Ocean warming pattern effects on future changes in East Asian atmospheric rivers

We examine large-ensemble simulation outputs called d4PDF (Mizuta et al 2017) developed using the Meteorological Research Institute Atmospheric General Circulation Model (MRI-AGCM) version 3.2 (Mizuta et al 2012). Mizuta et al (2017) provided details of the model and experiments. Horizontal resolution of this AGCM is TL319 (equivalent to 60 km grid). The number of vertical level is 64 (with the top at 0.01 hPa). To conduct current climate simulation for 1951–2010, the model was forced by observed SST, sea ice (Hirahara et al 2014), and historical radiative forcing agents (greenhouse gases, aerosols, and ozone). The ensemble simulations were developed with random initial and SST perturbations (Hirahara et al 2014). In this study, a 10-member ensemble mean was examined to reduce the effect of atmospheric internal variability (Kamae et al 2017a, 2017b).

We detect ARs using a methodology modified from Mundhenk et al (2016). First, vertically integrated water vapor transport (IVT) is calculated using 6-hr atmospheric variables. IVT anomaly is calculated by comparing with its climatology. Next, shape (e.g. length > 1500 km, area > 7.8 × 10⁵ km², length–width ratio > 1.325) and intensity (140 kg m^–1 s^–1) of anomalous IVT are checked to detect ARs. The detection scheme can filter out circular IVT anomalies associated with tropical cyclones via the shape criterion (see figure A2 of Mundhenk et al 2016). Note that AR-like IVT anomalies examined in this study are sometimes influenced by remote effects of tropical cyclone (Kamae et al 2017c). AR frequency examined in this study is defined as the fraction of time periods during which an AR exists to the total number of periods. Kamae et al (2017b) reported that this model can realistically simulate the climatology, seasonal cycle, and interannual variability of North-Pacific AR compared to an atmospheric reanalysis.

In addition to the current climate simulation, outputs from global warming simulations (Mizuta et al 2017) were also examined to evaluate future change in AR activity. In this set of simulations, the model was forced by constant radiative forcing, SST warming patterns, and anomalous sea ice concentration. Anomalous radiative forcing corresponding to the level of year 2090 in the Representative Concentration Pathway (RCP) 8.5 scenario adopted in CMIP5 was added to the radiative forcing in the current climate simulation. Six SST warming patterns derived from cluster analyses of CMIP5 multi-model results (figure S1 is available online: stacks.iop.org/ERL/14/054019/mmedia; Mizuta et al 2014) were added to historical SST. The six SST warming patterns were based on RCP8.5 simulations by CCSM4 (hereafter CC), GFDL-CM3 (GF), HadGEM2-AO (HA), MIROC5 (MI), MPI-ESM-MR (MP), and MRI-CGCM3 (MR). Each SST warming pattern was scaled so that the simulated global-mean surface air warming from the pre-industrial level is equal to 4 K (Mizuta et al 2017). The six patterns exhibit different zonal and meridional gradients over the Pacific, Indian and Atlantic Oceans (table S1, figure S1). For example, CC run was forced by La Niña-like SST pattern in the tropical Pacific (larger warming over the western equatorial Pacific than central-to-eastern equatorial Pacific), in contrast to El Niño-like warming pattern prescribed in other runs (figure S1; see section 3.3). Among the six runs, tropical Atlantic warming and meridional warming contrast between 30°–60°N and EQ–30°N in the North Pacific are strongest in MI and GF runs, respectively (table S1, figures S1, S2). In each run, ensemble was developed in a similar way to the current climate simulation (section 2.1). The AR detection scheme described in section 2.1 and used in Kamae et al (2017b) was applied to the outputs of global warming simulation.

