dataesr/scientific-paragraphs-categorization huggingface dataset:
The rectified lake area laid a solid foundation for subsequent estimation of lake volume changes. Seminal research has demonstrated the large potential of remote sensing data for retrieving lake water levels, using mainly satellite radar data (e. g. , Topex/Poseidon, EN-VISAT, Jason, and CryoSat-2) [10,  13, [15] [16] [17]. Our results indicated that ICESat and ICESat-2 data had measurement errors of 35. 5 cm and 26. 0 cm, respectively (Figure  S6a ), better than previous radar measurements (Figure  S6b )  [55]. Moreover, compared with the ESA CCI database, a typical satellite radar-based water level product, ICESat/ICESat-2 observed more medium-sized lakes (i. e. , lakes with an area of 10-50 km 2 ), which are important habitats and water supplies in arid and semi-arid regions  [5]. Another operating altimeter, GEDI (i.
e. , Global Ecosystems Dynamics Investigation) lidar onboard the ISS (i. e. , International Space Station) has a theoretical observation capability comparable to GLAS or ATLAS due to its small footprint (~25 m). However, the orbital inclination angle of the ISS is 51. 6 • , which prevents the GEDI lidar from observing lakes at high latitudes  [56]. Thus, ICESat/ICESat-2 altimetry data currently remains the best option for global-scale lake water level inversion. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission will soon enable a more comprehensive estimate of lake volume, including the exceedingly small water bodies (<1 km 2 in area)  [57]  over which ICESat/ICESat-2 laser shots are sparse; thus, it is expected to contribute enormously to the conservation of global lakes.
195, p = 0. 025). This is expected because paired observations were the inputs of our sampling-based method. With the accumulation of ICESat-2 data in the near future, our methods are expected to monitor the dynamics of a larger number of lakes, with possibly higher accuracy. With more ICESat-2 observations, it is also possible to quantify more accurately the codynamics between lake area and lake water level based on stratified sampling. We here established a constant, linear regression slope (between lake area and lake water level) per lake. However, the codynamics between lake area and lake water level could be nonlinear or time-dependent, which have been reported in, e. g. , Lake Urmia, Lake Mead, and Lake Tana  [20, 31].
Validation and Uncertainties Assessment of the Estimated Lake Volumes Prior to the analysis of lake volume changes, we assessed the accuracy of the estimated lake volumes. In spite of the difficulty of measuring lake volume on the ground, we were able to compile in situ lake volumes for 14 lakes from multiple sources (Table  S3 ). To our knowledge, this dataset is the largest compilation for the validation of satellite-based lake volumes. Compared with in situ volumes, our estimated volumes showed NRMSEs ranging from 3. 5% to 37. 5%, which are similar to a previous estimation based on 30 years of altimetry data  [31]. The validation also confirmed that two major results of this study are robust. First, our calculation that 9065 lakes increased in volume by 597 km 3 with a 90% confidence interval of 239-2618 km 3 , is reliable.
Data Availability Statement: The HydroLAKES database was downloaded from the official website (https://www. hydrosheds. org/page/hydrolakes; accessed on 18 January 2022). The ICESat and ICESat-2 data were obtained from the NSIDC (https://nsidc. org/; accessed on 18 January 2022). The GSW database, TWS database, and ERA5 database were publicly available, and were downloaded using Google Earth Engine. The database containing monthly volume series of 9065 lakes can be downloaded via https://doi. org/10. 5281/zenodo. 5902774 (accessed on 18 Janurary 2022).
Recent studies have shown a weakening of the Atlantic Niño variability in the past decades  [22] [23] [24]. The changes in eastern equatorial Atlantic SST variability have been attributed to the combined effect of a weakening of the Bjerknes feedback  23  (BF) and increased heat flux damping  23, 24  and to a basin-wide warming related to climate change  22. These studies used observational and reanalysis datasets to investigate changes in the SST variability during the historical period. Extensive analysis of the projections from the Coupled Model Intercomparison Project (CMIP) indicates that ENSO events will become stronger under global warming, but large uncertainties exist  [25] [26] [27] [28] [29] [30]. Large climate model biases in the tropical Atlantic sector  [31] [32] [33] [34]  have discouraged the climate community from carrying out a similar in-depth assessment of climate change in the area, as have the large uncertainties in the projected weakening of the Atlantic Meridional Overturning Circulation, in simulated Atlantic Multi-decadal Variability and in their influence on Atlantic Niño variability  4.
