dataesr/scientific-paragraphs-categorization huggingface dataset:
For the independent datasets, the mismatch with observations is smaller for E LUC-H and E LUC-DGVM (0. 9 and 1. 2 Pg C yr -1 , respectively), reaching 2. 0 Pg C yr -1 for E LUC-B. Part of the discrepancy observed during the plateau period in AGR estimated using Houghton and BLUE datasets results from the consistently higher values of BLUE over the whole century. The relative variation in AGR B and AGR Blc during 1940-1950 relative to the 1920s and 30s roughly matches the one observed in the other two datasets. However, it should be noted that DGVMs also differ considerably in their estimates of the land sink during 1940-1950, with one model (LPJmL) even estimating a terrestrial source rather than a sink during the period (Table  2 ).
Testing the hypothesis for the plateau As shown previously, the datasets of carbon budget terms (E FF , O K , L DGVM , and the different E LUC data) lead to overestimation of AGR by 0. 9 to 2. 0 Pg C yr -1 for E LUC-H and E LUC-B respectively. Thus, there is a sink missing in the budget that could be explained by (i) decadal variability in the ocean sink not represented in O K ; (ii) processes absent from all the TRENDY models causing extra land uptake in ecosystems without LUC; or (iii) LUC processes that lead to carbon uptake and are not, or not sufficiently, included in the current datasets.
Ocean variability Despite both ocean reconstructions (O J and O K ) generally agreeing on the long-term trend of the ocean sink (Figs. 2 and 3), O K presents a smooth increase, consistent with the evolution of atmospheric CO 2 , while O J points to the existence of large multi-decadal variations superimposed on the increasing trend, the largest of them coinciding with the plateau period. During 1940-1950 the two datasets differ by about 0. 5 Pg C yr -1 (O K ca. 0. 7 Pg C yr -1 and O J ca. 1. 2 Pg C yr -1 on average), providing a reference value for the possible contribution of natural variability in the ocean to the sink required to stabilize atmospheric CO 2. It is important to evaluate whether an enhancement of the ocean sink of the magnitude reported by  Joos et al. (1999)  is likely to have occurred during the first half of the 20th century.
Discussion We find that the datasets of anthropogenic CO 2 emissions combined with the reconstructions of carbon uptake by terrestrial ecosystems and the ocean are not able to reproduce the decrease in atmospheric CO 2 growth rate between 1940 and 1950 registered in the observations. A further sink of at least 0. 9 Pg C yr -1 is still required. While uncertainty in emissions from fossil fuels is much smaller than the sink required, uncertainty in the other terms is very high.
CO 2 sinks during the plateau An ocean sink of about 1. 2 Pg C yr -1 during the 1940s, as in the  Joos et al. (1999)  dataset, is needed to explain partly the observed CO 2 plateau; such a sink is compatible with the occurrence of a strong ocean uptake anomaly due to natural climate variability superimposed on the anthropogenic perturbation trend. The variation range of different realizations of the IPSL-CMA5 model forced with perturbed initial conditions is within the variability range found for ENSO impacts on oceanic CO 2 uptake in the late 20th century (0. 1-0. 5 Pg C yr -1 ;  Bousquet et al. , 2000; Rödenbeck et al. , 2003). Other works have suggested an ocean uptake of 2-2. 5 Pg C yr -1 during the 1940s  (Trudinger et al. , 2002a)  or in response to later ENSO events  (Keeling et al.
Given the lack of observation-driven datasets able to capture these variabilities, this hypothesis remains speculative. Considering a contribution of 0. 5 Pg C yr -1 due to natural variability in the ocean, as estimated by  Joos et al. (1999)  and recent observations, a further (terrestrial) sink of 0. 4-1. 5 Pg C yr -1 is required. DGVMs used to characterize the land sink during the 20th century indicate that terrestrial ecosystems constituted an important CO 2 sink, taking up about 0. 8 Pg C yr -1 during the period between 1940 and 1950 in response to generally warmer and wetter conditions. The models estimate a small decrease of the terrestrial sink during the strong El Niño event of 1940-1942 (and even enhancement in some models), which is not fully consistent with the more recent observations of the terrestrial response to ENSO  (Sarmiento et al.
