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 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 Examining soil carbon uncertainty in a global model: response of microbial decomposition to temperature, moisture and nutrient limitation

2013 ◽  
Vol 10 (6) ◽  
pp. 10229-10269
Author(s):  
J.-F. Exbrayat ◽  
A. J. Pitman ◽  
Q. Zhang ◽  
G. Abramowitz ◽  
Y.-P. Wang
Keyword(s):  
Soil Carbon ◽  
Nutrient Limitation ◽  
Historical Change ◽  
Cmip5 Models ◽  
Earth System ◽  
Microbial Decomposition ◽  
Soil C ◽  
System Models ◽  
C And N ◽  
Earth System Models

Abstract. Reliable projections of future climate require land–atmosphere carbon (C) fluxes to be represented realistically in Earth System Models. There are several sources of uncertainty in how carbon is parameterized in these models. First, while interactions between the C, nitrogen (N) and phosphorus (P) cycles have been implemented in some models, these lead to diverse changes in land–atmosphere fluxes. Second, while the parameterization of soil organic matter decomposition is similar between models, formulations of the control of the soil physical state on microbial activity vary widely. We address these sources uncertainty by implementing three soil moisture (SMRF) and three soil temperature (STRF) respiration functions in an Earth System Model that can be run with three degrees of biogeochemical nutrient limitation (C-only, C and N, and C and N and P). All 27 possible combinations of a SMRF with a STRF and a biogeochemical mode are equilibrated before transient historical (1850–2005) simulations are performed. As expected, implementing N and P limitation reduces the land carbon sink, transforming some regions from net sinks to net sources over the historical period (1850–2005). Differences in the soil C balance implied by the various SMRFs and STRFs also change the sign of some regional sinks. Further, although the absolute uncertainty in global carbon uptake is reduced, the uncertainty due to the SMRFs and STRFs grows relative to the inter-annual variability in net uptake when N and P limitations are added. We also demonstrate that the equilibrated soil C also depend on the shape of the SMRF and STRF. Equilibration using different STRFs and SMRFs and nutrient limitation generates a six-fold range of global soil C that largely mirrors the range in available (17) CMIP5 models. Simulating the historical change in soil carbon therefore critically depends on the choice of STRF, SMRF and nutrient limitation, as it controls the equilibrated state to which transient conditions are applied. This direct effect of the representation of microbial decomposition in Earth System Models adds to recent concerns on the adequacy of these simple representations of very complex soil carbon processes.

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

<p>The particle hygroscopic growth impacts the optical properties of aerosols and, in turn, affects the aerosol-radiation interaction and calculation of the Earth’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.</p><p>The first effort of the AeroCom Phase III – 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).</p><p>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 Ångström 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.</p><p>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 > 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.</p><p> </p><p>Burgos, M.A. et al.: A global view on the effect of water uptake on aerosol particle light scattering. Sci Data 6, 157. https://doi.org/10.1038/s41597-019-0158-7, 2019.</p><p>Burgos, M.A. et al.: A global model–measurement evaluation of particle light scattering coefficients at elevated relative humidity, Atmos. Chem. Phys., 20, 10231–10258, https://doi.org/10.5194/acp-20-10231-2020, 2020.</p><p>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.</p>

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 Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models

2017 ◽  
Vol 14 (22) ◽  
pp. 5143-5169 ◽  
Author(s):  
Sarah E. Chadburn ◽  
Gerhard Krinner ◽  
Philipp Porada ◽  
Annett Bartsch ◽  
Christian Beer ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Nutrient Limitation ◽  
Vascular Plants ◽  
Carbon Stocks ◽  
The Arctic ◽  
Earth System ◽  
Area Index ◽  
System Models ◽  
Soil Carbon Stocks ◽  
Earth System Models

