scholarly journals Spatially resolved evaluation of Earth system models with satellite column averaged CO<sub>2</sub>

2020 ◽  
Author(s):  
Bettina K. Gier ◽  
Michael Buchwitz ◽  
Maximilian Reuter ◽  
Peter M. Cox ◽  
Pierre Friedlingstein ◽  
...  
Keyword(s):  
Satellite Data ◽  
Satellite Observations ◽  
Negative Trend ◽  
Earth System ◽  
System Models ◽  
Data Coverage ◽  
Earth System Models ◽  
Satellite Product ◽  
Trend Reversal

Abstract. Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO2 concentrations. By comparing the simulations with satellite observations, in this study we find slight improvements in the ESMs participating in the new Phase 6 (CMIP6) compared to CMIP5. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission driven CMIP5 and CMIP6 simulations with satellite data of column-average CO2 mole fractions (XCO2). The satellite data are a combined data product covering the period 2003­–2014 based on the SCIAMACHY/ENVISAT (2003–2012) and TANSO-FTS/GOSAT (2009–­2014) instruments. In this study the Observations for Model Intercomparisons Project (Obs4MIPs) format data product version 3 (O4Mv3) with a spatial resolution of 5° × 5° and monthly time resolution has been used. Comparisons of XCO2 time series show a large spread among the model ensembles both in CMIP5 and CMIP6, with differences in the absolute concentrations of up to approximately 20 ppmv relative to the satellite observations. The multi-model mean has a bias of approximately +10 and +2 ppmv in CMIP5 and CMIP6, respectively. The derived atmospheric XCO2 growth rate (GR) is typically slightly overestimated in the models, with a slightly better average and lower spread for CMIP6. The interannual variability of the growth rate is well reproduced in the multi-model mean. All models capture the expected increase of the seasonal cycle amplitude (SCA) with increasing latitude, but most models underestimate the SCA. Most models from both ensembles show a positive trend of the SCA over the period 2003–2014, i.e. an increase of the SCA with XCO2, similar to in situ ground-based measurements. In contrast, the combined satellite product shows a negative trend over this period. Any SCA derived from sampled data can only be considered an effective SCA, as sampling can remove the peaks or troughs. This negative trend can be reproduced by the models when sampling them as the observations. The average effective SCA in the models is higher when using the SCIAMACHY/ENVISAT instead of the TANSO-FTS/GOSAT mean data coverage mask, overall leading to a negative trend over the full period similar to the combined satellite product. Models with a larger difference in the average effective SCA between the two coverages also show a stronger trend reversal. Therefore, this trend reversal in the satellite data is due to sampling characteristics, more specifically the different data coverage of the two satellites that can be reproduced by the models if sampled the same way. Overall, the CMIP6 ensemble shows better agreement with the satellite data in all considered quantities (XCO2, GR, SCA and trend in SCA), with the biggest improvement in the mean XCO2 content of the atmosphere. This study shows that the availability of column-integral CO2 from satellite provides a promising new way to evaluate the performance of Earth System Models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations.

scholarly journals Spatially resolved evaluation of Earth system models with satellite column-averaged CO2

2021 ◽  
Author(s):  
Bettina K. Gier ◽  
Michael Buchwitz ◽  
Maximilian Reuter ◽  
Peter M. Cox ◽  
Pierre Friedlingstein ◽  
...  
Keyword(s):  
Satellite Data ◽  
Near Infrared ◽  
Early Period ◽  
Coupled Model ◽  
Negative Trend ◽  
Positive Trend ◽  
Earth System ◽  
Evaluation Tool ◽  
System Models ◽  
Earth System Models

