dataesr/scientific-paragraphs-categorization huggingface dataset:
6b-c ). The standard deviation of the mean diurnal cycle is 43 W m -2 on average for the latent heat flux and 10 W m -2 for the sensible heat flux, which is a similar amplitude as the respective mean diurnal range (66 W m -2 for the latent heat flux and 14 W m -2 for the sensible heat flux). Indeed,  Marion (2014)  raises the point that these flux estimates are not accurate under the weak wind conditions of this period. Nevertheless, this dataset provides an idea of the amplitude and phasing of the diurnal turbulent flux cycle.
This highlights the fact that such a 1D con- figuration is a relevant tool to investigate the parameterization of ocean mixing processes. In this case study, it has been possible to run the model for periods longer than 10 d without imposing a large-scale ocean circulation. However, it should be highlighted that this is probably not the case everywhere in the ocean, and thus the use of this model for other case studies would require getting such a large-scale ocean forcing. It should be stressed, though, that the neglect of large-scale circulation is a common practice for single-column ocean modeling experiments (e. g. ,  Reffray et al. , 2015, and references therein). The case study used here is particularly relevant to study processes related to the SST diurnal cycle. In an atmospheric forced model, the SST is a forcing and the capability of representing its diurnal cycle mainly depends on the availability of such datasets.
Acknowledgements. This study has been conducted using data from the  US Department of Energy  ( DOE) Atmospheric Radiation Measurement (ARM) program  and  EU Copernicus Marine Service Information (CMEMS). Data from the CINDY2011. DYNAMO campaign were provided by  NCAR/EOL  under the sponsorship of the  National Science Foundation  (https://data. eol. ucar. edu/,last access: 6 September 2018). We acknowledge the kind help of  Chris Fairall  and  Ludovic Bariteau  in using these data. We finally thank  Guillaume Reffray  for helpful discussion on the NEMO SCM configuration and the anonymous reviewers for their constructive comments. Financial support. This research has been supported by the  Agence Nationale de la Recherche  project  COCOA  (grant no. ANR-16-CE01-0007TS5 ). Romain Roehrig  acknowledges support by the  GdR DEPHY.
Code availability. ARPEGE-Climat is only available to registered users for research purposes. SURFEX v8, OASIS3-MCT, and NEMO v3. 6 can be downloaded at https://doi. org/10. 5281/zenodo. 5772666 (Voldoire, 2021). Due to the restricted availability of ARPEGE-Climat, CNRM-CM6-1D is only available to registered users on demand from the corresponding author. Data availability. Data from the CINDY2011/DYNAMO campaign were downloaded from https://doi. org/10. 5065/D6KP80J9 (Edson et al. , 2016). Outputs from the numerical experiments analyzed are available from https://doi. org/10. 5281/zenodo. 5772666 (Voldoire, 2021).
4a ) that was approximately three-fold higher than the decadal average (Fig. S8 ). Furthermore, this examined Sakha Republic region is 7. 5× smaller than Canada above 60°N, but the area burnt there in 2014 was two-thirds of the total area burnt in Canada above 60°N (Fig. S8  and Supplemental Text). The 2014 fires in Sakha, Russia were exceptionally large, and thus may provide an indication of future fire behavior. Atmospheric circulation can transport continental wildfire smoke from Russia northwards to the Arctic Ocean. The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset (Fig. 2 ) and satellite images combined with backtrajectory analysis (Fig. 3b ) both show how wildfire smoke emitted from Russia (Fig. 3a ) traveled over the Laptev Sea and the eastern Eurasian Basin to reach the anomalous Arctic bloom of August 2014.
4b ). Since Arctic Ocean waters are relatively stratified compared to other ocean basins, the estimated residence time of these atmospherically deposited aerosols could be sufficiently long to allow any associated N to accumulate, over a period of days  35  , to a sufficiently high enough concentration for phytoplankton uptake and subsequent growth. To assess whether the fires provided sufficient exogenous N to enhance production, we established a nutrient budget for the region based on current models of atmospheric N deposition (Table  1 ). Several lines of evidence suggest that the Fire INventory from NCAR (FINN) emission dataset  36  , used here in CESM simulations, likely underestimates boreal fire emissions. First, McCarty et al. (2021)  4  showed that Arctic FINN fire emissions are between a factor of 2-5 lower than all other major fire emission inventories.
