scholarly journals Enhancement for bitwise identical reproducibility of Earth system modeling on the C-Coupler platform

2015 ◽  
Vol 8 (3) ◽  
pp. 2403-2435 ◽  
Author(s):  
L. Liu ◽  
R. Li ◽  
C. Zhang ◽  
G. Yang ◽  
B. Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Numerical Simulation ◽  
System Modeling ◽  
Critical Role ◽  
Model Development ◽  
Identical Result ◽  
Earth System ◽  
Software Environment ◽  
Earth System Modeling ◽  
Software Platforms

Abstract. Reliable numerical simulation plays a critical role in climate change study. The reliability includes bitwise identical reproducibility, i.e. bitwise identical result of numerical simulation can be reproduced. It is important to Earth system modeling and has already been used intra modeling groups for the model development. However, it is rarely considered in a wider range even worldwide. To help achieve the worldwide bitwise identical reproducibility, we introduce the detailed implementations for the bitwise identical reproducibility on the Community Coupler (C-Coupler) platform, a uniform runtime software environment that configures, builds and runs the models in the same manner. Moreover, we share a series of experiences and suggestions regarding the bitwise identical reproducibility. We believe that these implementations, experiences and suggestions can be easily extended to other model software platforms and can prospectively advance the model development and scientific researches in the future.

Changing How Earth System Modeling is Done to Provide More Useful Information for Decision Making, Science, and Society

2014 ◽  
Vol 95 (9) ◽  
pp. 1453-1464 ◽  
Author(s):  
Matthew J. Smith ◽  
Paul I. Palmer ◽  
Drew W. Purves ◽  
Mark C. Vanderwel ◽  
Vassily Lyutsarev ◽  
...  
Keyword(s):  
Climate Model ◽  
Current Knowledge ◽  
System Modeling ◽  
Model Development ◽  
Earth System ◽  
Earth System Modeling ◽  
Sources Of Uncertainty ◽  
Present Bias ◽  
Mitigation And Adaptation ◽  
Central Tenet

New details about natural and anthropogenic processes are continually added to models of the Earth system, anticipating that the increased realism will increase the accuracy of their predictions. However, perspectives differ about whether this approach will improve the value of the information the models provide to decision makers, scientists, and societies. The present bias toward increasing realism leads to a range of updated projections, but at the expense of uncertainty quantification and model tractability. This bias makes it difficult to quantify the uncertainty associated with the projections from any one model or to the distribution of projections from different models. This in turn limits the utility of climate model outputs for deriving useful information such as in the design of effective climate change mitigation and adaptation strategies or identifying and prioritizing sources of uncertainty for reduction. Here we argue that a new approach to model development is needed, focused on the delivery of information to support specific policy decisions or science questions. The central tenet of this approach is the assessment and justification of the overall balance of model detail that reflects the question posed, current knowledge, available data, and sources of uncertainty. This differs from contemporary practices by explicitly seeking to quantify both the benefits and costs of details at a systemic level, taking into account the precision and accuracy with which predictions are made when compared to existing empirical evidence. We specify changes to contemporary model development practices that would help in achieving this goal.

scholarly journals Importance of bitwise identical reproducibility in earth system modeling and status report

2015 ◽  
Vol 8 (6) ◽  
pp. 4375-4400 ◽  
Author(s):  
L. Liu ◽  
S. Peng ◽  
C. Zhang ◽  
R. Li ◽  
B. Wang ◽  
...  
Keyword(s):  
System Modeling ◽  
Model Development ◽  
Large Body ◽  
Scientific Research ◽  
Fundamental Principle ◽  
Status Report ◽  
Earth System ◽  
Earth System Modeling ◽  
Improve Model ◽  
Mean Climate

Abstract. Reproducibility is a fundamental principle of scientific research. Bitwise identical reproducibility, i.e., bitwise computational results can be reproduced, guarantees the reproduction of exactly the same results. Here we show the importance of bitwise identical reproducibility to Earth system modeling but the importance has not yet been widely recognized. Modeled mean climate states, variability and trends at different scales may be significantly changed or even lead to opposing results due to a slight change in the original simulation setting during a reproduction. Out of the large body of Earth system modeling publications, few thoroughly describe the whole original simulation setting. As a result, the reproduction of a particular simulation experiment by fellow scientists heavily depends on the interaction with the original authors, which is often inconvenient or even impossible. We anticipate bitwise identical reproducibility to be promoted as a worldwide standard, to guarantee the independent reproduction of simulation results and to further improve model development and scientific research.

scholarly journals Enhancing reproducibility of numerical simulation result on the C-Coupler platform

2014 ◽  
Vol 7 (4) ◽  
pp. 4429-4461 ◽  
Author(s):  
L. Liu ◽  
R. Li ◽  
C. Zhang ◽  
G. Yang ◽  
B. Wang
Keyword(s):  
Climate Change ◽  
Numerical Simulation ◽  
Critical Role ◽  
Model Development ◽  
Model Simulations ◽  
Numerical Simulation Result ◽  
Climate Change Study ◽  
Runtime Environment ◽  
Technical Reproducibility

Abstract. Reliable numerical simulation plays a critical role in climate change study. The reliability includes the technical reproducibility, i.e. bit-identical results of numerical simulation can be reproduced. It is very important for model development and scientific researches but has not been satisfactorily addressed yet so far. To address the technical reproducibility, necessary information about it is firstly analyzed, and how to enhance it on the Community Coupler (C-Coupler) platform, a uniform runtime environment that can operate various kinds of model simulations in the same manner, is then detailed. Moreover, we share a series of experiences and suggestions with scientists and model groups for achieving the technical reproducibility. We believe that, the proposed implementations, experiences and suggestions can be easily extended to other model platforms, and can prospectively advance model development and scientific researches in future.

scholarly journals Ubiquity of human-induced changes in climate variability

2021 ◽  
Vol 12 (4) ◽  
pp. 1393-1411
Author(s):  
Keith B. Rodgers ◽  
Sun-Seon Lee ◽  
Nan Rosenbloom ◽  
Axel Timmermann ◽  
Gokhan Danabasoglu ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Adaptation ◽  
Southern Oscillation ◽  
System Modeling ◽  
Greenhouse Warming ◽  
Earth System ◽  
Wide Range ◽  
State Changes ◽  
Mean State

Abstract. While climate change mitigation targets necessarily concern maximum mean state changes, understanding impacts and developing adaptation strategies will be largely contingent on how climate variability responds to increasing anthropogenic perturbations. Thus far Earth system modeling efforts have primarily focused on projected mean state changes and the sensitivity of specific modes of climate variability, such as the El Niño–Southern Oscillation. However, our knowledge of forced changes in the overall spectrum of climate variability and higher-order statistics is relatively limited. Here we present a new 100-member large ensemble of climate change projections conducted with the Community Earth System Model version 2 over 1850–2100 to examine the sensitivity of internal climate fluctuations to greenhouse warming. Our unprecedented simulations reveal that changes in variability, considered broadly in terms of probability distribution, amplitude, frequency, phasing, and patterns, are ubiquitous and span a wide range of physical and ecosystem variables across many spatial and temporal scales. Greenhouse warming in the model alters variance spectra of Earth system variables that are characterized by non-Gaussian probability distributions, such as rainfall, primary production, or fire occurrence. Our modeling results have important implications for climate adaptation efforts, resource management, seasonal predictions, and assessing potential stressors for terrestrial and marine ecosystems.

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