Moreover, DGVM simulations rely on CRU-NCEP v4, which uses the CRU dataset for monthly data and NCEP/DOEII reanalysis to generate 6 h variability. As NCEP does not extend to earlier than 1948, to generate the 6 h variations in CRU-NCEP v4, the variability of a random year between 1948 and 1960 is applied to each year before 1948. This may partly explain why the quality of the simulations before 1948 is not as good as afterwards. If precipitation was higher than average during this El Niño event in the regions that usually experience drought instead (as indicated in the CRU/NCEP data), the reasons for these opposite anomalies could be understood. Li et al. (2013) , using a 700-year reconstruction of Nino3. 4, have shown that the later decades of the 20th century were characterized by unusually high ENSO variability, while the 1940s registered a peak of low ENSO variance.
The contribution of LUC The different estimates of emissions from LUC differ significantly during most of the 20th century, but their estimates diverge to a greater extent in the earlier decades of the 20th century. We find that the LUC emission estimates from the latest inter-model comparison exercise (TRENDY v4) present good agreement with the bookkeeping data from  Houghton (2003). The two BLUE datasets  (Hansis et al. , 2015)  differ with the former two datasets by up to 1 Pg C yr -1. The discrepancies between BLUE and the other datasets likely result from the use of different methodologies, definitions, and assumptions in each study  (Gasser and Ciais, 2013; Hansis et al. , 2015) , such as the definitions of pasture areas or the way gross transitions are estimated. Moreover, the closer agreement of E LUC-H and E LUC-DGVM is incidental, as the models differ in the processes represented and definitions used  (Houghton et al.
1 database  (Klein Goldewijk et al. , 2011)  and thus the dataset by  Hurtt et al. (2011)  rely on extrapolating these country-level statistics back in time using population dynamics. In HYDE 3. 1, the cropland and pasture values per capita are allowed to change "slightly" prior to 1961  (Klein Goldewijk et al. , 2011). Although changes in per-capita values between the 1940s and 1960s amount to only 1 ‰ when averaged over all countries, they may be as high as 50 % in individual countries. The uncertainty in the LUC emissions during the Second World War period thus remains high. Although statistics about food production, population, or industrial output were kept because of their direct interest to the war effort management  (Harrison, 2000) , information about other processes relevant for E LUC may not be accurate (e.
Thus, the abandonment of cropland might be even higher for the most affected war territories of Ukraine and Belarus, where agricultural production was severely reduced due to a shortage of manpower and destruction of infrastructure. The interruption in agricultural production extended beyond the war period, recovering only slowly. The crop area in Russia returned to the pre-war level only in the early 1950s  (Lyuri et al. , 2010). Also, at the time of the Second World War, the reliance of Russian population on fuel wood was likely much larger than in the last decades of the Soviet Union. In China, the war-related mortality during the Second World War in China is estimated to be of about 14 million people  (Mitter, 2013)  and mass migration movements were also reported  (Lary, 2010). The cropland area likely decreased during the war period and only started to recover after 1949, according to Chinese Historical Cropland Database, which is not represented in HYDE 3.
1 dataset  (He et al. , 2013). A decade of reduced agricultural production and harvest in the war-stricken regions, not accounted for in the HYDE 3. 1 dataset, could lead to substantial missing carbon uptake during this period. The analysis of δ 13 signatures corresponding to each of the idealized experiments shows that differences in LUC datasets as extreme as the ones tested here could still be compatible with the observed δ 13 record, although changes in land use of the magnitude of our idealized tests are unlikely. Furthermore, effects of agricultural abandonment and halting of deforestation due to historical events have little effect on atmospheric CO 2 when persisting only for short periods of time (few decades or less), as model experiments suggest delayed emissions from past LUC, in particular from soils, persist, and regrowth takes time to reach its full potential  (Pongratz et al.
The effects of fire occurrence, changes in nutrient availability and the devastating socioeconomic consequences of the Second World War are examples of processes currently not well represented in the models. It should be noted that the high-resolution Law Dome record is unique in its precision and quality. However, the large measurements errors in even the best δ 13 C ice-core data currently available make it difficult to accurately quantify variations in the oceanic and terrestrial sinks. In high accumulation sites such as DE08, new measurements of δ 13 with improved accuracy should reveal the high-resolution information contained in the ice sheet and reduce the scatter of current estimates. It would also be advantageous to get another insight into atmospheric CO 2 and δ 13 C changes during 1940s from a second high-resolution core.
