dataesr/scientific-paragraphs-categorization huggingface dataset:
ECMWF ERA HTESSEL The ECMWF Reanalysis (ERA) is a global 3-D variational data assimilation (3D-Var) product that uses HTESSEL in the forecast system. HTESSEL has a surface runoff component and accounts for a global nonuniform soil texture unlike the old TESSEL model  (Balsamo et al. , 2009). This is an offline model simulation, and HTESSEL is driven by meteorological forcing output from the forecast runs. Photosynthesis in the model is computed independently (i. e. , with its own canopy conductance) from LE, so that the carbon cycle does not interact with the water cycle at the stomata level, adding errors. We use LE, H , and GPP estimates from ERA HTES-SEL provided on a 0. 25 • × 0. 25 • geographic grid with daily temporal resolution.
MODIS-GPP The MODIS sensor is onboard the sun-synchronous NASA satellites Terra (10:30 LT overpasses) and Aqua (13:30 LT overpasses). It provides 44 global data products  (Justice et al. , 2002)  from 36 spectral bands, including visible, infrared, and thermal infrared spectrums to monitor and understand Earth's surface: atmosphere, land, and ocean processes. The MODIS GPP/NPP project (MOD17) provides gross and net primary production estimates covering the whole land surface and is useful for analyzing the global carbon cycle and monitoring environmental change. The MOD17 algorithm is based on a light-use efficiency approach proposed by  Monteith and Moss (1977) , which states that GPP is proportional to the product of incoming photosynthetically active radiation (PAR), fraction of absorbed PAR (f APAR), and efficiency of radiation absorption in photosynthesis.
Eddy covariance tower estimates FLUXNET is a network of regional tower sites that measure turbulent flux exchanges (water vapor, energy fluxes, and carbon dioxide) between ecosystems and the atmosphere  (Baldocchi et al. , 2001). FLUXNET comprises over 750 sites covering five continents. Measurements from the FLUXNET towers provide valuable information for evaluating satellitebased retrievals of surface fluxes. In this study, FLUXNET measurements from the FLUXNET 2015, the La Thuile Synthesis dataset, and the Large-scale Biosphere-Atmosphere (LBA) experiment in Brazil are used for evaluation (details are provided in Sect. 4. 2). FLUXNET 2015 tier 1 and tier 2 data were retrieved from http://fluxnet. fluxdata. org/data/fluxnet2015-dataset/. The data have been systematically quality controlled with a standard format throughout the dataset (http://fluxnet.
fluxdata. org/data/fluxnet2015-dataset/data-processing/,  Pastorello et al. , 2014)  and gap-filled using ERA meteorological forcing downscaling. From the LBA experiment in Brazil, we use data from sites in Rondônia at the edge of a deforested region (BR-Ji1 and BR-Ji2) and near São Paulo (BR-Sp1). As the data did not span recent years, we instead use a climatology of the fluxes for comparison from 1999 to 2003. We note that, of course, the interannual variability in the region (such as El Niño and La Niña) could alter the seasonality and magnitude of the fluxes in the region. We also use data from the La Thuile Synthesis Dataset (http://fluxnet. fluxdata. org/data/la-thuile-dataset/) covering 24 sites. These data are part of the free and fair use version of the dataset. A total of 85 sites from the three datasets are selected for evaluation of WECANN retrievals spanning a large climatic and biome gradient (Fig.
S2  and Table  S1 ). For Ameri-Flux towers, if measurements from both the FLUXNET 2015 dataset and the La Thuile dataset were available, we used the FLUXNET 2015 data. We have only selected sites that had at least 24 months of continuous measurements during 2007-2015. Any site that would have fallen outside of the WECANN land mask (Fig. S1 ) is excluded (several sites in coastal regions).
The weights from the last iteration before the occurrence of the divergence represent the final solution. The testing subset is used to assess the ANN performance after the training phase. As an additional measure to avoid overfitting, we repeated the training for several ANNs with an increasing number of neurons in the hidden layer (1 to 15). For one to five neurons, the R 2 value between the target data and the ANN estimates increased with an increasing number of neurons. For more than five neurons, little change in the skill was observed when increasing the number of hidden layer neurons (Fig. S3 ). Thus, an ANN with five hidden layer neurons represents the simplest ANN that can converge to a solution and model the nonlinear relationship between the satellite inputs and the surface flux estimates.