To evaluate the forced response to radiative forcing and SST warming, 10-member ensemble mean, 60-yr climatology of AR frequency in each run is compared. In this study, projected changes between global warming and current climate simulations scaled by tropical-mean (30°S–30°N) SST anomaly are represented as Δ(K^–1). Average of Δ among six warming simulations can be regarded as a robust response to global warming simulated in this model. Deviations of Δ in individual warming simulations from the averaged Δ can be considered as the effect of ΔSST deviation from the averaged warming pattern (figure S2).

We first examine simulated changes in atmospheric circulation and AR frequency in each season robustly found among the six warming simulations. Figure 1 shows six-run-mean sea level pressure (SLP) response (ΔSLP), change in AR frequency, and prescribed ΔSST. Averaged ΔSST pattern is almost identical to the ensemble mean of CMIP models: stronger warming over the mid- and high-latitude North Pacific than the tropics, and stronger warming over the tropical eastern Pacific than western Pacific (e.g. Xie et al 2010, Mizuta et al 2014). During boreal summer, the North Pacific High (NPH) weakens (negative ΔSLP) in response to climate warming (figure 1(e); table 1), consistent with CMIP5 ensemble mean response reported in previous studies (He et al 2017, Song et al 2018). In this model, the western North Pacific subtropical high (WNPSH) around the Philippines is enhanced (figure 1(e); table 1), although model responses are divergent among CMIP5 models (He and Zhou 2015). Despite seasonal variations in ΔSLP and associated low-level circulation response (figures 1(a), (c), (e), (g)), AR frequency is increased over the middle and high latitudes during almost all the seasons (figures 1(b), (d), (f), (h)). The general increasing trend of ARs under climate warming can be attributed to thermodynamic effect (e.g. Lavers et al 2013, Gao et al 2015). Atmospheric water vapor increase and resultant positive ΔIVT should result in more AR count because the anomalous IVT threshold (section 2.1) is identical between current climate and global warming simulations.

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ARs become more frequent over East Asia (0.91 ± 0.15 % K^–1; table 1), western North America, eastern North America, and Europe (figures 1(b), (d), (f), (h)). During boreal summer, large increases in AR frequency over the subtropical North Pacific and East Asia (25°–40°N) are concurrent with anomalous low-level circulations associated with the weakened NPH and the enhanced WNPSH. Anomalous moist southwesterlies on the southeastern flank of the weakened NPH and on the northwestern flank of the enhanced WNPSH increase AR occurrences over those regions (figure 1(e); detailed in section 3.2). Such circulation-AR relationship is consistent with what found in interannual variability (Kamae et al 2017b; detailed in section 3.3). Such a dynamic effect of atmospheric circulation on regional changes in AR frequency (e.g. Payne and Magnusdottir 2015, Shields and Kiehl 2016; see section 1) suggests the importance of ΔSST pattern in future projections of North-Pacific AR activity. In this study, we mainly focus on North-Pacific ARs during boreal summer. AR responses during other seasons and over other regions will be examined in future studies.

Next, we examine sensitivity to ΔSST pattern. Figure 2 shows changes in boreal summer AR frequency over East Asia and the North Pacific simulated in the six runs. All the simulations produce a general increase in North-Pacific AR frequency, but the circulation and AR responses exhibit large spreads among the six runs: limited enhancement of WNPSH (limited weakening of NPH) in CC, GF, MP and MR runs (CC run) compared to other runs (table 1). The differences in the quantitative changes in East-Asian ARs (table 1) are partly related to the spreads of the WNPSH response. For example, MI run shows the largest enhancement of WNPSH among the six runs and correspondingly the largest increase in East-Asian AR occurrence (table 1) especially over southeastern China (figure 2(d)), while the changes are relatively weak in GF and MP runs (table 1, figures 2(b), (e), S3). These results suggest that a significant portion of the uncertainty in ΔARs is due to the SST-mediated dynamic effect (see section 3.3).

