dataesr/scientific-paragraphs-categorization huggingface dataset:
(2012)  for Greenland and  Depoorter et al. (2013)  for Antarctica). We provide them as the total (i. e. , liquid plus solid) river runoff in the main dataset. The separate components are also provided, if available (Appendix C). We used a monthly climatology of Greenland runoff for JRA55-do, because the interannually varying dataset is available only until 2010. This will be modified when an update to  Bamber et al. (2012) , which extends the seasonally-varying time-series to 2016 and will be imminently available. In CORE, the runoff is uniformly distributed on the coastal grid cells. Therefore, the non-uniform distribution of the 10°bins reflects the different numbers of coastal grid cells contained in each bin. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.
) (Bamber et al. , 2018)  (Appendix C). The climatology is based on the period 1961-1990, after which the total river runoff started to increase rapidly  (Bamber et al. , 2012). During the 1961-1990 period, the annual mean total river runoff from Greenland is 0. 0278 Sv in JRA55-do and 0. 002 Sv in CORE. That is, the total river runoff from Greenland is an order of magnitude higher in JRA55-do than that in CORE. Fig. 20  compares the monthly climatology of the total river runoff from Greenland in the two datasets. The seasonal variability of the total river runoff also differs between JRA55-do and CORE. Bamber et al. (2012)  utilises a high-resolution regional climate model, validated with in-situ observations, alongside observations of solid ice discharge. However, there was no gauges along the coastal Greenland in the runoff dataset of  Dai et al.
For the 1998-2007 period, the global ocean-integrated annual mean runoff in JRA55-do totals 1. 2566 Sv, approximately 0. 03 Sv larger than in CORE (1. 2229 Sv). The difference is mainly sourced from the different runoffs of Greenland and Antarctica in the two datasets. The Fig. 22. Time-series of the annual mean global ocean averaged total heat flux, and its separate components ( -W m 2 ). Compared are the results from three datasets; CORE inter-annual forcing version 2 (green), JRA55-raw (blue), and JRA55-do (red). The lower boundary condition is taken from COBESST  (Ishii et al. , 2005). Albedo and bulk formula are taken from  Large and Yeager (2009) , and the formulae for the air properties are from  Gill (1982)  (see Appendix B). For JRA55-do, the SST dataset by  Hurrell et al. (2008)  which was used to evaluate the CORE dataset by  Large and Yeager (2009)  is applied and plotted with grey lines.
(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) original intent was that any new river runoff dataset incorporated into this project would be adjusted to match its long-term mean  (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  with that of  CORE (1. 22 Sv). However, we have now decided to refer to new datasets to the total runoff of the present dataset (1. 26 Sv). Taking the oceanic area as 3. 654 × 10 14 m 2 , this runoff is equivalent to a global ocean averaged freshwater flux of × - -- 3. 44 10 kg m s 6 2 1.
Final adjustment for achieving global balance After making the main and additional adjustments described in the previous sections, we computed the heat and freshwater fluxes using COBESST  (Ishii et al. , 2005) , the albedo and bulk formulas of  Large and Yeager (2009) , and a set of formulas to compute properties of moist air presented in Appendix B. The fluxes for evaluating the global ocean heat and freshwater balances were calculated on the 1°× 1°grid of COBESST. As explained in Section 3. 2. 3, we impose exact closure on long-term mean global ocean averaged heat and freshwater fluxes in the satellite era. To achieve this, we adjusted the downward fluxes (i. e. , the shortwave and longwave radiation and precipitation fluxes) by globally and temporally constant factors. These factors are applied for the entire dataset period.
The heat budget is closed by applying a common factor (a) to the downwelling shortwave and longwave radiative fluxes: ). Results are derived from three datasets; CORE inter-annual forcing version 2 (green), JRA55-raw (blue), and JRA55-do (red). The black line in (b) is the precipitation of GPGP v2. 3. (c) Same as the upper panels, but for evaporation minus precipitation. Bulk formula and air-properties formulas are taken from  Large and Yeager (2009)  and  Gill (1982) , respectively. In JRA55-raw, the precipitation and the evaporation minus precipitation time-series were omitted because the precipitation was too high and out of range. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) 26 ) and (  24 ), the factor a is calculated as + + + + + + = + + a ( ) 0.