This study thus allows us to identify a number of key aspects of the global carbon budget that require deeper attention, if we are to better characterize the coupled carbonclimate variability in the 20th century. 6 Data availability δ 13 C-CO 2 data from  Rubino et al. (2013)   Land use change emissions from Houghton (  2003 ) is available at: http://cdiac. ornl. gov/trends/landuse/houghton/ houghton. html. The OSCAR model code will be made publicly available upon the release of the final version of the description paper by  Gasser et al. (2016). The Supplement related to this article is available online at doi:10. 5194/bg-13-4877-2016-supplement.
are publicly available as Supporting Material in http://onlinelibrary. wiley. com/store/10. 1002/jgrd. 50668/asset/supinfo/Dataset. xls?v= 1&s=f9c57096abf252b215b245220a3c1c523b3519f6. Fossil fuel emissions from CDIAC are available from their website at: http://cdiac. ornl. gov/ftp/ndp030/global. 1751_2013. ems. Doubledeconvolution data from Joos et al. (1999) is publicly available at: http://www. climate. unibe. ch/~joos/ OUTGOING/JOOSetal_GRL_1999/.
Acknowledgements. The authors would like to thank  S. Khatiwala  for providing the dataset of anthropogenic CO 2 ocean uptake. Cathy M. Trudinger  acknowledges the support of the  Australian Climate Change Science Program. The work is supported by the Commissariat à l'énergie atomique et aux énergies alternatives (CEA). Edited by: S. Zaehle Reviewed by:  R. A. Houghton ,  I. G. Enting , and one anonymous referee.
This notion of connectedness-between climate, landscape, and life; between our daily work and the expression of its meaning; between planetary-scale drivers and humble living things; between the abstraction and rigor of data and the reality and complexity of their importance; and especially between one generation and the next-inspires and informs much of the work within this volume. Our cover images this year reflect these intimate connections. Many of the shapes in the images are drawn, quite literally, from time series represented in this volume. The artist, Jill Pelto, is a practicing Earth scientist whose work reflects her field experience and her interpretation of the connection between global change, landscape, and life. Her father, Mauri, is both a longtime contributor to the State of the Climate series and a steward of a prominent global glacier dataset.
Sidebars included in each chapter are intended to provide background information on a significant climate event from 2015, a developing technology, or an emerging dataset germane to the chapter's content. A list of relevant datasets and their sources for all chapters is provided as an Appendix. The following ECVs, included in this edition, are considered "fully monitored," in that they are observed and analyzed across much of the world, with a sufficiently long-term dataset that has peerreviewed documentation:.
These peaks were especially high over oceans, consistent with the generally warmer air. These warmer, moister conditions tend to lag El Niño by a few months, and the event was ongoing at year end. In the cryosphere, Northern Hemisphere snow cover extent was slightly below average. However, alpine glacier retreat continued unabated and, with an update to the now 41-reference glacier dataset, 2015 became the 36th consecutive year of negative mass balance. In addition to the strong El Niño, 2015 saw mostly positive Antarctic Oscillation (AAO) conditions throughout the year, contributing to stronger wind speed anomalies both at the surface and aloft (850 hPa). This typically leads to reduced west Antarctic Peninsula (WAP) sea ice extent, but it was opposed in 2015 by the El Niño, which is more often associated with a weaker polar jet stream.
1. b. Temperature 1) Surface temperature-A. Sánchez-Lugo, C. Morice, and P. Berrisford The 2015 global land and ocean temperature set new records, exceeding the previous records set in  2014 (and 2010  depending on the in situ dataset) by a wide margin of 0. 13°-0. 18°C. Much-warmer-thanaverage conditions across much of the world's surface and a strong El Niño contributed to the highest temperature since records began in the mid-to late 1800s, according to four independent in situ analyses (NASA-GISS,  Hansen et al. 2010; HadCRUT4, Morice et al. 2012; NOAAGlobalTemp, Smith et al. 2008; JMA, Ishihara 2006). The 2015 globally averaged surface temperature was 0. 42°-0. 46°C (Table  2. 1) above the 1981-2010 average. Note that ranges of temperature anomalies provided in this summary are ranges of best estimates for the assessed in situ datasets.
