment or experiments they wish to run, would facilitate an efficient exploration of boundary condition uncertainty (experimental design vertex of the PMIP Triangle). The final point of the PMIP Triangle to be considered is uncertainties in the interpretation of proxies which provide SAT and SST estimates. One of the most important of these uncertainties surrounds chronology and the time-averaged nature of the PRISM3D data set. Limitations in correlating one marine or land site to another over large geographical distances, favoured the establishment of a time slab in the Pliocene to which the ages of marine or terrestrial sites could be more confidently attributed  (Dowsett and Poore, 1991).
It also increased the potential amount of geological data that could be incorporated, and would therefore underpin any environmental reconstruction. The current PRISM time slab for marine reconstruction is 240 000 yr wide. The vegetation reconstruction is constructed by considering information from the entire Piacenzian Stage (1 million years wide). Therefore, at each individual site the current PRISM palaeoenvironmental reconstruction represents an average of warm climate signals that occurred during the PRISM time slab  (Dowsett et al. , 2010; Salzmann et al. , 2008). The PRISM reconstruction should not be considered as a reconstruction of environmental conditions that existed together at a discrete moment in time (i. e. a time slice). In terms of mPWP climate modelling studies using AGCMs this does not present a serious problem.
e. a discrete moment in time during the mPWP). In reality climate model simulations run for 500 integrated years, using only a single realisation of orbit, CO 2 and other forcings, cannot reproduce a reconstruction of average warm climate conditions over either 240 000 or 1 million years, which reflect multiple changing and interacting forcing mechanisms (e. g. orbital forcing, trace gasses etc. ). Whilst the PRISM palaeoenvironmental reconstruction provides outstanding insights into the Pliocene, the methods used in its construction are not ideal for the specific purpose of data/model comparison. We hypothesise that a component of the observed model-data inconsistency is related to the time slab nature of the proxy data within the PRISM3D data set. Progress in ameliorating potential discrepancies between models and data for the mPWP in the future relies upon the identification of a discrete time slice, or slices, for environmental reconstruction within the Pliocene epoch.
At higher latitudes in the Northern Hemisphere point-to point data/model comparisons indicate that models potentially the magnitude of change on land and in the oceans. Comparison of regional averages highlight Russia and Siberia as particular areas of concern. Whilst these results provide justification for new sensitivity studies specifically targeted towards improving the match between data and models in the higher-latitudes of the Northern Hemisphere, they also highlight the need for reduced uncertainties in temperature estimates from geological temperature proxies. We highlight a strategy towards the adoption of more tightly constrained time slices  (Haywood et al. , 2013) , rather than the current time slab approach, where proxy data may be derived from a window of time as wide as one million years, to help reduce uncertainties in proxy data and the experimental design used in future climate model simulations.
Acknowledgements. A. M. H. , A. M. D. and S. J. P. acknowledge that the research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 278636. A. M. D. acknowledges the  UK Natural Environment Research Council  for the provision of a  Doctoral Training Grant. D. J. H. acknowledges the  Leverhulme Trust  for the award of an  Early Career Fellowship  and the  National Centre for Atmospheric Research  and the  British Geological Survey  for financial support. C. S. and G. L. received funding through POLMAR and PACES. Z. Z. would like to thank  Mats Bentsen ,  Jerry Tjiputra ,  Ingo Bethke  from  Bjerknes Center for Climate Research  for the contribution to the development of the NorESM-L.
Data and measurement The cruises that contribute tracer data are listed in Table  1. Mediterranean observations of tritium began in 1965, while those of 3 He were delayed up to the mid 1970s. Major transient-tracer surveys in the EMed were carried out by the F. S. Meteor (cruises  M5/6, 1987; M31/1, 1995; M44/4, 1999; M51/2, 2001; and M84/3, 2011 , for which tritium/helium data are reported here). In the WMed there was a tritium survey in 1981, and surveys of both tracers in 1995 and 2011. Below we will mostly address subsets of the available data that are deemed adequate to characterise the tracer distributions and their relevant features. The full datasets are, however, available for future work. Helium and Ne concentrations are reported in cc(STP)/kg and 3 He as δ 3 He = ( 3 He/ 4 He)/R a -1 (R a = atmospheric 3 He/ 4 He ratio).
This achievement depends on the particularly low 3 He content of terrigenic He and on tritium concentrations high enough to allow precise measurement. The resulting dataset of tritium and tritiugenic 3 He is useful to study the circulation and mixing of the Med. One tool is tritium-3 He dating, which we applied in the LIW at different locations and repeated in time. We find trends of increasing age with the calendar year of observation, which we ascribe to transit time dispersion of the LIW flow. We deduce average LIW transit times (between 12 and 22 yr), which are compatible with other estimates. As a further application we present detailed, quasi-zonal sections through the EMed of tritium-3 He age and terrigenic He for 1987. The highest ages in the East Mediterranean Deep Water (EMDW) are approximately 24 yr, far lower than the turnover time of this water mass (∼ 100 yr;  Roether and Schlitzer, 1991).
