G Reverdin
email: gilles.reverdin@locean-ipsl.upmc.fr

A R Friedman

L Chafik

N P Holliday

T Szekely

H Valdimarsson

I Yashayaev

North Atlantic extratropical and subpolar gyre variability during the last 120 years: a gridded dataset of surface temperature, salinity, and density. Part 1: Dataset validation and RMS variability

Keywords: sea surface temperature, sea surface salinity, surface density, North Atlantic, decadal variability

We present a binned annual product (BINS) of sea surface temperature (SST), sea surface salinity (SSS) and sea surface density (SSD) observations for 1896-2015 of the subpolar North Atlantic between 40°N and 70°N, mostly excluding the shelf areas. The product of bin averages over spatial scales on the order of 200 to 500 km, reproduces most of the interannual variability in different time series covering at least the last three decades or of the along-track ship monitoring. Comparisons with other SSS and SST gridded products available since 1950 suggest that BINS captures the large decadal to multidecadal variability. Comparison with the HadSST3 SST product since 1896 also indicates that the decadal and multidecadal variability is usually well reproduced, with small differences in long-term trends or in areas with marginal data coverage in either of the two products. Outside of the Labrador Sea and Greenland margins, interannual variability is rather similar in different seasons. Variability at periods longer than 15 years is a large part of the total interannual variability, both for SST and SSS, except possibly in the south-western part of the domain. Variability in SST and SSS increases towards the west, with the contribution of salinity variability to density dominating that of temperature in the western Atlantic, except close to the Gulf Stream and North Atlantic Current in the southwest area. Weaker variability and larger relative temperature contributions to density changes are found in the eastern part of the gyre and south of Iceland.

Introduction

The North Atlantic subpolar gyre (SPG) receives salty surface water from the subtropical North Atlantic as well as fresh water originating from the Arctic and the Nordic seas and circulating mostly on and along the shelves. It is a formation site of deep and intermediate waters and thereby a major contributor to the lower limb of the Atlantic meridional overturning circulation (AMOC) [START_REF] Rhein | Deep water formation, the subpolar gyre, and the meridional overturning circulation in the subpolar North Atlantic[END_REF]. There is wintertime densification of the surface Labrador and Irminger Seas resulting in intermittent deep convection reaching and exceeding 2400 m in the mid-1990s and 2000 m in recent years [START_REF] Yashayaev | Further intensification of deep convection in the Labrador Sea in 2016[END_REF]. Deep dense overflows from the Nordic Seas and the associated mixing with and entrainment of local waters also add important hydrographic connections between the regions and the surface and deep layers. In both instances, one expects that changes in surface density will modulate these vertical exchanges and the properties of the deep and intermediate waters formed, and thus the AMOC. Many model studies (at various spatial resolution) also suggest an influence of surface salinity changes in this region on changes in the AMOC [START_REF] Frankignoul | The Role of Salinity in the Decadal Variability of the North Atlantic Meridional Overturning Circulation[END_REF][START_REF] Rahmstorf | Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation[END_REF][START_REF] Böning | Emerging Impact of Greenland Meltwater on Deepwater Formation in the North Atlantic Ocean[END_REF].

Whereas the connection between these surface changes and AMOC is still subject to discussion [START_REF] Lozier | Overturning in the North Atlantic[END_REF][START_REF] Williams | Mechanisms of heat content and thermocline change in the subtropical and subpolar North Atlantic[END_REF][START_REF] Buckley | Observations, Inferences, and Mechanisms of the Atlantic Meridional Overturning Circulation: A Review[END_REF], the relation between changes of surface density and upper or intermediate water formation is rather well established [START_REF] Yashayaev | Hydrographic changes in the Labrador Sea, 1960-2005[END_REF]Loder 2016 2017;[START_REF] Piron | Gyre-scale deep convection in the subpolar North Atlantic Ocean during winter 2014-2015[END_REF]. Seawater density is a function of temperature and salinity, with the relative influence of salinity increasing when temperature decreases. Thus, investigating surface density variability and attempting to establish its past patterns of change requires a concurrent analysis of temperature and salinity fields.

Hydrographic data in the North Atlantic and neighboring seas have been used to analyze salinity and temperature on decadal time scales with a low spatial resolution , but only since the mid-1960s and in more restricted areas since 1950 [START_REF] Polyakov | Multidecadal Variability of North Atlantic Temperature and Salinity during the Twentieth Century[END_REF][START_REF] Skliris | Salinity changes in the World Ocean since 1950 in relation to changing surface freshwater fluxes[END_REF][START_REF] Holliday | Multidecadal variability of potential temperature, salinity, and transport in the eastern subpolar North Atlantic[END_REF][START_REF] Yashayaev | Hydrographic changes in the Labrador Sea, 1960-2005[END_REF][START_REF] Yashayaev | Hydrographic changes in the Labrador Sea, 1960-2005[END_REF]Loder 2016 2017). These data also rarely provide direct indications of winter surface density field (regional estimates can be found in [START_REF] Yashayaev | Hydrographic changes in the Labrador Sea, 1960-2005[END_REF]Loder (2016 2017)) that is the variable key to the connection between the surface ocean and the ocean interior. In the last twenty years, the advent of profiling floats [START_REF] Lavender | Mid-depth recirculation observed on the interior Labrador and Irminger seas by direct velocity measurements[END_REF][START_REF] Roemmich | The Argo Program: Observing the global ocean with profiling floats[END_REF][START_REF] Riser | Fifteen Years of Ocean Observations with the Global Argo Array[END_REF]) has allowed year-round three-dimensional monitoring of large-scale upper and intermediate ocean variability [START_REF] Tesdal | Salinity Trends within the Upper Layers of the Subpolar North Atlantic[END_REF], but these measurements do not yet help to resolve the decadal to multidecadal frequencies. Multidecadal time series with at least annual or seasonal resolution have also been produced at a few sites: Rockall Trough [START_REF] Holliday | Multidecadal variability of potential temperature, salinity, and transport in the eastern subpolar North Atlantic[END_REF], the Faroe-Shetland Channel [START_REF] Hughes | Variability in the ICES/NAFO region between 1950 and 2009: observations from the ICES Report on Ocean Climate[END_REF], south of Iceland (Icelandic hydrographic surveys), in the center of the Labrador Sea [START_REF] Yashayaev | Hydrographic changes in the Labrador Sea, 1960-2005[END_REF]Loder 2016 2017), or at station Mike and hydrographic sections in the Norwegian Sea [START_REF] Yashayaev | The role of the Atlantic water in multidecadal ocean variability in the Nordic and Barents Seas[END_REF].

Otherwise, mapped monthly fields of T and S have been constructed by different objective mapping methods based on the EN4 [START_REF] Good | EN4: Quality Controlled Ocean Temperature and Salinity Profiles and Monthly Objective Analyses with Uncertainty Estimates[END_REF], CORA [START_REF] Cabanes | The CORA Dataset: Validation and Diagnostics of in-Situ Ocean Temperature and Salinity Measurements[END_REF] and [START_REF] Ishii | Steric sea level changes estimated from historical ocean subsurface temperature and salinity analyses[END_REF] datasets that could have some coverage in a large part of this domain for the last 65 years. Monthly mapped sea surface temperature (SST) fields have also been produced with sufficient coverage into large parts of the SPG since the 1890s using mostly ship log data with a shift in the last two decades to drifter SST data. Carefully estimated bias correction has been applied to subsets of these data, such as done in Hadley Centre SST (HadSST3; Kennedy et al. 2011a and2011b). However, these binned SST datasets present gaps in some northern regions, in particular before 1922.

Long-term box averages in the SPG with near-annual resolution were presented in [START_REF] Reverdin | North Atlantic Subpolar Gyre Surface Variability (1895-2009)[END_REF] and [START_REF] Friedman A R, Reverdin | A new record of Atlantic sea surface salinity from 1896 to 2013 reveals the signatures of climate variability and long-term trends[END_REF]. [START_REF] Reverdin | North Atlantic Subpolar Gyre Surface Variability (1895-2009)[END_REF] Using similar box averaging as in these previous two studies, we present a higher-resolution binned product of temperature and salinity since 1896 in the subpolar North Atlantic and southern Nordic seas and draw first conclusions obtained with the dataset. Focusing on the region north of 40°N allows us to describe co-located SSS and SST at higher spatial scales (less than 500km) than presented previously, without having large periods with data gaps (except during or just after WWI and WWII in some sub-regions).

In this paper we present and validate the binned product at interannual time scales, and also discuss its RMS variability. A second part of this study (in preparation) examines the decadal and multidecadal variability, and relationship with North Atlantic climate variations. The dataset sources and construction are described in Section 2. In Section 3 we validate the dataset and compare it with other products. In Section 4, we examine the spatial distribution of temporal variability of SSS, SST, and surface density, followed by summary and discussion in Section 5.

Data and methods

Dataset construction

The main data sources are described in [START_REF] Reverdin | Surface Salinity of the North-Atlantic -Can We Reconstruct Its Fluctuations Over the Last 100 Years[END_REF], [START_REF] Reverdin | North Atlantic Subpolar Gyre Surface Variability (1895-2009)[END_REF][START_REF] Friedman A R, Reverdin | A new record of Atlantic sea surface salinity from 1896 to 2013 reveals the signatures of climate variability and long-term trends[END_REF]. A significant part of the temperature and salinity (reported as practical salinity) data was collected on merchant and other selected vessels (including the weather ships), in particular before the mid-1970s north of 50°N, and before WWII and since 1977 south of 50°N. This is also the case since 1993, mostly along two ship tracks between southern Newfoundland and Reykjavik and between the North Sea and Greenland [START_REF] Reverdin | North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density[END_REF], as well as near France and between the English Channel and eastern North America.

For the first two tracks, intake temperature was measured part of the time, XBTs were dropped and water samples collected, which allows for identification / estimation of possible temperature and salinity biases in the records from thermosalinographs (TSG) [START_REF] Alory | The French contribution to the voluntary observing ships network of sea surface salinity[END_REF][START_REF] Reverdin | North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density[END_REF]. The identification of the temperature biases is less certain for the other vessels since 1993, but water samples were also collected to correct salinity biases in TSG records. This is complemented by near-surface hydrographic data during cruises, and since 1996, by measurements from drifters, and upper level data (often near 5-8m depth) from Argo floats [START_REF] Roemmich | The Argo Program: Observing the global ocean with profiling floats[END_REF][START_REF] Riser | Fifteen Years of Ocean Observations with the Global Argo Array[END_REF], as well as from earlier PALACE floats [START_REF] Lavender | Mid-depth recirculation observed on the interior Labrador and Irminger seas by direct velocity measurements[END_REF][START_REF] Davis | Profiling ALACEs and other advances in autonomous subsurface floats[END_REF].

