dataesr/scientific-paragraphs-categorization huggingface dataset:
2020 and UAHNMAT, Junod and Christy 2020)  show a marked cooling in 2021 compared to 2020 but also relative to values over the last decade (Fig. 2. 4). For CLASSnmat, the average global temperature anomaly (relative to the 1961-90 average) during 2021 was +0. 38°C, a value comparable to that recorded in 2006. As such, 2021 was only the 13th highest in the series, which dates back to 1880. For UAHNMAT, the global mean anomaly value was +0. 33°C in 2021; this is also the 13th highest annual mean temperature in its 1900-2021 record. These values contrast with the HadSST4 sea surface temperature dataset  (Kennedy et al. 2019) , which shows that 2021 was the fifth highest in the 1850-2021 period, with a global average anomaly of +0. 67°C. A similar difference of about -0. 3°C in the 2021 NMAT averages, relative to HadSST4, is seen across other large-scale regions (Table  2.
2). In this comparison of the three datasets, colocated gridded data are used to avoid any complication to the analysis from differing global coverage. In particular, the NMAT datasets have poor coverage south of 40°S. The principal reason for the lower temperature anomalies during 2021, compared to recent years, is the extended La Niña conditions that were present during the year (see section 4b). Historically, the NMAT data show a clear response to La Niña conditions, with a pattern across the Pacific that is similar in form and magnitude to that observed in the SST data. However, differences are apparent in the NMAT data compared to SST beyond the typical La Niña pattern and is the reason for the lower global average anomalies in the two NMAT datasets for 2021 compared to 2020 (Figs. 2.
5d-f). The negative temperature anomalies in the central Pacific are more widespread in NMAT compared to SST, and the two nodes of positive anomalies in the North and southwestern Pacific are not clearly defined in either NMAT dataset. In addition, while positive anomalies for 2021 are evident in most grid cells in the SST data across the Atlantic and Indian Oceans, these are much weaker in the NMAT datasets, and there are many more grid cells in these regions with negative anomalies. Some of these regional differences can be explained by different data availability in the SST and NMAT datasets. Most notably, across the southern Pacific there is greater uncertainty in the NMAT values because there are few ship observations in the region; this is mitigated in the SST through the use of drifting buoy measurements in the grid cell averages.
However, this does not fully explain the differences seen in these results as there is relatively good observational coverage in the NMAT datasets across the North Atlantic. The data used in Fig. 2. 5 were detrended, prior to averaging, in order to remove the long-term trend from the data by calculating the residuals from a linear regression fit to the data per grid cell. This detrending allows the response of the SST and NMAT data to La Niña conditions to be compared more directly. Over approximately the last 50 years, global mean NMAT has increased at a slower rate than  SST (Fig. 2. 4; Cornes et al. 2021) ; this accounts for the difference in rankings in the NMAT datasets compared to HadSST4. The reasons for this trend-differential are not well understood, and work is ongoing to determine if this is a true feature of the data or an artifact in the NMAT and/or SST datasets.
It is also not clear if the interannual variability, and notably the relatively cool conditions observed during 2021, is related to this difference in long-term trends since taking into account this trend differential, the -0. 3°C offset between NMAT and SST in 2021 was unusually large. The average number of warm days (TX90p, Table  2. 3) over land was the second most on record, according to the observational dataset GHCNDEX  (Donat et al. 2013; Fig. 2. 6 , using the 1961-90 reference period), with 68 days, though we note the incomplete spatial coverage. The average number of cool nights (TN10p) was 22, which was 14 fewer than the expected average, but not at record values. The expected values for these two indices are 36. 5 days over the reference period, which is used in their calculation.
The average values of the spatially complete reanalysis dataset ERA5  (Hersbach et al. 2020 (Hersbach et al. , using the 1981 (Hersbach et al. -2010  reference period) over land are similar to those from GHCNDEX, and TX90p was at its third highest value at 65. 6 days, after 2016 and 2020 (Fig. 2. 6). The spatial patterns of these indices  (Plates 2. 1d, e; Appendix Fig. A2. 6)  show especially high numbers of warm days over Africa and Asia and relatively low numbers over Australia, which also had high numbers of cool nights in 2021. Below we describe some of the low and high temperature events of 2021 in more detail (see also  WMO 2022) , primarily from the global north where these details were more readily available. 1956, 1965, 1971, 1974, 1976, 1989, 1999, and   During 2021 there were a number of notable cold periods, starting with the after effects of a major storm on the Iberian Peninsula in January, with widespread temperatures below -20°C in northeast Spain, and a new national record of -34.
