dataesr/scientific-paragraphs-categorization huggingface dataset:
The same temporal misalignment problem is present for the CH 4 differences. However, the temporal misalignment problem is masked by the CH 4 precision of the GC system (±2. 3 nmol mol -1 ), which constitutes the lower limit for the variability of the differences and is exemplified in Fig. 5. Since this statistical error of the CH 4 difference is already large it masks the systematic error. Thus the deviation of the CH 4 histogram from the Gauss fit is much smaller. From the good, absolute agreement between the GC and the TCI we can conclude that it is possible to use the TCI in its current set-up to assess the performance of a field station. The variability of the differences is much smaller at the two field stations compared to that at the Heidelberg reference station. This has two reasons: first, the temporal resolution of the CRDS is 1 min and can, after proper smoothing, be more accurately compared with the 3 min data of the TCI; and secondly, the variability of the ambient concentrations in CBW and OPE are much smaller than in Heidelberg.
With the dedicated component tests, like the SIS test and the calibration check, the travelling instrument approach represents a true diagnostic evaluation directly at the field station under its potentially unique conditions. The travelling instrument approach is, however, labour intensive, since the TCI has to be referenced to independent instrumentation at its home base before and after each field trip. This reduces the comparison capacity of one TCI to only four or five station visits per year. From our experience, conducting the TCI approach, including preparation and data evaluation, would need the capacity of one fulltime scientist, at least during the first year. In addition, as long as the FTIR is used as TCI, the campaigns have to be supported by a technician or a student helper.
Thus, CRDS data were only available a few weeks after the station visits. -The example of insufficient flushing times in the CBW set-up has shown that a check of the station's calibration standards with the travelling instrument is not sufficient. Measuring the TCI working standards at the station instrument will give additional information on how well the scale is transferred from the station calibration standards to the station instrument. Our approach was vice versa and thus checked the accordance of the two standard sets and the scale transfer to the TCI. In our comparison we had only one direct cylinder comparison at both instruments, which already highlighted a respective problem. -For future TCIs it will be mandatory for the TCI host laboratory to have a separate intake line for the TCI instrument and to use the same line flushing pump as during the field comparisons.
Acknowledgements. We wish to thank  Pim van den Bulk (ECN) ,  Cyrille Vuillemin  (LSCE), and  Maxime Simon  ( ANDRA ) for their very helpful support at the stations. Dominik Schmithüsen (IUP) is gratefully acknowledged for his help during the OPE campaign. We thank the three anonymous reviewers for their valuable comments that helped to improve this paper. This work has been funded by the  European Commission  under the  ICOS Preparatory Phase Project  ( INFRA-2007-211574 ) Edited by:  A. C. Manning.
A modified cumulus convection parametrisation scheme is presented. This scheme computes the mass of air transported upward in a cumulus cell using conservation of moisture and a detailed distribution of convective precipitation provided by a reanalysis dataset. The representation of vertical transport within the scheme includes entrainment and detrainment processes in convective updrafts and downdrafts. Output from the proposed parametrisation scheme is employed in the National Institute for Environmental Studies (NIES) global chemical transport model driven by JRA-25/JCDAS reanalysis. The simulated convective precipitation rate and mass fluxes are compared with observations and reanalysis data. A simulation of the short-lived tracer 222 Rn is used to further evaluate the performance of the cumulus convection scheme.
This approach allows tracer transport to be calculated within the CTM while the GCM provides the meteorological variables (including parametrised fields) that are used to drive tracer transport. The options available for on-line CTMs are generally not available for off-line models, which are operated using a limited set of time-averaged, predefined meteorological parameters taken from a gridded reanalysis dataset. Reanalysis datasets often do not contain all fields that are necessary for tracer transport modelling, such as parameters associated with subgrid-scale mixing; consequently, the implementation of parametrisation schemes in off-line CTMs presents a significant challenge  (Mahowald et al. , 1995 (Mahowald et al. , , 1997)). Previous studies have demonstrated that transport model simulations of the distribution of 222 Rn fields are strongly sensitive to the parametrisation used to represent cumulus convection  (Brost and Chatfield, 1989; Jacob et al.