Impact of model biases Coupled general circulation models show large biases in the tropical Atlantic region  [31] [32] [33] [34] 43, 44  and, in particular, a warm SST bias in JJA in the eastern equatorial Atlantic, where projected changes in SST variability are largest. We fi d that the models with smaller bias have a stronger reduction in SST variability (Fig. 4a ), a stronger reduction in the third BF component (that is, the thermocline feedback) (Fig. 4b ) and a larger SST change between future scenario and historical (Fig. 4c ). Therefore, biases in the models seem to suppress the reduction of The change here is defined as the difference of the mean of the scenario period (2050-2099) minus the mean of the historical period (1950-1999). The vertical grey line in a represents an estimation of the third component of the BF from observation and reanalysis datasets over the period 1982-2017.
The grey shading depicts the 95% confidence interval of the linear regression. The confidence interval is obtained by taking the 2. 5th and 97. 5th percentile of the distribution of the linear regressions of the 10,000-time resampled datasets. The blue (black) numbers correspond to the CMIP6 (CMIP5) models listed in Supplementary Table  1. the SST variability in a future warmer climate through a reduction of the weakening of the thermocline feedback. This relation with SST bias is expected. The strength of the equatorial cold tongue and the thermocline feedback are connected through the intensity of equatorial upwelling and upper ocean vertical temperature gradients. Thereby, a stronger cold tongue is associated with a stronger thermocline feedback (explained variance of 28%, Fig. 4b ) and shallower thermocline  4.
Articles NATure ClIMATe CHANge methods Data. We use monthly mean model output obtained from the two latest CMIP generations: CMIP5 (ref. 37 ) and CMIP6 (ref. 38 ). We use the following fields from the CMIP models: SST, UAS, surface heat fluxes and ocean potential temperature. The latter is used to derive the depth of the 20 °C isotherm depth, which serves as a proxy for thermocline depth (Z20, hereafter). We use the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST)  50  to compute the model SST biases over the period 1950-1999. We also use an ensemble composed of the European Centre for Medium-range Weather forecast (ECMWF) Re-Analysis version 5 (ERA5)  51  , the NOAA Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5)  52  , the HadISST and the Optimum Interpolation SST analysis version 2 (OISSTv2)  53  to estimate the ATL3-averaged JJA SST variability over the period 1982-2017.
However, most of our results are based on the comparison between historical simulation and the highest emissions scenario simulations RCP85 and SSP585. The climate models used in this study are listed in Fig. 1  and Supplementary Table  1. We use one ensemble member for each of the models, and all model data have been interpolated to a common horizontal 1° × 1° grid. Statistical metrics. We use the standard deviation of the JJA SSTa and the MJJ UASa as metrics to investigate the changes in variability between the simulated historical and future climate periods. The season JJA (MJJ) is chosen for the SST (UAS) variability as it is the season of largest SST (UAS) variability in CMIP5 and CMIP6 models. For this analysis we use the 50-year periods January 1950 to December 1999 and January 2050 to December 2099 for the historical and scenario simulations, respectively.
Acknowledgements This study was supported by the  TRIATLAS  project funded under European Union's  Horizon 2020 programme  (grant agreement number  817578 ; L. R. C. , N. K. , S. K. , E. S. -G. ); and by the  Bjerknes Climate Prediction Unit  with funding from the  Trond Mohn Foundation  (grant  BFS2018TMT01 ; N. K. , L. S. ). This study was partially supported by the  German Federal Ministry of Education and Research  as part of the  BANINO  project ( 03F0795A ; A. P. ). We also acknowledge Norwegian national computing and storage resources provided by  UNINETT Sigma2 AS  ( NN9039K ,  NS9039K ).
