dataesr/scientific-paragraphs-categorization huggingface dataset:
Clearly, each product has various strengths and weaknesses and it is not straight forward to present a "best" estimate. The availability of multiple independent (or methodologically independent at least) products provides valuable information about the likely uncertainty in estimates of surface humidity. Note that spatial coverage between the in situ estimates and the reanalyses is drastically different-with little in situ information available outside the Northern Hemisphere. Over land, ERA-Interim has been masked to match the coverage of HadISDH when creating the series plotted in Fig. 2. 15. The NOCSv2. 0  (Berry and Kent 2009, 2011)  dataset has not been updated for 2016 due to the increasing sparseness of the marine observations and decrease in the quality of the observing system (e. g.
Similar to last year's report  (John et al. 2016) , global scale monitoring of upper tropospheric humidity (UTH) is achieved using two independent satellite UTH datasets: (1) the infrared-based HIRS dataset  (Shi and Bates 2011) , and (2) the microwavebased UTH dataset  (Chung et al. 2013 ). These datasets were constructed through careful bias corrections and intersatellite calibration. In these datasets, UTH represents a Jacobian weighted average of relative humidity in a broad layer which is roughly between 500-and 200-hPa but varies depending upon atmospheric humidity profile. The area-weighted mean deseasonalized anomaly time series of UTH for 60°N-60°S is shown in Fig. 2. 18. The anomalies are computed relative to the 2001-10 base period because the UTH dataset only begins in 1999. Positive relative humidity anomalies are observed in the second half of 2016, in contrast to the 2015 anomalies which were negative, indicating the shift from El Niño to La Niña conditions.
A near-zero decadal trend in the upper tropospheric relative humidity time series indicates an increase in absolute (specific) humidity in step with the warming upper troposphere, and hence is consistent with a positive water vapor feedback  (Chung et al. 2016). It is encouraging to see good agreement between the two independent datasets despite their differences in sampling: microwave data have an almost all-sky sampling whereas HIRS data sample mainly clear-sky scenes  (John et al. 2011). Extreme anomalies for the HIRS time series arise from the sampling issues as demonstrated by  John et al. (2011). The annual average UTH anomalies for 2016 relative to the 2001-10 base period (Plate 2. 1o for microwave data; Fig. 2. 19 for HIRS data) show moist anomalies over the central and eastern tropical Pacific and dry anomalies over the maritime continent which result from the still-strong El Niño during the first half of the year.
Sampling of only clear-sky scenes by the HIRS dataset reduces the range of UTH between moist and dry regions, explaining the smaller magnitude of anomalies (~ ±2%) compared with the microwave dataset (~ ±5%). 4) precipitation-R. S. Vose, R. Adler, A. Becker, and X. Yin Precipitation over global land areas was above the 1961-90 average in 2016 (Fig. 2. 20). However, there were two distinct "above-normal" story lines for the year depending upon the analyses considered. The first story line-one of slightly wetter-than-normal conditions-is supported by the Global Precipitation Climatology Centre (GPCC) dataset  (Becker et al. 2013)     Vose 1997), both of which are based on surface stations. The 1961-90 base period is used here because it maximizes the number of stations available for a global analysis.
Consistent with fading El Niño conditions, the strong positive anomalies over the tropical Pacific were less extreme than in 2015, as were the strong negative anomalies over the Maritime Continent. Broadly similar patterns are found for 2016 by the other precipitation datasets. The emergence of several new global satellite cloud datasets in recent years has provided an opportunity to improve understanding of cloud feedbacks and their microphysical composition, and subsequently how they might be efficiently parameterized in numerical models. This is important as the role of clouds in the climate system is multifaceted. Clouds help modulate the global energy budget through cooling (reflection of incoming solar radiation) and warming (trapping outgoing terrestrial radiation) processes. Clouds affect the hydrological cycle through the storage and precipitation of atmospheric water.
This pattern can be seen clearly in online Fig. S2. 15, although the El Niño pattern is apparent in the annual average as well, as seen in Plate 2. 1n. There were also several continental cloudiness anomalies significant at the 5% level relative to the 36year PATMOS-x/AVHRR dataset, which frequently coincide with drought (negative anomalies) or flooding (positive anomalies) conditions. The significant anomalies in 2016 were almost all of the negative type. Alaska and portions of northern Canada experienced reduced cloudiness for much of the year, which coincided with unusually warm and dry conditions. For Alaska the largest negative cloudiness anomalies also corresponded with negative phases of the Arctic Oscillation. Portions of South America, encompassing Ecuador, Peru, parts of Bolivia, and the southern tip of Chile, also experienced significantly reduced cloudiness.
