dataesr/scientific-paragraphs-categorization huggingface dataset:
Pinatubo (1991) ], which imparted a warm pulse for about two years following each eruption. The satellite MSU channel 4 datasets (RSS, NOAA, and UAH) and four recent reanalysis datasets (CFSR; ERA-Interim; JRA-55; MERRA-2) also show general agreement with the radiosonde time series. Table  2. 3 provides the trends for various time periods for the radiosondes, satellites, and reanalyses. There is variability among the datasets in the cooling trend from 1979 to 1995, with RATPAC having the great-est cooling of the radiosonde datasets and MERRA-2 having the greatest cooling of the reanalyses, while ERA-Interim and JRA-55 have the least cooling. The post-1995 trends also vary considerably. All three satellite, JRA-55, RATPAC, and NSW trends are near neutral. The ERA-Interim and MERRA-2 reanalyses, RICH, and RAOBCORE have a slightly positive trend.
Regionally, the frequencies of warm days and warm nights were the highest on record in western North America, parts of central Europe, and central Asia  (Plates 2. 1d, e). The GHCNDEX dataset  (Donat et al. 2013 ) is used to monitor temperature extremes for 2015. GHCNDEX is a quasi-global gridded dataset of land-based observed temperature and precipitation extremes. A suite of temperature and precipitation extremes indices  (Zhang et al. 2011 ) is first calculated for daily station time series from the GHCN-Daily archive  (Menne et al. 2012) , before interpolating the indices on global grids. At the time of writing, and similar to  Dunn et al. (2015) , some of the indices fields have limited spatial coverage for 2015, especially those derived from minimum temperatures across central and eastern Asia, compared to those calculated from maximum temperatures.
org). Monthly SCE for the NH, EU, NA, contiguous U. S. , Alaska, and Canada are also posted, along with information on how to access weekly areas and weekly and monthly gridded products. 3) aLpine gLacierS and ice SheetS-M. S. Pelto The World Glacier Monitoring Service (WGMS) record of mass balance and terminus behavior provides a global index for alpine glacier behavior. The WGMS dataset for terminus change contains 42 000 observations from 2000 glaciers extending from the mid-19th century. There are 5200 geodetic and glaciological mass balance observations in this dataset. Annual mass balance is the annual change in volume due to snow and ice accumulation and snow and ice losses. Here, WGMS mass balance is reported in mm of water equivalent (Fig. 2. 13). In 2014 mean mass balance was -798 mm for the 41 long-term reference glaciers and -586 mm for all 130 observed glaciers.
A land-and-ocean time-latitude plot derived from JRA-55 (Fig. 2. 17) indicates that the long-term increase in TCWV is occurring at all latitudes, with less variability outside the tropics. The El Niño events are clear, especially the 1997/98 event. The previous strong El Niño events during 1983/84 and 1997/98 showed pronounced drying in the northern tropics that accompanied moistening on the equator and the southern subtropics. Although similar in strength in terms of sea surface temperature, the TCWV response to the current El Niño does not show this feature (see  Sidebar 1. 1; Online Fig. S2. 13). 3) upper tropoSpheric humidity-V. O. John, L. Shi, and E. -S. Chung Global scale monitoring of upper tropospheric relative humidity (UTH) was first reported last year, using one dataset of satellite origin and one reanalysis.
However, the reanalysis data showed drying of the upper troposphere since 2001 that was not present in the satellite data. Therefore, for this year, two independent UTH satellite datasets are used. One is the infrared-based HIRS dataset  (Shi and Bates 2011)  which was used last year, and the other is the microwave-based UTH dataset  (Chung et al. 2013). UTH represents a weighted average of relative humidity in a broad layer, roughly between 500 and 200 hPa. Humidity distribution at these levels of the atmosphere is a key climate variable due to its strong control on the outgoing longwave radiation (OLR) which makes a strong feedback factor in the climate system. Area-weighted anomaly time series of UTH for the 60°N-60°S latitude belt are shown in Fig. 2  4) precipitation-R. S. Vose, A. Becker, K.
