dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that rainplus-snow equals the total precipitation (P). 2007, 2013). The assimilation window of the base-system (6 hours) is unchanged for JRA-55. To derive the atmospheric fields for driving the ocean-sea-ice models, we obtained 3-hourly outputs from the forecast phase (the 3rd and 6th hours after initialisation) rather than the initial (analysis) state. Meteorological variables such as temperature, humidity, wind vectors, and sea level pressure were obtained from instantaneous data, while fluxes such as radiation and precipitation were determined from three-hour mean data. Here, fluxes should be averaged quantities for any budget analyses using this dataset to be done consistently. Separately from the main atmospheric four-dimensional variational analysis, the JRA-55 system also performs a two-dimensional optimal interpolation analysis for the surface temperature and specific humidity at 2 m height, and the surface wind vector at 10 m height  (Kobayashi et al.
, 2015). Hereafter, this analysis is called JRA-55anl. This analysis takes the forecast fields of the four-dimensional analysis of JRA-55 as the first guess. The same correlation scales as the four-dimensional analysis are used for the error of the first guess. Thus, the analysis fields of JRA-55anl are not significantly smoothed relative to the four-dimensional analysis fields of JRA-55. However, the analysis fields of JRA-55anl do not constitute the initial state for the forecast phase of the four-dimensional analysis. For developing JRA55-do, this separate surface analysis was extensively used as a reference dataset for adjusting the surface marine meteorological variables, as explained in the following sections. A subset of JRA-55, JRA-55 Conventional (JRA-55C;  Kobayashi et al. , 2014) , uses the JRA-55 data-assimilation system but excludes satellite observations.
As a result, it assimilates only the conventional surface and upper air observations. The JRA-55C project aims to improve the inhomogeneity of long-term datasets by removing the historical changes in satellite observing systems. For this purpose, JRA-55C was branched off from JRA-55 in November of 1972, just before a vertical temperature profile radiometer (VTPR) was installed in 1973. From the JRA-55C data, we can assess the biases in the pre-satellite (pre-1973) period assuming that these biases have persisted into the recent period, where they can be quantified by referencing to satellite data. For reference, the acronyms containing JRA-55 or JRA55 are listed in Table  3.
First, the time scale of the variance to be adjusted, such as stochastic, seasonal, or interannual, would need to be chosen. Then, it would be necessary to reconstruct a time-series for both JRA55-raw and the reference dataset to constrain the variance for the chosen time scale. The choice of the time scale might be limited by the availability of the reference data. It would also be necessary to confirm that the adjustment on the variance for a particular time scale would do no harm to variability in other time scales. This approach might be taken for a future version, after consideration of results from ocean model simulations that use JRA55-do. For this study, we focussed on adjusting the Table  3  List of acronyms of JRA-55 subproducts. mean state using a single factor, either multiplicative or offsetting.
1). We then made additional adjustments as explained in the next subsection. The adjusted atmospheric fields are designated as JRA55-adj to distinguish them from the final product JRA55-do. JRA55-do was obtained after the global-balance adjustment of JRA55-adj (see Section 5). Note that we did not adjust the sea level pressure because there is not good data to be used as a reference field. Therefore, when applying the dataset to (for example) storm surge modelling, the user should be aware of the possible inconsistencies between the surface pressure and the adjusted wind fields. The atmospheric fields in JRA-55 were sometimes shifted by changes in the observation systems  (Kobayashi et al. , 2015). To accommodate these shifts, we divided the dataset into several phases and separately adjusted each phase.
On the other hand, the year 1973 marks the inclusion of VTPR and the separation of JRA-55C from JRA-55. Fig. 1  compares the time series of the downward shortwave radiation and precipitation in the JRA-55, JRA-55C, and CORE datasets. From 1973, the shortwave radiation was clearly lower in JRA-55 than JRA-55C. After 1998, the precipitation was clearly increased, which was not seen in the CORE dataset, and the shortwave radiation further lowered, in JRA-55 relative to JRA-55C. In JRA-55, AMSU-A (included in Aug 1998) corrected the warm bias in the upper troposphere over the ocean, whereas AMSU-B (included in Oct 2000) corrected the dry bias in the upper and middle troposphere over the ocean (see Figs. 9 and 10 of  Kobayashi et al. (2015) ). Both corrections increased the overall precipitation over the ocean and possibly influenced the shortwave radiation.
