Alexander Gavrikov

Sergey K Gulev

Margarita Markina

Natalia Tilinina

Polina Verezemskaya

Bernard Barnier

Ambroise Dufour

Olga Zolina

Yulia Zyulyaeva

Mikhail Krinitskiy

Ivan Okhlopkov

Alexey Sokov

RAS-NAAD: 40-yr High-Resolution North Atlantic Atmospheric Hindcast for Multipurpose Applications (New Dataset for the Regional Mesoscale Studies in the Atmosphere and the Ocean)

published or not. The documents may come

Introduction

Subsynoptic and mesoscale atmospheric dynamics over the North Atlantic Ocean are of great interest for understanding the mechanisms of highly localized precipitation, heat and moisture transports, and low-level baroclinicity in the atmosphere. Changes in the intensity and location of the North Atlantic storm tracks are critically important for the quantification of the impact of highly variable atmospheric processes onto air-sea fluxes and associated ocean signals and for understanding the responses of cyclone activity to those ocean signals [START_REF] Minobe | Influence of the Gulf Stream on the troposphere[END_REF](Minobe et al. , 2010;;[START_REF] Woollings | Response of the North Atlantic storm track to climate change shaped by ocean-atmosphere coupling[END_REF]Tilinina et al. 2018). Many works hint at the critical role of mesoscale dynamics in air-sea interaction in the North Atlantic, first of all in forming cold-air outbreaks (Zolina and Gulev 2003;[START_REF] Bond | Regional weather patterns during anomalous air-sea fluxes at the Kuroshio Extension Observatory (KEO)[END_REF][START_REF] Papritz | A climatology of cold air outbreaks and their impact on air-sea heat fluxes in the high-latitude South Pacific[END_REF][START_REF] Kim | Atmospheric conditions associated with Labrador Sea deep convection: New insights from a case study of the[END_REF]) over the Gulf Stream, the Labrador Sea, and the Greenland-Iceland-Norwegian (GIN) Seas and in generating polar lows characterized by small scales and extreme surface fluxes in the subpolar regions [START_REF] Kolstad | A global climatology of favourable conditions for polar lows[END_REF][START_REF] Condron | The impact of polar mesoscale storms on northeast Atlantic Ocean circulation[END_REF]Stoll et al. 2018, among others). Extremely high turbulent heat and momentum surface fluxes associated with these phenomena are highly localized in space and in time and require high temporal and spatial resolution for their adequate representation in models (Gulev and Belyaev 2012;[START_REF] Vihma | Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: A review[END_REF].

Many lower-troposphere responses to the ocean signals are also associated with mesoscale processes, including the low-level baroclinicity over the western boundary currents [START_REF] Nakamura | Response of storm tracks to bimodal Kuroshio path states south of Japan[END_REF][START_REF] Ogawa | Dependence of the climatological axial latitudes of the tropospheric westerlies and storm tracks on the latitude of an extratropical oceanic front[END_REF]Small et al. 2014;Ma et al. 2017;[START_REF] Duvivier | Winter atmospheric buoyancy forcing and oceanic response during strong wind events around southeastern Greenland in the Regional Arctic System Model (RASM) for 1990-2010[END_REF] Denotes content that is immediately available upon publication as open access. [START_REF] Parfitt | The atmospheric frontal response to SST perturbations in the Gulf Stream region[END_REF]Parfitt et al. , 2017) ) and anomalous convective precipitation in warm seasons [START_REF] Minobe | Influence of the Gulf Stream on the troposphere[END_REF][START_REF] Hand | Simulated response to inter-annual SST variations in the Gulf Stream region[END_REF]. High-resolution regional model experiments demonstrated the responses of the lower atmosphere to the ocean signals at length scales of less than 30-50 km, suggesting ocean-atmosphere coupling at mesoscales and submesoscales [START_REF] Small | Air-sea interaction over ocean fronts and eddies[END_REF](Small et al. , 2014(Small et al. , 2019;;Ma et al. 2017;[START_REF] Parfitt | The atmospheric frontal response to SST perturbations in the Gulf Stream region[END_REF][START_REF] Bishop | Scale dependence of midlatitude air-sea interaction[END_REF]. Subsynoptic and mesoscale processes are also crucial for better understanding the mechanisms of atmospheric moisture transports, first of all in the atmospheric rivers (ARs; Lavers et al. 2011;[START_REF] Lavers | The contribution of atmospheric rivers to precipitation in Europe and the United States[END_REF][START_REF] Gimeno | Atmospheric rivers: A mini-review[END_REF] providing strong ocean-to-continent moisture intrusions associated with abundant precipitation. All of these phenomena cannot always be adequately captured by global reanalyses, such as ERA-Interim, JRA-55, MERRA-2, and ERA5 [START_REF] Dee | The ERA-Interim reanalysis: Configuration and performance of the data assimilation system[END_REF][START_REF] Kobayashi | The JRA-55 Reanalysis: General specifications and basic characteristics[END_REF][START_REF] Gelaro | The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)[END_REF]; Copernicus Climate Change Service 2017; for definitions of acronyms, see https:// www.ametsoc.org/PubsAcronymList), partly because of their relatively coarse resolution but also because of the use of hydrostatic model configurations. Remarkably, the Arctic System Reanalysis (ASR; [START_REF] Bromwich | Arctic System Reanalysis improvements in topographically forced winds near Greenland[END_REF][START_REF] Boisvert | Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses[END_REF] performed with the polar Weather Research and Forecasting (WRF) Model [START_REF] Bromwich | Development and testing of Polar Weather Research and Forecasting model: 2. Arctic Ocean[END_REF]) demonstrated a considerable improvement of the representation of many phenomena in the Arctic atmosphere (Tilinina et al. 2014;Moore et al. 2015[START_REF] Wilson | A comparison of the regional Arctic System Reanalysis and the global ERA-Interim reanalysis for the Arctic[END_REF][START_REF] Smirnova | Comparing polar lows in atmospheric reanalyses: Arctic system reanalysis versus ERAinterim[END_REF][START_REF] Boisvert | Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses[END_REF]Justino et al. 2019, among others).

Ongoing ocean modeling activities also require highresolution forcing functions accounting for mesoscale atmospheric features. Existing datasets used for forcing model experiments such as Drakkar forcing set (DFS4/5), Coordinated Ocean-Ice Reference Experiments (CORE), and JRA-55-do are based on global reanalyses (Large andYeager 2004, 2009;[START_REF] Brodeau | An ERA40-based atmospheric forcing for global ocean circulation models[END_REF][START_REF] Danabasoglu | North Atlantic simulations in Coordinated Ocean-Ice Reference Experiments phase II (CORE-II). Part I: Mean states[END_REF][START_REF] Kim | Atmospheric conditions associated with Labrador Sea deep convection: New insights from a case study of the[END_REF][START_REF] Tsujino | JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do)[END_REF]) with a spatial resolution of approximately 50-100 km. At the same time, modern eddy-resolving ocean general circulation models use resolutions finer than 1/108, equivalent to a few kilometers at subpolar latitudes [START_REF] Deshayes | Oceanic hindcast simulations at high resolution suggest that the Atlantic MOC is bistable[END_REF][START_REF] Sérazin | Intrinsic variability of sea level from global ocean simulations: Spatiotemporal scales[END_REF][START_REF] Guo | Baroclinic instability of the Faroe Bank Channel Overflow[END_REF][START_REF] Rudnick | Cyclonic eddies in the Gulf of Mexico: Observations by underwater gliders and simulations by numerical model[END_REF][START_REF] Behrens | Composition and variability of the Denmark Strait Overflow Water in a high-resolution numerical model hindcast simulation[END_REF], and up to 1/508-1/608 in some regional simulations [START_REF] Chassignet | Impact of horizontal resolution (1/128 to 1/508) on Gulf Stream separation, penetration, and variability[END_REF][START_REF] Fresnay | Reconstruction of the 3-D dynamics from surface variables in a high-resolution simulation of North Atlantic[END_REF][START_REF] Fallmann | Impact of high-resolution ocean-atmosphere coupling on fog formation over the North Sea[END_REF]. These experiments are focused on essentially small-scale ocean features. The role of small-scale atmospheric processes, however, remains unclear when using relatively coarse-resolution forcing. Similarly, modern spectral wave models account for highly nonlinear wave generation and development processes strongly dependent on the submesoscale wind structure [START_REF] Ardhuin | Numerical wave modeling in conditions with strong currents: Dissipation, refraction, and relative wind[END_REF][START_REF] Hanley | A global climatology of wind-wave interaction[END_REF][START_REF] Semedo | A global view on the wind sea and swell climate and variability from ERA-40[END_REF][START_REF] Zieger | Observation-based source terms in the third-generation wave model WAVEWATCH[END_REF]Markina et al. 2019). At the same time, in most cases these advanced configurations are forced with relatively coarse-resolution reanalysis winds.

In summary, there is a high demand from different communities for long-term high-resolution atmospheric hindcasts performed with high-resolution model configurations for the North Atlantic where mesoscale and submesoscale processes are of high relevance. Facing this challenge, the P.P. Shirshov Institute of Oceanology of the Russian Academy of Sciences (IORAS) in cooperation with the Institut des Géosciences de l'Environnement (IGE) developed a high-resolution (14 km) atmospheric downscaling experiment for the North Atlantic Ocean [North Atlantic Atmospheric Downscaling (NAAD)]. In NAAD, the nonhydrostatic WRF Model was forced at the lateral boundaries by the ERA-Interim reanalysis over the 40-yr period (1979-2018). In the following, we describe the NAAD model configuration and production strategy in section 2 followed by a short description of NAAD products and data availability (section 3). Then we turn to the NAAD evaluation (section 4) and pilot applications (section 5). In the conclusive section 6 we discuss the NAAD added value and perspectives of further developments of the product.