The paper is organized as follows. The DCC parametrisation and model formulation are described in Sect. 2. The reanalysis convective precipitation rates and simulated convective mass fluxes are validated against reanalysis and observational data in Sect. 3. The simulated surface concentrations and vertical profiles of 222 Rn are evaluated relative to in situ observations from aircraft and surface stations and compared with output from models that contributed to the TransCom-CH 4 TMI in Sect. 4. Here we also analyzed the performance of the other TransCom-CH 4 transport models. The results and conclusions of the study are summarized in Sect. 5.
13 z top -z base -1 [ km -1 ], a = 0. 2. ( 5 ) The total amount of water condensed in the cloud during the life-cycle of the convective cell is given by C = x 1 • P c , (6) where P c denotes the total amount of convective precipitation at the surface [kg] and x 1 is a constant in the range 2. 10. We selected x 1 = 3 to be consistent with  (Austin and Houze, 1973) , who recommend value between 3 and 4. In tracer transport applications, the vertical convective mass flux MF U is of primary practical importance. This flux can be determined as MF U = M U /(S • t), where S is the area of the model grid cell, and t is the time step used in the model integration. The total amount of convective precipitation can be similarly determined as P c = P R •S • t, where P R is the surface convective precipitation rate [kg m -2 s -1 ] provided by a reanalysis dataset.
( 10 ) This off-line algorithm based on moisture balance (Eqs. 8-10) has several limitations. Firstly, to determine updraft mass flux we should find the total horizontal moisture convergence (Eq. 9) and the divergence of the air mass (Eq. 10   (Mizuta et al. , 2006). However, for users this meteorological dataset is available with a resolution of 0. 5 • × 0. 5 • for 21 pressure levels. Secondly, the scheme requires additionally using surface evaporation data S evap. Usually, this data are different from the reanalysis implemented for advection and may introduce inconsistencies in the transport. Thus, implementation of parameterisation based on moisture balance (Eqs. 8-10) in offline model induces more errors in estimation of updraft mass flux than the more straightforward approach of Eq. (  7 ).
The implementation of this convective parametrisation scheme additionally requires a priori knowledge of convective precipitation rate at the surface and the levels of cloud top and cloud base. These parameters must be supplied by a meteorological reanalysis dataset or calculated using the Kuo parametrisation scheme  (Kuo, 1965 (Kuo, , 1974)). This latter technique has been implemented in the National Center for Atmospheric Research/Pennsylvania State University Mesoscale Model (MM5), as discussed by  Grell et al. (1994). The level of the cloud base (σ b ) is defined as the lowest level where condensation would occur during dry adiabatic ascent, which is often referred to as the lifting condensation level. This level is identified by slightly perturbing the humidity and temperature fields at levels below 700 hPa and determining where condensation would occur if the perturbed air parcel were lifted adiabatically.
Evaluation of convective precipitation data As discussed above, the convective parametrisation scheme additionally requires a priori knowledge of the convective precipitation rate at the surface and the altitudes of cloud top and cloud base. This information may be supplied by a meteorological reanalysis dataset. All meteorological data used in the NIES TM simulations discussed in this paper (except PBL height) are provided by a reanalysis dataset produced by the Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry (CRIEPI). This dataset combines the Japanese 25-yr Reanalysis (JRA-25), which covers the 25-yr time period from 1 January 1979 through 31 December 2004, and the JMA Climate Data Assimilation System (JCDAS), which covers the time period from 1 January 2005 to the present  (Onogi et al.
, 2007). The JRA-25/JCDAS data is provided on a T106 Gaussian horizontal grid (320 × 160 gridcells) with 40 hybrid sigmapressure levels. The JRA-25/JCDAS convective precipitation data is evaluated by comparing it with monthly mean data from the global Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and special product data from the Modern Era Retrospective-analysis for Research and Applications (MERRA). The CMAP dataset has been constructed by merging several individual data sources with different characteristics  (Xie and Arkin, 1997). These data sources include monthly analyses from the Global Precipitation Climatology Centre, observations provided by multiple satellites and precipitation forecasts from the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis.
