dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021; Cooley et al. , 2022). The key question in the present study is whether ESMs simulate the observed ocean deoxygenation and O 2 variability during the historical era (defined here as changes in O 2 concentration or inventory in the past 5 decades)  (Ito et al. , 2017; Schmidtko et al. , 2017; Ito, 2022). In this study, we conduct a comprehensive assessment of simulations from CMIP6 models  (Eyring et al. , 2016)  focusing on the global upper ocean (0-700m) deoxygenation in the past 5 decades (1960s-2010s) and how they compare to a recent observational dataset  (Ito, 2022). Using model outputs from the Ocean Model Intercomparison Project 1 and 2 (OMIP1 and OMIP2)  (Griffies et al. , 2016; Orr et al. , 2017)  and CMIP6 Historical simulations, we examine how differences in model structure and atmospheric forcing (i.
e. forced ocean-only vs coupled configurations) lead to differences in the simulated oceanic O 2 content response to the recent warming and compare these simulated trends to the most recently available gridded observational dataset  (Ito, 2022). Various estimates of changes in ocean O 2 levels exist, varying mainly in their observational dataset construction methods (such as interpolation and gap filling procedures)  (Helm et al. , 2011; Ito et al. , 2017; Schmidtko et al. , 2017; Ito, 2022; Ito et al. , 2023) , though the availability of these products to the public via a community O 2 data platform and their comparison is still underway (e. g. Greǵoire et al. , 2021). We thus focus on the publicly available dataset from Ito, 2022 (and some complementary analysis based on the previous gridded O 2 dataset from 2017;  Ito et al.
, 2017)  and include a discussion on observational uncertainty in our comparison with the simulations. Our overall aim is to provide a comprehensive overview of model simulations of historical upper ocean deoxygenation and identify remaining challenges for the state-of-the-art models as guidelines for future coordinated efforts for ocean biogeochemistry simulations. The structure of the paper is as follows. We first describe the CMIP6 models, configurations, observational dataset and data analysis methods used herein (Section 2). Section 3 outlines the OMIP simulations' equilibrium states of sea water temperature and upper ocean O 2 along with key differences in climatological mean states of upper ocean O 2 between OMIP, CMIP6 Historical simulations and observations. In Section 4, we compare the observed and simulated changes in upper ocean O 2 over the past 5 decades, focusing on the upper 700m, where most of the O 2 changes and warming are expected in recent decades.
Ocean biogeochemistry simulations We use model outputs from two sets of simulations from the forced ocean-only model simulations and a set of fully coupled Earth System Model (ESM) simulations from the CMIP6 Historical configuration  (Sefeŕian et al. , 2020). More specifically, the forced ocean-only model simulations are based on the Ocean Model Intercomparison Project 1 (OMIP1) and Project 2 (OMIP2) protocols  (Griffies et al. , 2016; Orr et al. , 2017; Tsujino et al. , 2020)  with two additional simulations based on the ECMWF ERA-20C and the NCEP-NCAR Reanalysis 1 atmospheric dataset  (Kalnay et al. , 1996; Poli et al. , 2016). Forced oceanonly model simulations include ocean physics and biogeochemistry, with the atmosphere represented by noninteractive (and observation-derived) reanalysis fields of properties with bulk formulae to calculate ocean heat, moisture, and air-sea gas exchange fluxes, and momentum transfer.
, 2018) , respectively. The JRA55-do atmospheric forcing covers a more recent period and increase in spatio-temporal resolutions compared to the predecessor  (Tsujino et al. , 2018; Tsujino et al. , 2020). The atmospheric states of the original JRA-55 have been adjusted to match reference states based on observations or the ensemble means of atmospheric reanalysis products, which makes the atmospheric forcing dataset more realistic compared to the CORE-II forcing  (Tsujino et al. , 2018). These datasets are used to force the ocean biogeochemistry models and have been cyclically applied for the simulations. For the OMIP1 simulations, the length of the simulation is 310 years, conducted by repeating 5 cycles of the CORE-II forcing  (Large and Yeager, 2009). For the OMIP2 simulations, the length of the simulation is 366 years, completed by repeating 6 cycles of JRA55-do forcing  (Tsujino et al.
