Preliminary assessment on CMIP6 decadal prediction ability of air temperature over China

doi:10.12006/j.issn.1673-1719.2020.029

Abstract

Abstract:

The prediction ability of air temperature over China is evaluated in this paper based on the outputs of the Canadian CanESM5 model and the Japanese MIROC6 model participating in the Decadal Climate Prediction Project (DCPP) of the sixth Coupled Model Intercomparison Project (CMIP6). Comparing decadal prediction with historical simulation, both models’ decadal hindcasts show higher prediction skill for surface air temperature (SAT), which proves oceanic initialization improves the prediction skill of SAT in China on decadal scale. The models can capture the variation of annual mean temperature, and the prediction skill of seasonal mean temperature is the highest in autumn and comparatively lower in winter. Although both models have good performances in predicting annual and seasonal mean temperatures in various regions of China, the prediction skills are higher in the southern and western China than the northern China. As the lead time increases, the prediction skills of annual, spring and winter mean temperatures decrease, while those of summer and autumn mean temperature increase. The prediction skills of subregions share the same characteristics with the whole country.

Key words: CMIP6, Climate model, Decadal prediction, Near-surface air temperature over China

TANG Zi-Chen, LI Qing-Quan, WANG Li-Juan, WU Li-Quan. Preliminary assessment on CMIP6 decadal prediction ability of air temperature over China[J]. Climate Change Research, 2021, 17(2): 162-174.

Figures/Tables 10

Fig. 1 Sub-region of China

Fig. 2 1961-2010 averaged temperatures of observation (a, d, g, j, m), CanESM5’s historical simulation (b, e, h, k, n) and MIROC6’s decadal hindcasts (c, f, i, l, o) at lead years 1-5 (a-c) annual, (d-f) spring, (g-i) summer, (j-l) autumn, (m-o) winter

Tab.1 Average (1961-2010) pattern correlation coefficient (PCC) of annual and seasonal mean temperatures between model results (decadal and historical experiments of CanESM5 and MIROC6 models) and observations at lead years of 1-5

Fig. 3 Anomaly correlation coefficient (ACC) of annual and seasonal mean temperature between observation and CanESM5 historical experiment (a-e) and decadal reforecast (f-j) at lead years 5-9 (Spotted area denotes passing significant test at 0.1 level) (a, f) annual, (b, g) spring, (c, h) summer, (d, i) autumn, (e, j) winter

Fig. 4 Root mean square error (RMSE) of annual and seasonal mean temperature between observation and MIROC6 historical experiment (a-e) and decadal reforecast (f-j) at lead years 5-9 (a, f) annual, (b, g) spring, (c, h) summer, (d, i) autumn, (e, j) winter

Fig. 5 ACC between annual mean temperature of observation and that of CanESM5 reforecast at lead years (a) 1-5, (b) 2-6, (c) 3-7,(d) 4-8, (e) 5-9, (f) 6-10 (Spotted area denotes passing significant test at 0.01 confident level)

Tab.2 China’s average RMSE of annual mean temperature from model reforecasts at different lead years ℃

Tab.3 China’s average ACC of seasonal mean temperature from model reforecasts at different lead years

Fig. 6 RMSE of seasonal mean temperature of two model’s average reforecast at lead year 1-5 (a) spring, (b) summer, (c) autumn, (d) winter

Fig. 7 ACC between observation temperature and average temperature of two models’ reforecasts in various regions of China (Black chain lines represent thresholds at 0.1 significance level) (a) annual, (b) spring, (c) summer, (d) autumn, (e) winter

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