Monopole Mode of Precipitation in East Asia Modulated by the South China Sea Over the Last Four Centuries

Abstract

Precipitation in East Asia affects one quarter of the global population. However, the mechanisms governing precipitation changes at the century scale remain unclear. Reconstructions of warm season precipitation over the last 531 years show that the dominant mode of variability is a monopole covering most of China. However, this mode is mostly absent from Coupled Model Intercomparison Project Phase 5 results. In contrast, experiments using data assimilation reproduce this monopole mode well. Results show that sea surface temperature in the South China Sea is a major driver of the monopole mode of precipitation via a Gill-type response. Warm sea surface temperatures induce a distinct baroclinic structure over the central part of eastern China comprising a low-pressure cyclone in the lower troposphere and a high-pressure anticyclone in the upper troposphere with rising airflow, resulting in water vapor convergence and increased precipitation in East Asia.Plain Language Summary Precipitation in East Asia affects more than one billion people.Although recent changes in precipitation depend on latitude, reconstructions based on paleoclimate archives show that over the last four centuries, East Asia has been characterized by a spatially homogeneous change in precipitation. However, this feature is not present in current climate model simulations. Here, we reproduce this monopole pattern using a combination of model results and observational data using a data assimilation approach and identify the physical processes that lead to this pattern. The observed increase in precipitation over China is due to increased water transport and rising air in this region. These atmospheric circulation changes are linked to a heat source at the surface of the South China Sea caused by higher water temperatures. This work demonstrates that the dominant mode of precipitation variability in East Asia over the past four centuries may differ from the current mode. the annual mean (July-June) Niño 3.4 region SST anomalies and the data assimilation-based and proxybased reconstructed MJJAS precipitation indicates a weak north-south dipole ( Figure S18). ConclusionsThe dominant monopole mode of precipitation variability over the period 1470-1849 found in proxybased reconstructions in East Asia is reproduced by combining precipitation data and model results from CESM-LME applying a data assimilation technique based on a particle filter method. The EOF main loading of the monopole mode is located in the middle and lower reaches of the Yangtze and Yellow Rivers. This suggests that the monopole mode of the precipitation conforms to physical climate laws used by climate models. Based on an atmospheric circulation analysis, the monopole mode of MJJAS precipitation variability over East Asia is linked with warm SST anomalies in the SCS, which leads to a distinct baroclinic structure over the central part of eastern China via a Gill-type response. Correspondingly, local rising airflow, more water vapor convergence, a greater cloud fraction, and more prec...

Diverse Regional Sensitivity of Summer Precipitation in East Asia to Ice Volume, CO2 and Astronomical Forcing

“…Many studies show that the spatial variability of the summer precipitation in East Asia at present-day and in the last 500 years is characterized by a dipolar or tripolar mode pattern (e.g., Shi et al, 2019). Due to the strong regional dependence of the EASM (Wang, B., et al, 2008), in this study the East Asia (15°N-40°N and 105°E−120°E) is divided into five sub-regions every 5° along the meridian.…”

Interdecadal to Multidecadal Variability of East Asian Summer Monsoon Over the Past Half Millennium

“…A weight is calculated for each particle depending on its likelihood computed from the difference between the precipitation obtained in this simulation and the proxy‐based precipitation for China from the extended summer MJJAS precipitation field reconstruction using the OIE method (Shi, Zhao, et al., 2017). This weight is then used for all the simulated variables to obtain the other reconstructions for each of them (e.g., wind velocity and temperature) from the CESM‐LME simulation, the mean reconstruction being simply the weighted mean of the particles (Shi et al., 2019). This data assimilation‐based reconstruction can accurately reproduce the precipitation pattern and corresponding circulation information (Shi et al., 2019).…”

“…This weight is then used for all the simulated variables to obtain the other reconstructions for each of them (e.g., wind velocity and temperature) from the CESM‐LME simulation, the mean reconstruction being simply the weighted mean of the particles (Shi et al., 2019). This data assimilation‐based reconstruction can accurately reproduce the precipitation pattern and corresponding circulation information (Shi et al., 2019). Correspondingly, the data assimilation‐based zonal wind anomalies at 850 hPa is used to calculate the data assimilation‐based WF EASM index, which is compared with the proxy‐based reconstructed WF EASM index to define the strong/weak monsoon events.…”

500‐Year Periodic Vegetation and Monsoonal Climate Oscillations During the Last Deglaciation in East Asia

“…Holocene climate records from East Asia indicate that the ∼500-yr monsoon cyclicity is triggered by ENSO variability on the centennial scale (Xu et al, 2019(Xu et al, , 2020Zhu et al, 2017). This phenomenon can be explained by ENSO changes on the interannual scale, as revealed by modern observations and models (Chen et al, 2019;Shi et al, 2019;Wang et al, 2000Wang et al, , 2017. The mechanism responsible for this forcing is that high-frequency El Niño/La Niña events on the interannual scale can: (a) weaken/strengthen the trade winds and lower/raise the sea surface temperature (SST) of the Indo-Pacific warm pool and northwest Pacific; (b) trigger south-east/north-west movements of the western Pacific subtropical high (WPSH), with changes in the western Pacific anticyclone; and (c) weaken/strengthen the EASM intensity (Huang, 2004;Wang et al, 2000Wang et al, , 2017.…”