The depth of sampling and the methods of collection and analysis have changed in time, as well as data accuracy, and thus some subsets of the data require correction of identified biases, both for temperature and salinity, as commented in the earlier papers. Bias correction follows [START_REF] Reverdin | Surface Salinity of the North-Atlantic -Can We Reconstruct Its Fluctuations Over the Last 100 Years[END_REF] for ship data before the 1990s (based on comparison with surface data from Nansen casts). There is nonetheless the possibility of seasonal stratification between the data collected below the surface (for example, from TSG data, Argo floats and CTD/Nansen casts) and the surface data (for example from drifters, or in earlier times from bucket sampling). In particular, the change in depth of sampling in time can result in artificial time variability, due to near-surface stratification. In other parts of the world, this has been documented due to surface heating and evaporation or precipitation [START_REF] Boutin | Satellite and In Situ Salinity : Understanding Near-Surface Stratification and Sub-footprint Variability[END_REF]. In regions of sustained wind conditions, and away from shelves with surface freshwater sources (melting sea ice or icebergs, outflows from fjords, river plumes), we expect stratification on the order of or less than 0.01 in practical salinity and 0.1°C in temperature. Sustained wind conditions are commonly melt in all seasons near 55-65°N, and probably a little less so in particular in spring/summer further south and north.

We construct annual time series of deviations from the seasonal cycle in boxes for four different seasons, using a similar methodology as in [START_REF] Friedman A R, Reverdin | A new record of Atlantic sea surface salinity from 1896 to 2013 reveals the signatures of climate variability and long-term trends[END_REF] and applied from 1896-2015 for both T and S, over smaller spatial bins. This new box-product will be referred to as BINS. The boundaries of the 34 boxes were redesigned to better fit with oceanic fronts and main currents than in [START_REF] Friedman A R, Reverdin | A new record of Atlantic sea surface salinity from 1896 to 2013 reveals the signatures of climate variability and long-term trends[END_REF]. One box in the central Labrador Sea corresponds to what is used by [START_REF] Yashayaev | Recurrent replenishment of Labrador Sea water and associated decadal-scale variability[END_REF] for creating a combined Labrador Sea time series, and box boundaries in the eastern subpolar North Atlantic are chosen as in [START_REF] Holliday | Multidecadal variability of potential temperature, salinity, and transport in the eastern subpolar North Atlantic[END_REF]. Except for one box off southwest Greenland and one box on the southern part of the Grand Banks, the boxes do not overlap with the shelves and with areas of large seasonal sea ice cover. In the northeastern SPG, the resulting boxes provide a slightly lower spatial resolution than what was used in [START_REF] Reverdin | North Atlantic Subpolar Gyre Surface Variability (1895-2009)[END_REF], but for which the different time series were rather well correlated. To give an idea of the resolution achieved, the SSS and SST late-winter climatologies are mapped onto the BINS grid boxes (Fig. 1), which shows that the main characteristics of spatial variability are retained by this choice of grid.

The construction of box time series and their error estimates is described in Friedman et al (2017). Here, the main steps are summarized, with specifics and additions related to BINS further described. The SSS and SST climatologies were constructed on a 1°×1° grid with little spatial smoothing and gap-filling, with an adjustment to the mean and up to three sinusoidal harmonics of the year, when data was sufficient. Deviations from this climatology of individual data are estimated (T being in °C and S reported in the practical salinity scale 1978 (PSS-78;[START_REF] Fofonoff | Physical properties of seawater: A new salinity scale and equation of state for seawater[END_REF][START_REF] Unesco | The Practical Salinity Scale 1978 and the International Equation of State of Seawater 1980[END_REF]. For T, outliers were removed using a spatially variable threshold (between 3°C and 6°C), inspired by the local standard deviation. We compared this to a fixed 3°C error threshold, which yielded generally similar results albeit with smaller interannual excursions. Largest differences were found in the central Labrador Sea in the 1920s, a period with extremely low data coverage. S outliers were removed similarly, with less influence on the variability. In each box, these individual deviations are then combined for the individual seasons and years, by median-averaging the individual deviations. The seasonal anomalies are 1-2-1 smoothed over successive years (not applied at the start and end years) and then averaged annually from December-November, inversely weighted by error. [The annual period was incorrectly stated as March-February in Friedman et al. (2017)]. Before being combined, the seasonal anomalies were adjusted to a common baseline over the 40-year period from 1956-1995, which was chosen due to data coverage.

The fall season (September-November) is not included in most areas north of 45°N in the western Atlantic, including in the central Labrador and southwest Labrador boxes due to poorly correlated T or S time series in the fall with the other seasons in these regions. In the Norwegian Sea, central Labrador, southwest Labrador, and West Greenland, the winter season (December-February) data are not included before 1947 due to insufficient coverage.

The total number of years with data is shown in Fig. 2a. Coverage is below 100 years in the North Irminger Sea and central Labrador Sea grid boxes; all other grid boxes have more than 100 years of coverage. The periods of missing data occur mainly during WWI and WWII, and around 1900 (Fig. 2b). Multi-year data gaps are linearly interpolated, and attributed an error, equal to the standard deviation of the whole time series (which is probably an overestimate).

A few missing end years (mostly 1896) in some boxes were filled by extending the first nonmissing value. As in Friedman et al. (2017), regional average error terms are constructed by RMS averaging the error terms of their constituent individual boxes (which assumes that the errors are uncorrelated in different boxes). The BINS SSS regional average fields are compared with those of [START_REF] Friedman A R, Reverdin | A new record of Atlantic sea surface salinity from 1896 to 2013 reveals the signatures of climate variability and long-term trends[END_REF] in Appendix A for the common period 1896-2013, showing differences most commonly within the ±2 standard error ranges.

Figs. 2c-2d present an estimate of the average interannual grid box error as the mean error term divided by the error term during the year when there was no coverage and the error is maximum (1943, except in the West Greenland box, when it is 1917). This is thus an indication of the ratio of noise relative to signal. This shows for S, the smallest relative errors are in the central SPG. For temperature, the largest relative errors are in the western part of the domain, where intra-annual variability and eddy variability are largest and not adequately sampled in this data set. The normalized area-averaged error for SSS and SST is shown in Fig. 2e and mirrors data coverage, with a larger value in the last year (2015) because error is not reduced in that year by the filtering.

Density is computed from the annual binned SST and SSS values using EOS80 [START_REF] Unesco | The Practical Salinity Scale 1978 and the International Equation of State of Seawater 1980[END_REF], and referring the annual anomalies to the March average T and S values. Because SST is lower in winter and density becomes less sensitive to temperature variations when temperature decreases, this choice slightly decreases the dependency of density on SST compared to using an annual average reference. What motivated this choice was that we wanted to have an estimate typical of winter conditions. Density errors are calculated from the errors in interannual SSS and SST, assuming that they are uncorrelated.

Seasonal dependency of the interannual variability

The combination of the four seasonal time series to create an annual time series in each bin implicitly assumes that the four seasons portray comparable signals/patterns of interannual variability. This is particularly important if one season is missing, as is the case for the winter season in some boxes for the first part of the records. To get a sense of how well this holds for individual seasons, we combine the time series of individual bins into seven main multi-box regions (Fig. 3a): the central Labrador Sea / West Greenland Shelf (3 grid boxes), the southern Nordic seas (3 grid boxes), the north-east part of the SPG (5 grid boxes), the central SPG (6 grid boxes), a large central area of the intergyre gyre south of 50°N (8 boxes), an eastern Atlantic region (two boxes), and four boxes in the southwestern region around the Grand Banks. The regions are chosen for similar source data and coherent variability signals (except for the last one). For example, the two Norwegian Sea grid boxes contain mostly Norwegian and Swedish surface data before WWII, and afterwards much hydrographic data.

The central Labrador Sea / West Greenland region contains hydrographic data from the Ice Patrol and other cruises in the southwest, and a mix of hydrographic data and merchant ship data until 1960 in the shelf box close to Greenland. South of 50°N, many different countries contributed to the surface data before WWII, and the French data sources are a large contribution since the mid-1970s. Three grid boxes are not included in any of these regions due to low seasonal correlations in S and more varied data sources: the southwest Labrador Sea, south Greenland/ southwest Irminger Sea, and the western area north of the Gulf Stream.

The seasonal correlations are summarized in Table 3. For the central SPG and north-east SG regions (Fig. 3b-c, rows 1 and 2), the time series are most complete in all seasons. There, the different seasons present rather similar variability, both for T and S, although for T, the summer (June-August season) deviates at times from the other seasons and presents more variance in the central SPG. Furthermore, after 1960, S anomalies in Sep-Nov in central SPG tend to be below the ones in the other seasons, and the opposite before. This might either be the sign of a real long-term seasonal change, or the effect of changes in the dataset around 1960 (transition in this region from bucket collection to other means of collecting water, for example).

In the southern Nordic seas (Fig. 3b-c, bottom row), the different seasons also present rather similar variability, at least since 1950. Before that, this remains largely true for SSS, albeit with more variability in summer. For SST, this also holds before WWI, however between 1920 and 1940, the different seasons present non-coherent variability. We suspect that this might be caused by inhomogeneities in the dataset, either spatially, with a southward shift of the sampling in late autumn to early spring in this period, or due to lower data quality and larger random as well as systematic errors affecting the results and lowering correlations, such as the documented use of engine room or intake temperature data from Norwegian vessels. For this reason and the limited winter sampling coverage, we chose not to incorporate the winter season in the annual averages before 1947 in the two Norwegian Sea boxes.