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However, the WNPSH response cannot explain all the spreads of East Asian ΔARs. For example, CC run (figure 2(a)) produces larger East-Asian ΔARs than GF, HA, MP and MR runs (table 1) but not a larger enhancement of WNPSH. Another feature found in CC run is a limited weakening of NPH and associated North-Pacific circulation response (figure S3). Here circulation anomaly associated with the NPH response is important for ΔAR pattern over the North Pacific. GF run shows the largest weakening of NPH, causing a southeastward shift of seasonal-mean AR frequency over the North Pacific: a moderate increase over the middle latitudes on the northwestern flank of the anomalous NPH with a greater increase over the subtropics on the southeastern flank (figure 2(b)). Such a meridional shift is not apparent in CC run (figure 2(a)), suggesting that the NPH response is also an important factor for the spatial pattern of ΔARs over the North Pacific.

The spreads in circulation and AR responses result from differences in ΔSST prescribed in these runs. Here SST-circulation-AR relationship found in the global warming simulations appears consistent with that observed in interannual variability. In the next subsection, we examine the relationship found in interannual variability and compare it with the results shown above.

Figure 3 shows major modes of interannual variability of East-Asian summertime ARs in the current climate simulation for 1951–2010 based on empirical orthogonal function (EOF) analysis of 10-member ensemble mean. Note that EOF1 is nearly identical to the ENSO-related interannual variability in reanalysis and d4PDF (Kamae et al 2017b). The EOF1 is modulated by the Indo-western Pacific Ocean capacitor effect (IPOC; Xie et al 2016): eastward propagating Kelvin waves and an anomalous WNPSH (figure 3(a)) induced by the North Indian Ocean (NIO) and the South China Sea warming after wintertime El Niño development (figure 3(c); Xie et al 2009). Note that warm SST anomaly over the equatorial Pacific (5° S–5°N, 170°–120°W) associated with EOF1 rapidly diminishes after the wintertime El Niño peak, resulting in very modest anomaly during summer (figure 3(c); see figure 8 of Kamae et al 2017b). The strengthened southwesterlies on the northwestern flank of the WNPSH increase AR occurrence over East Asia (figure 3(a); Kamae et al 2017b). Here the meridional atmospheric teleconnection (Pacific-Japan pattern) associated with IPOC (Xie et al 2016) results in a pair of anticyclone and cyclone over the subtropical western North Pacific and over Okhotsk Sea, producing a tripolar pattern of AR anomalies, as reported in Kamae et al (2017b).

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In contrast to the tripolar pattern, the EOF2 is characterized by a dipole pattern of AR frequency over the North Pacific: an increase over the extratropics and a decrease over the subtropics (figure 3(b)). Atmospheric circulation associated with EOF2 exhibits an anticyclonic anomaly over the North Pacific, indicating an enhanced NPH. The circulation-AR relationship, an increase (decrease) in ARs on the northwestern (southeastern) flank of the anomalous anticyclone, is similar to that in EOF1 although positions and horizontal extent are different. The anomalous circulation (figure 3(b)) facilitates more frequent landfalling ARs over East Asia (especially northeastern China, Korean Peninsula, and northern Japan) and suppresses ARs off the Pacific coasts of Japan. Figure 3(d) shows the anomalous SST pattern corresponding to EOF2. Negative SST anomalies are found over the central and eastern tropical Pacific, indicating a summertime development of La Niña. The relationship between summertime SST anomaly and the NPH is consistent with previous studies (e.g. Wang et al 2013, Paek et al 2018). Details of La Niña-induced anomalies in atmospheric circulation and ARs over the North Pacific are currently being extensively examined and will be reported in a separated paper.