3 10 kg m s ocean o ocean 7 2 1 ). Here, is determined in a simulation forced by a preliminary version of the JRA55-do dataset and should be revised when an observational estimate or an ensemble mean of multi-model outputs becomes available. and represent the long-term mean global ocean averaged evaporation and runoff, respectively. The factor b is calculated as = - + + b. ( 29 ) Given the evaporation = -× - -- 3. 971 10 kg m s , over the 1988-2007 period, b was computed as 1. 02118 (denoting an approximate enhancement of 2. 1%).
Evaluation This section compares the atmospheric fields and surface fluxes derived from the JRA55-do, CORE, and JRA55-raw datasets, and aims to motivate the replacement of CORE by JRA55-do in the OMIP framework. For this purpose, we focus on the large-scale features and global balances of the surface fluxes. We first interpolated the atmospheric fields on the 1°× 1°grid of COBESST, then calculated the heat and freshwater fluxes as described in the previous section. Note that COBESST is commonly used as the lower boundary condition for all of the three datasets in order to compare only the set of atmospheric variables. The wind speed and surface wind stress cannot be suitably computed using this approach because any temporal or spatial interpolation of the wind vector slightly weakens both variables, complicating the comparison with observations.
Therefore, the equivalent neutral wind speeds and surface wind stresses in JRA55-do and JRA55raw were computed on their original normal TL319 grid points, using their surface-atmospheric variables and JRA-55 brightness temperature, which is based on COBESST. As a reference, a detailed comparison of surface meteorological variables with buoy observations is given in Appendix F. JRA55-do showed reduced RMS misfits and increased correlations in comparison with JRA55-raw and CORE. An atlas of annual mean surface fluxes of JRA55-do and their comparison with the CORE dataset is given in Appendix G. Several notable features have also been described there.
) increase between these periods is slightly larger in JRA55-do than in JRA55-raw, which would also act to reduce the evaporation and latent heat flux change. region f o Q AO f o Q SW f o Q LW f o Q LA f o Q SE f o F AO f o E P R 1 - There are some notable differences between the JRA55-do and CORE analyses. The time series of longwave radiation and sensible heat fluxes are steadier in JRA55-raw and JRA55-do than in CORE. The large decadal variations in latent and sensible heat fluxes seen in CORE during the 1970s and 1980s are not seen in JRA55-do. The lower boundary condition is COBESST in JRA-55, but another SST dataset is used in the NCEP/NCAR reanalysis and the ISCCP-FD longwave radiation calculation. In general, the surface air temperature is strongly influenced by the SST used as the lower boundary condition.
Therefore, we may expect less variability in the difference between surface air temperature and SST in JRA-55 than CORE in the present flux computation, which commonly uses COBESST as the lower boundary condition. Our assessment on the JRA-55 downward longwave radiation at the sea-surface implies that the interannual variation of the downward longwave radiation correlated well with that of the surface air temperature, which is influenced by the SST (i. e. , COBESST). Therefore, we may generally expect less variability in the difference between upward and downward longwave radiation fluxes in JRA-55 than in CORE if COBESST is used as the lower boundary condition. However, the use of another SST dataset  (Hurrell et al. , 2008)  as the lower boundary condition only slightly enhanced variability (grey lines in Fig.
22 ). This would imply that the surface air temperature of JRA-55 follows more closely the variation of the SST than that of CORE. A detailed analysis on the boundary layer scheme of the atmospheric model used by JRA-55 would be required to resolve this issue, which is beyond the scope of this paper. It should also be noted that the effects of the Pinatubo volcanic eruption in 1991 represented as the decline of the downward shortwave radiation flux in CORE are missing from the radiative fluxes of JRA-55 (Fig. 24a ). However, the simulated fields forced by JRA55-do might be cooled by the reduced air temperature assimilated into JRA-55 in the 1991-92 period (see Fig. 25b ). The long-term  (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  averages of the three datasets are summarised in Table  9.