In the central Labrador Sea / West Greenland Shelf region (Fig. 3b-c, row 3), on the other hand, while there is some similarity between the different seasons in SST (less so for the summer season), there is much less coherence in SSS, even during the last 50 years of more regular sampling. Notice in particular the large differences between the spring and autumn seasons, which originate from the two boxes in central Labrador Sea and along west Greenland. These differences (and the much larger RMS variability in autumn) justify the choice of not including the autumn season when combining the different seasons to create the BINS time series in this region. In the Grand Banks region, however, we have less correlation with winter season but with similar variance (Table 3), so that we chose to retain the winter season. Elsewhere, the statistics associated with the comparison between the seasonal time series (Table 3) justify the choice to have retained all seasons in order to reduce the sampling uncertainty in the BINS interannual time series.

Other products

In the next section, we validate BINS with some of the few published compiled hydrographic time series that are available in the SPG, either from repeated seasonal cruises or a compilation of data from different origins: the Rockall Trough time series since 1975 [START_REF] Holliday | Multidecadal variability of potential temperature, salinity, and transport in the eastern subpolar North Atlantic[END_REF]; South Icelandic stations (Sevlogsbanki 5 (Sb5) and Stokksnes 5 (St5)) since the 1970s; and the central Labrador Sea since 1941 [START_REF] Yashayaev | Hydrographic changes in the Labrador Sea, 1960-2005[END_REF]Loder, 2009 2016).

Notice also the time series in the Norwegian Sea summarized by [START_REF] Yashayaev | The role of the Atlantic water in multidecadal ocean variability in the Nordic and Barents Seas[END_REF]. We will also compare BINS to gridded datasets. These include the 1°×1° EN4, version 4.2.1 [START_REF] Good | EN4: Quality Controlled Ocean Temperature and Salinity Profiles and Monthly Objective Analyses with Uncertainty Estimates[END_REF]) fields from 1950-2015, as well as the 1950-2012 1°×1° product of [START_REF] Ishii | Steric sea level changes estimated from historical ocean subsurface temperature and salinity analyses[END_REF], version 6.12 (referred to as ISHII). We also compared BINS to the CORA5.0 fields [START_REF] Cabanes | The CORA Dataset: Validation and Diagnostics of in-Situ Ocean Temperature and Salinity Measurements[END_REF]) that have been extended to 1950, but that exclude Nansen cast data, and thus cannot be used for the analysis of salinity before 1980, and have much coarser spatial sampling in temperature than EN4 or ISHII. On the other hand, when sampling is sufficient (mostly, in the northeastern part), results with CORA are comparable to those of ISHII, and will not be discussed further. For long-term surface temperature, we also compare BINS with the HadSST3 monthly product (Kennedy et al. 2011a and2011b).

Dataset validation

In order to validate the BINS SST and SSS time series, we compare it first to local compilations of high-quality hydrographic data. We also compare the binned time series to different gridded time series to better characterize the properties of BINS. In addition, Appendices A and B show comparisons with Friedman et al (2017) and [START_REF] Reverdin | North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density[END_REF], which are based on different binning (in time and space) of similar data sources.

BINS and hydrographic time series

The sites of the different time series are presented in Fig. 4a. The Rockall Trough and central Labrador time series fit rather well within some BINS grid boxes, and the South Icelandic time series are just to the north of one BINS grid box. The results based on hydrographic time series often comprise vertical averages, excluding the near-surface (5 to 10 m thick) layer, which is often determined by specifics of performing oceanographic stations and technological limitations. This averaging depth range was not optimized, but in Rockall Trough and near Iceland, it fits with the expected local maximum winter mixed layer. The time series are then averaged over annual means (after removal of an annual cycle), and smoothed with a 1-2-1 filter over successful years, similar to BINS.

The comparisons of these upper ocean time series with BINS are presented in Fig. 4b (with statistics in Table 4). At each site BINS is correlated with the vertically integrated time series, although the surface time series tend to have larger amplitudes. In the Labrador Sea, we compared different ranges, with the best correlation for the comparison to 20-50 m, which is within the mixed layer at least 6 months of the year and closely follows variations in seasonal discharge of freshwater and its interannual variations. There is still a correlation when considering the averages over the 20-200 m layer, which is within the winter mixed layer most years. The thicker layer time series however present less extreme amplitudes than the shallower ones. In the Rockall Trough, we also plot the surface time series based only on winter data, which is also correlated with the vertically integrated time series. The largest difference is found for South Iceland, with a low BINS S in the late 1980s that is not found in the hydrographic time series. However, there is also a difference in latitude between the BINS box and the site further north of the hydrographic time series.

Overall, we find that BINS surface time series are significantly correlated at 0-lag with upper ocean time series from hydrography, both for T and S (correlation coefficients are in the range 0.73 to 0.88 for the different time series of salinity or temperature), and exhibit very comparable RMS variability, as described in Table 4a. The analysis of lag correlation (summarized in Table 4b) indicates that the annually averaged BINS T and S precede by one year the vertically integrated upper ocean analysis both at the Rockall and Southern Iceland sites (for SST, the one-year lag correlation coefficients are respectively 0.80 and 0.89; and for SSS, respectively 0.77 and 0.88), as expected from winter ventilation of the upper ocean and time series analysis of the 20 to 40-year long weather-ship time series in the SPG [START_REF] Reverdin | Decadal variability of hydrography in the upper northern North Atlantic 1948-1990[END_REF]. Interestingly, this is not the case for the Norwegian Sea time series.

BINS and gridded objective analyses

We compare the BINS time series with other gridded products of hydrographic data at their near-surface level that we average over the bins of the BINS analysis and filter in time to correspond to the same time resolution (1-2-1 running average over successive years December to November).

Because of the differences in sampling and mapping, the comparison in individual boxes with BINS often presents a large scatter. We will thus average the time series over the same domains as for the seasonal time series (Fig. 3a). [One difference is that the central SPG and north-east SPG are combined into one SPG region]. Except in the Labrador Sea or near the Grand Banks, the time series of the three products (Fig. 5) are well correlated (Table 5).

Correlation coefficients are larger for SST than for SSS, and for SSS, are usually larger between ISHII and BINS than between EN4 and BINS. In the Labrador Sea (row 2), there are differences both for SST and for SSS. For SSS, the large positive peak in BINS originates from a data gap in one of the three bins that are averaged in the regional time series. On the other hand, the altogether weaker SST-signal in ISHII in this box, something also found, but to a lesser domain in the other regions might result from the objective mapping method used or from more strident tests on outliers (Ishii and Kimoto, 2009). We also notice in the southern Labrador Sea for the last 20 years smaller positive SSS (and SST) anomalies in BINS than in the other two products, possibly a result of the thermosalinograph (TSG) data incorporated in BINS and not in the other products [START_REF] Reverdin | North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density[END_REF]). On the other hand, near the Grand Banks during the last 20 years, T is higher in BINS than in the other products.

Elsewhere, the major variability is portrayed with similar amplitude and roughly at the same time in the different products. For example in the SPG or southern Nordic seas, the salinity time series portray the events sometimes described as great salinity anomalies [START_REF] Belkin | Great Salinity Anomalies" in the North Atlantic[END_REF]), the largest one in the central SPG happening in the early 1970s and referred to as the Great Salinity Anomaly [START_REF] Dickson R R, Meincke | The ''Great Salinity Anomaly'' in the northern North Atlantic 1968 -1982[END_REF]. The figures also suggest larger differences between the different products in the 1990s, in particular in the intergyre region.

The high overall agreement might result from a share of common hydrographic data incorporated in the different products. However, there is also a large portion of specific data that are not in all the products (T from mechanical or expandable bathythermographs are used in EN4 and ISHII, but not in BINS, and different sets of surface data are in BINS, but not used in EN4 and ISHII). There are also large methodological differences between the different products, both in data selection and validation and in mapping techniques and how seasonal anomalies are grouped or not. These comparisons, as well as the comparisons presented in 3.1, thus reinforce our assessment of usually small sampling errors for the BINS product during that period, and that the product captures the major events of interannual variability at the grid box scale since 1950.

BINS and HadSST3

SST data in BINS and HadSST3 originate from different data streams. Although some data are certainly incorporated in both products, the overlap is probably rather minimal, as discussed in [START_REF] Reverdin | Surface Salinity of the North-Atlantic -Can We Reconstruct Its Fluctuations Over the Last 100 Years[END_REF]. In particular, the data before 1920 north of 58°N in BINS originate mostly from Danish and Norwegian sources which are not incorporated in HadSST3. In the last two decades, data from TSGs on merchant vessels or from profiling floats are a large contribution to BINS but are not included in HadSST3, whereas the drifter SST data used in HadSST3 are mostly not in BINS. Even in the post-WWII to mid-1970s period, when ocean weather ships contribute both to HadSST3 and BINS, temperature data in the two products often originate from different sensors. BINS and HadSST3 also account for biases and uncertainties differently. HadSST3 does not apply smoothing or interpolation between neighboring grid cells, facilitating more direct regional comparisons.

We examine the monthly HadSST3 anomalies, version 3.1.1.0 (median realization). We areaaverage the HadSST3 grid boxes into similar sub-regions similar to those shown in Fig. 3a. The HadSST3 and BINS regional time series are compared in Fig. 6, with the respective 1896-2015 and 1950-2015 correlations listed in Table 6. Overall, the products are very highly correlated since 1950. In the first half of the record, the largest differences between the products are generally found during periods of data gaps and linear interpolation near the end of WWI and WWII, when BINS error estimates are also large. Among regions, the agreement is less good in the southern Nordic seas (Fig. 6b) before the 1940s; we also find that the HadSST3 SST in different seasons are also much less correlated there before the 1940s (not shown). To some extent, the early differences come from the most poorly sampled northwestern part of this region, but also from Danish and Norwegian data incorporated in this product and not in HadSST3. Furthermore, in the 1930s, the differences might also originate from Norwegian data interpreted in BINS as recorded continuously on a water line inside the ship, and thus attributed a large positive bias. The central Labrador Sea / West Greenland shelf (Fig. 6a) also shows less agreement before 1930. The products correspond very well after the 1930s, although HadSST3 has less of a cooling in the 1980s. The two SPG regions are both very strongly correlated over the length of the record (Fig. 6c,d). The Southwest Labrador Sea grid box (Fig. 6e) generally corresponds well after the gap periods in the early 1900s. In the regions further south (Fig. 6f,g,h), the two time series usually show a good agreement, and are most of the time compatible with the BINS error estimate. The largest differences are near the Grand Banks (Fig. 6g) before 1920, maybe as a result of different seasonal coverage, but also in the 1990s, when BINS tends to present lower values.