The precipitation increase occurred mainly in the tropical regions  (Kobayashi et al. , 2015). It should also be noted that from May 1997, JRA-55 included the scatterometer winds  (ERS from May 1997 to Jan 2001 , QuikSCAT from Jul 1999 to Nov 2009 , and ASCAT from Jan 2008). Thus, the 1997-98 period is also a suitable transition point for wind-field adjustment. We also note that in Fig. 1  the long-term mean global ocean averaged downward shortwave radiation and precipitation were biased high relative to those of the CORE dataset, which warrants adjustment on JRA-55 fluxes. Based on these assessments, we divided the dataset period of JRA-55 into three phases  1958 -1972 (phase-I); 1973 -1997 (phase-II); (phase-II); and 1998-present (phase-III). We then applied different adjustment factors in each phase.
Closing the budgets The adjusted atmospheric dataset (JRA55-adj) was tested to see whether it could satisfy well-recognised constraints (as discussed below) on the global surface-heat and freshwater flux budgets when a realistic surface condition was applied (Section 5). When calculating the global ocean surface flux budget (excluding the sea-ice region), we applied COBESST  (Ishii et al. , 2005)  as the lower boundary condition (sea-surface temperature and sea-ice distribution). Appendix D briefly introduces COBESST. The latitude-dependent albedo and the bulk formulae (including the saturation of the transfer coefficients in the highwind regime) were taken from  Large and Yeager (2009). The air properties were computed as described in Appendix B. COBESST is analysed on a 1°× 1°grid and its SST structure is smooth.
Although a high-resolution SST product can give more detailed and presumably more accurate structure of fluxes, a low-resolution product (COBESST) has been selected for this study because it is anticipated that the majority of ocean-climate models that participate in CMIP6-OMIP will adopt a nominal 1-degree horizontal resolution. A model integration for about 300 years is required for the physical OMIP. In the biogeochemical OMIP, many tracers are added to the model and much longer integrations will be required for the spin-up. Fulfilling these requirements with a mesoscale eddy permitting model is too computationally challenging for many modelling centres. The SST structure of low-resolution products is what a nominal 1-degree oceanclimate model ideally reproduces. Thus, as a forcing dataset to be used in the framework of OMIP, JRA55-do was evaluated and adjusted in terms of the global ocean averaged surface flux budget by using a lowresolution SST product (COBESST).
The adjustment factor is then applied for the entire dataset period. Details will be explained in Section 5. The precipitation (P) was similarly readjusted. After adjustment, the global freshwater input to the ocean-seaice system formally balances with the evaporation (E), river runoff (R) and sublimation over the sea-ice (S), i. e. , + + + ∼ 0 during the same multi-decadal time-period as used for the heat flux. Here, the use of instead of E implies an integration over a global ocean as well as an averaging over a multi-decadal time scale. In JRA55-do, the runoff is imported from other sources. Provided that the adjustment is minor, any river runoff dataset that is optionally added to JRA55-do will be adjusted to match its long-term  (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  mean with that of the first version (1.
26 Sv, where 1 Sv = -10 m s 6 3 1 ; see Section 4). The final adjusted fields comprise the JRA55-do dataset. Note that the long-term closure adopted here will still allow for the study of interannual variability in the ocean-sea-ice system. However, it may preclude investigation of long-term trends, although a trend may be imposed by a user by slightly modifying the globally uniform factors applied to the downward fluxes. On the other hand, the exact closure may be suited for simulations run for multiple cycles of the nearly 60year forcing in the OMIP experiments  (Griffies et al. , 2016) , because a long-term trend will not be imposed a priori on the simulations.
Reference dataset A reliable long-term dataset for adjusting the surface air temperature and specific humidity with consistently high data quality could not be found. Consequently, we computed the reference values for these variables from reanalysis data using an ensemble-mean approach. For this purpose, we selected seven atmospheric reanalysis products: NCEP-R1, NCEP-R2, 20CRv2, ERA-interim, MERRA2, NCEP-CFSR, and JRA-55anl. Data were first mapped onto the reduced TL319 grid of JRA-55 before the ensemble mean was computed. When computing the ensemble mean, we weighted the modern, high-resolution reanalysis products (ERA-interim, MERRA2, NCEP-CFSR, and JRA-55anl) higher than the old, low-resolution reanalysis products (NCEP-R1, NCEP-R2, and 20CRv2). The weighting factors for the modern and older products were 1.