Acknowledgements. We thank  Toste Tanhua, Kiel , for organising the 2011 Meteor cruise and supporting our participation;  E. Hümpel, Kiel , took the helium and tritium samples. Figures 2, 3 and 7 were contributed by  B. Klein ,  Hamburg. We also thank  Liliane Merlivat  and  Chantal Andrié, Paris , for providing the Origny 1971/72 and Phycemed-83 data. All Meteor cruises that delivered data for the present work and the M84/3 tracer measurement carried out at Bremen were funded by the  Deutsche Forschungsgemeinschaft, Bonn-Bad Godesberg, Germany. We are grateful to the masters and crew of the various cruises and to the people that helped to produce the dataset used in this work. Edited by:  S. Sparnocchia Ocean Sci. , 9, 837-854, 2013 www. ocean-sci. net/9/837/2013/W. Roether et al. : Tritium and 3 He in the Mediterranean Sea.
A semi-analytical bio-optical algorithm is used to help interpret regional to global SeaWiFS chlorophyll observations from using three independent, well-validated ocean color data products; the chlorophyll a concentration, absorption by CDM and particulate backscattering. First, we show that observed long-term, global-scale trends in standard chlorophyll retrievals are likely compromised by coincident changes in CDM. Second, we partition the chlorophyll signal into a component due to phytoplankton biomass changes and a component caused by physiological adjustments in intracellular chlorophyll concentrations to changes in mixed layer light levels. We show that biomass changes dominate chlorophyll signals for the high latitude seas and where persistent vertical upwelling is known to occur, while physiological processes dominate chlorophyll variability over much of the tropical and subtropical oceans.
Autonomous sampling platforms equipped with sensors capable of measuring relevant ocean ecosystem parameters have been developed (e. g. ,  Bishop et al. , 2002; Johnson et al. , 2009)  but these advances are relatively recent and appropriate data over the global ocean are not yet available. Global observations of phytoplankton dynamics have resulted largely from satellite ocean color sensors that sample the entire Earth surface in just a few days  (McClain, 2009). The longest and most complete satellite ocean color record stems from the Sea viewing Wide-Field of view Sensor (SeaWiFS), which was launched on August 1, 1997 and operated until December 14, 2010. A key objective for the SeaWiFS mission was the determination of surface layer phytoplankton chlorophyll a concentration (hereafter referred to simply as chlorophyll).
SeaWiFS observations of remote sensing reflectance SeaWiFS was a polar-orbiting, sun-synchronous, crosstrack-scanning instrument with six visible "ocean color" wavebands and two near- infrared (NIR) "atmospheric correction" bands  (McClain et al. , 2004). SeaWiFS collected imagery at a nominal 1-km spatial resolution (globally subsampled to 4 km) and achieved near-complete global measurement coverage every 2 days. Accurate ocean color retrievals are among the most difficult Earth-system satellite data to acquire, in part because only ~10% of the signal measured by the satellite sensor at the topof-atmosphere originates from the ocean (the remaining ~90% comes from the atmosphere and surface reflectance). SeaWiFS's NIR bands provide critical data for removing the atmospheric contribution to radiances measured at the 6 ocean color bands.
, 1998). Nearly 2000 paired observations of ocean remote sensing reflectance and chlorophyll concentration from version 2 of the NASA bio-Optical Marine Data set (NOMADv2) archive of biooptical field measurements  (Werdell & Bailey, 2005)  were used to derive the OC4v6 algorithm. Performance of the OC4v6 algorithm is excellent (Table  1 ) when applied to the NOMADv2 data set of ocean remote sensing reflectance spectra and compared with the in situ chlorophyll measurements (http://seabass. gsfc. nasa. gov/seabasscgi/nomad. cgi). This in situ data comparison can be thought of as measuring the inherent variability in the OC4v6 algorithm, as the NOMADv2 observations comprised nearly all of the data used to determine the OC4v6 model coefficients. SeaWiFS remote sensing reflectance spectra can be also used to assess the performance of the OC4v6 algorithm.
A matchup data set of SeaWiFS remote sensing reflectance spectra and in situ observations (largely from NOMADv2) is constructed using the single level-3 pixel (~9 km) containing the in situ observation for that day. The performance of the Chl OC4 algorithm is again excellent (Table  1 ); with an R 2 value of 0. 795, slopes approaching unity, and a log-transformed intercept near zero. Similar validation results for Chl OC4 are found in the analyses conducted by the NASA Ocean Biology Processing Group following procedures laid out by  Bailey & Werdell, 2006. Thus, the SeaWiFS band ratio chlorophyll algorithm (OC4v6) has excellent performance when compared with the NOMADv2 in situ chlorophyll data, both using in situ reflectance determinations and SeaWiFS reflectances.