Note that with the  Hobday et al. (2016)  MHW definition the long-term trend is not removed before detecting events. This means that warming (cooling) areas will generally experience more (fewer) MHWs in the present than in the past, with the inverse observed for MCSs. GHCNDEX  (Donat et al. 2013 ) is based on the daily GHCND dataset  (Menne et al. 2012). The extremes indices developed by the former ETCCDI (WMO Expert Team in Climate Change Detection and Indices,  Zhang et al. 2011 ) are calculated for each station and then interpolated onto a regular 2. 5° grid. Reduced spatial coverage in the most recent years (Fig. 2. 6) arises because of late arriving data; hence for complete global land coverage we also use the ERA5 reanalysis  (Hersbach et al. 2020) , not including the preliminary release of the 1950-78 data.
TROPOSPHERIC TEMPERATURE-S. Po-Chedley, J. R. Christy, L. Haimberger, and C. A. Mears  The 2021 global lower-tropospheric temperature (LTT) was approximately 0. 25°C higher (0. 14°-0. 34°C, depending on dataset) than the long-term climatological  average. This places 2021 among the 10 warmest years on record (since 1958) despite La Niña conditions during most of 2021, which typically reduce the temperature of the troposphere. Earth's tropospheric temperature is influenced by several natural factors such as volcanic eruptions, solar variability, and internal climate variability. Anthropogenic emissions of greenhouse gases and aerosols also affect LTT and collectively contribute to long-term warming of the global troposphere (Table  2. 4). Since August of 2020, the El Niño-Southern Oscillation (ENSO) has largely been in a La Niña state (Fig.
9, and RAOBCORE v1. 9) , satellite-based microwave sounding observations  (RSS v4, UAH v6, UW v1, and NOAA STAR v4. 1) , and atmospheric reanalysis products (MERRA-2, ERA5, and JRA-55). Each dataset is constructed with different strategies to remove biases, resulting in slightly different time series. In the ERA5 and MERRA-2 reanalysis datasets, 2021 was the fifth-warmest year on record for global LTT  (Hersbach et al. 2020; Gelaro et al. 2017). 2021 was the sixth-warmest year in the RATPAC, RICH, and RSS datasets  (Free et al. 2005; Mears and Wentz 2016; Haimberger et al. 2012)  and eighth in the JRA-55, RAOBCORE, and UAH datasets  (Kobayashi et al. 2015; Spencer et al. 2017). In general, observations of the global LTT (Fig. 2. 9) and tropical tropospheric temperature (TTT) have similar interannual variations, but exhibit non-negligible structural uncertainty for long-term trends, which range from 0.
13° to 0. 23°C decade -1 since 1979 (Table  2. 4). The estimated trend uncertainty for individual satellite datasets is approximately 0. 04°C decade -1 and the uncertainty in converting level temperatures to synthetic satellite brightness temperatures is approximately 0. 02°C decade -1 (Po-Chedley et  al. 2021). Efforts to intercompare and understand differences across tropospheric temperature datasets is ongoing  (Steiner et al. 2020). For example,  Christy et al. (2018)  compares radiosonde and satellitebased measurements of tropospheric temperature and concludes that satellite datasets likely have spurious warming over the 1990s. On the other hand, this analysis also documents unexplained cooling in satellite datasets over the early 2000s. This latter finding is consistent with recent work by  Zou et al.
(2021) , who note that existing satellite datasets underestimate tropospheric temperature trends relative to a new post-2002 dataset, which relies on observations from the most recent generation of microwave sounding instruments and/or satellites in stable sun-synchronous orbits. Consistent with physical expectations, recent research shows that climate models exhibit a close coupling between atmospheric moistening and warming in the tropics. If model simulations are sufficiently accurate, relationships between distinct geophysical fields (e. g. , water vapor and sea surface temperature observations) may be used to investigate potential observational biases in tropospheric temperature datasets  (Santer et al. 2021). As a result of collective efforts to better understand and intercompare existing observational datasets, records of tropospheric warming are continually evolving and improving.
