dataesr/scientific-paragraphs-categorization huggingface dataset:
2016). Subsequent data releases will occur annually such that the data can inform the annual assessment of global CO 2 sources and sinks provided by the Global Carbon Project (www. globalcarbonproject. org). The increased data coverage and new mapping techniques make it possible to obtain air-sea CO 2 fluxes at monthly time scales, allowing investigation of variability on subannual to decadal time scales and the causes thereof. An important recent result illuminated by these improved approaches is the reinvigoration of the Southern Ocean carbon sink since 2002  (Landschützer et al. 2015) , which had previously been found to be decreasing (Le  Quéré et al. 2007 ). The newly released datasets have been used to verify the magnitude of the anthropogenic air-sea CO 2 fluxes over the last decade and in 2014.
4 region (5°N-5°S, 170°-120°W) using ERSST. v4 data  (Huang et al. 2015 ). NOAA's Climate Prediction Center classifies ENSO events historically using the ONI. At the end of 2015 ONI values were +2. 25°C, comparable to the strongest El  Niño (1997/98)  in the 1950-2015 record. The EQ-SOI measures the difference in surface air pressure anomalies between Indonesia and the eastern equatorial Pacific Ocean, two large areas along the equator  (Barnston 2015). Therefore, the EQ-SOI is a more robust measure of ENSO than the traditional SOI, which is based on measurements at only two stations, both of which are off-equatorial (Tahiti at 18°S, Darwin at 12°S  ; Troup 1965; Trenberth 1984). Large negative values as seen during 2015 typify El Niño (Fig. 4. 1b), and reflect the combination of decreased surface air pressure over the eastern equatorial Pacific and increased air pressure over Indonesia.
During 2015, an El Niño event that had established itself in March continued to intensify through the end of the year. The monsoon of the western North Pacific extended far to the east to bring unusually strong and persistent westerly winds to the date line and beyond. Sea surface and subsurface temperatures were much warmer than normal, and the convergence zones were more active. For most months from May to December, the NASA ENSO Precipitation index (ESPI;  Curtis and Adler 2000)  was close to +2 or more, well above the +1 threshold associated with El Niño conditions. With the demise of the TRMM satellite in mid-2015, the rainfall data for this year's chapter are taken from NOAA's "CMORPH" global precipitation analysis  (Joyce et al. 2004 ). This dataset, derived from low orbiter satellite microwave observations (as is TRMM 3B43), is available at the same 0.
e. Tropical cyclones 1) Overview-H. J. Diamond and C. J. Schreck The IBTrACS dataset comprises historical tropical cyclone (TC) best-track data from numerous sources around the globe, including all of the WMO Regional Specialized Meteorological Centers (RSMC;  Knapp et al. 2010 ). To date, IBTrACS represents the most complete compilation of global TC data and offers a unique opportunity to revisit the global climatology of TCs. Using IBTrACS data  (Schreck et al. 2014 ) a 30-year average value for storms (from WMO-based RSMC numbers) is noted for each basin. The global tallying of total TC numbers is challenging and involves more than simply adding up basin totals because some storms cross basin boundaries, some basins overlap, and multiple agencies are involved in the tracking and categorization of TCs.
Compiling the activity using the IBTrACS dataset over all seven TC basins (Fig. 4. 18), the 2015 season (2014/15 in the Southern Hemisphere) had 101 named storms [wind speeds ≥ 34 knots (kt; 1 kt = 0. 51 m s -1 ) or 18 m s -1 ], which is well above the 1981-2010 average of 82  (Schreck et al. 2014)  and 10 more than the 2014 total of 91  (Diamond 2015). The 2015 season also featured 62 Hurricanes/Typhoons/Cyclones (HTC; wind speeds ≥ 64 kts or 33 m s -1 ), which is also well above the 1981-2010 average of 46 HTCs  (Schreck et al. 2014). Of these, 36 storms reached major HTC status (wind speeds ≥ 96 kts or 49 m s -1 ; WMO 2015), which is well above the average of 21. To assist in tallying the basin totals, this year we normalized the counts by basing them on WMO-defined basin boundaries and by using the Saffir-Simpson scale to represent intensities for all basins, realizing that the Saffir-Simpson scale is not operationally used in all basins.
