Geoscientific Model Development

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Combined list of the recent articles of the journal Geoscientific Model Development and the recent discussion forum Geoscientific Model Development Discussions
Updated: 1 day 2 hours ago

Multi-variate factorisation of numerical simulations

Fri, 06/05/2020 - 17:51
Multi-variate factorisation of numerical simulations
Daniel John Lunt, Deepak Chandan, Harry J. Dowsett, Alan M. Haywood, George M. Lunt, Jonathan C. Rougier, Ulrich Salzmann, Gavin A. Schmidt, and Paul J. Valdes
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2020-69,2020
Preprint under review for GMD (discussion: open, 0 comments)
Often in science we carry out experiments with computers in which several factors are explored. For example, in the field of climate science, how the factors of greenhouse gases, ice, and vegetation affect temperature. We can explore the relative importance of these factors by "swapping in and out" different values of these factors, and can also carry out experiments with many different combinations of these factors. This paper discusses how best to analyse the results from such experiments.

CAPRAM reduction towards an operational multiphase halogen and dimethyl sulfide chemistry treatment in the chemistry transport model COSMO-MUSCAT(5.04e)

Thu, 06/04/2020 - 17:51
CAPRAM reduction towards an operational multiphase halogen and dimethyl sulfide chemistry treatment in the chemistry transport model COSMO-MUSCAT(5.04e)
Erik H. Hoffmann, Roland Schrödner, Andreas Tilgner, Ralf Wolke, and Hartmut Herrmann
Geosci. Model Dev., 13, 2587–2609, https://doi.org/10.5194/gmd-13-2587-2020, 2020
A condensed multiphase halogen and DMS chemistry mechanism for application in chemical transport models has been developed and applied by 2D simulations to explore multiphase marine chemistry above the pristine open ocean. The model simulations have demonstrated the ability of the mechanism in studying aerosol cloud processing effects in the marine atmosphere. First 2D simulations have shown significant differences in the DMS processing under convective and stratiform cloud conditions.

The Flexible Ocean and Climate Infrastructure version 1 (FOCI1): mean state and variability

Wed, 06/03/2020 - 17:51
The Flexible Ocean and Climate Infrastructure version 1 (FOCI1): mean state and variability
Katja Matthes, Arne Biastoch, Sebastian Wahl, Jan Harlaß, Torge Martin, Tim Brücher, Annika Drews, Dana Ehlert, Klaus Getzlaff, Fritz Krüger, Willi Rath, Markus Scheinert, Franziska U. Schwarzkopf, Tobias Bayr, Hauke Schmidt, and Wonsun Park
Geosci. Model Dev., 13, 2533–2568, https://doi.org/10.5194/gmd-13-2533-2020, 2020
A new Earth system model, the Flexible Ocean and Climate Infrastructure (FOCI), is introduced, consisting of a high-top atmosphere, an ocean model, sea-ice and land surface model components. A unique feature of FOCI is the ability to explicitly resolve small-scale oceanic features, for example, the Agulhas Current and the Gulf Stream. It allows to study the evolution of the climate system on regional and seasonal to (multi)decadal scales and bridges the gap to coarse-resolution climate models.

Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0

Wed, 06/03/2020 - 17:51
Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0
Sam J. Silva, Colette L. Heald, and Alex B. Guenther
Geosci. Model Dev., 13, 2569–2585, https://doi.org/10.5194/gmd-13-2569-2020, 2020
Simulating the influence of the biosphere on atmospheric chemistry has traditionally been computationally intensive. We describe a surrogate canopy physics model parameterized using a statistical learning technique and specifically designed for use in large-scale chemical transport models. Our surrogate model reproduces a more detailed model to within 10 % without a large computational demand, improving the process representation of biosphere–atmosphere exchange.

Calibrating soybean parameters in JULES5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O3 experiment

Wed, 06/03/2020 - 17:51
Calibrating soybean parameters in JULES5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O

Image Processing Based Atmospheric River Tracking Method Version 1 (IPART-1)

Wed, 06/03/2020 - 17:51
Image Processing Based Atmospheric River Tracking Method Version 1 (IPART-1)
Guangzhi Xu, Xiaohui Ma, Ping Chang, and Lin Wang
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2020-135,2020
Preprint under review for GMD (discussion: open, 0 comments)
We observed considerable limitations in existing atmospheric river (AR) detection methods, and looked into other disciplines for inspirations of tackling the AR detection problem. A new method is derived from an image-processing technique, and encodes the spatio-temporal scale information of AR systems, which is a key physical ingredient of ARs that is more stable than the vapor flux intensities, making it more suitable for climate scale studies when models often have different biases.