In JRA55-do, the net fluxes of shortwave radiation ). Table  9  also lists the averages of heat and fresh water flux components for the overlapping period  of CORE and JRA-55. The differences for JRA55-do between  1958-2009 and 1988-2007  are less than -1W m 2 for heat fluxes and × -0. 1 10 kg s 9 1 for fresh water fluxes. These differences are less than or equal to those between datasets (JRA55-do and CORE). As a result, it can be concluded that the constraints imposed on the surface heat and freshwater flux, specifically that the long-term mean globally averaged heat flux and globally integrated freshwater flux into the ocean-sea-ice system are both nearly zero or slightly positive, approximately hold for the entire dataset period for JRA55-do within the accuracy range of the major components of surface heat (∼ --0 1Wm 2 ) and freshwater (∼ -× -0 1 10 kgs 8 1 ) flux budgets.
We instead applied the corrections de- scribed by  Large and Yeager (2009). Following their computation of the meridional heat transport (Fig. 10a  of  Large and Yeager (2009) ), the global heat flux imbalance of 0. 6 -W m 2 in CORE (Table  9 ) was assumed to be uniformly distributed except in the Atlantic Ocean. The exclusion of the Atlantic Ocean was based on the assumption that there was no bias and oceanic heat storage in the Atlantic basin, which gave the most consistent results with the estimated oceanic heat transports for the CORE datasets as explained by  Large and Yeager (2009). Thus, we subtracted ∼ × - - A 0. 8Wm ( 0. 6Wm / 2 2 GlobalOcean - A A [ ], GlobalOcean AtlanticOcean where A represents horizontal area) from the total heat flux Q in (30) in all oceanic regions except the Atlantic Ocean.
Summary and conclusions In this paper, we revisited the scientific and engineering foundations required to produce surface forcing datasets for driving coupled ocean-sea-ice models, and in so doing we produced the JRA55-do dataset of use within the CORE/OMIP framework. When generating the JRA55-do forcing dataset, we followed the methods of  Large and Yeager (2009)  and advanced several aspects of their methods by utilising newly available datasets and considering feedback from the ocean modelling community. In the CORE dataset provided by  Large and Yeager (2009) , the surface-atmospheric variables (e. g. , air temperature and specific humidity, wind vectors, and sea level pressure) are based on the NCEP/ NCAR reanalysis  (Kalnay et al. 1996; Kistler et al. 2001) , whereas the downward fluxes affected by clouds (e.
g. , radiative fluxes and precipitation) are based on other sources such as satellite data and direct observations. Our new JRA55-do dataset is based on a recent long-term (more than 50 years) reanalysis projects, the JRA-55 project, which uses an atmospheric model with refined horizontal resolution ( ∼ 55 km) and state-of-the-art data-assimilation techniques such as 4D-Var  (Kobayashi et al. , 2015). The atmospheric variables are based on the forecast phase of JRA-55 and the temporal interval (3 h) can marginally resolve the diurnal cycle, further advancing JRA55-do over the CORE dataset. Besides applying the JRA-55 reanalysis product, we made several updates to the data production process. We retained QuikSCAT as the reference dataset for the wind-field adjustment and used the full ten years available.
Moreover, the synoptic variabilities of the surface-atmospheric variables are more self-contained and mutually consistent than the earlier dataset. We also possess the inter-annual variations in the pre-satellite era (before the 1980s), which are absent in the CORE dataset. As references, we adopt CERES-EBAF-Surface_Ed2. 8 for the shortwave and longwave radiation fluxes, and CORE for the precipitation. The CERES shortwave radiation data were slightly reduced in the tropics as well as over sea-ice before being applied as the reference field. A state-of-the-art atmospheric reanalysis product still necessitates the introduction of adjustment factors. Specifically, changes in the observing system and the input data density and quality cause transitions in the quality of the assimilation product, which manifest as abrupt shifts in the JRA-55 data quality.
To remove these shifts requires time-dependent adjustments. Fortunately, these adjustments can be calculated with some fidelity, because the JRA-55C dataset was acquired by the same data-assimilation system as JRA-55 but contains only the conventional surface and upper air observations (i. e. , contains no satellite observations). Therefore, the biases in the pre-satellite period can be estimated from JRA-55C data in recent years. Although the inter-annual variability of the adjusted fields raises concern, the adjustment factors used in the pre-satellite and satellite periods are qualitatively similar. Furthermore, the time-dependent adjustments introduce no artificial transitions in the time series of the global means  (Figs. 24 and 25). These findings guarantee consistency in the quality of large-scale climatological features over the entire dataset period.
