Evaluation of global teleconnections in CMIP6 climate projections using complex networks

Dalelane, Clementine; Winderlich, Kristina

doi:10.5194/esd-14-17-2023

Cited by 16 publications

(11 citation statements)

References 96 publications

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“…Errors related to overestimation/underestimation of links in the ITCZ can also be seen in the degree difference between reanalysis and forecast networks of OLR (Supporting Information Figure S6d). However, as our purpose here is not the evaluation of climate interactions predicted by models (Steinhaeuser and Tsonis, 2014; Boers et al ., 2015; Gregory et al ., 2022; Dalelane et al ., 2023), we do not seek a detailed understanding of the differences between the reanalysis and forecast network connectivity structure. But from our aforementioned discussion, it is clear that the topological structure of the forecast error correlation network of the climate variable indeed highlights the primary source of structured error in that variable, which may not be revealed from the connectivity structure of the variable itself.…”

Section: Resultsmentioning

confidence: 99%

“…The climate network approach has been used to study patterns of climate variability in different climate variables, such as temperature, pressure, geopotential height, wind, and precipitation, at various scales (Tsonis and Roebber, 2004; Yamasaki et al ., 2008; Donges et al ., 2009a; Ludescher et al ., 2013; Radebach et al ., 2013; Runge et al ., 2015; Gelbrecht et al ., 2017; Boers et al ., 2019; Gupta et al ., 2021; Lu et al ., 2022; Gupta et al ., 2023). The methodology has also been used in previous studies for the purpose of model evaluation in order to identify the underestimation or overestimation of statistical links, and hence teleconnection patterns, by comparing the depiction of climate interactions in the reanalysis data with that in the forecasts (Steinhaeuser and Tsonis, 2014; Boers et al ., 2015; Feldhoff et al ., 2015; Di Capua et al ., 2022; Gregory et al ., 2022; Dalelane et al ., 2023).…”

Section: Introductionmentioning

confidence: 99%

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Analysis of spatially coherent forecast error structures

Gupta

Banerjee

Marwan

et al. 2023

Quart J Royal Meteoro Soc

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Understanding error properties is an essential part in numerical weather prediction. Predictable relationship between errors of different regions due to some underlying systematic or random process can give rise to correlated errors. Estimation of error correlation is crucial for improvement of forecasts. However, the size of the corresponding correlation matrix is larger than what is possible to represent on geographical maps in order to diagnose its full spatial variation. Here, we propose a complex network‐based approach to analyse forecast error correlations that enables us to estimate the spatially varying component of the error. A quantitative study of the spatio‐temporal coherent structures of medium‐range forecast errors of different climate variables using network measures can reveal common sources of errors. Such information is crucial, especially in cases such as the outgoing long‐wave radiation, in which errors are correlated across very long distances, indicating an underlying climate mechanism as the source of the error. We show that the spatial patterns of forecast error co‐variability may not be the same as that of the corresponding climate variable itself, thereby implying that the mechanisms behind the correlated errors may be different from the climate processes responsible for the spatio‐temporal interactions of the climate variable. Our results highlight the importance of diagnosing the full spatial variation of error correlations to understand the origin and propagation of forecast errors, and demonstrate complex networks to be a promising diagnostic tool in this regard.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of spatially coherent forecast error structures

Gupta

Banerjee

Marwan

et al. 2023

Quart J Royal Meteoro Soc

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“…For iso-contours in scalar ensemble fields, Ferstl et al [15] assess the spatial correlation of their occurrence at different locations in the domain. Global teleconnections are visualized by Delalene et al [16] to analyze in interannual to decadal climate variability. They use dependence measures to infer spatial functional networks between clustered subdomains, in combination with correlation matrices to visualize such dependencies.…”

Section: Ensemble Correlation Analysismentioning

confidence: 99%

Interactive Focus+Context Rendering for Hexahedral Mesh Inspection

Neuhauser

Wang

Westermann

2021

IEEE Trans. Visual. Comput. Graphics

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Visualizing spatial structures in 3D ensembles is challenging due to the vast amounts of information that need to be conveyed. Memory and time constraints make it unfeasible to pre-compute and store the correlations between all pairs of domain points. We propose the embedding of adaptive correlation sampling into chord diagrams with hierarchical edge bundling to alleviate these constraints. Entities representing spatial regions are arranged along the circular chord layout via a space-filling curve, and Bayesian optimal sampling is used to efficiently estimate the maximum occurring correlation between any two points from different regions. Hierarchical edge bundling reduces visual clutter and emphasizes the major correlation structures. By selecting an edge, the user triggers a focus diagram in which only the two regions connected via this edge are refined and arranged in a specific way in a second chord layout. For visualizing correlations between two different variables, which are not symmetric anymore, we switch to showing a full correlation matrix. This avoids drawing the same edges twice with different correlation values. We introduce GPU implementations of both linear and non-linear correlation measures to further reduce the time that is required to generate the context and focus views, and to even enable the analysis of correlations in a 1000-member ensemble.

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“…The method has been applied to investigate the nonlinear relationship between air pollution and meteorological variables [45], gene-gene interactions [46], etc. Dalelane et al [47] evaluated the global teleconnections in CMIP6 climate projections using the distance correlation. However, the method has not yet been used to describe the nonlinear relationship between SST and EP.…”

Section: Introductionmentioning

confidence: 99%

A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

Yang

Chen

Singh

et al. 2023

Water

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Monthly extreme precipitation (EP) forecasts are of vital importance in water resources management and storage behind dams. Machine learning (ML) is extensively used for forecasting monthly EP, and improvements in model performance have been a popular issue. The innovation of this study is summarized as follows. First, a distance correlation-Pearson correlation (DC-PC) method was proposed to identify the complex nonlinear relationship between global sea surface temperature (SST) and EP and select key input factors from SST. Second, a random forest (RF) model was used for forecasting monthly EP, and the physical mechanism of EP was obtained based on the feature importance (FI) of RF and DC–PC relationship. The middle and lower reaches of the Yangtze River (MLYR) were selected as a case study, and monthly EP in summer (June, July and August) was forecasted. Furthermore, the physical mechanism between key predictors with a large proportion of FI and EP was investigated. Results showed that the proposed model had high accuracy and robustness, in which R2 in the test period was above 0.81, and RMSE as well as MAE were below 10 mm. Meanwhile, the key predictors in the high SST years could cause eastward extension of the South Asian High, westward extension of the Western Pacific Subtropical High, water vapor rising motion and an increase in the duration of atmospheric rivers exceeding 66 h, which lead to increasing EP in the MLYR. The results indicated that the DC–PC method could replace Pearson correlation for investigating the nonlinear relationship between SST and EP, as well as for selecting the factors. Further, the key predictors that account for a large proportion of FI can be used for explaining the physical mechanism of EP and directing forecasts.

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Evaluation of global teleconnections in CMIP6 climate projections using complex networks

Cited by 16 publications

References 96 publications

Analysis of spatially coherent forecast error structures

Analysis of spatially coherent forecast error structures

Interactive Focus+Context Rendering for Hexahedral Mesh Inspection

A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

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