Time series of global monthly temperature anomalies from the middle troposphere to the upper stratosphere based on satellite measurements are shown in Fig. 2. 10. As discussed in  Steiner et al. (2020) , the middle and upper stratosphere data are merged products combining infrared emission measurements (Stratospheric Sounding Unit [SSU] 1, 2, 3 from 1979 to 2006) with more recent satellite data from microwave and infrared limb sounders for a continuous record. Merged datasets from microwave emission measurements provide layer-averaged temperatures for the lower stratosphere (TLS) and for the middle troposphere (TMT). In addition to long-term stratospheric cooling (due to CO 2 increase and stratospheric ozone changes) and tropospheric warming due to greenhouse gas increases (section 2b5), transient variations arise from a variety of causes including ENSO (e.
4 cm decade -1 for the period 1981-2020 (Fig. 2. 14). Long-term observation of permafrost relies on field observations of ALT, permafrost temperatures, and, since 2021, on rock glacier velocity  (Streletskiy et al. 2021; Pellet et al. 2021 ). International data are collected by the Global Terrestrial Network for Permafrost (GTN-P) as part of the Global Climate Observing System (GCOS). Permafrost temperatures are logged in boreholes reaching at least the DZAA, with a measurement accuracy assumed to be 0. 1°C  (Biskaborn et al. 2019; Noetzli et al. 2021b; Streletskiy et al. 2021). ALT is determined by mechanical probing where possible (accuracy of ~1 cm) and otherwise interpolated from borehole temperature measurements. The global coverage of permafrost monitoring sites is sparse and particularly limited in regions such as Siberia, central Canada, Antarctica, and the Himalayan and Andes Mountains.
ALPINE GLACIERS-M. Pelto In the hydrological year 2020/21, observed World Glacier Monitoring Service (WGMS) reference glaciers experienced a mass balance loss of -900 mm water equivalent (mm w. e. ), compared to -700 mm w. e. in 2019/20. From 1970 to 2021 the eight most negative mass balance years were all recorded after 2010. A value of -1000 mm w. e. per year represents a mass loss of 1000 kg m -2 of ice, or an annual glacier-wide thickness loss of about 1100 mm yr -1. Figure  2. 16 illustrates glacier mass balance for the WGMS global reference glaciers with more than 30 years of data for the period 1970 to 2020. Global values are calculated using a single value (averaged) for each of 19 mountain regions in order to avoid a bias to well observed regions. In 2021, a negative annual mass balance was reported from 31 of the 32 reference glaciers reported to the WGMS as of 1 June 2022.
The exceptional heat wave during late June and early July in western North America (section 2b4; Sidebar 7. 1) set the stage for the large glacier mass loss  (Pelto et al. 2022; Fig. 2. 17). In Alaska, three of the four glaciers measured had a negative mass balance, with a mean annual balance of -528 mm. In South America, 2021 mass balance data were reported from three glaciers in Argentina, two glaciers in Chile, and one in Colombia; all were negative with a mean of -861 mm. This is greater than the 2000-18 average loss observed in the Andes of -720 ± 220 mm yr -1  (Dussaillant et al. 2019). In High Mountain Asia, 15 of 18 glaciers reported negative balances in 2021. The average mass balance was -468 mm. Early winter of 2021 was warm and dry across the Himalayan region. This was capped off by record warmth in the Mount Everest region, leading to the snow line on glaciers rising and snow-free glaciers up to 6000 m  (Pelto et al.
(2021). We obtained in situ ice phenology for 110 lakes: Canada (  5 ), United States (53), Estonia (2), Finland (27), Iceland (1), Norway (18), Poland (1), Sweden (1), Russia  (1) , and Japan  (1). In addition, we obtained annual maximum ice cover (%) data for each of the Laurentian Great Lakes from 1973 to 2020 (https://www. glerl. noaa. gov/ data/ice/). Surface air temperature data for the NH cold season (November-April average) were downloaded from the NASA GISS surface temperature analysis  (Lenssen et al. 2019; GISTEMP Team 2022). To create the time series figure, the ERA5 data were averaged across all 0. 25° grids and consisted of a much larger sample size relative to the in situ data where the anomalies were averaged across 110 lakes.
d. Hydrological cycle 1. SURFACE HUMIDITY-K. M. Willett, D. A. Lavers, M. Bosilovich, and A. J. Simmons Global mean specific humidity (q) in 2021 was lower compared to 2020, although remaining well above the 1991-2020 average in all datasets (+0. 07 to +0. 18 g kg -1 for q land , +0. 07 to +0. 20 g kg -1 for q ocean ) except for ERA5, which placed global q land just below average at -0. 01 g kg -1 (Figs. 2. 21a-d). For global mean relative humidity (RH), 2021 was less saturated than 2020 in all datasets over land (-1. 32 to -0. 70 %rh) (Figs. 2. 21e,f). Both JRA-55 and ERA5 show a large difference between 2020 and 2021, placing 2021 as the least saturated year on record  (since 1958 and 1967, respectively)  by a large margin (-0. 23 %rh and -0. 45 %rh, respectively). It was the second driest year on record after 2019 in HadISDH.