The MERRA dataset has been produced using the National Aeronautics and Space Administration (NASA) global data assimilation system, which employs NASA Goddard Earth Observing System global atmospheric model version 5 (GEOS-5). Using a non-hydrostatic finite-volume dynamical core coupled with advances in the moist physics and convective parameterization the model has been used to perform cloud-system resolving experiments at resolutions as fine as 3. 5-to 14km globally  (Putman and Suarez, 2011). This data assimilation system incorporates information from a variety of modern observing systems (such as the Earth Observing System, a coordinated series of polar-orbiting and low-inclination satellites that provides long-term global observations of the climate system) into a reanalysis framework for climate applications  (Wong et al.
, 2011). Figure  1  shows zonal mean convective precipitation (CP) and total precipitation (TP) rates (mm d -1 ) from the JRA-25/JCDAS, MERRA and CMAP datasets respectively averaged for June-July-August (JJA) and December-January-February (DJF) 2006. The JRA-25/JCDAS dataset generally captures the latitudinal variation of CP reported in the MERRA and in good agreement with TP from CMAP datasets in tropical regions, where fraction of convective precipitation in total precipitation budget is quite large. JRA-25/JCDAS convective precipitation rate even exceeds total CMAP precipitation. While the estimates from CMAP and MERRA bracket that from JRA-25/JCDAS in mid-latitudes. The JRA-25/JCDAS CP and CMAP TP are consistent in shape and only slightly different in magnitude in the tropics, whereas the MERRA dataset is biased low compared to the other estimates by 2 mm d -1 during both seasons.
All three datasets indicate seasonal and geographic variations in precipitation (Table  1 ). CP and TP is consistently higher in the Northern Hemisphere than in the Southern Hemisphere and also consistently higher during the boreal summer season (JJA) than during boreal winter (DJF). Hot weather during the boreal summer season leads to frequent convective storms in northern temperate latitudes (Fig. 2 ). The largest amounts of CP occur in equatorial areas and over India and Southeast Asia during the monsoon season (Myoung and Nielsen-Gammon, 2010). The potential for DCC over the ocean persists throughout the night, and convective cells over the ocean commonly reach altitudes in the upper troposphere  (Schumacher and Houze, 2003). The seasonal   variability of convective rainfall is small over warm seas and oceans.
Evaluation of the simulated convective mass flux The MERRA special products dataset, which has been developed in conjunction with the chemistry community, provides support for chemistry transport modelling  (Wong et al. , 2011). This dataset includes a number of meteorological fields that are necessary for tracer transport simulations, including fields that describe convective motion (such as upward convective mass flux). This reanalysis dataset can therefore be used to evaluate simulated convective mass fluxes calculated using Eq. (  7 ). However, the MERRA reanalysis also depends on a model that includes a parameterization of convective processes. Although meteorological fields in the reanalysis are optimized against observations they are still not independent of the specific parameterization scheme.
This is therefore more a comparison between the results of different parameterization schemes. The convective mass flux calculated by the proposed convection scheme is generally in good agreement with MERRA year-round in the tropics (Fig. 3 ). This agreement between the two datasets is particularly good over Southeast Asia, the Pacific coast of Central America and the tropical Atlantic Ocean. The proposed convection scheme underesti- mates convective mass fluxes over the oceanic regions of the western coastlines of North and South America, Africa and Australia, and in the Southern Ocean off the northern coast of Antarctica. The differences over mid-latitude oceanic regions are larger in both hemispheres during DJF (boreal winter/austral summer) than during JJA. The agreement between the two datasets is better over the continents.
Both datasets indicate that convective mass fluxes are fairly evenly distributed over land areas, and discrepancies in the simulated intensity of convective mixing are much lower. Convective precipitation rates in the JRA-25/JCDAS dataset used to drive the model and the MERRA dataset agree most closely over the continents and tropical oceans (Fig. 2 ), exactly where the two convective flux datasets agree. The regional mismatches in intensity described above indicate that convective precipitation may not always accompany upward convective mass flux. Equation (  7 ) represents only deep convection accompanied by convective precipitations, however precipitation occurs only when a cloud water threshold is exceeded, so convection may exist without precipitation. Moreover, convective upward and fall of convective precipitations may occur at a different moment of time or in a different grid cell if the convective column moves.
