the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ENSO statistics, teleconnections, and atmosphere-ocean coupling in the Taiwan Earth System Model version 1
Yi-Chi Wang
Yu-Luen Chen
Shi-Yu Lee
Huang-Hsiung Hsu
Hsin-Chien Liang
Abstract. This study provides an overview of the fundamental statistics and features of the El Niño Southern Oscillation (ENSO) in the historical simulations of the Taiwan Earth System Model version 1 (TaiESM1). Compared with observations, TaiESM1 can reproduce the fundamental features of observed ENSO signals, including seasonal phasing, thermocline coupling with winds, and atmospheric teleconnection during El Niño events. However, its ENSO response is approximately two times stronger than the observance in the spectrum, resulting in powerful teleconnection signals. The composite of El Niño events shows a strong westerly anomaly extending fast to the east Pacific in the initial stage in March, April, and May, initiating a warm sea surface temperature anomaly (SSTA) there. This warm SSTA maintains through September, October, and November (SON) and gradually diminishes after peaking in December. Analysis of wind stress-SST and heat flux-SST coupling proposes that biased positive SST-shortwave feedback contributes significantly to the strong warm anomaly over the eastern Pacific, especially in SON. Our analysis demonstrates TaiESM1’s capability of simulating ENSO—a significant tropical climate variation on interannual scales with strong global impacts and provides insights into mechanisms in TaiESM1 related to ENSO biases, laying the foundation for future model development to reduce uncertainties in TaiESM1 and climate models in general.
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Yi-Chi Wang et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2023-41', Anonymous Referee #1, 10 Apr 2023
This paper evaluates the Taiwan Earth System Model version 1 (TaiESM1), a recent addition to the class of CMIP models, against various data sets. The model is shown to have a too strong and regular ENSO cycle similar to the model is it derived from (CESM). The model also exhibits the usual systematic errors, like a cold tongue bias and a positive SST-SW radiation feedback in the eastern Pacific which the authors argue accounts for many of the biases in the model ENSO cycle. The paper will be useful addition to the literature for those interested in the analysis of CMIP models, particularly their ENSO variability. There are a few issues that that authors should address in a revision of the manuscript, listed in order of appearance in the paper. The most significant issues are raised in points #3 and #4.
1. Lines 82-83. Is this the resolution of the ocean component, the atmospheric component, or both?
2. Line 102. Why did you use a base period for the model that was different than for the data? What are the differences between the model base period used and a 1970-2000 base period?
3. The authors use a composite of eight observed ENSO events (lines 110-111) to compare with the model output. However this set is comprised of a combination of eastern Pacific (EP) and central Pacific (CP) El Ninos with distinctly different spatial structures (McPhaden et al., 2011; Capotondi et al., 2021). The authors should include a discussion of how well TaiESM1 simulates ENSO diversity as this is one of the most important problems in ENSO research today.
4. Lines 127-28. The comment about diminishing ENSO amplitude is interesting but not further elaborated on. Is the background state in the model changing like in observations, i.e. becoming more La Nina like? We know changes in background state affect ENSO (Fedorov et al., 2021; Cai et al, 2021). This sentence warrants further elaboration since ENSO in a changing climate is also one of the most important problems in ENSO research today.
5. Line 176. I don’t understand the meaning of “fledges” as used here.
6. Lines 321-25. The authors describe what needs to be done to resolve the causes of the biases in this model. But they don’t say that the needed actions will actually be taken. Is there a plan to carry out more analyses to resolve the problems?
References
Cai, W., A. Santoso, M. Collins, et al., 2021: Changing El Niño Southern Oscillation in a warming climate. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00199-z.
Capotondi, A., A.T. Wittenberg, J.-S. Kug, K. Takahashi, and M.J. McPhaden, 2021:ENSO Diversity. In El Niño Southern Oscillation in a Changing Climate (eds M.J. McPhaden, A. Santoso and W. Cai). AGU Monograph, doi:10.1002/9781119548164.ch4.
Fedorov, A. V., S Hu, A. T. Wittenberg, A. F. Z. Levine, and C. Deser, 2021: ENSO Low‐frequency Modulation and Mean State Interactions. AGU Monograph, doi:10.1002/9781119548164.ch8.
McPhaden, M. J., T. Lee, and D. McClurg, 2011: El Niño and its relationship to changing background conditions in the tropical Pacific. Geophys. Res. Lett., 38, L15709, doi:10.1029/2011GL048275.
Citation: https://doi.org/10.5194/gmd-2023-41-RC1 -
RC2: 'Comment on gmd-2023-41', Anonymous Referee #2, 05 May 2023
I don't know how this journal exactly works, but this manuscript does not present a new model development, nor the first decription of this model. However, it provides the first detailed analysis of ENSO characteristics of this model. If this falls within the scope of the journal that would be ok then. Overall the quality of the analysis of ENSO characteristics is very good. However, I have one major concern, and a few minor comments.
Major: The TaiESM1 shows a very reasonable ENSO in various measures, but has a too strong magnitude. The authors provide some analysis why this may be the case and point to the solar radiation flux increase in El Nino events due to stratos cloud reduction. While this impact is clearly identified, I think it cannot be argued that this is the mechnism clearly responsible for too strong ENSO amplitude. For example, Fig. S2 clearly shows that the net surface heatfluxes much more strongly oppose the dynamically induced ENSO SST anomalies in the model compare to observations. Therefore, it seems more likely that other positive feedback that lead to stonger westerly wind anomalies in the central Pacific are relevant. More analysis is needed here. Perhaps looking at thermocline structure. Overall, while the shown solar radiation positive feedback is certainly there, other heatflux feedback do overcompensate this, leading to a strong negative net heatflux feedback. The authors have to make more effort if they want to convince that this solar radiation feedback is working.
Minor:
1. Line 128: Perhaps also quote the observed Nino3.4 standard deviation in the Satellite period (1981 to 2022), which would be substantially larger than 0.84 and closer to the TaiESM1.
2. Fig. 4: What is the box shown and why?
3. Fig. 6: Also mention the dominance of IOD-like response in modelin DJF compared to obs, where we see already the dominance of an IOBM response.
4. Fig. 8: Please provide an additional (supplementary) figure which shows the equatorial temperature structure in a longiture-depth plot to also see the thermocline structure. Perhaps also zonal currents could be shown there.
Citation: https://doi.org/10.5194/gmd-2023-41-RC2
Yi-Chi Wang et al.
Data sets
CMIP6.CMIP.AS-RCEC.TaiESM1.historical Wei-Liang Lee, Hsin-Chien Liang https://doi.org/10.22033/ESGF/CMIP6.9755
Model code and software
post-processing code for "ENSO statistics, teleconnections, and atmosphere-ocean coupling in the Taiwan Earth System Model version 1"" Yu-Luen Chen https://doi.org/10.5281/zenodo.7740033
Taiwan Earth System Model v1.0.0 Wei-Liang Lee et al. https://doi.org/10.5281/zenodo.3626654
Yi-Chi Wang et al.
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