To complete the dataset, we imported independent river runoff datasets. The river runoff is determined by a river-routing model (CaMa-Flood) forced by input runoff from the land-surface component of JRA-55  (Suzuki et al. , 2017). The input is adjusted to ensure similar long-term variabilities in JRA55-do and the CORE dataset based on  Dai et al. (2009). The river runoffs from Greenland and Antarctica, which involve ice-sheets, are taken from  Bamber et al. (2012)  and  Depoorter et al. (2013) , respectively. The dataset details are summarised in Appendix C. We plan to regularly update the JRA55-do dataset thus facilitating real-time numerical experiments. Other global surface datasets with similar features are based either on satellite data (HOAPS;  Andersson et al. 2010 and J-OFURO; Tomita et al.
2010)  or the synthesis of several sources (OAflux;  Yu and Weller 2007). It is important to understand that the purposes and merits of these datasets differ from those of the datasets customised for driving ocean-sea-ice models. The former datasets tend to provide realistic fluxes, yet omit regions of large uncertainty or non-availability of observations. The area covered by sea-ice is a typical example of such regions. This treatment is unsuitable for driving global ocean-sea-ice models. When compiling a dataset for ocean-sea-ice models, an atmospheric reanalysis (which omits no region) is a suitable starting point. However, by adopting this approach we inevitably compromise the data accuracy which in turn requires careful assessments and corrections. The JRA-55 project uses a relatively low-resolution SST dataset (COBESST) as the lower boundary condition.
Thus a global adjustment on freshwater fluxes is always warranted in a global ocean-sea-ice model simulation if the freshwater fluxes are treated explicitly. (This is not necessary if the freshwater fluxes are converted to the corresponding virtual salt fluxes. ) To minimise these false adjustments in forced ocean-sea-ice models, the global flux budgets based on a realistic SST should preferably be balanced. In the present study, we took a relatively simple approach to close a long-term  (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  surface flux budget by applying a globally and temporally constant multiplicative factor on downwelling radiations and precipitation. The factors are applied for the entire dataset period.
These adjustment factors can be revised if major regional biases in any variables of the present version are identified. After assessing the large-scale features and global balances of the fluxes, we confirmed that JRA55-do is a valid alternative to the CORE dataset for driving global ocean-sea-ice models. We hypothesise that the technical advances incorporated into JRA55-do will improve many aspects of ocean modelling studies. However, the production processes will be continually reviewed and revised based on experience from the wide suite of anticipated studies. Specifically, the downward fluxes affected by clouds (radiation and precipitation fluxes) were derived from a reanalysis product. The validity of this choice should be carefully checked and reviewed in future studies. Also, the adjustment methods of all variables require large improvements near the coast (e.
g. ,  Taboada et al. , 2018). The horizontal resolution of JRA-55 ( ∼ 55 km) is still insufficient for capturing the meteorological features affected by orography in the coastal regions, and the reference datasets in the present adjustment methods (except for the scatterometer wind products) are even less well-resolved. Our group is planning to write several papers related to the present study. For the simulation paper, a number of international groups will conduct long-term inter-annual simulations forced by both CORE and JRA55-do, and compare their mean states and variabilities. The normal or repeat-year forcing paper will propose typical years that represent the present climate, and display the results of model integrations forced by repeated use of each of those years. Additionally, we expect various studies will arise from the CMIP6/OMIP process  (Griffies et al.
, 2016) , which makes use of both the CORE and JRA55-do forcing. The JRA55-do dataset is registered with "input datasets for Model Intercomparison Project (input4MIPs)" and can be obtained from the input4MIPs website. 1  The program codes used to generate the JRA55do dataset can be obtained from a repository on Github 2. B1. 1. Gill (1982)  The saturation vapor pressure e sw (in units hPa) of pure water vapor over a plane water surface is given by.