Thus, the proposed convection scheme successfully captures most of the large-scale upward convective mass flux that is accompanied by convective precipitation, but does not appear to reproduce smaller intensity upward fluxes. Bottom panel (Fig. 3e, f ) shows upward convective mass flux calculated by previous version of NIES TM (NIES-05, described by  Maksyutov et al. , 2008)  with the original Kuotype scheme (8-10) using NCEP reanalysis. In general the parameterization is able to reproduce convective mass flux, but underestimates intensity in comparison to MERRA and proposed scheme. It is especially visible in tropical regions, where significant contribution is coming from second term of Eq. (  9 ) the surface evaporation S evap. This term is used to compensate insufficient reproduction of moisture convergence due to imbalance in initial data caused spatial and temporal interpolations.
9c ). Complicating factors in the model-measurement comparison include the strong dependence of observed surface 222 Rn on the structure of the boundary layer, the coarse horizontal resolution of the models and the possible errors in estimation of local emissions in the 222 Rn fluxes used in the simulations  (Jacob et al. , 1997). We have to note that the observed concentration data have high scatter about 2. 0-2. 5 mBq m -3. The majority of the models are successful in reproducing the monthly variations at Cape Grim (Fig. 9d ). The exception is ACCESS. As a coastal site, the monthly variations are determined by meteorology (i. e. the proportion of time that air reaching the site is oceanic or continental in origin). ACCESS is unable to capture this variation since its transport is not nudged to analyses.
Comparison with observed seasonal variations Figure  10  shows the seasonal variation of 222 Rn concentrations at continental sites (Fig. 10a-d ), remote island sites (Fig. 10e-h ) and coastal sites (Fig. 10i-l ). We also analyzed model-observation correlations, to obtain a quantified characteristic of the models performance (Fig. 11 ), despite high models biases and small seasonal cycle of the tracer. The models qualitatively reproduce seasonal variability in radon concentration in Beijing (Fig. 10a ), but the amplitude of this variability is severely underestimated. Zhang et al. (2008)  reported that the annual mean of these measurements is a factor of two higher than the annual mean of measurements taken on the fourth floor of a building in Beijing (16 m above the ground). This discrepancy between observational datasets suggests that the model simulations may also properly characterize the annual mean concentration in Beijing.
Discussion The availability of global meteorological analysis and forecast products at fine resolutions makes it possible to resolve many dynamical features, but the lack of datasets that contain information about sub-grid scale processes creates challenges for the implementation of convective parametrisation schemes. The convective scheme presented in this work relaxes this limitation by linking vertical convective fluxes to convective precipitation rates. The proposed convective scheme can successfully reproduce deep cloud convection, as shown from comparison with MERRA reanalysis, radon vertical and near surface profiles. However, some issues should be discussed. The proposed convection scheme underestimates the convective mass flux across significant areas (Fig. 3 ). The modeled convective rain rate is very different in different reanalysis datasets (  the convection scheme used, the model resolution and other model features.
However, model results agree well with observations at coastal sites, because seasonal cycles at such sites are driven by large-scale circulation. Reproducing the complicated seasonal cycles at continental sites is the most challenging. On the other hand, the model-to-model difference in 222 Rn concentration is still large indicating very different performance of DCC parameterizations. Results of NIES TM with offered parameterisation are consistent with the results of considered TransCom-CH 4 on-line and off-line models. Unfortunately, carrying out a more detailed analysis to check model behavior is complicated due to a lack of observations. Convective precipitation rates and other necessary parameters are calculated explicitly by GCMs and are available in reanalysis datasets at high spatial and temporal resolutions, so the NIES TM with described parameterization scheme may be employed using different reanalysis.
