Sintex 4 Layer

[Manric Hock]

Reviewedby: Siva Reddy Sanikommu, King Abdullah University of Science and Technology, Saudi Arabia; F. Feba, University of Hyderabad, India; P. A. Francis, Indian National Centre for Ocean Information Services, India

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In this study, we explored impacts of interannual variations of chlorophyll on seasonal predictions of the tropical Pacific by the SINTEX-F2 dynamical climate prediction system, which is highly skillful at predicting El Nio/Southern Oscillation (ENSO) and other tropical climate phenomena. We conducted twin re-forecast experiments; one system used the observed climatology of chlorophyll to compute the shortwave absorption in the upper ocean, while the other used the observed chlorophyll with year-to-year variations. Although the chlorophyll impacts on predictions of the Nio 3.4 index were limited, improvements are noticed in the predictions of sea surface temperature over the eastern edge of the Western Pacific Warm Pool. This region corresponds to the separation between warm, low-salinity waters of the warm Pool and cold, high-salinity upwelled waters of the Pacific cold tongue in the central-eastern equatorial Pacific. The improvement was very striking in the 2015 case, when a super El Nino occurred.

Phytoplankton and chlorophyll (Chla) can affect the absorption of shortwave radiation and thus the vertical distribution of heat in the upper ocean (Lewis et al., 1983). Some previous works already showed that seasonal cycle of Chla is important for modifying and reducing the bias of annual mean and seasonal cycle of shortwave absorption and, especially in the tropics, where the thermocline depth is shallow, the Chla concentration is large, and the shortwave radiation is strong (Nakamoto et al., 2001; Sweeney et al., 2005; Lptien et al., 2009).

To the best of our knowledge, an exploration of possible impacts of the interannual variations of Chla on seasonal predictions by a dynamical prediction system has not yet been presented, thus it is the focus of this study. We evaluated such a response by using the SINTEX-F2 prediction system, which has demonstrated its outstanding performance of predicting ENSO, Indian Ocean Dipole, and other tropical climate phenomena (see Section Methods).

For the mixed layer heat budget analysis, we used the daily outputs of on-line computation of the terms of the heat budget at the time-stepping by the ocean component of the SINTEX-F2 (NEMO); total rate of change, contributions due to the sum of latent, shortwave, longwave and sensible heat fluxes, the zonal, meridional, and vertical advection, the lateral and vertical diffusion, and the tendency of temperature due to Asselin time filtering (e.g. Jouanno et al., 2011; Madec and the NEMO Team, 2016). The discretized form of the heat budget equations used in the model imposes to compute the entrainment term as the residual of those heat budget terms (Vialard and Delecluse, 1998).

Comparing the observed and predicted time series of the SST averaged in the target region, we found that the difference between the F2 and F2chT was striking in June 2015, which was statistically significant above the 90% confidence levels on a paired t-test (Figures 2B, 4A,B). The observation shows development of negative SST anomaly in June 2015, which was associated with occurrence of a super El Nio (Chen et al., 2017). Different from the observed anomalies, the ensemble mean prediction by the F2 system shows development of positive SST anomaly from January 1st, 2015, which reached the maximum in June 2015. That unrealistic development of the positive SST anomaly was toned down by the F2chT system.

The F2chT still could not capture well the development of the negative SST anomaly as seen in the observation. The observed cooling process in the target region is mainly due to the air-sea coupling dynamical development processes of the 2015 super El Nio (Chen et al., 2017; Ineson et al., 2018). However, the positive SST anomaly in the Nino3.4 region, an important aspect of the occurrence of the 2015 super El Nio, was not predicted well (Figure 2A). The deficiency might be due to the so-called spring prediction barrier of ENSO events (Latif et al., 1998; Ren et al., 2016; Behera et al., 2021). Actually, the SINTEX-F2 model captured the occurrence of the 2015 super El Nio after May 2015, although the amplitude was underestimated in the ensemble mean (Doi et al., 2019a). A strong westerly wind burst activity in 2015 may be important for successful prediction of the 2015 tropical Pacific condition (Ineson et al., 2018). However, the SINTEX-F2 as well as many climate models in the world have significant biases in their representation of the westerly wind burst activity (Tan et al., 2020). We need further efforts to improve the intraseasonal variability in the model (Baba, 2021), which is beyond the scope of this study.

