How much of the interannual variability of East Asian summer rainfall is forced by SST?
- 1.3k Downloads
It is widely accepted that the interannual variability of East Asian summer rainfall is forced by sea surface temperature (SST), and SST anomalies are widely used as predictors of East Asian summer rainfall. But it is still not very clear what percentage of the interannual rainfall variability is contributed by SST anomalies. In this study, Atmospheric general circulation model simulations forced by observed interannual varying SST are compared with those forced by the fixed annual cycle of SST climatology, and their ratios of interannual variance (IAV) are analyzed. The output of 12 models from the 5th Phase of Coupled Model Intercomparison Project (CMIP5) are adopted, and idealized experiments are done by Community Atmosphere Model version 4 (CAM4). Both the multi-model median of CMIP5 models and CAM4 experiments show that only about 18 % of the IAV of rainfall over East Asian land (EAL) is explained by SST, which is significantly lower than the tropical western Pacific, but comparable to the mid-latitude western Pacific. There is no significant difference between the southern part and the northern part of EAL in the percentages of SST contribution. The remote SST anomalies regulates rainfall over EAL probably by modulating the horizontal water vapor transport rather than the vertical motion, since the horizontal water vapor transport into EAL is strongly modulated by SST but the vertical motion over EAL is not. Previous studies argued about the relative importance of tropical Indian Ocean and tropical Pacific Ocean to East Asian summer rainfall anomalies. Our idealized experiments performed by CAM4 suggest that the contributions from these two ocean basins are comparable to each other, both of which account for approximately 6 % of the total IAV of rainfall over EAL.
KeywordsRainfall Interannual variance Sea surface temperature
Summer is the rainy season for East Asia which is affected by the monsoon. The interannual variability of the East Asian summer rainfall is regulated by multiple factors, such as the atmospheric internal dynamics (Lu et al. 2006; Kosaka et al. 2012; Song et al. 2013), the sea surface temperature (SST) forcing (Chang et al. 2000; Wang et al. 2013; Zuo et al. 2013), and land–atmosphere interaction (Zhang et al. 2011; Duan et al. 2012; Li et al. 2015). Among the factors affecting East Asian summer rainfall, it is widely accepted that the most important source of predictability originates from the SST anomalies (Lin et al. 2012; Yim et al. 2014; Wang et al. 2015), especially the SST anomalies (SSTA) directly or indirectly associated with El Nino-Southern Oscillation (ENSO) (Yang et al. 2008, 2012; Zuo et al. 2013; Wang et al. 2013). SST anomalies over some key regions are widely used as predictors for East Asian summer rainfall anomaly (Wu et al. 2009; Cao et al. 2013; Wang et al. 2013).
The SST anomalies affect East Asian summer rainfall anomalies by modulating water vapor transport associated with anomalous circulation (Zhang 2001; Zhou and Yu 2005; Li et al. 2014). As a result of warm SST anomalies over tropical Indian Ocean (Li et al. 2008; Xie et al. 2009; Wu et al. 2010) or cold SST anomalies over equatorial central Pacific Ocean (Wang et al. 2013; Xiang et al. 2013), an anomalous anticyclone forms over the western North Pacific. By modulating horizontal water vapor transport, this anomalous anticyclone induces excessive rainfall over the Yangtze River Valley, and deficient rainfall over South China and North China (Chang et al. 2000; Zhang 2001; Zhou and Yu 2005; Li et al. 2014). Tropical Indian Ocean (TIO) and Tropical Pacific Ocean (TPO) are both claimed as key regions responsible for summer rainfall anomalies over East Asia. Some previous studies emphasized the importance of TIO (Xie et al. 2009; Kosaka et al. 2013) but others argued the TPO is more important to East Asian summer rainfall anomalies (Wang et al. 2013; Xiang et al. 2013).
Despite its crucial role on the precipitation variability in the tropics, the impact of SST on rainfall variability is much limited in the extra-tropics, especially in summer (Koster et al. 2000; Conil et al. 2007; Sun and Wang 2014). Studies focused on drought have revealed that the drought events are stochastically generated over most mid-latitude land regions, while SST only plays a secondary role (Ferguson et al. 2010; Stevenson et al. 2015). However, most of these studies are focused on North America or from a global perspective, and it is still unclear to what extent is the interannual variability of East Asian summer rainfall forced by SST.
