scholarly journals Deep Learning Optimizes Data-Driven Representation of Soil Organic Carbon in Earth System Model Over the Conterminous United States

2020 ◽  
Vol 3 ◽  
Author(s):  
Feng Tao ◽  
Zhenghu Zhou ◽  
Yuanyuan Huang ◽  
Qianyu Li ◽  
Xingjie Lu ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Earth System Model ◽  
System Model ◽  
Data Driven ◽  
Earth System

scholarly journals CAM-chem: description and evaluation of interactive atmospheric chemistry in the Community Earth System Model

2012 ◽  
Vol 5 (2) ◽  
pp. 369-411 ◽  
Author(s):  
J.-F. Lamarque ◽  
L. K. Emmons ◽  
P. G. Hess ◽  
D. E. Kinnison ◽  
S. Tilmes ◽  
...  
Keyword(s):  
United States ◽  
Atmospheric Chemistry ◽  
Surface Ozone ◽  
The United States ◽  
Earth System Model ◽  
System Model ◽  
Ozone Production ◽  
Positive Bias ◽  
Earth System ◽  
Community Earth System Model

Abstract. We discuss and evaluate the representation of atmospheric chemistry in the global Community Atmosphere Model (CAM) version 4, the atmospheric component of the Community Earth System Model (CESM). We present a variety of configurations for the representation of tropospheric and stratospheric chemistry, wet removal, and online and offline meteorology. Results from simulations illustrating these configurations are compared with surface, aircraft and satellite observations. Major biases include a negative bias in the high-latitude CO distribution, a positive bias in upper-tropospheric/lower-stratospheric ozone, and a positive bias in summertime surface ozone (over the United States and Europe). The tropospheric net chemical ozone production varies significantly between configurations, partly related to variations in stratosphere-troposphere exchange. Aerosol optical depth tends to be underestimated over most regions, while comparison with aerosol surface measurements over the United States indicate reasonable results for sulfate , especially in the online simulation. Other aerosol species exhibit significant biases. Overall, the model-data comparison indicates that the offline simulation driven by GEOS5 meteorological analyses provides the best simulation, possibly due in part to the increased vertical resolution (52 levels instead of 26 for online dynamics). The CAM-chem code as described in this paper, along with all the necessary datasets needed to perform the simulations described here, are available for download at www.cesm.ucar.edu.

scholarly journals Field-warmed soil carbon changes imply high 21st century modeled uncertainty

2018 ◽  
Author(s):  
Katherine Todd-Brown ◽  
Bin Zheng ◽  
Thomas Crowther
Keyword(s):  
Organic Carbon ◽  
Soil Carbon ◽  
21St Century ◽  
Temperature Sensitivity ◽  
Carbon Stock ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Soil Carbon Stock ◽  
Q10 Values

Abstract. The feedback between planetary warming and soil carbon loss has been the focus of considerable scientific attention in recent decades, due to its potential to accelerate anthropogenic climate change. The soil carbon temperature sensitivity is traditionally estimated from short-term respiration measurements – either from laboratory incubations that are artificially manipulated, or field measurements that cannot distinguish between plant and microbial respiration. To address these limitations of previous approaches, we developed a new method to estimate temperature sensitivity (Q10) of soil carbon directly from warming-induced changes in soil carbon stocks measured in 36 field experiments across the world. Variations in warming magnitude and control organic carbon percentage explained much of field-warmed organic carbon percentage (R2 = 0.96), revealing Q10 across sites of 2.2, [1.6, 2.7] 95 % Confidence Interval (CI). When these field-derived Q10 values were extrapolated over the 21st century using a post-hoc correction of 20 CMIP5 Earth system model outputs, the multi-model mean soil carbon stock changes shifted from the previous value of 83 ± 156 Pg-carbon (weighted mean ± 1 SD), to 16 ± 156 Pg-carbon with a Q10 driven 95 % CI of 245 ± 194 to −99 ± 208 Pg-carbon. On average, incorporating the field-derived Q10 values into Earth system model simulations led to reductions in the projected amount of carbon sequestered in the soil over the 21st century. However, the considerable parameter uncertainty led to extremely high variability in soil carbon stock projections within each model; intra-model uncertainty driven by the measured Q10 was as great as that between model variation. This study demonstrates that data integration may not improve model certainty, but instead should strive to capture the variation of the system as well as mean trends.

scholarly journals Field-warmed soil carbon changes imply high 21st-century modeling uncertainty

2018 ◽  
Vol 15 (12) ◽  
pp. 3659-3671 ◽  
Author(s):  
Katherine Todd-Brown ◽  
Bin Zheng ◽  
Thomas W. Crowther
Keyword(s):  
Organic Carbon ◽  
Soil Carbon ◽  
21St Century ◽  
Temperature Sensitivity ◽  
Carbon Stock ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Soil Carbon Stock ◽  
Q10 Values