Over oceans, RH remained mixed with HadISDH and JRA-55 behaving similarly, placing 2021 RH ocean at +0. 20 and +0. 16 %rh above the 1991-2020 average, respectively, which is slightly above 2020, while ERA5 dropped below average at -0. 09 %rh, continuing a short drying trajectory since 2019. Spatially, missing data regions of HadISDH (Plate 2. 1g) over much of northwestern, central, and eastern Africa are regions that showed strong negative q anomalies in ERA5, indicating that the water vapor content was lower than average  (Appendix Fig. A2. 8). This might explain the discrepancy in global land averages between ERA5 and HadISDH. Central Africa is a key region for differences between the reanalyses, with MERRA-2 (Appendix Fig. A2. 9) showing strong high water vapor content anomalies there and more expansive high water vapor content anomalies generally, likely contributing to the MERRA-2 high 2021 q land anomaly.
6 ), all datasets continue to show long-term increasing water vapor over land and ocean (+0. 04 to +0. 10 g kg -1 decade -1 ), while the air over land has become less saturated (-0. 23 to -0. 45 %rh decade -1 ). This means that the water vapor content of the air has increased more slowly than the water vapor carrying capacity of the air, which increases exponentially with temperature. The trend magnitudes for q are similar over land and ocean for each dataset. HadISDH and MERRA-2 show similar larger trends in q (+0. 09 to +0. 10 g kg -1 decade -1 ,  respectively), while ERA5 and JRA-55 show more moderate trends (+0. 04 to +0. 06 g kg -1 decade -1 , respectively). While all datasets show a negative trend for RH ocean , these trends are small, relatively widespread (-0. 03 to -0. 18 %rh decade -1 ), and not considered to be significant for HadISDH.
We conclude that there is large uncertainty in whether there is any real change in RH ocean. Agreement between the monitoring products in terms of both long-term trajectories and yearto-year variability is generally good (Fig. 2. 21), with the exception of RH ocean. Greater discrepancy over ocean is expected given the sparse observing network available from the Voluntary Observing Ships (VOS; https://www. vos. noaa. gov/vos_scheme. shtml) that make up the HadISDH record and the difference in methodological approaches between datasets. For example, ERA5 does not incorporate any in situ near-surface temperature or dew point temperature measurements over ocean, whereas HadISDH, MERRA-2, and JRA-55 do. The uncertainty range estimated for HadISDH is larger over ocean than over land, reflecting both the larger observational uncertainty since 2015, when digitized ship metadata provision ended, and larger spatial coverage uncertainty compared to over land.
TOTAL COLUMN WATER VAPOR-C. A. Mears, J. P. Nicolas, O. Bock, S. P. Ho, and X. Zhou In 2021, global land and ocean averages of total column water vapor (TCWV) were above the 1991-2020 climatological averages, but were generally lower than the 2015-20 period which showed high vapor values. This is not surprising because La Niña conditions, which were present for most of the year, have a cooling effect on global surface and tropospheric temperatures, thus lowering the water-holding capacity of the global atmosphere. In reanalysis output, where time series are available for the entire 1979-2021 period, 2021 was the fourth (MERRA-2, anomaly = 0. 477 kg m -2 ), seventh (ERA5, anomaly = 0. 264 kg m -2 ), and eighth (JRA55, anomaly = 0. 383 kg m -2 ) highest vapor year since 1979. Time series of global averages agree well in all datasets (Fig.
2. 22). Part of the discrepancy between the Global Navigation Satellite System (GNSS) time series and reanalysis over land is due to the limited spatial sampling afforded by the GNSS network. While TCWV decreased sharply from 2020 to 2021 globally and over ocean in all the datasets used in this assessment, there is substantial spread in the amount of this decrease over land. The global map of TCWV anomalies from MERRA-2 (presented as percent of annual mean values to show extratropical changes more clearly) for 2021 (Plate 2. 1i) reveals large low vapor anomalies in the eastern South Pacific, with low vapor anomalies close to the equator along a northwest-southeast oriented band and high vapor anomalies farther south. This pattern denotes the southwest displacement of the South Pacific Convergence Zone (SPCZ) that is typically associated with La Niña conditions  (Brown et al.