Next, while the CAMS reanalysis dataset reproduces AOD observations at Tiksi (Fig. 2 ), both in magnitude and timing, atmospheric modeling of aerosol transport using FINN emissions in CESM captures only the timing of the AOD peaks; the AOD magnitude is lower than both CAMS and observations by a factor of 2-5, with the best-captured peak being the first one which may not have reached the bloom. In addition, Eckhardt et al. (2015)  37  showed that global modeling, on average, underpredicts measured aerosol observations of EC taken at Tiksi by a factor of 3. Finally, CESM CO concentrations (a tracer of combustion) are lower than pan-Arctic observations by the same factor of ~2-5 (Fig. S11 ). Applying adjustments of 2-5× suggests that wildfires provided between ~12 and 30% (Table  1 : standard model ×2 and ×5) of the N required to fuel the enhanced biomass associated with the bloom.
However, the atmospheric deposition model we used is missing a significant source of N in sub-polar environments from N-rich peat fires (Fig. 4a ), which are not represented in the FINN emission dataset. Assuming a ×3. 5 factor is required to account for additional N emissions from peat fires (see "Methods"), the successive N deposition events we observed in the summer of 2014 could then support between ~40 and 100% of the total N content of the bloom (Fig. 4b  and Table  1 ). Despite uncertainties in our knowledge of N emissions, and subsequent atmospheric deposition to aquatic ecosystems, the budget reconciles N transport when including peat fires in this region. Thus, in very N-depleted and highly stratified conditions in the Arctic Ocean, especially in the Central Arctic, significant deposition of N from peat fires will likely impact Arctic Ocean phytoplankton growth and productivity.
Methods Ocean color and surface parameters Ocean color. Chlorophyll a concentration, was inferred from retrievals of the three main ocean color satellite missions in orbit over the time period of this study (2014): MODIS-Aqua, MODIS-Terra, and VIIRS-SNPP; note that similar processing was used to generate the daily chlorophyll a climatology using satellite data available between 2003 and 2019. The composite products of the three missions were generated based on the recommendations of the Ocean Color -Climate Change Initiative 52 , but for a finer spatial resolution corresponding to a pixel edge of ~1 km. The remote sensing reflectance R rs and chlorophyll a were obtained after atmospheric correction performed through the SeaDAS software  53  and the combination of the spectral ratio and color index algorithms for chlorophyll a  54, 55.
Note that the latter parameter was also estimated through regionally-tuned algorithms showing similar results (see Fig. S12 ). Image reprojection, binning, and aggregation were performed using the Sentinel Application Platform (SNAP) software, developed by Brockmann Consult. Thanks to the high number of data acquisitions in this period (up to 8 for a given satellite per day), bad quality pixels were filtered out, and basic statistics were performed to provide quality controlled daily values. First, for each individual image, pixels were filtered out if one of the following quality flags provided by the SeaDAS algorithm is true: ATMWARN, ATMFAIL, HIGLINT, HILT, HISATZEN, STRAYLIGHT, CLDICE, CHLFAIL, and MAXAERITER. Due to particularly complex environmental conditions in Arctic seas for optical remote sensing, another pixel filtering procedure was performed when at least one the spectral bands exhibited R rs values smaller than 0.
Atmospheric transport modeling. CESM version 1. 5  33  was used with the interactive chemistry version of the Community Atmospheric Model (CAM-chem) as the atmospheric component  56  following the set-up described in Bernstein et al. (2021)  57. Aerosols in CAM-chem are represented by four modes (Aitken, accumulation, coarse, and a primary carbon)  58. All simulations were performed on a horizontal resolution of 0. 9 × 1. 25°and 56 vertical levels with offline meteorology nudged to GEOS5 59 meteorological analysis. Dust and sea salt are prognostically calculated following the MAM4 default configuration  58. Anthropogenic emissions are taken from HTAP-2  60. Daily fire emissions (from wildfires, agricultural fires, and prescribed burning) are taken from the Fire INventory from NCAR (FINN) dataset  36  version 1.
