dataesr/scientific-paragraphs-categorization huggingface dataset:
Here we analyze the return periods of low precipitation over the central United States for 2012 under two scenarios: actual forcing and natural-only forcing. Data and methods. Values of observed monthly precipitation for the years 1895-2012 were obtained from the U. S. National Climatic Data Center (NCDC) Climate at a Glance dataset (ftp://ftp. ncdc. noaa. gov/ pub/data/cirs/). The values were spatially averaged over eight states that were strongly afflicted by the drought: Colorado, Nebraska, Kansas, Oklahoma, Iowa, Missouri, Arkansas, and Illinois. We used the UK Meteorological Office's Had-AM3P atmospheric circulation model  (Pope et al. 2000; Gordon et al. 2000; Jones et al. 2004 ; 1. 875° × 1. 25°, 19 levels, 15-minute time step) to simulate the atmospheric and land-surface climate from December 2011 through November 2012 under two distinct scenarios.
The monthly SSTs in the modeled year 2012 for a single member from the "Natural" ensemble were subtracted from the corresponding SSTs in a single member from the "All Forcings" ensemble (e. g. , see the second figure in "The Use of a Very Large Atmospheric Model Ensemble to Assess Potential Anthropogenic Influence on the United Kingdom Summer 2012 High Rainfall Totals" in this report). This resulting pattern of modeled SST changes was then subtracted from the observed OSTIA SST pattern. The net increase in SST from the "Natural" to the "All Forcings" scenarios was 0. 5°C averaged over the period December 2011 to August 2012 and between 70°S and 70°N. Globally, the area covered by sea ice was decreased by 8. 9% averaged over the same period. Baseline initial conditions for each of the two scenarios were taken as the final conditions from simulations like those described above, but for the period, December 2010 through November 2011 and with "All Forcings" SSTs derived from the HadISST observational dataset  (Rayner et al.
This decrease occurred with a concomitant positive shift of 1. 7°C or 1. 6°C across the distributions of spring and summer temperature, respectively, between the "Natural" and "All Forcings" scenarios (results not show). The absence of a detectable signal in the tail of the precipitation distribution may in part be associated with the sample size. However, it is not merely a matter of the 95% confidence intervals from each scenario overlapping; the empirical distributions from the "Natural" and "All Forcings" runs converge at return periods greater than about 10 years. Consequently, weakening the significance criteria (e. g. , decreasing the confidence intervals from 95% to 67%) would not lead to a separation of the confidence intervals in the tail between 10-and 50-year return periods. Beyond 50 years, inferences cannot be made with confidence due to the sample size of the "Natural Forcings" dataset.
3. 1h and 3. 1k) , with large areas of the central United States requiring a mean of greater than ten PI periods but less than two 20C periods (Supplementary Fig. S3. 2). In contrast, most areas of the north-central and northeastern United States exhibit greater frequency of a 2012-magnitude soil moisture event in the preindustrial forcing (Fig. 3. 1i) than in the current forcing (Fig. 3. 1l). However, the soil moisture anomalies should be viewed with caution, as different reanalysis datasets exhibit different patterns and magnitudes of anomalies across the United States (Fig. 3. 1, and Supplementary Fig. S3. 4), and there is greater discrepancy between the reanalysis and 20C simulations for soil moisture than for temperature or geopotential height (Fig. 3. 2, and Supplementary Fig. S3. 4).
(We note that a 2012-magnitude soil moisture event occurs more often in the 20C simulations than in the PI simulations over much of the western United States in both reanalysis datasets that we evaluate; Fig. 3. 1, and Supplementary Fig. S3. 4. ) Like the regional mean (Figs. 3. 1g and 3. 2), the PDF of grid-point anomalies reveals a shift towards more frequent occurrence of 2012-magnitude temperatures over the north-central and northeastern United States in the 20C simulations (Fig. 3. 2). To quantify the difference in likelihood of a 2012-magnitude temperature event over this region, we compare the fractional difference in occurrence between all possible combinations of 33-year periods in the CMIP5 PI and 20C simulations (Fig. 3. 2). Following the Intergovernmental Panel on Climate Change (IPCC) uncertainty guidance  (Mastrandrea et al.
