Regional response of annual-mean tropical rainfall to global warming

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1 ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 15: (2014) Published online 25 November 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: /asl2.475 Regional response of annual-mean tropical rainfall to global warming Ping Huang* Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing , China *Correspondence to: Dr. P. Huang, Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing , China Received: 6 August 2013 Revised: 22 October 2013 Accepted: 24 October 2013 Abstract Regional response of annual-mean tropical rainfall to global warming is investigated based on 18 models from the Coupled Model Intercomparison Project 5. With surface warming, the climatological ascending circulation pumps up increased surface moisture and leads to a rainfall increase over the convergence zone, while the change in ascending flow induces a rainfall increase over the region with sea surface temperature (SST) increase exceeding the tropical mean, with a concomitant modification of background surface moisture and SST. These two effects form a hook-like pattern of rainfall change over the tropical Pacific and an elliptic pattern over the northern Indian Ocean. Keywords: tropical rainfall; global warming; regional response 1. Introduction Under global warming, tropical precipitation change plays important role to the changes in atmospheric convection, circulation and energy transportation (Allen and Ingram, 2002; Held and Soden, 2006; Meehl et al., 2007). Projecting rainfall response to global warming is a major challenge (Solomon et al., 2007; Hsu et al., 2012; Kitoh et al., 2013), but the regional patterns of precipitation change have great uncertainties both in observation and simulation (Meehl et al., 2007; Zhang et al., 2007; Ma and Xie, 2013). Simulated by global climate models (GCMs), the increased precipitation change peaks over the deep tropic (Figure 1(a)) (e.g. Chou et al., 2009). Generally, there are two theories to explain the spatial distribution of precipitation response. One argues that the rainfall increases along with the precipitation climatology (wet-get-wetter) and decreases in the margins of convective regions (upped-ante) (Chou and Neelin, 2004; Held and Soden, 2006; Chou et al., 2009; Chou et al., 2013a); the other suggests increased rainfall where the sea surface temperature (SST) increase exceeds the tropical mean warming (warmer-get-wetter) (Xie et al., 2010). The wet-get-wetter (WeGW) mechanism emphasizes the role of vertical moisture transport change by mean circulation (Chou et al., 2009), while the SST change ( SST) pattern overlooked in WeGW is considered to dominate the regional precipitation change by influencing local convective instability in warmerget-wetter (WaGW) (Xie et al., 2010). As shown in Figure 1(a), however, both climatological rainfall and SST partly overlap the annual-mean rainfall change pattern in GCMs but also have some discrepancies. More studies notice that the rainfall change can be divided into two components, the thermodynamic and dynamic components, which are related to the specific humidity change and circulation change, respectively (e.g. Chou et al., 2009; Seager et al., 2010). It has obtained a consensus that the thermodynamic component exhibits a WeGW effect, but different views exit regarding the dynamic component (Bony et al., 2013; Chadwick et al., 2013; Huang et al., 2013). Chadwick et al. (2013) claim that the dynamic component of rainfall change can be mainly divided into one part related to circulation slowdown (anti-wegw) and the other related to SST pattern (WaGW), the anti-wegw part cancels largely out the thermodynamic component (WeGW), and then the rainfall change mainly exhibits a WaGW pattern. To explain the seasonal pattern of zonal-mean precipitation change, Huang et al. (2013) suggests that WaGW effect contributes more to the annual mean whereas WeGW effect to the seasonal anomalies because of the almost seasonally independent SST and seasonally varying precipitation climatology. This study investigates the spatial distribution of precipitation change based on the multi-model ensemble (MME) results of the Coupled Model Intercomparison Project Phase 5 (CMIP5) model outputs (Taylor et al., 2012). The role of SST pattern is verified using atmospheric model experiments forced by spatial uniform SST increase and spatial patterned SST increase (Bony et al., 2011). A simplified vertical moisture transportation is diagnosed for the contribution of WeGW and WaGW effects as in Huang et al. (2013). 2. Models We use the outputs of 18 models participating CMIP5 including the historical experiment for and representative concentration pathway 4.5 (RCP 4.5) 2013 Royal Meteorological Society

