dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The new ALADIN-Climate version 6 has been coupled to SURFEX and run at a 12-km horizontal resolution over the France metropolitan area. It has been compared with the SAFRAN analyses data and evaluated for the four seasons independently. The precipitation bias does not exceed ± 0. 5 mm day -1 , and T min and T max are correctly simulated with acceptable biases ranges comparing to previous studies. The main defect of the model is the lack of cloudiness leading to too high incoming solar radiation and resulting in a T max overstimation with a positive bias of 2. 8 °C. ALADIN-Climate has then be used to perform a sensitivity analysis on three different representations of urban areas: CITY corresponding to explicit description and modeling of cities with a specific urban parameterization, ROCK that is the conventional approach of ALADIN-climate model describing impervious urban covers as rock, and VEG for which cities are replaced by natural covers.
Without explicit modeling of cities, the impervious covers are converted in natural land surfaces to be dealed by ISBA. The Ecoclimap II classification is projected on ALA-DIN-Climate simulation grid, so that each mesh consists in a combination of land covers. The parameters associated to each land cover class (as defined in Ecoclimap II database) are aggregated over the mesh according to respective cover fractions. The tile fractions of sea, inland water, natural land surface and urban areas can be derived, as well as input data required by the surface models. Figure  12  illustrates for the three ALADIN-Climate scenarios the methodology that has been applied to compute the cover fractions of natural and urban tiles for each model grid cell, starting from the fractions of land cover classes provided by Ecoclimap II.
CERA-20C is a coupled reanalysis of the twentieth century which aims to reconstruct the past weather and climate of the Earth system including the atmosphere, ocean, land, ocean waves, and sea ice. This reanalysis is based on the CERA coupled atmosphere-ocean assimilation system developed at ECMWF. CERA-20C provides a 10 member ensemble of reanalyses to account for errors in the observational record as well as model error. It benefited from the prior experience of the retrospective atmospheric analysis ERA-20C. The dynamical model and the data assimilation systems initially developed for NWP had been modified to take into account the evolution of the radiative forcing and the observing system. To limit the impact of changes in the observing system throughout the century, only conventional surface observations have been used in the atmosphere.
Introduction Since its creation in 1975, ECMWF has been a key player in the production of reanalyses, which provide a numerical description of the recent climate by combining models with observations. The initial focus was on producing atmospheric reanalyses covering the satellite observing period, from 1979 onward. The first of these reanalyses, FGGE, was produced in the 1980s, followed by ERA-15, ERA-40  (Uppala et al. , 2005) , and ERA-Interim  (Dee et al. , 2011). The fifth reanalysis in this series, ERA5, is now in production after years of research since ERA-Interim, and intense technical preparations  (Hersbach & Dee, 2016). ECMWF is also producing ocean reanalyses with the replacement in 2016 of ORAS4 (Ocean Reanalysis System 4) by ORAS5 to incorporate the latest improvements in ocean models, data assimilation methods, and forcing fluxes  (Balmaseda et al.
Reanalyses make it possible to study the interannual variability of forecast skill and to test new model versions on past severe weather cases. At ECMWF, this is true for both the medium-range/monthly ensemble (ENS) and for the Journal of Advances in Modeling Earth Systems seasonal ensemble (SEAS5). ERA-Interim and ORAS5 are the current operational atmospheric and ocean reanalyses at ECMWF, continuing in near real time. They are both created using versions of the data assimilation system and model that are kept unchanged during the reanalysis production, while ingesting a variety of satellite and in situ observations to provide the best state estimate over the data-rich time period since 1979. Several other weather and research centers apply the same methodology to produce recent global atmospheric reanalyses based on their model and data assimilation systems.