To train the ANN, we used LE, H , and GPP estimates from the years 2008-2010. The target dataset was generated through a TC-based merging of triplets of the flux estimates introduced in Sect. 2. 1 (details are discussed in Sect. 3. 2). After completion of the training, the performance of the ANN and its ability to generalize was evaluated using the LE, H , and GPP target data from 2011. Finally, WECANN retrievals are evaluated against other global products and eddy covariance tower data. Results of these comparisons are presented in Sect. 4.
Target dataset: a Bayesian prior using triple collocation One of the key issues in the design of an ANN to retrieve any geophysical variable is defining a good target dataset. One practice has been to use outputs from a land surface model as the target  (Aires et al. , 2005; Jiménez et al. , 2013; Kolassa et al. , 2013; Rodriìguez-Fernández et al. , 2015). However, all observations and models contain random errors and biases. Therefore, the retrieval based on the ANN exhibits some of the biases of the original target dataset even if the ANN is able to make corrections to its original target data (e. g. , correction of an imperfect seasonal cycle, as demonstrated by  Jiménez et al. , 2009). To address this issue, we use three datasets, which are sufficiently independent so that the training can learn from each dataset and benefit from all of them, synergistically.
We implement a pseudo-Bayesian training by probabilistically weighting the occurrence of each training dataset by its likelihood and define a target dataset. The three datasets are listed in Table  1  for each variable. To define this prior distribution, we use the TC technique. TC is a method to estimate the RMSE (and, if desired, correlation coefficients) of three spatially and temporally collocated measurements by assuming a linear error model between the measurements  (McColl et al. , 2014; Stoffelen, 1998). This methodology has been widely used in error estimation of land and ocean parameters, such as wind speed, sea surface temperature, soil moisture, evaporation, precipitation, f APAR, and in the rescaling of measurement systems to reference system for data assimilation purposes  (Alemohammad et al.
, 2014 )   σ ε 1 σ ε 2 σ ε 3   =         Q 11 - Q 12 Q 13 Q 23 Q 22 - Q 12 Q 23 Q 13 Q 33 - Q 13 Q 23 Q 12         , (2) in which Q ij is the (ith or j th) element of the covariance matrix between the three measurements. Since the triplet of datasets used for training each of the fluxes (see Table  1 ) is derived through different semiempirical approaches with different sources of errors, the assumption of uncorrelated errors is more likely to be met. In the following, we will calculate the standard deviation of the random error component of Eq. (1) using TC for each of the surface fluxes and use them as TC-based errors of each product. The TC-estimated errors for the surface fluxes and GPP are shown in Figs. S4-S6. The white regions represent missing retrievals or discarded negative estimates due to an insufficient data record.
, 2016). For the GLEAM and ECMWF data, thick vegetation generally induces biases compared to the satellite observations, especially in tropical regions  (Anber et al. , 2015). Finally, we use the TC-based RMSE estimates at each pixel to compute the a priori probability (P i ) of selecting a particular dataset in each pixel, if that pixel is used as part of the training dataset: P i = 1 σ 2 ε i 3 i=1 1 σ 2 ε i , (3) in which P i is the probability of selecting dataset i when sampling from three measurements. We assume that these probabilities are time independent as we are limited by the currently available duration of the input data; however, future versions will explore the use of seasonally varying probabilities.
e. , East Asia, the eastern US, and the north and east Australian coasts with over 60 W m -2 difference between winter and summer months. One exceptional case is South Asia, where LE does not significantly rise in spring, likely due to the effects of the monsoonal climate. In eastern South America, the ET estimates are relatively high compared to GPP estimates. This difference can be caused by either low water use efficiency or significant rain reevaporation and soil evaporation. Moreover, the SIF relationship with GPP likely changes in C4 plants. However, we did not impose the C4-C3 delimitation in the ANN as it would be highly dependent on the quality of the classification map used. We note that all training products used here include C3-C4 delimitation and therefore the C3-C4 delimitation is implicit in the training dataset; therefore, it can be learned by the network.
Evaluation with FLUXNET data Direct validation of the WECANN retrievals is challenged by the fact that no global, error-free estimates of LE, H , and GPP are available. Remote sensing or model products such as those used for training have their own errors. When three datasets with uncorrelated errors (commonly assumed to be true if the sources of error in each dataset have no common physical origin) are available, TC provides a valuable technique to evaluate large-scale datasets in the absence of a known truth. However, WECANN's use of different training datasets will cause the presence of some correlated errors between WECANN retrievals and any of the datasets used for the training. Instead, we evaluate the retrievals by comparing them to data from a set of FLUXNET eddy covariance towers.