The southwest Labrador

Large differences are also found in the intergyre region (Fig. 6f) in the early 1950s. This is the only place where the error estimate does not explain the difference between the curves, maybe because, for a few years in the early 1950s, data in BINS for some of these boxes were only available in the summer season, and thus are less representative of annual averages.

Characteristics of T, S and surface density RMS variability

In this section, we examine the linear trends and the RMS variability of the linearly detrended interannual time series. The 1896-2015 BINS least-squares linear trends are shown in Fig. 7a-7c. We construct error estimates for the linear trends by block resampling using the time series error estimates (resampled every 2 years to account for the 1-2-1 filter). The SSS trend (Fig. 7a) shows a negative trend throughout most of the domain, except for the southwest and part of the eastern boundary; the pattern is consistent with the 1896-2013 trend shown in [START_REF] Friedman A R, Reverdin | A new record of Atlantic sea surface salinity from 1896 to 2013 reveals the signatures of climate variability and long-term trends[END_REF]. The SST trend (Fig. 7b) shows strong warming in the Gulf Stream, and to a lesser extent along the eastern margin. There is cooling in West Greenland / Central Labrador, and near 52-55°N/30°W. However, error bars due to higher frequency variability or sampling are often large. Both the increase in T and the decrease in S contribute to an overall decrease in surface density (Fig. 7c).

The 1896-2015 HadSST3 trend is shown in (Fig. 7d). The trends are calculated for grid boxes with 5 or fewer years with missing data. [Note that specific seasons are not removed from the spatial grid boxes as for the HadSST3 indices discussed above]. Error terms for HadSST3 are computed by multiplying the white-noise standard error of the slope by a factor of sqrt(2) on account of the 1-2-1 filter. Like BINS, the HadSST3 trend shows significant warming in the Gulf Stream extension, and also along the eastern margin. HadSST3 also shows a region of cooling in the central SPG from 50-60°N, referred to as the 'warming hole' [START_REF] Drijfhout | Is a Decline of AMOC Causing the Warming Hole above the North Atlantic in observed and modeled warming patterns?[END_REF], which is not well reproduced in BINS. Checking this particular difference suggests that data in the pre-1917 period originated mostly from one group of data from the ICES archive, often reported at set longitudes, and for which we applied an overall correction of -0.2°C on temperature, but with little comparison data to define it [START_REF] Reverdin | Surface Salinity of the North-Atlantic -Can We Reconstruct Its Fluctuations Over the Last 100 Years[END_REF]. Notice however that nonetheless, for the area average of the central SPG (Fig. 6c), the difference between BINS and HADSST3 is not significant for that period.

The detrended RMS standard deviation of the interannual T and S time series (Figs. 8a and8b) correspond to the expectation that large-scale temperature RMS standard deviation diminishes from west to east; whereas the salinity RMS values are largest in the west, then near 50°N with a decrease from west to east and from the southern to the northern SPG. This pattern is indicative on one hand of larger air-sea and hydrographical/heat forcings in the western SPG, and on the other hand of the presence during some winters of a rather shallow mixed layer in the western SPG (even in the Labrador Sea, where deep convection also often takes place [START_REF] Yashayaev | Recurrent replenishment of Labrador Sea water and associated decadal-scale variability[END_REF]). In mid-ocean near 45-50°N, there might also be a contribution of displacements of hydrographical fronts. Interestingly, in the westernmost part of the domain, except near the Gulf Stream, the salinity contribution to density variability dominates over the temperature contribution (Fig. 8d), whereas in the eastern and northern parts of the SPG, temperature dominates. Thus in the first region, density tends to be positively correlated with SSS and SST, whereas in the other region it is negatively correlated with SSS and SST. In the northern part, this was not expected, as temperature is lower than further south, which would contribute to diminish the contribution of temperature variations to density variations. However, this is counteracted by a lower ratio of SSS over SST variability in the northern regions. In general, the SSS contribution is slightly larger for the non-detrended data (not shown), due to its relatively larger centennial trends in this part of the ocean (discussed in part 2 of this study).

Next, we show the total RMS variability contained at multidecadal frequencies. We low-pass filter the time series using a spectral filter with half power at a period close to 15 years. Fig. 9 shows the percentage of the detrended interannual variance explained by the low-pass filtered time series. The low-pass filtered variance percentage of SSS (Fig. 9a) and SST (Fig. 9b) are less correlated spatially than overall RMS variability. For S, what striking is the lower percentages in the western part (as low as 40%) than in the eastern part of the domain (larger than 60%), whereas for T, it is mostly the northern part which has a larger percentage of variance in the multidecadal frequencies, with percentage of detrended variance reaching up to 80%. The low percentages of multidecadal variability for SSS in the northwest is consistent with the penetration in the ocean interior of short-lived freshwater pulses originating from the western shelves and the rim of the Labrador Sea (examples of recent short-term SSS variability in this region are presented in [START_REF] Tesdal | Salinity Trends within the Upper Layers of the Subpolar North Atlantic[END_REF]).

The low-pass filtered time series explain comparatively less density variance, particularly in the western SPG region (Fig. 9c, values usually less than 50%), which suggests coordinated SSS and SST variations at lower frequencies with thus partial compensation on their contribution to density changes. Indeed, the low-pass filtered correlation of SSS and SST (Fig. 9d) is positive in most grid boxes, with large magnitudes in the central SPG, around

Greenland and in the south-west north of the Gulf Stream as well as around the Grand Banks.

Notice however, less significant correlation in other areas of the western, southern, and eastern parts of the domain close to Europe. The relationship between SSS and SST variations will be examined further in part 2 of this study.

Discussion and Conclusions

We have constructed a spatially averaged binned product BINS of interannual T, S, and surface density for 1896-2015 north of 40°N, similar to what was done for S in Friedman et al. ( 2017), but at a higher spatial resolution. We deliberately excluded the shelf regions (except for one box along southwestern Greenland and one box on the southern Grand Banks), which have different dynamics, variability and sampling issues [START_REF] Reverdin | North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density[END_REF]. We have shown that in recent decades, the BINS interannual variability of SST and SSS is coherent with published time series of upper ocean heat and salinity based on high quality hydrographic data. It provides also rather similar results to an analysis of SST and SSS variability along two ship tracks since 1993, based on the same in situ data set for its main part [START_REF] Reverdin | North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density[END_REF].

Furthermore, BINS compares well with two gridded products of hydrographic data (EN4 from 1950-2015and ISHII from 1950[START_REF] Lozier | Overturning in the North Atlantic[END_REF], which use different data selection and mapping methods. This suggests that in this region, the processing and spatial averaging of the individual data in the spatial bins done in BINS is usually sufficient to portray the interannual variability of SST on the grid box scale, at least since 1950, and that during this period, the data set used does not contain large biases compared to these other data sets. On interannual time scales, BINS SST also fits largely with what is portrayed in HadSST3, except in the pre-WWII period for the Labrador Sea and the Norwegian Sea. Insufficient coverage or erroneous data, either in HadSST3 or BINS, might be causing these discrepancies. Thus, more care should be taken when interpreting variability in BINS before 1950.

The product combines time series of interannual variability in different seasons. This was required to reduce sampling errors to a reasonable level (in particular pre-1950), but this might also mask or alias important differences between the seasons. For example in the northern part of the subtropical gyre (thus, south of the region we investigate) a strong seasonal modulation in the recent trends towards increasing SSS has been documented, associated to important seasonal changes of the hydrological cycle [START_REF] Yu | Poleward Shift in Ventilation of the North Atlantic Subtropical Underwater[END_REF]). In the Labrador Sea, too, on an interannual time scale, large summer-time anomalies of SSS have been documented in the recent decade (2008 and 2012) that don't have a clear counterpart in other seasons [START_REF] Tesdal | Salinity Trends within the Upper Layers of the Subpolar North Atlantic[END_REF]. Such events, and also the seasonality of atmospheric forcing, seasonal thermal stratification and mixing, could induce large interannual differences between the anomalies in different seasons. Indeed, Fig. 3 Nonetheless, a more homogeneous 25-year dataset in the central SPG resolving seasonal variability along well sampled ship lines [START_REF] Reverdin | North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density[END_REF]) also pointed out a similarity in interannual variability for the different seasons, although interannual RMS variability tended to be larger there too in early summer for SST.

This aside, the seasonal inhomogeneity in data coverage, with less sampling in winter in particular before 1955, should have little impact on the reconstructed interannual time series.

We however warn that this might not be a wise substitute for the seasonal time series, in particular for studies of winter conditions the central Labrador Sea or water mass formation, but then obviously the available binned data present more gaps, and the original seasonal time series have larger error bars.

Density is a non-linear function of temperature and salinity, with the character of nonlinearity increasing at low T. We deliberately emphasized winter characteristics when estimating density from temperature and salinity time series, but use the BINS time series which combine SST and SSS anomalies in the four (or three) different seasons. We noticed that in some regions interannual variability has smaller amplitude in winter (Table 3 summarizing Fig. 3), thus this choice might result in an overestimation of winter-time interannual density variability. It would be also misleading to use these density interannual time series as a proxy for water density in other seasons.

We do not have a direct comparison of SSS BINS time series prior to the 1950s. We estimate that they provide reasonable estimates for this early period mostly based on comparisons of the SST BINS time series with HadSST3 binned data. However, before 1950 in the Nordic Seas and Labrador Sea, the binned data have larger uncertainties and winter seasons are poorly (or not at all) sampled. Thus, clearly our estimates for SSS are also less certain in these regions prior to 1950. A possibility for further validation would be to compare with independent proxy-based estimates of surface variability. Few such proxy time series have been derived in this region resolving the multidecadal variability in SST and even less in SSS, and their uncertainties are probably too large for such a validation [START_REF] Hall | Surface and deep ocean coupling in the subpolar North Atlantic during the last 230 years[END_REF], for a site in the Iceland Basin (Gardar drift); [START_REF] Richter | Late Holocene (0-2.4 ka BP) surface water temperature and salinity variability, Feni Drift, NE Atlantic Ocean[END_REF], for a site in the northern Rockall Trough area (Feni drift); [START_REF] Moffa-Sanchez | North Atlantic variability and its links to European climate over the last 3000 years[END_REF] and references therein as well as [START_REF] Thornhalley | Anomalously weak Labrador Sea convection and Atlantic overturning during the past 150 years[END_REF], for a site in the southeastern Labrador Sea).

