1. Introduction
Tropical precipitation drives the large-scale atmospheric circulation and is important to global and regional climate. Under global warming, the change in tropical precipitation is associated with the atmospheric circulation, leading to regional climate change (Xie et al. 2015) and great impacts on human society and economy. The tropical precipitation change is mainly attributed to two factors (Seager et al. 2010; Bony et al. 2013): the thermodynamic change due to the increase in water vapor and the atmospheric circulation change (the dynamic response) (Held and Soden 2006; Xie et al. 2010; Chadwick et al. 2013).
The thermodynamic component of precipitation change is tied to the climatological precipitation and circulation, increasing in regions/seasons of surface wind convergence (Chou and Neelin 2004; Held and Soden 2006; Chou et al. 2009). This relationship between the climatology and change of precipitation is known as the wet-get-wetter mechanism. By contrast, the dynamic response of precipitation is associated with the changes in both strength and spatial distribution of atmospheric circulation. The strength change is directly linked to the slowdown of mean circulation under global warming (Held and Soden 2006; Vecchi et al. 2006; Vecchi and Soden 2007; Tokinaga et al. 2012; Ma et al. 2012), while the circulation shift is mostly due to the spatially patterned SST warming, known as the warmer-get-wetter mechanism (Xie et al. 2010). The latter mechanism argues that the mean ocean warming pattern regulates the precipitation change over tropical oceans, with increased rainfall in regions of SST warming greater than the tropical mean and vice versa. In coupled model projections, the dynamic response due to the weakening mean circulation largely offsets the thermodynamic change (Chadwick et al. 2013). As a result, the warmer-get-wetter mechanism becomes important for the annual-mean tropical precipitation change (Xie et al. 2010).
Previous studies have investigated the seasonal variation of tropical rainfall change under global warming (Sobel and Camargo 2011; Huang et al. 2013; Kent et al. 2015; Lazenby et al. 2018; Lan et al. 2019). Huang et al. (2013) suggested that for the zonal-mean seasonal cycle of tropical precipitation change, the wet-get-wetter effect becomes important. By using a precipitation decomposition method (Chadwick et al. 2013), however, Kent et al. (2015) suggested that the seasonal variation in the horizontal distribution of tropical rainfall change is closely related to the circulation spatial shift term that mainly represents the warmer-get-wetter effect. Therefore, it is still unclear what controls the seasonal variation in spatial distribution of tropical precipitation change.
The purpose of the present study is to investigate the mechanisms for seasonal variations in tropical rainfall change in response to global warming, specifically whether the seasonality of climatological SST or that of SST warming is more important. By designing a set of experiments and using a diagnostic method of precipitation change decomposition, our study aims to investigate the characteristics of each component and its contribution to the seasonality of tropical precipitation change.
The rest of the paper is organized as follows. Section 2 describes the experimental design and the decomposition method as well as the data used in this study. Section 3 presents the annual-mean and seasonal-mean tropical precipitation change under global warming. Section 4 shows the contributions of different components to the seasonality of tropical precipitation change. Section 5 is a summary with discussions.
2. Data and methods
a. Atmospheric general circulation model
The Community Atmosphere Model (CAM) is the atmospheric component of the Community Earth System Model (CESM). We use the CAM, version 4 (CAM4) for experiments, which shares the same deep convection parameterization (Zhang and McFarlane 1995) with CAM5. Patterns of mean tropical precipitation in the two models are similar (Xie et al. 2012; Chen and Dai 2019), indicating that skills in simulating mean tropical precipitation of CAM4 and CAM5 are comparable. The comprehensive description of CAM4 can be found in Neale et al. (2010). We choose a horizontal 1.9° × 2.5° grid (“f19_f19”) with 26 vertical sigma levels.
We carry out four experiments for comparison. The control run, called AMIP, is forced by the observed monthly mean SST and sea ice concentration. In the experiment AMIP4K, we impose a spatially uniform SST increase (SUSI) of 4 K superimposed on the observed monthly climatological SST. The SST increase is also constant in season. In the experiment AMIPFuture, we impose the spatially patterned SST increase (SPSI) derived from the multimodel-mean CMIP3 quadruple CO2 (1% to 4×) simulation. The SST increase varies by month. In the last experiment, called AMIPFuture annual-mean ΔSST (shortened to AMIPFAM), we set the imposed SST increase as the annual mean of SST change in AMIPFuture, so it is spatially variable but constant in time. All runs last for 22 years (from 1979 to 2000). The results of the last 20-yr mean (1981–2000) are analyzed.
