dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Kasischke et al. , 2002; Stocks et al. , 2003)  and/or simulation models, have been used to produce estimates of burned area and associated emissions going back to the beginning of the 20th century  (Mouillot and Field, 2005; Mouillot et al. , 2006; Schultz et al. , 2008; Mieville et al. , 2010). Both remote sensing data and historical reconstructions can be used to evaluate model performance, but the pre-1990s period -especially before the 1960s -is quite data-poor. This first phase of FireMIP will thus serve to produce an ensemble estimate of global fire activity during that time. Sensitivity experiments will be used to diagnose potential causes of mismatches between simulations and observations. However, fire models can be evaluated only in conjunction with their associated vegetation models: a model that reproduces burned area perfectly but simulates wildly incorrect patterns of aboveground biomass, for example, would be less than ideal.
Likewise, it is possible for biases in a model to cancel each other out, resulting in the right output for the wrong reasons. A number of important vegetation-related variables have observational data available, and FireMIP will assess model simulations of these in addition to fire-related variables so as to holistically evaluate model performance. A major goal of FireMIP is to provide well-founded estimates of future changes in fire regimes. In the second phase of FireMIP, we will evaluate how different fire models respond to large changes in climate forcing by running a coordinated paleoclimate experiment. Past climate states provide the possibility to test the models under environmental conditions against which they were not calibrated  (Harrison et al. , 2015) , using charcoal records.
Baseline and sensitivity experiments The baseline simulation in FireMIP is a fully transient simulation from 1700 to 2013 (SF1; Table  1 ). This simulation involves specification of the full set of driving variables and will allow individual model performance to be evaluated against a number of available benchmarking datasets (Sect. 4. 1). A series of sensitivity experiments (SF2) will allow the reasons for inter-model agreements and/or discrepancies to be diagnosed by analyzing the impact of each of the main drivers of fire activity separately (Table  1 ). These experiments use the same input and setup as the SF1 run, but keep key variables constant: 1. "World without fire" (SF2_WWF): Fire is turned off to evaluate the impact of fire on ecosystem processes and biogeography. 2. "Pre-industrial climate" (SF2_CLI): Climate forcings are fixed to repeated 1901-1920 levels to analyze the impact of historical climate changes on photosynthesis and consequent impacts on fire and other ecosystem processes.
Input datasets The FireMIP baseline experiment is driven by a set of standardized inputs, which include climate, population, land use, and lightning. The climate forcing is based on a merged product of Climate Research Unit (CRU) observed monthly 0. 5  climatology  (1901 -2013; Harris et al. , 2014)  and the hightemporal-resolution NCEP reanalysis. The merged CRU-NCEP v5 product has a spatial resolution of 0. 5 • and a 6hourly temporal resolution  (Wei et al. , 2014). Global atmospheric CO 2 concentration was derived from ice core and NOAA monitoring station data (Le  Quéré et al. , 2014)  and is provided at annual resolution over the period 1750-2013. Many of the participating models were developed using different climate forcing data. Figure  1  illustrates how serious an impact this can be, using the JSBACH-SPITFIRE fire model  (Lasslop et al.
, 2014). This model configuration was originally parameterized using the CRU-NCEP forcing data. When the CRU-NCEP wind forcing is substituted with that from the WATCH data  (Weedon et al. , 2011) , modeled burned area decreases by ca. 27 % with important spatial changes in regional patterns. Because the use of different input data -in this case wind speed -can produce such major differences in outputs, participating groups were allowed to re-parameterize their fire models to adjust for the idiosyncrasies of the FireMIP-standardized input data. Annual data from 1700 to 2013 at 0. 5 • resolution on the fractional distribution of cropland, pasture, and wood harvest -as well as transitions among land-use types -were taken from the dataset developed by  Hurtt et al. (2011). This dataset is based on gridded maps of cropland and pasture from version 3.