4) western nOrth pacific basin-S. J. Camargo (i) Introduction The WNP is unique in that TCs are tracked simultaneously by several agencies in that region. Among these are the United States military's Joint Typhoon Warning Center (JTWC) and the WMO-sanctioned RSMC-Tokyo, Japan Meteorological Agency (JMA). Data from JTWC are used here; best-track dataset for the period 1945-2014 and from the JTWC's preliminary operational data for 2015. The best-track data from the RSMC-Tokyo, Japan Meteorological Agency (JMA), was used in Fig. 4. 28b. All other figures were produced using JTWC TC data. Climatology is defined using the period 1981-2010, with the exception of landfall statistics, where 1951-2010 is used.
The 1981-2010 median ACE for the Atlantic is 92, and NOAA defines any season with less than 66 ACE units as a below-average season. The remainder of the Northern Hemisphere basins (the eastern North Pacific, the western North Pacific, and the north Indian) were conversely quite active. Some of the most notable records set during this record-breaking year for these three basins individually, then collectively, for the Northern Hemisphere are documented. Table  SB4. 1 summarizes the statistics by basin and denotes records achieved in 2015. All statistics described are based on operational advisories from the National Hurricane Center, Central Pacific Hurricane Center, and Joint Typhoon Warning Center, and are then compared with archived best-track data compiled by those agencies. The data in these basins date back to 1851 in the North Atlantic, 1949 in the northeast Pacific, 1945 in the northwest Pacific, and 1972 in the north Indian; however, it should be noted that the data quality among these datasets is not uniform prior to about 1985  (Chu et al.
gov/aboutgloss. shtml#s). Until recently, TCs were believed not to form in the Mediterranean Sea, the Atlantic basin south the equator, and the far eastern Pacific basin south of the equator  (Gray 1968 ). Here we describe a subtropical storm identified in the southeastern Pacific basin off the Chilean coast farther east than any in the historic record as documented in either the IBTrACS  (Knapp et al. 2010)  or SPEArTC  (Diamond et al. 2012 ) datasets and outside of the responsibility of any global RSMC. The formation of Hurricane Catarina off the coast of  Brazil in 2004 (McTaggart et al. 2006; Gozzo et al. 2014 ) demonstrated that TCs can occasionally form in previously unsuspected areas such as the South Atlantic. The existence of possible TCs in the Mediterranean Sea, which are overwhelmingly subtropical in nature, has also generated interest in recent years  (Moscatello et al.
2). The NCEP-NCAR reanalysis data  (Kalnay et al. 1996)  for 29 April at 1200 hours UTC showed a broad low pressure area located near 25°S, 102°W. As the system developed, it drifted toward the southeast before stalling near 28°S, 100°W for approximately 36 hours. From 2 to 3 May, the system moved west then northwest, dissipating on 6 May near 18°S, 110°W. The location of the system was in a "no man's land" of sorts as it is not within the forecast or warning area of responsibility of any RSMC. It was too far to the east of the Nadi RSMC's area of responsibility, and while there is no formal RSMC covering the area east of 120°W, the system will be incorporated into the SPEArTC dataset under the informal name of Katie. Therefore, while the system will not necessarily be formally picked up by the IBTrACS dataset, which reflects RSMC tracks on behalf of the World Meteorological Organization's TC Programme, the more research-oriented SPEArTC dataset, which focuses on the southwest Pacific, will include this storm in its listing of 2014/15 storms with appropriate notation of its unique subtropical nature.
2015). Figures 5. 25c,d therefore also compare UVI anomalies measured by OMI and ground-based instruments deployed throughout the Arctic and Scandinavia. Anomalies derived from the two datasets agree to within ±12% at all locations, with the exception of Andøya for April (OMI overestimates the actual anomaly by 16%) and Jokioinen for March (overestimate by 27% or 0. 3 UVI units). The large discrepancy for Jokioinen can be explained by early snowmelt on 9 March while the OMI climatology assumes snow cover through the month of March. Persistent cloud cover in the second half of March also contributed to this discrepancy. Monthly average noontime UVIs for March 2015 were below the 2005-14 means in a belt stretching from the Greenland Sea and Iceland in the east to Hudson Bay and the Canadian Arctic Archipelago in the west (Fig.
For 2015, the NOAA/NCEI GHCN daily precipitation dataset showed a considerable amount of missing data. For some stations, the daily rainfall amount was incomplete, whereas in other cases the value was flagged because it did not pass a quality control test. Precipitation historical records for the above-mentioned stations were recovered from Central American national weather services (NWS). The station climatology  and anomalies for 2015 were recalculated using NWS data by filling the gaps in the daily data records of the NOAA/NCEI database (especially those considered initially as zero based on the flags listed in the metadata of this database). In some stations (e. g. , David and Choluteca), differences in precipitation totals between NWS data and the NOAA/NCEI dataset were as high as 420 and 560 mm, respectively, for 2015.