Then, 50 passes of a 5-point smoothing filter is applied to moderate the transition from zero to unity. Because the atmospheric fields over the ocean in those regions are represented by only a few grid points in the low-resolution products, we decided that they do not have sufficient accuracy to be used as a reference for adjusting higher resolution reanalysis products. Surface air temperature over sea-ice can differ widely between the different reanalysis products (e. g. ,  Lindsay et al. , 2014) , and so the ensemble mean air temperature is a dubious reference for data above sea-ice. Therefore, data over ice-covered regions is referenced to the JRA-55anl data. Because JRA-55anl uses the JRA-55 forecast as a first guess, we can expect modest differences between them (that is, adjustment factors will be small relative to other possible reference datasets).
Before JRA-55anl is applied as a reference over sea-ice, the surface air temperatures in the Arctic region of JRA-55anl were adjusted with respect to the International Arctic Buoy Programme (IABP) / Polar Exchange at the Sea Surface (POLES) (IABP-NPOLES;  Rigor et al. , 2000). Specifically, the monthly climatological difference between JRA-55anl and IABP/NPOLES for the period 1979-1998 (which is the period covered by IABP/NPOLES), were used to adjust JRA-55anl for the entire dataset period (see also footnotes of Table  5 ). The general effect of the adjustment was to lower the air temperature of JRA-55anl in the Arctic Ocean. In the Southern Hemisphere, JRA-55anl was used as a reference field over sea-ice without modification.
Additional adjustments To correct errors introduced by various aspects of the JRA-55 configuration (detailed below), we further adjusted the air temperature and specific humidity as follows. We explain these adjustments in the order of their implementation. Note that because the temperature and specific humidity of the CORE dataset, which are given at 10 m height, are used as the reference fields for the additional adjustments explained below, the height of the temperature and specific humidity data was shifted from 2 m to 10 m prior to the additional adjustments, as described in Appendix A. 2. Also, the data on the reduced TL319 grid were interpolated onto the normal TL319 grid, as explained in Appendix A. 1. The 10-m values were computed using the sea-surface temperature from COBESST, the ice-surface temperature from the brightness temperature of JRA-55, the adjusted equivalent neutral 10-m wind field explained in Section 3.
Thus, only positive differences appear in the marginal sea-ice zones. Cut-off of extremely low air temperature around Antarctica. The air temperature around Antarctica was adjusted as described by  Large and Yeager (2004; 2009)  for the CORE datasets. In the CORE datasets, the extremely low temperatures were cut off by applying an   1980 -1996 (JRA-55C), 1980 -1996 , and 1999 -2014 , respectively, for the three phases. annual cycle of the minimum temperature based on weather station and drifting buoy  (Bryan, Personal Communication, 2002). For JRA55-do, we used the same formula for the annual cycle of the minimum temperature. Specifically, T min (ϕ, t) is defined as a function of latitude (ϕ) south of 60°S obtained by fitting sinusoidal curves to the observed monthly minimum temperature: = + + + T ϕ t b b ϕ c t a a ϕ ( , ) ( ) ( )( ), min 0 1 0 0 1 (15) with = - ∘ a 21.
In the pre-satellite period , the sea-ice distribution of COBESST in the Southern Hemisphere is largely based on climatology, so the inter-annual variabilities of the air temperature and specific humidity in the high-latitude Southern Hemisphere are unreliable. In the Northern Hemisphere, the sea-ice distribution of COBESST is based on  Walsh and Chapman (2001)  in the pre-satellite period, but the sea-ice extent of COBESST tends to be higher than the original data in the marginal sea-ice zones. Identifying the reason for this is beyond the scope of this paper -we simply consider how to remedy the discrepancy. In NCEP-R1, which is used as the basis for air temperature and specific humidity in the CORE dataset, sea-ice does not show such extensive distributions. Furthermore, NCEP-R1 uses various sea-ice analyses in addition to  Walsh and Chapman (2001)  prior to 1978 when available  (Kalnay et al.