BINS has sufficient spatial resolution to resolve large-scale patterns of RMS standard deviation of the variability in SST, SSS and density (this was done after removing linear trends). RMS variability in SSS and to a lesser degree in SST increases towards the west, with the contribution to density of SSS variability dominating that of SST in the west, whereas in the eastern and northern parts of the SPG, temperature typically dominates the variability. The portion of variability in multidecadal frequencies (here with a cut-off at 15 years) could be ascertained. It is found both for SST and SSS to be a very large part of interannual variability, with lesser relative contribution in the west for SSS and in the southern part for SST. For density, it is less prominent, except in the northeastern part of the SPG, where SST variability contribution dominates and is very much at multidecadal frequencies (also see [START_REF] Holliday | Multidecadal variability of potential temperature, salinity, and transport in the eastern subpolar North Atlantic[END_REF]. The higher frequencies are more influenced by the sampling uncertainties, in particular the effect of interpolation and interannual smoothing during data gaps in either specific seasons or all seasons (near WWII, for example). Thus this analysis of the relative importance of multidecadal variability is more dataset-dependent than the RMS variability with EN4 (1950with EN4 ( -2015) ) and ISHII (1950ISHII ( -2012) ) surface products. The regional domains are the ones presented in Fig. 3. Here, the SPG region includes both the central SPG and the northeast SPG (and the south Greenland/southwest Irminger Sea box). One standard deviation rms error bars are added on the BINS series.

  described a record of SST and SSS in the northeastern part of the SPG from 1895-2009. There, co-variability of temperature (T) and salinity (S) was found on decadal or multidecadal time scales in different seasons, with a slight dominance of temperature on density variability (partially compensated by the salinity contribution). Friedman et al. (2017) examined interannual (1-2-1 smoothed over successive years) salinity time series in larger boxes over the Atlantic from 20°S-70°N (the temperature reports associated with the salinity data were not examined). The largest salinity root mean square (RMS) variability was found in the northern tropics, from 5°-20°N. In the subpolar North Atlantic, RMS variability was shown to decrease to the northeast from the Labrador Sea to the Nordic seas (Friedman et al. 2017, Fig. 1), though the large grid box size did not permit finer examination of the spatial structure.

  Sea BINS grid box is also examined, which closely corresponds to one HadSST3 grid box. Generally however, the large 5°×5° HadSST3 grid boxes do not align directly with BINS grid boxes; the specific grid boxes are shown in Fig. C1. We average the HadSST3 anomalies into 3-month seasons (skipping over missing values). The seasonal anomalies are 1-2-1 smoothed (excluding endpoints) over successive years, and the seasonal anomalies are averaged into Dec-Nov annual means (again skipping over missing values). The winter season is not included in the southern Nordic, central Labrador, or southwest Labrador annual means before 1947 to match the BINS coverage. Likewise, the fall season is excluded from the central Labrador and southwest Labrador sub-regions. In general, we find that the BINS data are more seasonally coherent at low frequencies before WWII (not shown).

  Surface density standard deviation (Fig.8c) diminishes both from west to east and south to north. The relative contributions of T and S to detrended density variability are shown in Fig.8d. The contributions of SST and SSS to surface density variability are opposite in most places (the effects of these variables on density variability tend to compensate each other, as there is a positive correlation between SST and SSS variability on intra-annual to centennial time scale in a large part of this domain). This also might explain a pattern of density rms variability which presents weaker zonal gradients in the west than what is found in SST or SSS.

  suggests that the differences between seasons are large in the central Labrador Sea, but for the other open-ocean regions, different seasons present more similar decadal and longer frequencies. Possibly, the insufficient accuracy of binned time series and the change in collection and measurement techniques could preclude further investigation of the seasonality of the decadal or longer variability with this dataset. For example, in boxes of the southern SPG, we noticed SSS differences in the autumn around 1960 that could be a data artifact. Differences with HadSST3 taking place around 2000 could also be data-related (either in HadSST3 with drifting buoy data becoming more numerous, or in BINS with the Argo data afterwards).

Figures. Figure 1 .

 1 Figures.

Figure 4 .

 4 Figure 4. (a) Map showing hydrographic time series locations with BINS grid boxes. (b)Comparison for S of BINS (in red) with hydrographic time series low passed 1-2-1 over successive years; Central Labrador Sea (top; green and blue for different layer averages), Rockall Trough (top middle; the green curve is from BINS but for the winter (Dec-Feb.) season; blue is the vertically averaged (30-800m) S time series), North Iceland Basin (lower middle; two vertically averaged station time series are presented); and eastern Norwegian Sea (bottom; the blue and green time series is a compilation of the offshore data (median averaged) in 3 Norwegian Sea sections presented in[START_REF] Yashayaev | The role of the Atlantic water in multidecadal ocean variability in the Nordic and Barents Seas[END_REF], for two different depth ranges). One standard deviation rms error bars are added on the BINS series.

Figure 5 .

 5 Figure5. Comparison of regional T (left, °C) and S (right, PSS-78) time series from BINS withEN4 (1950EN4 ( -2015) ) andISHII (1950ISHII ( -2012) ) surface products. The regional domains are the ones presented in Fig.3. Here, the SPG region includes both the central SPG and the northeast SPG (and the south Greenland/southwest Irminger Sea box). One standard deviation rms error bars are added on the BINS series.

Figure 6 .

 6 Figure 6. Area-averaged BINS and HadSST3 SST anomalies: (a) central Labrador / West Greenland, (b) southern Nordic, (c) central SPG, (d) north-east SPG, (e) south-west Labrador, (f) intergyre, (g) Grand Banks / Labrador Current, (h) east Atlantic. Shading indicates ±2 BINS error terms. The corresponding regions are shown in Fig. C1.

Figure 7 .

 7 Figure 7. Trend over 1896-2015 (a) SSS; (b), SST; and (c) density; per 100 years. Pluses indicate where the slope magnitude is larger than twice the estimated error. (d) HadSST3 trend, per 100 years.

Figure 8 .

 8 Figure 8. Detrended interannual (1-2-1 filtered) RMS variability for (a) SSS, (b) SST, and (c) density (𝜌). (d) Ratio of the contributions of detrended SSS and SST to density RMS variability (plotted as a logarithm).

Figure 9 .

 9 Figure 9. Percentage of detrended interannual (1-2-1) variance explained by the 15-year lowpass filtered time series for (a) SSS, (b) SST, and (c) density. (d) Detrended correlation coefficient between the low-pass filtered SSS and SST. Circles indicate where the correlation is significant at p<0.05, estimated with a random-phase bootstrap test to account for serial autocorrelation[START_REF] Ebisuzaki | A method to estimate the statistical significance of a correlation when the data are serially correlated[END_REF].

Table 3 .

 3 Seasonal RMS and Pearson correlation coefficient. RMS values are in °C for T, and PSS-78 for S (×1000). Corresponding regions are shown on Fig.3a. of common points, and varying number of boxes; thus not reliable, in particular for S. Both for Southern Nordic and Central Labrador, very different number of years in winter and other seasons (only on the order of 60 years with winter data in some of the boxes included in the average)

	Region	Length	12-2 3-5	6-8	9-11	12-2 3-5	6-8	9-11
	Central SPG 112-116 yrs	T RMS 0.304 0.407 0.505 0.404 S RMS 73 60	72	79
			T corr.			S corr.
			12-2	0.58 0.52 0.57 12-2	0.72 0.77 0.78
			3-5		0.55 0.71 3-5		0.79 0.82
			6-8			0.62 6-8			0.86
	Region	Length	12-2 3-5	6-8	9-11	12-2 3-5	6-8	9-11
	N-E SPG	113-115 yrs	T RMS 0.339 0.338 0.375 0.336 S RMS 42 41	44	55
			T corr.			S corr.
			12-2	0.65 0.54 0.60 12-2	0.72 0.75 0.73
			3-5		0.76 0.71 3-5		0.81 0.78
			6-8			0.74 6-8			0.80
			12-2 3-5	6-8	9-11	12-2 3-5	6-8	9-11
	Southern Nordic	T RMS 0.358 0.351 0.425 0.524 S RMS 41 40	47	41
			T corr.			S corr.
			12-2	0.31 0.40 0.61 12-2	0.50 0.43 0.66
			3-5		0.69 0.41 3-5		0.66 0.52
			6-8			0.57 6-8			0.61
			12-2 3-5	6-8	9-11	12-2 3-5	6-8	9-11
	Central Labrador/	T RMS 0.47 0.385 0.482 0.602 S RMS 130 95	131 162
	West Greenland*	T corr.			S corr.
			12-2	0.81 0.20 0.60 12-2	0.37 0.50 0.27
			3-5		0.60 0.78 3-5		0.56 0.50
			6-8			0.52 6-8			0.42

Table 5 .

 5 RMS variability and correlation with BINS: (a) EN4, (b) ISHII.

	(a) EN4 / BINS		RMS (S)	corr (S)	RMS (T)	corr (T)
			(PSS-78)		°C
	Region	Time period	EN4 BINS		EN4 BINS
	SPG	1950-2015	0.050 0.046 0.78	0.36 0.34 0.93
	Central Labrador/	1950-2015	0.173 0.121 0.49	0.76 0.63 0.87
	West Greenland
	Central Labrador/	excluding 1976-1979	0.169 0.112 0.67	0.78 0.64 0.87
	West Greenland
	Southern Nordic S 1950-2015	0.045 0.039 0.75	0.34 0.32 0.96
	Intergyre	1950-2012	0.057 0.057 0.78	0.33 0.43 0.85
	Grand Banks	1950-2012	0.098 0.114 0.40	0.47 0.69 0.42
	East Atlantic	1950-2012	0.037 0.042 0.73	0.26 0.32 0.84
	(b) ISHII / BINS		RMS (S)	corr (S)	RMS (T)	corr (T)
			(PSS-78)		°C
	Region	Time period	ISHII BINS		ISHII BINS
	SPG	1950-2012	0.034 0.045 0.90	0.38 0.34 0.95
	Central Labrador/	1950-2012	0.075 0.124 0.55	0.50 0.64 0.74
	West Greenland
	Central Labrador/	excluding 1976-79	0.077 0.115 0.61	0.52 0.65 0.75
	West Greenland
	Southern Nordic	1950-2012	0.028 0.040 0.91	0.35 0.32 0.9
	Intergyre	1950-2012	0.034 0.045 0.71	0.34 0.34 0.62
	Grand Banks	1950-2012	0.068 0.124 0.45	0.41 0.65 0.42
	East Atlantic	1950-2012	0.030 0.040 0.73	0.28 0.32 0.65

Table 6 .