NAAD model configuration and production strategy

In NAAD we used the nonhydrostatic WRF Model, version 3.8.1 [START_REF] Skamarock | A description of the Advanced Research WRF version 3[END_REF][START_REF] Powers | The Weather Research and Forecasting Model: Overview, system efforts, and future directions[END_REF]. The domain covers the North Atlantic from 108 to 808N and from 908W to 58E (Fig. 1), with the center at 458N, 458W. The initial and lateral boundary conditions [including sea surface temperature (SST)] were provided by the ERA-Interim reanalysis [START_REF] Dee | The ERA-Interim reanalysis: Configuration and performance of the data assimilation system[END_REF]) at 0.78 3 0.78 spatial resolution and 60 levels in the vertical direction. The spatial resolution in the basic NAAD high-resolution experiment (HiRes) was 14 km (551 3 551 points) and 50 terrain-following, dry hydrostatic pressure levels, starting from around 10-12 m above the ocean surface to 50 hPa with ;15 levels in the boundary layer (http://www.naad.ocean.ru). Besides the HiRes experiment, we also conducted a moderately low resolution experiment (LoRes) with the hydrostatic setting of the WRF Model at 77-km resolution (110 3 110 points) with 50 vertical levels (as in HiRes). The LoRes experiment (with resolution comparable to ERA-Interim) will be used to quantify the added value of the HiRes experiment, which cannot be directly compared with ERA-Interim (constrained by data assimilation and using a very different model configuration). All experiments were run for the 40-yr period from January 1979 to December 2018. Details of the model settings for the HiRes and LoRes experiments are presented in Table 1.

Most parameterizations were used in both HiRes and LoRes NAAD experiments. We used the Kain-Fritsch (KF) convective parameterization scheme [START_REF] Kain | The Kain-Fritsch convective parameterization: An update[END_REF]). The RRTMG longwave and shortwave radiation schemes [START_REF] Iacono | Radiative forcing by longlived greenhouse gases: Calculations with the AER radiative transfer models[END_REF] were used for terrestrial and solar radiation processes, which additionally utilize effective cloud water, ice and snow radii from the single-moment 6-class (WSM6) scheme for microphysics [START_REF] Hong | The WRF single-moment 6class microphysics scheme (WSM6)[END_REF] in HiRes, and subgrid convective cloud information from KF for a more accurate estimation of atmospheric optical depth. The surface layer was parameterized by the MM5 scheme of [START_REF] Skamarock | A description of the Advanced Research WRF version 3[END_REF]) based upon similarity theory, accounting for a viscous sublayer and incorporating the COARE 3.0 algorithm [START_REF] Fairall | Bulk parameterization of air-sea fluxes: Updates and verification for the COARE algorithm[END_REF] for calculating thermal and moisture roughness lengths (or exchange coefficients for heat and moisture) over the ocean surface. For the planetary boundary layer (PBL) we used the Yonsei University (YSU) nonlocal scheme [START_REF] Hong | The WRF single-moment 6class microphysics scheme (WSM6)[END_REF]) and the Noah land surface model [START_REF] Chen | Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity[END_REF]). An important issue is the number of vertical levels captured by the PBL. In NAAD (see the specification of vertical levels at http:// www.naad.ocean.ru), 15 vertical levels are below 850 hPa. Computation of the PBL height (not shown) reveals the highest PBL exceeding 1000 m over the regions with active convection and the lowest PBL of less than 200 m in the polar regions. This implies that the number of vertical levels in PBL range from as few as 5-6 to as many as 15-16 over the NAAD domain. In this respect, for example, version 2 of ASR (ASRv2; [START_REF] Boisvert | Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses[END_REF]) with approximately 10-12 levels in the PBL (implied by 25 levels below MAY 2020

G A V R I K O V E T A L .
850 hPa) is more effective in polar latitudes; however, ASRv2 is based on the local Mellor-Yamada-Janjic ṔBL scheme, which may not necessarily be effective over the whole North Atlantic region.

Since the Noah scheme updates deep soil temperature, the skin sea surface temperature is calculated using the [START_REF] Zeng | A prognostic scheme of sea surface skin temperature for modeling and data assimilation[END_REF] formulation. The PBL scheme is responsible for vertical subgrid-scale fluxes due to eddy transports in the whole atmospheric column, and not only in the boundary layer. Horizontal eddy viscosity coefficients are obtained in the WRF dynamic core independently using the Smagorinsky first-order closure approach. Parameterizations of microphysics were nevertheless different in HiRes and LoRes. Thus, the WSM6 scheme for microphysics [START_REF] Hong | The WRF single-moment 6class microphysics scheme (WSM6)[END_REF] was used in the NAAD-HiRes and WSM 5-class (WSM5; Hong et al. 2004). Additionally, employing entrainment information from KF was applied in the NAAD-LoRes case. For the long-term runs with WRF in both HiRes and LoRes experiments, the RRTMG scheme uses climatological ozone and aerosol data. The ozone data were adapted from the Community Atmospheric Model radiation scheme with latitudinal (2.828 step), height, and temporal (monthly) variation. The aerosol data were based on the [START_REF] Tegen | Contribution of different aerosol species to the global aerosol extinction optical thickness: Estimates from model results[END_REF] dataset with relatively coarse spatial (58 in longitude and 48 in latitude) and temporal (monthly) resolution.

The WRF settings used for NAAD HiRes, with some modifications, was applied in a number of applications. In the Polar WRF used in ASRv2 [START_REF] Boisvert | Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses[END_REF]) the major difference was in the use of the Mellor-Yamada-Nakanishi-Niino [START_REF] Nakanishi | Improvement of the Mellor-Yamada turbulence closure model based on large-eddy simulation data[END_REF][START_REF] Nakanishi | Improvement of the Mellor-Yamada turbulence closure model based on large-eddy simulation data[END_REF]Niino 2004, 2006) 2.5-level PBL parameterization. However, the nonlocal YSU scheme used in NAAD is effective to resolve strong convective processes in the midlatitudes and tropical regions. This parameterization was used in a number of RCM simulations [START_REF] Bukovsky | Precipitation simulations using WRF as a nested regional climate model[END_REF][START_REF] Otte | Does nudging squelch the extremes in regional climate modeling?[END_REF][START_REF] Gao | Evaluation of WRF mesoscale climate simulations over the Tibetan Plateau during 1979-2011[END_REF][START_REF] Tang | Impact of spectral nudging on regional climate simulation over CORDEX East Asia using WRF[END_REF].

At the ocean surface, we used ERA-Interim SST and sea ice, which was updated every 6 h during the simulation. ERA-Interim SST is combined from different sources [START_REF] Dee | The ERA-Interim reanalysis: Configuration and performance of the data assimilation system[END_REF]. [START_REF] Kumar | Sea surface temperatureprecipitation relationship in different reanalyses[END_REF] demonstrated that in different reanalyses, the intraseasonal SST-precipitation relationship is dependent on the SST used. In this respect we understand that the relatively coarse (with respect to HiRes) resolution of ERA-Interim SST may have an effect on the atmospheric surface layer and PBL. Currently, several high-resolution SST datasets, while limited in time coverage, are available [START_REF] Chelton | Global microwave satellite observations of sea surface temperature for numerical weather prediction and climate research[END_REF][START_REF] Chao | Blending sea surface temperatures from multiple satellites and in situ observations for coastal oceans[END_REF][START_REF] Ricchi | On the use of a coupled ocean-atmosphere-wave model during an extreme cold air outbreak over the Adriatic Sea[END_REF]. Nevertheless, for the 40-yrlong NAAD experiments we used ERA-Interim SST, which is considered to be homogeneous and adequate for multidecadal scales.

To reduce unrealistic atmospheric dynamics in the regional domain in both LoRes and HiRes experiments, we applied throughout the 40-yr period the procedure of spectral interior nudging [START_REF] Jeuken | On the potential of assimilating meteorological analyses in a global climate model for the purpose of model validation[END_REF][START_REF] Miguez-Macho | Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations[END_REF]). The spectral nudging technique optimizes the adjustment of the large-scale dynamics inside the domain to that implied by the boundary conditions. The nudging procedure was applied to the zonal and meridional wind components, air temperature, and the perturbation of the geopotential height. We did not nudge the moisture fields because their variability is not always represented adequately in the coarse-resolution ERA-Interim [START_REF] Miguez-Macho | Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations[END_REF][START_REF] Otte | Does nudging squelch the extremes in regional climate modeling?[END_REF]. Configuration of nudging was set according to the sensitivity study of [START_REF] Markina | Developing configuration of WRF model for long-term high-resolution wind wave hindcast over the North Atlantic with WAVEWATCH III[END_REF], which implied the optimal wavelength cutoff being 1100 km, applied only above the PBL. For determining the optimal nudging strength, we performed sensitivity experiments with the nudging strength coefficients increasing from 3 3 10 25 to 3 3 10 23 s 21 . These experiments implied an optimal value of the nudging strength coefficient of 3 3 10 24 s 21 (equivalent to a damping scale of about 1 h). This value is also consistent with other studies [START_REF] Miguez-Macho | Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations[END_REF][START_REF] Otte | Does nudging squelch the extremes in regional climate modeling?[END_REF]Tang et al. 2017, among others). Note also that the ASRv2 [START_REF] Boisvert | Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses[END_REF]) used spectral nudging with the strength 3 3 10 23 s 21 (i.e., an order of magnitude stronger relative to NAAD) for all levels in the outer domain and above 100 hPa in the inner domain. However, a direct comparison is unlikely to be possible here, as the ARSv2 assimilates a lot of observational data in the surface layer using the same technique (Newtonian relaxation, also called ''observational nudging''; [START_REF] Jeuken | On the potential of assimilating meteorological analyses in a global climate model for the purpose of model validation[END_REF].

The dynamical solver of WRF uses a Cartesian grid. The difference between the geographical and the model horizontal distance (map-scale factor) should not deviate significantly from unity in order to match the CFL criterion. The NAAD grid is based on a cylindrical equidistant projection (lat-lon in the namelist; http://www.naad.ocean.ru) with the rotated pole in order to locate the equator at the middle of the domain ( pole_lat 5 45; pole_lon 5 180; stand_lon 5 245). For this projection, the maximum map-scale factor amounts to 1.2 in the northernmost and southernmost regions of the domain (Fig. 1). This allowed for keeping a constant third-order Runge-Kutta time step of 30 s in HiRes and 240 s in LoRes. In NAAD we used the USGS topography data with 10 min spatial resolution.

NAAD products and data availability

NAAD products (see http://www.naad.ocean.ru for the parameter identification and namelist) include surface and upper-troposphere variables provided at the native NAAD grid for HiRes and LoRes products with resolutions of 14 and 77 km, respectively, for the period of 1979-2018. Since the NAAD core model (WRF-ARW) uses a staggered Arakawa C grid, all 3D fields are preinterpolated from the C grid on the mass points (unstaggered grid). No interpolation was applied for surface variables and fluxes, provided at the mass points. The entire archive of the NAAD data amounts to 150 terrabytes (TB) with individual annual files ranging from approximately 140 MB in LoRes to 3.3 GB in HiRes for surface variables on to 165 GB for HiRes 3D fields. The whole NAAD data output is organized as annual NetCDF files by variable and is available online (http://www.naad.ocean.ru) for download using FTP and Network Data Access Protocol (OPeNDAP) accesses.

