Oceanic Drivers and Empirical Prediction of Interannual Rainfall Variability in Late Summer Over Northeast China

Abstract

Northeast China (NEC) is located between the subtropical monsoon and temperate-frigid monsoon regions and exhibits two successive rainy seasons with different natures: the northeast cold vortex rainy season in early summer (May–June) and the monsoon rainy season in late summer (July–August). Summer rainfall over NEC (NECR) has a fundamental influence on society, yet its successful seasonal prediction remains a long-term scientific challenge to current dynamical models. The poor NECR prediction skill is partly attributed to the large NECR variability at both the interannual and interdecadal time scales. Here, we focus on the oceanic drivers of the late summer NECR variability and associated physical processes at interannual time scale. Then, we establish an empirical prediction model to predict the interannual variability of summer NECR at one-month lead time (in June). The analysis of observations spanning 40 years (1963–2002) reveals three physically and synergistically influencing predictors of the late summer NECR interannual variability. Above-normal NECR is preceded in the previous spring by (a) warm sea surface temperature (SST) anomalies in the tropical northern Indian Ocean, (b) a positive thermal contrast tendency in the tropical West–East Pacific SST, and (c) a positive tendency of the North Atlantic tripolar SST. These precursors enhance the anomalous low-level anticyclone over the Northwest Pacific and southerly anomalies over NEC in late summer, which are beneficial to enhancing NECR. An empirical prediction model built on these three predictors achieves a forecast temporal correlation coefficient (TCC) skill of 0.72 for 1961–2019, and a 17-year (2003–2019) independent forecast shows a significant TCC skill of 0.70. The skill is substantially higher than that of five state-of-the-art dynamical models and their ensemble mean for 1979–2019 (TCC=0.24). These results suggest that the proposed empirical model is a very meaningful approach for the prediction of NECR, although the dynamical prediction of NECR has considerable room for improvement.

A hybrid statistical downscaling prediction model with timescale decomposition for summer precipitation over Hainan Island, China

“…This correlation pattern weakens in spring (Figure 3b) but becomes stronger in the tropical North Atlantic (TNA) in summer (Figure 3c). The positive (negative) SST anomalies over the TNA also persist from winter to summer, and these anomalies can enhance (suppress) the western North Pacific anomalous anticyclone (WNPAC) through zonal westward extension of the overturning circulation over the Pacific-Atlantic Ocean (Hong et al, 2014;Zhao et al, 2022Zhao et al, , 2023Zuo et al, 2019Zuo et al, , 2020, which can affect the HNSP A directly. Thus, the JA NAT A predictor reflects the remote and transseasonal impacts of the North Atlantic on HNSP A .…”

“…These results indicated that the SD prediction model outperformed the dynamic model predictions to some extent. Inspired by the timescale decomposition approach, Zhao et al (2022) established an oceanic driver and empirical prediction of interannual precipitation variability in late summer over Northeast China, drastically improving the region's prediction accuracy. Additionally, the hybrid statistical downscaling prediction model (HSDPM), which integrates well-known external forcing signals with effective dynamic model predictions, has recently emerged as a new method for short-term climate prediction.…”

A Physics‐Based Empirical Model for the Seasonal Prediction of the Central China July Precipitation

“…However, state‐of‐the‐art dynamical models commonly exhibit essential biases in simulating the ENSO‐related characteristics largely owing to the common excessive Pacific cold tongue bias (G. Li et al., 2019), seriously limiting the prediction skill of the EASM (Huang et al., 2018; Y. Liu et al., 2019; B. Liu et al., 2021; Zhu et al., 2020). The statistical models have a relatively higher skill in predicting the EASM (B. Liu et al., 2021; Z. Wu et al., 2009; Yim et al., 2016; Zhao et al., 2022). But there still exists a deficient capture for the ENSO characteristics in the statistical models, such as the EDR.…”