Previous studies (Huang 2014; Kent et al. 2015; Zhou et al. 2018) demonstrate that the atmospheric general circulation models (AGCMs) can well reproduce the response of tropical precipitation in coupled GCMs. Chadwick et al. (2014) show a weak precipitation response to the direct CO2 effect over tropical oceans, supporting the AGCM reconstruction of the century-scale pattern of tropical precipitation change. Thus, we use the AGCM runs to analyze tropical precipitation response to different SST warming patterns in our study.
b. AMIP
To test our result of the circulation response to uniform warming, we analyze 12 Atmospheric Model Intercomparison Project (AMIP) experiments in the output of phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive (Table 1): the AMIP control run and the AMIP4K. The AMIP control run is forced by the observed SST and the AMIP4K is forced by the observed SST plus a spatially uniform SST warming of 4 K. All model outputs are interpolated onto a common 2.5° × 2.5° grid, and only the first-member run (r1i1p1) of each model is analyzed.
List of 12 AMIP models from CMIP5 used in this study.
c. Decomposition of precipitation change
Based on the moisture budget equation in Trenberth and Guillemot (1995) and Seager et al. (2010), precipitation change can be approximated as follows (Huang et al. 2013; Long et al. 2016):
where P, ρw, ω, and q are precipitation, the density of water, upward vertical velocity, and specific humidity, respectively. The overbar denotes the present climatology and Δ denotes change under global warming. For tropical precipitation, it is a good approximation to choose ω as 500-hPa pressure velocity and q as surface specific humidity (Held and Soden 2006; Huang et al. 2013; Huang 2014; Long et al. 2016). Here we multiply the 500-hPa pressure velocity by −1 to make ω positive upward. Horizontal moisture advection and the residuals have been neglected.
The precipitation response to global warming can be diagnostically decomposed into thermodynamic and dynamic components (Emori and Brown 2005; Seager et al. 2010; Chou and Lan 2012; Bony et al. 2013; Chadwick et al. 2013). Based on Eq. (1), we decompose the ΔP into thermodynamic component (ΔPt,
Here we divide the dynamic component into three parts (Table 2): the SUSI effect
List of dynamic subcomponents of ΔPd (SUSI is spatially uniform SST increase.)
Chadwick et al. (2013) decomposed the dynamic effect into components due to the circulation weakening and shift. The circulation weakening component is obtained by regressing the change in mass flux against the mean, opposing the thermodynamic effect on rainfall change. We use a different decomposition method by using additional AMIP simulations. Here, the dynamic effect is decomposed into three subcomponents induced by uniform warming, the annual-mean warming pattern, and the seasonally varying warming pattern, respectively. We will show that the dynamic subcomponent induced by uniform warming does not follow a simple linear relationship with the thermodynamic component. Our method further isolates the effects of the annual mean and seasonal variation of SST change.
3. Tropical precipitation change under global warming
First, we analyze the change in annual-mean tropical precipitation under global warming (Fig. 1). Consistent with previous studies (Xie et al. 2010; Chadwick et al. 2013; Huang 2014), rainfall mainly increases in the tropical Pacific, the northern Indian Ocean, and the equatorial Atlantic with a maximum in the equatorial central Pacific (Fig. 1a). We then decompose the total rainfall change (ΔP) into the thermodynamic (ΔPt, Fig. 1c) and dynamic (ΔPd, Fig. 1b) components. The thermodynamic component ΔPt shows positive anomalies in the South Pacific convergence zone (SPCZ) and the intertropical convergence zone (ITCZ), and also over the Maritime Continent, central Africa, and South America (Fig. 1c). The dynamic component ΔPd predominantly shows positive rainfall change over the equatorial central Pacific, corresponding to the El Niño–like SST warming pattern (Fig. 1b). Note that ΔPd is quite different from ΔPt, mainly in the ITCZ and SPCZ and also over the Maritime Continent. We further decompose ΔPd into rainfall changes induced by SUSI (
Annual-mean ΔP under global warming (shading): (a) ΔP, (b) ΔPd, (c) ΔPt, (d)
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
In June–August (JJA), ΔP shows robust positive anomalies in the Northern Hemisphere and relatively weak changes in the Southern Hemisphere (Fig. 2a). Contributed by the thermodynamic component ΔPt, rainfall mainly increases in the Asian monsoon region and the ITCZ (Figs. 2a,c). Rainfall also increases in the equatorial central Pacific due to the dynamic component ΔPd (Figs. 2a,b). The ΔPd is mainly due to
Seasonal-mean ΔP in JJA under global warming (shading): (a) ΔP, (b) ΔPd, (c) ΔPt, (d)
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
In December–February (DJF), ΔP is mainly positive over the ocean near the equator (Fig. 3a). The SUSI term
As in Fig. 2, but for seasonal-mean precipitation change under global warming in DJF.