, 2015). Extending these reanalyses further back in time is a tremendous scientific challenge as the observing system is very sparse before the availability of satellite data from the 1970s onward, and especially before the arrival of radiosonde measurements in the 1930s  (Stickler et al. , 2014). Information about the nature and quality of early instrumentation is often incomplete  (Kennedy, 2014). Furthermore, locating and gaining access to early weather observations requires dedicated efforts in data rescue and digitization, especially in parts of the world that are most affected by climate change and variability  (Allan et al. , 2011). The skill of reanalyses is limited in the Southern Ocean by a lack of historical observations. The ERA-CLIM and ERA-CLIM2 projects have imaged information on surface meteorological variables for the Southern Ocean for the early twentieth century.
This is the motivation for CERA-20C, a reanalysis of the twentieth century using the coupled data assimilation CERA. The CERA system simultaneously ingests atmospheric and ocean observations in the coupled Earth system model used for ECMWF's ensemble forecasts  (Laloyaux et al. , 2016a). This approach accounts for interactions between the atmosphere and the ocean during the assimilation process and has the potential to generate a more balanced and consistent Earth system climate reconstruction. CERA-20C is the first 10 member ensemble of coupled reanalyses of the twentieth century. The main features of some of the reanalyses produced at ECMWF are summarized in Table  1. This article is organized as follows. In section 2, the dynamical coupled model and the observation data sets used to produce CERA-20C are described.
The modifications made to the data assimilation system to compute a retrospective coupled analysis over the twentieth century are presented in section 3. The quality of the atmospheric reconstruction of CERA-20C against other centennial reanalyses and independent observations is assessed in section 4. Different weather and climate signals are discussed in section 5 putting an emphasis on the interactions between the different components of the Earth system. Section 6 presents some preliminary results on the uncertainties that can be estimated from the ensemble reconstruction. Further possible directions of research and development in coupled data assimilation are finally described in section 7.
At the air-sea interface, the sea surface temperature (SST) is relaxed toward the HadISST2 monthly ensemble product  (Titchner & Rayner, 2014)  to limit the model drift while enabling the simulation of coupled processes. The relaxation coefficient is set to 200 Wm 22 C 21 which is equivalent to about a 2-3 day time-scale over a depth of 10 m. The sea-ice concentration is not relaxed toward HadISST2 but its extension is constrained by the SST. The HadISST2 data set is produced by the Met Office Hadley Centre. Observations used in HadISST2 include in situ SST measurements, AVHRR (Advanced Very High Resolution Radiometer) SST retrievals, and retrievals from AATSR (Advanced Along-Track Scanning Radiometer) reprocessing. Both the radiative forcing and the HadISST2 incorporate a proper long-term evolution of climate trends in the twentieth century, and the occurrence of major events, such as El Niño Southern Oscillations and volcanic eruptions.
Within the ERA-CLIM project and its follow-up ERA-CLIM2, large amounts of historical observations were rescued, compiled, processed, and made available to the reanalysis efforts. The newly rescued data includes millions of historical surface and upperair observations  (Stickler et al. , 2014)  which are embedded within the international Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative to ensure exchange of information across various projects  (Allan et al. , 2011). The CERA-20C reanalysis does not assimilate the full observing system at any time, but only observation types with a good enough spatial and temporal coverage over the entire century are used. As in ERA-20C, surface pressure and marine wind observations are assimilated in the atmospheric model, from the ISPDv3.
2. 6  (Cram et al. , 2015)  and ICOADSv2. 5. 1  (Woodruff et al. , 2011)    assimilated. EN4 is a collection of subsurface profiles obtained across the global oceans from profiling floats, moorings, and ships. The evolution of the number of temperature and salinity observations assimilated in CERA-20C is plotted on the bottom plot of Figure  1. Although the data set is available over the period 1,900 to present, the first part of the century is barely observed. The observation coverage improves considerably with the introduction in the last decade of the Argo global array of about 3,800 free-drifting profiling floats that measures the temperature and salinity of the upper 2,000 m of the ocean. Although no oceanic satellite data is assimilated in CERA-20C, the changes in the in situ component of the ocean observing system has a substantial effect on the temporal variability of the ocean estimate  (de Boisseson et al.