2 104 P. Huang Figure 1. Annual-mean (a) precipitation change, (b) the precipitation climatology and (c) SST change in RCP 4.5 and in MME of 18 CMIP5 models. In (a), the green curves show the 7 mm day 1 contour of the precipitation climatology, and the red curves show the 1.1 C contour of SST. experiment for at (Taylor et al., 2012). The eighteen models are BCC- CSM1.1, CanESM2, CCSM4, CNRM-CM5, CSIRO- Mk3.6.0, FGOALS-s2, GFDL-CM3, GFDL-ESM2G, GISS-E2-R, HadGEM2-ES, INM-CM4, IPSL-CM5A- LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3 and NorESM1 (see details at availability.html). The current climatology is defined as the long-term mean for in historical experiment, whereas the change under global warming is defined as the difference between in RCP 4.5 and in historical. The MME is defined as the average results of 18 models. Six atmospheric models (CanAM4, CNRM-CM5, HadGEM2-A, IPSL-CM5A-LR, MIROC5 and MRI- CGCM3) in CMIP5 that provide the following set of atmospheric experiments are also used (Bony et al., 2011; Taylor et al., 2012): the amip experiment prescribes observed SST (control run), amip4k and amipfuture, respectively, add a spatially uniform SST increase (SUSI) of 4 K and a spatially patterned SST increase (SPSI) from CMIP3 1% per year CO 2 increase to quadrupling experiments as the boundary condition. The SST used in SPSI run is similar to that of coupled GCMs from CMIP5 with peak on the equator. The climate changes in SUSI and SPSI runs are defined as the differences of 20-year monthly climatology for from the control runs. For comparing the changes among RCP 4.5, SUSI and SPSI runs, all changes are normalized using the tropical (20 S 20 N) mean SST change in corresponding runs. 3. Results Figure 1 shows the MME and annual-mean precipitation change, mean precipitation (P, the overbar denotes the mean averaged from 1981 to 2000 in historical run) and SST. The increased precipitation change primitively exhibits a hook-like pattern over the deep equatorial Pacific and an elliptic pattern over the northern Indian Ocean (Figure 1(a)). The hooklike pattern of rainfall change ( P) over the tropical Pacific resembles neither the distribution of mean convergence zone (Figure 1(b)) nor the El Niño-like pattern of SST (Figure 1(c)) (e.g. Liu et al., 2005; Xie et al., 2010; Lu and Zhao, 2012), but their combination. The pattern of P over the northern Indian Ocean approximately coincides with the Indian Ocean SST, but the maximum SST is located over the Arabian Sea, east of the central northern Indian Ocean with maximum P (Hsu and Li, 2012). The discrepancies of P pattern with SST and P patterns indicate that the spatial distribution of annual-mean P does not simply follow the pattern of climatological rainfall or SST. To isolate the SST effect, we analyze a group of atmospheric experiments in CMIP5, the SUSI and SPSI runs, forced by uniform and patterned SST, respectively. In SUSI runs, rainfall change generally peaks over the tropical convergence zones, present a quasi-wegw effect (Figure 2(a)). On the other hand, in SPSI runs, the maximum positive P is located over the equatorial central Pacific with weak negative change over the Maritime Continent, similar to the distribution of P in RCP 4.5 runs

3 Regional response of annual-mean tropical rainfall to global warming 105 (Figure 2(b)). Some small differences between SPSI and RCP 4.5 could be attributed to the different SST pattern in CMIP3 and CMIP5 (Yeh et al., 2012) or to the lack of radiative forcing in SPSI (Bony et al., 2013). Vertical velocity change ( ω) plays important role on rainfall change. In SUSI, ascending changes occur over the northwestern Pacific, the cold tongue and southeastern Pacific and descending changes over the Indian Ocean, Maritime Continent, Africa and Amazon (Figure 2(d)). The ω pattern in SPSI is very different from that in SUSI over the oceans but similar to the ω in RCP 4.5 run (Figures 2(e) and 3(a)). The SST-induced difference of ω consists well with that of P, ascending changes corresponding to positive rainfall changes and vice versa (Figure 2(c) and (f)). Therefore, the SST-induced ω makes a major contribution to the different P in two runs. As shown in Figure 2(d) (f), the magnitude of circulation change over oceans in SPSI is much stronger than that in SUSI (the variance of oceanic ω in SUSI is only 30% of that in SPSI), and the SST-induced ω can explain around 65% variance of oceanic ω in SPSI. Therefore, the SST-induced ω contributes the majority of ω in SPSI. It is consistent with the model result of previous studies, in which the slowdown of the tropical Pacific circulation is attributed to the SST pattern (Tokinaga et al., 2012a, 2012b). The SST pattern can explain little ω over the Africa and Amazon, which could be driven by the land sea warming contrast (Bayr and Dommenget, 2013). Although the SST-induced ω dominates oceanic ω in SPSI, the SST-induced rainfall change has significant differences from P in SPSI run and RCP 4.5 run (Figures 1(a) and 2(b) and (c)). The SSTinduced rainfall shows an El Niño-like response with negative change over the Maritime Continent and the flanks of tropical Pacific as strong as the positive change over the central Pacific and the northern Indian Ocean (Figure 2(c)), whereas P in SPSI and RCP 4.5 has little negative change over the tropics (DiNezio et