Almost all of the previous studies which quantitatively assessed the potential predictability of climate are based on only one model (e.g., Conil et al. 2007; Koster et al. 2000; Sun and Wang 2014; Stevenson et al. 2015). It is inevitable that their results may be model dependent (Conil et al. 2007). Recently, the 5th phase of Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012) has released a large set of simulations performed by multiple models, motivating us to quantitatively assess the contribution of SST to East Asia summer rainfall variability under the multi-model framework. The following two scientific questions will be addressed in this study: (1) To what extent is the interannual variability of East Asian summer rainfall forced by SST? How does it differ from the surrounding western Pacific Ocean? (2) Which oceanic region contributes more to the East Asian summer rainfall variability, TIO or TPO?
The rest of this paper is organized as follows. Section 2 describes the models, data and the methods. A brief model evaluation against observation is presented in Sect. 3. A comparison of CMIP5-AGCMs between the fixed SST simulation and interannual-varying SST simulation is presented in Sect. 4. Furthermore, idealized experiments performed by Community Atmospheric Model Version 4 (CAM4) are analyzed in Sect. 5. Finally, the conclusion and discussion are presented in Sect. 6.
2 Models, data and methods
Information about the 12 CMIP5 models in this study
College of Global Change and Earth System Science, Beijing Normal University (GCESS)
Beijing Climate Center, China Meteorological Administration (BCC)
National Center for Atmospheric Research (NCAR)
Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence (CSIRO-QCCCE)
LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences (LASG-IAP)
NOAA Geophysical Fluid Dynamics Laboratory (NOAA GFDL)
Institute for Numerical Mathematics (INM)
Institut Pierre-Simon Laplace (IPSL)
Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies (MIROC)
Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology) (MPI-M)
Meteorological Research Institute (MRI)
It is worth noting that the fixed SST annual cycle in SSTClim simulation is derived from the pre-industrial control run, which is not exactly the same as the climatology in the AMIP run. To corroborate the results obtained from CMIP5 models, additional experiments are performed by CAM4 (Neale et al. 2010). The CAM4 experiments are performed under a finite volume dynamic core, which is equivalent to a horizontal resolution of 1.9° × 2.5°. The control simulation (CTL) is forced by interannual-varying monthly SST from 1900 to 2000 (Hurrell et al. 2008), similar to the AMIP simulation in CMIP5. Besides, F_SST experiment is performed by forcing CAM4 with the climatological annual cycle of SST which exactly equals the SST climatology in CTL run. The F_SST experiment is also run for 101 years, and the last 100 years of CTL and F_SST experiments are selected for analysis.
To investigate the relative importance of TIO SST and TPO SST to East Asian summer rainfall, another two experiments named F_TIO and F_TPO are done with CAM4. The only difference between F_TIO and CTL is that the SST over TIO (30°S–30°N, 40°–110°E) is fixed as the climatological annual cycle in F_TIO run. The only difference between F_TPO and CTL is that the SST over TPO (30°S–30°N, 150°E–90°W) is fixed as the climatological annual cycle. The F_TIO and F_TPO experiments are also run for 101 years and the last 100 years are selected for analysis.
Given the uncertainty in precipitation observation (Collins et al. 2013), two monthly precipitation datasets are adopted in the model evaluation part in Sect. 3. They include the version-2 Global Precipitation Climatology Project (GPCP) data (Adler et al. 2003), and the CPC Merged Analysis of Precipitation (CMAP) data (Xie and Arkin 1997). For all the model outputs and observational data, monthly values for June, July and August (JJA) of each year are averaged into seasonal mean before further analyses.
Two metrics were adopted by previous studies to measure the amplitude of interannual variability, including standard deviation (Lu and Fu 2010; Fan et al. 2014; Sun and Wang 2014) and variance (Kumar and Hoerling 1995; Koster et al. 2000; Ferguson et al. 2010; Stevenson et al. 2015). In this study, we measure the interannual variability in terms of Interannual Variance (IAV) calculated as the variance of the 8-year high-pass filtered time series. The variance of the sum of two independent random variables equals the sum of their variance, but such law doesn’t apply to standard deviation. This is why we choose to use variance rather than standard deviation.