Data availability The CMIP6 data can be found at https://esgf-data. dkrz. de/search/cmip6-dkrz/. The CMIP5 data can be found at https://esgf-node. llnl. gov/search/cmip5/. The ERA5 data can be found at https://cds. climate. copernicus. eu/cdsapp#!/dataset/ reanalysis-era5-single-levels-monthly-means?tab=form. The ERSSTv5 data can be found at https://psl. noaa. gov/data/gridded/data. noaa. ersst. v5. html. The OI-SST data can be found at https://psl. noaa. gov/data/gridded/data. noaa. oisst. v2. highres. html. The HadISST data can be found at https://www. metoffice. gov. uk/hadobs/hadisst/. The NCEP-DOE Reanalysis 2 data can be found at https:// psl. noaa. gov/data/gridded/data. ncep. reanalysis2. html. The ORA-S4 data can be found at https://www. cen. uni-hamburg. de/en/icdc/data/ocean/easy-init-ocean/ ecmwf-ocean-reanalysis-system-4-oras4.
Code availability The data analysis was conducted using the software CDO (https://code. mpimet. mpg. de/projects/cdo/embedded/cdo. pdf), Matlab (https://www. mathworks. com/ products/matlab. html) and Python (https://www. python. org/). The code that was used for data processing, model analysis and figure production is available at https://doi. org/10. 5281/zenodo. 6815433  62.
1 ). Lightning reported at northern latitudes in recent studies  [5] [6] [7]  , especially above the 2-bar level  7  , points to moist convection within ammonia clouds as a powerful source of energy in Jupiter's polar regions. However, even though the dynamical link between small-scale moist convection and large-scale vortices has been investigated at high latitudes in recent modelling studies  [8] [9] [10] [11]  , it has never been supported by observations. Here, we investigate this link using high-resolution infrared images taken by JIRAM at the north pole of Jupiter on 2 February 2017, from which two complementary datasets are derived. First, we use wind velocities (Extended Data Fig. 1a ) derived by tracking clouds in sequence of infrared images (see ref. 4  for the methodology) in a domain greater than 7,000 × 20,000 km (Fig.
1 , black rectangle), where overlapping observations over short intervals are available. The wind field has a resolution of 10 km, which is slightly higher than previous measurements  12. Second, the optical depth, defined as log(I 0 /I), with I the brightness and I 0 = 0. 55946 W m -2 the maximum brightness, is directly obtained from the infrared measurements  3  with the same resolution as the wind field (Fig. 2a ). Optical depth is related to variations in cloud thickness such that thick cloud produces high optical depth  3, 13. The analysis of these two unique datasets using a theoretical framework (the surface quasi-geostrophy (SQG) framework  [14] [15] [16] [17] [18]  , described in Moist convection is a KE source at 100 km scales section) allows us to uncover a dynamical regime on Jupiter, which highlights the key role played by moist convection at Jovian high latitudes.
Methods Datasets. Infrared images. Infrared images are taken by JIRAM's imaging channel in the M band, which operates at 4. 8 μm wavelength. These are images of ammonia clouds shielding the thermal emission of the planet at different viewing angles  2. During the perijove 4 orbit, JIRAM performed a series of overlapping observations of the poles of Jupiter in the hours bracketing perijove. In this paper, we consider only the north polar sequences because they provide better overlap among the images and shorter intervals between images than at the south pole (Extended Data Table  1 ). Observations were taken on 2 February 2017 and have a resolution of ~10 km per pixel, depending on foreshortening and spacecraft motion. The image processing, from the raw data to the polar orthographic projected maps, is thoroughly described in ref.
In Main, we show the results for one typical mosaic (n02, Fig. 2a ), taken at mid-time between the two mosaics used to derive the wind field described below. Wind measurements. Horizontal winds are computed by tracking cloud features between two overlapping infrared observations separated by 16 min using Tracker 3 software from JPL 4 with a resolution of 10 km and an error of 2. 1 m s -1. That dataset has filename 'velocity vector' in the Supplementary Data. To remove remaining small-scale noise, we apply a Butterworth filter of order 1 with a cutoff wavelength of 250 km. We analyse a total of two wind mosaics (derived from a total of 48 infrared images), yielding similar results (Extended Data Figs. 8  and 9 ). In Main, we show results for the area with broader coverage (Extended Data Fig.