In the station climatology, the largest differences were found in  David and Liberia (490 and 820 mm, respectively). Previous years' station climatology from the NOAA/NCEI database and procedures used for all variables can be found in  Amador et al. (2011). (i) Temperature Mean temperature (Tm) frequency distributions for the nine stations are shown in Fig. 7. 8. Most stations, with the exception of Limon and Liberia, experienced a higher frequency of above-average daily mean temperatures in 2015. There was a near-normal negative skewness in Tm at Philip Goldson (Tm1) and Puerto Barrios (Tm2) on the Caribbean slope and a near-average number of cold surges during the winter months. Stations in Panama (Tm5 and Tm6) and Honduras (Tm8) show a shift to the right of the Tm distribution with a higher frequency of warm Tm values during 2015.
e. Africa In 2015, most of Africa experienced above-average temperatures and below-average precipitation. Extreme weather caused loss of life and property damage in many parts of the continent. This extreme weather included torrential rains across western Africa and heavy rainfall related to a tropical storm over western Indian Ocean island countries. In contrast, eastern African countries, including Ethiopia, Somalia, and parts of Kenya, were impacted by drought. The drought in Ethiopia, the worst in several decades, was associated with the El Niño that developed early in the year. Extreme high temperatures were observed over northern, southern, and southwestern parts of Africa. The 2015 climate assessment for Africa is based on the 1981-2010 reference period. Both observed and reanalysis datasets are presented for analysis.
1) overvieW Europe was, on average, much warmer than normal in 2015. The mean land surface air temperature for the European region (35°-75°N, 10°W-30°E) from the CRUTEM4 dataset  (Jones et al. 2012 ) was +1. 51°C above the 1961-90 normal, only 0. 2°C short of the previous record set in  2014 (Fig. 7. 32). According to the E-OBS dataset  (van der Schrier et al. 2013b; Chrysanthou et al. 2014) , which uses different meteorological stations over an area extending farther west and east (25°W-45°E), the European annual mean land surface temperature was the highest on record (+0. 93°C above the 1981-2010 average; Fig. 7. 33). However, differences between both datasets are within the level of uncertainty (allowing for the different base periods). Across Europe and the Middle East, temperature anomalies ranged between +1°C in northwestern areas and +3°C in northeastern and Alpine regions (Fig.
7. 36e, dotted). In contrast, the British Isles, Scandinavia, and northern European Russia were influenced by frequent low pressure systems. These regions recorded surface temperature anomalies of 0° to -1°C accompanied by above-average rain amounts of up to 170% of normal (Fig. 7. 37c). In autumn the atmospheric circulation featured above-average 500-hPa heights (Fig. 7. 36g), and temperatures were warmer than normal in nearly all regions. Scandinavia and the eastern Mediterranean, including the Black Sea region, were especially affected by high pressure conditions and recorded significantly positive surface and 850-hPa temperature anomalies of more than +3°C in places. According to the E-OBS dataset, it was the third warmest autumn since 1950 for the European region. Eastern Turkey and the Balkan States received localized precipitation totals of more than 200% of normal (Fig.
Global (or representative) average time series for essential climate variables. Anomalies are shown relative to the base period in parentheses although original base periods (as shown in other sections of the report) may differ. The numbers in the square brackets that follow in this caption indicate how many reanalysis (blue), satellite (red), and in situ (black) datasets are used to create each time series in that order. ; (ac) Biomass burning [0,2,0]; (ad) Soil moisture [0,1,0]; (ae) Terrestrial groundwater storage [0,1,0]; (af) FAPAR [0,1,0]; (ag) Land surface albedo-visible (solid) and infrared (dashed) [0,2,0]. THE 2015/16 EL NIÑO COMPARED WITH OTHER RECENT EVENTS-D. E. PARKER, K. M. WILLETT, R. ALLAN, C. SCHRECK, AND D. S. ARNDT The climate of 2015 was clearly influenced by the strong 2015/16 El Niño.
18. The anomalies are computed relative to 2001-10 because the microwave-based UTH dataset begins only in 1999. A slightly below-average 2015 anomaly is observed. A near-zero trend in the UTH time series indicates an increase in specific humidity in the warming upper troposphere and 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 samples mainly clearsky areas. The annual average of UTH for 2015 (Plate 2. 1n; Online Fig. S2. 14) shows large moist anomalies over the central and eastern tropical Pacific and dry anomalies over the Maritime Continent, which results from the strong El Niño of 2015.