Data and methods. Our observed September ASIE analysis for the satellite period 1979-2012 uses the National Snow and Ice Data Center (NSIDC) sea ice index  (Fetterer et al. 2009 ) and observed September SAT gm data is from the NCEP/NCAR Reanalysis  (Kalnay et al. 1996). The model outputs from the CMIP5 archive combine 20th century All-Forcing (anthropogenic and natural combined) simulations with the representative concentration pathway (RCP) 4. 5 future emission scenario experiments for years beyond 2005. We selected 19 CMIP5 models, with 88 All-Forcing ensemble members, requiring each model have at least three RCP4. 5 ensemble members and 100 years of preindustrial control run. For observations and models, ASIE is defined as total Northern Hemisphere marine area with sea ice concentrations of at least 15%.
As suggested by others (e. g. , "The Extreme European Summer 2012" in this report), this is consistent with a southwards shift of the summer storm track. While NW Europe is anomalously wet, southern Europe has about 50%-75% of normal precipitation. We explore three hypotheses for the recent NW European rainfall anomaly: (i) internal atmospheric variability, (ii) forced by recent changes in both SST and sea ice, and (iii) forced only by changes in SST. To explore these hypotheses we used the Had-AM3P Atmosphere General Circulation Model  (Rowell 2005) , which is a N96 (1. 25° × 1. 875°) resolution version of HadAM3  (Pope et al. 2000)  with some other changes. We drove it with the HadISST dataset  (Rayner et al. 2003)  with both SSTs and sea ice area-averaged to N96 resolution. HadISST uses SSM/I (Special Sensor Microwave Imager) data to estimate sea-ice.
SSMI degraded in early 2009 and was replaced with data from AMSR-E (Advanced Microwave Scanning Radiometer -Earth Observing System). Screen (2011) investigated the impact of this and found it had a large impact in the southern hemisphere but found no apparent step-changes in Arctic sea-ice extent. Nevertheless, there remains the possibility of time-varying biases in the HadISST record. The model was also driven with historical concentrations of greenhouse gases, and both the direct and indirect effect on cloud brightness  (Jones et al. 2001)  were included with SO 2 emissions using the historical IPCC emissions. Land-surface values were set to standard climatological values, and ozone values used the ozone forcing dataset of  Tett et al. (2007). A single simulation driven with these boundary conditions was run from December 1959 to August 2012 ("Historical").
We estimate the unpredictable component from the pooled intra-ensemble variances of the three 12-member ensembles for 2007-12. This is about 70% of the Historical simulation's variance. Cautiously assuming that simulated and smoothed intra-ensemble variability for the 2007-12 period is representative of the entire 1960-2012 period, then SST variability contributes about 30% of the variance in NW European precipitation. The results from the single Historical simulation are largely consistent at the two-sigma level with observations for 1960-2012 (Fig. 11. 1). However, there is one period around 1975 when the difference between the two-filtered time series is significant. In a time series, we would expect differences to arise solely by chance, and to test for this possibility, we use a Kolmogorov-Smirnov test on the two datasets and an F-test on the sum-of-squares of the differences weighted by twice the intra-ensemble variance.
Hence, we make two ensembles, one representing 2012 and the other representing an imagined analogous year in the preindustrial period. This involves the prescription of differences in three sets of driving conditions between these scenarios: (i) changes of atmospheric gas constituents (most notably raised levels of carbon dioxide), (ii) different SSTs, and (iii) different sea ice fractions. SST and sea ice fraction values for 2012 are taken from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) dataset  (Stark et al. 2007; Donlon et al. 2012 ). To estimate preindustrial conditions for SSTs, we calculate SST differences between nonindustrial and present day simulations of the HadGEM2-ES model and subtract them from the OSTIA SST data for 2012. For nonindustrial sea ice, we adopt conditions that correspond to the year of maximum sea ice extent in each hemisphere of the OSTIA record.
The differences in this plot are large, especially for the United Kingdom. In fact, the rainfall amounts from the ECMWF reanalysis dataset are greater than the 99th percentile for the All Forcings ensemble distribution for most locations in the United Kingdom. This implies one of two possibilities: either (1) our findings are correct for the prescribed atmospheric CO 2 concentrations, SST, and sea ice fractions appropriate to 2012, then the chances of the observed summer UK rainfall patterns occurring are in fact incredibly small, or (2) there are systematic biases in our atmospheric modeling structure. These biases possibly correspond to raised rainfall amounts falling incorrectly over the seas more to the north of the United Kingdom, rather than over the United Kingdom as seen in the observations.
