A new approach for in situ analysis in fully coupled earth system models

The particle hygroscopic growth impacts the optical properties of aerosols and, in turn, affects the aerosol-radiation interaction and calculation of the Earth&#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.The first effort of the AeroCom Phase III &#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).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 &#197;ngstr&#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.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.&#160;Burgos, M.A.&#160;et al.:&#160;A global view on the effect of water uptake on aerosol particle light scattering.&#160;Sci Data&#160;6,&#160;157. https://doi.org/10.1038/s41597-019-0158-7, 2019.Burgos, M.A. et al.: A global model&#8211;measurement evaluation of particle light scattering coefficients at elevated relative humidity, Atmos. Chem. Phys., 20, 10231&#8211;10258, https://doi.org/10.5194/acp-20-10231-2020, 2020.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.

Download Full-text

The Transient Response of the Carbon-Cycle-Climate Continuum to CO2 Emissions is Pathway Dependent

10.5194/egusphere-egu21-15392 ◽

2021 ◽

Author(s):

Alexander J. Winkler ◽

Ranga B. Myneni ◽

Markus Reichstein ◽

Victor Brovkin

Keyword(s):

Carbon Cycle ◽

Emission Rate ◽

Transient Temperature ◽

Emission Rates ◽

Earth System ◽

Max Planck ◽

System Models ◽

The Impact ◽

Fully Coupled ◽

Earth System Models

<div> <div> <div> The prevailing understanding of the carbon-cycle response to anthropogenic CO2 emissions suggests that it depends only on the magnitude of this forcing, not on its timing. However, a recent study (Winkler et al., Earth System Dynamics, 2019) demonstrated that the same magnitude of CO2 forcing causes considerably different responses in various Earth system models when realized following different temporal trajectories. Because the modeling community focuses on concentration-driven runs that do not represent a fully-coupled carbon-cycle-climate continuum, and the experimental setups are mainly limited to exponential forcing timelines, the effect of different temporal trajectories of CO2 emissions in the system is under-explored. Together, this could lead to an incomplete notion of the carbon-cycle response to anthropogenic CO2 emissions. We use the latest CMIP6 version of the Max-Planck-Institute Earth System Model (MPI-ESM1.2) with a fully-coupled carbon cycle to investigate the effect of emission timing in form of four drastically different pathways. All pathways emit an identical total of 1200 Pg C over 200 years, which is about the IPCC estimate to stay below 2 &#176;K of warming, and the approximate amount needed to double the atmospheric CO2 concentration. The four pathways differ only in their CO2 emission rates, which include a constant, a negative parabolic (ramp-up/ramp-down), a linearly decreasing, and an exponentially increasing emission trajectory. These experiments are idealized, but designed not to exceed the observed maximum emission rates, and thus can be placed in the context of the observed system. We find that the resulting atmospheric CO2 concentration, after all the carbon has been emitted, can vary as much as 100 ppm between the different pathways. The simulations show that for pathways, where the system is exposed to higher rates of CO2 emissions early in the forcing timeline, there is considerably less excess CO2 in the atmosphere at the end. These pathways also show an airborne fraction approaching zero in the final decades of the simulation. At this point, the carbon sinks have reached a strength that removes more carbon from the atmosphere than is emitted. In contrast, the exponentially increasing pathway with high CO2 emission rates in the last decades of the simulation, the pathway usually studied, shows a fairly stable airborne fraction. We propose a new general framework to estimate the atmospheric growth rate of CO2 not only as a function of the emission rate, but also include the aspect of time the system has been exposed to excess CO2 in the atmosphere. As a result, the transient temperature response is a function not only of the cumulative CO2 emissions, but also of the time the system was exposed to the excess CO2. We also apply this framework to other Earth system models and observational records of CO2 concentration and emissions. </div> </div> </div><div> <div> <div> The Earth system is currently in a phase of increasing, nearly exponential CO2 forcing. The impact of excess CO2 exposure time could become apparent as we approach the point of maximum CO2 emission rate, affecting the achievability of the climate targets. </div> </div> </div>

Download Full-text

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

10.5194/gmd-2018-179 ◽

2018 ◽

Cited By ~ 1

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.