, several of the existing datasets compare well against each other and against in situ measurements. These datasets open new pathways to diagnose large-scale drought and irrigation needs, and to improve water resources management and the characterization of hydrological cycles. Satellite-based evaporation estimates respond to long-term changes in Earth's water and energy budgets and are able to capture fluctuations due to internal climate variability. The mean distribution of evaporation anomalies in 2015 (Fig. SB2. 2) is a clear example of the underlying effects of multidecadal climate trends and climate oscillations on the terrestrial water cycle. strong 1997/98 El Niño (Fig. 2. 28). Indeed, the other post-1950 years with scPDSI drought areas as large as in 2015 (31% in moderate drought; e.
Surface windS-R. J. H. Dunn, C. Azorin-Molina, C. A. Mears, P. Berrisford, and T. R. McVicar During 2015, over land, observational datasets have revealed generally higher surface wind speeds (Plate 2. 1s; Fig. 2. 33a) than in the last 20 years. This "recovery" continues the behavior observed since 2013 and concurs with Kim and Paik (2015), who reported a break from the decreasing trend in surface wind speed around the Republic of Korea during the most recent decade.
3. 3a), where the peaks and valleys in the global ocean SSTA often correspond with those in the tropical Pacific SSTA (Fig. 3. 3b). The mean SSTA in the tropical Pacific increased by 0. 23°-0. 29°C from 2014 to 2015, and 2015 surpassed 1997 as the warmest year since 1950. Partially owing to the strong warming in the tropical Pacific, the mean SSTA in the global ocean increased by 0. 08°-0. 11°C from 2014 to 2015, depending on the dataset examined, and 2015 surpassed 2014 as the warmest year since 1950.
Measured annual climatic mass balance (B clim ) of glaciers in Alaska, the Canadian Arctic, Iceland, Svalbard, and northern Scandinavia for 2013/14 and 2014/15, along with the 1980-2010 mean and standard deviation for each glacier (column 3). Mass balance data are from the World Glacier Monitoring Service (2015; 2016), with corrections to Svalbard data provided by J. Kohler and to Alaska data provided by S. O'Neel, and with updates from the Norwegian Water Resources and Energy Directorate (NVE) database. Numbers in column 1 identify glacier locations in Fig. 5. 1. Note that 2014/15 results may be based upon data collected before the end of the 2015 melt season and may be subject to revision.
7. 53), and Erromango and Tanna suffered a direct hit, making Pam the single worst natural di-saster in the history of Vanuatu. The cyclone crippled infrastructure, with an estimated 90% of Vanuatu's buildings impacted by the storm. Communications were devastated and there was a shortage of water for several days following the storm. At least 132 000 people were affected by Pam, including 54 000 children. There were at least 15 fatalities. 4) aUStralia-C. Ganter and S. TobinThe information presented here has been prepared using the homogenized Australian temperature dataset (ACORN-SAT) for area-averaged temperature values and the observational dataset (AWAP) for area-averaged rainfall values and mapped analyses for both temperature and rainfall. See www. bom. gov. au/climate/change/acorn-sat/ and w w w.
(Mühle et al. 2010;Miller et al. 2010)ace means for the indicated calendar year determined from the NOAA global cooperative air sampling network(Hofmann 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 brackets are the differences between the 2015 and 2014 global mean mole fractions. Global means derived from multiple NOAA measurement programs ("Combined Dataset").
ACKNOWLEDGMENTS We wish to thank the AMS Journals' editorial staff, in particular Melissa Fernau, for facilitating the document. We thank the NCEI visual communications team for laying the document out and executing the countless number of technical edits needed. We also wish to express our sincere and deep gratitude to Dr. Rick Rosen, who served as the AMS special editor for this report. Dr. Rosen's handling of the reviews was at the same time rigorous and responsive, and greatly improved the document.
Plate 1. 1. |SIDEBAR 1. 1 : 1111 Global (or representative) average time series for essential climate variables. Anomalies are shown relative to the base period in parentheses although original base periods (as shown in other sections of the report) may differ. The numbers in the square brackets that follow in this caption indicate how many reanalysis (blue), satellite (red), and in situ (black) datasets are used to create each time series in that order. ; (ac) Biomass burning [0,2,0]; (ad) Soil moisture [0,1,0]; (ae) Terrestrial groundwater storage [0,1,0]; (af) FAPAR [0,1,0]; (ag) Land surface albedo-visible (solid) and infrared (dashed) [0,2,0]. THE 2015/16 EL NIÑO COMPARED WITH OTHER RECENT EVENTS-D. E. PARKER, K. M. WILLETT, R. ALLAN, C. SCHRECK, AND D. S. ARNDT The climate of 2015 was clearly influenced by the strong 2015/16 El Niño.