1 of the History Database of the Global  Environment (HYDE; Klein Goldewijk et al. , 2010) , which are generated based on country-level FAO statistics of agricultural area in combination with algorithms to estimate population, land use, and settlement patterns into the past. HYDE also provides gridded maps of historical population density, which participating FireMIP groups used if needed. A global, time-varying dataset of monthly cloud-toground lightning was developed for this study at 0. 5 • and monthly resolution (J. Kaplan, personal communication, 2015) , comprising global lightning strike rate (strikes km -2 day -1 ), for the period 1871-2010. This dataset incorporates interannual variability in lightning activity using the method described by  Pfeiffer et al. (2013)  by scaling a mean monthly climatology of lightning activity  (covering 2005 -2014; Virts et al.
, 2013)  using convective available potential energy (CAPE) anomalies  (Compo et al. , 2011). The participating models (Table  2 ) have different spatial and temporal resolutions; groups were thus allowed to interpolate inputs from their original resolution to that appropriate for their model. This was done so as to preserve totals as close as possible to the canonical data. Some models required additional input datasets -for example, nitrogen deposition rates or soil properties. These were not standardized.
However, because the CRU-NCEP and lightning forcing data were not available for 1700-1900, the 1901-1920 forcings were recycled for the first 200 years of the simulation; this allowed natural climate variability to be captured while incorporating only minimal human influence. From 1901 to 2010, time-varying values of all variables were used. Finally, the lightning dataset did not include 2011-2013, so the 2010 values were used for the last three years of the experiment. A visualization of the time periods covered by each input in the spinup and historical model runs can be found in Fig. 2. Although agriculture (cropland and pasture) were specified inputs, each model calculated natural vegetation according to its standard set-up and no attempt was made to standardize this. The biogeography of natural vegetation, rep-Table  2.
CTEM fire module The Canadian Terrestrial Ecosystem Model (CTEM v. 2. 0;  Melton and Arora, 2016)  represents disturbance as both nat-ural and human-influenced fires. The original fire parameterization is described in  Arora and Boer (2005) , with  Melton and Arora (2016)  describing recent changes and its implementation in CTEM v. 2. 0. The only changes between the version of the model used here and that described by  Melton and Arora (2016)  are for the vegetation biomass thresholds for fire initiation (S L ; Table  S4 ) and the PFT-specific fractional combustion of leaves ( FC l,leaf ), stems ( FC l,stem ), and litter ( FC d,litter ; see Table  S16 ).
LM3-FINAL The Fire Including Natural & Agricultural Lands model (FI-NAL;  Rabin, 2016; Rabin et al. , 2017)  simulates global fires within the Geophysical Fluid Dynamics Laboratory Land Model version 3 (LM3;  Shevliakova et al. , 2009; Milly et al. , 2014; Sulman et al. , 2014). FINAL follows the structure of  Li et al. (2012 Li et al. ( , 2013) )  closely for prediction of wildland fires with lightning and human ignition, but does not have special modules for deforestation and peatland fires. Previous work  (Magi et al. , 2012; Rabin et al. , 2015)  estimated the amount of burned area from cropland, pasture, and non-agricultural fires based on total observed burned area from the Global Fire Emissions Database version 3 including small fires (GFED3s;  Randerson et al. , 2012). The nonagricultural burned area estimates from  Rabin et al.
, 2011) , and maximum crown area (increased to 300 m 2 based on  Seiler et al. , 2014 , but taking a more conservative value appropriate for a global parameterization). For details see Table  S22. Furthermore, a simple land-use scheme was implemented for compliance with the FireMIP protocol. A timeevolving fraction of patches was designated as pasture or cropland based on the HYDE land-use dataset  (Klein Goldewijk et al. , 2010). When natural patches were converted to cropland or pastures, 90 % of the aboveground carbon was immediately respired to the atmosphere and 10 % was added to a woody product carbon pool with a 25-year residence time (following  Lindeskog et al. , 2013). In cropland and pasture patches, tree establishment is forbidden, so only grass PFTs are present. Lightning ignitions occur in both cropland and pasture, but human ignitions were forbidden in croplands.