WECANN uses three training datasets, one of which (FLUXNET-MTE) is based on upscaling FLUXNET eddy covariance tower estimates. This might cause some dependence between WECANN retrievals and the tower estimates. However, WECANN learns from all three training datasets collectively and uses remote sensing observations as input. Therefore, this dependence is negligible. In situ estimates from eddy covariance towers with a footprint of a few hundred meters to kilometers may not be representative of the entire 1 • × 1 • pixel and are known to have problems with energy closure  (Foken et al. , 2010). However, in the comparison against tower data the impact of large-scale climate variability and seasonality can still be seen even on different spatial scales. For instance, the phenology has a strong impact on the seasonal cycle of the LE, H , and GPP and in the following examples; it is clearly highlighted when comparing different products to flux tower estimates.
All products overestimate the reported GPP, though WECANN is closest to the observations and captures the seasonal dynamics better compared to other products. Overall, across the different sites, the WECANN retrieval performs better than other products, especially in terms of the seasonality of LE, H , and GPP. Several factors contribute to the improved retrieval of WECANN compared to other products, even at those smaller footprint sites. First, the SIF measurements that are directly correlated with GPP provide a better constraint on estimating LE, H , and GPP. The ANN approach in WECANN also uses a novel training technique based on probabilistically merging different datasets to remove outliers from its target dataset. Therefore, WECANN retrievals learn collectively from the different datasets (and remote sensing observations) and are closer to the truth than each of the individual target datasets.
Comparison against other products based on remote sensing In this section, we compare the WECANN-based estimates to other datasets used in the training to better understand how WECANN differs from those training data. Figure  8  shows the comparisons for LE and indicates that our product has a relatively similar R 2 with the three products (R 2 = 0. 96 with FLUXNET-MTE and ECMWF and R 2 = 0. 94 with GLEAM). However, the scatter plot with FLUXNET-MTE is more concentrated and aligned along the 1 : 1 line, further emphasizing the consistency between the two datasets (RMSD of 6. 42 W m -2 for FLUXNET MTE versus 8. 47 and 9. 72 W m -2 for GLEAM and ECMWF, respectively). Differences in spatial patterns shown in Fig. 8a -c reflect that WE-CANN exhibits smaller spatial differences with FLUXNET-MTE than GLEAM or ECMWF and such differences exhibit a narrower range between -10 and 10 W m -2.
The differences and similarities of WECANN retrievals with the three target datasets are consistent with the error estimates from TC. For example, Fig. S4  shows that FLUXNET-MTE has the smallest error in LE estimates globally compared to GLEAM and ECMWF, other than across India. WECANN retrievals also have better agreement with FLUXNET-MTE. The differences in H estimates are more complex (Fig. 9 ). First, the R 2 values between WECANN and the other datasets are slightly lower than for LE. ECMWF and FLUXNET-MTE yield a higher R 2 with WECANN (0. 92) while GLEAM has an R 2 of 0. 87. GLEAM exhibits lower H in most of the NH, especially in seasonally dry regions, potentially due to its simple formulation of ground heat flux (G). H estimates are relatively higher over the Amazon and Congo but lower over Indonesia for GLEAM.
In the southern Sahara and northern Sahel as well as in eastern Asia and Canada, GLEAM has lower H compared to WECANN and FLUXNET-MTE. ECMWF exhibits higher values in seasonal dry regions such as the western US, Brazilian savannas, southern Congo, and the Sahel compared to WECANN and smaller values in the Amazon, Indonesia, over desert areas of the Sahara and Arabian Peninsula, and Southeast Asia. The GLEAM and ECMWF H difference maps show many similar patterns: the Sahara, eastern Europe, and East Asia are underestimated, while southern Africa and the eastern part of the Amazon are overestimated. Similarly the errors patterns estimated from TC (Fig. S5 ) are consistent with the comparison of WECANN and the target datasets. Figure  S5  shows that ECMWF has higher errors in the Sahel, southern Congo, and Brazilian savanna, and GLEAM has higher errors in the Amazon, East Asia, and Central Africa.