Reference dataset The main reference dataset for the surface wind vectors is the Remote Sensing Systems QuikSCAT Ku-2011 Daily Ocean Vector Wind on 0. 25 °grid Version 4  (Ricciardulli et al. 2011; Ricciardulli and Wentz 2015 ; hereafter referred to as QuikSCAT). We first computed the time series of the monthly scalar wind speeds, and the zonal and meridional components of the wind vectors, from daily data on the original 0. 25°g rid. At each grid point, the monthly data are used if available on more than one-third of the total number of days in that month. The timeseries data were then mapped onto the reduced TL319 grid of JRA-55. The data gaps are caused by the shifting satellite orbits, rain contaminations, and the presence of sea-ice. In grid cells where the monthly data were not available, JRA-55anl wind data was used.
This treatment should smooth the transition of the adjustment factors in the marginal sea-ice zones. From the derived monthly dataset, we generated time-series of the annual mean and its climatology on the reduced TL319 grid. QuikSCAT wind is available from Aug 1999 to Oct 2009, sufficiently long for referencing phase-III. This is an improvement relative to the CORE dataset for which only 5 years of reference data was available for adjustment. In phases I and II, the wind speed was adjusted using an SSM/I-based wind speed product  (Wentz, 2013). Because this product extends from 1988 to the present, the SSM/I wind speeds over the entire period were adjusted relative to the QuikSCAT wind speed during the Nov 1999 to Oct 2009 period. The adjusted speed then provided the reference for adjusting the JRA-55 wind speed.
Users of the present dataset should be aware of this limitation and its possible impact on their simulated fields (e. g. ,  Abel et al. , 2017). In future work, the absolute wind vector ( → U A ) could be estimated from the surface current ( → U O ) de- rived in an oceanic reanalysis or state estimation (e. g. ,  Rio et al. , 2014). For this version, we provide a climatological mean surface current ( → U O clim ) computed from the dataset of  Rio et al. (2014). The climatology is based on the period used to construct the reference wind field from QuikSCAT (Nov1999-Oct2009). This climatological surface current could be added to the time series of the wind vector of this dataset to approximate the time-series of the absolute wind vector.
Reference dataset We used the CERES-EBAF-Surface_Ed2. 8 data  (Kato et al. , 2013)  as the reference dataset for the downward radiative fluxes. CERES-EBAF-Surface_Ed2. 8 consists of surface irradiance fluxes as well as other variables derived from the Clouds and Earth's Radiant Energy System (CERES) satellite measurements of Top of Atmosphere (TOA) radiation  (Loeb et al. , 2012). EBAF (Energy balanced and Filled) refers to adjustments made to CERES data within its uncertainty to make it consistent with the estimated global average heat storage in the ocean of -0. 50Wm 2  (Loeb et al. , 2012). The estimates of surface fluxes, derived from radiative transfer theory using data on clouds, atmosphere, surface and aerosol properties, are constrained to give TOA radiances that match CERES-EBAF in turn  (Kato et al.
, 2013). CERES-EBAF-Surfa-ce_Ed2. 8 (hereafter referred to as CERES) is considered a state-of-thescience product replacing the earlier ISCCP-FD dataset  (Zhang et al. , 2004)  used in CORE. As the latest dataset of its kind, CERES has been widely used as a reference in verification studies. CERES data now cover 15 years, sufficiently long for comparisons with other data. The ratio of the total energy explained by the first CEOF mode on which the rotating factor shown in (a) is based. The rotating factor with a low ratio is tapered with a formula given by Eq. (  23 ). Following  Large and Yeager (2004; 2009) , who produced the CORE data using ISCCP-FD radiation, we compared the CERES data with buoy observations and evaluated the necessity of adjusting them before their application to the reference fields.