NAAD evaluation a. Surface

Surface-state variables and fluxes are particularly significant as they are used for forcing ocean models and for the diagnostics of extreme events. Figures 2a and2b show that surface air temperature diagnosed by NAAD HiRes is colder than in LoRes. The differences between LoRes and HiRes are smaller than 0.28C over most of the domain and increase to 0.48C in the western midlatitude North Atlantic. Larger surface air temperatures in HiRes compared to LoRes, amounting to 18C, are observed over the ice-covered regions and occur primarily in the winter months. This regional bias can be explained by using a coarse-resolution sea ice mask from ERA-Interim in both HiRes and LoRes NAAD simulations. Compared to ERA-Interim (Fig. 2c), HiRes shows 0.28 to 0.48C lower surface air temperatures over most of the North Atlantic and considerably colder surface air temperatures in the ice-covered regions in the subpolar North Atlantic. A similar pattern of differences in surface air temperature is revealed for ASRv2 (Fig. 2d) for the area of overlap of the two domains. Surface relative humidity (Figs. 2e,f) over open ocean midlatitudes and subtropics is slightly smaller in HiRes compared to LoRes with the differences exceeding 1% identified in the western tropics. At the same time, somewhat more humid surface conditions in HiRes compared to LoRes are identified in the subpolar western North Atlantic and in the offshore region of the subtropical eastern Atlantic. Here the differences amount to 0.6%-0.8%. Compared to ERA-Interim and ASRv2 (Figs. 2g,h), NAAD HiRes shows a relative humidity higher by 2%-3% over the North Atlantic with the strongest differences (.6%) in the eastern North Atlantic subtropics.

Figure 3 shows the evaluation of climatological winter winds in NAAD HiRes and LoRes. NAAD HiRes and LoRes surface winds are consistent south of 458N with the differences within 60.15 m s 21 . At the same time, in the Irminger Sea NAAD HiRes shows stronger mean winds by 0.2-0.6 m s 21 , thus likely reflecting a better representation of the mesoscale features such as tip jets and katabatic winds in this area. The comparison with ERA-Interim (Fig. 3c) shows stronger trade winds in the NAAD HiRes experiment, somewhat lower wind speeds in the western midlatitude North Atlantic, and also stronger winds in the subpolar North Atlantic along the eastern Greenland coast. North of 408N, differences between HiRes and ASRv2 (Fig. 3d) are consistent with those for ERA-Interim. [START_REF] Dukhovskoy | Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometer-based wind products over the Nordic Seas and the northern North Atlantic and their application for ocean modeling[END_REF] noted differences between high-resolution satellite wind products and reanalyses attributing them to the subsynoptic and mesoscale processes. The impact of high-resolution and nonhydrostatic setting onto the wind field is especially evident for extreme winds (Figs. 3e-h). We note much stronger katabatic winds and tip jets along the Greenland coast, with the differences in surface winds between HiRes and LoRes experiments being locally over 4 m s 21 (about 20% of mean values of 99th percentile of wind speed). Relative to ERA-Interim, the HiRes experiment shows extreme winds stronger by more than 7 m s 21 . Important is that HiRes also shows much better localization of katabatic winds near the coast in HiRes compared to LoRes and ERA-Interim. Figure 3d shows the localization of maximum extreme winds much closer to the Greenland coast in agreement with the case studies [START_REF] Moore | Tip jets and barrier winds: A QuikSCAT climatology of high wind speed events around Greenland[END_REF]Moore et al. 2015). Extreme wind speed differences between NAAD HiRes and ASRv2 in the Irminger Sea (Fig. 3h Seas. In the subpolar latitudes, the stronger HiRes fluxes are explained by the differences in wind speed (Fig. 3) with a significant contribution from surface temperature and humidity gradients, especially for sensible heat flux (no figure shown). In the midlatitudes and subtropics, the differences in turbulent heat fluxes between HiRes and LoRes are associated with surface temperature and humidity vertical gradients, as wind speed differences here are close to zero (Fig. 3). Differences with ERA-Interim (Fig. 5c) are 30%-50% larger than those with NAAD LoRes, while the direct comparison here is difficult because of the differences between the ERA-Interim surface flux algorithm and the COARE 3.0 flux algorithm [START_REF] Fairall | Bulk parameterization of air-sea fluxes: Updates and verification for the COARE algorithm[END_REF]) used in NAAD (Brodeau et al. 2017). Of interest is also the evaluation of extreme surface turbulent fluxes, which might be strongly dependent on mesoscale processes [START_REF] Ma | Winter extreme flux events in the Kuroshio and Gulf Stream extension regions and relationship with modes of North Pacific and Atlantic variability[END_REF]) and demonstrate differences between different products not consistent with those for mean values (Gulev and Belyaev 2012;[START_REF] Bentamy | Review and assessment of latent and sensible heat flux accuracy over the global oceans[END_REF]. Figures 5d-f presents extreme fluxes quantified by the 99th percentile of the modified Fisher-Tippett distribution (Gulev and Belyaev 2012). Relative to LoRes, HiRes shows stronger extreme fluxes over the Gulf Stream and the North Atlantic Current where differences may locally exceed 30 W m 22 (up to 5%-10% of the mean values). In the Irminger Sea the differences amount to 200 W m 22 , being more than 20%-25% of the mean values of extreme turbulent fluxes. Extreme fluxes diagnosed by HiRes are also considerably stronger than in ERA-Interim (Fig. 5f), with maximum differences locally exceeding 300 W m 22 . b. Storm tracks and cyclone dynamics NAAD opens a new avenue in the analysis of cyclone dynamics and storm tracks. Cyclone tracks were diagnosed using the IORAS algorithm [START_REF] Zolina | Improving the accuracy of mapping cyclone numbers and frequencies[END_REF][START_REF] Tilinina | Comparing cyclone life cycle characteristics and their interannual variability in different reanalyses[END_REF], tested within the IMILAST project [START_REF] Neu | IMILAST: A community effort to intercompare extratropical cyclone detection and tracking algorithms[END_REF]. Tracking was performed on the NAAD HiRes grid. For tracking cyclones in the limited area over the North Atlantic we applied an approach that accounts for the so-called entry-exit uncertainties (Tilinina et al. 2014). Postprocessing was further applied to cutoff cyclones with migration distances smaller than 1000 km and lifetimes shorter than 24 h [START_REF] Tilinina | Comparing cyclone life cycle characteristics and their interannual variability in different reanalyses[END_REF].

NAAD HiRes (Fig. 6a) captures well the main North Atlantic storm tracks that are consistent with cyclone climatologies based on the global reanalyses (e.g., [START_REF] Neu | IMILAST: A community effort to intercompare extratropical cyclone detection and tracking algorithms[END_REF][START_REF] Tilinina | Comparing cyclone life cycle characteristics and their interannual variability in different reanalyses[END_REF]. NAAD HiRes shows 30%-60% larger local cyclone numbers compared to LoRes. Also, NAAD LoRes shows slightly larger number of cyclones compared to ERA-Interim while the differences are within 3%-5%. Over the North Atlantic storm track, the number of cyclones in NAAD HiRes is close to that in ERA5 (Fig. 6d) with slightly larger cyclone counts over the storm formation region in the Northwest Atlantic and slightly smaller counts in the central North Atlantic and the subpolar regions.

Figure 7 shows the winter [December-February (DJF)] time series of the domain integrated number of cyclones with different intensities (quantified by central pressure). NAAD HiRes allows for identification of ;2 times more cyclones compared to LoRes, which indicates a high consistency with the global reanalyses, except for ERA5, which reveals practically the same number of cyclones as NAAD HiRes (Fig. 7a). Importantly, these differences between HiRes and all other products (including LoRes) are formed mostly by moderately deep and shallow cyclones (Figs. 7b,d). At the same time, the number of deep cyclones in HiRes is more consistent with LoRes and reanalyses showing 10%-15% higher counts. Overall, the winter cyclone activity in NAAD is very close to that in ERA5 and considerably more intense compared to the other reanalyses. Summer results (not shown) are qualitatively similar with even higher differences especially for shallow cyclones dominating during the warm season.

Figure 6 demonstrates relatively strong differences between cyclone counts in NAAD and in all reanalyses over the North American continent. This is likely associated with the fact that the finer-resolution NAAD HiRes is capable of identifying cyclone generation events at an earlier stage than the global reanalyses. Our analysis of cyclogenesis events (not shown) demonstrates considerably larger counts of cyclone generation events over the North American storm track in NAAD when compared with even ERA5, while the total number of tracks is close in both products (Fig. 7). Also, we note that the cyclone effective radius, characterizing cyclone size [START_REF] Rudeva | Climatology of cyclone size characteristics and their changes during the cyclone life cycle[END_REF] 2013, NAAD HiRes is in a very good agreement with GPM, demonstrating very small (relative to the other products) differences for all seasons (Fig. 8b). The consistency with GPM is, however, somewhat better in autumn-winter than in spring-summer, likely because of a stronger contribution of the convective precipitation (requiring even higher resolution than that in HiRes) during the warm season.

Figures 9a-c shows annual mean total precipitation in NAAD HiRes and the differences with LoRes and ERA-Interim. NAAD HiRes when compared with LoRes shows stronger precipitation in the western Atlantic tropics and over the Gulf Stream by up to 1 mm day 21 and lower, by 0.3-0.5 mm day 21 , precipitation in the North Atlantic subpolar gyre. Relative to ERA-Interim (Fig. 9c), NAAD HiRes shows stronger precipitation over the Gulf Stream (up to 2 mm day 21 ) and weaker precipitation over the subpolar latitudes. The precipitable water content (PWC) (Figs. 9d,e) is lower in NAAD HiRes than in LoRes by 4%-6%, with the strongest absolute differences in the western tropics. ERA-Interim, however, shows a slightly higher PWC than NAAD HiRes over most of the domain except for the western tropics and subtropics (Fig. 9e). Differences in precipitation and PWC suggest stronger tropical water recycling in NAAD HiRes and somewhat weaker recycling in mid-and subpolar latitudes relative to LoRes and ERA-Interim.