C3. Planned updates and older versions The latest version of JRA55-do as of April 2018 is 1. 3. The plan is to continually update this version using near real-time updates to the raw JRA-55 reanalysis. The version number will advance, accompanied by a detailed document, whenever new datasets or adjustment methods are incorporated. We will soon upgrade (in version 1. 4) the Greenland runoff to include interannualy-varying forcing from 1958 to 2016, incorporating an update to  Bamber et al. (2012)  that extends the seasonally-varying time-series to 2016  (Bamber et al. , 2018). The updated time-series will also include runoff from Arctic glaciers and ice caps which have also started to rapidly lose mass since the early 2000s  (Bamber et al. , 2018) , in addition to those over Greenland as reported by  Bamber et al.
3. 1, Fig. 2 ). • (Upgrade) The region of full ice-cover (fraction > 0. 99) used for smoothing temperature and specific humidity was determined based on daily, instead of monthly, COBESST data to facilitate quicker updating in the future (Section 3. 3. 4). • (Upgrade) When calculating surface fluxes using the bulk formula to determine the globally uniform, constant adjustment factors applied to downward fluxes, scalar wind speed is first calculated on the original grid of the dataset and then interpolated onto the COBESST (1°× 1°) grid. In the older version, the wind vector components were first interpolated onto the COBESST grid before computing the scalar wind speed (Sections 5 and 6). Fig. 34. Same as Fig. 33  but for the meridional wind-stress. Fig. 35  shows the comparison of surface fresh water fluxes between versions 1.
, 2015). For more details on the analysis method and the evaluation of the COBESST dataset, readers are referred to  Ishii et al. (2005). ) of JRA55-do-v1. 1 (blue), and JRA55-do-v1. 2 (red). Light blue line in (a) is the evaporation using the older treatment of scalar wind speed (see text). In (a), the blue (v1. 1) and the red (v1. 2) lines lie on top of each other. Black line in (b) is the precipitation of GPGP v2. 3. Bulk formula and air-properties formulas are taken from  Large and Yeager (2009)  and  Gill (1982) , respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) Fig. 36  shows a long-term mean (1981-2010) SST from COBESST and the difference to  Hurrell et al. (2008) , which was used by  Large and Yeager (2009)  for evaluating surface fluxes based on the CORE dataset.
Acknowledgements The  CERES EBAF-Surface  product (CERES EBAF-Surface_Ed2. 8)data were obtained from the  NASA Langley Research Center  CERES ordering tool at http://ceres. larc. nasa. gov/. International Arctic Buoy Programme (IABP) / Polar Exchange  at the  Sea Surface (POLES) Arctic  surface air temperature data are available and downloaded from http:// research. jisao. washington. edu/data_sets/iabppoles/. QuikSCAT and SSM/I wind products are produced by  Remote Sensing Systems  and were obtained from http://www. remss. com/. GlobCurrent products were obtained from http://globcurrent. ifremer. fr/. GPCP Precipitation data are provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their Web site at http://www. esrl. noaa. gov/psd/. Feedback and generous supports provided by  Patrik Hyder at UK Met Office ,  Kazutoshi Oonogi ,  Masahiro Hosaka , and  Masayoshi Ishii  at  MRI ,  Tracy Ippolito  at  FSU , and participants of  CLIVAR Ocean Model Development Panel (OMDP) Mini Workshop on Forcing ocean-Sea-ice Models , which was held in January 2015 in Grenoble, France, Extended CLIVAR-OMDP panel meeting on forcing ocean-ice climate models, which was held in January 2016 in Yokohama, Japan, and CLIVAR OMDP panel meetings, which were held in September 2016 in Qingdao, China and in October 2017 in Exeter, UK, and the continuous support from the members of CLIVAR-OMDP as well as the many other modelling groups that have tested versions of  JRA55-do , are gratefully acknowledged.
The use of a CEOF analysis for the wind direction adjustment was suggested by  Yutaka Yoshikawa  at  Kyoto University. This study was supported by  JSPS KAKENHI  Grant Number  15H03726. NCAR contribution to this study was supported by the  NOAA Climate Program Office Climate Variability and Predictability Program. NCAR is sponsored by the  US National Science Foundation. The PCMDI/LLNL contribution to this study was performed under the auspices of the  U. S. Department of Energy  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344. We also acknowledge support from the  U. S. Department of Energy, Office of Science, Climate and Environmental Sciences Division, Regional and Global Modeling and Analysis Program. The present effort to produce the  JRA55 -do dataset is a project separated from the  JRA-55  project conducted by  Japan Meteorological Agency.