2012; Wagner et al. 2012). The latest dataset combines 11 different sensors between late 1978 and December 2016 and now also includes data from the Soil Moisture Ocean Salinity (SMOS) mission  (Dorigo et al. manuscript submitted to Remote Sens. Environ. ). The dataset has been used for a wide range of applications (see  Dorigo and de Jeu 2016; Dorigo et al. submitted, )  and has been benchmarked against a large number of land surface models and in situ datasets  (Albergel et al. 2013; Dorigo et al. 2015b; Fang et al. 2016; Loew et al. 2013). Based on the ESA CCI Soil Moisture (SM) dataset the yearly and monthly anomalies are computed here with respect to a 1991-2015 climatology. For 2016, spatial anomaly patterns (Plate 2. 1g) are markedly different from 2015, which was characterized by El Niño conditions throughout the year  (Dorigo et al.
g. , 2004/05). This limited impact of El Niño on global soil moisture was already observed for 2015 and may be because other climate oscillations may have partly counterbalanced the negative effects of El Niño  (Dorigo et al. 2016). Instead, globally and for both hemispheres, average soil moisture was slightly above normal in 2016 (Fig. 2. 28). No evident large-scale long-term global soil moisture trends can be observed (Fig. 2. 28). This does not exclude, however, the existence of long-term trends at regional or local scales  (Dorigo et al. 2012; Rahmani et al. 2016; S. Wang et al. 2016). Trends in average global soil moisture should be treated with caution owing to dataset properties changing over time and the inability to observe beneath dense vegetation or in mountain areas and frozen soils (cf.
2013a ) is presented, using precipitation and Penman-Monteith potential ET from an early update of the CRU TS 3. 25 dataset  (Harris et al. 2014). Moisture categories are calibrated over the complete 1901-2016 period to ensure that "extreme" droughts and pluvials relate to events that do not occur more frequently than in approximately 2% of the months. This choice affects direct comparison with other hy-drological cycle variables in Plates 2. 1g-r which use a different baseline period. Other drought indices may give varied results (see  van der Schrier et al. 2015). Following the rapid expansion in the overall area of drought across the globe during 2015  (Osborn et al. 2016) , drought area for 2016 was among the largest in the post-1950 record (Fig. 2. 29). Every month of 2016 had at least 12% of global land experiencing severe drought conditions (scPDSI < -3), matched only by  1984 and 1985 (although 1983, 1986, and 1992   Although the area in drought during 2016 is no greater than at the end of 2015, the annual average extreme drought area in 2016 is around double the annual average for 2015 because 2015 began with a relatively small area in drought.
Nonetheless, pioneering efforts to infer evaporation from its satellite-observed environmental and climatic drivers (e. g. ,  Price 1982; Nemani and Running 1989; Su 2002)  have culminated in a range of datasets that can be used to monitor land evaporation dynamics at the global scale (e. g. ,  Fisher et al. 2008; Miralles et al. 2011; Vinukollu et al. 2011; Yo. Zhang et al. 2016). While several international efforts remain focused on characterizing their uncertainties  (McCabe et al. 2016; Miralles et al. 2016 ), these evaporation products are also being used to study a range of process behaviors, including: (1) trends in response to global warming  (Jung et al. 2010; Yo. Zhang et al. 2016) , (  2 ) effects of internal climate variability on hydrology  (Miralles et al. 2014b ), (3) irrigation requirements in agricultural regions  (Anderson et al.
2016a). Although negative anomalies were reported for the North America (-0. 083 m s -1 ), Europe (-0. 073 m s -1 ), and Australia (-0. 008 m s -1 ) in 2016, all of them are less negative than the lowest values reached (in around 2013). This recent strengthening in terrestrial wind speed is more evident in central (+0. 108 m s -1 ) and East (+0. 084 m s -1 ) Asia, both showing positive anomalies in 2016. The occurrence of moderate (>3 m s -1 ) and strong (>10 m s -1 ) winds (Figs. 2. 37b,c) exhibited a different longterm behavior: moderate winds displayed no trend at all or a weakly increasing trend in agreement with the recent recovery mentioned above. Strong winds showed the opposite pattern, reaching the second lowest percentage of occurrence (2. 04%) globally in 2016. The assessment of the spatial and temporal variability of surface wind speed in 2016 is based on two quality-controlled datasets from anemometer observation records: (1) the global HadISD2  (1973 -2016; Dunn et al.