Representation of summer precipitation over the western North Atlantic is important for quantifying the lower-atmosphere responses to the ocean frontal signals in the Gulf Stream [START_REF] Minobe | Influence of the Gulf Stream on the troposphere[END_REF](Minobe et al. , 2010;;[START_REF] Parfitt | The atmospheric frontal response to SST perturbations in the Gulf Stream region[END_REF]. Precipitation responses in summer are typically associated with convective processes and mostly located over the westernmost part of the Gulf Stream. In winter, precipitation responses are associated with the atmospheric frontal activity and enhanced precipitation over the central and eastern Gulf Stream proper (Minobe et al. 2010). Figure 10 demonstrates precipitation for July 2015 as revealed by NAAD HiRes and LoRes as well as by ERA-Interim, ERA5, GPM, and GPCP. NAAD HiRes with its capability to capture convective processes shows the best agreement with GPCP in the structure of precipitation pattern and in magnitude. NAAD LoRes and ERA-Interim tend to underestimate precipitation rates by 2-4 and 3-6 mm day 21 , respectively. ERA5 is most consistent with HiRes in spatial structure but shows smaller precipitation rates of 2-3 mm day 21 . Comparison with ASRv2 (no figure shown) is somewhat difficult as the ASR domain boundary cuts a considerable part of the region analyzed. However, for the overlapping part of the domain the precipitation pattern in ASRv2 is comparable in structure to that of NAAD HiRes.

Capabilities of the high-resolution NAAD in representing moisture transports can be analyzed through the For the detection of ARs, we used the 85th percentile of the integrated water vapor transport (IVT) along with a fixed lower IVT limit of 100 kg m 21 s 21 [START_REF] Guan | Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies[END_REF]. The 85th percentile of IVT was computed from the 20-day sliding windows in the NAAD-HiRes and LoRes outputs, and moisture transports were computed according to [START_REF] Dufour | Atmospheric moisture transport to the Arctic: Assessment of reanalyses and analysis of transport components[END_REF]. Figure 11 shows the case study for 5 December 2015, with an AR associated with the deep cyclone east of Iceland and clearly seen in IVT fields. Associated daily accumulated precipitation in the NAAD HiRes closely matches that diagnosed by GPM, while, for example, ERA-Interim shows the shift in the location of the AR with respect to GPM. NAAD HiRes precipitation in the AR landfall areas over the U.K. and Norway coasts is over 66 mm day 21 . This is greater than the values diagnosed by NAAD LoRes and ERA-Interim by up to 30 mm day 21 (Fig. 11f). We also note an accurate location of the coastal precipitation maxima in the landfall areas in NAAD HiRes (Fig. 11f). The maximum of water vapor transport in the AR (Fig. 12a) at 2.5-km height is associated with locally strong winds amounting to more than 40 m s 21 . Figure 12b shows that the AR core in HiRes is characterized by 10%-15% stronger transports relative to LoRes, reflecting the fact that ARs in the HiRes experiment are narrower. In climatological context, this reduces the time during which western Europe coasts are exposed to ARs; however, this makes the impact of individual ARs stronger and highly localized. Our estimate for 2015, performed using a method similar to [START_REF] Guan | Detection of atmospheric rivers: Evaluation and application of an algorithm for global studies[END_REF], shows that AR landfalls in HiRes happen during 10%-15% of the time, which is less than in LoRes and MERRA (15%-20%).

d. Mesoscale features

To demonstrate the representation of mesoscale features in the NAAD, we use kinetic energy (KE) wavenumber spectra (SKE) derived from the wind speed data and averaged over the layer between 3-and 5-km height over the whole domain (Figs. 13a,b). These spectra characterize the atmospheric turbulence at different scales by the power law SKE(k) ; k 2g , where k is the wavenumber and g is changing from approximately 23 to approximately 25/3 between the ranges of space scales larger and smaller than 500 km [START_REF] Waite | The mesoscale kinetic energy spectrum of a baroclinic life cycle[END_REF][START_REF] Condron | The impact of polar mesoscale storms on northeast Atlantic Ocean circulation[END_REF][START_REF] Dukhovskoy | Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometer-based wind products over the Nordic Seas and the northern North Atlantic and their application for ocean modeling[END_REF]. Figure 13 demonstrates marked differences in the wavenumber spectra for HiRes and LoRes experiments. For the total and geostrophic KE, the HiRes spectrum is better matching k 23 than LoRes is. It is important, however, that the spectral decay rate for both geostrophic and ageostrophic components at smaller scales (,500km) is considerably smaller in HiRes than in LoRes. This reflects stronger pressure gradients (and, thus, stronger winds) in synoptic and mesoscale transients in the HiRes experiment. [START_REF] Dukhovskoy | Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometer-based wind products over the Nordic Seas and the northern North Atlantic and their application for ocean modeling[END_REF] noted a similar tendency in ASR relative to global coarser-resolution reanalyses in the subpolar North Atlantic.

Figure 13c shows the wavenumber kinetic energy spectra near the surface using data from NAAD HiRes and ASRv2 over the overlapping part of the two domains. As in the case of the free troposphere (Figs. 13a,b), the NAAD HiRes spectrum built from the surface data closely matches k 25/3 decay rate in the range of scales less than 700 km. The ASRv2 spectrum demonstrates a very similar behavior in terms of change in g from ;23 to ;25/3 and the decay rate in the range from 700 to at least 100-150 km.

In this respect it is of interest to consider the representation of polar lows in NAAD. Polar lows represent highly localized maritime atmospheric phenomena associated with extreme winds and advection of very dry cold air, playing an important role in high-latitude atmospheric dynamics and air-sea interaction processes [START_REF] Zahn | A long-term climatology of North Atlantic polar lows[END_REF]Condron et al. 2008;Condron and Renfrew 2013, among others). They are hardly detectable in global reanalyses primarily due to their small size [START_REF] Zappa | Can polar lows be objectively identified and tracked in the ECMWF operational analysis and the ERA-Interim reanalysis?[END_REF][START_REF] Stoll | An objective global climatology of polar lows based on reanalysis data[END_REF]. Many publications, however, report the capability of the WRF Model even without data assimilation to effectively simulate polar lows [START_REF] Wagner | The mesoscale structure of a polar low: Airborne lidar measurements and simulations[END_REF][START_REF] Wu | Piecewise potential vorticity diagnosis of the development of a polar low over the Sea of Japan[END_REF][START_REF] Føre | A 'hurricane-like' polar low fuelled by sensible heat flux: High-resolution numerical simulations[END_REF]Kolstad et al. 2016). [START_REF] Kolstad | A global climatology of favourable conditions for polar lows[END_REF] developed a unique database of 63 polar lows (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) in the subpolar and subarctic North Atlantic and Arctic using AVHRR and QuikSCAT winds. Of the 21 events identified by [START_REF] Kolstad | A global climatology of favourable conditions for polar lows[END_REF] in the NAAD domain, NAAD HiRes was able to successfully detect 20 polar lows.

Figure 14 shows a case study for the polar low that developed on 2 March 2008 in the Irminger Sea. NAAD HiRes detects well the location of the pressure minimum identifying a 978-hPa central pressure, which is deeper than that in ERA-Interim (986 hPa) and even in ERA5 (984 hPa). Also, NAAD HiRes demonstrates the well-detectable comma-type structure not present in ERA-Interim and ERA5 and less evident in ASRv2. Note that NAAD LoRes results (no figure shown) are very close to ERA-Interim for this case. The associated maximum surface wind speed in the polar low amounts in HiRes to 32 m s 21 , with 26-27 m s 21 in ERA-Interim and ERA5, and nearly 30 m s 21 in ASRv2 (Fig. 14). Note that [START_REF] Gutjahr | A model-based comparison of extreme winds in the Arctic and around Greenland[END_REF] clearly demonstrated that an accurate representation of the tip jets and associated extreme winds around Greenland requires resolutions of at least 15 km. Remarkably, it is also the case that surface turbulent fluxes (sensible plus latent) are considerably stronger in HiRes (up to 900 W m 22 ) than in ERA-Interim (600 W m 22 ) and are comparable to both ERA5 and ASRv2. We note that the direct comparison of surface fluxes between these products should be taken with caution because of the use of somewhat different flux algorithms. However, the analysis of surface flux PDFs and the surface flux relative extremeness (Gulev and Belyaev 2012;Tilinina et al. 2018) show that the strong flux event during 2 March 2008 in NAAD HiRes contributed more to the total monthly flux as compared with ERA5 and ASRv2.

The capability of the NAAD to capture the mesoscale dynamics in the tropics is evaluated in Fig. 13d showing wavenumber spectra of the kinetic energy near the surface (10 m) and at 1500 m in HiRes and LoRes simulations. Remarkably, the spectra near the surface and at 1500 m are qualitatively close to each other for both simulations and are also close to the spectra for the free troposphere (Figs. 13a,b) and to the surface spectra in the subpolar region (Fig. 13c). At the same time, tropical spectra for HiRes demonstrate a k 25/3 decay rate for the wave lengths from 200 to 1000 km, while LoRes spectra follow k 23 and a slightly stronger decay rate in this range. This implies more energetic mesoscale features of the same size in the tropics in HiRes than in LoRes.

NAAD is also capable of identifying tropical cyclones generated and propagating north of the southern margin of the domain. Figure 15 shows the diagnostics of Hurricane Gaston, which developed over the North Atlantic between 22 August and 2 September 2016. At the moment of maximum development, NAAD HiRes diagnoses the lowest central pressure (6 hPa deeper when compared with ERA5 and more than 15 hPa deeper than NAAD LoRes and ERA-Interim) as well as winds stronger by 5-8 m s 21 than in ERA5. Associated precipitation in NAAD HiRes is considerably stronger than in LoRes and ERA-Interim and consistent in magnitude with ERA5. However, the precipitation pattern in HiRes more accurately captures the shape implied by GPM than ERA5.