, 2018). The initial conditions are based on the observational dataset from the World Ocean Atlas 2013 and GLODAPv2  (Olsen et al. , 2016; Garcia et al. , 2019). For further detailed information of the forced ocean-only model simulations and experimental designs, see the OMIP protocol references  (Griffies et al. , 2016; Orr et al. , 2017; Tsujino et al. , 2020). Our focus is mainly on the past 5 decades from 1960s to 2010s. This corresponds to the last cycle from the OMIP1 and OMIP2 simulations. We also analyzed two additional forced ocean-only model simulations to complement the analysis of OMIP simulations. One is based on the ECMWF ERA-20C atmospheric reanalysis forcing 1900-2009 for the MPI-ESM1. 2 model, the ocean model MPI-OM with an ocean biogeochemistry model HAMOCC6  (Ilyina et al.
The NEMOv3. 6-PlankTOM12. 1 model was integrated from  1750-1947 (198 years)  based on the NCEP-NCAR  Reanalysis 1 climatological (a 1948 Reanalysis 1 climatological (a -1977  daily climatology) atmospheric forcing followed by 61 years of hindcast simulation using the interannual atmospheric forcing from 1948-2009. Further details of these two additional simulations are provided in the Supplementary Material. While the analysis periods differ slightly among the models and configurations, these differences were not found to significantly impact our results. We compare these simulations with the most recent and publicly available observational O 2 dataset  (Ito, 2022). Before focusing on the analysis of model outputs from the recent decades, we briefly examine the models' equilibrium states of upper ocean temperature and O 2 from OMIP1 and OMIP2 using the model output from the full length of the simulation cycles.
Observational dataset The observational product used in this study is the global threedimensional, time varying O 2 dataset based on the quality-control bottle O 2 data from the World Ocean Database 2018 (WOD2018) from 1965-2015  (Ito, 2022) (hereafter ITO2022 dataset). This product is a 1-degree gridded product based on the optimal interpolation method to fill in spatial gaps. In this dataset, the number of O 2 samples is denser in 1970s and 80s, with more samples in the North Pacific and North Atlantic Oceans. The global coverage of samples is more even after 1990s, but overall, the samples remain relatively sparse, particularly after the year 2000 Additional information for each model may be found in the references cited herein. The full five-cycle simulations for OMIP1 (5×62 years) are labeled as 1700-2009 and the full six-cycle simulations for OMIP2 (6×61 years) are labeled as 1653-2018.
Some OMIP1 models do not include the full length of simulations (see labels) due to differences in the configuration-specific settings. (see Figure  1  from Ito, 2022). Despite the interpolation, smoothing and gap filling procedures, the signals could be skewed towards the data dense regions in certain periods. Note that this product differs from the previous product  (Ito et al. , 2017) , which includes Conductivity Temperature Depth (CTD) profiler measurements. The O 2 data product from ITO2022 dataset is presented as an anomaly relative to the long-term  mean  (Ito, 2022) , where O 2 concentrations are shown as yearly anomalies calculated using a 5-year moving averaging window. Note that a 5-year moving averaging window reduces the variability on timescales shorter than 5 years  (Ito, 2022).
The estimate of global ocean deoxygenation from this latest observational product is smaller than the previous works  (Ito et al. , 2017; Schmidtko et al. , 2017)  and thus provides a lower bound estimate of global ocean deoxygenation  (Ito, 2022). We compare the upper ocean heat content (OHC) based on five dataset products to the simulated OHC from OMIP1, OMIP2, and CMIP6 configurations. These dataset products include the ECMWF Ocean Reanalysis System 4 (ORAS4) (following the previous observational O 2 and OHC study from  Ito et al. , 2017) , EN4. 2. 1 from UK Met Office, Cheng dataset from the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (http://www. ocean. iap. ac. cn/), Ishii dataset from Japan Meteorological Agency, and Levitus data from NOAA NCEI  (Gouretski and Reseghetti, 2010; Levitus et al.
, 2012; Balmaseda et al. , 2013; Ishii et al. , 2017; Cheng et al. , 2022). We additionally use the sea water temperature and salinity data from the ECMWF ORAS4, EN4. 2. 1, Cheng, and Ishii datasets to calculate oxygen saturation (O 2,sat and Apparent Oxygen Utilization (AOU) (in sections 2. 3 and 3. 4). For climatological comparison of upper ocean O 2 (in section 3. 1), we use the observations-based climatology from the World Ocean Atlas 2018  (Garcia et al. , 2019).