&lt;p&gt;Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO&lt;sub&gt;2&lt;/sub&gt; concentrations. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission-driven CMIP5 and CMIP6 simulations with satellite data of column-average CO&lt;sub&gt;2&lt;/sub&gt; mole fractions (XCO&lt;sub&gt;2&lt;/sub&gt;). XCO&lt;sub&gt;2&lt;/sub&gt; time series show a large spread among the model ensembles both in CMIP5 and CMIP6. Using the satellite observations as reference, the CMIP6 models have a &lt;span&gt;l&lt;/span&gt;ower bias in the the multi-model mean than CMIP5, but the spread remains large. The satellite data are a combined data product covering the period 2003&amp;#8211;2014 based on the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY)/Envisat (2003&amp;#8211;2012) and Thermal And Near infrared Sensor for carbon Observation Fourier transform spectrometer/Greenhouse Gases Observing Satellite (TANSO-FTS/GOSAT) (2009&amp;#8211;2014) instruments. While the combined satellite product shows a strong negative trend of decreasing &lt;span&gt;seasonal cycle amplitude (SCA)&lt;/span&gt; with increasing XCO&lt;sub&gt;2&lt;/sub&gt; in the northern midlatitudes, both CMIP ensembles instead show a non-significant positive trend in the multi-model mean. The negative trend is reproduced by the models when sampling them as the observations, attributing it to sampling characteristics. Applying a mask of the mean data coverage of each satellite to the models, the SCA is higher for the SCIAMACHY/Envisat mask than when using the TANSO-FTS/GOSAT mask. This induces an artificial negative trend when using observational sampling over the full period, as SCIAMACHY/Envisat covers the early period until 2012, with TANSO-FTS/GOSAT measurements starting in 2009. Overall, the CMIP6 ensemble shows better agreement with the satellite data than the CMIP5 ensemble in all considered quantities (mean XCO&lt;sub&gt;2&lt;/sub&gt;, growth rate, SCA and trend in SCA). This study shows that the availability of column-integral CO&lt;sub&gt;2&lt;/sub&gt; from satellite provides a promising new way to evaluate the performance of Earth system models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations.&lt;/p&gt;

scholarly journals Spatially resolved evaluation of Earth system models with satellite column-averaged CO<sub>2</sub>

2020 ◽  
Vol 17 (23) ◽  
pp. 6115-6144
Author(s):  
Bettina K. Gier ◽  
Michael Buchwitz ◽  
Maximilian Reuter ◽  
Peter M. Cox ◽  
Pierre Friedlingstein ◽  
...  
Keyword(s):  
Satellite Data ◽  
Missing Values ◽  
Near Infrared ◽  
Early Period ◽  
Negative Trend ◽  
Positive Trend ◽  
Earth System ◽  
Evaluation Tool ◽  
System Models ◽  
Earth System Models

Abstract. Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO2 concentrations. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission-driven CMIP5 and CMIP6 simulations with satellite data of column-average CO2 mole fractions (XCO2). XCO2 time series show a large spread among the model ensembles both in CMIP5 and CMIP6. Compared to the satellite observations, the models have a bias of +25 to −20 ppmv in CMIP5 and +20 to −15 ppmv in CMIP6, with the multi-model mean biases at +10 and +2 ppmv, respectively. The derived mean atmospheric XCO2 growth rate (GR) of 2.0 ppmv yr−1 is overestimated by 0.4 ppmv yr−1 in CMIP5 and 0.3 ppmv yr−1 in CMIP6 for the multi-model mean, with a good reproduction of the interannual variability. All models capture the expected increase of the seasonal cycle amplitude (SCA) with increasing latitude, but most models underestimate the SCA. Any SCA derived from data with missing values can only be considered an “effective” SCA, as the missing values could occur at the peaks or troughs. The satellite data are a combined data product covering the period 2003–2014 based on the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY)/Envisat (2003–2012) and Thermal And Near infrared Sensor for carbon Observation Fourier transform spectrometer/Greenhouse Gases Observing Satellite (TANSO-FTS/GOSAT) (2009–2014) instruments. While the combined satellite product shows a strong negative trend of decreasing effective SCA with increasing XCO2 in the northern midlatitudes, both CMIP ensembles instead show a non-significant positive trend in the multi-model mean. The negative trend is reproduced by the models when sampling them as the observations, attributing it to sampling characteristics. Applying a mask of the mean data coverage of each satellite to the models, the effective SCA is higher for the SCIAMACHY/Envisat mask than when using the TANSO-FTS/GOSAT mask. This induces an artificial negative trend when using observational sampling over the full period, as SCIAMACHY/Envisat covers the early period until 2012, with TANSO-FTS/GOSAT measurements starting in 2009. Overall, the CMIP6 ensemble shows better agreement with the satellite data than the CMIP5 ensemble in all considered quantities (XCO2, GR, SCA and trend in SCA). This study shows that the availability of column-integral CO2 from satellite provides a promising new way to evaluate the performance of Earth system models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations.