Pilot ocean applications a. Effects in modeled ocean circulation

To demonstrate the NAAD capabilities for driving long-term experiments with regional configurations of ocean general circulation models, we developed surface Comparative model experiments were performed with NAAD HiRes and NAAD LoRes atmospheric forcings; the experiments are referred to as NAAD-OHR and NAAD-OLR, respectively. Significant differences in characteristics of turbulent and radiative heat fluxes as well as momentum fluxes between the two forcings at the ocean surface result in large differences in the simulated ocean mean state. Thus, the domain-averaged simulated SST is approximately 0.68C lower in NAAD-OHR, with summer differences amounting to more than 1.58C (Fig. 16), in close agreement with European Space Agency (ESA) SST (http://www.esa-sst-cci.org). Note that trends in SST are highly consistent in both NAAD-OHR and NAAD-OLR. The strongest SST negative differences of 18-1.58C and lower sea surface salinity (0.15-0.2 psu) in NAAD-OHR relative to NAAD-OLR are observed in the Labrador and Irminger Seas. Consistent with SST, NAAD-OHR shows a lower ocean heat content for both the upper (0-700 m) and intermediate ocean (700-1500 m), suggesting a more intense ventilation of the ocean by convective processes in NAAD-OHR (Fig. 16). Since almost all ocean models in noncoupled experiments have a tendency toward a warmer and saltier ocean [START_REF] Tréguier | The North Atlantic subpolar gyre in four high-resolution models[END_REF][START_REF] Rattan | Towards an understanding of Labrador Sea salinity drift in eddy-permitting simulations[END_REF], the colder upper-ocean temperatures in NAAD-OHR should be considered as an improvement. The OHR forcing also appears to drive significant changes in the boundary currents around Greenland and in the different branches of the central North Atlantic Current, which were found to be more intense and more variable in NAAD-OHR than in NAAD-OLR, as revealed by the partition of eddy kinetic energy (not shown). NAAD-OHR also shows a somewhat deeper mixed layer depth (MLD) relative to NAAD-OLR (not shown) in regions known for being strongly ventilated by winter convection (the southwest sector of the Labrador Sea and the central Irminger Sea). A lesser ventilation is noticed in the areas where ocean eddies are known to counterbalance the effects of strong surface fluxes onto MLD (e.g., [START_REF] Chanut | Mesoscale eddies in the Labrador Sea and their contribution to convection and restratification[END_REF]).

b. Effects in ocean wind-wave modeling

NAAD can be also effectively used for forcing spectral wave models whose solutions are critically dependent on the quality and spatial resolution of atmospheric forcing [START_REF] Cavaleri | Wave modeling-Missing the peaks[END_REF][START_REF] Ardhuin | Numerical wave modeling in conditions with strong currents: Dissipation, refraction, and relative wind[END_REF]. In this respect, the mesoscale activity in the lower atmosphere might be of critical importance for capturing extreme wind waves (Condron et al. 2006;[START_REF] Zappa | Can polar lows be objectively identified and tracked in the ECMWF operational analysis and the ERA-Interim reanalysis?[END_REF]Markina et al. 2019).

We used NAAD HiRes and LoRes outputs for the WAVEWATCH III spectral wave model (WW3DG 2016) over the NAAD domain. Experiments (referred to as NAAD-WHR and NAAD-WLR for HiRes and LoRes forcing, respectively) were performed with the spatial resolution of 0.28 and spectral model resolution being 36 directions and 25 frequencies spanning from 0.04 Hz with an increment of 1.1. The WAVEWATCH-III configuration included ST6 parameterization for wave energy input and dissipation [START_REF] Zieger | Observation-based source terms in the third-generation wave model WAVEWATCH[END_REF][START_REF] Liu | Observation-based source terms in the third-generation wave model WAVEWATCH III: Updates and verification[END_REF]) calibrated for WRF winds [START_REF] Markina | Developing configuration of WRF model for long-term high-resolution wind wave hindcast over the North Atlantic with WAVEWATCH III[END_REF]) and the ice source term package (IC0; Tolman 2003) implying the exponential attenuation of waves in partially sea ice-covered regions. To account for the ocean surface current impact on growing waves we used daily surface current velocities from the NEMO-based Ocean Reanalysis System 5 (ORAS5; [START_REF] Zuo | The ECMWF operational ensemble reanalysis-analysis system for ocean and sea ice: A description of the system and assessment[END_REF]. [START_REF] Cavaleri | Wave modeling-Missing the peaks[END_REF] argued that the increase in spatial resolution of forcing per se does not necessarily result in the increase in significant wave height (SWH); rather, the formation of high waves is associated with higher winds, changes in the duration of wind action, and the length of fetch. In this sense, NAAD HiRes with its stronger winds (Fig. 3), smaller cyclone sizes and larger number of synoptic transients (Figs. 5 and6) likely acts locally rather than on a larger scale.

Figures 17a and17b show winter (January-March) climatological SWH for 1979-2018 in NAAD-WHR and the differences between NAAD-WHR and NAAD-WLR. In NAAD-WHR the highest SWH amounts to 5.4 m in the eastern subpolar North Atlantic in very close agreement with VOS observations (Gulev et al. 2003;[START_REF] Gulev | Variability of the winter wind waves and swell in the North Atlantic and North Pacific as revealed by the voluntary observing ship data[END_REF]. Differences between NAAD-WHR and NAAD-WLR SWH over most of the North Atlantic midlatitudes and subtropics are generally within 0.3 m but strongly increase in the subpolar North Atlantic where they amount to 0.8 m in the Irminger Sea. The highest extreme strong waves quantified as 95th percentile amount in winter in NAAD-WHR to 9 m (Fig. 17c), being higher than in NAAD-WLR by 0.2-0.5 m in the central and eastern subpolar North Atlantic (Fig. 17d). At the same time the most distinctive differences between the NAAD-WHR and NAAD-WLR experiments are identified along the southeast Greenland coast where the extreme SWH in NAAD-WHR is higher than that in NAAD-WLR by more than 1 m (up to 20% of mean values). This likely reflects a more accurate representation of katabatic winds and tip jets in this area in NAAD-WHR.

Summary and outlook

We presented NAAD-a new 3D multidecadal atmospheric dataset for the North Atlantic produced with a WRF nonhydrostatic model at mesoscale resolution (NAAD HiRes). In parallel, a coarser-resolution dataset (NAAD LoRes) was produced with a spatial resolution close to ERA-Interim used in both experiments as a lateral boundary condition.

Our evaluation demonstrates reasonably realistic representations of most climatological characteristics in both NAAD HiRes and NAAD LoRes datasets. The main differences are identified in the ice-covered subarctic regions, especially for surface air temperature and partly for surface humidity. At the same time, atmospheric dynamics was quite adequately represented. The major purpose of the NAAD at this stage was not to provide extremely close comparability of NAAD LoRes with, for example, ERA-Interim. This is hardly achievable because ERA-Interim (as well as the other reanalyses) is largely constrained by data assimilation. The major NAAD focus was rather to develop a highresolution atmospheric dataset that allows a better analysis of subsynoptic and mesoscale features-the task still not resolved by global reanalyses over the North Atlantic. In this respect, the objectives for NAAD are similar to those posed for the other regional reanalyses (e.g., ASR; [START_REF] Boisvert | Intercomparison of precipitation estimates over the Arctic Ocean and its peripheral seas from reanalyses[END_REF]). The NAAD model configuration was quite close to the one used in ASR, the lateral conditions are the same (ERA-Interim) and the resolutions of the two products are similar. At the same time ASR (both ASRv1 and ASRv2) used extensive data assimilation input that exceeds data assimilation in the global reanalysis (e.g., ERA-Interim). Our capabilities for the direct comparisons of NAAD with ASR were limited to the subpolar latitudes. However, the analysis of kinetic energy spectra and extreme winds and fluxes associated with polar lows (Figs. 13 and14) clearly demonstrated that the differences between NAAD HiRes and ASRv2 are the smallest relative to the other reanalyses, and this is reassuring. The comparative analysis of surface fluxes, winds, and precipitation in the North Atlantic midlatitudes is not representative, as these regions are close to the boundary of the ASR domain.

Extensive evaluation of ASR (e.g., Moore et al. 2015;[START_REF] Bromwich | Arctic System Reanalysis improvements in topographically forced winds near Greenland[END_REF]Tilinina et al. 2014) Higher extreme turbulent fluxes in NAAD HiRes and a better representation of the convective precipitation over the Gulf Stream make NAAD potentially useful for quantifying ocean-atmosphere interactions at mesoscales and depicting ocean impacts on the lower atmosphere and associated responses in the dynamics of midlatitude storm tracks. NAAD is also capable of capturing highly localized mechanisms of atmospheric moisture transports such as ARs, more accurately locating them, and quantifying their intensity and impacts. In the tropics, NAAD HiRes also effectively captures mesoscale features, including an improved representation of tropical cyclones, especially in terms of central pressure, wind, and precipitation. Applications of NAAD to ocean modeling demonstrated the effect of HiRes onto the modeled ocean state and eddy kinetic energy distribution, specifically showing smaller surface temperature and upper-ocean heat content consistently with observations. Being applied to wind-wave modeling, NAAD HiRes resulted in higher simulated extreme wind waves in the eastern subpolar North Atlantic, reflecting stronger extremeness of surface winds.

Further near-time developments of the NAAD will include the adaptation of ERA5 as a source for lateral boundary conditions and changing to a finer spatial resolution of at least ;3 km with a higher number of vertical layers. This will also include at least for the period after 2000 the use of high-resolution SST and sea ice data, available from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system [START_REF] Roberts-Jones | Daily, global, high-resolution SST and sea ice reanalysis for 1985-2007 using the OSTIA system[END_REF][START_REF] Donlon | The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system[END_REF] as well as from Multisensor Analyzed Sea Ice Extent (MASIE) AMSR2 (MASAM2; [START_REF] Fetterer | MASAM2: Daily 4 km Arctic sea ice concentration, version 1. National Snow and Ice Data Center[END_REF]. Improved representation of sea ice will help to minimize biases in temperature and humidity in the northern North Atlantic. Also planned is the domain extension to the full coverage of the North Atlantic tropics that will make it possible to provide accurate diagnostics of tropical cyclone dynamics including the poleward shift in the trajectories [START_REF] Studholme | Concurrent changes to Hadley circulation and the meridional distribution of tropical cyclones[END_REF][START_REF] Sharmila | Recent poleward shift of tropical cyclone formation linked to Hadley cell expansion[END_REF]. Finer spatial and vertical resolution will also provide a better representation of mesoscale features and will better demonstrate the value added by nonhydrostatic model configurations. For selected years (provisionally in the 2010s), we are also planning to develop ensemble simulations (up to 10 members). In parallel, on a midterm scale we will work on the transition of NAAD to the North Atlantic regional reanalysis with assimilating all available information over the domain that will make it possible to develop the product for assessing the impact of mesoscale processes onto longer-term climate variability in the atmosphere and the ocean. the editor. Their insightful suggestions helped to largely improve the first version of the paper. We thank Peter Koltermann of MSU for useful comments on the paper text. Our analysis of mesoscale dynamics was strongly motivated by Peter Arseev of PhIRAS and Alexey Morozov of ITEP. Discussions on the model setting with Andrey Glazunov of INMRAS are appreciated. We acknowledge the access to the facilities of HPC computing resources at Lomonosov Moscow State University as well as support of the IORAS administration and RSC-Technologies Group in maintaining supercomputers and data storage systems at IORAS.