Hilburn, G. Huffman, M. Kruk, and X. Yin Precipitation over the global land surface in 2015 was far below the long-term average (Fig. 2. 19). In fact, 2015 was the driest year on record in two prominent global products: the Global Precipitation Climatology Centre (GPCC) dataset  (Schneider et al. 2011; Becker et al. 2013) , which is based on surface stations, and the Global Precipitation Climatology Project (GPCP) version 2. 3  (Adler et al. 2003) , which is based on both satellite data and surface stations. Last year was also among the five driest years on record in a new (experimental) version of another prominent global product, the Global Historical Climatology Network (GHCN) dataset  (Peterson and Vose 1997; Menne et al. 2012) , which contains about five times as many surface stations as its operational counterpart (version 2).
Minnis et al. 2015)  shows an increase of 1. 1%, and CLARA-A2 (Cloud, ALbedo and RAdiation dataset;  Karlsson et al. 2013)  shows a modest decrease of 0. 25%. Three of the records-PATMOS-x, CLARA-A2, and SatCORPS-are derived from the AVHRR (Advanced Very High Resolution Radiometer) on the NOAA Polar Orbiter Environmental Satellite series and more recently the EUMETSAT Polar System Metop series. SatCORPS is the most recent addition and was developed through the NOAA Climate Data Record program. CLARA-A2 is the successor to CLARA-A1 and includes several changes, the most notable to impact global cloudiness being improvements in cloud detection over semiarid regions. In addition to instrument sensitivity and calibration, several factors contribute to differences among the records. For example, the AVHRR-derived records use different methods to account for satellite diurnal drift, while HIRS is primarily focused on detection of cirrus cloud.
2012; Wagner et al. 2012). The current version of the dataset combines nine different sensors (SMMR, ERS-1/2, TMI, SSM/I, AMSR-E, ASCAT, WindSat, AMSR2, and SMOS) between late 1978 and December 2015. It has been used for a wide range of applications (e. g. ,  Dorigo and De Jeu 2016)  and has been benchmarked against a large number of land surface models and in situ datasets  (Albergel et al. 2013; Dorigo et al. 2015b; Loew et al. 2013) , revealing a good performance across the globe except for densely vegetated areas. The surface soil moisture content sensed by the microwave satellites is closely linked to that of the root zone  (Paulik et al. 2012) , except for very dry conditions where they may become decoupled  (Hirschi et al. 2014). Based on the ESA CCI SM dataset, the yearly and monthly anomalies are computed here with respect to a 1991-2014 climatology.
No evident large-scale, long-term global soil moisture trends can be observed  (Figs. 2. 26, 2. 27 ). However, this does not exclude the existence of longterm trends at the regional or local scale  (Dorigo et al. 2012). 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, for mountain areas, or frozen soils (cf. gray regions in Plate 2. 1f and Online Fig. S2. 17).
9) monitoring g Lo b a L d ro u g h t u S i n g t h e Se Lf -caLibrating paLmer drought Se verit y i n d e x-T. J. Osborn, J. Barichivich, I. Harris, G. van der Schrier, and P. D. Jones Hydrological drought results from a period of abnormally low precipitation, sometimes exacerbated by additional evapotranspiration (ET), and its occurrence can be apparent in reduced river discharge, soil moisture, and/or groundwater storage, depending on season and duration of the event. Here, an esti-mate of drought called the self-calibrating Palmer drought severity index is presented (scPDSI;  Palmer 1965; Wells et al. 2004; van der Schrier et al. 2013a ) using precipitation and Penman-Monteith potential ET from an early update of the CRU TS 3. 24 dataset  (Harris et al. 2014). Moisture categories are calibrated over the complete 1901-2015 period to ensure that "extreme" droughts and pluvials relate to events that do not occur more frequently than in approximately 2% of the months.
To date, land evaporation cannot be observed directly from space. However, a range of approaches have been proposed to indirectly derive evaporation by applying models that combine the satellite-observed environmental and climatic drivers of the flux (e. g. ,  Price 1982, Nemani and Running 1989; Anderson et al. 1997; Su 2002). Pioneering efforts targeting the global scale  (Mu et al. 2007; Fisher et al. 2008)   Nonetheless, continental evaporation remains one of the most uncertain components of Earth's energy and water balance. Both the WACMOS-ET and LandFlux projects have brought to light the large discrepancies among widely used, observationbased evaporation datasets, particularly in semiarid regimes and tropical forests (e. g. ,  Michel et al. 2016; Miralles et al. 2016; McCabe et al. 2016).