The differences between the two re-forecast experiments disappeared when the model was initialized on July 1st, 2015 (Figure 3B). The SINTETX-F2 ensemble mean has an amplitude underestimation bias of the ENSO development for both experiments (Figure 3). We need further analysis from a viewpoint of probabilistic prediction as well as deterministic prediction for the rare and extreme case dynamics by conducting large ensemble members' re-forecast experiments (Doi et al., 2019a).

In order to present a skill assessment from a view point of impacts of interannual variations of Chla on seasonal predictions, we conducted twin re-forecast experiments with the SINTEX-F2 dynamical seasonal prediction system: one system used the observed climatology of Chla to compute the shortwave absorption in the upper ocean, while the other used the observed Chla with year-to-year (interannual) variations. Although the interannual Chla impacts on predictions of SST in the central-eastern equatorial Pacific were limited, improvements in predictions of SST were found over the eastern edge of the Western Pacific Warm Pool, in particular for the 2015 super El Nio year. The results showed that considering the interannual variation of Chla in a dynamical seasonal prediction system is potentially important for improvement of seasonal climate predictions.

Several mixed-layer processes are found to play a role in the improvements in the SST predictions. Those seem partly related to the damping process of El Nio via Chla. However, the exact contributions of those processes are complicated to be fully understood at this stage.

Several factors could affect the results. For example, although this study used the satellite-observational data of surface Chla concentration, the maximum Chla concentration is actually observed at the subsurface ocean in the tropical Pacific (Le Borgne et al., 2002; Lee et al., 2014; Yasunaka et al., 2021). Developing a sustainable ocean observing system with Biogeochemical (BGC) Argo (Bittig et al., 2019) will shed light on 3-dimentional variations of Chla, which could not be observed only by the satellites. Along with the development of the observational system, a new scheme to compute the shortwave absorption in the upper ocean based on the vertical profile of Chla should be developed. For example, Manizza (2005) uses the entire vertical profile of Chla to compute the induced biological heating at each vertical level. Such a parameterization might enhance the impact of Chla in the tropical Pacific (Park et al., 2014). In addition, a seasonal prediction system based on a global Earth system model to resolve physical-biogeochemical feedbacks via Chla may open a new door to discovery of predictability of variations in the marine system associated with the ENSO, including ocean warming, acidification, deoxygenation, biological production, and biodiversity (e.g. Park et al., 2019). On the other hand, we should be careful of possible skill degradation by those approaches. For example, Lim et al. (2018) demonstrated that an overestimation bias of mean Chla state in the tropics in their earth system model enhanced cold SST bias in the tropical Pacific. Further studies in such research streams are necessary. The tropical Pacific may serve as an optimal test-bed for those studies, because the interannual dynamical variations associated with the ENSO are already predictable with high accuracy relative to other basins.

TD performed the seasonal prediction experiments, analyzed the observation data, and model prediction outputs. Both authors contributed to designing the research, interpreting results, and writing the manuscript.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

We are sincerely grateful to Drs. Wataru Sasaki, Jing-Jia Luo, Sebastian Masson, Andrea Storto, Antonio Navarra, Silvio Gualdi and our European colleagues of INGV/CMCC, L'OCEAN, and MPI for their contributions in developing the prototype prediction system. We would also like to thank three reviewers for their constructive comment and Drs. Toshio Yamagata and Hyung-Gyu Lim for their helpful comments and suggestions. The GrADS software was used for creating the figures and the maps.

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