FIG. 2 .

 2 FIG. 2. (a) Annual mean (1979-2018) 2-m air temperature in NAAD HiRes and differences in the annual mean 2-m air temperatures (b) between NAAD HiRes and NAAD LoRes, (c) between NAAD HiRes and ERA-Interim, and (d) between NAAD HiRes and ASRv2. Also shown is (e) annual mean (1979-2018) 2-m relative humidity in NAAD HiRes and differences in the annual mean 2-m relative humidity (f) between NAAD HiRes and NAAD LoRes, (g) between NAAD HiRes and ERA-Interim, and (h) between NAAD HiRes and ASRv2. For LoRes and ERA-Interim, the differences are for the period of 1979-2018; for ASRv2, the differences are for the period of 2000-16.

  ) amount to 1.5-2 m s 21 . Comparison of NAAD winds with QuikSCAT winds (Ricciardulli and Wentz 2015) (Fig. 4) indicates that both NAAD HiRes and LoRes have considerably

  FIG. 3. (a) January mean (1979-2018) scalar 10-m wind speed (colors) in NAAD HiRes and wind vectors in NAAD HiRes (red) and ERA-Interim (black), along with differences in 10-m wind speed (b) between NAAD HiRes and LoRes, (c) between NAAD HiRes and ERA-Interim, and (d) between NAAD HiRes and ASRv2. Also shown is (e) the January 99th percentile of 10-m wind speed over subpolar North Atlantic in NAAD HiRes and differences in 99th percentile of 10-m wind speed (f) between NAAD HiRes and LoRes, (g) between NAAD HiRes and ERA-Interim, and (h) between NAAD HiRes and ASRv2. For LoRes and ERA-Interim, the differences are shown for the period of 1979-2018; for ASRv2, differences are shown for the period of 2000-16.

FIG. 4 .

 4 FIG. 4. Histograms of the differences between NAAD HiRes (red), NAAD LoRes (orange), and ERA-Interim (blue) winds with respect to QuikSCAT winds for 2005.

  FIG. 5. (a) January (1979-2018) sensible plus latent turbulent heat fluxes in NAAD HiRes and differences in sensible plus latent turbulent heat flux (b) between NAAD HiRes and LoRes and (c) between NAAD HiRes and ERA-Interim. Also shown is the (d) January (1979-2018) 99th percentile of sensible plus latent turbulent heat flux in NAAD HiRes and differences in 99th percentile of sensible plus latent turbulent heat flux (e) between NAAD HiRes and LoRes and (f) between NAAD HiRes and ERA-Interim.

  FIG. 6. (a) Winter (DJF; 1979-2018) number of cyclones in NAAD HiRes and the differences in the DJF number of cyclones (b) between NAAD LoRes and ERA-Interim, (c) between NAAD HiRes and ERA-Interim, and (d) between NAAD HiRes and ERA5. Units are cyclone tracks per season (DJF) per circle with a radius of 28 latitude [equivalent to approximately 155 000 km 2 ; see Tilinina et al. (2013, 2014) for the mapping metrics].

FIG. 7 .

 7 FIG. 7. Time series of the seasonal (DJF) (a) total number of cyclones, as well as numbers of (b) moderately deep, (c) deep, and (d) shallow cyclones in NAAD HiRes, NAAD LoRes, and different reanalyses. Thin lines show winter (DJF) annual values, and thick lines show 5-yr running means.

FIG. 8 .

 8 FIG. 8. Time series of the (a) annual mean domain-averaged precipitation (mm day 21 ) for 1979-2017 and (b) monthly mean domain-averaged precipitation (mm day 21 ) for 2014-17 in NAAD HiRes (red), NAAD LoRes (orange), ERA-Interim (blue), ERA5 (cyan), GPCP (green), and GPM (magenta).

FIG. 9

 9 FIG. 9. (a) Annual (1979-2018) mean precipitation (mm day 21 ) in NAAD HiRes and differences in the annual mean precipitation (b) between NAAD HiRes and NAAD LoRes and (c) between NAAD HiRes and ERA-Interim. Also shown are (d) annual (1979-2018) mean atmospheric precipitable water content (kg m 22 ) in NAAD HiRes and differences in the annual mean precipitable water content (e) between NAAD HiRes and NAAD LoRes and (f) between NAAD HiRes and ERA-Interim (f).

FIG. 10 .

 10 FIG. 10. July 2015 monthly precipitation rates (mm day 21 ) in (a) NAAD HiRes, (b) LoRes, (c) ERA-Interim, (d) GPM, (e) GPCP, and (f) ERA5.

  FIG. 12. (a) Moisture transport across the AB section (see Fig. 11a) on 5 Dec 2015 in NAAD HiRes (colors) and the component of wind speed orthogonal to the section (contours) as well as (b) the difference in the moisture transport across the AB section on 5 Dec 2015 between NAAD HiRes and NAAD LoRes.

FIG. 13 .

 13 FIG. 13. Wavenumber kinetic energy spectra derived from wind speed data and averaged over the layer between 3-and 5-km height over the whole NAAD domain for (a) NAAD HiRes and (b) NAAD LoRes. In (a) and (b), spectra for the total (black), geostrophic (green), and ageostrophic (yellow) kinetic energy are shown. Dotted lines show power laws of k 23 and k 25/3 , as labeled. (c) Wavenumber kinetic energy spectra derived from surface wind speed diagnosed by NAAD HiRes (green) and ASRv2 (blue) for the area of overlap of the NAAD and ASRv2 domains. The area of overlap of NAAD and ASRv2 is outlined in red in the inlay map, and the gray line in the inlay shows the southern boundary of the ASRv2 domain in the Atlantic region. (d) Wavenumber kinetic energy spectra derived from NAAD HiRes (black) and NAAD LoRes (gray) for the surface (solid lines) and 1500 m (dashed lines) for the tropical domain outlined in red in the inlay map.

FIG. 14 .

 14 FIG. 14. Diagnostics of the polar low on 2 Mar 2008. Shown is the surface 10-m wind speed (colors) and mean sea level pressure (MSLP; contours) as revealed by (a) NAAD HiRes, (b) ERA-Interim, (c) ERA5, and (d) ASRv2. Also shown is sensible plus latent heat flux (colors) and MSLP (contours) diagnosed by (e) NAAD HiRes, (f) ERA-Interim, (g) ERA5, and (h) ASRv2.

  demonstrated the added value of high-resolution nonhydrostatic model settings in improving the representation of polar lows and tip jets, as well as extratropical cyclones. In this respect, our comparison of HiRes and LoRes simulations confirms the conclusions drawn from the ASR evaluation. It also clearly demonstrates the added value of high-resolution and nonhydrostatic model settings in NAAD over the whole North Atlantic. Specifically, NAAD HiRes provides the possibility to resolve mesoscale dynamics associated with high winds, first of all in the North Atlantic subpolar latitudes characterized by small-scale polar lows and tip jets. Here NAAD HiRes demonstrated stronger extreme winds and their better localization compared to the LoRes version and modern reanalyses. Much higher resolution of the WRF Model in NAAD HiRes provides a new view of the North Atlantic extratropical cyclone activity with 2 times as large a total number of cyclones counted in NAAD HiRes relative to LoRes and most reanalyses. This difference was primarily due to cyclones that are smaller in size and relatively shallow, which were poorly simulated in the LoRes experiment and in global reanalyses.

FIG. 15 .

 15 FIG. 15. Diagnostics of Hurricane Gaston in the moment of maximum development on 0000 UTC 31 Aug 2016, showing (a) the precipitation pattern diagnosed by GPM and the MSLP (contours), 10-m wind speed vectors (arrows), and precipitation (colors) as diagnosed by (b) NAAD HiRes, (c) NAAD LoRes, (d) ERA-Interim, and (e) ERA5.

  FIG. 16. (top) Domain-averaged simulated ocean SST in NAAD-OHR (orange) and NAAD-OLR (magenta), along with ESA SST (green). Time series of ESA SST are shown only for the period of data availability (1993-2010). (bottom) Domain-averaged ocean heat content for the 0-700-m layer (upper curves) and the 700-1500-m layer (lower curves) in NAAD-OHR (orange) and NAAD-OLR (magenta). The inlay map shows the domain of the NNATL12 ocean general circulation model. Thick lines in the bottom panels show monthly means, and dashed lines show annual means.

TABLE 1 .

 1 NAAD HiRes and LoRes experimental design (see text for details).

	Attribute

J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 59

Acknowledgments. We acknowledge the support by the Russian Academy of Sciences (ST-ASS-0149-2018-0001) and the Ministry of Science and Higher Education of Russian Federation under the Agreement 14.W0331.0006. Authors Tilinina and Markina were also supported by Grant 17-77-20112 from the Russian Science Foundation. The analysis of the hydrological cycle (authors Zolina, Dufour, and Krinitskiy) benefited from Helmholtz-RSF Grant 18-47-06202. We greatly appreciate critically important comments of the two anonymous reviewers and FIG. 17. (a) Mean SWH in NAAD-WHR and (b) difference in the mean SWH between NAAD-WHR and NAAD-WLR over the period of 1979-2018, as well as (c) mean 95th percentile of SWH in NAAD-WHR and (d) difference in 95th percentile of SWH between NAAD-WHR and NAAD-WLR.