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
Overall, tropical rainfall response to SST warming features two major characteristics. First, the response over the ocean mainly coincides with the SST pattern response (warmer-get-wetter effect) and is largely captured in the annual-mean SST pattern
4. Contributions of different components to the seasonality of tropical precipitation change
a. Thermodynamic component (ΔPt)
The spatial distribution of the thermodynamic component (ΔPt) is dominated by the spatial pattern of the mean upward vertical velocity
For any location,
where the angle brackets and the asterisk denote the zonal mean and deviation, respectively. The maximum of
Zonal mean (contours) and zonal standard deviation (shading) of (a)
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
In the subtropics of the winter hemisphere, due to the robust subsidence of the Hadley cell (blue contours in Fig. 4a), the zonal mean of ΔP is nearly zero and shows a “dry-get-drier” pattern in 10°–20°N from December to May approximately (Fig. 4b). In the summer hemisphere, the zonal variation of ΔP is large (Fig. 4b), corresponding to large
Figure 4 also shows a seasonal delay of zonal mean ΔP with regard to its climatology (
b. Dynamic component under uniform warming
In DJF,
To diagnose the large-scale atmospheric circulation (Ma et al. 2012; Ma and Xie 2013; He et al. 2014), we analyze the ensemble mean of 300-hPa velocity potential (χ) of the AMIP and AMIP4K runs from 12 CMIP5 models. Figure 5 shows that with the upper-level divergence center in the tropical western Pacific and convergence center in the eastern Pacific, the Walker circulation in JJA is stronger than that in DJF. The slowdown of the Walker circulation is apparent in both JJA and DJF (Figs. 5a,b), but the strongest change in velocity potential displaces west of the climatological center of divergence in JJA in the Asian monsoon region. Similar to JJA, a weak westward shift of the velocity potential change relative to the mean also occurs in DJF (Fig. 5b). Thus, the circulation change under uniform warming is not a simple slowdown of the mean circulation.
(a),(b) Multimodel ensemble mean and (c),(d) intermodel standard deviation of the 300-hPa velocity potential change (shading) from AMIP4K-AMIP in (top) JJA and (bottom) DJF. The AMIP climatology is in contours (interval of 1.6 × 106 m2 s−1; the zero contour is thickened in gray). The solid (dashed) lines denote positive (negative) velocity potential.
