International Journal of Marine Science, 2024, Vol.14, No.3, 218-230 http://www.aquapublisher.com/index.php/ijms 223 These SST variations influenced regional climate, highlighting the role of IOD events in climate variability. The study underscores the critical impact of SST anomalies on atmospheric conditions and regional weather patterns. Figure 2 Evolution of SST anomalies (℃) in the TIO in 2016 and 2017 with respect to 1981–2010 reference period (Adopted from Khan et al., 2021) Image caption: (a) yearly mean of SST anomalies in 2016, (b) yearly mean of SST anomalies in 2017. The monthly distribution of SST anomalies in 2016 and 2017 is shown within the map. The two boxes represent WTIO (10° S–10° N, 50–70° E) and ETIO (10° S–Eq, 90–110° E). Data obtained from NOAA OISSTv2.1 (Adopted from Khan et al., 2021) These case studies underscore the critical role of Indo-Pacific ocean circulation in shaping extreme weather events, with significant implications for climate variability and change. 6 Modeling and Prediction of Ocean Circulation 6.1 Numerical modeling techniques Numerical modeling techniques are essential for understanding and predicting ocean circulation patterns in the Indo-Pacific region. These models integrate various data sources, including in situ observations, remote sensing, and palaeo proxy networks, to simulate the complex interactions within the ocean system. For instance, the use of satellite-observed sea surface temperature (SST) data has been instrumental in examining the multi-time scale variabilities of the Indo-Pacific Warm Pool, which is crucial for understanding seasonal and interannual changes in ocean circulation (Yin et al., 2020). Additionally, climate models such as the CESM1 Large Ensemble and CMIP6 models have been employed to project changes in precipitation, low-level winds, and sea-level pressure under global warming scenarios, providing insights into the enhanced interannual variability in the region (Wang et al., 2022). 6.2 Challenges in prediction Despite advancements in numerical modeling, several challenges persist in predicting ocean circulation. One significant challenge is the model biases and intermodel variability, which contribute to uncertainties in projecting climate mode changes in a warming climate (Zheng, 2019). For example, discrepancies between observed and modeled trends in sea-surface temperature and sea-level pressure highlight the limitations of current climate models in accurately reproducing historical climate patterns (Wills et al., 2022). Furthermore, the sparse observational network in certain regions, such as the subsurface of the Indian Ocean, adds to the difficulty in assessing contemporary changes and their attribution to anthropogenic or natural variability (Ummenhofer et al., 2021). The complex interactions between different climate modes, such as ENSO, IOD, and PDO, also complicate the prediction of ocean circulation patterns (Kumar et al., 2021).
RkJQdWJsaXNoZXIy MjQ4ODYzNA==