The best-estimate global average surface temperatures were 1. 03°-1. 09°C above the mid-to late 19th century average in assessed datasets. Fourteen of the 15 warmest years on record have occurred since the beginning of the 21st century, with 1998 the only exception (ranking between third and eighth warmest year, depending on the dataset). Every estimate of global average temperature has inherent uncertainty. The main sources of un-certainty in observational datasets tend to be associated with changes in measurement practices and with sparse spatial sampling, both of which can vary with time. When taking into consideration the estimated uncertainty of the global land and ocean annual temperature in the annual ranking, following the method of  Arguez et al. (2013) , it is virtually certain that 2015 was the warmest year since records began, with a probability >99%, according to the NOAAGlo-balTemp dataset (see  Arguez and Applequist 2015).
The near-surface temperature analyses assessed here are derived from air temperatures observed at weather stations over land and sea surface temperatures (SST) observed from ships and buoys. While each analysis differs in methodology, all four analyses are in close agreement (Fig. 2. 1). Plate 2. 1c and Online Figs. S2. 1, S2. 2, and S2. 3  show the differences between the datasets, which are mainly due to how each methodology treats areas with little to no data and how each analysis accounts for changes in measurement methods [for more details see  Kennedy et al. (2010) ;  Hansen et al. (2010) ; and  Huang et al. (2015) ]. Global average surface air temperatures are also estimated using reanalyses, which blend information from a numerical weather prediction model with observations. Reanalysis produces datasets with uniform spatial and temporal coverage, but suffers from model biases and problems arising from time variations in amount and/or quality of assimilated observations.
Surface temperatures from reanalyses are consistent with observations in regions of good observational coverage at the surface, due in part to the large volumes of assimilated observations (e. g. , more than 40 billion to date in the ERA-Interim reanalysis). According to ERA-Interim  (Dee et al. 2011) , the 2015 globally averaged, analyzed 2-m temperature was the highest since records began in 1979. The temperature was 0. 38°C above the 1981-2010 average (Table  2. 1) and 0. 10°C above its previous record set in  2005. The magnitude of the anomaly would be larger had the temperature analyses been corrected for changes in the source of the prescribed SST, which was uniformly cooler by about 0. 1°C relative to Had-CRUT4 from 2002 onwards  (Simmons and Poli 2014). The only land areas with temperatures below average, according to the in situ datasets, were parts of southern South America, eastern Canada, Greenland, and Antarctica.
Overall, the globally averaged annual temperature over land [including the landonly Berkeley Earth analysis  (Rohde et al. 2013) ] was 0. 61°-0. 72°C above average-the highest on record. This exceeds the previous 2007 record (and, depending on the in situ dataset, 2010) by 0. 12°-0. 26°C. The strong El Niño maturing during 2015 resulted in record high SSTs across much of the tropical Pacific Ocean. However, areas in the North Atlantic, South Pacific, and the waters south of South America experienced below-average conditions (Plate 2. 1c). The globally averaged annual temperature across the oceans was 0. 33°-0. 39°C above average-the highest on record according to the in situ datasets, surpassing the previous record set in 2014 by 0. 10°-0. 12°C (see section 3b for more detailed SST information).
Similarly, ERA-Interim for 2015 shows warmerthan-average conditions over many, but not all, regions of the world (Online Fig. S2. 1). The average analyzed 2-m temperature over land was 0. 65°C above average (0. 09°C above the previous 2007 record), and, over the oceans, it was 0. 28°C above average (0. 10°C above the previous 2005 record). 2) Lower and midtropoSpheric temperatureS-J. R. Christy The 2015 globally averaged annual temperature of the lower troposphere (LT, the bulk atmosphere below 10 km altitude or roughly the lower 70% by mass) was approximately +0. 3°C above the 1981-2010 mean. This placed 2015 first to fourth warmest of the past 58 years, depending on the dataset, and was on average about 0. 2°C cooler than the warmest year, 1998, varying from just above the 1998 value in two radiosonde datasets to 0.