Download Full-text

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

Ocean Science ◽

10.5194/os-12-561-2016 ◽

2016 ◽

Vol 12 (2) ◽

pp. 561-575 ◽

Cited By ~ 32

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.

Download Full-text

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

Biogeosciences ◽

10.5194/bg-16-917-2019 ◽

2019 ◽

Vol 16 (4) ◽

pp. 917-926 ◽

Cited By ~ 1

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.

Download Full-text

The iron CLAW

Environmental Chemistry ◽

10.1071/en07066 ◽

2007 ◽

Vol 4 (6) ◽

pp. 396 ◽

Cited By ~ 7

Author(s):

Mike Harvey

Keyword(s):

Feedback System ◽

Environmental Context ◽

Sulfur Cycle ◽

Earth System ◽

Phytoplankton Productivity ◽

System Models ◽

Iron Sulfur ◽

Fully Coupled ◽

Earth System Models ◽

Complex Manner

Environmental context. A ‘climate stabilising’ feedback system known as the CLAW hypothesis, which involves the phytoplankton driven influence on cloud reflectivity through the cycling of sulfur was proposed ~20 years ago, and because of its complexity, it remains unproven today. Since the CLAW proposal, experiments that have added iron to the ocean have proven that iron can significantly limit phytoplankton productivity and can also affect the marine sulfur cycle in a complex manner. Because of a range of possible feedbacks between iron, sulfur and climate, it is likely that future advances in understanding the CLAW hypothesis will require a comprehensive process-based description that can be tested in fully coupled earth-system models.

Download Full-text

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

10.5194/bg-2018-342 ◽

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.

Download Full-text

Observing carbon cycle–climate feedbacks from space

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1716613115 ◽

2018 ◽

Vol 115 (31) ◽

pp. 7860-7868 ◽

Cited By ~ 21

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.

Download Full-text

From the last interglacial to the future – new insights from modeling the last glacial-interglacial cycle in PalMod

10.5194/egusphere-egu21-11083 ◽

2021 ◽

Author(s):

Kerstin Fieg ◽

Mojib Latif ◽

Michael Schulz ◽

Tatjana Ilyina

Keyword(s):

Climate Variability ◽

Ice Sheet ◽

Earth System ◽

Glacial Cycle ◽

System Models ◽

Last Glacial ◽

The Future ◽

Fully Coupled ◽

Earth System Models ◽

The Last Glacial

We present new insights from the project PalMod, which started in 2016 and is envisioned to run for a decade. The modelling initiative PalMod aims at filling the long-standing scientific gaps in our understanding of the dynamics and variability of the climate system during the last glacial-interglacial cycle. One of the grand challenges in this context is to quantify the processes that determine the spectrum of climate variability on timescales that range from seasons to millennia. Climatic processes are intimately coupled across these timescales. Understanding variability at any one timescale requires understanding of the whole spectrum. If we could successfully simulate the spectrum of climate variability during the last glacial cycle in Earth system models, would this enable us to more reliably assess the future climate change? Such simulations are necessary to deduce, for example, if a regime shift in climate variability could occur during the next centuries and millennia in response to global warming. PalMod is specifically designed to enhance our understanding of the Earth system dynamics and its variability on timescales up to the multimillennial with complex Earth System Models.The following major goals were achieved up to now:<ul><li>Full coupling of atmosphere, ocean and ice-sheet models, enabling investigation of Heinrich Events and bi-stability of the AMOC, and millennial-scale transient climate-ice sheet simulations.</li> <li>Implementation of a coupled ocean and land biogeochemistry enabling simulations with prognostic atmospheric CO2 concentrations and including improved representation of methane (CH4) in transient deglaciation runs.</li> <li>Systematic comparison of newly compiled proxy data with model simulations.</li> </ul>The major goal for the next two years is to set up the fully coupled physical-biogeochemical model which will be tested for three time periods: deglaciation, glacial inception and Marine Isotope Stage 3 (MIS3). This fully coupled model will be eventually used to simulate the complete glacial cycle and project the climate over the next few millennia.

Download Full-text

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

Geoscientific Model Development ◽

10.5194/gmd-12-233-2019 ◽

2019 ◽

Vol 12 (1) ◽

pp. 233-259 ◽

Cited By ~ 2

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.

Download Full-text