 6 HadSST3 correlation with BINS SST

	Region	Time period	corr	Time period	corr
	Central SPG	1896-2015	0.84	1950-2015	0.95
	N-E SPG	1896-2015	0.87	1950-2015	0.93
	Central Labrador/	except 1900-01,	0.57	1950-2015	0.72
	West Greenland	1910
	SW Labrador	except 1900-01,	0.56	1950-2015	0.71
		1904-1905, 1912
	Southern Nordic	1896-2015	0.65	1950-2015	0.93
	Intergyre	1896-2015	0.74	1950-2015	0.84
	Grand Banks	1896-2015	0.67	1950-2015	0.82
	East Atlantic	1896-2015	0.82	1950-2015	0.89

Acknowledgements

This is a contribution to the French SSS observation service, which is supported by French agencies INSU/CNRS, IRD, CNES and IPEV, as well as from SOERE CTDO2. The Rockall Trough time series were provided with support from the UK Natural Environment Research Council (Extended Ellett Line program, National Capability). The station time series south of Iceland were provided with Icelandic support. The annual oceanographic monitoring of the Labrador Sea was initiated as a Canadian contribution to the World Ocean Circulation Experiment in 1990 and is presently conducted as a core component of the Atlantic Zone Off-Shelf Monitoring Program (AZOMP) run by the Bedford Institute of Oceanography of Fisheries and Oceans Canada. The International Argo Program is part of the Global Ocean

Observing System [START_REF] Argo | Argo float data and metadata from Global Data Assembly Centre (Argo GDAC)[END_REF]. Argo data are available from the Coriolis Global Data Center, Institut français de recherche pour l'exploitation de la mer (Ifremer). The HadSST3 and EN4 data were provided by the Met Office Hadley Center, and the ISHII data were provided by the NCAR Research Data Archive. A.R.F. was supported by SOERE CTDO2 and the ERC funded project TITAN (EC-320691). L.C. acknowledges support from the Swedish National Space Board (SNSB; Dnr 133/17). Comments by two reviewers and by Semyon Grodsky were appreciated. The BINS product is available at https://dx.doi.org/10.6096/TSD-BINS-NASPG.

Appendix B. Comparison with Reverdin et al. (2018)

As a check on the BINS time series, we compare the boxes with time series constructed with mostly similar data, monthly binned along two ship routes since mid-1993, intersecting near 59.5°N/32°W: AX02 between Iceland and southern Newfoundland and AX01 between the North Sea and southern Greenland, mostly along 59.5°N [START_REF] Reverdin | North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density[END_REF], shown in Fig. B1a. Time series along AX02 start in July 1993 with few gaps, whereas for AX01 some large data gaps were filled until late 1997. These time series, referred to as B-AX01 and B-AX02, provide increased spatial resolution at seasonal time scales, and portray very coherent variability where they intersect.

We illustrate the comparison of interannual variability of T and S between B-AX01 and BINS for overlapping boxes in the common period 1993-2015 (Fig. B1b). For S, the corresponding


Xavier Hinaut
email: xavier.hinaut@inria.fr

Which Input Abstraction is Better for a Robot Syntax Acquisition Model? Phonemes, Words or Grammatical Constructions?

, one still encounters considerable cases of wrong sentences recognition. Thus speech recognition can not be considered as solved especially when an utterance is considered in isolation of its context. Particular solutions, that can be adapted to different Human-Robot Interaction applications and contexts, have to be found. In this perspective, the way children learn language and how our brains process utterances may help us improve how robot process language. Getting inspiration from language acquisition theories and how the brain processes sentences we previously developed a neuro-inspired model of sentence processing. In this study, we investigate how this model can process different levels of abstractions as input: sequences of phonemes, sequences of words or grammatical constructions. We see that even if the model was only tested on grammatical constructions before, it has better performances with words and phonemes inputs.

I. INTRODUCTION

A. Robots as models to study language acquisition Robots are interesting for studying language in many perspectives. Some of the long lasting questions are, for instance how languages evolve or emerge [START_REF] Spranger | Emergent functional grammar for space[END_REF], [START_REF] Steels | The synthetic modeling of language origins[END_REF], how language or symbols in general could be grounded [START_REF] Harnad | The symbol grounding problem[END_REF], [START_REF] Roy | Learning visually grounded words and syntax for a scene description task[END_REF] or how the linguistic or non-linguistic symbols may emerge from grounding [START_REF] Taniguchi | Symbol emergence in robotics: a survey[END_REF]. In particular, one may be interested to have a robot able to mix vision and dialog interaction in order to vocally command the robot to grasp some objects in complex environments [START_REF] Bastianelli | Effective and robust natural language understanding for human-robot interaction[END_REF], [START_REF] Hatori | Interactively picking real-world objects with unconstrained spoken language instructions[END_REF], [START_REF] Tenorth | Knowrob: A knowledge processing infrastructure for cognition-enabled robots[END_REF]. However, even if some of these systems provide some transparency on how they work 1 , they rarely help to understand how our brain processes languages or how children could acquire one. Developmental architectures [START_REF] Gaspers | Constructing a language from scratch: Combining bottom-up and top-down learning processes in a computational model of language acquisition[END_REF], [START_REF] Iwahashi | Robots that learn language: Developmental approach to human-machine conversations[END_REF] are inspired from children development and do not require to have all (vocabulary or syntactic) abilities prefixed since the beginning of the learning period. Some studies have used different cognitively inspired frameworks with robotics, such as Embodied Construction Grammar [START_REF] Eppe | Exploiting deep semantics and compositionality of natural language for human-robot-interaction[END_REF] and 1 For instance, when they do not include multiple ad hoc "hacks" to make them work in the desired experimental conditions construction grammar [START_REF] Gaspers | Constructing a language from scratch: Combining bottom-up and top-down learning processes in a computational model of language acquisition[END_REF], [START_REF] Panzner | Learning linguistic constructions grounded in qualitative action models[END_REF]. Our brains process utterances in a robust fashion in a variety of contexts: we believe that the lack of brain-inspiration in these systems results in a gap of robustness with human performance. In our approach, we try to build an architecture that is able to tackle several of these points and get a step closer to the understanding of brain processes, language developmental strategies and symbol grounding.

B. Our question and hypothesis

Considering a system that learns to parse a sentence given a stream of inputs, one question is what is the optimal level of abstraction of the inputs2 : phonemes, words or grammatical constructions?

Here, we only compare purely symbolic input representation and do not consider raw acoustic signals or distributed representation of coding, such as word embedding. In particular we want also to see the robustness to noise of these different representations. This is particularly important when such a system is used with real speech signals, and have to deal with the misrecognition of words. We previously started a step in that direction by enabling the model to generalize on sentences with unknown (or unrecognized) words [START_REF] Hinaut | A recurrent neural network for multiple language acquisition: Starting with english and french[END_REF].

C. Broca sentence parsing model

The neural parser proposes to model how the human brain processes sentences and is inspired from several studies in neuroscience [START_REF] Dominey | A neurolinguistic model of grammatical construction processing[END_REF], [START_REF] Hinaut | Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing[END_REF]. A schema of how the global architecture is and how inputs are processed can be seen on Figure 1.

The model is an analogy to a sub part of Broca's area (a region of prefrontal cortex, involved for instance in syntax processing) and the striatum (a subpart of the basal ganglia). Both are generally involved in sequence processing and learning, and in particular in sentence parsing.

Because there are probably as many neuro-inspired models as the number of modellers, we want to make clear our claims about this neuro-plausibility and give some high-level Fig. 1. Sentence parsing model with different input conditions. The system processes inputs as follows: (top left) from a sentence as input, the model outputs (middle right) an action command that can be performed by a robot. The processing of the sentence is sequential: each symbol of the sequence (phoneme, word, ...) is given one at a time as a one-hot encoding. The final thematic roles for each SW is read-out at the end of the sentence (but partial predictions can be read-out when the parsing is on-going). Before entering the neural network, the sentence is preprocessed depending on the main condition (PHON, WORD or CONST) and on the optional condition (INF and/or NOISE). Semantic Words (nouns, verbs, ...) are replaced by a SW symbol. Infrequent function words (IWs) are replaced by the & symbol. Here, the input layer only represents word symbols, but in the PHON condition these are replaced by phonemes. Example of input sequences for different conditions can be seen in Figure 2. Figure modified from [START_REF] Hinaut | A recurrent neural network for multiple language acquisition: Starting with english and french[END_REF]. information about our model in order to easily compare it with other models:

• the computations rely on distributed units (leaky average firing rate neurons), • these computations are generic and not hand-or task-designed3 contrary to some models (e.g. Bayesian) 4 : there is no task a priori applied on the computations, • it processes a sentence word by word instead of taking the whole sentence in one shot or as a bag of words5 , • it is an anytime algorithm: it can give partial results (i.e. predictions) before the end of a sentence, • it can be trained in one-shot [START_REF] Hinaut | Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing[END_REF] or incrementally with a Hebbian learning rule6 , • the learning algorithm does not unfold time contrary to the Backpropagation Through Time (BPTT) algorithm [START_REF] Werbos | Backpropagation through time: what it does and how to do it[END_REF], [START_REF] Werbos | Beyond regression: New tools for prediction and analysis in the behavioral sciences[END_REF],

• we do not use a specific framework for language, on the contrary it is based on the general Reservoir Computing (RC) paradigm, • this RC paradigm is inspired from neuroscience and recently inspired neurobiologists in return: e.g. how to Fig. 2. Symbol sequences given as input to the neural network depending on the conditions. The same sentence (see 2nd line, WORD condition) is given as input in order to see the effect of the different conditions. In PHON cond., a sequence of phonemes is inputed into the network using CMU's dictionary representation: e.g. point is replaced by the sequence of symbols "P, OY1, N, T". In CONST cond., semantic words are replaced by a SW symbol. In INF cond., infrequent words are replaced by "&" symbol: here, then is replaced by "&". In NOISE cond., 5% of the words are randomly replaced by another one: here, and is replaced by put.

decode electrophysiological activity in the prefrontal cortex of a monkey 7 .