The total IAV in reality (IAV(Tot)) is contributed by SST-forced component (IAV(SST)) and Non-SST factors (IAV(Non)). Supposing the SST forced variability is independent from the Non-SST induced variability, the relationship IAV(Tot) = IAV(SST) + IAV(Non) holds. For CMIP5 models, The IAV in AMIP simulation (IAV(AMIP)) is an estimation of IAV(Tot), and the IAV in SSTClim simulation (IAV(SSTClim)) is an estimation of IAV(Non). Therefore, the SST contribution to the total IAV can be estimated as 1-IAV(SSTClim)/IAV(AMIP) for CMIP5 models. The fraction of SST contribution for each model is calculated, and then the MMM is obtained. For CAM4 experiments, the IAV in CTL and the F_SST experiments are the estimations of IAV(Tot) and IAV(Non), respectively. The SST contribution to the total IAV can be estimated as 1-IAV(F_SST)/IAV(CTL) in CAM4.
The relative contributions of SST forcing from TIO and TPO can be estimated by comparing F_TIO and F_TPO experiments with CTL experiment of CAM4. The contribution of TIO SST to the total IAV can be estimated as 1-IAV(F_TIO)/IAV(CTL), since the interannual variability in F_TIO experiment is induced by all the other factors except the TIO SST forcing. Similarly, the contribution of TPO SST can be estimated as 1-IAV(F_TPO)/IAV(CTL), since the interannual variability in F_TPO experiment is induced by all the other factors except the TPO SST forcing.
3 Model evaluation on the IAV of summer rainfall
Given the magnitudes of IAV in precipitation are different among East Asian land (EAL), tropical western Pacific (TWP) and mid-latitude Pacific (MWP), the regional averaged IAVs are shown separately for these three regions as bar charts in Fig. 2b. The MMM estimated regional averaged IAVs for the EAL and TWP are higher than both GPCP and CMAP, but close to the observations over MWP. Although the IAV for the TWP is overestimated for about four times in bcc-csm1.1 and MRI-CGCM3 (denoted as “2” and “12” in Fig. 2b), the MMM is not substantially misled by these outlier models, suggesting the superiority of multi-model median to multi-model average (Gleckler et al. 2008). We will use all of the 12 models to construct the MMM in the following section for three reasons. First, the multi-model median is not sensitive to outliers. Second, outliers in climatology simulation are not necessarily outliers in other aspects (this will be stated in the following section). Third, excluding outlier models may result in a too small sample size.
4 SST contribution estimated by CMIP5 models
Given to noise seen in the spatial pattern, it may be useless to discuss the fraction of SST-contribution at a single grid point or over a small area. Only the regional average over large areas are meaningful. The EAL and MWP regions are large enough to contain several negative centers and several positive centers with relatively large values, and the regional averaging over EAL and MWP regions could effectively suppress the noise. The regional averaged values of 1-IAV(SSTClim)/IAV(AMIP) for MMM and all the 12 models are shown in Fig. 4b, which are estimations of the percentages of SST contribution to the total IAV over EAL, TWP and MWP, respectively. The MMM estimated percentages of contribution by SST are 18.4 % for EAL, 58.2 % for TWP, and 26.2 % for MWP. Except for CSIRO-Mk3.6.0 (denoted as “4” in Fig. 4b), all of the other models are consistent that the fractions of SST contribution over EAL and MWP are smaller than TWP, and most of the individual models agree that the fractions of SST contribution to EAL and MWP are comparable with each other. In brief, the SST-contributed fractions to total IAV in summer rainfall are comparable between EAL and MWP, which are far lower than the TWP.
For the two outlier models bcc-csm1.1 and MRI-CGCM3 in the simulation of climatology, bcc-csm1.1 (denoted as “2” in Fig. 4b) seems an outlier again in terms of negative SST contribution over EAL, but it is close to the MMM over the TWP and MWP (Fig. 4b). The SST-contributed fractions estimated by MRI-CGCM3 are close to the MMM for all the three regions (denoted as “12” in Fig. 4b). Therefore, it is not reasonable to exclude a model simply because it is an outlier in a certain aspect of the climatology simulation, since it is not necessarily outliers in other aspects.