Conclusions In this work, we presented the modified cumulus convection parametrisation scheme, which computes the mass of air transported upward in a cumulus cell using conservation of moisture and a detailed distribution of convective precipitation provided by JRA-25/JCDAS reanalysis dataset. This approach is more straightforward for off-line transport models, than the original Kuo-type scheme which integrates the total horizontal moisture convergence to calculate updraft mass flux, because the new scheme does not require to implement divergence operator to fields distorted by additional spatial and temporal interpolations. The proposed moist convection parametrisation was incorporated into the NIES offline global transport model, with the calculation of convective updraft mass fluxes based on convective precipitation rates taken from the JRA-25/JCDAS reanalysis.
Necessary convective precipitation rate at the surface and the levels of cloud top and cloud base locations of cloud top and cloud base are provided by a meteorological dataset, as in this work, or estimated using the Kuo parametrisation scheme. The JRA-25/JCDAS convective precipitation rate and simulated mass fluxes are compared with observations and reanalysis data. The convective precipitation data is evaluated by comparing it with monthly mean data from CMAP data and MERRA reanalysis. The MERRA analysis was also used to evaluate simulated convective mass fluxes calculated using the proposed scheme. The proposed convection scheme successfully captures most of the intensive upward convective mass flux that is accompanied by convective precipitation. A simulation of the short-lived tracer 222 Rn is used to further evaluate the performance of the cumulus convection scheme.
Acknowledgements. The authors are grateful to  Kai Zhang  ( Pacific Northwest National Laboratory, Richland, USA ) and  Wuhu Feng  ( NCAS, University of Leeds, UK ) for helpful comments and discussions. We thank  Charmaine Franklin  ( CSIRO Marine and Atmospheric Research, Australia ) for helpful comments on the manuscript. Annemarie Fraser  is supported by the  UK Natural Environment Research Council National Centre for Earth Observation. The contribution by the LLNL authors was prepared under Contract  DE-AC52-07NA27344 , with different parts supported by the  IMPACTS  project funded by the  US DOE (BER)  and project ( 07-ERD-064 ) funded by the  LDRD program  at  LLNL. The JRA-25/JCDAS datasets used in this study were provided by the  Japan Meteorological Agency. NIES TM numerical simulations were performed on the  National Institute for Environmental Studies (NIES ) supercomputer system ( NEC SX-8R/128M16 ).
The ACCESS model uses the  Met Office Unified Model ™ (UK)  with a CSIRO land-surface scheme (CABLE). ACCESS and CCAM simulations were undertaken as part of the  Australian Climate Change Science Program  and used the  NCI National Facility in Canberra, Australia. The research leading to the IFS results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7 THEME [SPA. 2011. 1. 5-02 ])under grant agreement no. 283576  in the context of the  MACC-II  project ( Monitoring Atmospheric Composition and Climate -Interim Implementation ). Edited by:  C. Gerbig.
2. 4-13 C Incubation 13 C incubation experiments were conducted for each species and limited nutrient. A photosynthetron (modified from Babin et al. [4] ) was used to perform in situ incubations. A U shaped dimmable fluorescent tube (OSRAM, DULUX L, 2G11, 55W/ 12-950, daylight) produced the light, and the temperature in the photosynthetron was maintained at 18uC by a water circuit. One litre of each culture was inoculated with NaH 13 CO 3 (98 atom %, Sigma-Aldrich) corresponding to an enrichment of about 15% of the dissolved inorganic carbon already present. The inoculated culture was shared among twenty 62 mL culture flasks placed in the photosynthetron. Light intensity in each flask was measured using a micro-spherical quantum sensor (US-SQS; Walz) connected to a LI-COR 1400 data logger, and one flask was maintained in the dark to estimate non-photosynthetic inorganic carbon incorporation.
2. 6-Data Analyses To study the effect of treatments on biological (a*, carbon/Chl a ratio) and photosynthetic parameters (F v /F m , ETR Car max , P Car max ), analyses of variance (ANOVA) were performed using SigmaPlot 11. 0 (Systat Software Inc. Chicago, USA). Analyses of covariance (ANCOVA) were performed to study the linear relationship between ETR Chl and P Chl. ANCOVA was performed using XLStat 2007. Logarithmic regressions were carried on some of the data using SigmaPlot 11. 0 (Systat Software).