, 2017). No data assimilation is performed for the land, ocean wave, and sea-ice components in CERA-20C. However, the use of the coupled model ensures a dynamically consistent Earth system estimate at any time. Climate reanalysis can be used to quality control the observations from the databanks as the data assimilation system compares in a systematic way the observations with a physically consistent model. ECMWF climate reanalyses include the production of Observation Feedback Archives (OFA) designed from the outset for user investigations  (Hersbach et al. , 2015b). This archive is organized by observation report type and source. It contains the observations assimilated (surface pressure and marine wind) and other observations that can be handled by the IFS model. This includes visual observations (present and past weather, visibility, cloud-base height, amounts, and types) and observations that can be exploited quantitatively (surface pressure tendency, air temperature and humidity, and seawater temperature).
Upper-air observations have not been processed and they are therefore not available in the OFA. The observation feedback includes in particular the background departure (observation minus background) and the analysis departure (observation minus analysis) for many observations. For example, the ERA-20C feedback information was analyzed in detail prior to CERA-20C, to detect stations reporting suspicious measurements. The CERA-20C feedback information was analyzed similarly. This includes stations or ships with systematically too large background departure, ships with unrealistic speed due to position errors and ships reporting observations over land, or stations reporting constant values for a long-time (stuck sensor). The lists of stations were sent to the data providers to improve the quality control of their data sets (to be used in future reanalyses).
However, the RMSE of CERA-20C is about twice as large as the CERA-20C analysis ensemble spread. This global offset between the spread and RMSE can be seen in the vertical profiles (Figure  18 ). CERA-20C is thus overly confident in the data compared to the actual error. Figure  19  shows a similar study but in observation space using the independent upper-air temperature measurements from radiosondes as another proxy for the truth. One can see a good correspondence between the CERA-20C ensemble spread at the observation locations and the monthly standard deviation of analysis departures averaged over the period 1959-1960, although the ensemble spread is globally too small by a factor of 2. To improve the uncertainty estimations in future reanalysis, the size of the ensemble and the way the perturbations are generated in the different members when the EDA system is used will require further investigation to reduce the current offset between the ensemble spread and the actual error.
The CERA-20C ensemble spread for the wave component has been assessed using the same methodology. The left plot of Figure  20  shows the ensemble mean for significant wave height against all available in situ data for a 2 year period  (2005) (2006). The observations are mostly from moored buoys, with the exception  of platform data from the Gulf of Mexico and the North Sea. The quality control procedure and collocation method are described in  Bidlot (2017)  and  Bidlot et al. (2002). In this validation against observations, there is no special account of the uncertainty and representativeness error of the observations as demonstrated in  Saetra and Bidlot (2004). Generally, the quality of CERA-20C wave heights is rather good, with exception in nearshore areas, with a marked degradation, usually associated with a tendency to underestimate (not shown).
Too low spread in the winds will yield too low spread in the waves, knowing that at first order, the wind waves generation is proportional to the square of the wind speed. The significant wave height is an integral parameter that includes all contributions from the different wave components, including the swell components generated from storms across the ocean basins days earlier. The CERA system does not necessarily propagate that information in time and space. For these reasons, it is not surprising that the wave height spread is underestimated. Instead of only measuring uncertainties, the ensemble spread can also be used to directly estimate the confidence in the data relative to climatology. One possibility is to define the confidence as 12 ensemble spread climatological spread where the ensemble spread is the square root of time-averaged analysis ensemble variance, and the climatological spread is the temporal standard deviation of the CERA-20C ensemble mean over a longer reference period.