Some examples of evaporation datasets targeting near-real-time simulation at continental scales include the Land Surface Analysis Satellite Applications Facility (LSA SAF) product  (Ghilain et al. 2011 ) and the Atmosphere-Land Exchange Inverse (ALEXI) datasets  (Anderson et al. 1997 (Anderson et al. , 2011)). While GLEAM was not deliberately designed with an operational intent, the current version 3 dataset has been updated to include 2015, using observations from the Soil Moisture and Ocean Salinity (SMOS) mission (www. gleam. eu). Figure  SB2. 2 shows the anomalies in evaporation for 1980-2015 based on this new dataset. Periods of global decline in evaporation typically coincide with El Niño conditions, and are associated with drought in the water-limited ecosystems of the Southern Hemisphere  (Miralles et al.
(2008)  are used. In Australia, the positive anomaly made 2015 the fourth windiest year in the 1979-2015 record. There were positive anomalies in all other regions, with the exception of a noticeable negative anomaly in North America. Over this latter region, 2015 was the ninth calmest year in the observed record, with a slightly lower occurrence of both moderate (>3 m s -1 ) and strong (>10 m s -1 ) winds  (Figs. 2. 33b, c) , in agreement with  Iacono and Azorin-Molina (2014). Overall increases in Europe, central Asia, and East Asia reflected a higher occurrence of moderate winds, and particularly of strong winds in  Europe (Fig. 2. 33b, c). Adapting the approach of  Berrisford et al. (2015) , two quality-controlled wind speed datasets from instrumental records are used: 1) the global HadISD  (1973 ( -2015 ( , Dunn et al.
2012)) , with the highest station density in the Northern Hemisphere, and 2) an Australian database  (1979 ( -2015 ( , McVicar et al. 2008 ). The 10-m wind speed fields from ERA-Interim  (Dee et al. 2011 ) are also used to investigate the spatial and temporal variability of winds over regions that have few observations. Over land surfaces with high-density wind observations, the large-scale anomaly patterns from ERA-Interim (Plate 2. 1s) are relatively consistent with the instrumental records. Reanalysis products provide contiguous global information but have shortcomings in their representation of surface layer processes and hence near-surface winds speeds (see  McVicar et al. 2008; Pryor et al. 2009; Vautard et al. 2010 for examples). In addition, reanalyzed winds are representative of larger spatial and temporal scales than point observations.
2. 34) is consistent with the observational HadISD dataset, the magnitude of changes is underestimated, as previously noted for other reanalysis products  (McVicar et al. 2008 ;   Pryor  et al. 2009; Vautard et al. 2010). This worldwide slowdown of land surface wind speed observed since the 1980s has been reported over many regions (see  McVicar et al. 2012  for a review). The precise causes of this weakening in wind speed remain largely uncertain and do not necessarily reflect wind tendency at higher altitudes  (McVicar and Körner 2013)  than the standard 10-m observations  (Vautard et al. 2010; Troccoli et al. 2012). Increase of surface roughness due to forest growth, land use changes, and urbanization  (Vautard et al. 2010; Bichet et al. 2012; Wever 2012; Wu et al. 2016) ; changes in large-scale atmospheric circulation  (Azorin-Molina et al.
2011). The absorbed shortwave (TSI -RSW) anomaly started the year with a value of 0. 2 W m -2 , increased to just over 1. 1 W m -2 in September, but then decreased the last few months of the year. The positive values towards the latter half of the year were large enough to dominate the annual average, leading to a large absorbed shortwave anomaly for the year. The total net anomaly, which contains the combined OLR and absorbed shortwave anomalies, began 2015 with a value of -0. 7 W m -2 , then jumped to positive values, peaking in September at 0. 9 W m -2 before falling below 0 W m -2 by the end of the year. The positive absorbed shortwave anomaly dominates the net, resulting in the positive annual total net anomaly. Long-term trend analyses that include the last two months of the merged dataset are discouraged due to the natural fluctuation in ERB components, the uncertainty from the data merging process, and potential for drift in the FLASHFlux product.
Global-annual TSI data are from the Total Irradiance Monitor (TIM) instrument aboard the Solar Radiation and Climate Experiment (SORCE) spacecraft  (Kopp and Lean 2011)  and the Royal Meteorological Institute of Belgium (RMIB) composite dataset  (Dewitte et al. 2004) , both renormalized to the SORCE Version 15 data. RSW and OLR data were obtained from the Clouds and the Earth's Radiant Energy System (CERES) mission  (Wielicki et al. 1996 (Wielicki et al. , 1998)) , deriving flux data from the Terra and Aqua spacecraft. Time series (Fig. 2. 38) were constructed from the CERES EBAF (Energy Balanced And Filled) Ed2. 8 product  (Loeb et al. 2009 (Loeb et al. , 2012) )  from March 2000 to October 2015 and the CERES Fast Longwave and Shortwave Radiative Fluxes (FLASHFlux) products  (Kratz et al.