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
We have also examined the changes in individual CMIP5 models. The westward shift of the change relative to the mean is obvious in most models, except for FGOALS-g2 (see Figs. S1 and S2 in the online supplemental material). The intermodel diversity of the velocity potential change peaks in the tropical northwest Pacific in JJA, roughly in the same region of the maximum upper-level divergence in the present climatology (Fig. 5c). The result in DJF is similar in spatial distribution but weak in magnitude, compared to JJA (Fig. 5d). This indicates that large model uncertainty exists regarding the circulation slowdown and the magnitude or position of the westward shift under global warming. The physical mechanisms for the circulation shift (Ma et al. 2012), however, are still unclear. Previous studies suggested that it is associated with the land–sea contrast in surface warming and specific humidity (Chadwick et al. 2014, 2019; He et al. 2014; Kent et al. 2015). The zonal shift between Δχ and mean χ in JJA is consistent with positive (negative)
c. Dynamic component under annual-mean SST pattern
The SST change under global warming features an El Niño–like pattern (contours in Figs. 1–3). The equatorial peak of ΔSST strengthens slightly in June (Fig. 6a). Away from the equator, ΔSST increases in late summer through fall. The seasonal variation of ΔSST is weak in the tropics (Fig. 6b) but the annual range exceeds 0.4°C at 20°N, a latitude that rainbands of the Asian summer monsoon reach. The climatological SST shows strong seasonality, nearly symmetric about the equator in the deep tropics during March–May (MAM), but is warmer north than south of the equator in other months (Fig. 6c), corresponding to that of climatological precipitation (Fig. 7). Figure 7 shows that the maximum of climatological precipitation in the Northern Hemisphere is in the summer season (JJA), whereas the maximum in the Southern Hemisphere is in MAM due to that of the climatological SST. The strong seasonality of rainfall change and circulation change could be due to that of the climatological SST.
Zonal-mean seasonal cycle of (a) ΔSST under global warming, (b) seasonal deviation of ΔSST from the annual mean under global warming, and (c) the observed monthly mean climatological SST (from 1981 to 2000). Contours in (c) denote the climatological SST of 27.5°C.
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
Zonal-mean seasonal cycle of climatological precipitation
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
Figure 8a shows a robust anomalous ascent near the equator in the first half of the year under global warming. This is due mainly to the annual-mean ΔSST (Fig. 8c). The robust anomalous ascent is displaced slightly south of the equator and induces anomalous compensating subsidence north of the equator with anomalous cross-equatorial northerlies (Huang et al. 2013). The ω responses to uniform warming and seasonal ΔSST are both weak (Figs. 8b,d), indicating that the seasonality of Δω is dominated by that of climatological SST, rather than ΔSST.
Zonal-mean seasonal cycle of Δω in (a) ΔPd, (b) the SUSI effect
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
The warmer-get-wetter effect on Δω and ΔP can be understood from moist instability change due to ΔSST and Δq (Xie et al. 2010). Over the eastern equatorial Pacific, despite the El Niño–like ΔSST and increased Δq, Δω, and ΔP are weak in the annual average (Huang 2014). This suggests the crucial role of the climatological SST in modulating the warmer-get-wetter effect. Figure 8c shows that the maximum of Δω collocates with the positive peak of annual-mean ΔSST near the equator in the first half of the year. In the second half of the year, however, the anomalous ascent is weak near the equator due to low climatological SSTs. In the present climate, SST above 27.5°C is required for large-scale deep convection (Fig. 7; Graham and Barnett 1987). The threshold is approximately 1°C higher than the tropical mean SST (~26.5°C). Since the SST threshold for convection increases in tandem with the tropical mean SST under global warming (Johnson and Xie 2010), we consider the regions where local SST is greater than the threshold of the tropical mean +1°C to be active in deep convection in a changing climate. Figure 8c shows that the annual-mean ΔSST has a considerable impact on deep convection from January to May, while in the second half of the year, the impact on deep convection is small because of lower climatological SST on the equator.
Figure 9 shows the strong seasonality of Δω forced by the annual-mean SST pattern. The robust anomalous ascent takes place in the central and eastern equatorial Pacific in MAM (Fig. 9a), while it is confined to the central equatorial Pacific in other seasons (Figs. 9b–d). The robust anomalous ascent in MAM in the eastern equatorial Pacific (Fig. 9a) is due to the seasonal increase of climatological SST that draws the ITCZ closest to the equator. The seasonality of Δω largely follows the seasonality of the climatological SST, especially in the eastern equatorial Pacific (Fig. 10). Therefore, only when the climatological SST is close to or exceeds the threshold for convection does the warmer-get-wetter effect on rainfall change get turned on.
Spatial patterns of Δω forced by the annual-mean SST pattern in (a) MAM, (b) JJA, (c) SON, and (d) DJF. The red (blue) solid (dashed) contours are positive (negative) deviation of the annual-mean ΔSST from the tropical mean (with a 0.3°C interval; values between −0.2° and +0.2°C are omitted).