Several models of sentence acquisition, comprehension or production have been designed [START_REF] Gaspers | Constructing a language from scratch: Combining bottom-up and top-down learning processes in a computational model of language acquisition[END_REF], in particular models based on neural networks [START_REF] Brouwer | A neurocomputational model of the n400 and the p600 in language processing[END_REF], [START_REF] Chang | Symbolically speaking: A connectionist model of sentence production[END_REF], [START_REF] Elman | Finding structure in time[END_REF], [START_REF] Frank | Strong systematicity in sentence processing by an echo state network[END_REF]- [START_REF] Frank | Neural network models of language acquisition and processing[END_REF], [START_REF] Miikkulainen | Subsymbolic case-role analysis of sentences with embedded clauses[END_REF]). However, to our knowledge none of such models combine all these properties, even if not based on RC paradigm. More information about previous versions of the model available in supplementary materials 8 .

D. How to deal with Out-of-Vocabulary words?

In a developmental perspective it is important, for a child or a robot, to be able to deal with unknown words. Even if they do not need to infer their meaning yet. In such a developmental approach it is also interesting to not only rely on a fixed vocabulary, but on an evolving one [START_REF] Iwahashi | Robots that learn language: Developmental approach to human-machine conversations[END_REF]. In speech recognition and natural language processing (NLP) in general, some processed words may be unknown: i.e. not in the vocabulary list of the processing system. Theses words are called Out-of-vocabulary (OOV), and are often represented by the marker UNK for unknown. How to deal with these OOV words is a well-known problem in the field [START_REF] Jurafsky | Speech and Language Processing: An Introduction to Natural Language Processing[END_REF]. One can use ad hoc and hand-crafted tricks to deal with such a problem, which seems often the case in HRI, even if not explicitly stated in the papers. On the contrary, we take advantage of the generalization properties of our neural architecture and incorporate the fact that a word was not recognized instead of discarding it (see subsection II-E and [START_REF] Hinaut | A recurrent neural network for multiple language acquisition: Starting with english and french[END_REF] for more details).

II. METHODS

A. Echo State Networks (ESN)

The neural parser is based on an ESN [START_REF] Jaeger | The "echo state" approach to analysing and training recurrent neural networks[END_REF]: a particular kind of recurrent neural network (RNN) in which inputs are projected to a random recurrent layer, and only the output layer (called the "read-out") is modified by learning.

The units of the recurrent neural network have a leak rate (α) which corresponds to the inverse of a time constant. These equations define the update of the ESN:

x(t + 1) = (1 -α)x(t) + αf (W in u(t + 1) + Wx(t)) (1)
y(t) = W out x(t) (2)
where u(t), x(t) and y(t) are the input, the reservoir (i.e. hidden) state and the read-out (i.e. output) states respectively at time t. α is the leak rate. W in , W and W out are the input, the reservoir, and the read-out matrices respectively. f is the tanh activation function. After the collection of all reservoir states, the following equation defines how the readout (i.e. output) weights are trained. In order to prevent from overfitting 9 , we use ridge regression (also known as regression with Tikhonov regularization), which probably provides the most stable solution in this context [START_REF] Lukoševičius | A practical guide to applying echo state networks[END_REF]:

W out = Y d X T (XX T + βI) -1 (3)
where Y d is the concatenation of the desired outputs, X is the concatenation of the reservoir states (over all time steps for all trained sentences), β is the regularization coefficient (called ridge in the remaining of the paper) and M T is the transpose of matrix M.

B. Corpus

The corpus was obtained by asking naive users (agnostic about how the system works) to watch several actions in a video and give the commands corresponding to the motor actions performed, as if they wanted a robot to perform the same action. The video used is available online with the first experiments we did with robots [START_REF] Hinaut | Exploring the acquisition and production of grammatical constructions through humanrobot interaction with echo state networks[END_REF]. Five users were recruited and each user provided 38 commands: this gives a total of 190 sentences. Note that some sentences provided by users are complex and some times probably ungrammatical (see Table I).

C. Experiments

Based on a previously developed neural parser model [START_REF] Hinaut | Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing[END_REF], [START_REF] Hinaut | A recurrent neural network for multiple language acquisition: Starting with english and french[END_REF] we enhanced it in order to make it able to process a given sentence at three different levels of abstraction:

• sequence of phonemes (PHON) • sequences of words (WORD) • grammatical construction (CONST) From these three kinds of inputs we define the three different conditions of our experiments. All inputs are given symbol by symbol, in a one-hot (localist) encoding. Please refer to the supplementary materials and to [START_REF] Hinaut | Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing[END_REF], [START_REF] Hinaut | A recurrent neural network for multiple language acquisition: Starting with english and french[END_REF] in order to have more details on the model. In the case of PHON condition, we extended the model [START_REF] Hinaut | Real-time parallel processing of grammatical structure in the fronto-striatal system: a recurrent network simulation study using reservoir computing[END_REF] to process directly sequence of phonemes instead of sequence of words. Each word is replaced by its corresponding list of phonemes based on the Carnegie Mellon University wordphoneme correspondence dictionary (CMUdict v0.07) 10 . No additional space or any other clue enabling the model to detect boundary of words was added. Each sentence is then encoded as a succession of input unit activations in a localist (i.e. onehot) encoding: 1 for the corresponding phoneme, and 0 for others. The outputs are generated as in the previous model, as if the grammatical constructions were processed. Thus, the model has no simple cue indicating that a semantic word is being processed (i.e. activation of the SW input unit, as it is the case in the previous model).

E. Infrequent symbols category

For the sequence of words (WORD) and grammatical construction (CONST) inputs we also add a condition where we replace infrequent words by an "&" marker in order to see if this enables better generalization. Before training, we simply replace the most infrequent words in the training corpus. We count the number of occurrences of the words in the training corpus and define a threshold θ (θ = 5). The words that have a lower number of occurrences are replaced by an Infrequent Word (IW) marker "&". This enables our system to smartly process unknown words during test phase [START_REF] Hinaut | A recurrent neural network for multiple language acquisition: Starting with english and french[END_REF]. The idea behind is the following: the reservoir is trained to have such IW markers at different positions inside sentences, thus enabling it to not "freak out" when an unknown word appears. This word replacement was not used in the PHON condition for several reasons:

• if we use phonemes we may not have access to word recognition, so the problem of misrecognizing a word does not exists in this case. • although it could help, the aim is to assess if relying on PHON without speech recognition is possible.

F. Dealing with noisy inputs

As we are interested in the performance in real world scenarios of this neural parser, we would like the system to be able to handle and recover from speech recognition errors. In Human-Robot Interaction studies, it is common that this issue is not considered (badly recognized sentences are just discarded) or ad hoc methods are applied (e.g. researchers write by hand a correspondence dictionary in order replace words that are phonetically close to the desired ones). Even using mainstream APIs, such as the Google Speech API, doesn't prevent this issue (i.e. several words are not correctly recognized) from occurring in many sentences. Such APIs seem to be optimized for web searches, etc., but not for HRI vocabulary. Twiefel et al. [START_REF] Twiefel | Improving domain-independent cloud-based speech recognition with domaindependent phonetic post-processing[END_REF], [START_REF] Twiefel | Using Natural Language Feedback in a Neuro-inspired Integrated Multimodal Robotic Architecture[END_REF] have been able to enhance the results from Google API by post-processing its results: by decomposing the sentences obtained in sequences of phonemes and then finding the most probable sentence based on Levenshtein distance. Although the performances of the robotic architecture tested in these studies provide robust results, even in the presence of significant ambient noise 11 , we want to enable the core part of the architecture (i.e. the recurrent network) to be intrinsically more robust.

G. Training and testing

For all but noisy experiment conditions, we did a 10-fold cross-validation. For all experiments we averaged over 50 instances 12 . In order to evaluate the performance, we record the activity of the read-out layer at the last time step, which is when the final dot is given as input. We first discard the activations that are below a threshold of 0.5. Finally, if there are several possible roles for a particular SW, we do a winnertake-all and keep the role unit with the highest activation.

More implementation details on the training and testing procedures, about simulating noisy speech, and about hyperparameters are available in the supplementary materials III. RESULTS

A. Phonemes, words or grammatical constructions?

In Table II are presented the overall results for all the condition combinations. For PHON, WORD and CONST conditions, we obtain generalization errors of 18.49% (+1.76), 18.12% (+1.38) and 21.46% (+1.41) respectively. If we add the option of replacing infrequent words 13 , error for WORD and CONST decrease to 16.51% (+1.26) and 17.71% (+1.49) respectively 14 . In Table II, one can see that the result of (PHON, default) condition have been pasted in the (PHON, INF) condition, this is because there is no need to consider infrequent words in the PHON cond.

From these results we conclude that in general conditions (without word replacement), the WORD condition (i.e. sequence of words) performs better than the two other conditions, although the improvement of performance is not striking. It is surprising that the condition using phonemes has a very close performance although it processes more complex inputs with the same reservoir size: the inputs streams are longer, and there is no cue indicating the boundaries between words. We can also notice that replacing infrequent words helps the WORD, and particularly the CONST, conditions to improve generalization. Therefore this option (of replacing infrequent words) should be used by robotic systems confronted to OOV words.

Additionally to the global hyper-parameter (HP) search, for each condition we made a HP search in order to select the optimal regularization coefficient for each condition using hyperopt with TPE (Tree of Parzen Estimators) algorithm [START_REF] Bergstra | Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms[END_REF] for a hundred evaluations 15 . Surprisingly PHON and WORD conditions have a similar optimal ridge parameter (2.5e-04), compared to CONST condition (5e-06). Although, from the landscape formed by the parameter exploration: WORD condition has its best values around the optimal ridge with much worse performances for other ridge values, and PHON condition has similar performances when the ridge has a lower value (i.e. less regularization). Due to this landscape, we could speculate that PHON condition is less prone to overfitting than WORD condition.