First global assessment of modelled aerosol hygroscopicity in the context of other aerosol optical properties

2021 ◽  
Author(s):  
Maria Ángeles Burgos Simón ◽  
Elisabeth Andrews ◽  
Gloria Titos ◽  
Angela Benedetti ◽  
Huisheng Bian ◽  
...  
Keyword(s):  
Optical Properties ◽  
Light Scattering ◽  
Relative Humidity ◽  
Water Uptake ◽  
Chem Phys ◽  
Earth System ◽  
Aerosol Optical Properties ◽  
System Models ◽  
Earth System Models

&lt;p&gt;The particle hygroscopic growth impacts the optical properties of aerosols and, in turn, affects the aerosol-radiation interaction and calculation of the Earth&amp;#8217;s radiative balance. The dependence of particle light scattering on relative humidity (RH) can be described by the scattering enhancement factor f(RH), defined as the ratio between the particle light scattering coefficient at a given RH divided by its dry value.&lt;/p&gt;&lt;p&gt;The first effort of the AeroCom Phase III &amp;#8211; INSITU experiment was to develop an observational dataset of scattering enhancement values at 26 sites to study the uptake of water by atmospheric aerosols, and evaluate f(RH) globally (Burgos et al., 2019). Model outputs from 10 Earth System Models (CAM, CAM-ATRAS, CAM-Oslo, GEOS-Chem, GEOS-GOCART, MERRAero, TM5, OsloCTM3, IFS-AER, and ECMWF) were then evaluated against this in-situ dataset. Building on these results, we investigate f(RH) in the context of other aerosol optical and chemical properties, making use of the same 10 Earth System Models (ESMs) and in-situ measurements as in Burgos et al. (2020) and Titos et al. (2021).&lt;/p&gt;&lt;p&gt;Given the difficulties of deploying and maintaining instrumentation for long-term, accurate and comprehensive f(RH) observations, it is desirable to find an observational proxy for f(RH). This observation-based proxy would also need to be reproduced in modelling space. Our aim here is to evaluate how ESMs currently represent the relationship between f(RH), scattering &amp;#197;ngstr&amp;#246;m exponent (SAE), and single scattering albedo (SSA). This work helps to identify current challenges in modelling water-uptake by aerosols and their impact on aerosol optical properties within Earth system models.&lt;/p&gt;&lt;p&gt;We start by analyzing the behavior of SSA with RH, finding the expected increase with RH for all site types and models. Then, we analyze the three variables together (f(RH)-SSA-SAE relationship). Results show that hygroscopic particles tend to be bigger and scatter more than non-hygroscopic small particles, though variability within models is noticeable. This relationship can be further studied by relating SAE to model chemistry, by selecting those grid points dominated by a single chemical component (mass mixing ratios &gt; 90%). Finally, we analyze model performance at three specific sites representing different aerosol types: Arctic, marine and rural. At these sites, the model data can be exactly temporally and spatially collocated with the observations, which should help to identify the models which exhibit better agreement with measurements and for which aerosol type.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Burgos, M.A.&amp;#160;et al.:&amp;#160;A global view on the effect of water uptake on aerosol particle light scattering.&amp;#160;Sci Data&amp;#160;6,&amp;#160;157. https://doi.org/10.1038/s41597-019-0158-7, 2019.&lt;/p&gt;&lt;p&gt;Burgos, M.A. et al.: A global model&amp;#8211;measurement evaluation of particle light scattering coefficients at elevated relative humidity, Atmos. Chem. Phys., 20, 10231&amp;#8211;10258, https://doi.org/10.5194/acp-20-10231-2020, 2020.&lt;/p&gt;&lt;p&gt;Titos, G. et al.: A global study of hygroscopicity-driven light scattering enhancement in the context of other in-situ aerosol optical properties, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-1250, in review, 2020.&lt;/p&gt;

scholarly journals Towards in-situ visualization integrated earth system models: RegESM 1.1 regional modelling system

2018 ◽  
Author(s):  
Ufuk Utku Turuncoglu
Keyword(s):  
Data Analysis ◽  
System Model ◽  
Earth System ◽  
System Models ◽  
Modeling Systems ◽  
Temporal And Spatial ◽  
Model Components ◽  
Earth System Models ◽  
Modelling System