Future Evolution of the CERA System The coupled atmosphere-ocean assimilation system used to produce CERA-20C has been recently upgraded to demonstrate the feasibility of a coupled reanalysis over the satellite-era. In this context, the resolution of the CERA system has been increased to a 55 km horizontal grid with 137 levels in the atmosphere and to a quarter of a degree in the ocean. All types of observations including upper-air and satellite measurements are assimilated. Data assimilation is no longer restricted to the ocean and the atmosphere but is also  performed in the land, wave, and sea-ice components. This proof of concept, called CERA-SAT, will deliver a reanalysis over the period 2008-2016 which will be publicly accessible. The configuration of the atmosphere and ocean waves is based on that of the ensemble component of the ERA5 reanalysis, which at the time of writing was being produced at ECMWF as part of the Copernicus Climate Change Service.
Further research and development in coupled data assimilation is being investigated to improve the quality of subsequent versions of the CERA system. In CERA-20C, the sea surface temperature is nudged toward the HadISST2 analysis. The ocean surface temperature is therefore tightly constrained to this preexisting analysis, and the air-sea interactions in the coupled model are not allowed to evolve freely. This has the advantage of preventing drifts in the coupled model, and makes use of the large amount of effort and expertise that was put into the development of the HadISST2 analysis, but has a number of disadvantages. For instance, the HadISST2 objective analysis has spatially and temporally varying errors which are larger in areas of large data voids, but it is difficult to take these into account in a nudging scheme.
The horizontal spreading of information in both the surface and subsurface of the ocean also requires further development if SST observations are to be assimilated directly. A scheme which could allow spreading of information over large spatial scales when the data are sparse, while making good use of the higherresolution measurements during the satellite era is the ultimate aim. Developments based on similar ideas to those of  Karspeck et al. (2012)  whereby empirical orthogonal functions (EOFs) are used to model the large-scale error covariances, applied to surface and sub-surface data are being investigated. These can be used in conjunction with the existing local-scale error covariance model currently used in CERA-20C for subsurface ocean data. The use of the EDA technique in the atmospheric assimilation system showed some clear benefits by changing dynamically the weight given to the background depending on the observation density.
Conclusions The ERA-CLIM and ERA-CLIM2 projects have initiated an important European program to extend the current global reanalysis capability including data rescue, in order to meet the challenging requirements for climate monitoring, climate research, and the development of climate services. Built upon the experience from the ERA-20C atmospheric climate reconstruction, CERA-20C is the first coupled reanalysis of the twentieth century which includes the atmosphere, ocean, land, waves, and sea ice. Making optimal use of available observations is key to any successful reanalysis. To this end, a new coupled atmosphere-ocean assimilation system has been implemented to simultaneously ingest atmospheric and ocean observations in the ECMWF coupled Earth system model. Particular attention has been given to the specification of the observation and background error to ensure a consistent analysis across geophysical variables, space and time.
This provides a unique source for detection and correction of biases or other errors in the assimilated observations. The scientific community and data set users also provide feedback and raise important issues that need to be addressed in future reanalyses. This is why reanalysis is an ongoing activity that should never be regarded as completed. New climate reanalyses need to be produced periodically to benefit from the latest updates in the models and data assimilation systems developed for numerical weather prediction. Historical reanalyses, in particular, also benefit from more (and more accurate) observations as historical observations are rescued, digitized, and quality-controlled. The coupled atmosphere-ocean assimilation system used to produce CERA-20C has been recently upgraded to demonstrate the feasibility of a coupled reanalysis over the satellite-era.
This proof of concept called CERA-SAT has delivered a reanalysis over the period 2008-2016 at higher resolution and assimilating all type of observations including upper-air and satellite measurements. The CERA system, originally designed for climate reanalysis, could pave the way for more advanced data assimilation to be used in weather forecasting. CERA-20C enables users to develop weather and climate-sensitive applications in a wide range of fields by providing a longer data set that extends back to the early instrumental record.