e. an increase in AOU is shown as a decrease in -1 × AOU, which translates to a decrease in O 2 ) (section 3. 4). All the model outputs are re-gridded to a 1-by-1 degree horizontal grid using distance weighted average remapping method (climate data operators (CDO); remapdis)  (Schulzweida, 2022). The vertical interpolation is based on linear level interpolation (climate data operators (CDO); intlevel)  (Schulzweida, 2022) , interpolated from the model native levels to 102 vertical levels following the observational product, though our analysis focuses primary on the upper depth range (0-700m) of the ocean (i. e. the top 41 vertical levels). The spatial resolution of the regridded model output is the same as the ITO2022 dataset used in this study (Section 2. 2). The main diagnostics of ocean deoxygenation and associated ocean warming for the past 5 decades are based on the anomaly of O 2 and sea water temperature relative to the long-term mean (i.
e. the past 5 decades). The calculations of the anomaly of O 2 from the model output basically follows the ITO2022 dataset. We focus on upper ocean O 2 and heat content changes as this is where the majority of ocean physical and biogeochemical changes due to atmospheric forcing on inter-annual to decadal timescales are expected. Also the upper ocean environmental changes are highly relevant for marine ecosystems (e. g. Longhurst and Harrison, 1989). We calculate globally integrated O 2 inventory anomaly and OHC time series for comparing models and observations evaluations. OHC is in anomaly by definition (e. g. Levitus et al. , 2012). Ocean biogeochemistry simulations require a long term spin-up to reach equilibrium and can exhibit a long-term drift in their solution  (Sefeŕian et al. , 2016).
, 2020. To assess the models' drift from forced ocean-only model simulations, we explore upper ocean global mean (covering 89. 5°S -65°N in this study following the spatial coverage of the ITO2022 dataset) properties as a metric to evaluate the models' equilibrium states for the variables on which we are focusing. Specifically we examine the global volume-weighted mean sea water temperature and O 2 from 0-700m from the OMIP1 and OMIP2 simulations (Figure  1 ). The time series of the global volume-weighted mean O 2 and sea water temperature from the upper ocean for the full cycles show individual model transitions to their respective quasiequilibrium states for both properties. We show that for the final 50-60 years of the simulations, the analysis of ocean deoxygenation during this period is not significantly impacted by model drift in the upper ocean (see Figure  1 ).
We used linear trend analysis to quantify multidecadal changes in upper ocean O 2 inventory and OHC. Linear trend analysis is based on annual model output and observational dataset. The statistical significance test uses a modified Mann-Kendall test, which takes into account autocorrelations within the dataset  (Yue and Wang, 2004; Hussain and Mahmud, 2019; Martıńez-Moreno et al. , 2021).
, 2019). The volcanic effects were previously assessed in the large ensemble simulations (e. g. Eddebbar et al. , 2019; Fay et al. , 2023)  and emerge similarly here in the CMIP6 Historical multi-model mean (see the Pinatubo-related pause in ocean deoxygenation and heat loss in CMIP6 Historical in Figures  3A, B ), but this signal is challenging to detect in OMIP simulations and observations. The discrepancy in OMIP simulations could be due to the confounding effect of atmospheric driven climate variability (i. e. such as a common wind forcing in OMIP simulations modulating the ocean circulation) or OMIP simulations are less sensitive to volcanic effects. Detection of volcanic effect in observations is particularly challenging given the large gaps and uncertainties in the dataset. The complementary MPI-ESM1.
The signature of the major volcanic eruptions are also pronounced in these simulations (Figures  3A, B , particularly for Agung  (1963) (1964) (1965)  and  Pinatubo (1991 Pinatubo ( -1994)) , in line with the expected cooling of the upper ocean (Figure  3B ) and intrusion of O 2 following eruptions which acts to temporarily slow down the advance of ocean deoxygenation  (Eddebbar et al. , 2019; Fay et al. , 2023). The O 2 loss shown by the CMIP6 Historical simulations occurs alongside significant heat uptake which is also in line with the observed estimate of changes in the upper OHC (Figure  3B ). Simulated O 2 shows weaker variability than observed at depth  (Long et al. , 2016) , an issue that likely persists in the OMIP and CMIP6 simulations (Figures  3C, E, G ). About half of the models show lower temporal SD in global O 2 inventory compared to the latest ITO2022 dataset (magnitude ∼ 0.