2012 Dunn et al. , 2016b) )  and (  2 ) an Australian database  (1974 -2016; McVicar et al. 2008). As the spatial representativeness of these site-specific wind speed observations is limited (2625 stations in total), with low-density in, for example, South America and Africa, 10-m wind speed fields from the  MERRA-2 (1980 MERRA-2 ( -2016;; Gelaro et al. 2017 ) and the ERA-Interim reanalysis  (1979 -2016; Dee et al. 2011 ) are used to cover contiguous global scales (including ocean surfaces). Reanalysis products have the ability to provide global wind speed data, although investigations have detected shortcomings in the simulation of near-surface layer processes (e. g. ,  McVicar et al. 2008; Pryor et al. 2009; Vautard et al. 2010). Over land, during 1979-2016, observed longterm trends of wind speed were slightly negative for all regions, with a global declining change of -0.
7 W m -2 and a minimum of 0 W m -2. Indeed, the amplitude of the anomalies for the first five to six months was often greater than 0. 8 W m -2. The rapid decrease in the strength of the El Niño thereafter corresponded well with the MEI values. Overall, this led to a large positive annual OLR anomaly (see  of -0. 3 W m -2 , fell to -1. 5 W m -2 , then recovered to positive values peaking in June at +1. 4 W m -2 before remaining mostly positive, oscillating between -0. 15 and +0. 74 W m -2 for the remainder of the year. The periods of negative net anomalies occurred during the El Niño, and the positive net anomalies occurred after the El Niño had decayed. These offsetting positive and negative values tended to cancel, thereby producing essentially no annual total anomaly in the net flux. The TSI data used in this study are provided by the Total Irradiance Monitor aboard the Solar Radiation and Climate Experiment (SORCE) spacecraft  (Kopp and Lean 2011)  and by the Royal Meteorological Institute of Belgium composite dataset  (Dewitte et al.
2006) , except for PFC-14, PFC-116, and HFC-23 which were measured by AGAGE  (Mühle et al. 2010; Miller et al. 2010). Changes indicated in parentheses are the differences between the 2016 and 2015 global surface mean mole fractions. c Preliminary estimate. d Global mean estimates derived from multiple NOAA measurement programs ("Combined Dataset"). also contribute to radiative forcing. Atmospheric mole fractions of some CFCs, such as CFC-12 and CFC-11, have been decreasing for a decade or more in response to production and consumption restrictions imposed by the Montreal Protocol and its Amendments (Fig. 2. 44d). The atmospheric burdens of many CFC-replacement compounds (HCFCs and HFCs) continued to increase in 2016 (Fig. 2. 45; Table  2. 6). The NOAA Annual Greenhouse Gas Index (AGGI) summarizes trends in the combined direct radiative forcing by five major LLGHGs (CO 2 , CH 4 , N 2 O, CFC-11, and CFC-12) and 15 minor gases  (Hofmann et al.
2012). GFAS is calibrated to partly match the Global Fire Emissions Database (GFED) which estimates emissions based on burned area and fuel consumption with a much longer latency (van der  Werf et al. 2010). The combined use of GFAS  (2001 ( -16) and GFED (1997 ( -2014) )  indicates that fire emissions were on average 2. 1 Pg C yr -1 (1 Pg = 10 15 g; C is carbon) with substantial interannual variability mostly stemming from tropical deforestation zones and the boreal regions where fire activity varies more from year to year than in most savanna areas. In 2016, negative regional anomalies of around -75 Tg C (1 Tg = 10 12 g) in each of tropical Asia, Australia, and Southern Hemisphere America led to total global fire emissions below average (-6%; see Table  2. 7, and Plate 2. 1ad). Tropical Asia stands out with an 85% drop in fire activity from 2015 to 2016 (Fig.