Julien Cenes
email: cenes@laplace.univ-tlse.fr

Gilbert Teyssedre
email: teyssedre@laplace.univ-tlse.fr

Séverine Le Roy
email: leroy@laplace.univ-tlse.fr

Laurent Berquez
email: berquez@laplace.univ-tlse.fr

Virginie Eriksson
email: virginie.eriksson@borealisgroup.com

Wendy Loyens
email: wendy.loyens@borealisgroup.com

Marc Bailleul
email: marc.bailleul@borealisgroup.com

Pierre Hondâa
email: pierre.hondaa@rte-france.com

Christophe Moreau
email: christophe.moreau@edf.fr

Laurent Berquez Laplace

Assessment of the impact of the electrical stress on the ageing for a HVDC model cable

Keywords: XLPE, HVDC, Ageing, Space charge, Cables, Conductivity

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

In the field of electricity transmission (high voltage underground cables), the challenge lies in the development of increasingly compact, reliable and environmentally friendly systems. In this context, polyethylene, notably XLPE has become a material of choice for HVDC cable insulation. However, with the significant growth of the technology over the last 20 years at increasing voltage levels, the behaviour of XLPE insulation needs to be better known under higher DC stress. Many ageing studies have been performed on XLPE, but over relatively short periods of time, at temperatures well above the melting temperature without electrical stress or with relatively low electrical stress [START_REF] Eriksson | Long term performance of XLPE insulation materials for HVDC cables[END_REF]- [START_REF] Bessissa | Lifetime estimation and diagnosis of XLPE used in HV insulation cables under thermal ageing: arithmetic sequences optimised by genetic algorithms approach[END_REF].

The objective of this study is to characterize and compare the electrical and physico-chemical evolutions of a model cable system subjected to thermoelectric or thermal stress for a long period of time. To do so, the applied thermal stress, although above typical operating temperatures of full size cable systems, was kept under the material's melting temperature, while a significantly higher electrical stress was applied than the typical nominal electric stresses of full size cable systems in operation. Ageing and characterizations are performed on model cables thus taking into account the cylindrical geometry, as well as the presence of semi-conducting layers (SC). Current measurements and space charge measurements for the electrical characterizations, and Infra-Red spectroscopy, as well as DSC have been performed on unaged and aged samples.

SAMPLES AND METHODS

The sample used for the study is model cable extruded on a laboratory CCV line using HVDC grade materials. The main advantages of this type of sample compared to plaques are the cylindrical geometry as well as the presence of the semi-conductive layers (SC). The model cables consist of a copper core and a three layer insulation system (SC / XLPE / SC). The core radius is r1=0.7mm, the internal SC radius r2=1.5mm, the insulator radius r3=3mm and the external SC radius r4=3.7mm. Electrical and physicochemical characterizations were performed on an unaged model cable to provide a reference state of the insulation. These characterizations were also performed on aged model cable to observe the evolution with ageing time. The electrical characterization consists in current measurements, to deduce the conductivity, and space charge measurements using the Pulsed Electro-Acoustic (PEA) method. The physicochemical characterizations were carried out using infrared spectroscopy (FTIR), calorimetric analysis (DSC). Thermogravimetric analysis (TGA) and dynamic mechanical analysis (DMA) were also conducted, but are not presented here as results did not provide any significant information.

Ageing set-up

Two ageing protocols were conducted in parallel in ovens with air circulation for 24 months:  A thermal ageing: 100°C isothermal;  A thermoelectric ageing: The temperature is also 100°C isothermal. A voltage of 90kV is applied at the core of the cable providing an electric field of 87kV/mm at the internal SC/XLPE interface and 43kV/mm at the external XLPE/SC interface. The average electric field over the thickness is 60kV/mm.

For each ageing protocol, samples are collected every 6 weeks for electrical and physico-chemical characterizations. The ageing test bench is described elsewhere [START_REF] Cenes | Assessment of ageing in the insulation of HVDC model cables under thermo-electric stress[END_REF].

Electrical characterization

Current measurements on unaged cable: Current measurements were performed under 5 temperatures (25-45-70-90-100°C), and 4 fields per temperature (5-10-15-20 kV/mm). For each electric field, a protocol of 4 hours polarization / 4h depolarization is applied. A unique sample is used for all the measurements, in fields and temperature.

Current measurements

on aged cables: Characterization after ageing is performed only under 70°C for the 4 electric fields (5-10-15-20 kV/mm).

Space charge measurements on unaged cable: Space charge measurements are performed by PEA method directly on model cable. The length of the cable sample for PEA measurement is of 3m. The unaged cable is characterized under three average electric fields (30, 50 and 60 kV/mm) and a thermal gradient of 4.3°C (TSCin= 66.2°C; TSCout= 61.9°C). It is important to note that the gradient within the PEA test cell is the same (4.3°C) but the internal and external temperature values are lower due to the heat sink effect of the cell (TSC_in= 34.3°C; TSC_out= 30°C). For each electric field, a protocol of 4 hours polarization / 4h depolarization is applied. A unique sample is used for all the applied electric fields.

Space charge measurements on aged cables: Space charge measurements after ageing are performed with the same thermal gradient as the unaged cable (4.3°C) but under an average field of 45 kV/mm. The electric field is still applied for 4 hours of polarization and depolarization.

Physico-chemical characterization

For physico-chemical analysis, measuring conditions for unaged cable and aged cable are identical.

FTIR:

Measurements are carried out under controlled atmosphere (air) in transmission mode, on samples of thickness 500 µm that have been sliced directly in the centre of the insulator.

DSC:

The degree of crystallinity as well the melting temperature of XLPE are determined by DSC [START_REF] Gray | Polymer crystallinity determinations by DSC[END_REF]. The sample mass analyzed is 6 mg, taken from the core of the insulation. The DSC thermogram is obtained under nitrogen for a temperature ramp of 10°C/min

RESULTS ON UNAGED SAMPLES

Current measurements

Fig. 2 presents the current as a function of time for the different applied electric fields, at 70°C. The transient current under 5 kV/mm has a special pattern. First a plateau is observed during a short duration (< 200s); then, two declining slopes appear: a steep one for the period between 100 and 3000s, followed by a more moderate one. This trend has been observed for temperatures from 25 to 70°C, but not for higher temperatures for unaged samples. The presence of the plateau could be due to the transport of ions in the sample, driven by the applied electric field. This plateau is shorter in time when the temperature is increased, and is no longer visible at a temperature of 90°C and higher. This could be due to faster transport phenomena at higher temperatures. For higher applied electrical fields, the plateau is not observed, and only the two decreasing slopes are recorded. It is to note that current measurements have been performed consecutively on the same sample: charges (electronic or ionic) can therefore not be totally released during depolarization.

Based on these measurements, the conductivity is determined using equation 1, supposing that it is independent on the field.

Fig. 1: Current function of time on an unaged cable at 70°C for different averaged electric fields

Here Ic represents the mean current over the last minute after 4h of polarization, is the length of the collecting electrode (≈ 30cm) and U the voltage.

[1]

The conductivity follows an Arrhenius law, of the form: [START_REF] Zhang | Conductivity properties of XLPE insulation used for HVDC cable after accelerated thermal ageing[END_REF] where kb is the Boltzmann's constant, Ea the activation energy and o a pre-exponential factor. Fig. 3 presents the variation of the conductivity as a function of temperature for 5 and 20 kV/mm. The estimated activation energy, calculated using equation 2, is of 0.82 eV, and does not vary with the electric field over the tested range.

Fig. 2: Conductivity vs. temperature on an unaged cable at 5 and 20 kV/mm

Space charge measurements

Space charge cartographies are shown in Fig. 3 for average electric fields of 30 and 50 kV/mm. Such stress values, which are significantly higher than those observed in industrial applications, are intended to amplify the phenomena. For an average field of 30 kV/mm (Fig. 3a), a small amount of positive charges is present in the insulation. For higher field (Fig. 3b), the formation of a positive space charge from the internal SC and the propagation of this charge throughout the material are clearly observed. The same conclusions also hold for an average applied field of 60 kV/mm (not presented here). Hence, the charge density generated is higher when the electric field increases. Similarly, the propagation speed of the positive charge is higher when the electric field is higher. No negative charges are observed under these conditions. It is important to note also the relaxation of the The presence of a thermal and an electric gradient under DC conditions implies a conductivity gradient. This variation of the conductivity within the cable produces a redistribution of the electric field, and the appearance of a charge density, of the form: [START_REF] Bessissa | Lifetime estimation and diagnosis of XLPE used in HV insulation cables under thermal ageing: arithmetic sequences optimised by genetic algorithms approach[END_REF] where  is the dielectric permittivity and E the electric field. The charge density and electric field distribution have been calculated using the activation energy previously calculated (Ea=0.82eV), with the hypothesis that the activation energy is the same at 30 kV/mm. Fig. 4 presents the electric field redistribution and the charge density issued from the conductivity gradient at steady state. With an activation energy of 0.82 eV, the charge density is maximum close to the internal electrode (> 0.4 C/m

3 ) and minimum on the external electrode (<0.2 C/m 3 ). The presence of positive charges in the whole insulation layer implies a homocharge zone close to the internal electrode and a heterocharge zone close to the outer electrode. This, in turn, leads to a decrease in the electrical field close to the internal electrode and an increase close to the external electrode, compared to the Laplacian field.

Infrared Spectroscopy (FTIR)

To analyse the FTIR results, we focussed on the carbonyl index, which evolution reflects a modification of the amount of carbonyl groups present in the material and therefore the rate of oxidation. This index is calculated by the ratio of the areas between the carbonyl vibration band (between 1674 cm -1 and 1759 cm -1 ) and the CH2 deformation band (between 1974 cm -1 and 2098 cm -1 )[6] For the unaged material, the carbonyl content is 0.26.

Differential scanning calorimetry (DSC)

Only one peak is observed on the second melting cycle, the maximum of which corresponds to the melting temperature of the material, being Tm= 107.7°C for the unaged material. On the other hand, the calculated crystallinity, linked to the area under the melting peak, is estimated at 44%.

Current measurements Thermoelectric ageing

The evolution of the conductivity as a function of ageing time is presented on Fig. 5, for the different electric fields, at 70°C. The decrease is relatively rapid over the first weeks of ageing (≈30 weeks), and seems to remain stable after that, or increase slightly for the longest ageing times.

The conductivity values are one order of magnitude lower than the ones measured for the reference samples. As a first approximation one possible explanation could be that, the aged material under thermoelectric stress would trap more and more charges over the first 60 weeks of ageing. After this threshold, its propensity to trap charges would decrease. This phenomenon could be explained either by the saturation of the traps present thus preventing a trapping of the charges or by a modification of its structure, which will be analysed by physicochemical measurements. material. In the case of thermoelectric ageing, it would then mean that during 54 weeks, ions are still present in the insulation, but their density decreases with ageing time. After 60 weeks, there would be no more mobile ions, and the 'plateau' is no more visible. It is to note that the conductivity on Fig. 5 for thermoelectric ageing presents a minimum after 60 weeks, and seems to increase after this time. This should be linked to the change of physical processes at play after this time.