The SST-circulation-AR relationship found here is helpful for detecting the SST pattern effect on North-Pacific ΔARs under global warming. The SST forcing prescribed in MI run exhibits larger warming over the NIO and the South China Sea and weaker warming over the central equatorial Pacific than the six-run mean (table S1, figures 4, S1, S2). The larger enhancement of WNPSH under global warming in MI run is consistent with CMIP5 inter-model spreads (He and Zhou 2015) and the SST-circulation relationship found in interannual variability (figure 3). CC run was forced by La Niña-like ΔSST pattern (zonal ΔSST gradient between the western equatorial Pacific and central equatorial Pacific; table S1, figures S1, S2), in contrast to El Niño-like Pacific ΔSST found in the multi-model ensemble mean (e.g. Xie et al 2010, Mizuta et al 2014). The ΔSST in CC run is consistent with the weak NPH response compared to other runs (table 1, figures 2(a), S3(a)). These results suggest that the anomalous NIO and South China Sea warming and La Niña-like SST gradient are important factors for the inter-model spreads in ΔARs over East Asia and the North Pacific. Note that ΔSST over other regions including the Atlantic Ocean (table S1, figures S1, 2) may also play some roles (e.g. Chen et al 2014, Hong et al 2014), which is not discussed here since our focus in this study is on the tropical Indian and Pacific Ocean.

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Figure 4(a) shows the interannual relationship among NIO SST (averaged over 50°–125°E, 5°–25°N), zonal SST gradient between the tropical western Pacific (TWP) and tropical central Pacific (TCP; 100°–150°E, 5 S°–5°N minus 150E°–140°W, 5 S°–5°N), and East-Asian summertime AR frequency for 1951–2010. Removing linear trends for 1951–2010 from these variables does not change the results significantly. Here the two SST indices are not significantly correlated (r = 0.07). If we use these two as explanatory variables, East Asian ARs (%) can be approximated using a multiple linear regression (R² = 0.57) as follows:

$$\begin{eqnarray}&&{\rm{A}}{\rm{R}}{\rm{s}}=\mbox{--}83.79+3.23{X}_{1}+0.39{X}_{2},\end{eqnarray} \tag{ 1 }$$

where X₁ is NIO SST (°C) and X₂ is TWP SST minus TCP SST (K). Years with higher NIO SST tend to produce more frequent East Asian ARs as reported in Kamae et al (2017b). In addition, warmer TWP than TCP (La Niña-like SST gradient) is associated with increased East Asian ARs. Similarly, SSTs in these two regions are important for the spreads of East-Asian ΔARs among the six warming simulations. Figure 4(b) shows relationship between NIO ΔSST, TWP ΔSST minus TCP ΔSST, and East Asian ΔARs in these runs. As a reference, we also plot ΔSST simulated in CMIP5 models (Taylor et al 2012; table S2). The six d4PDF runs effectively cover uncertainty ranges of the tropical ΔSST pattern in CMIP5 models (Mizuta et al 2014) except two models (strong La Niña-like SST gradient simulated in FGOALS-g2 and weak NIO warming in INMCM4). The East Asian ΔARs is approximated as follows:

$$\begin{eqnarray}&&{\rm{\Delta }}{\rm{A}}{\rm{R}}{\rm{s}}=\mbox{--}0.82+1.66{X}_{1}+0.94{X}_{2},\end{eqnarray} \tag{ 2 }$$

where X₁ is NIO ΔSST (K) and X₂ is TWP ΔSST minus TCP ΔSST (K). R² of equation (2) is 0.66. Among the d4PDF runs, the larger increases in SST over the NIO and the South China Sea in MI and MR runs (table S1, figure 4(b), S2) are associated with larger increases in East-Asian ΔARs (table 1, figures 2, 4(b)). In addition, the La Niña-like ΔSST gradients in CC and MI runs (figure 4(b), S2) are also related to greater increases in East-Asian ΔARs (table 1, figures 2, 4(b)). Based on these results, we suggest that the two ΔSST indices are important sources of uncertainty in East-Asian ΔARs. Note that the regression coefficients obtained in equation (2) based on the limited samples size (n = 6) may be sensitive to increasing sample size. Relative importance of NIO SST (X₁) compared to the zonal SST gradient (X₂) under global warming should be further tested using sensitivity experiments prescribed with idealized SST warming patterns.