2014; Stackhouse et al. 2006) , for November and December 2015. The FLASHFlux data are normalized to the EBAF data using the following procedure based on overlapping data from January 2009 through December 2014. First, successive versions of globally-averaged FLASHFlux TOA components are normalized to each other relative to the current version 3B. Then, this unified 6-year FLASHFlux dataset is cross-calibrated to the corresponding EBAF Ed2. 8 data using TOA fluxes from both datasets, accounting for multiyear bias, linear change, and seasonal dependent differences. Finally, these coefficients are used to cross-normalize FLASHFlux to EBAF and provide an estimate of monthly globally averaged TOA flux components. The resulting 2-sigma monthly uncertainty of the normalization procedure for the 6-year overlap period was ±0.
Combustion and chemical in situ sources typically produce similar amounts of CO each year. New in 2015 is a CAMS-based retrospective analysis of CO for the period 2003-15 based on total column CO retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument  (Deeter et al. 2013, Version 5). This dataset is part of the CAMS interim reanalysis of atmospheric composition, an extended and temporally more consistent dataset than the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis  (Inness et al. 2013). The MACC has been used in previous State of the Climate assessments of CO and aerosols. MOPITT retrievals between 65°N and 65°S were assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) that has been extended to simulate atmospheric chemistry  (Flemming et al.
To this end, we use the data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS) developed at the ECMWF since 2005. The interim CAMS reanalysis is an improved version of the previous MACC reanalysis  (Inness et al. 2013)  and is updated in quasi nearreal time. Observational datasets used, among others, are the NASA MODIS Aerosol Optical Depth Collection 5 product  (Remer et al. 2005 ) and the MOPITT V5 total column carbon monoxide (CO) retrievals. A reanalysis dataset provides a dynamically consistent 3D estimate of the climate state at each time step and can be considered a good proxy for atmospheric conditions, including variables that are not directly observed. Here, 2015 anomalies of CO and carbonaceous aerosols are determined from the 2003-15 CAMS reanalysis, while the ozone anomalies are based on the 2005-14 ozone records from NASA's Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS)  (Ziemke et al.
In temperate and boreal regions, fires tend to occur less frequently and can be either human or lightning ignited. Since the late 1990s, fire occurrence and the associated burned area has been routinely detected by satellites. The Global Fire Assimilation System (GFAS) builds on active fire detections and their associated fire radiative power to estimate emissions in near-real time  (Kaiser 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 which have a much longer latency (van der  Werf et al. 2010). The combined use of  GFAS (2001-15)  and  GFED (1997 GFED ( -2014) )  indicates that fire emissions were on average 2. 1 Pg C year -1 (Pg = 10 15 g), with substantial interannual variability, the latter mostly stemming from tropical deforestation zones and the boreal region where fire activity varies more from year to year than in most savanna areas.
v3b; Smith et al. 2008) ; 3) the Met Office Hadley Centre's sea ice and SST dataset (HadISST;  Rayner et al. 2003) ; and 4) the recent update of  ERSST, version 4 (ERSST. v4; Huang et al. 2015). OISST is a satellite-based analysis that uses in situ data for bias adjustments of Advanced Very High Resolution Radiometer (AVHRR) data with 1° resolution, available since November 1981. ERSST. v3b, ERSST. v4, and HadISST are historical analyses beginning in the 19th century, and all apply statistical methods to data from the recent period to extend the SST analysis back in time when in situ observations were sparse. ERSST. v3b and ERSST. v4 include in situ data only and are produced at 2° resolution; both are presented here because v4 is new to this report. HadISST includes both in situ measurements and AVHRR SST retrievals from 1982 onward, available at 1° resolution.
3. 5b) from 2004 to 2015 that approaches 0. 15°C decade -1 near the surface, declines to around 0. 02°C decade -1 by 400 dbar, and remains near that rate down to 2000 dbar. This warming trend is found mostly south of the equator since 2006  (Roemmich et al. 2015; Wijffels et al. 2016). A decade is short for defining long-term trends with statistical confidence, especially in the upper ocean where ENSO causes large interannual perturbations, so the analysis is extended further back in time and deeper using historical data collected mostly from ships. Five different estimates of globally integrated in situ upper (0-700 m) OHCA (Fig. 3. 6a) all reveal a large increase since 1993 and indicate a record high OHCA value in 2015. Causes of the differences among estimates are discussed in previous reports (e.