75 × 10 14 mol) and much smaller than the temporal SD from the previous dataset from Ito et al. , 2017 (Figures  3C, E, G ). While the CMIP6 Historical simulations show a relatively strong 50-year downward trend (Table  2 ) in the global O 2 inventory, major biases associated with physical and biogeochemical processes (e. g. zonal equatorial currents and eddies) in hinders their ability to reproduce observed variability of O 2 (e. g. Busecke et al. , 2019). The temporal SD of global OHC time series from OMIP1 and OMIP2 simulations are smaller compared to the CMIP6 Historical simulations. Compared to the recent observational OHC time series, the simulated temporal SD might underestimate the observed fluctuations compared to the temporal SD from the Cheng and Levitus datasets. The ECMWF ORAS4 and EN4.
2. 1 show higher SD compared to the simulations and other observational datasets. The 50-year linear trend in upper ocean global O 2 inventory and OHC for models and observations are summarized in Table  2. The magnitude of the total decrease of global O 2 inventory over the historical simulation period is about 4-5×10 14 [mol] for the CMIP6 Historical simulations (Figure  3A  and Supplementary Material), which is much stronger than the OMIP simulations. The latest estimates based on observational dataset shows a similar range, though discrepancies between observational products arising from gaps in observational coverage and differences in interpolation methods continue to generate large uncertainties in the rate of global ocean deoxygenation  (Ito, 2022). A detailed observationsmodel comparison requires sub-sampling, gridding, and interpolation of model outputs in similar fashion to the observational estimates and will be addressed in a separate study  (Ito et al.
Linear trend values are based on Theil-Sen slope estimator and values with asterisk denotes statistically significant trends based on a modified Mann-Kendall test (p < 0. 05). The ± ranges indicate the multimodel standard deviation for each configuration (OMIP1, OMIP2, CMIP6 Historical) and the standard deviation among the five observational dataset for OHC (± 0. 34). For the observational global O 2 inventory, we present results from two different datasets with three different estimates based on  Ito et al. , 2017; Ito, 2022. Volume normalized (denoted as VN) value is the grid-volume normalized value presented in  Ito et al. , 2017. The observational global O 2 inventory trend values in square brackets denote linear trend estimates with uncertainties (the 2. 5 th and 97. 5 th percentiles of the slope distribution based on a bootstrapping method).
The Hovmöller diagram of sea water temperature anomaly include results from the models (A-C) and four different observational datasets (D-G). Ensemble data spread (SD) for observational datasets is included in (H). the strong changes in the CMIP6 Historical simulations towards negative O 2 saturation as much as -1 mmol m -3 and increasing AOU, which results in stronger ocean deoxygenation trends in these simulations, consistent with the trends shown in Figures  3, 4. The strong negative O 2 saturation and AOU changes are consistent with the intense simulated ocean heat uptake in CMIP6 Historical simulations, and observations (Figure  9 , black lines and dots).
Potential impact of atmospheric forcing Differences in the long-term trend of the surface wind stress fields between OMIP1 (CORE-II) and OMIP2 (JRA55-do) (in Figure  12 ) could lead to bias in spun-up states and subsequent decadal response in the upper ocean O 2 inventory. A spurious trend in zonal wind stress in the tropical Pacific Ocean (also discussed in  Yeager et al. , 2018; Ito et al. , 2019  and see Figure  12A ) likely leads to O 2 inventory and OHC biases in the Pacific Ocean (see Figure  4 ) through changes in the equatorial currents and upwelling in this Correlation coefficient patterns of column OHC (i. e. two-dimensional data) against the globally integrated upper ocean O 2 inventory time series (i. e. one-dimensional data) averaged over all models (A-C) and from an observational dataset (D).
No de-trending applied to the data to focus on the trend signals. Black dots designate robustness, as defined by at least 80%model sign agreement in correlation coefficients. The correlation analysis is based on the period of  1958 -2007 for OMIP1, 1969 -2018 for OMIP2, 1965 -2014 for CMIP6 Historical, and 1965 -2015  for the observations [WOD2018 and Cheng's dataset  (Cheng et al. , 2022; Ito, 2022) ]. Correlation coefficients based on other observational column OHC dataset are in Supplementary Material. region. These biases overall impact on the global O 2 inventory and OHC the OMIP1 simulations. Therefore, while the OMIP1 simulations struggle to simulate the observed O 2 loss, OMIP2 simulations show improvement to some extent in simulating global ocean deoxygenation likely due to reduced bias in zonal wind stress and increased resolution of the wind fields  (Oschlies et al.
g. Deutsch et al. , 2011; Ito and Deutsch, 2013; Deutsch et al. , 2014; Eddebbar et al. , 2017; Duteil et al. , 2018; Ito et al. , 2019; Poupon et al. , 2023). Ocean biogeochemistry models are known to have major issues in simulating the observed patterns and magnitude of global ocean deoxygenation in past decades  (Stramma et al. , 2012; Oschlies et al. , 2017). Our analysis shows some improvements in simulating the observed global ocean deoxygenation. The observational datasets contains major uncertainties, with varying rates of O 2 loss across studies from the estimates of  Ito et al. (2017)  to more conservative estimates of  Schmidtko et al. (2017) and Ito (2022). Recent efforts to combine existing datasets and new observations from autonomous floats e. g. Addey (2022)  along with more robust quantification of uncertainties associated with data coverage and interpolation, alongside improved comparisons to models will be critical for a comprehensive assessment.