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
Seasonal cycle of Δω in the eastern equatorial Pacific (1°S–1°N) forced by the annual-mean SST pattern. Contours are the climatological SSTs with a 1°C interval. The 26°C (red) contours are thickened.
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
Figure 11 shows the standard deviation of monthly mean Δω and ΔP induced by the annual-mean SST pattern effect. In the eastern equatorial Pacific, the seasonal variations of Δω and ΔP are large due to the aforementioned effect of the climatological SST seasonal cycle. Both Δω and ΔP feature strong seasonal variations in the tropical northwestern Pacific and Asian monsoon region (Fig. 11) where the background seasonal cycle is strong. Enhanced seasonal variance of ΔP in Fig. 11 resembles the response of an AGCM to an El Niño SST pattern that is kept constant in time (Fig. 5a of Xie and Zhou 2017). All this shows the crucial role of climatological SST in determining local convective activity, which can further affect the rainfall change and its seasonality.
Spatial patterns of the standard deviation of monthly mean (a) Δω and (b) ΔP from the annual mean forced by the annual-mean SST pattern.
Citation: Journal of Climate 33, 18; 10.1175/JCLI-D-20-0032.1
The spatially patterned SST warming, especially the equatorial warming peak, causes the ITCZ to contract equatorward (Huang et al. 2013; Zhou et al. 2019). The ITCZ’s equatorward shift shows a robust seasonality (Fig. 8a) that can be reproduced with annual-mean ΔSST (Fig. 8c). Thus, the seasonality of the ITCZ contraction is due mostly to that of climatological SST, but not so much to that of SST warming.
5. Summary and discussion
We have used AGCM experiments to investigate the mechanisms for the strong seasonal cycle of tropical precipitation change in response to global warming. The precipitation change consists of thermodynamic and dynamic components. We further use model experiments to decompose the dynamic component into three subcomponents due to uniform warming and to the annual mean and seasonal variation of the SST warming pattern, respectively.
The spatial pattern of thermodynamic component is mainly controlled by the mean upward vertical velocity
The dynamic component is due mainly to SUSI and the annual-mean SST pattern, while the effect of seasonally varying SST pattern is small. In addition to the slowdown of the mean circulation, we identify a robust westward shift of 300-hPa velocity potential change from the climatological distribution. The cause of this shift needs further investigation.
The seasonality of Δω under SPSI is dominated by the seasonal cycle of the climatological SST rather than ΔSST. Specifically, in the eastern equatorial Pacific, moist instability change induced by annual-mean ΔSST can only cause robust rainfall and circulation change in MAM when the background SST reaches the annual peak and allows deep convection to occur. Furthermore, although the equatorward contraction of the ITCZ is driven by the equatorial peak in ΔSST (Huang et al. 2013; Zhou et al. 2019), its seasonal cycle is mainly due to the strong seasonality of the background SST.
Overall, the seasonal precipitation response to global warming results from a combination of wet-get-wetter and warmer-get-wetter effects (Huang et al. 2013). This study highlights that the climatological seasonal cycle of SST dominates the seasonality of tropical precipitation change by modulating both wet-get-wetter and warmer-get-wetter effects.
Projections of precipitation change suffer large intermodel uncertainty and internal variability (Deser et al. 2012; Rowell 2012; Ma and Xie 2013; Long et al. 2016; Chadwick 2016; Lazenby et al. 2018). Our results show that the seasonal cycle of background SST is important for tropical rainfall change. Climate models suffer large biases in climatology (Zhou and Xie 2015), for instance in the ITCZ and the equatorial cold tongue (Li et al. 2016). Such mean biases affect the SST warming pattern (Li et al. 2016; Ying et al. 2019; Seager et al. 2019) and regional rainfall projections (Zhou and Xie 2015; Li et al. 2017). These issues remain to be investigated quantitatively in future studies.
Acknowledgments
We wish to thank three anonymous reviewers for constructive comments. Y.F.G. and C.Y.W. are supported by the National Key Research and Development Program of China (2016YFA0601804 and 2018YFA0605702) and S.P.X. is supported by the National Science Foundation (AGS 1637450). The National Center for Atmospheric Research (NCAR) provided the CESM model. Computational resources were provided by National Supercomputer Centre in Guangzhou (NSCC-GZ), China.
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