2020) , MERRA-2  (Gelaro et al. 2017) , and JRA-55  (Kobayashi et al. 2015) , as well as measurements made by satellite-borne microwave radiometers over the ocean (RSS Satellite;  Mears et al. 2018) , Global Positioning System-Radio Occultation (GPS-RO) observations from the COSMIC, Metop-A, -B, and -C and COSMIC2 satellite missions (satellite RO;  Ho et al. 2020a Ho et al. ,b, 2010a,b;,b; Teng et al. 2013; Huang et al. 2013) , and from ground-based GNSS stations  (Bock 2020). The RSS satellite measurements are only available over the ocean, while GNSS stations are generally located on land. GPS-RO is available for both land and ocean. All three reanalyses assimilate satellite microwave radiometer and GPS-RO data and are therefore not fully independent from these two datasets. Ground-based GNSS measurements are not assimilated and are thus independent.
Upper tropospheric humidity (UTH) was slightly below the 1991-2020 average in 2021. Figures 2. 24a, b  show the monthly time series of the humidity datasets and a difference time series between the temperature and water vapor brightness temperature measurements in the UT, respectively. The large-scale relative humidity in the upper troposphere remains roughly constant, as expected from theoretical considerations  (Ingram 2010) ; however, moistening in terms of the water vapor content of the UT is clearly evident from the lower panel. Here, there is a positive trend in the difference between T2 (brightness temperature of MSU Channel 2/AMSU-A Channel 5, which is sensitive to the UT temperature) and T12 (brightness temperature of HIRS Channel 12, which is sensitive to the UT water vapor). The trend in T2 and T12 indicates that the   1984, 1988, 1995, 1998, 1999, 2005, 2007, 2010, 2011, 2017,   and 2020   emission level of the HIRS water vapor channel is shifting higher due to an increase in water vapor over time, while the emission level of the temperature sounding channel located in the 60-GHz oxygen absorption band remains unchanged, because the oxygen concentration does not change over time  (Chung et al.
2016). Water vapor is the strongest greenhouse gas in the atmosphere. Therefore, monitoring of water vapor in the upper troposphere (UT), owing to the cold temperature there, is crucial to determine one of the strongest positive feedback factors to the anthropogenic warming of the climate system (Coleman and Soden 2021). Three of the four datasets used in this work to monitor changes of water vapor in the UT are satellite based: 1) infrared based upper tropospheric humidity (UTH) derived from HIRS measurements starting in the late 1970s  (Shi and Bates 2011) , 2) microwavebased UTH derived from AMSU-B and MHS measurements starting in the late 1990s  (Chung et al. 2013) , and 3) mid-to-upper tropospheric temperature derived from MSU/AMSU-A measurements starting in the late 1970s  (Zou and Wang 2011).
The fourth dataset is relative humidity in the UT from the ERA5 reanalysis  (Hersbach et al. 2020). The agreement among the three UTH datasets is fairly good; the correlations of the HIRS and ERA5 data with the microwave data during their common period (1999-2021), are 0. 6 and 0. 5, respectively, despite their differences. For example, satellite data represent a layer average UTH with one satellite sampling the same location over Earth only two times a day while ERA5 data represent the 400-hPa level RH with hourly sampling. The microwave data have almost all-sky sampling while the HIRS data sample have only clear-sky conditions; this sampling difference is one reason for the higher interannual variability in the HIRS data as illustrated in  John et al. (2011). In recent years, the HIRS data behave differently from the other two datasets: the mean value of UTH anomaly during 2021 for the HIRS data is 0.
2013)  and GHCNDEX data show positive anomalies corresponding to a prolonged period of heavy rain in eastern parts of New South Wales, Australia, in late March, with some areas recording more than 500 mm of rain in 48 hours (Floodlist 2021b) and more than 50 new daily records for March (Bureau of Meteorology 2021a). More than 60 daily records were broken for November across New South Wales and Queensland, with widespread flooding (Bureau of Meteorology 2021b,c). Combined, these contributed to considerable positive anomalies of  R10mm and R20mm (Figs. 2. 26c, d). The datasets examined here are broadly consistent over southeast Asia in showing aboveaverage frequencies of rainfall extremes, although the GPCC data show below-average intensities in Rx1day (Plate 2. 1l). Cyclone Seroja produced 15 new monthly records for April over Western Australia (Bureau of Meteorology 2021d), after generating significant accumulations over Timor, Indonesia, and Singapore (WMO 2021b; Floodlist 2021c).