Investigating the sensitivity to resolving aerosol interactions in downscaling regional model experiments with WRFv3.8.1 over Europe

Tue, 06/02/2020 - 17:51
Investigating the sensitivity to resolving aerosol interactions in downscaling regional model experiments with WRFv3.8.1 over Europe
Vasileios Pavlidis, Eleni Katragkou, Andreas Prein, Aristeidis K. Georgoulias, Stergios Kartsios, Prodromos Zanis, and Theodoros Karacostas
Geosci. Model Dev., 13, 2511–2532, https://doi.org/10.5194/gmd-13-2511-2020, 2020
Our study investigates the role of aerosols in the climate of Europe by using a computer model and exploring different aerosol options available in this model as well as different aerosol datasets. Results show that aerosols can have a considerable impact on many aspects of the climate. Aerosols reduce solar radiation and temperature at the surface. Precipitation is not particularly affected in any specific direction. The cloudiness amount change is small. Also, changes in wind pattern are seen.

Adaptive lossy compression of climate model data based on hierarchical tensor with Adaptive-HGFDR (v1.0)

Tue, 06/02/2020 - 17:51
Adaptive lossy compression of climate model data based on hierarchical tensor with Adaptive-HGFDR (v1.0)
Zhaoyuan Yu, Zhengfang Zhang, Dongshuang Li, Wen Luo, Yuan Liu, Uzair Bhatti, and Linwang Yuan
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2020-124,2020
Preprint under review for GMD (discussion: open, 0 comments)
Few methods consider the uneven distribution of compression errors affecting compression quality. Here we develop an adaptive lossy compression method with the stable compression error for earth system model data. The results show that our method has higher compression ratio and more uniform error distributions, compared with other commonly used lossy compression methods, such as the Fixed-Rate Compressed Floating-Point Arrays method.

Role of atmospheric horizontal resolution in simulating tropical and subtropical South American precipitation in HadGEM3-GC31

Tue, 06/02/2020 - 17:51
Role of atmospheric horizontal resolution in simulating tropical and subtropical South American precipitation in HadGEM3-GC31
Paul-Arthur Monerie, Amulya Chevuturi, Peter Cook, Nick Klingaman, and Christopher E. Holloway
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2020-125,2020
Preprint under review for GMD (discussion: open, 0 comments)
In this study, we assess how increasing the horizontal resolution of HadGEM3-GC31 can allow simulating better tropical and subtropical South American precipitation. We compare simulations of HadGEM3-GC3.1, performed at three different horizontal resolutions. We show that increasing resolution allows decreasing precipitation biases over the Andes and north-east Brazil, and, improves the simulation of daily precipitation distribution.

Integrated Modeling of Photosynthesis and Transfer of Energy, Mass and Momentum in the Soil-Plant-Atmosphere Continuum System

Tue, 06/02/2020 - 17:51
Integrated Modeling of Photosynthesis and Transfer of Energy, Mass and Momentum in the Soil-Plant-Atmosphere Continuum System
Yunfei Wang, Yijian Zeng, Lianyu Yu, Peiqi Yang, Christiaan Van de Tol, Huanjie Cai, and Zhongbo Su
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2020-85,2020
Preprint under review for GMD (discussion: open, 0 comments)
This study integrates photosynthesis and transfer of energy, mass, and momentum in the soil-plant-atmosphere continuum system, via a simplified 1D root growth model. The results indicated that the simulation of land surface fluxes was significantly improved due to considering the root water uptake, especially when vegetation is experiencing severe water stress. This finding highlights the importance of enhanced soil heat and moisture transfer on simulating ecosystem functioning.

Multi-Scale Sea Ice Kinematics Modeling with a Grid Hierarchy in Community Earth System Model (version 1.2.1)

Tue, 06/02/2020 - 17:51
Multi-Scale Sea Ice Kinematics Modeling with a Grid Hierarchy in Community Earth System Model (version 1.2.1)
Shiming Xu, Jialiang Ma, Lu Zhou, Yan Zhang, Jiping Liu, and Bin Wang
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2020-160,2020
Preprint under review for GMD (discussion: open, 0 comments)
A multi-resolution tripolar grid hierarchy is constructed and integrated in CESM (version 1.2.1). The resolution range includes 0.45-deg, 0.15-deg, and 0.05-deg. Based on atmospherically forced sea ice experiments, we show that the model simulates reasonable sea ice kinematics and scaling properties. Besides, landfast ice thickness can be systematically shifted due to non-convergent solutions to EVP rheology. This work serve as a framework of multi-scale modeling of ocean and sea ice with CESM.

Correcting a bias in a climate model with an augmented emulator

Fri, 05/29/2020 - 17:51
Correcting a bias in a climate model with an augmented emulator
Doug McNeall, Jonny Williams, Richard Betts, Ben Booth, Peter Challenor, Peter Good, and Andy Wiltshire
Geosci. Model Dev., 13, 2487–2509, https://doi.org/10.5194/gmd-13-2487-2020, 2020
In the climate model FAMOUS, matching the modelled Amazon rainforest to observations required different land surface parameter settings than for other forests. It was unclear if this discrepancy was due to a bias in the modelled climate or an error in the land surface component of the model. Correcting the climate of the model with a statistical model corrects the simulation of the Amazon forest, suggesting that the land surface component of the model is not the source of the discrepancy.