Problems in capturing heavy precipitation with this model have also been noted in "Are Recent Wet North Western European Summers a Response to Sea-Ice Retreat?" in this report. Figure  12. 1c shows the precipitation difference between the 50th percentile All Forcings simulation minus that of Natural. There is a distinct geographical feature where, in general, northerly regions experience higher JJA rainfall amounts, whereas for southern Europe the opposite is true. The United Kingdom lies close to the nodal line of this pattern. In Fig. 12. 2a, we present the SST changes prescribed to our modeling system, which are based on HadGEM2-ES GCM. When compared to the differences in the observationally based HadISST dataset  (Rayner et al. 2003).
This is because, as noted above, the event occurs beyond the 99th percentile of the All Forcings model ensemble distribution. Instead, we suspect that there remain deficiencies in our atmospheric model that require resolution before making any such definitive statements regarding human influence on changed probabilities of rainfall events. The major differences in modeled and measured forcing SSTs also require consideration. Our initial argument was that until recently (and certainly before satellite retrievals), the number of measurements contributing to SST climatologies is relatively small  (Rayner et al. 2003 ) and model derived differences may be more trustworthy. However, we now plan to make a new set of simulations using both the longer HadISST dataset directly and SST warming patterns based on other GCMs that have contributed to the CMIP5 database.
Data. We used in situ measurements provided by the European Climate Assessment dataset at more than 4500 stations over the period  1948 -2012 (Klein-Tank et al. 2002). We computed anomalies relative to a 1971-2000 climatology. The daily climatology is obtained by averaging over each calendar day in 1971-2000, then smoothing by splines. We selected 351 stations on the basis of (i) an altitude lower than 800 m, longitudes between 8°W and 40°E and lati-tudes between 30°N and 72°N, (ii) the availability of more than 70% of daily values between 1 January 1971 and 31 December 2000 for a reliable estimate of the climatology, and (iii) more than 90% of daily values between 1 January 2012 and 31 December 2012 for a reliable estimate of the 2012 anomalies. We averaged precipitation values over Northern Europe (50°N-72°N, 8°W-35°E) after selecting (iv) only one station per 0.
2012). For 2011-12, data were derived from the monitoring product (2007-present) that only uses a network of near-real-time stations. Previous work on Mediterranean drought events (e. g. ,  García-Herrera et al. 2007; Trigo et al. 2010 ) has shown a high consistency between these two GPCC products during their overlapping period. Droughts in partially semiarid regions, such as the IP, can evolve with some complexity in both spatial and temporal domains. It is highly advisable to avoid use of the calendar year and instead evaluate the anomalous hydrological year, which extends from September of year n-1 through August of year n. The accumulated precipitation over the IP between September 2011 and August 2012 dropped to roughly 50% of the 1950-2000 climatological average over the southwestern  IP (Fig.
This begs the question whether anthropogenic effects on the climate have affected the ENSO-extreme rainfall relationship, thus altering the probability of extreme rainfall in this area. Data and methods. Observed monthly total rainfall and Rx5day amounts were calculated from the Australian Water Availability Project (AWAP) gridded dataset of daily rainfall  (Jones et al. 2009 ) interpolated onto a regular 0. 5° grid. Anomalies from the 1900-2012 means of total rainfall and Rx5day were calculated for each grid box separately. SSTs in the Niño-3. 4 region (5°N-5°S, 170°W-120°W) were used as an index for ENSO from the HadISST dataset  (Rayner et al. 2003). Observed total and Rx5day anomalies were averaged across the SE Australia region as defined by the boxed area (25°S-40°S, 135°E-154°E) in Fig.
1). As ENSO strongly drives Australian rainfall variability, models that do not capture ENSO variability cannot replicate the observed teleconnection. These ten models adequately capture the amplitude of variability in Niño-3. 4 region surface air temperatures seen in the observational HadCRUT4 dataset  (Morice et al. 2012). The absolute SE Australia Rx5day amounts were taken from data downloaded from Environment Canada's CLIMDEX website (http:// www. cccma. ec. gc. ca/data/climdex/; Sillmann et al. 2013 ). Rx5day anomalies were first calculated for each grid box in each model run and then averaged over the SE Australia domain. The anomalies were calculated in the same way as for the observations, so the area-averaged Rx5day anomaly values are not biased towards wetter areas. Sea surface temperature anomalies (SSTAs) in the Niño-3.
