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CO2-induced heat source changes over the Tibetan Plateau in boreal summer-part II: the effects of CO2 direct radiation and uniform sea surface warming

Abstract

Under the global warming, the influence of increased CO2 on regional climate change is driven by two main effects: CO2 direct radiation and oceanic warming. Based on the outputs of CMIP5 (phase 5 of the Coupled Model Intercomparison Project), the present study found that CO2 direct radiation and uniform oceanic warming are mainly responsible for the heat source (HS) enhancement led by increased CO2 during June–September over the Tibetan Plateau (TP). As CO2 increases, the resulting uniform sea surface warming induces atmospheric warming and increased atmospheric moisture over the TP, which locally enhances the latent heating (LH). In addition, the uniform sea surface warming narrows the land-sea thermal contrast between the Asian continent and the Indo-Pacific and reduces the ascending motion of the air over the TP. This reduction of ascending motion is offsetted by the effect of CO2 direct radiation, which enhances the thermal contrast and the ascending motion. The combined effect of the two causes a generally mild change in ascending motion. Evaporation intensification led by uniform sea surface warming partly contributes to the LH increase. Thus, the changes in LH lead to the enhancement of the TP HS. Additionally, the net radiation of the atmosphere over the TP slightly increases and partly hinders the HS increase, which is mainly associated with the effect of uniform sea surface warming. The leading intermodel spread of the TP HS features an overall positive/negative deviation pattern relative to the multi-model ensemble (MME) mean response caused by the LH diversity, which stems from the uncertainties of uniform sea surface warming and the corresponding temperature response over the TP among the models.

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Acknowledgement

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.” The study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20060501), the National Natural Sciences Foundation of China (Grant No. 41831175), Key Deployment Project of Centre for Ocean Mega-Research of Science, Chinese Academy of Sciences (COMS2019Q03), the National Natural Sciences Foundation of China (Grant No. 41530425) and the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0102).

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Appendix: Comparison of 11-model results with 30-model results

Appendix: Comparison of 11-model results with 30-model results

In the results of the 11 models, the JJAS HS is enhanced in response to increased CO2, which is similar to the results of the 30 models (the model information is listed in Table S1 in the supplemental material). During JJAS, in the MME results of 11 models, the TP is dominated by a significantly increased HS. The largest increase mainly occurs over the southern TP, and the increased HS gradually weakens from south to north. The largest increase (~ 94 W m−2) occurs over the southern TP in July (Fig. S1 in the supplemental material). The results of the 30 models display a similar response. However, the area reaching a 95% significance level is larger than that of the 11-model results, which may be due to the greater number of samples when calculating the significance.

The TP-averaged response of the HS and associated components do not display a significant difference between the 11- and 30-model results. In the MME of the 11-model results, the TP-averaged HS increases by 12.6 W m−2, whereas in the MME of the 30-model results, the HS increases by 16.1 W m−2 (Fig. S2 in the supplemental material). However, the difference does not reach the 95% significance level. For the components of the HS, the differences are even smaller.

Consistent with the 30-model results, the LH is the main contributor to the HS pattern. Table S2 in the supplemental material demonstrates the spatial pattern correlation (of each variable with HS response) and the ratios of the spatial standardized deviation (of each variable to that of HS response). In the MME of 11-model results, the clear-sky HS is the closest to the HS (spatial correlation coefficient 1.00; ratios of the spatial standardized deviation 0.94), indicating that the cloud-radiation feedback is also negligible. In addition to the clear-sky HS, the LH is the closest among the components (spatial correlation coefficient 0.97; ratios of the spatial standardized deviation 0.97), revealing that the LH is the main contributor to the HS pattern. The spatial correlation coefficient and ratios of the spatial standardized deviation of sensible heating at the surface and net radiation flux into the atmosphere are much lower. These findings are similar to those in the 30-model results.

The intermodel spreads of the JJAS HS response over the TP in the 11- and 30-model results are compared. In both the 11- and 30 model results, the leading modes feature generally uniform diversity over the TP, with a maximum over the southern TP. The leading mode of the 11-model results accounts for 46% of the total intermodel variance, whereas that of the 30-model results accounts for 30.7% of the variance. When an intermodel regression of the variables is performed against the normalized leading principle components, the LH displays the most apparent results in both the 11- and 30-model results (Fig. S3 in the supplemental material), indicating that the LH contributes most to the leading intermodel spread of the HS over the TP in both cases. The second mode of the intermodel EOF features a seesaw structure over the central TP and the southeastern TP in both the 11- and 30-model results, which account for 20.3% and 22.9% of the variance, respectively. Consistent with the leading intermodel spread, the LH is also the most important contributor among the components (figures not shown). Thus, consistent with the 30-model results, the LH is mainly responsible for the intermodel spread in the 11-model results.

To summarize, in terms of the spatial pattern, the differences in the area average and intermodel spread of the HS over the TP between the 11-model and 30 model results are insignificant.

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Qu, X., Huang, G. CO2-induced heat source changes over the Tibetan Plateau in boreal summer-part II: the effects of CO2 direct radiation and uniform sea surface warming. Clim Dyn 55, 1631–1647 (2020). https://doi.org/10.1007/s00382-020-05349-5

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Keywords

  • The tibetan plateau
  • Heat source
  • CO2 direct radiation
  • Uniform sea surface warming