An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models: application to GEOS-Chem version 12.0.0

Thu, 05/28/2020 - 17:51
An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models: application to GEOS-Chem version 12.0.0
Lu Shen, Daniel J. Jacob, Mauricio Santillana, Xuan Wang, and Wei Chen
Geosci. Model Dev., 13, 2475–2486, https://doi.org/10.5194/gmd-13-2475-2020, 2020
Chemical mechanisms in air quality models tend to get more complicated with time, reflecting both increasing knowledge and the need for greater scope. This objectively improves the models but increases the computational burden. In this work, we present an approach that can reduce the computational cost of chemical integration by 30–40 % while maintaining an accuracy better than 1 %. It retains the complexity of the full mechanism where it is needed and preserves full diagnostic information.

Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research

Thu, 05/28/2020 - 17:51
Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research
Robin D. Lamboll, Zebedee R. J. Nicholls, Jarmo S. Kikstra, Malte Meinshausen, and Joeri Rogelj
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2020-138,2020
Preprint under review for GMD (discussion: open, 0 comments)
Many models project how human activity can lead to more or less climate change, but most of these models do not project all climate-relevant emissions, potentially biasing climate projections. This paper outlines a Python package called Silicone which can add missing emissions in a flexible yet high-throughput manner. It does this 'infilling' based on more complete literature projections. It facilitates a more complete understanding of the climate impact of alternative emission pathways.

HydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sources

Wed, 05/27/2020 - 17:51
HydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sources
Harsh Beria, Joshua R. Larsen, Anthony Michelon, Natalie C. Ceperley, and Bettina Schaefli
Geosci. Model Dev., 13, 2433–2450, https://doi.org/10.5194/gmd-13-2433-2020, 2020
We develop a Bayesian mixing model to address the issue of small sample sizes to describe different sources in hydrological mixing applications. Using composite likelihood functions, the model accounts for an often overlooked bias arising due to unweighted mixing. We test the model efficacy using a series of statistical benchmarking tests and demonstrate its real-life applicability by applying it to a Swiss Alpine catchment to obtain the proportion of groundwater recharged from rain vs. snow.

Satellite-derived leaf area index and roughness length information for surface–atmosphere exchange modelling: a case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0

Wed, 05/27/2020 - 17:51
Satellite-derived leaf area index and roughness length information for surface–atmosphere exchange modelling: a case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0
Shelley C. van der Graaf, Richard Kranenburg, Arjo J. Segers, Martijn Schaap, and Jan Willem Erisman
Geosci. Model Dev., 13, 2451–2474, https://doi.org/10.5194/gmd-13-2451-2020, 2020
Chemical transport models (CTMs) are important tools to determine the fate of reactive nitrogen (Nr) emissions. The parameterization of the surface–atmosphere exchange in CTMs is often only linked to fixed, land-use-dependent values. In this paper, we present an approach to derive more realistic, dynamic leaf area index (LAI) and roughness length (

Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x)

Tue, 05/26/2020 - 17:51
Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x)
Vijay S. Mahadevan, Iulian Grindeanu, Robert Jacob, and Jason Sarich
Geosci. Model Dev., 13, 2355–2377, https://doi.org/10.5194/gmd-13-2355-2020, 2020
Accurate climate modeling of coupled Earth systems requires mapping of solution field data between dependent components that use non-matching discrete meshes. While existing workflows provide a pathway to generate the projection weights as an offline step, severe bottlenecks impede flexible setup of high-resolution models. In this paper, we present new algorithmic approaches to simplify the E3SM computational workflow using a scalable software infrastructure to generate the remapping operators.

An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1

Tue, 05/26/2020 - 17:51
An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1
Michael Jähn, Gerrit Kuhlmann, Qing Mu, Jean-Matthieu Haussaire, David Ochsner, Katherine Osterried, Valentin Clément, and Dominik Brunner
Geosci. Model Dev., 13, 2379–2392, https://doi.org/10.5194/gmd-13-2379-2020, 2020
Emission inventories of air pollutants and greenhouse gases are widely used as input for atmospheric chemistry transport models. However, the pre-processing of these data is both time-consuming and requires a large amount of disk storage. To overcome this issue, a Python package has been developed and tested for two different models. There, the inventory is projected to the model grid and scaling factors are provided. This approach saves computational time while remaining numerically equivalent.

Ocean biogeochemistry in the Norwegian Earth System Model version 2 (NorESM2)

Tue, 05/26/2020 - 17:51
Ocean biogeochemistry in the Norwegian Earth System Model version 2 (NorESM2)
Jerry F. Tjiputra, Jörg Schwinger, Mats Bentsen, Anne L. Morée, Shuang Gao, Ingo Bethke, Christoph Heinze, Nadine Goris, Alok Gupta, Yan-Chun He, Dirk Olivié, Øyvind Seland, and Michael Schulz
Geosci. Model Dev., 13, 2393–2431, https://doi.org/10.5194/gmd-13-2393-2020, 2020
Ocean biogeochemistry plays an important role in determining the atmospheric carbon dioxide concentration. Earth system models, which are regularly used to study and project future climate change, generally include an ocean biogeochemistry component. Prior to their application, such models are rigorously validated against real-world observations. In this study, we evaluate the ability of the ocean biogeochemistry in the Norwegian Earth System Model version 2 to simulate various datasets.

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