Acknowledgements A portion of this work was carried out at the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with the  National Aeronautics and Space Administration (NASA) , grant/cooperative agreement no. 80NSSC20K0555 , and a contract with the Juno mission, which is administered for NASA by the  Southwest Research Institute. L. S. was first supported by a  Caltech-JPL postdoctoral research  grant.
Data availability JIRAM data are available at the Planetary Data System (PDS) online (https://atmos. nmsu. edu/PDS/data/PDS4/juno_jiram_bundle/data_calibrated/). Data products used in this study include calibrated, geometrically controlled, radiance data mapped onto an orthographic projection centred on the north pole and velocity vectors derived from the radiance data. The raw data used in this study are listed in the Extended Data Table  1. Brightness maps and velocity vectors can be found in the Supplementary Data.
Photoacoustic imaging (PAI) is an emerging modality that has shown promise for improving patient management in a range of applications. Unfortunately, the current lack of uniformity in PAI data formats compromises inter-user data exchange and comparison, which impedes: technological progress; effective research collaboration; and efforts to deliver multi-centre clinical trials. To overcome this challenge, the International Photoacoustic Standardisation Consortium (IPASC) has established a data format with a defined consensus metadata structure and developed an open-source software application programming interface (API) to enable conversion from proprietary file formats into the IPASC format. The format is based on Hierarchical Data Format 5 (HDF5) and designed to store photoacoustic raw time series data.
The Digital Imaging and Communications in Medicine (DICOM) format is the current international standard for handling clinical imaging datasets  [9] , but the format is designed to store reconstructed images only and targeted towards clinical use. The lack of a standardised format for raw PAI time series data impedes access, exchange and usability of acquired data between different researchers, vendors and organisations. A standard photoacoustic data format would have two substantial benefits. First, it would assist those working with photoacoustic data on a day-to-day basis by facilitating better data handling. For example, it could aid in comparison of data obtained from different sites or using instruments from different vendors. Second, a standard data format, if widely adopted, would help those developing photoacoustic technologies and visualisation software to present a united front to clinicians, data scientists, and other potential users.
For example, a common format could accelerate the development and testing of new image reconstruction and processing methods by making PAI data more transparent. Nonetheless, the development of a standardised format for PAI is associated with challenges. As highlighted above, system configurations for PAI are diverse, leading to a wide variability in the available (and relevant) metadata between systems. Moreover, time series data can be large in size and high-dimensional. Additionally, image acquisition parameters that can be highly relevant for post-processing algorithms may be unavailable (for example, because of hardware constraints or intellectual property protection) or subject to uncertainties. The development, introduction, and adoption of a standard file format relies heavily on the widespread participation of researchers within the community.
Terminology and definitions This section provides definitions of terms to avoid ambiguity. For further details, please refer to the IPASC 'Terms and Definitions'  [11]  and 'Photoacoustic Data and Device Parameters' consensus documents  [12]  that have been adopted by IPASC and are available on the IPASC website. Raw Time Series Data: A time series refers to the time-sampled signal from one detection element. Raw time series data refers to an unaltered set of such time series, one for each detection element. Device: A specific make and model of a photoacoustic device (hardware and/or software). This may include lab prototypes, systems sold for research use, or medical devices regulated in the United States by the Food and Drug Administration (FDA) and in the European Union (EU) by CE marking  [13].
The IPASC data format A PAI dataset consists typically of one (or more) images representing the projection of a target volume onto an image plane (twodimensional (2D) imaging), a series of images representing volume (three-dimensional (3D) imaging), or multiple acquisitions of the same 2D or 3D dataset over time and/or wavelength to produce a dynamic series of acquisitions (multi-dimensional imaging). The IPASC data format is designed to store measurements as raw time series data and not as reconstructed images to prevent loss of information and decrease the complexity of the data format. The raw time series data are accompanied by relevant metadata to enable reproducible reconstruction. To accommodate the varied nature of PAI datasets, the Hierarchical Data Format (HDF5)  [15]  was chosen as the data format due to the following benefits: HDF5 (1) is able to store and organise large amounts of data within a single file container; (2) is platform-independent; (3) supports an unlimited variety of data types; (4) is widely used among the scientific community; (5) is openly available; (6) implements a high-level API with, for example, MATLAB,  4  Python  [16] , C++,  5  or Java interfaces; 6  and (6) contains the descriptive metadata within the data file, so metadata cannot get lost when exchanging files.