ORCHIDEE-SPITFIRE The ORCHIDEE-SPITFIRE model was developed by incorporating the SPITFIRE model  (Thonicke et al. , 2010)  into the land surface model ORCHIDEE. All equations as described in  Thonicke et al. (2010)  were implemented, except for changes to lightning ignitions and combustion completeness, as well as the addition of a fuel-dependent ignition efficiency term (as described in  Yue et al. , 2014 Yue et al. , , 2015)). Combustion completeness values were updated to those in  Yue et al. (2014 Yue et al. ( , 2015)) , based on data published in  van Leeuwen et al. (2014). Regional scaling factors for burned area were also introduced, to adjust simulated regional burned area for 1997-2009 to agree with that reported in version 3 of the Global Fire Emissions Database (GFED3;  Giglio et al.
, 2010). The regions used were the 14 GFED regions (van der  Werf et al. , 2006). Finally, the standard FireMIP lightning dataset was adjusted to account for the fact that the original model  (Yue et al. , 2014 (Yue et al. , , 2015) )  was calibrated using the LIS/OTD lightning flash rate climatology  (Cecil et al. , 2014, http ://gcmd. nasa. gov/records/GCMD_lohrmc. html). Specifically, the cloud-to-ground numbers provided were scaled to total (i. e. , cloud-to-ground plus within-cloud) flashes, so that the mean annual global lightning flash rate during 1997-2009 was the same as that given in the LIS/OTD data.
These standardized statistics allow straightforward comparison of model performance with regard to variables that may have differences in units of many orders of magnitude. Kelley et al. (2013)  also introduced the idea of creating a kind of statistical control for putting these metric scores into context. The "mean model" consists of a dataset of the same size as the observations, where every element is replaced with the observational mean. Similarly, the "random model" is produced by bootstrap resampling of the observations. These datasets allow the performance of the actual models to be compared against external standards in addition to each other for individual processes of interest. If a model does not perform significantly better than one using the mean or random data, its usefulness may be limited.
Additionally, as the metrics used represent normalized "distance" between models and observations, a comparison of scores shows how much closer to reality one model is than another. For example, a model's score of 0. 5 is exactly 33 % closer to the observations than another of 0. 75 (0. 5/0. 75 = 33 %). Conversely, the second model would need to improve by 33 % in order to provide as good a match to observations as the first. This benchmarking system can be used to evaluate model performance with regard to aspects of land and vegetation other than fire. In addition to burned area and fire emissions, we will use observational datasets of vegetation properties and hydrology to evaluate how well the models simulate the land-vegetation system as a whole. This is especially important because burning affects a wide range of Earth system processes, often in a non-linear manner.
g. ,  Daniau et al. , 2012; Bistinas et al. , 2014). Model outputs can then be interrogated to determine whether the model reproduces these relationships (e. g. ,  Lasslop et al. , 2014; Li et al. , 2014). We plan to apply GLMs to both observational datasets and to the corresponding model forcing variables and model outputs to identify relationships between fire activity and potential climatic, vegetative, and socio-economic drivers. This will allow us to analyze the sensitivity of the simulated fire activity to various controls, as well as to evaluate how well the models recreate emergent relationships seen in observational data.
Observational data The observational database assembled for FireMIP consists of a collection of datasets selected to allow systematic evaluation of a range of model processes. The system is an updated and extended version of that presented by  Kelley et al. (2013). As in  Kelley et al. (2013) , the site-based and remotely sensed observational datasets were chosen to fulfill a num-Table  4. Summary description of the observational datasets to be used for model evaluation. "Frequency" refers to the temporal resolution at which the analyses will be performed, which may be coarser than the native resolution of the data. ber of criteria. They are all global in coverage or provide an adequate sample of different vegetation types on each continent. The datasets are also all independent, in that they do not require the calculation of vegetation properties from the same driving variables as the fire-enabled DGVMs.
