An Artificial Neural Networks‐Based Tree Ring Width Proxy System Model for Paleoclimate Data Assimilation

Abstract. We investigate relationships between climate and tree-ring data on a global scale using the process-based Vaganov–Shashkin Lite (VSL) forward model of tree-ring width formation. The VSL model requires as inputs only latitude, monthly mean temperature, and monthly accumulated precipitation. Hence, this simple, process-based model enables ring-width simulation at any location where monthly climate records exist. In this study, we analyse the growth response of simulated tree rings to monthly climate conditions obtained from the CRU TS3.1 data set back to 1901. Our key aims are (a) to assess the VSL model performance by examining the relations between simulated and observed growth at 2287 globally distributed sites, (b) indentify optimal growth parameters found during the model calibration, and (c) to evaluate the potential of the VSL model as an observation operator for data-assimilation-based reconstructions of climate from tree-ring width. The assessment of the growth-onset threshold temperature of approximately 4–6 °C for most sites and species using a Bayesian estimation approach complements other studies on the lower temperature limits where plant growth may be sustained. Our results suggest that the VSL model skilfully simulates site level tree-ring series in response to climate forcing for a wide range of environmental conditions and species. Spatial aggregation of the tree-ring chronologies to reduce non-climatic noise at the site level yielded notable improvements in the coherence between modelled and actual growth. The resulting distinct and coherent patterns of significant relationships between the aggregated and simulated series further demonstrate the VSL model's ability to skilfully capture the climatic signal contained in tree-ring series. Finally, we propose that the VSL model can be used as an observation operator in data assimilation approaches to reconstruct past climate.

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Data assimilation: Particle filter and artificial neural networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/135/1/012073 ◽

2008 ◽

Vol 135 ◽

pp. 012073 ◽

Cited By ~ 2

Author(s):

Helaine Cristina Morais Furtado ◽

Haroldo Fraga de Campos Velho ◽

Elbert Einstein Nehrer Macau

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Data Assimilation ◽

Particle Filter ◽

Artificial Neural

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Data assimilation by artificial neural networks for the global FSU atmospheric model: Surface pressure

2015 Latin America Congress on Computational Intelligence (LA-CCI) ◽

10.1109/la-cci.2015.7435937 ◽

2015 ◽

Cited By ~ 4

Author(s):

Rosangela Cintra ◽

Haroldo de Campos Velho ◽

Juliana Anochi ◽

Steven Cocke

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Data Assimilation ◽

Surface Pressure ◽

Atmospheric Model ◽

Model Surface ◽

Artificial Neural

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Evaluation of a dendroclimatic process-based model (MAIDEN) over the last century using the PAGES2k tree-ring width database

10.5194/egusphere-egu2020-8247 ◽

2020 ◽

Author(s):

Jeanne Rezsöhazy ◽

Hugues Goosse ◽

Joël Guiot

Keyword(s):

Tree Ring ◽

Ring Width ◽

Climatic Conditions ◽

System Model ◽

Test Case ◽

Tree Ring Width ◽

Modeling And Analysis ◽

Temperature And Precipitation ◽

Wide Range ◽

Statistical Approaches

Trees are one of the main archives to reconstruct the climate of the last millennium at high resolution. The links between tree-ring proxies and climate have usually been estimated on the basis of statistical approaches, assuming linear and stationary relationships. Both assumptions can be inadequate and this issue can be overcome by ecophysiological models such as MAIDEN (Modeling and Analysis In DENdroecology), which simulates tree-ring growth starting from temperature and precipitation daily inputs. A protocol for the application of MAIDEN to potentially any site with tree-ring width data in the extratropical region has been developed in Rezs&#246;hazy et al. (2019) (in review). In this study, the applicability of the model has been tested over the twentieth century using as a test case tree-ring observations from twenty-one Eastern Canadian taiga sites and three European sites. The paper highlights the potential of MAIDEN as a complex mechanistic proxy system model to analyse the links between tree growth and climatic conditions in paleoclimatic applications. Following on from this recent work, MAIDEN is here applied to the PAGES2k tree-ring width database over the last century using the protocol developed in Rezs&#246;hazy et al. (2019) (in review). We show how this larger network allows refining our protocol. We identify the regions and sites where MAIDEN can be successfully applied, as well as estimate the uncertainty associated with the use of MAIDEN for a wide range of sites.&#160;Rezs&#246;hazy, J., Goosse, H., Guiot, J., Gennaretti, F., Boucher, E., Andr&#233;, F., and Jonard, M.: Application and evaluation of the dendroclimatic process-based model MAIDEN during the last century in Canada and Europe, Clim. Past Discuss., https://doi.org/10.5194/cp-2019-140, in review, 2019.