Acknowledgments The submission has been approved and assigned LA-UR number  LA-UR-22-29410. This research used resources of the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. We thank  Charles Stock  and  Dave Munday  for the comments on the manuscript and  Taka Ito  for the early discussion on observational dataset.
Data availability statement Publicly available datasets were analyzed in this study. The CMIP6 and OMIP data can be found here: https://esgf-node. llnl. gov/search/cmip6/. The gridded observational dissolved oxygen dataset can be found in the websites (https://o2. eas. gatech. edu/ data. html; https://www. bco-dmo. org/dataset/816978). Ocean temperature and salinity data could be found here: https://www. cen. uni-hamburg. de/en/icdc/data/ocean. html. Cheng's dataset can be found here: http://www. ocean. iap. ac. cn/ and Ishii's ocean heat content dataset can be found at the Japan Meteorological Agency's (JMA) website: https://www. data. jma. go. jp/gmd/kaiyou/english/ ohc/ohc_global_en. html.
Introduction New aircraft configurations such as the strut-braced wing require experimental studies, as results coming from simulations are less trustworthy than those regarding traditional and well-tested configurations. Data should be collected to assess both static and dynamic aeroelastic behavior, with the ultimate goal of validating numerical models and techniques. After a scaling process of a typical strut-braced wing reference aircraft provided by ONERA, a 1:10 scale model for wind tunnel testing is designed by Polimi and IBK allowing for some variations in the geometry and kinematics of the strut. A pneumatic system is designed for the excitation of the model inside the wind tunnel when turbulence is not enough. The dynamic response is measured both in terms of accelerations and displacements, using accelerometers and an optical system to track the motion of some markers on the model.
Aeronautics and Astronautics - AIDAA XXVII International Congress Materials Research Forum LLC Materials Research Proceedings  37 (2023)  34-37  https://doi. org/10. 21741/9781644902813-8  Acknowledgement This project  U-HARWARD  has received funding from the  Clean Sky 2 Joint Undertaking (JU)  under grant agreement No  886552. The JU receives support from the  European Union 's  Horizon 2020 research and innovation programme  and the  Clean Sky 2  JU members other than the  Union.
Data preprocessing The EEG was recorded using a 64-electrode channel system. Data pre-processing and analysis were performed with Brainstorm (Tadel et al 2011) and Mat-lab2021b (MathWorks, Inc. Natick, 158 Massachusetts, USA), using the Automagic toolbox  (Pedroni et al 2019). The preprocessing pipeline was applied per subject, on their whole recording. The data were resampled to 1024 Hz. The data were notch filtered at 50 Hz. A high pass and low pass filter were set respectively to 1 and 45 Hz. Automagic was used to automatically pre-process every dataset, with a manual inspection at the end. The ICA MARA algorithm was used  (Winkler et al 2011 (Winkler et al , 2014)). The residual bad channels were eliminated, identifying the ones with a standard deviation exceeding 20 µV. Finally, bad electrodes were interpolated.
Data processing-BNS identification From this final dataset, BNSs segmentation was performed using clustering algorithms to identify stable periods over time. The analysis was done based on some functions developed in the EEGLab Microstate toolbox  (Poulsen et al 2018). This toolbox is designed for EEG voltage topography, but we adapted the pipeline to use it with functional brain network as an input. Since the n × n connectivity matrix (n = 56) is symmetric, we vectorized it in a (n * (n -1)/2) = 1540 elements vector, leading a total of 158 matrices of size 478 × 1540. Those matrices have been averaged across the 158 subjects, to obtain a final averaged matrix of size 478 × 1540, that will be used as the input for the state segmentation. Then, the modified k-means algorithm (Pascual-Marqui et al 1995) was used as a clustering algorithm, and the BNS were sorted according to global explained variance (GEV)  (Murray et al 2008 , Poulsen et al 2018).