(2013)  and then used to derive 1° monthly mean gridded salinity anomalies relative to a long-term monthly mean for years  1955 -2006 (World Ocean Atlas 2009; Antonov et al. 2010 ) at standard depths from the surface to 2000 m  (Boyer et al. 2012). In recent years, the single largest source of salinity profiles for the world's ocean is the Argo program with its fleet of profiling floats  (Riser et al. 2016). These data are a mix of real-time (preliminary) and delayed-mode (scientific quality controlled). Hence, the estimates presented here could change after all data have been subjected to scientific quality control. The SSS analysis relies on Argo in situ data downloaded in January 2016, with annual maps generated following  Johnson and Lyman (2012)  as well as monthly maps from BASS  (Xie et al.
2014 ), a bulk (as opposed to skin) SSS data product that blends in situ SSS data with data from the Aquarius (Le  Vine et al. 2014)  and SMOS (Soil Moisture and Ocean Salinity;  Font et al. 2013 ) satellite missions. The Aquarius mission ended in June 2015, leaving SMOS as the sole source for satellite SSS for the rest of 2015. BASS maps can be biased fresh around land (including islands) and should be compared carefully with in situ data-based maps at high latitudes before trusting features there. Salinity is measured as a dimensionless quantity and reported on the 1978 Practical Salinity Scale, or PSS-78  (Fofonoff and Lewis 1979). Surface salinity values in the open ocean range from about 32 to 37. 5, with seasonal variations exceeding 1 in a few locations  (Johnson et al. 2012). 2) Sea Surface SalInIty (SSS)-G.
The GPCP precipitation dataset shows that changes over land and ocean during El Niño or La Niña years balance to first The Gulf Stream and Kuroshio Extension are the largest oceanic heat loss regions in the Northern Hemisphere. Associated with that enormous winter cooling are deep winter mixed layers, which become what is referred to as subtropical mode water. Mode waters constitute a large portion of the upper ocean volume and are composed of nearly uniform temperature and salinity. These water masses outcrop at the surface during winter, at which point they are stamped with the current atmospheric conditions. Vigorous convection drives the creation of deep mixed layers that entrain heat, freshwater, and anthropogenic CO 2 into the upper ocean. When air-sea heat flux changes sign, generally in the spring, the upper ocean begins to restratify, and the thick subtropical mode water layer (Fig.
3. 23) and  AMHT (Fig. 3. 24). This time series has not been updated since last year's report  (Baringer et al. 2015a,b)    that span the full basin and include direct transport measurements in the boundary currents as part of the large RAPID-MOC/MOCHA/WBTS 26°N mooring array  (Smeed et al. 2015). The data from these moorings are collected every 18 months, most recently in December 2015; too late to be calibrated and finalized for this report. The 26°N data shown here extend from April 2004 to March 2014 (see last year's report for full details). At 16°N, a mooring array of inverted echo sounders, current meters, and dynamic height moorings  (Send et al. 2011 ) measures the flow below 1000 m (the southward flowing part of the AMOC "conveyor belt") that sends North Atlantic Deep Water toward the equator; hence, the AMOC estimate at this latitude (Fig.
1) aIr-Sea carbon dIoxIde fluxeS Ocean CO 2 uptake can be estimated from airsea differences in CO 2 partial pressure (pCO 2 ) and gas transfer velocity, which is mainly a function of wind speed. Significant improvement in global and regional CO 2 flux estimates have been made in the past year as part of Surface Ocean pCO 2 Mapping Intercomparison (SOCOM), comparing 13 independent data-based methods of global interpolation of pCO 2  (Rödenbeck et al. 2015). Recent research has also decreased uncertainty on the equations used to estimate CO 2 exchange from air-sea pCO 2 differences  (Wanninkhof 2014; Ho and Wanninkhof 2016). Large increases in autonomous pCO 2 measurements over time have been achieved with ships of opportunity (SOOP-CO 2 ) and moorings. The third update of the Surface Ocean CO 2 Atlas (SOCAT) with over 14 million data points was released to the public in 2015  (Bakker et al.