Abstract. It is important that climate models can accurately simulate the terrestrial carbon cycle in the Arctic due to the large and potentially labile carbon stocks found in permafrost-affected environments, which can lead to a positive climate feedback, along with the possibility of future carbon sinks from northward expansion of vegetation under climate warming. Here we evaluate the simulation of tundra carbon stocks and fluxes in three land surface schemes that each form part of major Earth system models (JSBACH, Germany; JULES, UK; ORCHIDEE, France). We use a site-level approach in which comprehensive, high-frequency datasets allow us to disentangle the importance of different processes. The models have improved physical permafrost processes and there is a reasonable correspondence between the simulated and measured physical variables, including soil temperature, soil moisture and snow. We show that if the models simulate the correct leaf area index (LAI), the standard C3 photosynthesis schemes produce the correct order of magnitude of carbon fluxes. Therefore, simulating the correct LAI is one of the first priorities. LAI depends quite strongly on climatic variables alone, as we see by the fact that the dynamic vegetation model can simulate most of the differences in LAI between sites, based almost entirely on climate inputs. However, we also identify an influence from nutrient limitation as the LAI becomes too large at some of the more nutrient-limited sites. We conclude that including moss as well as vascular plants is of primary importance to the carbon budget, as moss contributes a large fraction to the seasonal CO2 flux in nutrient-limited conditions. Moss photosynthetic activity can be strongly influenced by the moisture content of moss, and the carbon uptake can be significantly different from vascular plants with a similar LAI. The soil carbon stocks depend strongly on the rate of input of carbon from the vegetation to the soil, and our analysis suggests that an improved simulation of photosynthesis would also lead to an improved simulation of soil carbon stocks. However, the stocks are also influenced by soil carbon burial (e.g. through cryoturbation) and the rate of heterotrophic respiration, which depends on the soil physical state. More detailed below-ground measurements are needed to fully evaluate biological and physical soil processes. Furthermore, even if these processes are well modelled, the soil carbon profiles cannot resemble peat layers as peat accumulation processes are not represented in the models. Thus, we identify three priority areas for model development: (1) dynamic vegetation including (a) climate and (b) nutrient limitation effects; (2) adding moss as a plant functional type; and an (3) improved vertical profile of soil carbon including peat processes.

scholarly journals Causes of variation in soil carbon predictions from CMIP5 Earth system models and comparison with observations

2012 ◽  
Vol 9 (10) ◽  
pp. 14437-14473 ◽  
Author(s):  
K. E. O. Todd-Brown ◽  
J. T. Randerson ◽  
W. M. Post ◽  
F. M. Hoffman ◽  
C. Tarnocai ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Net Primary Productivity ◽  
Carbon Stocks ◽  
Earth System ◽  
System Models ◽  
Soil Database ◽  
Empirical Estimates ◽  
Global Soil ◽  
Soil Carbon Stocks ◽  
Earth System Models

Abstract. Stocks of soil organic carbon represent a large component of the carbon cycle that may participate in climate change feedbacks, particularly on decadal and century scales. For Earth system models (ESMs), the ability to accurately represent the global distribution of existing soil carbon stocks is a prerequisite for predicting future carbon-climate feedbacks. We compared soil carbon predictions from 16 ESMs to empirical data from the Harmonized World Soil Database (HWSD) and Northern Circumpolar Soil Carbon Database (NCSCD). Model estimates of global soil carbon stocks ranged from 510 to 3050 Pg C, compared to an estimate of 890–1660 Pg C from the HWSD. Model predictions for the high latitudes fell between 60 and 800 Pg C, compared to 380–620 Pg C from the NCSCD and 290 Pg C from the HWSD. This 5.3-fold variation in global soil carbon across models compared to a 3.4-fold variation in net primary productivity (NPP) and a 3.8-fold variation in global soil carbon turnover times. The spatial distribution of soil carbon predicted by the ESMs was not well correlated with the HWSD (Pearson's correlations < 0.4, RMSE 9.4 to 22.8 kg C m−2), although model-data agreement generally improved at the biome scale. There was poor agreement between the HWSD and NCSCD datasets in northern latitudes (Pearson's correlation = 0.33), indicating uncertainty in empirical estimates of soil carbon. We found that a reduced complexity model dependent on NPP and soil temperature explained most of the spatial variation in soil carbon predicted by most ESMs (R2 values between 0.73 and 0.93). This result suggests that differences in soil carbon predictions between ESMs are driven primarily by differences in predicted NPP and the parameterization of soil carbon responses to NPP and temperature not by structural differences between the models. Future work should focus on accurately representing these driving variables and modifying model structure to include additional processes.

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.

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