6 and prescribed vertically following AeroCom recommendations  61  up to a maximum plume height of 5 km. The CAMS reanalysis dataset incorporates satellite-derived AOD reanalysis using the ECMWF Integrated Forecasting System  62. In this way, CAMS reduces bias with observations, and in the context of this study, provides an AOD reference with which to compare CESM results. The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT)  63  , developed by NOAA's Air Resources Laboratory, was used to simulate back trajectories from the location of the bloom (82°N, 130°E, August 12, 2014). Current fire emission inventories combine data from different sources, on fire fuel loading, biomass consumed, and species-specific emission factors to estimate emissions over the area burned. This results in substantial uncertainties between commonly used datasets which differ by a factor of 4 globally and by factors of 3-15 regionally  64, 65.
A second uncertainty is that when fire emissions are prescribed in global model simulations, there is a well-known low bias compared to aerosol optical depth measurements  2, 66  , leading to regional adjustments of fire emissions commonly between a factor of 2-3. The fire emission database FINN used here does not include peatland fire emissions, and thus model simulations do not transport any N from peat fires, despite fires occurring within regions containing peatlands (Fig. 4a ). For our study region, FINN currently contains the lowest estimates of fire carbon emissions between all inventories: the highest estimates being a factor of ~5 larger  14. In addition to peat fires being a large missing carbon source they are also a significant missing source of nitrogen owing to some species (e.
g. , HCN and NH 3 ) being emitted from smoldering peat fires at a factor of ~10 higher, per unit biomass consumed, than for flaming savanna fires 8 or a factor of 3. 5 higher than boreal fires  8. Therefore, to estimate the missing source contribution of peat fires to the N deposition flux, an emission ratio of 3. 5 (peat fires:boreal fires) is used as a bias correction, based on emission factor differences between boreal forests and peatlands for some N species 8 and the missing PM 2. 5 contribution from tropical peatland fires when using the FINN dataset  67. Assuming linearity in transport of peat fire emissions with those from forest fires, we applied this factor 3. 5 bias correction factor to the 'inclusion of peat biomass' nitrogen deposition flux in Table  1. Burned area was estimated using the collection 6 MODIS Global Burned Area Product MCD64CMQ (climate model grid)  68.
Acknowledgements M. A. was supported by the  Alg-O-Nord research  project of the  CNES (Center National d'Etudes Spatiales ),  High Impact Publications Program of ArcticNet  and a  European Union 's  Horizon 2020 Marie Sklodowska-Curie grant  (no. 746748 ). This work represents a contribution to IRL Takuvik,  Stanford University, Sorbonne Université, Sentinelle Nord ,  Québec Océan  and  Institut Nordique du Québec. We would like to thank the international agencies and programs for support, including the  U. S. Department of Energy (DOE , grant Number:  DE-SC0021302 ) and  Cornell Atkinson Center for Sustainability. We would like to acknowledge the computational resources from Cheyenne provided by  NCAR's Computational and Information Systems Laboratory , sponsored by the  National Science Foundation.
Data availability The NABOS oceanographic dataset in the Laptev Sea was obtained from https://uaf-iarc. org/nabos/. AERONET data for the Tiksi site (PI: Brent Holben) were downloaded from https://aeronet. gsfc. nasa. gov/. Ocean color data from MODIS-Aqua, MODIS-Terra, and VIIRS-SNPP and sea surface temperature from MODIS-Aqua were downloaded from https://oceancolor. gsfc. nasa. gov and https://ladsweb. modaps. eosdis. nasa. gov. The sea ice concentration and melt onset dates were obtained from the NSIDC (https://nsidc. org). The daily CESM aerosol optical depth and nitrogen deposition data for July and August 2014 used in this study can be downloaded from https://doi. org/10. 5281/zenodo. 6862522. We use the NASA collection 6 MODIS Global Burned Area Product MCD64CMQ to estimate burnt area in each year, which is available to download from https://modis-land.