We performed additional experiments (available in supplementary materials) in order to (1) explore whether adding a feedback connection from the output units to the reservoir could improve the performance, and to (2) explore how the number of neurons inside the reservoir influence the performance of the three main conditions.

B. Tasks difficulty

In order to evaluate if each condition was of the same difficulty -in a different manner than evaluating purely generalization performance -, we did a supplementary parameter exploration. We did a TPE search changing only the number of neurons inside the reservoir without using regularization, i.e. we simply use the pseudo inverse [START_REF] Lukoševičius | A practical guide to applying echo state networks[END_REF] instead of ridge regression. We did this search with the infrequent word replacement option for WORD and CONST conditions. For the current data set, the optimal number of neurons are of approximately of 160, 280 and 300 neurons for the WORD, CONST and PHON conditions respectively. If we assume the optimal number of neurons (when no regularization is applied) is representative of task difficulty 16 , then we could conclude that the WORD condition is easier than CONST and PHON condition. Thus it is somehow "unfair" to compare the three conditions with a number of reservoir units fixed to 500, and not allowing CONST and PHON to have more reservoir units as the tasks seem more difficult 17 .

C. Noisy speech recognition

Results in Table II show that adding noisy input affect the performances 18 , although they do not dramatically fall. For the PHON condition we pass from an error of 18.49% (+1.76) to 33.11% (+0.77); for WORD from 18.12% (+1.38) to 29.73% (+0.48); for CONST from 21.46% (+1.41) to 40.53% (+0.77). Interestingly the noise do not affect the three conditions in the same way. The CONST condition is the most affected by noisy inputs. This could be explained by the fact that the system could only rely on function words to parse the sentence. PHON condition is bit more affected than the WORD condition. Further explorations would be necessary to understand if this comes from the hyper-parameters that could be optimized specifically or not. Actually, the way the noisy condition is designed is "unfair" to the PHON condition, because if phonemes are used as input one would expect to have noise on the phonemes and not on the whole words.

IV. DISCUSSION

Considering a system that learns to parse a sentence given a stream of inputs, we answered the question what could be the optimal level of abstraction of the inputs (phonemes, words or grammatical constructions) given a particular English corpus of robot commands provided by users. This optimal input abstraction may be different for other corpora, in particular if they are of different size. In this study, we only compared purely symbolic input representations and do not consider raw acoustic signals or distributed encoding representations, such as word embedding. Although we have unpublished results showing that our system is able to process words with word2vec representation [START_REF] Mikolov | Distributed representations of words and phrases and their compositionality[END_REF] (a particular kind of embedding), we did not focus on it in this study 19 .

One could argue that comparing different levels of abstraction is probably more relevant to a neuro-cognitive robot than to a child: because in order to represent words she needs to chunk phonemes, and to have constructions she should chunk groups of words and identify some words as being variables 17 Effect of reservoir size is discussed in supplementary materials. 18 All results were averaged over 50 instances. 19 We did not focus on such representations in order to keep the comparison between levels of symbolic abstraction only. Otherwise we should also have compared it with embedding for phonemes and for grammatical constructions.

(e.g. semantic words). However, this study tries to understand what kind of information in the inputs are most relevant for a robot to learn to parse sentences.

In a nutshell, this study raise the question whether processing sentences as sequence of words (WORD condition) is always optimal or not. This assumption is often made by default in HRI experiments, but this may depend on the context. Our results suggest that the little advantage of a particular kind of input could change for slightly different conditions or corpora 20 .

A first remark on the results is that we outperformed previous results obtained on the same corpus in CONST condition: here we obtain 17.71% (+1.49) instead of 21.4% (+2.2) in previous study [START_REF] Hinaut | A recurrent neural network for multiple language acquisition: Starting with english and french[END_REF] using the same number of reservoir units. Results showed that WORD condition (i.e. sequence of words) is the one performing best in normal conditions, but only from a short increase in performance. It remains to be explored whether these results depend on the training dataset and on its size. In particular, we speculate that the CONST condition is not performing well because the user-based corpus is small and contains a lot of sentence variants to say similar things (e.g. see Table I). Thus, the model in the CONST cond. doesn't have enough repetitions of these sentence variants in order to robustly generalize only on grammatical constructions (i.e. sentence templates). Conversely, in the WORD cond. the model can rely on the precise semantic words used in a sentence as supplementary cues to help generalization: which explains the better generalization of WORD over CONST for this small corpus.

Surprisingly the PHON cond. performs nearly as well as the WORD cond. in default experiments. The task seems more difficult in the PHON cond. because the network has to deal with longer time scales and no word segmentation is provided. We believe the PHON condition would also benefit from a bigger corpus enabling the network to extract more statistical regularities from phoneme sequences. Thus, we aim to repeat the experiment on a much larger corpus in order to demonstrate better performance in both PHON and CONST experiments.

We also explored noisy conditions, where 5% of the words were randomly replaced by other words. WORD and PHON conditions resisted better to noise than CONST condition; with WORD cond. maintaining its leadership. However, more realistic noisy conditions21 could be obtained by randomly replace/insert/delete phonemes: we speculate that with such noise design the PHON condition would outperform the two other conditions. Consequently, we will extend this work to the processing of sequences and phonemes provided by a speech recognizer such as DeepSpeech [START_REF] Hannun | Deep speech: Scaling up end-to-end speech recognition[END_REF]: this will provide more

1

  touch the circle after having pushed the cross to the left 1 put the cross on the left side and after grasp the circle 2 move the circle to the left then the cross to the middle 3 put first the triangle on the middle and after on the left 4 push the triangle and the circle on the middle 5 hit twice the blue circle 5 grasp the circle two times 6 put the cross to the right and do a u-turn 7 put both the circle and the cross to the right D. Details on the Phoneme Extension of the Model

TABLE I SOME

 I SENTENCE EXAMPLES FROM THE NOISY ENGLISH CORPUS. DIFFERENT TYPE OF SENTENCES ARE GIVEN: 1. SEQUENCE OF ACTIONS 2. IMPLICIT REFERENCE TO VERB 3. IMPLICIT REFERENCE TO VERB AND OBJECT 4. CROSSED REFERENCE 5. REPEATED ACTION 6. UNLIKELY ACTION 7. PARTICULAR FW

	TYPE	SENTENCE EXAMPLE

TABLE II MEAN

 II ERROR IN PERCENT (AND STANDARD DEVIATION) FOR FULL SENTENCE COMPREHENSION FOR DIFFERENT CONDITIONS.

Given a particular corpus, because one could assume that this could change on the size and complexity of the corpus.

In particular, the computations performed in the input and recurrent layers.

Only input and outputs units are encoded with localist (i.e. one-hot) symbols, but there is no reason to think that a distributed coding should make the model work differently. Because each symbol given as input is already subject to a random projection.

Like a spoken sentence which comes like a flow and is available only once: once has to rely on working memory to keep the information about the whole sentence.

X. Hinaut and S. Wermter, "An incremental approach to language acquisition: Thematic role assignment with echo state networks," in Proc. of ICANN 2014, pp.33-40.

Neuroscience studies borrowed this idea of decoding non-linear and highdimensional computations through time.

[START_REF] Eppe | Exploiting deep semantics and compositionality of natural language for human-robot-interaction[END_REF] Supplementary material and source code are available at https://github. com/neuronalX/Hinaut2018_icdl-epirob

For the current data set, the optimal number of neurons is approximately of 160, 280 and 300 neurons for the WORD, CONST and PHON conditions. Thus we need to regularize if we want to use more units in the reservoir (i.e. 500).

Available at http://www.speech.cs.cmu.edu/cgi-bin/cmudict

We successfully tested the system in a crowded noisy environment, Night of Science in Hamburg in fall 2015.

Erratum: in a previous version of the paper, it was erroneously written 100 instances instead of 50. This does not affect the results and order between the results. This can be checked with the code published online.

i.e. occurring less than five times in the data set

We outperformed previously obtained results from[START_REF] Hinaut | Exploring the acquisition and production of grammatical constructions through humanrobot interaction with echo state networks[END_REF] and[START_REF] Hinaut | A recurrent neural network for multiple language acquisition: Starting with english and french[END_REF] that were applied only on CONST.

The ideal ridge regularization coefficient is told to be dependent on each reservoir instance. However we prefer to use a general value a priori which is more useful for applications.

Of course this is also tightly linked to overfitting problems. Another parameter search by changing the ridge actually indicates that WORD condition is much more sensitive to ridge parameter than the PHON condition.

We did not perform statistical comparisons between the different conditions because such tests are likely to not have enough statistical power given the size of the corpus

[START_REF] Jurafsky | Speech and Language Processing: An Introduction to Natural Language Processing[END_REF] More realistic noisy conditions could be obtained with a speech recognizer giving access to the sub-word information (i.e. phonemes recognized), instead of Google speech API for instance.

realistic phoneme/word recognition errors, and supposedly favor the PHON condition.

Despite the small corpus used, the current performances are already interesting and useful for small corpus applications in Human-Robot Interaction experiments. Because the core part of the model is a generic neural architecture, it could be easily reused or adapted for other computational or robotic experiments in language acquisition. In particular, we would like to extend this work by integrating our neural parser with multi-modal (e.g. vision, sensori-motor, ...) and behavioral robotic experiments [START_REF] Dominey | Developmental stages of perception and language acquisition in a perceptually grounded robot[END_REF]. For instance, the semantic and syntactic information of such complex sentences could be integrated into robotic experiments grounding linguistic symbols to robot behavior and to the visual modality [START_REF] Stramandinoli | The grounding of higher order concepts in action and language: a cognitive robotics model[END_REF], [START_REF] Sugita | Learning semantic combinatoriality from the interaction between linguistic and behavioral processes[END_REF], [START_REF] Yamada | Dynamical integration of language and behavior in a recurrent neural network for human-robot interaction[END_REF]. Syntactic richness of natural language sentences are often simplified in such experiments (for the benefit of motor or visual modalities), and rather rely on stereotypical sequence of few semantic words without function words (e.g. "hit left blue"). Our model could help in such architectures by increase the syntactic variability a robotic architecture could deal with.

Supplementary material and source code are available at https://github.com/neuronalX/Hinaut2018_icdl-epirob ACKNOWLEDGMENT The author would like to thank Bhargav Teja Nallapu for valuable feedback on paper, and Johannes Twiefel for interesting discussions.