Abstract. The data volume being produced by regional and global multi-component earth system models are rapidly increasing due to the improved spatial and temporal resolution of the model components, sophistication of the used numerical models in terms of represented physical processes and their non-linear complex interactions. In particular, very short time steps have to be defined in multi-component and multi-scale non-hydrostatic modelling systems to represent the evolution of the fast-moving processes such as turbulence, extra-tropical cyclones, convective lines, jet streams, internal waves, vertical turbulent mixing and surface gravity waves. Consequently, the used small time steps cause extra computation and disk I/O overhead in the used modelling system even if today's most powerful high-performance computing and data storage systems are being considered. Analysis of the high volume of data from multiple earth system model components at different temporal and spatial resolution also poses a challenging problem to efficiently perform integrated data analysis of the massive amounts of data by relying on the conventional post-processing methods available today. This study basically aims to explore the feasibility and added value of integrating existing in-situ visualization and data analysis methods with the model coupling framework (ESMF) to increase interoperability between multi-component simulation code and data processing pipelines by providing easy to use, efficient, generic and standardized modeling environment for earth system science applications. The new data analysis approach enables simultaneous analysis of the vast amount of data produced by multi-component regional earth system models (atmosphere, ocean etc.) during the run process. The methodology aims to create an integrated modeling environment for analyzing fast-moving processes and their evolution in both time and space to support better understanding of the underplaying physical mechanisms. The state-of-art approach can also be used to solve common problems in earth system model development workflow such as designing new sub-grid scale parametrizations (convection, air–sea interaction etc.) that requires inspecting the integrated model behavior in a higher temporal and spatial scale during the run or supporting visual debugging of the multi-component modeling systems, which usually are not facilitated by existing model coupling libraries and modeling systems.

scholarly journals Carbon-based phytoplankton size classes retrieved via ocean color estimates of the particle size distribution

Ocean Science ◽  
2016 ◽  
Vol 12 (2) ◽  
pp. 561-575 ◽  
Author(s):  
Tihomir S. Kostadinov ◽  
Svetlana Milutinović ◽  
Irina Marinov ◽  
Anna Cabré
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Ocean Color ◽  
Earth System ◽  
System Models ◽  
Size Classes ◽  
Carbon Biomass ◽  
Earth System Models

Abstract. Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the “unit of accounting” in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size – picophytoplankton (0.5–2 µm in diameter), nanophytoplankton (2–20 µm) and microphytoplankton (20–50 µm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield  ∼  0.25 Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the No parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.

scholarly journals Evaluating the simulated mean soil carbon transit times by Earth system models using observations

2019 ◽  
Vol 16 (4) ◽  
pp. 917-926 ◽  
Author(s):  
Jing Wang ◽  
Jianyang Xia ◽  
Xuhui Zhou ◽  
Kun Huang ◽  
Jian Zhou ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Soil Carbon ◽  
Earth System ◽  
Soil C ◽  
Global Database ◽  
System Models ◽  
Transit Times ◽  
Negative Linear Correlation ◽  
Earth System Models

Abstract. One known bias in current Earth system models (ESMs) is the underestimation of global mean soil carbon (C) transit time (τsoil), which quantifies the age of the C atoms at the time they leave the soil. However, it remains unclear where such underestimations are located globally. Here, we constructed a global database of measured τsoil across 187 sites to evaluate results from 12 ESMs. The observations showed that the estimated τsoil was dramatically shorter from the soil incubation studies in the laboratory environment (median = 4 years; interquartile range = 1 to 25 years) than that derived from field in situ measurements (31; 5 to 84 years) with shifts in stable isotopic C (13C) or the stock-over-flux approach. In comparison with the field observations, the multi-model ensemble simulated a shorter median (19 years) and a smaller spatial variation (6 to 29 years) of τsoil across the same site locations. We then found a significant and negative linear correlation between the in situ measured τsoil and mean annual air temperature. The underestimations of modeled τsoil are mainly located in cold and dry biomes, especially tundra and desert. Furthermore, we showed that one ESM (i.e., CESM) has improved its τsoil estimate by incorporation of the soil vertical profile. These findings indicate that the spatial variation of τsoil is a useful benchmark for ESMs, and we recommend more observations and modeling efforts on soil C dynamics in regions limited by temperature and moisture.

scholarly journals Evaluating the simulated mean soil carbon transit times by Earth system models using observations

2018 ◽  
Author(s):  
Jing Wang ◽  
Jianyang Xia ◽  
Xuhui Zhou ◽  
Kun Huang ◽  
Jian Zhou ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Soil Carbon ◽  
Interquartile Range ◽  
Earth System ◽  
Soil C ◽  
Global Database ◽  
System Models ◽  
Transit Times ◽  
Earth System Models