That said, approximately half of the analyses found some evidence that anthropogenic climate change was a contributing factor to the extreme event examined, though the effects of natural fluctuations of weather and climate on the evolution of many of the extreme events played key roles as well. In this second issue of the annual BAMS attribution report, an important innovation is that we have now included multiple analyses of a single event. The differences in the results also provide insights into the structural uncertainty of event attribution, that is, the uncertainty that arises directly from the differences in analysis methodology. For example, there are four different studies of the wet summer in northern Europe using different methodological approaches, observational datasets, and climate models.
19. AN ATTRIBUTION STUDY OF THE HEAVY RAINFALL OVER EASTERN AUSTRALIA IN MARCH 2012nikolaos Christidis, peter a. stott, david J. karoly, and andreW Ciavarella Introduction. Heavy rains at the end of summer 2012 in comparison with the summer months (Fig. 19. 1a). across eastern Australia (Bureau of Meteorology In fact, March 2012 ranks as the third wettest after 2012) led to swollen rivers and widespread flooding 2011 and 1956 in the Global Historical Climatology that swamped agricultural land, caused loss of life, Network (GHCN) dataset used to provide rainfall and forced tens of thousands of people to evacuate observations for this study their homes. The event came only a year after the catastrophic floods in Queensland, when the larg- est part of the north-eastern state was declared a disaster zone.
Data analysis Correspondence analysis (CA) was used to ordinate bird communities along a gradient of forest composition and structure ranging from riparian forests to poplar plantations. As there were only three riparian forests, which were mature and not ploughed, three adult non-harrowed poplar plantations were used in the analyses for comparative purposes. Analyses of variance (ANOVAs) and Tukey's HSD post-hoc test were used to test for the effect of habitat type on species richness and abundance index in poplar plantations. Mann-Whitney tests were also used to detect whether species richness and abundance index differed in riparian forests and adult non-harrowed poplar plantations. A two-step approach was used to evaluate the relative influence of habitat and landscape variables on bird communities in poplar plantations.
2012). At each distance class, 1. 000 permutations were run to conduct a two-sided significant test of whether the value of Moran's I differed from zero (α = 0. 05). Analyses were carried out using the "ncf" package  (Bjørnstad 2013 ) implemented in the R software environment (R Foundation for Statistical Computing, Vienna, Austria). Second, other multiple regressions were carried out to test the effects on bird species richness or bird abundance index of the single dendrometric, understory and landscape variables that were best correlated with the selected axes (using the broken-stick method) of PCA calculated for different buffer radii. Canonical correspondence analysis (CCA) was applied to study the influence of the habitat and landscape variables (buffer radius selected by the two-step approach) on the bird assemblages in poplar plantations.
1 ). Examination of the dataset corresponding to the 24 poplar plantations revealed that a total of 45 species were recorded, of which 16 occurred in only one stand. The most abundant species were as follows, in decreasing order: Fringilla coelebs, Corvus corone, Turdus merula, Anthus trivialis, Phylloscopus collybita, Carduelis chloris and Columba palumbus. Analysis of variance for mean species richness per stand (N = 24) revealed a significant interaction between age and management (N = 24, F = 5. 35, p = 0. 03). Tukey's HSD test revealed that species richness was lowest in the adult harrowed stands and highest in the young harrowed stands. By contrast, bird abundance index did not differ according to the type of poplar plantations (Fig. 2 ). Principal component analyses (PCAs) and multiple regressions were used to test the first assumption of the two-step approach, i.
Datasets Five C. nigra individuals from three distinct subspecies were analyzed (Table  1 , Additional file 1: Figure  S1 ). Besides C. nigra, samples from the congeneric red-footed tortoise C. carbonaria and from the Spanish pond turtle Mauremys leprosa (one individual each) were also collected. The dataset was completed by transcriptome data from the previously published European pond turtle Emys orbicularis (10 individuals) and pond slider Trachemys scripta (three individuals, Table  2 ).