Finally, we compare annual anomalies of WECANN retrievals and the three training datasets globally and in different climatic zones. The zones are defined as polar (90-60 • N), NH midlatitude (60-10 • N), tropics (10-15 • S), and SH midlatitude (15-60 • S). Results are presented in Fig. S7. WECANN anomalies are derived from the mean values between 2007 and 2015. However, not all the training products are available for this period. Therefore, their anomalies are calculated from their respective temporal domain, which is 2007-2011 for  FLUXNET-MTE, GLEAM, and MODIS and 2008-2011  for ECMWF. Anomalies from the four products have similar patterns in general, while their absolute values differ. Anomalies in GPP have better agreement across different products compared to LE and H. Evaluation of the discrepancies between these anomalies is beyond the scope of this paper and will be characterized in detail in a future study.
Conclusions This study introduces a new statistical approach to retrieving global surface latent and sensible heat fluxes as well as gross primary productivity using remotely sensed observations on a monthly timescale. The methodology is developed based on an artificial neural network that uses six input datasets including solar-induced fluorescence, precipitation, net radiation, soil moisture, snow water equivalent, and air temperature. Moreover, a Bayesian approach is implemented to optimally integrate information from three target datasets for training the ANN, using triple collocation to calculate a priori probabilities for each of the three target datasets based on their uncertainty estimates. The new global product, referred to as WECANN, is evaluated using target datasets as well as FLUXNET tower observations.
The evaluation results compared with training datasets show that our retrieval has similar correlation with the three products, while it has the smallest RMSD with FLUXNET-MTE for LE (RMSD = 6. 42 W m -2 ), H (RMSD = 7. 84 W m -2 ), and www. biogeosciences. net/14/4101/2017/ Biogeosciences, 14, 4101-4124, 2017 GPP (RMSD = 0. 88 gC m -2 day -1 ), which is believed to be one of the most realistic global datasets. It also has the lowest RMSE based on our TC error estimates (Fig. S4 -S6), despite its reported underestimated interannual variability due to the use of climatological values for several meteorological drivers  (Miralles et al. , 2014a (Miralles et al. , , 2016)). Such a tendency can also be summarized from the global difference maps, which show that FLUXNET-MTE has the best agreement with WECANN retrievals.
The WECANN and FLUXNET-MTE approaches are both based on machine learning, although the FLUXNET-MTE retrievals use a regression tree rather than an ANN. Nevertheless, this commonality of methods may also contribute to the greater correspondence between these two datasets. The retrieval maps indicate that LE, H , and GPP have similar seasonal variability and distribution, which are determined by the annual phenological cycle in energy-limited northern latitude regions, dryness in Mediterranean and monsoonal climates, and by light availability in rainforests. Seasonal radiation has a great impact for all three variables on some regions, such as the eastern US, Europe, and East Asia, which have wet conditions, are highly vegetated and located in midlatitudes. Conversely, the seasonal variability in LE, H , and GPP in some low-latitude and wet condition regions, such as Amazon rainforest, southern Africa, and Southeast Asia, as well as some low-latitude arid regions, such as the southwestern US, Western Australia, North Africa, and western Asia, are not significant, as there is less seasonal solar radiation variability in the aforementioned regions.
The inputs of WECANN include six remotely sensed variables introduced in Sect. 2. 2 and Table2: SIF, net radiation, air temperature, soil moisture, precipitation, and snow water equivalent (SWE). These are used to retrieve LE, H , and GPP. Different observation-and/or model-based datasets are used as the training dataset and are explained in Sect. 2. 1 and summarized in Table.
The inputs of WECANN include six remotely sensed variables introduced in Sect. 2. 2 and Table2: SIF, net radiation, air temperature, soil moisture, precipitation, and snow water equivalent (SWE). These are used to retrieve LE, H , and GPP. Different observation-and/or model-based datasets are used as the training dataset and are explained in Sect. 2. 1 and summarized in Table • × 0. 05 • MODIS-based Land Cover Type product (MCD12C1 v051) (NASA LP DAAC, 2016). 2 Data.