2). Direct measurement of the LT bulk temperature utilizes radiosonde datasets since 1958, complemented by satellites since late 1978. The datasets are described in  Christy (2015)  with new additions of the UNSW radiosonde dataset from  Sherwood and Nishant (2015)  and, for use in the tropical midtroposphere, two satellite datasets with similar construc-tion methods [NOAA  (Zhou and Wang 2011)  and UW  (Po-Chedley et al. 2015) ]. Previously utilized datasets from UAH and RSS have been updated (UAHv6. 0, Spencer et al. 2016  submitted to Asia-Pacific J. Atmos. Sci. ; RSSv4. 0 for the midtroposphere, Mears and Wentz 2016). In addition, three reanalyses products are also shown. There is close agreement in the interannual variability between all products; ERA-Interim is used here to provide the spatial depictions (Plate 2.
S2. 4). The long-term global LT trend based on radiosondes (starting in 1958) is +0. 15° ± 0. 02°C decade -1 and based on both radiosondes and satellites (starting in 1979) is +0. 13° ± 0. 03°C decade -1. The range represents the variation among the different datasets, which then serves as an estimate of structural uncertainty in Fig. 2. 2. When taking into account the magnitude of the year-to-year variations, there is a statistical confidence range of ± 0. 06°C decade -1 , meaning that the trends are significantly positive. Major volcanic events in 1963 Major volcanic events in , 1982 Major volcanic events in , and 1991  contributed to cooler temperatures during the early part of the LT record, especially in the satellite era, thus increasing the upward trend to some extent. With this edition we introduce the midtropospheric temperature (MT, surface to around 70 hPa) product for the tropical atmosphere (Fig.
2. 3). The MT profile extends higher than that of LT, entering the stratosphere, but only slightly in the tropics where the tropopause is at approximately 16-km altitude. The dominant signal of this product is in the mid-to-upper troposphere, thus capturing the layer in the tropics which represents the maximum response to forcing (e. g. , increased greenhouse gases, warm surface waters from El Niño, volcanic cooling, etc. ). MT is constructed from the Microwave Sounding Unit (MSU) channel 2 and the Advanced MSU  channel 5  (Christy et al. 2003). MT tropical trends are inf luenced by lower stratospheric cooling by approximately 0. 03°-0. 04°C decade -1 , which is fully accounted for in comparison with theory. Examining the various datasets of tropical MT trends , there are two clusters of results: +0.
08°C decade -1 (most radiosonde datasets and satellite UAH) and +0. 12°C decade -1 (RICH radiosonde dataset and satellite datasets of RSS, NOAA, and UW;  Table 2. 2). A significant difference between UAH and the other satellite datasets is evident over the oceans. This suggests the disagreement is due to differing assumptions regarding basic calibration issues rather than corrections for the diurnal drift of the spacecraft, and is an active area of research. The MT time series and trends (Fig. 2. 3, Table  2. 2) through 2015 continue the characteristic noted in past State of the Climate reports that observed MT trends tend to be below estimates anticipated from basic lapse-rate theory-the theory that indicates a magnification of trend with height  (Christy 2014). This is especially true in the tropics where theory suggests amplification by a factor of 1.
4 ± 0. 2 of the mid tropospheric trend over the surface trend. The range of trends for 1979-2015 from the different radiosonde datasets is +0. 07° to +0. 11°C decade -1 and from satellites is +0. 07° to +0. 14°C decade -1 compared with the tropical surface trend (average of NOAAGlo-balTemp and HadCRUT4) of +0. 12°C decade -1. The median trend of all observational datasets examined here is between +0. 09 and 0. 10°C decade -1. Thus, the current tropical MT/surface ratio from observations since 1979 (0. 8 ± 0. 3) continues to be less than theory.
A cooler stratosphere is consistent with a warmer troposphere in the case of rising greenhouse gases, as more outgoing energy is trapped in the troposphere. The TLS is a weighted layer-mean temperature of the part of the atmosphere observed by specific channels from satelliteborne microwave sounding instruments. It ranges from around 200-20 hPa (12-27 km) and is entirely in the lower stratosphere polewards of 35°. But equatorward of this, it extends into the upper troposphere, which needs to be accounted for when assessing latitudinal trends. For further details see  Long and Christy (2015). All radiosonde datasets (RATPAC, RAOBCORE, RICH, NSW) show a cooling trend in the lower stratosphere from 1958 to 1995. However, after 1995 there is not much of a trend to the present (Fig. 2. 4). The pre-1995 cooling trend is only interrupted by several volcanoes  [Agung (1963 ), El Chichón (1982 ), and Mt.