Turtle phylogenomics We first analyzed a dataset of 248 orthologous genes in eight turtle species. The tree topology we obtained (Figure  1 ) was consistent with published Testudines phylogenies  [25, 26]. Branch lengths in Figure  1  are proportional to time, whereas branch thickness reflects the estimated per million years rate of synonymous substitution, obtained by dividing synonymous branch lengths by absolute divergence time. The three tropical lineages (Phrynops, Pelodiscus, and Chelonoidis) showed a higher synonymous substitution rate than the marine (Caretta caretta) and temperate ones (Emys, Trachemys, Mauremys), consistent with a recent report in turtles of a negative correlation between synonymous substitution rate and latitude in turtles  [27]. Colors in Figure  1  reflect lineage-specific d N /d S ratio.
Gene expression analysis Gene-specific expression profiles were estimated in the five turtle species for which Illumina data were available. Correlation coefficients of log-transformed, normalized orbicularis. π S : average nucleotide diversity at synonymous sites; π 5 : average nucleotide diversity at 5′-UTR sites; π 3 : average nucleotide diversity at 3′-UTR sites. Very similar results were obtained when we only analyzed predicted cDNAs for which both coding sequence and 3′ UTR sequence were available, and when we restricted the analysis to the 500 first bases of the 3′ UTR regions. expression levels across genes were calculated for each pair of species. The Testudinidae (C. nigra/C. carbonaria) and Emydidae (E. orbicularis/T. scripta) pairs showed the highest level of correlation of gene expression (r2 = 0.
Sampling and sequencing Blood from five adult C. nigra, one C. carbonaria and one M. leprosa individuals were sampled from public European zoological collections (Table  1 ). The sampling and animal handling have been done by veterinarians and staff of the Montpellier Zoo, Zurich Zoo, Rotterdam Zoo, and A Cupulatta Zoo according to the Code of Practice and Code of Ethics established by the European Association of Zoos and Aquarias. RNA was extracted and cDNA libraries were sequenced using Genome Analyzer II (Illumina®, see Additional file 2). Sequence reads were deposited in the NCBI Sequence Read Archive database as bioproject PRJNA230239, biosample accession SRS509366-SRS509372 (see Table  1 ).
Gene ontology analysis The generic GO-slim ontology  [23]  was used to explore the distribution of the π N /π S ratio across functional categories of genes in C. nigra vs. E. orbicularis, in search for gene sets showing a specific increase/decrease of selective pressure in one of the two species. GO-slim is a contracted version of the Gene Ontology database including a relatively small number of high-level, little-overlapping terms. A term-averaged log-transformed π N /π S ratio was calculated for each species and each term, and compared between species. A resampling procedure was designed to quantify, for each term, the significance of the normalized difference in π N /π S ratio between the two species (see Additional file 2).
Acknowledgments We thank  Henk Zwartepoorte ,  Willem Schaftenaar ,  Samuel Furrer ,  Pierre Moisson ,  Cedric Libert ,  Carmen Palacios ,  Olivier Vernau , the  Diergaarde Blijdorp -Rotterdam Zoo ,  Zurich Zoo ,  A Cupulatta , and  Montpellier Lunaret Zoo  for their support and help with the sampling. This work was supported by  European Research Council  grant  232971  'PopPhyl' and Fundação para a Ciência e Tecnologia (Portugal), postdoctoral fellowships  SFRH/BDP/73515/2010.
We find a robust relationship between 47 and growth temperature. We also find that the slope of a linear regression through all the 47 data for bivalves plotted against seawater temperature is significantly shallower than previously published inorganic and biogenic carbonate calibration studies produced in our laboratory and go on to discuss the possible sources of this difference. We find that changing seawater saturation state does not have significant effect on the 47 of bivalve shell carbonate in two taxa that we examined, and we do not observe significant differences between 47 -temperature relationships between calcitic and aragonitic taxa.
Field collected samples Specimens were collected at the locations given in Table 2. The length of bivalve mollusk growing season will vary somewhat between taxa and this presents an additional source of uncertainty in the calibration. However, in the results section below we show that the slope of our calibration line is not significantly impacted by assumptions over the predominant season of field collected bivalve growth. In the figures and tables presented here we have assumed that there is a bias in the predominant season of shell growth to the three warmest months of the year. In order to obtain seawater temperatures at the sites where specimens were collected from we used the Levitus database  (Levitus and Boyer, 1994) , and in the case of the specimen from San Diego data from the Scripps Pier coastal water monitoring project (http://www.