al., 2010). Therefore, one other part is needed to P for balancing the negative change induced by SST. We decompose the tropical rainfall change into the contributions of circulation changes and moisture change as in Huang et al. (2013): P ω q + ω q (1) where q denotes surface specific humidity and ω is pressure velocity at 500 hpa. In Equation (1), the dynamic component ω q and thermodynamic component ω q represent the contributions of circulation change and moisture change, respectively (Chou et al., 2009; Seager et al., 2010). It is a good approximation for the tropics (Held and Soden, 2006), which reproduces the zonal-mean seasonal cycle of P well both in coupled models and atmospheric models (Huang et al., 2013). The decomposition of Equation (1) well reproduces the spatial distribution of P (Figures 1(a) and 3(c)), including the Pacific hook-like pattern and the elliptic pattern over the northern Indian Ocean. The spatial distributions of both ω q and ω q are dominated by vertical velocity (Figures 3(a) and 3(b)), because q and q have flat distribution relative to ω and ω. The dynamic component ω q induces positive rainfall change over the deep-tropical Pacific and the Arabian Sea, and negative change over the warm pool regions, the flanks of equatorial Pacific, Amazon and central Africa (Figure 3(a)). The distribution of dynamic component as well as circulation change generally coincides with the SST pattern as in SUSI run, although the tropical mean convective mass flux must weaken due to the energy constraint (Held and Soden, 2006). It reflects the WaGW effect. On the other hand, the thermodynamic component ω q coincides with the climatological rainfall zone, as the mean upward motion pumps up increased moisture under a warming climate (Figure 3(b)). It reflects the WeGW effect. The WeGW effect cancels out the negative change of WaGW effect (dynamic component) over the mean convergence zone, Amazon and central Africa. Except the conspicuous cancellation, the WeGW effect reinforces the positive change over the central Pacific and northern Indian Ocean, and induces the P peaks over the western Pacific and northern central Indian Ocean differing from the dynamic component (Figure 3(c)). It means that the rainfall change with the hook-like pattern over the tropical Pacific and the elliptic pattern over the northern Indian Ocean is a hybrid of WeGW and WaGW effects, like the seasonal pattern of zonal mean (Huang et al., 2013). Similar decomposition also can be performed on P in SPSI and SUSI (not shown). The WeGW effect dominated by positive change over the tropics also cancels out the negative change induced by SST in SPSI and SUSI. Because of greater ω in SPSI, the dynamic component contributes more to total P in SPSI, whereas less contribution of dynamic component to total P in SUSI is due to weaker ω. Therefore, the P pattern in SUSI generally shows a quasi-wegw pattern, whereas in SPSI it shows a quasi-wagw pattern (Xie et al., 2010). Although the relationship between SST pattern and circulation change has been investigated by previous study (Xie et al., 2010), the circulation change and dynamic component have some discrepancies with SST on close inspection (Figures 4(a) and 1(c)). ω shows a hook-like pattern with maximum over the central Pacific, whereas SST has maximum over the eastern Pacific. The effect of SST on circulation change can be understood from the moist instability theory. The moist instability of tropical atmosphere can be evaluated by I = δ p (T + L/C p q), where C p is specific heat capacity at constant pressure and L is latent heat of evaporation (Yu et al., 1998; Chou et al., 2013b). Because of the flat temperature change

4 106 P. Huang Figure 2. Annual-mean precipitation change (left) and circulation change (right) represented by ω at 500 hpa in SUSI and SPSI runs, and their difference. The green curves in (a) show the 6 mm day 1 contour of the precipitation climatology in control runs. Red contours in (d) (f) show the surface component of moist instability change in SUSI and SPSI and their difference (Unit: C). in upper troposphere, the distribution of instability change is dominated by its surface component. I s = T s + L/C p q s (2) In Equation (2), q also contributes to the moist instability change besides SST (Figure 4(b)). Under the restraint of Clausius Clapeyron equation, q q SST (Xie et al., 2010), the Pacific q on the equator is enhanced by SST pattern, and the maximum Pacific q is located in the central Pacific west of SST peak because of q with maximum over the warm pool (Figure 4(c)). With the contribution of q to q, the I s has almost zonally uniform distribution over the tropical Pacific, which is closer to ω compared to SST pattern (Figure 4(a)). The distribution of higher I s resembles well with ascending circulation change (Figure 4(a) and (b)). Other Figure 3. (a) The dynamic ( ) and (b) thermodynamic ( ) components of P and (c) their sum in RCP 4.5. In (a), the green curves are the Pa s contour of thermodynamic component; in (c), the red curves are the 0.4 mm day 1 contour of P.