3. 3f; 1. 00°C century -1 ) oceans. The SST increase for the period 1950-2016 was fastest in the tropical Indian (1. 47°C century -1 ), tropical Atlantic (1. 12°C century -1 ), and Southern (1. 02°C century -1 ) oceans; and slower in the tropical Pacific (1. 00°C century -1 ), North Atlantic (1. 00°C century -1 ), and North Pacific (0. 65°C century -1 ) oceans. The recent increase of the global mean SST (to the end of 2015) as seen in ERSSTv4 is consistent with the report by  Karl et al. (2015)  and corroborated by comparisons with independent and homogenous datasets  (Hausfather et al. 2017). In addition to long-term SST trends, internal SST variations can be seen in all global ocean basins, although the amplitude is typically smaller in the Southern Ocean. Variations associated with the Atlantic multidecadal oscillation (AMO) can clearly be identified  (Huang et al.
In the South Pacific, the southeast trade winds became weaker. It is yet to be examined whether the weakened trades were associated with the decaying El Niño or caused by the warm PDO phase that persisted through 2016 (see Fig. constructed for the period from 2001 onward, using the available period of the CERES EBAF dataset. The time series of P -E and wind stress started from the availability of SSM/I observations in 1988. The Q net time series trended slightly higher in recent years after a major dip in 2008. These Q net fluctuations may be related to ENSO, with the ocean gaining more heat as El Niño strengthens and less as La Niña builds  (Johnson and Birnbaum 2017). A similar dip is also present in P -E time series and wind time series, except that the dip in wind occurred one year later in 2009.
The P -E time series variations again may be owing to changes in transfers of water between land and sea with ENSO (e. g. ,  Cazenave et al. 2012 ). The P -E time series is up slightly in 2016, presumably reflecting El Niño's influence on tropical oceanic precipitation. To first order, the GPCP precipitation dataset shows that changes over land and ocean during El Niño or La Niña years approximately balance, giving a global time series that is more nearly constant than the land-only or ocean-only time series. Over the 29-year period, the P -E time series shows a slight decrease during the 1990s but no obvious trend thereafter. A strengthening of the global winds in the 1990s is also indicated in the global wind stress time series of 29 years. The global average wind stress remains near steady in recent years.
3. 17c). The opposite is true for Hawaii, where the multidecadal trend is small (Fig. 3. 15b), but the annual anomaly is large (Fig. 3. 16a) due to Rossby waves emanating from the eastern boundary during mid-to late 2016 in response to ENSO forcing along the equator. g. Surface currents-R. Lumpkin, G. Goni, and K. Dohan  This section describes ocean surface current changes, transports derived from ocean surface currents, and features such as rings inferred from surface currents. Surface currents are obtained from in situ (global array of drogued drifters and moorings) and satellite (altimetry, wind stress, and SST) observations. Transports are derived from a combination of sea height anomaly (from altimetry) and climatological hydrography. See  Lumpkin et al. (2012)  for details of the datasets used and calculations performed.
The difference between the regression constants from the earlier and later datasets are then used to estimate the changes in ocean carbon independent of any changes in the water mass distributions. Using a similar approach for the Atlantic,  Woosley et al. (2016)  showed that C anth storage increased from 5. 1 to 8. 1 Pg C decade -1 over the same time intervals. These results suggest that both increases in air-sea exchange of CO 2 at the surface as well as changes in ventilation within the ocean interior contribute to increased rates of uptake in the later decade. Because of the vast area of the subtropical gyres, the majority of the C anth inventory is stored in the subtropics. By contrast, upwelling regions near the equator, in the North Pacific, and in the Southern Ocean south of the Antarctic Circumpolar Current have low storage rates (Table  3.
The South African monsoon season (October-April) is typically suppressed during El Niño. This region recorded below-average precipitation from October 2015 through February 2016 (Fig. 4. 7g; see also  Bell et al. 2016 ). In the tropical region, the annual temperature for 2016 was the highest on record for the latitude range 24°N-24°S (0. 96°C above the 1951-80 average, according to the NASA GISS dataset) and for 30°N-30°S (0. 75°C above the 1961-90 average, according to the HadCRUT4 dataset). Outside of the tropics, typical El Niño surface temperature impacts  (Halpert and Ropelewski 1992 ) early in the year included aboveaverage temperatures across Canada and the northern U. S. , southern Africa, and eastern Australia. Typical La Niña surface temperature impacts later in the year included above-average temperatures across the southeastern U.