This excludes, for example, net primary productivity or evapotranspiration products that are based on the interpretation of remotely sensed data using a vegetation model. For variables that display significant seasonal or interannual variability, the data must be available for multiple years and seasonal cycles. And finally, the data must be publicly accessible, so that other modeling groups can subsequently use the benchmarks. The selected datasets provide information for vegetation properties, fire properties, hydrology, and fire emissions (Table 4). All remotely sensed data were re-gridded to a 0. 5 • grid and masked to a land mask common to all the models. There are multiple datasets available for some variables; we retained all of these products in order to be able to take account of observational uncertainties in the benchmarking procedure.
It should be noted that many of the individual datasets do not provide measures of uncertainty. The analytical protocol we have described is appropriately rigorous and transparent. However, the effectiveness of any model evaluation is dependent on the quality of its observational data, and FireMIP is no different. There are no data available at scales relevant to global models for a number of important fire-related variables -for example, ignition frequency or fraction of trees killed by fire. The variables that do have global data from remote sensing often suffer from substantial uncertainty, as discussed for burned area by  Hantson et al. (2016).
Similarly, the protocol has not standardized soil inputs -which will affect the water-balance calculations and hence control vegetation distribution -because this would likely require major re-calibrating of the models. However, differences in the soil inputs used by individual models could lead to differences in fire regimes at a regional scale. We anticipate that this is a second-order effect, and will rely on process-based diagnoses to identify the degree to which it explains inter-model differences. Finally, the exact implementation of land use and land cover change can cause important differences in model outputs, even given the same land-use driver dataset  (Brovkin et al. , 2013). The participating models vary in spatial resolution: most are run on a 0. 5 • grid but some are run at coarser resolution (Table  2 ) and provide outputs at the native resolution of the model.
Model parameterizations are specific to model resolution, and thus differences caused by differences in resolution are an inherent part of the structural uncertainty. However, resolution has an impact on the benchmarking metrics, with goodness-of-fit being inflated as resolution becomes coarser. Thus, the interpretation of the benchmarking metrics will need to take this into account by calculating appropriate null models for the different resolutions. Model benchmarking will examine several different aspects of the fire regime, but will also consider how well each model captures vegetation properties and hydrology (Table  4 ). There are multiple datasets available for some of these properties, including, for example, burned area. Padilla et al. (2015)  have shown that currently available burned area products differ considerably both in terms of global total and at a regional scale.
Differences between datasets effectively define the current range of uncertainty in observations, and this level of uncertainty needs to be taken into account when evaluating model performance. A total of 11 modeling groups are performing the baseline FireMIP simulations, but there are several other fire models in use. We hope that publishing this experimental and benchmarking protocol will encourage other fire modeling groups to participate in FireMIP. We provide a standardized modeling and benchmarking protocol for a wide variety of global fire-enabled ecosystem models. The wide variety of approaches taken by the participating models leads us to expect notable inter-model variation in results. Some models, for example, estimate energy release for calculations of fire behavior and effects, while others use simplifications -an important structural difference.
The Supplement related to this article is available online at doi:10. 5194/gmd-10-1175-2017-supplement. Acknowledgements. S. Rabin was supported by a  National Science Foundation Graduate Research Fellowship  and by the  Carbon Mitigation Initiative , and along with  S. Hantson  and  A. Arneth  would like to acknowledge support by the  EU   FP7  projects  BAC-CHUS  (grant agreement no. 603445 ) and  LUC4C  (grant agreement no. 603542 ). This work was supported, in part, by the  German Federal Ministry of Education and Research (BMBF) , through the  Helmholtz Association and its research programme ATMO , and the  HGF Impulse and Networking fund. F. Li  was funded by the  National Natural Science Foundation of China  under grant no. 41475099  and the  CAS Youth Innovation Promotion Association Fellowship.