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Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model

Advanced Applications for Artificial Neural Networks ◽

10.5772/intechopen.70791 ◽

2018 ◽

Cited By ~ 5

Author(s):

Rosangela Saher Cintra ◽

Haroldo F. de Campos Velho

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Data Assimilation ◽

General Circulation Model ◽

General Circulation ◽

Atmospheric General Circulation Model ◽

Circulation Model ◽

Atmospheric General Circulation ◽

Artificial Neural

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Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind–wave model for wave forecasting

Journal of Hydroinformatics ◽

10.2166/jh.2006.005 ◽

2006 ◽

Vol 8 (1) ◽

pp. 65-76 ◽

Cited By ~ 19

Author(s):

Zhixu Zhang ◽

Chi-Wai Li ◽

Yok-Sheung Li ◽

Yiquan Qi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Data Assimilation ◽

Wave Model ◽

Altimeter Data ◽

Northwestern Pacific ◽

Third Generation ◽

Model Output ◽

Artificial Neural ◽

Wave Forecasting

Although the third-generation formulation of the ocean wave model describes the wave generation, dissipation and nonlinear interaction processes explicitly, many empirical parameters exist in the model which have to be determined experimentally. With the advance in oceanographic remote-sensing techniques, information on oceanic parameters including significant wave height (SWH) can be obtained daily by satellite altimeters. The assimilation of these data into the wave model provides a way of improving the hindcasting results. However, for wave forecasting, no altimeter data exist during the forecasting period, by definition. To improve the forecasting accuracy of the wave model, Artificial Neural Networks (ANN) are introduced to mimic the errors introduced by the wave model. This is achieved by training the ANN using the wave model output as input, and the results after data assimilation as the targeted output. The trained ANN is then used as a post-processor of the output from the wave model. The proposed method has been applied in wave simulation in the northwestern Pacific Ocean. The statistical interpolation method is used to assimilate the altimeter data into the wave model output and a back-propagation ANN is used to mimic the relation between the wave model outputs with or without data assimilation. The results show that an apparent improvement in the accuracy of forecasting can be obtained.

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Artificial Neural Networks as Surrogate Models for Uncertainty Quantification and Data Assimilation in 2-D/3-D Fuel Performance Studies

Journal of Nuclear Engineering ◽

10.3390/jne1010005 ◽

2020 ◽

Vol 1 (1) ◽

pp. 54-62

Author(s):

Carlo Fiorina ◽

Alessandro Scolaro ◽

Daniel Siefman ◽

Mathieu Hursin ◽

Andreas Pautz

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Data Assimilation ◽

Uncertainty Quantification ◽

Monte Carlo Sampling ◽

Fuel Performance ◽

Fast Running ◽

Use Of Data ◽

Artificial Neural ◽

Trained Neural Network

This paper preliminarily investigates the use of data-driven surrogates for fuel performance codes. The objective is to develop fast-running models that can be used in the frame of uncertainty quantification and data assimilation studies. In particular, data assimilation techniques based on Monte Carlo sampling often require running several thousand, or tens of thousands of calculations. In these cases, the computational requirements can quickly become prohibitive, notably for 2-D and 3-D codes. The paper analyses the capability of artificial neural networks to model the steady-state thermal-mechanics of the nuclear fuel, assuming given released fission gases, swelling, densification and creep. An optimized and trained neural network is then employed on a data assimilation case based on the end of the first ramp of the IFPE Instrumented Fuel Assemblies 432.

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Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling

10.5194/cp-2018-120 ◽

2018 ◽

Cited By ~ 1

Author(s):

Robert Tardif ◽

Gregory J. Hakim ◽

Walter A. Perkins ◽

Kaleb A. Horlick ◽

Michael P. Erb ◽

...

Keyword(s):

Tree Ring ◽

Ring Width ◽

Surface Air Temperature ◽

Drought Severity ◽

Last Millennium ◽

Tree Ring Width ◽

Forward Models ◽

Proxy Records ◽

Skill Scores ◽

Paleoclimate Data

Abstract. The Last Millennium Reanalysis utilizes an ensemble methodology to assimilate paleoclimate data for the production of annually resolved climate field reconstructions of the Common Era. Two key elements are the focus of this work: the set of assimilated proxy records, and the forward models that map climate variables to proxy measurements. Results based on an extensive proxy database and seasonal regression-based forward models are compared to the prototype reanalysis of Hakim et al. (2016), which was based on a smaller set of proxy records and simpler proxy models formulated as univariate linear regressions against annual temperature. Validation against various instrumental–era gridded analyses shows that the new reconstructions of surface air temperature, 500 hPa geopotential height and the Palmer Drought Severity Index are significantly improved, with skill scores increasing from 10 % to more than 200 %, depending on the variable and verification measure. Additional experiments designed to isolate the sources of improvement reveal the importance of additional proxy records, including coral records for improving tropical reconstructions; tree-ring-width chronologies, including moisture-sensitive trees, for thermodynamic and hydroclimate variables in mid-latitudes; and tree-ring density records for temperature reconstructions, particularly in high northern latitudes. Proxy forward models that account for seasonal responses, and the dual sensitivity to temperature and moisture characterizing tree-ring-width proxies, are also found to be particularly important. Other experiments highlight the beneficial role of covariance localization on reanalysis ensemble characteristics. This improved paleoclimate data assimilation system served as the basis for the production of the first publicly released NOAA Last Millennium Reanalysis.

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