The fraction of SST contribution to horizontal water vapor transport shares similar spatial pattern as that of precipitation. Slightly higher contribution from SST is seen for horizontal water vapor transport than precipitation over EAL, and the difference between IAV(SSTClim) and IAV(AMIP) is significant at the 95 % confidence level over a large part of EAL and MWP within 20°N–40°N (Fig. 5a, c). As seen in the regional averages, the SST contributions to the total IAV of zonal water vapor transport are 44.8, 79.4, and 40.3 % for EAL, TWP and MWP, respectively (Fig. 5b, d), and the contributions of SST to the meridional water vapor transport are 34.4, 50.0 and 24.4 % for EAL, TWP and MWP, respectively. The contributions from SST forcing are comparable for the EAL and MWP, which are much lower than the TWP.
In contrast to the horizontal water vapor transport, the fraction of SST-contribution to vertical velocity at 500 hPa is approximately 0 over EAL, which explains why the SST contribution to rainfall is lower than the SST contribution to water vapor transport. There is a sharp decrease in the percentage of SST contribution from the south to the north along the southern coast of East Asia (Fig. 5e). The MME estimated regional average of 1-IAV(SSTClim)/IAV(AMIP) is −4.0 % for EAL and 5.3 % for MWP, both of which are approximately 0 (Fig. 5f), suggesting the IAV for the vertical velocity is not affected by the SST over EAL and MWP. As proposed by Johnson and Xie (2010), the deep convection in tropical oceans are strongly modulated by local SST relative to the tropical averaged SST, but this law doesn’t apply to land regions and mid-latitude oceans. This may explain why the SST contribution is far larger for TWP than over EAL and MWP.
The above evidences in Fig. 5 suggest that the SST impacts the IAV of rainfall over EAL and MWP by modulating the horizontal water vapor transport, rather than modulating the vertical velocity. Many previous studies revealed the role of horizontal water vapor transport in connecting the tropical SST and East Asian rainfall (e.g., Zhou and Yu 2005; Li et al. 2012, 2014), but few studies documented the relationship between vertical motion on EAL with the SST. Therefore, our results are consistent with previous studies.
In short, the above results based on CMIP5 models suggest two points. First, the percentage of SST contribution to the interannual variability of summer rainfall is much lower for the EAL and MWP compared with the TWP, but there is no significant difference between the southern part and the northern part of EAL. Second, the SST anomalies regulate the interannual variability of rainfall by modulating horizontal water vapor transport into EAL and MWP, while the IAVs of vertical velocity over EAL and MWP have little relation with SST forcing. The above results based on 12 CMIP5 models have provided a model-independent estimation of SST forcing. But a defect in the above analyses is that the climatology of SST differs between AMIP and SSTClim simulations, since AMIP simulation is forced by observed SST whereas SSTClim simulation is forced by the SST generated by coupled models. To overcome this defect, model experiments are performed with CAM4, using exactly the same climatology of SST in CTL and F_SST runs, and the results are discussed in Sect. 5.
5 SST contribution estimated by CAM4 experiments
As a comparison to EAL, the estimations for TWP and MWP are also shown in Fig. 8. The SST contribution to the IAV of rainfall over TWP is 58.1 and 41.9 % as estimated by MMM of CMIP5 models and CAM4, respectively. The SST contribution to the IAV of rainfall over MWP is 26.2 % as estimated by MMM of CMIP5 models, but an estimation of 7.8 % is shown by CAM4. Although in-consistent values are obtained for MWP, it is still evident that the percentage of SST contribution to the IAV of rainfall over MWP is much lower than TWP but comparable to EAL.
The relative contributions of TIO and TPO SST variabilities to the IAV of summer rainfall are also evaluated in Fig. 8, which are estimated as 1-IAV(F_TIO)/IAV(CTL) and 1-IAV(F_TPO)/IAV(CTL) based on CAM4 experiments. For EAL region, 5.7 % of the IAV is contributed by TIO SST and 5.5 % of the IAV is contributed by TPO, consistent with Wu et al. (2003) which argued that ENSO contributes to about 4 % of the East Asian summer rainfall. For TWP region, TIO contribution is 1.4 %, whereas TPO contribution is 27.1 %, suggesting the importance of local SST than the remote SST forcing. For both EAL and TWP, the sums of the contributions from TIO and TPO are smaller than the contribution of global SST (comparing the sum of purple and green bars with the black bars in Fig. 8). However, the contributions from TIO and TPO are estimated to be −4.7 and −5.0 % for the IAV of MWP rainfall. Besides chaotic factors, another possible cause for the negative values is that the interannual variability of WNP rainfall is determined by other oceanic region, e.g., local SST over subtropical WNP (Chung et al. 2011; Wang et al. 2013; He and Zhou 2014), and the remote forcing from TIO and TPO tend to offset the local SST-forced variability of MWP rainfall. In the F_TIO and F_TPO simulations, the negative contributions from TIO and TPO are absent, and the IAV of MWP rainfall would increase.