The Garver-Siegel-Maritorena ocean color algorithm and its interpretation Advances in ocean optics modeling have yielded tools to decompose satellite ocean color signals into multiple, specific optical contributions (e. g. ,  IOCCG, 2006). Here, the Garver-Siegel-Maritorena algorithm (GSM;  Maritorena et al. , 2002 Maritorena et al. , , 2010) )  is applied to SeaWiFS ocean reflectance data to estimate chlorophyll concentration (Chl GSM ), the combined absorption at 443 nm by CDOM and detrital particulates (CDM), and the particulate backscattering coefficient at 443 nm (BBP). The GSM model retrieves Chl GSM , CDM and BBP simultaneously by performing a non-linear least-squares minimization between the satellite and modeled reflectance for all available visible bands. Model parameters were evaluated through a constrained optimization using observed remote sensing reflectance, chlorophyll, CDM and BBP determinations mostly from open ocean, non-polar environments  (Maritorena et al.
However, long-term trends derived from them provide at best a single-point in a map like Fig. 4b. Clearly, data from many long-term monitoring sitessampling both areas of increasing and decreasing trendsare needed to create a global assessment of change. The trends diagnosed here show both similarities and differences with previous studies of temporal trends found using SeaWiFS observations. For example,  Gregg et al. (2005)  find a ~4% increase in global chlorophyll concentrations over the first six years of the SeaWiFS record (1998 to 2003), with the highest increases in coastal environments. Contrasting this,  Behrenfeld and Boss (2006)  find decreasing levels of global chlorophyll concentrations and rates of net primary production (NPP; from 1999 to 2005) that follow strong increases in both Chl OC4 and NPP during the transition in 1998 from El Niño to La Nina conditions.
Vantrepotte and Mélin (2009)  examine the first ten years of the SeaWiFS record and find trends for biogeochemical provinces consistent with the present analysis, which is expected considering the similarity in the periods studied. Methodology is likely to be important as well. The present study log-transforms the chlorophyll observations before global scale anomalies are calculated while the  Gregg et al. (2005)  study does not. Not assuming log-normality will likely amplify the influence of the fewer, highly productive sites in global summaries. Thus, record length, time period and assumptions in data analysis will all have important roles in quantifying trends. Evaluating changes on time scales longer than a single mission requires the merging of data across multiple satellite platforms and/or the inclusion of historical field observations.
Several researchers have link data from the Coastal Zone Color Scanner (CZCS)  (1978) (1979) (1980) (1981) (1982) (1983) (1984) (1985)  with the SeaWiFS data record (e. g. ,  Antoine et al. , 2005; Gregg et al. , 2003; Martinez et al. , 2009). Gregg et al. , 2003  find a global decline of NPP of more than 6% between CZCS observations in the early 1980s and the first five years of the SeaWiFS record, with most of the decreases occurring at high latitudes. Contrasting these results,  Antoine et al. (2005)  find a ~22% increase in global chlorophyll concentrations, where most of these increases are due to changes at low latitudes. These two analyses are based on fundamentally different satellite data processing approaches and underscore the importance of consistency of data processing and radiometric calibration standards across multiple missions  (McClain, 2009; National Research Council, 2011; Siegel & Franz, 2010).
Both approaches are limited in their ability to diagnose phytoplankton community structure information (e. g. ,  Higgins et al. , 2011; Kostadinov et al. , 2010)  and this is another area where advances in field methodologies are needed. Progress in understanding the optically complex ocean requires improvements and expansion of field data sets to develop and test bio-optical algorithms as well as advancements in space technology. All ocean color algorithms are empirical and the availability of high quality observations that span the application space for these algorithms is central for these developments. Many suggestions for improvements in field data needed for bio-optical model development have been made already. One important suggestion is refining the quality of existing data sets, such as NOMADv2, which will enable researchers to choose the highest quality or most extensive observations to develop and test their models.
SeaWiFS legacy and its use as a periscope to the future The SeaWiFS mission provided the first consistent, long-term, well-sampled and stable estimates of ocean remote sensing reflectance spectra and these observations were central for the analysis of temporal changes in regional to global phytoplankton dynamics presented here. The loss of the SeaWiFS data stream marks the end of an observational time-series largely free of instrument artifacts and a critical record against which other satellite ocean color sensors have been made useful (e. g. ,  Jeong et al. , 2011; Kwiatkowska et al. , 2008; Meister et al. , 2012). Establishing a quality replacement for SeaWiFS that will sustain, as well as advance, the ocean color time series is of paramount importance  (National Research Council, 2011; Siegel & Franz, 2010).