Thermal ageing

The conductivity for thermally aged samples is one decade higher than the one measured for thermoelectric ageing (Fig. 5). The conductivity decreases during 24 weeks and then stabilizes for all electric fields except 5 kV/mm. As for thermoelectric ageing, this behaviour is explained by the shape of the current transient (Fig. 6 b) which presents a kind of plateau throughout the ageing process. Moreover, the value of the current on this plateau, even if it decreases with ageing time, is more than two decades higher than the value for thermoelectric ageing for the same ageing time. This means that during thermal ageing, the ions density is higher and has not been consumed after 84 weeks of ageing. Hence, the thermoelectric stress seems to consume the reservoir of ions present in the model cable.

Space charge results

Thermoelectric ageing

Space charge measurements using the PEA method require a calibration before any measurement. As the model cables are aged and a test sample is cut every 6 weeks, the calibration should take place on an already aged and hence already charged sample. This would lead to the cancellation of the charge present before any characterization and to erroneous transfer function estimation. In order to account for residual charge, a specific data treatment has been developed, which allow obtaining the charge state of the as received samples, before any DC stressing. Fig. 7 shows the charge density profiles for different thermoelectric ageing times on as received test samples. During the first weeks of ageing, a positive charge, more intense close to the outer electrode, accumulates. This charge density tends to increase with ageing time. For 60 weeks of ageing, this positive charge becomes more distributed across the insulation (in fact it is observed between 54 and 60 weeks but not presented here), and the charge density still increases. This increase is also visible on the image charges at the SC/insulator interfaces at 1.5 and 3 mm. with an increase of the positive and negative charge densities. For an ageing time above 48 weeks in the present paper, but between 48 and 60 weeks of ageing when looking at the whole cartographies, a spread of these charges across the insulation thickness in observed (see for example Fig. 8c at 90 ageing weeks). For all characterizations, the charge density remains stable during the 4 hours of polarization. These charges mainly come from the ageing protocol (i.e. 100°C isothermal, 90 kV). If we compare these charge profiles to the simulation of Fig. 4, the measured charge density during ageing is largely higher than what would be expected only with a conductivity gradient. The measured charge density has another origin, not only linked to conductivity gradient.

From space charge measurements, it is also possible to calculate the Field Enhancement Factor (FEF),:

Where Eapp(r) corresponds to the Laplacian field. Results (not shown) reveal that the maximum of FEF remains relatively stable during ageing (1.5). The FEF is located close to the outer electrode during the first 54 weeks. It then spread in the middle of the insulation, with approximately the same value (1.5) for the rest of thermoelectric ageing.

Thermal ageing

Thermally aged samples were characterized in the same conditions as for thermoelectrically aged samples. In this case, no electric stress is applied, so there is no charge on as received test samples. Fig. 9 presents the evolution of the charge density for different ageing times. After 6 weeks (Fig. 9 a), positive heterocharges appear at the beginning of ageing, close to the outer electrode. The charge density increases with polarization time. During depolarization, there is a decrease of the charge density, and negative charges are also observed close to the inner electrode. After 30 weeks of thermal ageing, the space charge evolution with polarization time is almost the same as the one after 6 weeks, but the charge density is more than twice the one measured after 6 weeks. Positive charges are also released during depolarization. After 84 weeks (Fig. 9 c), the charge density, having the same dynamics, is however lower compared to the one after 30 weeks. It is to note that for each measurement, an injection of negative charges is measured at the beginning of polarization. These charges come from the outer electrode and cross the insulator in around 5min. This crossing time increases slightly for an ageing time of 30 weeks.

For the thermal ageing, the FEF is higher than the one calculated for the thermoelectric ageing, due to the presence of a higher charge density. The maximum of FEF is of the order of 2.6 and is located next to the outer electrode (at 24 weeks) and then it decreases. Hence, in the case of space charge measurements also, it can be concluded that the thermoelectric stress drives charges, leading to a lower amount of charges measured during characterization compared to thermal ageing.

Physico-chemical analyses

Infrared spectroscopy results

The FTIR analysis was performed on the 17 test samples taken each 6 week during the two ageing protocols (thermoelectric and thermal). Fig. 10 presents the evolution of the carbonyl index as a function of the ageing time for both protocols. The increase of the carbonyl index is visible for both ageing conditions. The impact of the thermoelectric stress compared to the thermal one is not visible for ageing times shorter than 60 to 80 weeks. After 80 weeks, the carbonyl index increases significantly for samples thermoelectrically aged. As no ageing was carried out after 84 weeks in the case of thermal ageing no comparison can be done. The increase of the carbonyl index can have different origins: some products have been consumed during this time, and are not able to slow down oxidation, or the electrical stress implies an additional oxidation, not visible at 'short' ageing times.

Fig. 10: Carbonyl index vs ageing time

The reason for this change of oxidation rate is yet not totally understood and would require deep analysis on several, different samples to understand possible causes.

Differential scanning calorimetry results

The crystallinity (Fig. 11 a) and the melting temperature (Fig. 11 b) do not show any significant evolution with ageing time, for both types of ageing. A significant change in the physical structure of the XLPE would result in a significant decrease of the crystallinity and/or melting point [START_REF] Boukezzi | DSC Study of Artificial Thermal Aging of XLPE Insulation Cables[END_REF]. After 102 weeks of thermoelectric ageing or 84 weeks of thermal ageing, we have not reached a significant ageing to observe this tendency, and only small fluctuations around the value for the reference material are detected.

DISCUSSION

Electrical measurements have shown a change in the electric behavior of the material under thermal and thermoelectric ageing, such as a substantial decrease in conductivity until a threshold of 60 weeks for thermoelectric ageing, and an increase in the space charge density. The major fact in these electrical measurements is that the observed behaviors are very different depending on whether thermal or thermoelectric ageing is considered. The first measurement of current after thermoelectric ageing at 5 kV/mm demonstrates a particular behavior. Up to 60 weeks, the plateau linked to the presence of ions is observed at short times. After this threshold in time it disappears. This behavior could be a marker of the presence of ions produced by dissociation or diffusion within the insulator that are slowly consumed with ageing time. On the contrary for thermal ageing, this specific plateau is still observed after 84 weeks. Space charge measurements show also a threshold in time at 60 weeks for thermoelectric ageing. Below this ageing time, a positive heterocharge, close to the outer electrode, is observed, with a charge density that increases with ageing time. Above 60 weeks, there is a spread of these positive charges inside the insulation. It is to note that no movement of charges is observed during the characterization time for this ageing. On the contrary for thermal ageing, space charge measurements show a comparable pattern along ageing time. Positive heterocharges are observed close to the outer electrode, which density increases during polarization time, and also when the ageing time increases, until 30 weeks. Then the charge density decreases with an increase of the ageing time. These electrical measurements show that the thermoelectric stress drives charges (ions and electronic charges), and apparently decreases the density of ions that are present in the insulation. After the threshold of 60 weeks, other dominant processes are at play that implies a small increase in the conductivity, and a spreading of the charges. All these results have been compared to physico-chemical analyzes to identify if a change in the structure of the material is the cause of the change in behavior. The FTIR measurement shows a relatively low increase of carbonyl index for both ageing for times lower than 80 weeks. After this time, oxidation seems to increase faster for the thermoelectric ageing. The DSC measurement shows no evolution of the crystallinity or the melting temperature with ageing time for the two protocols. The structure of the material does not seem to be affected by different applied stresses.

CONCLUSION

Electrical and physico-chemical characterizations have been performed on thermoelectrically and thermally aged model cables. Electrical stress coupled to thermal stress drives the charges and makes decrease the ion density present in the insulation. This lead to a decrease in the conductivity during ageing, and a conductivity that is one decade lower than for a thermally aged cable. After 60 weeks of thermoelectric ageing, the dielectric behavior of the insulation changes. It is at that time difficult to link this change with any structural modification, as the observed threshold for FTIR measurements seems to appear at longer time, and that no variation was observed on the DSC results. More work needs to be done to fully understand the obtained results.

  Linear fit at 5kV/mm Linear fit at 20kV/mm positive charges during the depolarization phase, the kinetics showing a decrease in the density of these charges in the volume.

Fig. 4 :

 4 Fig. 4: Impact of the conductivity gradient on charge density and electric field within the insulation

Fig. 5 :Fig. 3 :

 53 Fig. 5: Conductivity vs. ageing time for different average electric fields after thermoelectric and thermal ageing A particular behaviour is observed under 5 kV/mm (Fig. 6 a). Like for the unaged cable, a plateau is present during the first 54 weeks of aging. The value of the current on this plateau decreases with ageing time. At an ageing time of 60 weeks, it totally disappears. This plateau has been associated to the presence of ions for the unaged

Fig. 7 :

 7 Fig. 7: Charge profiles of the as received model cable for different ageing times (thermoelectric ageing) Fig. 8 shows the space charge cartographies during characterization, i.e. under an average electrical field of 45 kV/mm and a temperature gradient of 4.3°C, after different ageing times for thermoelectric ageing. After 6 weeks of ageing (Fig. 8 a), heterocharges are present close to both electrodes, during the 4h of polarization. The charge density seems almost stable over that time. The same conclusions hold for 48 weeks of ageing (Fig. 8 b),with an increase of the positive and negative charge densities. For an ageing time above 48 weeks in the present paper, but between 48 and 60 weeks of ageing when looking at the whole cartographies, a spread of these charges across the insulation thickness in observed (see for example Fig.8cat 90 ageing weeks). For all characterizations, the charge density remains stable during the 4 hours of polarization. These charges mainly come from the ageing protocol (i.e. 100°C isothermal, 90 kV). If we compare these charge profiles to the simulation of Fig.4, the measured charge density during ageing is largely higher than what would be expected only with a conductivity gradient. The measured charge density has another origin, not only linked to conductivity gradient.

Fig. 6 :

 6 Fig. 6: Current fonction of time under 5 kV/mm. a) thermoelectric ageing, b) thermal ageing

  Charge density for different thermal ageing time. a) 6 weeks, b) 30 weeks and c) 84 weeks

Fig. 11 :

 11 Fig. 11: Evolution of the crystallinity and the melting temperature of XLPE as a function of time, for a thermoelectric and a thermal ageing