Abstract. The feedback between planetary warming and soil carbon loss has been the focus of considerable scientific attention in recent decades, due to its potential to accelerate anthropogenic climate change. The soil carbon temperature sensitivity is traditionally estimated from short-term respiration measurements – either from laboratory incubations that are artificially manipulated or from field measurements that cannot distinguish between plant and microbial respiration. To address these limitations of previous approaches, we developed a new method to estimate soil temperature sensitivity (Q10) of soil carbon directly from warming-induced changes in soil carbon stocks measured in 36 field experiments across the world. Variations in warming magnitude and control organic carbon percentage explained much of field-warmed organic carbon percentage (R2 = 0.96), revealing Q10 across sites of 2.2 [1.6, 2.7] 95 % confidence interval (CI). When these field-derived Q10 values were extrapolated over the 21st century using a post hoc correction of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth system model outputs, the multi-model mean soil carbon stock changes shifted from the previous value of 88 ± 153 Pg carbon (weighted mean ± 1 SD) to 19 ± 155 Pg carbon with a Q10-driven 95 % CI of 248 ± 191 to −95 ± 209 Pg carbon. On average, incorporating the field-derived Q10 values into Earth system model simulations led to reductions in the projected amount of carbon sequestered in the soil over the 21st century. However, the considerable parameter uncertainty led to extremely high variability in soil carbon stock projections within each model; intra-model uncertainty driven by the field-derived Q10 was as great as that between model variation. This study demonstrates that data integration should capture the variation of the system, as well as mean trends.

Sensitivity of organic carbon fluxes from Arctic coastal erosion to climate change

2021 ◽  
Author(s):  
David Marcolino Nielsen ◽  
Patrick Pieper ◽  
Victor Brovkin ◽  
Paul Overduin ◽  
Tatiana Ilyina ◽  
...  
Keyword(s):  
Climate Change ◽  
Organic Carbon ◽  
Sea Ice ◽  
Coastal Erosion ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Carbon Release ◽  
Arctic Coast

<p>When unprotected by sea-ice and exposed to the warm air and ocean waves, the Arctic coast erodes and releases organic carbon from permafrost to the surrounding ocean and atmosphere. This release is estimated to deliver similar amounts of organic carbon to the Arctic Ocean as all Arctic rivers combined, at the present-day climate. Depending on the degradation pathway of the eroded material, the erosion of the Arctic coast could represent a positive feedback loop in the climate system, to an extent still unknown. In addition, the organic carbon flux from Arctic coastal erosion is expected to increase in the future, mainly due to surface warming and sea-ice loss. In this work, we aim at addressing the following questions: How is Arctic coastal erosion projected to change in the future? How sensitive is Arctic coastal erosion to climate change?</p><p>To address these questions, we use a 10-member ensemble of climate change simulations performed with the Max Planck Institute Earth System Model (MPI-ESM) for the Coupled Model Intercomparison Project phase 6 (CMIP6) to make projections of coastal erosion at a pan-Arctic scale. We use a semi-empirical approach to model Arctic coastal erosion, assuming a linear contribution of its thermal and mechanical drivers. The pan-Arctic carbon release due to coastal erosion is projected to increase from 6.9 ± 5.4 TgC/year (mean estimate ± two standard deviations from the distribution of uncertainties) during the historical period (mean over 1850 -1950) to between 13.1 ± 6.7 TgC/year and 17.2 ± 8.2 TgC/year in the period 2081-2100 following an intermediate (SSP2.4-5) and a high-end (SSP5.8-5) climate change scenario, respectively. The sensitivity of the organic carbon release from Arctic coastal erosion to climate warming is estimated to range from 1.52 TgC/year/K to 2.79 TgC/year/K depending on the scenario. Our results present the first projections of Arctic coastal erosion, combining observations and Earth system model (ESM) simulations. This allows us to make first-order estimates of sensitivity and feedback magnitudes between Arctic coastal erosion and climate change, which can lay out pathways for future coupled ESM simulations.</p><p> </p>

scholarly journals Predictabilityand feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model

2021 ◽  
Author(s):  
Alexandre Renchon ◽  
Roser Matamala ◽  
Miquel Gonzalez-Meler ◽  
Zoe Cardon ◽  
Sébastien Lacube ◽  
...  
Keyword(s):  
Deep Learning ◽  
Water Cycle ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Water Conductance

Integrating subsea permafrost into an Earth System Model (MPI-ESM)