Although not identical, CMORPH and TRMM 3B43 rainfall are very similar in pattern and magnitude at the broad scale discussed here. Figures 4. 12, 4. 13, and 4. 14 show typical ENSO displacement of the convergence zones, well-supported by rainfall observations from Pacific Islands (not shown). In January-March (Figs. 4. 12, 4. 13), both the ITCZ and SPCZ were located closer to the equator than normal, with substantially higher-than-normal rainfall at the peak latitude; this was especially marked from the date line eastward to 120°W in both hemispheres. Conversely, rainfall was reduced on the poleward side of both convergence zones. Figure  4. 13 shows that January-March had the steepest rainfall decline with latitude, north of the equator, of any year in the CMORPH dataset. The Northern Hemisphere peak rainfall, between approximately 3°-4°N in this smoothed representation, was also higher than any In the Southern Hemisphere, rainfall was much lower than normal in many of the early months of the year as documented in more detail in Chapter 7.
With the collapse of El Niño conditions in April-June, the two convergence zones behaved differently. The ITCZ showed a poleward displacement from May onwards (a La Niña signature), whereas the SPCZ remained unusually active for the time of year and maintained its equatorward displacement through the season (Fig. 4. 12b). In July-September (Fig. 4. 12c), the SPCZ convection had subsided and the ITCZ continued a more pronounced poleward displacement from its climatological position as weak La Niña conditions developed (see Section 4b). In  4. 14)  the poleward displacement of the ITCZ was quite marked. Despite of the La Nina being categorized as weak, Fig. 4. 14 suggests that the peak rainfall (near 8°N) in the 180°-150°W sector was the highest in the CMORPH dataset, with the ITCZ also displaced farther north between 10°N and 15°N than in any other year.
Matthew formed from a tropical wave as it neared the Lesser Antilles, and over the course of the following 12 days, it cut a path of devastation across portions of Hispaniola, Cuba, the Bahamas, and then along the U. S. southeast coast before finally becoming post-tropical. In this sidebar, several of Hurricane Matthew's most notable meteorological records are highlighted. All statistics for Matthew listed are from the operational b-decks, which are utilized to initialize the numerical model guidance on tropical cyclones in real-time every six hours. The b-decks are available at http://ftp. nhc. noaa. gov/atcf/btk. Historical statistics are calculated from the HURDAT2 database, which provides six-hourly estimates of historical Atlantic tropical cyclone wind speeds, pressures, and locations since 1851  (Landsea and Franklin 2013).
4) western nOrth pacific basin-S. J. Camargo (i) Introduction The tropical cyclone data used here are from the Joint Typhoon Warning Center (JTWC) western North Pacific (WNP) best-track dataset for the period from 1945-2015 and from the JTWC preliminary operational data for 2016 (also as embodied in IBTrACS). Climatology is defined using the period from 1981-2010, with the exception of landfall statistics, where 1951-2010 was used. The best-track data from the RSMC-Tokyo, Japan Meteorological Agency (JMA) was also used in Fig. 4. 31b. All other figures and statistics were obtained using JTWC best track data.
The percentage of typhoons that reached supertyphoon status in 2016 (46%) was in the top 5th percentile from 1981-2010, the third consecutive season with a high rate of supertyphoon occurrence (47% in 2015 and 58% in 2014). Climatologically, only 24% of typhoons become supertyphoons (75th percentile is 30%, 95th percentile is 43%). This is remarkably similar to the 1957-59 period when this ratio was also in the top 5th percentile for three consecutive years, although the data are less reliable from that period  (Chu et al. 2002). The JMA total for 2016 was 26 TCs (median is 25). Tropical Storm Rai was only considered to be a tropical depression by the JTWC and Tropical Storm Malou was not included in the JTWC database. Of those 26, 7 were tropical storms, 6 were severe tropical storms (both values equal to the median), and 13 were typhoons (bottom quartile).
Records were set in 2016 on an annual basis, on a seasonal basis (in spring, summer, and autumn), and in individual months (Table  5. 1). The annual average temperature in 2016 was record setting at most coastal observing stations in East Greenland. At Summit (elevation 3216 m above sea level), 2016 was +2. 2°C above average, second only to 2010. Data collected from the PROMICE network also indicate that the annual temperature in 2016 was above average by +1. 0° ± 1. 8°C, with substantial regional differences. As highlighted in Table  5. 1, new surface temperature records were set during the spring season at Kangerlussuaq, Aasiaat, and Summit. April was particularly warm, with new records set at Summit and eight other sites. Summer temperature anomalies were positive at all stations around the Greenland coastline, with new records set at the southeast coastal sites of Tasiilaq, Aputiteeq, and Illoqqortoormiut, at the northeast site of Danmarkshavn and Daneborg, and at the southern site of Narsarsuaq.