6 Conclusion and discussion
The contribution of SST forcing to the interannual variability of summer rainfall is about 18 % over East Asian land regions, which is quantitatively agreed by MMM of CMIP5 models and CAM4 experiments. The percentage of SST contribution decreases sharply from south to north along the southern coast of East Asia. No substantial difference is seen in the percentage of SST contribution between the southern part and the northern part of East Asia. The magnitude SST contribution to seasonal rainfall over East Asian land is much smaller than the tropical western Pacific, but is comparable to the mid-latitude western Pacific.
It is the horizontal water vapor transport, rather than the vertical velocity over EAL, which connects the interannual rainfall variability over EAL with the SST forcing. As agreed by CMIP5 models and CAM4 experiments, horizontal water vapor transport is more affected by SST than vertical velocity, and it may probably play a role as a mediator linking SST anomaly with rainfall anomaly in East Asian land. The IAV of vertical motion over East Asian land and mid-latitude western Pacific has little relation to the interannual SST variability, suggesting it is primarily controlled by the atmospheric internal dynamics.
By comparing the simulations with fixed TIO SST and fixed TPO SST, the relative importance of TIO SST and TPO SST to East Asian summer rainfall variability is investigated. It is shown that TIO contributes 5.7 % of the total rainfall IAV over East Asian land, while TPO contributes 5.5 %. In brief, the contributions from these two ocean sectors to the interannual rainfall variability over East Asian land are comparable with each other.
In addition to observational uncertainty in the mean state of rainfall (Collins et al. 2013; He and Zhou 2015), great observational uncertainty between GPCP and CMAP is seen in the IAV of rainfall, especially over the tropical western Pacific Ocean. The Multi-model median of IAV is close to the CMAP data, but higher than the GPCP data. Therefore, great effort should be devoted to reduce the observational uncertainty of precipitation, in terms of both mean state and its variability.
The comparison between AMIP simulation with SSTClim simulation in this paper is conducted under the assumption that the atmospheric internal variability is independent on the mean state of SST, since the mean state of SST in SSTClim simulation is slightly different from that of the AMIP simulation. We recommend an additional model experiment could be done in the next generation of CMIP, by forcing AGCMs with observed SST climatology, which exactly equals the SST climatology in AMIP experiment. This experiment could help separating the SST forced phenomenon from the atmospheric internal dynamics.
We wish to thank the PCMDI which provides model data and NOAA which provides observational data. We also wish to thank Dr Zhenyu Han (In CMA) and Dr Jiawen Zhu (in IAP) for useful discussions in performing the analyses and performing CAM4 experiments. This work was jointly supported by National Basic Research Program of China (2014CB953901), China Meteorological Administration Special Public Welfare Research Fund (GYHY201406001) and National Natural Science Foundation of China (41375095, 41575043, 41505067, 41330423, and 41205069).
- Adler RF, Huffman GJ, Chang A, Ferraro R, Xie PP, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J Hydrometeorol 4(6):1147–1167. doi: 10.1175/1525-7541(2003)004<1147:tvgpcp>2.0.co;2 CrossRefGoogle Scholar
- He C, Zhou T (2015) Responses of the Western North Pacific Subtropical High to global warming under RCP4.5 and RCP8.5 scenarios projected by 33 CMIP5 models: the dominance of tropical Indian Ocean–Tropical Western Pacific SST gradient. J Clim 28(1):365–380. doi: 10.1175/jcli-d-13-00494.1 CrossRefGoogle Scholar
- Neale RB, Richter JH, Conley AJ, Park S, Lauritzen PH, Gettelman A, Williamson DL, Rasch PJ, Vavrus SJ, Taylor MA, Collins WD, Zhang MH, Lin S-J (2010) Description of the NCAR Community Atmosphere Model (CAM4.0), USAGoogle Scholar
- Zhang R (2001) Relations of water vapor transport from Indian monsoon with that over East Asia and the summer rainfall in China. Adv Atmos Sci 18(5):1005–1017Google Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.