scholarly journals Call for Papers on Machine Learning and Earth System Modeling

Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
Janni Yuval ◽  
Mike Pritchard ◽  
Pierre Gentine ◽  
Laure Zanna ◽  
Jiwen Fan
Keyword(s):  
Machine Learning ◽  
System Modeling ◽  
Earth System ◽  
Earth System Modeling ◽  
Special Collection ◽  
Applications Of Machine Learning ◽  
New Machine

Contributions are invited to a new journal special collection on the use of new machine learning methodologies and applications of machine learning to Earth system modeling.

Easing and promoting the application of ML and AI in earth system sciences - introducing the KI:STE platform

2021 ◽  
Author(s):  
Thomas Seidler ◽  
Norbert Schultz ◽  
Dr. Markus Quade ◽  
Christian Autermann ◽  
Dr. Benedikt Gräler ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Analysis ◽  
State Of The Art ◽  
System Modeling ◽  
Earth System ◽  
Earth System Modeling ◽  
System Science ◽  
Software Frameworks ◽  
Learning Platform

<p>Earth system modeling is virtually impossible without dedicated data analysis. Typically, data are big and due to the complexity of the system, adequate tools for the analysis lie in the domain of machine learning or artificial intelligence. However, earth system specialists have other expertise than developing and deploying state-of-the art programming code which is needed to efficiently use modern software frameworks and computing resources. In addition, Cloud and HPC infrastructure are frequently needed to run analyses with data beyond Tera- or even Petascale volume, and corresponding requirements on available RAM, GPU and CPU sizes. </p><p>Inside the KI:STE project (www.kiste-project.de), we extend the concepts of an existing project, the Mantik-platform (www.mantik.ai), such that handling of data and algorithms is facilitated for earth system analyses while abstracting technical challenges such as scheduling and monitoring of training jobs and platform specific configurations away from the user.</p><p>The principles for design are collaboration and reproducibility of algorithms from the first data load to the deployment of a model to a cluster infrastructure. In addition to the executive part where code is developed and deployed, the KI:STE project develops a learning platform where dedicated topics in relation to earth system science are systematically and pedagogically presented.</p><p>In this presentation, we show the architecture and interfaces of the KI:STE platform together with a simple example.</p>

scholarly journals Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling

2020 ◽  
Author(s):  
Zhonghua Zheng ◽  
Jeffrey Curtis ◽  
Yu Yao ◽  
Jessica Gasparik ◽  
Valentine Anantharaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
System Modeling ◽  
Global Scale ◽  
Earth System ◽  
Mixing State ◽  
Earth System Modeling ◽  
Submicron Aerosol ◽  
Aerosol Mixing State

scholarly journals Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling

2020 ◽  
Author(s):  
Zhonghua Zheng ◽  
Jeffrey H. Curtis ◽  
Yu Yao ◽  
Jessica T. Gasparik ◽  
Valentine G. Anantharaj ◽  
...  
Keyword(s):  
Machine Learning ◽  
System Modeling ◽  
Global Scale ◽  
Earth System ◽  
Mixing State ◽  
Earth System Modeling ◽  
Submicron Aerosol ◽  
Aerosol Mixing State

scholarly journals A European Network for Earth System Modeling

Eos ◽  
2007 ◽  
Vol 88 (12) ◽  
pp. 143 ◽  
Author(s):  
Sophie Valcke ◽  
Reinhard Budich ◽  
Mick Carter ◽  
Eric Guilyardi ◽  
Marie-Alice Foujols ◽  
...  
Keyword(s):  
System Modeling ◽  
Earth System ◽  
European Network ◽  
Earth System Modeling

scholarly journals Bitwise identical compiling setup: prospective for reproducibility and reliability of Earth system modeling

2016 ◽  
Vol 9 (2) ◽  
pp. 731-748 ◽  
Author(s):  
R. Li ◽  
L. Liu ◽  
G. Yang ◽  
C. Zhang ◽  
B. Wang
Keyword(s):  
System Modeling ◽  
Computer Software ◽  
Scientific Research ◽  
Test Cases ◽  
Earth System ◽  
Earth System Modeling ◽  
Software Bugs ◽  
Fast Development ◽  
Software And Hardware ◽  
Simulation Results

Abstract. Reproducibility and reliability are fundamental principles of scientific research. A compiling setup that includes a specific compiler version and compiler flags is an essential technical support for Earth system modeling. With the fast development of computer software and hardware, a compiling setup has to be updated frequently, which challenges the reproducibility and reliability of Earth system modeling. The existing results of a simulation using an original compiling setup may be irreproducible by a newer compiling setup because trivial round-off errors introduced by the change in compiling setup can potentially trigger significant changes in simulation results. Regarding the reliability, a compiler with millions of lines of code may have bugs that are easily overlooked due to the uncertainties or unknowns in Earth system modeling. To address these challenges, this study shows that different compiling setups can achieve exactly the same (bitwise identical) results in Earth system modeling, and a set of bitwise identical compiling setups of a model can be used across different compiler versions and different compiler flags. As a result, the original results can be more easily reproduced; for example, the original results with an older compiler version can be reproduced exactly with a newer compiler version. Moreover, this study shows that new test cases can be generated based on the differences of bitwise identical compiling setups between different models, which can help detect software bugs in the codes of models and compilers and finally improve the reliability of Earth system modeling.

scholarly journals Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Min Chen ◽  
Chris R. Vernon ◽  
Neal T. Graham ◽  
Mohamad Hejazi ◽  
Maoyi Huang ◽  
...  
Keyword(s):  
Land Use ◽  
Global Change ◽  
System Modeling ◽  
Earth System ◽  
Analysis Model ◽  
Change Analysis ◽  
Rcp Scenarios ◽  
Earth System Modeling ◽  
Modeling Studies ◽  
Important Input

Abstract Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM) and a land use spatial downscaling model, named Demeter, under the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05° × 0.05°) and spreads under a more comprehensive set of SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. We compared our dataset with the Land Use Harmonization version 2 (LUH2) dataset and found our results are in general spatially consistent with LUH2. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts.

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