Methods Kinematic precise point positioning strategy. The high-rate position time series are obtained using the GD2P module of GIPSY-OASIS 6. 4 software  46  that is developed by the Jet Propulsion Laboratory (JPL). Our processing strategy is similar to that of Miyazaki and Larson  23  and Malservisi et al. 25. We use the precise point positioning strategy  47  including the phase ambiguity resolution from a single receiver  48. We use the final orbits and satellite clock estimates provided by JPL. We account for ocean loading effects using the FES2004 model  49. The troposphere delays are calculated using the VMF1 mapping functions  50. We account for higher order ionospheric terms using the IRI-2012b model  51. We set the input parameters as suggested by the GIPSY-OASIS documentation except for two parameters.
Detecting the onset time of postseismic displacement. To detect the onset time of the postseismic displacement, we design an algorithm to estimate when the mean of the position time series changes significantly and remains at its new level. For that, we assume that the position time series follow a normal distribution. The algorithm for the detection is based on the Chow-test  35  , which tests the significance of using two linear regressions to model a given dataset. The null hypothesis assumes that the time series do not exhibit a change of mean. Thus, the residual sum of squares for the null hypothesis is: (A) Sidereal filter. The coloured lines represent the six 24-hour time series preceding the mainshock. The black line is the average of the 6 time series after that they have been properly shifted and stacked.
Acknowledgements The continuous GNSS stations for the Maule and Illapel earthquakes are part of the  International GNSS Service (IGS)  and the  Chilean-French International Laboratory (LIA)  network. The stations for the Pedernales earthquake are part of the  IGEPN (Instituto Geofísico)  and  IRD (Institut de Recherche pour le Développement) network. We thank  Christophe Vigny Frederique Rolandone  and  Jean-Mathieu Nocquet  for facilitating the access to the GNSS data. This work is conducted within the  ANR JCJC E-POST  ( ANR-14-CE03-0002-01JCJC E-POST ). C. T. was funded by the  Centre National d'Études Spatiales  as well as by the  ANR JCJC E-POST. We also thank two anonymous reviewers for their comments that have helped to improve the manuscript. We also thank  Louisa Tsang  for proof-checking the manuscript and provide valuable suggestions.
Data and Methods Observed and reanalyzed data of meteorological and oceanic variables (e. g. , precipitation, SST, wind, specific humidity, velocity potential, stream function and vertical velocity/omega) are used for the period from 1960 to 2015 (or 2000 to 2015 for Quick Scatterometer (QuikSCAT) + Advanced Scatterometer (ASCAT) winds). Information on these data is summarized in Table  1.
Scalability of local climate change impacts observations To    [43]  and locations of the reviewed case studies along with the main climates according to the Koeppen-Geiger classification  [47, 48]. An important characteristic of the locations where observations of climate change impacts were documented is their distance to weather stations whose data is included in the datasets CRUTEM4 used for assessing anthropogenic climate change  [43]. Thus, half of the locations documented are in areas with <6 and <27 weather stations within a 200km and a 500km radius (compared to a maximum of 61 and 346 weather stations for a case study in Italy) (SM5). Given the deficient weather station coverage, observations of local climate change impacts could become an alternative data source to evaluate the performance of climate models in these areas.
Acknowledgements Research leading to this work has received funding from the  European Research Council (ERC)  under grant agreement No  771056-LICCI-ERC-2017-COG  and from the  Spanish government  through a grant of the  Ministry of Economy and Competitiveness  ( CSO2014-59704-P ). García-del-Amo and  Reyes-García  acknowledge financial support from the  Spanish Ministry of Economy and Competitiveness , through the  "María de Maeztu" Programme for Units of Excellence in R&D  ( MdM-2015-0552 ). Thanks to M. Gueze for cartographical help.
In the Calabrian Arc two competing interpretations-ranging from partially-locked to unlocked or inactive-were recently proposed  (Carafa et al. 2018; Nijholt et al. 2018 ). In the Hellenic Arc interpretations range from full locking  (Ganas and Parsons 2009)  to low coupling  (Shaw and Jackson 2010; Vernant et al. 2014) , but also the presence of important along-strike coupling variations was proposed  (Laigle et al. 2004). Rates used afterward for S-PTHA are inherited from the TSUMAPS-NEAM Project. We refer to the Project documentation for further details. We just list for completeness here that, depending on the alternative TSUMAPS-NEAM modelling strategy, either the SHEEC-EMEC seismic catalog (Stucchi et al. 2013; Gru ¨nthal and Wahlstro ¨m 2012), whose time span ranges from 1000 to 2006, was used; or, the b-values, convergence and coupling data from GEM Faulted Earth  (Christophersen et al.