Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5717-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-5717-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Nov 2021
Research article |
| 08 Nov 2021
| 08 Nov 2021
Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach
Tongtiegang Zhao
CORRESPONDING AUTHOR
Center of Water Resources and Environment, Southern Marine Science
and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering,
Sun Yat-Sen University, Guangzhou, China
Haoling Chen
Center of Water Resources and Environment, Southern Marine Science
and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering,
Sun Yat-Sen University, Guangzhou, China
Quanxi Shao
CSIRO Data61, Australian Resources Research Centre, Bentley, WA,
Australia
Tongbi Tu
Center of Water Resources and Environment, Southern Marine Science
and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering,
Sun Yat-Sen University, Guangzhou, China
Yu Tian
Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing, China
Xiaohong Chen
Center of Water Resources and Environment, Southern Marine Science
and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering,
Sun Yat-Sen University, Guangzhou, China
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Y. Y. Zhang, Q. X. Shao, A. Z. Ye, H. T. Xing, and J. Xia
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Z. Luo, E. Wang, H. Zheng, J. A. Baldock, O. J. Sun, and Q. Shao
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Soil carbon models are primary tools for projecting soil carbon balance under changing environment and management. This study shows that the carbon model produces divergent projections but accurate reproduction of measured soil carbon. This projection uncertainty is mainly due to an insufficient understanding of microbial processes and soil carbon composition. Climate conditions and land management in terms of carbon input also have significant effects.
Y. Y. Zhang, Q. X. Shao, A. Z. Ye, and H. T. Xing
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-9219-2014, https://doi.org/10.5194/hessd-11-9219-2014, 2014
Revised manuscript not accepted
Related subject area
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to the analysis of the extreme events.
This technique comes with advantages with respect to the previous ones
in terms of accuracy, simplicity, and robustness.
B. Asadieh and N. Y. Krakauer
Hydrol. Earth Syst. Sci., 19, 877–891, https://doi.org/10.5194/hess-19-877-2015, https://doi.org/10.5194/hess-19-877-2015, 2015
Short summary
Short summary
We present a systematic comparison of changes in historical extreme precipitation in station observations (HadEX2) and 15 climate models from the CMIP5 (as the largest and most recent sets of available observational and modeled data sets), on global and continental scales for 1901-2010, using both parametric (linear regression) and non-parametric (the Mann-Kendall as well as Sen’s slope estimator) methods, taking care to sample observations and models spatially and temporally in comparable ways.
A. I. J. M. van Dijk, L. J. Renzullo, Y. Wada, and P. Tregoning
Hydrol. Earth Syst. Sci., 18, 2955–2973, https://doi.org/10.5194/hess-18-2955-2014, https://doi.org/10.5194/hess-18-2955-2014, 2014
M. H. J. van Huijgevoort, P. Hazenberg, H. A. J. van Lanen, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 16, 2437–2451, https://doi.org/10.5194/hess-16-2437-2012, https://doi.org/10.5194/hess-16-2437-2012, 2012
D. Brochero, F. Anctil, and C. Gagné
Hydrol. Earth Syst. Sci., 15, 3307–3325, https://doi.org/10.5194/hess-15-3307-2011, https://doi.org/10.5194/hess-15-3307-2011, 2011
D. Brochero, F. Anctil, and C. Gagné
Hydrol. Earth Syst. Sci., 15, 3327–3341, https://doi.org/10.5194/hess-15-3327-2011, https://doi.org/10.5194/hess-15-3327-2011, 2011
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Short summary
This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection using the coefficient of determination. Three cases of attribution are effectively facilitated, which are significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection.
This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts...