5 Regional response of annual-mean tropical rainfall to global warming 107 Figure 4. Annual-mean (a) circulation change ( ω at 500 hpa; shaded), (b) surface component of moist instability change I s (shaded and red contours in (a)) and (c) specific humidity change q in RCP 4.5. Green curves in (a) and (b) are the 26.5 C contour of annual-mean SST in historical run. discrepancies between ω and I s are the weak ω over the cold tongue with strong instability change. It should be attributed to the background SST (green curves in Figures 4(a) and (b)). Because of the nonlinear relationship between tropical convection and SST and the weaker spatial deviation of SST relative to climatological SST, the relative high I s cannot lead to large ascending ω over the cold tongue (Graham and Barnett, 1987; Johnson and Xie, 2010). Therefore, the forcing of SST pattern (WaGW effect) is modified by the background q and SST, and then induces the hook-like pattern of ω and P over the tropical Pacific. The ω results in the atmospheric runs verify the explanation of ω in RCP 4.5 run by the moist instability theory. The SST-induced ω well matches the different surface moist instability change ( I s ) between SUSI and SPSI very well (Figure 2(f)). The ω in SPSI matches I s better than in SUSI run. In SUSI, the ascending change of ω over the Pacific follows the greater I s, but the relative greater I s over the Indian Ocean and Maritime Continent cannot explain the descending change of ω. It could be attributed to the weak spatial pattern of I s in SUSI, although I s is not spatial uniform under the spatial uniform SST forcing (Figure 2(d)) because of the contribution of non-uniform moisture change (Xie et al., 2010). When the spatial gradient of I s is weak, the relative contribution of other factors, such as the land sea contrast and mean advection of stratification change, on ω will become more significant (Ma et al., 2012; Bayr and Dommenget, 2013). Although it has been illustrated that the higher I s associated with higher SST can induce proportional ascending change and the descending change is always located over weaker I s (Figures 2(d) (f) and 4(b)), the descending change is not strictly proportional to the weak I s even if the other factors are removed in Figure 2(f). Therefore, the mechanism of convective suppression under global warming must be more complicated than the simplified I s mechanism. 4. Conclusions This study shows the spatial pattern of annual-mean P is a hybrid of the WeGW and the WaGW effects using 18 CMIP5 models. The two complementary effects best explains the regional pattern of annualmean rainfall response to global warming. The climatological mean circulation pumps up increasing low-level moisture and then induces positive P over current tropical convergence zone (WeGW effect), whereas the greater SST warming drives ascending circulation response and then induces positive P over the central Pacific and northwestern Indian Ocean (WaGW effect). The combined effects form a hook-like pattern of P over the tropical Pacific (with maximum over the western Pacific) and an elliptic pattern of P over the northern Indian Ocean. Although the dynamic component of P generally exhibits a WaGW pattern, the climatological distribution of surface moisture and SST also contribute to the dynamic component. The background

6 108 P. Huang moisture can influence the change in specific humidity that contributes to atmospheric instability change and then to the circulation change. On the other hand, the background SST can modify the influence of instability change on circulation change due to the nonlinear relationship of SST-convection (Graham and Barnett, 1987; Johnson and Xie, 2010). The climatological moisture and SST lead ω peaks over the tropical central Pacific, west of the maximum SST over the eastern Pacific. Although the dominant role of SST pattern on dynamic component of P is emphasized in this study, another factors such as weakening circulation induced by mean advection of stratification change, direct role of radiative forcing, the contribution of convection depth change, etc. can also contribute the dynamic component of P (Chou and Chen, 2010; Ma et al., 2012; Bony et al., 2013; Chadwick et al., 2013). In Huang et al. (2013), the unifying effect of WeGW and WaGW on the seasonal cycle of P was called WeWa effect. Here, it is demonstrated that the unifying WeWa effect including WeGW and modified WaGW also applies to the spatial distribution of annual-mean rainfall change under global warming. Under WeWa mechanism, the WeGW part of P pattern can be projected based on current rainfall, but the WaGW part has great uncertainties due to the uncertain SST response (Deser et al., 2010; Ma and Xie, 2013). However, the modification on WaGW by climatological moisture and SST decreases the uncertainties of WaGW pattern, and thus the uncertainties of P pattern should be decreased relative to around 50% uncertain P estimated by Huang et al. (2013) in which the climatology rainfall and uncertain SST pattern have almost equal contribution to P. 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