The UN75 Global Conversation -Peoples' Priorities and Their Ideas for Upgrading the United Nations In parallel to the political UN75 Declaration process, the UN Secretary-General launched a global conversation in January 2020, inviting people around the world to discuss how we can work better together in order to address shared global challenges. Through dialogues and surveys, more than 1. 5 million people in 193 countries shared their short-and long-term priorities, their ideas for action and their calls for a more inclusive, transparent UN to lead the response to pressing global challenges. Data was collected through five channels: 1. 3 million UN75 survey responses were received from 193 countries; 1,141 UN75 dialogue summaries were received from participants in 94 countries; a research mapping was conducted in six languages; two independent polls were carried out in 50 countries, by Edelman Intelligence and Pew Research Center; and media analysis was conducted in 70 countries by Edelman Intelligence.
The data was analyzed in collaboration with the Graduate Institute of International and Development Studies. 3 Working together with UNDP's Human Development Index team and the Institute for Economics and Peace, the Graduate Institute of International and Development Studies cross-analyzed the UN75 survey data with the Human Development Index and the Global Peace Index, uncovering some further regional trends and trends based on level of human development and levels of peacefulness in the countries where respondents reside. The full results of the analysis can be viewed in the January 2021 final UN75 report, "Shaping Our Future Together. " The raw data, analyses and interactive data visualizations are also available on UNDP's Data Futures Platform, under "Data and insights from UN75" (UNDP 2020d).
It is striking to see that across regions, sectors and age groups, several patterns and recurrent themes emerged in the data in terms of respondents' short-and long-term priorities. As COVID-19 reversed progress in human development and widened inequalities, many respondents prioritized access to basic services, tackling inequalities and international solidarity (UN75 Office 2021, 20). The shortfall in healthcare to address the pandemic saw universal access to healthcare as the top immediate priority of all respondents. As COVID-19 forced children out of schools, investment in education and youth also ranked high, especially in sub-Saharan Africa and Central and Southern Asia. While 3 billion people lack access to a basic hand-washing facility with soap and water, access to safe water and sanitation was a key priority across all human development levels.
A negative Indian Ocean dipole event became established during July, associated with a warmer east and cooler west Indian Ocean. Northern Hemisphere winters were affected by a negative phase of the North Atlantic Oscillation at both the beginning and end of 2021. In the Southern Hemisphere, a very strong positive Southern Annular Mode (also known as the Antarctic Oscillation) contributed to New Zealand's record warm year and to very cold temperatures over Antarctica. Land surface winds continued a slow reversal from the multi-decadal stilling, and over the ocean wind speeds were at their highest in almost a decade. La Niña conditions had a clear influence on the regional patterns of many hydrological variables. Surface specific humidity and total column water vapor over land and ocean were higher than average in almost all datasets.
Relative humidity over land reached record or near-record low saturation depending on the dataset, but with mixed signals over the ocean. Satellite measurements showed that 2021 was the third cloudiest in the 19-year record. The story for precipitation was mixed, with just below-average mean precipitation falling over land and below-average mean precipitation falling over the ocean, while extreme precipitation was generally more frequent, but less intense, than average. Differences between means and extremes can be due to several factors, including using different indices, observing periods, climatological base reference periods, and levels of spatial completeness. The sharp increase in global drought area that began in mid-2019 continued in 2021, reaching a peak in August with 32% of global land area experiencing moderate or worse drought, and declining slightly thereafter.
This is not possible for all datasets, as it is dependent on their length of record or legacy processing methods. While anomalies from the new climatology period are not so easily comparable with previous editions of this report, they more clearly highlight deviations from more recent conditions. b. Temperature  1. GLOBAL SURFACE TEMPERATURE-A. Sánchez-Lugo, C. Morice, J. P. Nicolas, and A. Argüez The year 2021 was among the seven warmest years since global records began in the mid-tolate 1800s, with a temperature departure between +0. 21° and +0. 28°C, according to six global temperature datasets (Table  2. 1). Depending on the dataset, 2021 was either the fifth, sixth, or equal with 2018 as sixth-warmest year on record (Fig. 2. 1). These datasets consist of four independent global in situ surface temperature analyses (NASA-GISS,  Lenssen et al.