The UK Met Office contribution was funded by  BEIS  under the  Hadley Centre Climate Programme contract  ( GA01101 ). G. A. Folberth also wishes to acknowledge funding received from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 641816  (CRESCENDO). J. O. Kaplan was supported by the  European Research Council  (COEVOLVE,  313797 ). The article processing charges for this open-access publication were covered by a  Research Center of the Helmholtz Association. We acknowledge support from the  Deutsche Forschungsgemeinschaft and the Open Access Publishing Fund of the Karlsruhe Institute of Technology. The authors would like to thank the editor and referees for their helpful comments. The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association.
Data availability. Once all runs are completed, model outputs will be made available to the public at https://bwfilestorage. lsdf. kit. edu/public/projects/imk-ifu/FireMIP. The FireMIP website (http:// www. imk-ifu. kit. edu/firemip. php) will also be kept up-to-date with the latest data access details in addition to project updates and summary information.
System under investigation To perform our experiments, we use the open source Eclipse MAT (Memory Analyzed Tool) 3 which is an OSGi standalone application that supports programmers to detect memory leaks. We choose Eclipse MAT because it is a medium-sized project which source code is freely available. Figure  5  depicts the dependencies between the system components according to the documentation that we have found by inspecting each JAR File composing the system. The core of the system is a component called api. It provides a set of routines, protocols, and tools for analyzing memory usage of a Java application. All of the remaining components either depends directly or indirectly on the api component except the report component which acts like a bridge between Eclipse MAT and Eclipse Business Intelligence and Reporting Tools.
The components ui, ui rcp and ui help are responsible for providing the user interface and interactive help for the tool. The component jruby resolver provides support for the JRuby JVM-based language. The parser 2 OSGi Alliance : www. osgi. org 3 Eclipse Memory Analyzer Tool : www. eclipse. org/mat component provides heap parsing utilities which are used by two components: dtfj and hprof. The first one accesses the heap dump and provides diagnostic tools for analyzing Java classes and objects and their classloaders that were present in the heap. The second one relies on the heap dump to present CPU usage, heap allocation statistics, and monitor contention profiles. The ibmdumps component provides support for IBM Java virtual machines. The two remaining components namely chart and chartui provide feature-rich charting functionalities.
The change-points appearing in the different rows are claimed to be change-points for the two-dimensional data either if they appear at least in one row (the associated ROC curve for this approach called HL1 is displayed with '+' in Figure  3 ) or if they appear in ([n/2] + 1) rows (the associated ROC curve for this approach called HL2 is displayed with ' ' in Figure  3 ). Since the procedures HL1 and HL2 are much slower than ours, the ROC curves are displayed for matrices of size 250 × 250. We can see from Figure  3  that our method outperforms the other ones. Numerical Performances. We implemented Algorithm 1 in C++ using the library armadillo for linear algebra  [12]  and also provide an interface to the R platform  [11]  through the R package blockseg which is available from the Comprehensive R Archive Network (CRAN).
All experiments were conducted on Linux workstation with Intel Xeon 2. 4 GHz processor and 8 GB of memory. We generated data as in Model  (10)  for different values of n: n ∈ {100, 250, 500, 1000, 2500, 5000} and different values of the maximal number of activated variables: s ∈ {50, 100, 250, 500, 750}. The median runtimes obtained from 4 replications (+ 2 for warm-up) are reported in Figures 4. Left of Figure  4  (resp. right) gives the runtimes in seconds as a function of s (resp. of n). These results give experimental evidence for the theoretical complexity O(mn 2 + ms 2 ) that we established in Section 3 and thus for the computational efficiency of our approach: applying blockseg to matrices containing 10 7 entries takes less than 2 minutes. Model Selection. In practice, we take s = K 2 max where K max is an upper bound for K 1 and K 2.
Conclusion In this paper, we proposed a novel approach for retrieving the boundaries of a block wise constant matrix corrupted with noise by rephrasing this problem as a variable selection issue. Our approach is implemented in the R package blockseg which will be available from the Comprehensive R Archive Network (CRAN). In the course of this study, we have shown that our method has two main features which make it very attractive. Firstly, it is very efficient both from the theoretical and practical point of view. Secondly, its very low computational burden makes its use possible on very large data sets coming from molecular biology.