Data Access ECMWF provides access to CERA-20C and other public data sets via a dedicated data portal (http://apps. ecmwf. int/datasets). This consists of a point-and-click web interface used by users to discover the data sets available. A WebAPI is provided so users can programmatically download large amounts of data, which would otherwise require tedious interactions with the web interface. For each data set, there are: 1. instructions on how to download the data efficiently, following the organization of the data set in the MARS archive, 2. documentation explaining how the data set was produced, spatial and temporal resolution and detailed product description, and 3. a link to the conditions of use, which users have to acknowledge before any data download.
Acknowledgments The authors acknowledge the work done by the two reviewers and the editor who have provided a critical reading with helpful comments on an earlier version of the manuscript. The authors would like to thank  Gil Compo  and  Laura Slivinski  for having shared their experience from the 20CR reanalyses and their thoughts on how to measure confidence in ensemble climate data sets. Support for the Twentieth Century Reanalysis Project version 2c and the International Surface Pressure Databank data set is provided by the  U. S. Department of Energy, Office of Science Biological and Environmental Research (BER) , and by the  National Oceanic and Atmospheric Administration Climate Program Office. The authors would like to thank  Manuel Fuentes ,  Sebastien Villaume ,  Matthew Manoussakis  ( ECMWF's Products Team ),  Olivier Treiber ,  Will Weir ,  Christian Weihrauch , and  Sami Saarinen  ( ECMWF's High Performance Computing Team ),  Stephen Richards ,  David Lee , and  Lennart Sorth  ( ECMWF's Data Handling System Team ) for their work on the production and the dissemination of  CERA-20C.
Acknowledgements We thank the many persons involved in logistics, drill developments and drilling, and ice-core processing and analysis in the field and in our laboratories. NEEM is directed and organized by the  Center of Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (NRCan/GSC), China (CAS) ,  Denmark (FIST), France (IPEV, CNRS/INSU, CEA and ANR) ,  Germany (AWI) ,  Iceland (RannIs), Japan (NIPR) ,  Korea (KOPRI) , The  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) ,  United Kingdom  ( NERC ), and the  USA (US NSF, Office of Polar Programs ). Long-term support of ice core research at the  University of Bern  by  SNF  is gratefully acknowledged.
l. m. ). The pump was placed next to the nearest main power line near the entrance to the marsh site, and connected to the analyzer by a 150 m long 2 vacuum line. A solenoid valve at the inlet to the QCL was used to switch from the sampling line to a stream of dry N 2 (ultrahigh purity) for a one-minute background correction every hour. Data from the QCL analyzer were recorded at 10 Hz and stored on the QCL hard drive. The RMS noise (1 σ) at 10 Hz was 11-18 parts per trillion in volume (p. p. t. v. ) for COS during chamber measurements. The leaf chamber was measured once per hour. We monitored chamber air concentrations during the five-minute measurement periods (i. e. , while the ventilation fan was off), as well as the ambient air for one minute before and after these periods (i. e. , while the fan was running).
Leaf fluxes were calculated from the transient changes with respect to the interpolated inlet (ambient) concentrations (Fig. 1 ). The apparent fluxes from blank chambers were characterized and were found to be negligible. Environmental data were obtained from various sensors including photosynthetically active radiation (PAR) (SQ-215, Apogee Instruments), ambient air temperature and humidity (HMP45AC, Vaisala), and chamber air and leaf temperature (type T thermocouples, PFA coated), and were stored at 10 s intervals on a datalogger (CR1000, Campbell Scientific). The datalogger also controlled the operation of the high-speed ventilation fan.
Acknowledgements. The work was performed at the  San Joaquin Freshwater Marsh (SJFM) Reserve of the University of California Natural Reserve System. We thank  Mike Goulden  at  UC Irvine  for help and discussions, and  Bill Bretz  and  Peter Bowler  for assistance at the  SJFM UC Reserve. This work was supported by the  European Research Council (ERC)   Starting Grant  no. 202835  and  NSF   CAREER Award 1455381 to U. S. 11 Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-431Manuscript under review for journal Biogeosciences Discussion started: 7 November 2017 c Author(s)  2017. CC BY 4. 0 License.