Reference dataset The precipitation adjustment was referenced to the CORE dataset. The precipitation field of the CORE dataset is comprised of several data sources. The global ocean is separated into several latitude bands and an appropriate dataset is chosen for each. From the south, the Global Precipitation Climatology Project (GPCP;  Huffman et al. , 1997)  for  70 N, and a combined Serreze and Hurst (2000) and Yang (1999) climatology for the Arctic poleward of 70°N. The data with interannual variability is only available after 1979. In the earlier version of the dataset, the global ocean averaged precipitation showed a rapid increase during the most recent decade, which was not found in the GPCP-v2. 3 dataset  (Adler et al. , 2003)  (see also Appendix C. 3. 2). This is thought to be caused by the introduction of GNSS-RO (Global Navigation Satellite System-Radio Occultation) in 2006 as well as the increase in the number of radiance observations from satellite water vapor channels.
Because it is not desired that the enhanced precipitation remains in the updated data in the future, phase-III is further divided at 2006 and different adjustment factors are computed for the first and second halves (phase-IIIa and phase-IIIb, respectively). We used the CORE dataset for the first half. In the second half, we used a product based on the GPCP-v2. 3 dataset because the CORE dataset is only available until 2009. Because GPCP-v2. 3 extends from 1979 to the present, the GPVP-v2. 3 precipitation over the entire period were first adjusted relative to the CORE dataset during the 1979 to 2009 period. Thus, the adjusted precipitation provides the reference for adjusting the JRA-55 precipitation for phase-IIIb.
Additional adjustment in the Mediterranean After the first adjustment, the precipitation remained exaggerated in the Mediterranean during the 1958-1978 period (Fig. 18 ). This is due to processing errors on some of the radiosonde data used in JRA-55 in 1960s to 1970s. The impact of this error on oceanic precipitation was limited except for the Mediterranean. This excessive precipitation, which might affect transport at the Strait of Gibraltar, was corrected by estimating the annual precipitation over the Mediterranean from 1958 to 1978. The estimation is described below. The precipitation on the land around the Mediterranean was first calculated using Global Precipitation Climatology Center (GPCC;  Schneider et al. , 2011)  data from 1958 to the present. Additionally, the precipitation over the Mediterranean was calculated from CORE for the 1979-2009 period (when the inter-annual variability of precipitation was available in this dataset).
Runoff The main part of the river runoff (freshwater discharge at river mouths) was taken from a dataset presented in  Suzuki et al. (2017). In this dataset, the river discharge to the ocean was calculated by the global river-routing model CaMa-Flood  (Yamazaki et al. 2011; Yamazaki et al. 2013). The input runoff from the JRA-55 land-surface model is then routed to oceans along the river network map, which is fitted to the land-surface model. The longitudinal and latitudinal resolution of the model is 0. 25°. The input runoff from the land-surface component of JRA-55 contains biases that were corrected by the multiplicative factors as explained by  Suzuki et al. (2017). In this correction, the input runoff was adjusted so that CaMa-Flood gives the river discharge into the ocean that fits with that reported by  Dai et al.
25°× 0. 25°spatial resolution. The river discharge from the remaining small rivers was separated into 12 divided ocean basins (the western and eastern part of the three major (Atlantic, Pacific, and Indian) Oceans, the Arctic Ocean, the Mediterranean, Black, Baltic, and Red Seas, and the Hudson Bay). In the above adjustment procedure, the total input runoff into the small rivers was adjusted to match the sum of the river discharges from the divided basins (excluding the discharges of the 45 major rivers). The depths and widths of the 38 major continental rivers in CaMa-Flood were tuned ad-hoc to fit the climatology of the seasonal cycle with that of  Dai et al. (2009). Fig. 19  compares the time series of the annual mean, global oceanintegrated river discharges into the oceans in the CORE dataset  (Dai et al.
, 2009)  and the CaMa-Flood simulation of  Suzuki et al. (2017). The simulated annual mean river discharges vary similarly to the discharges from the major continental rivers reported by  Dai et al. (2009) , partly because the long-term input was adjusted to  Dai et al. 's (2009)  data by the time-dependent multiplicative factor. The excessive river discharge in 1958 of JRA55-do occurred because the high runoff from the land-surface component of JRA-55 of that year could not be appropriately corrected by the present adjustment method that uses the multiplicative factor for 1963 during the period 1958-1962. The river model used by  Suzuki et al. (2017)  is suitable only for liquid water. Thus, in basins with significant runoff in the solid phase, such as Greenland and Antarctica, we used climatologies of existing datasets (namely, the climatologies of  Bamber et al.