Metadata attributes Metadata provide information about relevant aspects of data. Within the IPASC data format, each metadatum is characterised by a series of attributes to describe and define its use and boundary conditions (Table  1 ). If applicable, further specifications by nested attributes are given. All units of the metadata are given in the International System of Units (SI units) unless otherwise specified. The metadata of the IPASC data format are organised into three sub-categories: (1) Minimal Metadata, (2) Acquisition Metadata and (3) Device Metadata. Whilst the format tries to accommodate the most relevant metadata parameters for full PAI data description, the wide variety of existing systems and constant technological progress may require the addition of further parameters that have not been considered yet in our current framework.
(1) Minimal Metadata Minimal Parameters are indicated by the Necessity attribute and comprise all parameters that are required to read PAI data and reconstruct any image from the raw time series data. Any additional information should be reported in the metadata if available. The minimal parameter set contains: Container Format Metadata: The inherent features of the file format, which specify mandatory parameters. They include the unique universal identifier (UUID), the type of compression, and the type of encoding. Binary Data Metadata: The metadata that make the binary data machine-readable. They include specifications on data type, dimensionality, and the size of each dimension. A/D (Analogue/Digital) Sampling Rate: A single value referring to the rate at which samples of the analogue signal are taken to be converted into digital form.
This definition covers both the imaged medium and the coupling agent. Scanning Method: A string representing the scanning method that is used. The following descriptions can be used: (''composite_scan'', ''full_scan''). This flag determines the way the metadatum ''measurement'' is defined. Measurements Per Image: A single value describing the number of measurements that constitute the dataset corresponding to one image. Frequency Domain Filter: An array defining the frequency threshold levels that are applied to filter the raw time series data, containing [lower, higher] -3 dB points of the filter in Hertz. [lower, -1] denotes a high-pass filter and [-1, higher] denotes a low-pass filter.
(3) Device Metadata The Device Metadata carry all information necessary to describe a PAI device (Fig. 1 ). In contrast to the Acquisition Metadata, they aim to facilitate modelling of how individual systems acquire data, for example, to enable corrections for directivity, or the application of illumination geometry-specific fluence correction algorithms, such as described in  [17]  or  [18]. By collecting these metadata, a database of commercial and custom-built PAI devices can be created with all parameters necessary to construct a digital twin of the imaging device hardware. Each system is assigned with a unique identifier, which is referred to in the recorded photoacoustic data. The Device Metadata include: Universally Unique Identifier: A randomly generated hexadecimal string that can be used to reference the device.
Illumination Element Illuminator Position: Coordinates defining the position of the illuminator centroid. Illuminator Orientation: Coordinates defining the direction unit vector of the illuminating beam. Illuminator Geometry Type: A string describing the shape of the optical fibre (bundle) output. Illuminator Geometry: Values defining the numerical geometry of the optical fibre (bundle) output. The data type and content of this metadatum are determined by the illuminator geometry type field. Wavelength Range: An array of three values [minimum wavelength, maximum wavelength, accuracy] describing the wavelengths that can be generated by the illuminator. Laser Energy Profile: An array of two double arrays [wavelengths, energies] which describes the laser energy of the illuminator. Laser Stability Profile: An array of two double arrays [wavelengths, standard deviations] representing the standard deviation of the pulseto-pulse laser energy of the illuminator.
PACFISH: An open-source API for data access and conversion To facilitate the use of the IPASC data format, a prototype Pythonbased software tool (PACFISH = Photoacoustic Converter for Information Sharing) was implemented (Fig. 3 ). PACFISH serves three purposes: (1) it helps vendors to integrate the IPASC data format export into their standard software; (2) it assists scientists to read and write data in the consensus HDF5 format; and (3) it helps the PAI community to create custom adapters that convert proprietary file formats into the consensus HDF5 format. PACFISH is available open-source on GitHub 7  under the commercially-friendly BSD-3-Clause licence  8  and contributions to the continued development of PACFISH are welcomed. PACFISH is divided into the API, core, quality control, and iohandler modules.