2020 ◽  
Author(s):  
Stiig Wilkenskjeld ◽  
Paul Overduin ◽  
Frederieke Miesner ◽  
Matteo Puglini ◽  
Victor Brovkin
Keyword(s):  
Global Warming ◽  
Organic Carbon ◽  
Earth System Model ◽  
System Model ◽  
Arctic Shelf ◽  
The Arctic ◽  
Earth System ◽  
Long Time ◽  
Near Future ◽  
Subsea Permafrost

<p>Subsea permafrost on the Arctic Shelf originates as terrestrial permafrost which was submerged by ocean water following sea level rise during deglaciation. The thickness and depth of subsea permafrost are not well known on the circumpolar scale. Subsea frozen sediments contain organic carbon as well as preventing the upward diffusion of carbon-containing greenhouse gases. Thawing of subsea permafrost – which may accelerate as a consequence of global warming – makes this carbon available for release to the ocean-atmosphere system and thus constitutes a positive feedback to global warming. Present estimates of the carbon associated with subsea permafrost range over two orders of magnitude and are thus highly uncertain and the amount of stored organic carbon potentially huge. Due to the long time scales involved in thawing permafrost, subsea permafrost may become – especially in a future with low anthropogenic carbon emissions – a significant contributor to global carbon releases and thus to an enhanced global warming.</p><p> </p><p>The best tool for estimating the effects of future carbon releases are the Earth System Models (ESMs) which, however, are – due to their computational demands – not well suited for the long time scale of build-up and degradation of subsea permafrost. We therefore apply a novel two-model approach. The multiple glacial-cycle model <em>Submarine Permafrost Map (SuPerMap)</em> was used to obtain the pre-industrial distribution of permafrost based on 1D modelling of heat flow driven by glacial, marine and aerial surface upper boundary conditions. This state was then used to initialize JSBACH, the land surface component of the MPI Earth System Model (MPI-ESM), which was extended to allow subsea permafrost applications. JSBACH was used to generate present-day and near-future permafrost thaw by applying historical and future scenario forcings from the MPI-ESM runs performed within the Coupled Model Intercomparison Project, CMIP6. As a first step we here present the modelled physical state (temperature and ice content profiles) of the subsea sediments on the Arctic Shelf in the pre-industrial and present states as well as in the near future. SuPerMap generated a region of cryotic (<0°C) sediment on the Arctic Shelf of 2.5 million km<sup>2</sup>, more than 80% of which lay north of Eastern Siberia. In the JSBACH simulations, permafrost thawing rates accelerate after the mid-20<sup>th</sup> century. From about 2060 onwards, the choice of shared social-economic pathway (SSP) determines the rate of thaw<!-- Maybe add a sentence about how these results will make the world more wonderful – i.e. the first ESM with ocean-subsea permafrost interactions. --> and up to about 1/3 of the pre-industrial cryotic area is lost before year 2100. Regional aspects of the SSP projections will be presented.</p>

Korea Institute of Ocean Science and Technology Earth System Model and Its Simulation Characteristics

2021 ◽  
Author(s):  
Gyundo Pak ◽  
Yign Noh ◽  
Myong-In Lee ◽  
Sang-Wook Yeh ◽  
Daehyun Kim ◽  
...  
Keyword(s):  
Science And Technology ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Ocean Science

scholarly journals Climate Change Projection in the Twenty-First Century Simulated by NIMS-KMA CMIP6 Model Based on New GHGs Concentration Pathways

2021 ◽  
Author(s):  
Hyun Min Sung ◽  
Jisun Kim ◽  
Sungbo Shim ◽  
Jeong-byn Seo ◽  
Sang-Hoon Kwon ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Changes ◽  
Scientific Information ◽  
Earth System Model ◽  
First Century ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Future Projections ◽  
Twenty First Century

AbstractThe National Institute of Meteorological Sciences-Korea Meteorological Administration (NIMS-KMA) has participated in the Coupled Model Inter-comparison Project (CMIP) and provided long-term simulations using the coupled climate model. The NIMS-KMA produces new future projections using the ensemble mean of KMA Advanced Community Earth system model (K-ACE) and UK Earth System Model version1 (UKESM1) simulations to provide scientific information of future climate changes. In this study, we analyze four experiments those conducted following the new shared socioeconomic pathway (SSP) based scenarios to examine projected climate change in the twenty-first century. Present day (PD) simulations show high performance skill in both climate mean and variability, which provide a reliability of the climate models and reduces the uncertainty in response to future forcing. In future projections, global temperature increases from 1.92 °C to 5.20 °C relative to the PD level (1995–2014). Global mean precipitation increases from 5.1% to 10.1% and sea ice extent decreases from 19% to 62% in the Arctic and from 18% to 54% in the Antarctic. In addition, climate changes are accelerating toward the late twenty-first century. Our CMIP6 simulations are released to the public through the Earth System Grid Federation (ESGF) international data sharing portal and are used to support the establishment of the national adaptation plan for climate change in South Korea.

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