2 ) 2 Surface windS-R. J. H. Dunn, C. Azorin-Molina, C. A. Mears, P. Berrisford, and T. R. McVicar During 2015, over land, observational datasets have revealed generally higher surface wind speeds (Plate 2. 1s; Fig. 2. 33a) than in the last 20 years. This "recovery" continues the behavior observed since 2013 and concurs with Kim and Paik (2015), who reported a break from the decreasing trend in surface wind speed around the Republic of Korea during the most recent decade.
S2  and S3  in the supplemental material). Similarly, SGBP-B also bound to XyG and XyGO 2 with approximately equal affinities, although in both cases, K a values were nearly 10-fold lower than those for SGBP-A. Also in contrast to SGBP-A, SGBP-B also bound to XyGO 1 , yet the affinity for this minimal repeating unit was poor, with a K a value of ca. 1 order of magnitude lower than for XyG and XyGO 2. Together, these data clearly suggest that polysaccharide binding of both SGBPs is fulfilled by a dimer of the minimal repeat, corresponding to XyGO 2 (cf. Fig. 1A ). The observation by affinity PAGE that these proteins specifically recognize XyG is further substantiated by their lack of binding for the undecorated oligosaccharide cellotetraose (Table  1 ; see Fig. S3 ). Furthermore, D. SGBP-A binds cellohexaose with ~770-fold weaker affinity than XyG, while SGBP-B displays no detectable binding to this linear hexasaccharide.
S6 ). Additional residues surrounding the binding site, including Y369 and E412, may contribute to the recognition of more highly decorated XyG, but precisely how this is mediated is presently unclear. Hoping to achieve a higher-resolution view of the SGBP-B-xyloglucan interaction, we solved the crystal structure of the fused CD domains in complex with XyGO 2 (1. 57 Å, R work ϭ 15. 6%, R free ϭ 17. 1%, residues 230 to 489) (Table  2 ). The CD domains of the truncated and fulllength proteins superimpose with a 0. 4-Å RMSD of the C␣ backbone, with no differences in the position of any of the glycanbinding residues (see Fig. S7A  in the supplemental material). While density is observed for XyGO 2 , the ligand could not be unambiguously modeled into this density to achieve a reasonable fit between the X-ray data and the known stereochemistry of the sugar (see Fig.
Proteins were dialyzed against 20 mM HEPES-100 mM NaCl (pH 7. 0), and sugars were prepared using the dialysis buffer. The protein (45 to 50 M) was placed in the sample cell, and the syringe was loaded with 2. 5 to 4 mg/ml XyG polysaccharide. Following an initial injection of 0. 5 l, 26 subsequent injections of 2 l were performed with stirring at 350 rpm, and the resulting heat of reaction was recorded. Data were analyzed using the Nano Analyze software. All other ITC experiments were performed using a MicroCal VP-ITC titration calorimeter calibrated to 25°C. Proteins were dialyzed into 20 mM HEPES-100 mM NaCl (pH 7. 0), and polysaccharides were prepared using the dialysis buffer. Proteins (micromolar concentrations) were placed in the sample cell, and a first injection of 2 l was performed followed by 24 subsequent injections of 10 l of 2 to 20 mM oligosaccharide (cellotetraose, cellohexaose, XyGO 1 , or XyGO 2 ) or 1 to 2.
5 mg/ml XyG polysaccharide. The solution was stirred at 280 rpm, and the resulting heat of reaction was recorded. Data were analyzed using the Origin software program. DSC. Structural integrity of the SGBP-B mutants was verified by differential scanning calorimetry (DSC). DSC studies were performed on a MicroCal VP-DSC (Malvern Instruments). Experiments were carried out in 50 mM HEPES (pH 7. 0) at a scan rate of 60°C/h. All samples (40 M protein) were degassed for 7 min with gentle stirring under vacuum prior to being loaded into the calorimeter. Background excess thermal power scans were obtained with buffer in both the sample and reference cells and subtracted from the scans for each sample solution to generate excess heat capacity versus temperature thermograms. The melting temperature decreased from 57.