Abstract. One known bias in current Earth system models (ESMs) is the underestimation of global mean soil carbon (C) transit time (τsoil), which quantifies the mean age of the C atoms at the time they leave the soil. However, it remains unclear where such underestimations are located globally. Here, we constructed a global database of measured τsoil across 187 sites to evaluated results from twelve ESMs. The observations showed that the estimated τsoil was dramatically shorter from the soil incubations studies in the laboratory environment (median as 4 with the interquartile range of 1–25 years) than that derived from field in-situ measurements (31 with 5–84 years) with the shifts of stable isotopic C (13C) or the stock-over-flux approach. In comparison with the field observations, the multi-model ensemble simulated a shorter median (19 years) and a smaller spatial variation (interquartile range of 6–28 years) of τsoil across the same site locations. We then found a significant and negative linear correlation between the in-situ measured τsoil and mean annual air temperature, and the underestimations of modeled τsoil are mainly located in cold and dry biomes especially tundra and desert. Furthermore, we showed that one ESM (i.e., CESM) has improved its τsoil estimate by incorporation of the soil vertical profile. These findings indicate that the spatial variation of τsoil is a useful benchmark for ESMs, and we recommend more observation and modeling efforts on soil C dynamics in hydrothermal limited regions.

scholarly journals Observing carbon cycle–climate feedbacks from space

2018 ◽  
Vol 115 (31) ◽  
pp. 7860-7868 ◽  
Author(s):  
Piers J. Sellers ◽  
David S. Schimel ◽  
Berrien Moore ◽  
Junjie Liu ◽  
Annmarie Eldering
Keyword(s):  
Carbon Dioxide ◽  
Earth System ◽  
Climate Feedbacks ◽  
System Models ◽  
Component Processes ◽  
The Impact ◽  
Atmospheric Concentrations ◽  
Earth System Models

The impact of human emissions of carbon dioxide and methane on climate is an accepted central concern for current society. It is increasingly evident that atmospheric concentrations of carbon dioxide and methane are not simply a function of emissions but that there are myriad feedbacks forced by changes in climate that affect atmospheric concentrations. If these feedbacks change with changing climate, which is likely, then the effect of the human enterprise on climate will change. Quantifying, understanding, and articulating the feedbacks within the carbon–climate system at the process level are crucial if we are to employ Earth system models to inform effective mitigation regimes that would lead to a stable climate. Recent advances using space-based, more highly resolved measurements of carbon exchange and its component processes—photosynthesis, respiration, and biomass burning—suggest that remote sensing can add key spatial and process resolution to the existing in situ systems needed to provide enhanced understanding and advancements in Earth system models. Information about emissions and feedbacks from a long-term carbon–climate observing system is essential to better stewardship of the planet.

scholarly journals Toward modular in situ visualization in Earth system models: the regional modeling system RegESM 1.1

2019 ◽  
Vol 12 (1) ◽  
pp. 233-259 ◽  
Author(s):  
Ufuk Utku Turuncoglu
Keyword(s):  
Data Analysis ◽  
Small Time ◽  
Earth System ◽  
System Models ◽  
Modeling Systems ◽  
In Situ Visualization ◽  
Temporal And Spatial ◽  
Model Components ◽  
Earth System Models

Abstract. The data volume produced by regional and global multicomponent Earth system models is rapidly increasing because of the improved spatial and temporal resolution of the model components and the sophistication of the numerical models regarding represented physical processes and their complex non-linear interactions. In particular, very small time steps need to be defined in non-hydrostatic high-resolution modeling applications to represent the evolution of the fast-moving processes such as turbulence, extratropical cyclones, convective lines, jet streams, internal waves, vertical turbulent mixing and surface gravity waves. Consequently, the employed small time steps cause extra computation and disk input–output overhead in the modeling system even if today's most powerful high-performance computing and data storage systems are considered. Analysis of the high volume of data from multiple Earth system model components at different temporal and spatial resolutions also poses a challenging problem to efficiently perform integrated data analysis of the massive amounts of data when relying on the traditional postprocessing methods today. This study mainly aims to explore the feasibility and added value of integrating existing in situ visualization and data analysis methods within the model coupling framework. The objective is to increase interoperability between Earth system multicomponent code and data-processing systems by providing an easy-to-use, efficient, generic and standardized modeling environment. The new data analysis approach enables simultaneous analysis of the vast amount of data produced by multicomponent regional Earth system models during the runtime. The presented methodology also aims to create an integrated modeling environment for analyzing fast-moving processes and their evolution both in time and space to support a better understanding of the underplaying physical mechanisms. The state-of-the-art approach can also be employed to solve common problems in the model development cycle, e.g., designing a new subgrid-scale parameterization that requires inspecting the integrated model behavior at a higher temporal and spatial scale simultaneously and supporting visual debugging of the multicomponent modeling systems, which usually are not facilitated by existing model coupling libraries and modeling systems.

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