2019; HadCRUT5, Morice et al. 2021; NOAAGlobalTemp, Zhang et al. 2019; Berkeley Earth, Rhode and Hausfather 2020)  and two global atmospheric reanalyses (ERA5,  Hersbach et al. 2020 , Bell et al. 2021 ; JRA-55,  Kobayashi et al. 2015). The six datasets all agree that the last seven years  were the seven warmest years on record. Similarly, the datasets agree that the global average surface temperature has increased at an average rate of 0. 08°-0. 09°C decade -1 since 1880 and at a rate more than twice as high since 1981 (0. 18°-0. 20°C decade -1 , depending on the dataset).
Global The year began with a cold phase of the El Niño-Southern Oscillation (ENSO; see section 4b), also known as La Niña, across the eastern and central tropical Pacific Ocean, helping cool global temperatures slightly. The month of February had the smallest temperature anomaly of the year for the globe and was the coldest February since 2014, with global temperatures close to the 1991-2020 base period. La Niña dissipated by June, but re-emerged in August. With the exception of February, each month during 2021 had a global temperature that was above the 1991-2020 average. While it is common, and arguably expected, for each newly completed year to rank as a top 10 warmest year (see  Arguez et al. 2020) , the global annual temperature for 2021 was considerably lower than we would expect due to the secular upward trend alone, with trend-adjusted anomalies registering between the 10th and 40th percentiles (depending on the dataset) following the  Arguez et al.
Averaged as a whole, the global land-only surface air temperature for 2021 ranked among the sixth highest in the six datasets with a temperature departure of +0. 32° to +0. 41°C. The globally averaged SST was either sixth or seventh highest on record at +0. 14° to +0. 22°C, depending on the dataset. Even though each dataset might differ slightly on the annual rankings and anomalies, it is worth noting that these differences are small and that, overall, temperature anomalies for each dataset are in close agreement (for more details see  Simmons et al. 2017 Simmons et al. , 2021;; Morice et al. 2021). Global atmospheric reanalyses use a weather prediction model to combine information from a range of satellite, radiosonde, aircraft, and other in situ observations to reconstruct historical weather and climate across the whole globe.
The spatial distribution of 2021 anomalies for these lakes (Appendix Fig. A2. 5) all have positive values, but with strong variation across each of the lakes. The LSWT warm-season averages for midlatitude lakes are computed for summers, (July-September  [JAS]  in the extratropical Northern Hemisphere and January-March  [JFM]  in the extratropical Southern Hemisphere) and whole-year averages (January-December) are presented for tropical lakes (within 23. 5° of the equator). LSWT time series were derived from satellite observations from the series of Along Track Scanning Radiometers (ATSRs), the Advanced Very High Resolution Radiometers (AVHRRs) on  MetOp A and B (1996-2019) , and the Sea and Land Surface Temperature Radiometers (SLSTRs) on  Sentinel3A and 3B (2019-2021). The retrieval method of MacCallum and  Merchant (2012)  was applied on image pixels filled with water according to both the inland water dataset of  Carrea et al.
(2015)  and a reflectance-based water detection scheme. The satellite-derived LSWT data are spatial averages for each of a total of 963 lakes, for which high quality temperature records were available in 2021. The satellite-derived LSWT data were validated with in situ measurements with a good agreement (average satellite minus in situ temperature difference less than 0. 5°C). Lake-wide average surface temperatures have been shown to give a more representative picture of LSWT responses to climate change than single-point measurements  (Woolway and Merchant 2018). The averaged surface air temperature was calculated from the GHCN v4 (250-km smoothing radius) data of the NASA GISS surface temperature analysis  (Hansen et al. 2010; GISTEMP Team 2022). The in situ data for the Great Lakes were collected by the NOAA National Data Buoy Center.
3. NIGHT MARINE AIR TEMPERATURE-R. C. Cornes, T. Cropper, R. Junod, and E. C. Kent  Globally gridded datasets of night marine air temperature (NMAT) provide a useful and independent comparison against sea surface temperature (SST) data. Air temperature values have been recorded onboard vessels for centuries and continue to the present through the Voluntary Observing Ship (VOS) network. These observations can be used to construct global datasets of marine air temperature back to at least 1900. While temperature values are recorded throughout the day and night on these ships, only the nighttime observations are currently used to construct the gridded values due to the heating bias that arises in the daytime data, as a result of the superstructure of the ship. Global mean temperature anomalies calculated from the two gridded NMAT datasets (CLASSnmat,  Cornes et al.