1. Introduction
Precipitation variability has profound impacts on society and ecosystems because it is closely related to drought, floods, and water resources. Any useful prediction skills with regard to precipitation will be greatly beneficial and normally associated with certain climate modes within the coupled climate system, such as the interdecadal Pacific oscillation (IPO; Zhang et al. 1997; Power et al. 1999) and the Atlantic multidecadal oscillation (AMO; Kerr 2000). The AMO, for example, is found to be strongly coupled with the global water cycle (Vellinga and Wu 2004). Anthropogenic climate change is also affecting the global water cycle with regional impacts (Wu et al. 2013, 2015; Zhang et al. 2017; Kang and Eltahir 2018). There are certain regions over the world that are particularly sensitive to episodes of climate-related precipitation variability on decadal or multidecadal time scales, such as North China and the Southwest United States.
Both North China and the Southwest United States are typical arid and semiarid regions, often hard hit by serious droughts. Many studies have attributed their interdecadal precipitation variability to the IPO, although the underlying mechanism is still under intensive debate (Farneti et al. 2014; Di Lorenzo et al. 2015; Newman et al. 2016; Si and Hu 2017; Henley 2017; Henley et al. 2017). The warm IPO phase corresponds to below-normal precipitation over North China (Ma and Shao 2006; Ma 2007; Zhou et al. 2013; Qian and Zhou 2014; Huang et al. 2017; Yang et al. 2017a) but above-normal precipitation over the Southwest United States (Meehl and Hu 2006; Dai 2013), implying an obvious out-of-phase relationship for precipitation regime shift. Following the IPO cold-to-warm phase shift around 1977, there is an evident drying trend over North China, whereas a significant wetting trend is shown in the Southwest United States since the 1950s; increased precipitation in the Huang-Huai River region (southern North China) after 2000 and a robust drying trend over the Southwest United States since the 1980s are thought to be largely caused by the IPO warm-to-cold phase transition around 1999 (Ma 2007; Zhu et al. 2011; 2015; Dai 2013; Chylek et al. 2014; Qian and Zhou 2014). Additionally, the AMO contributes to the out-of-phase decadal precipitation pattern (Li and Bates 2007; Mo et al. 2009; Schubert et al. 2009; Feng et al. 2011; Zhu et al. 2016; Si and Ding 2016). The warm AMO phase tends to induce a stronger East Asia summer monsoon (EASM), leading to more northward transport of moisture and enhanced rainfall over North China, but dry conditions in the Southwest United States (Lu et al. 2006; Sutton and Hodson 2005; 2007; Hu et al. 2011; Feng et al. 2011). The combined effects of the IPO and AMO are complicated and important (Schubert et al. 2009; Zhu et al. 2016; Si and Hu 2017; Yang et al. 2017b). For instance, more than half (52%) of the spatial and temporal variance in multidecadal drought frequency over the United States is attributable to the AMO and the Pacific decadal oscillation (PDO; Mantua et al. 1997; Mantua and Hare 2002), which is the North Pacific component of the IPO (McCabe et al. 2004).
The purposes of this study are to identify the interdecadal seesaw of precipitation variability between North China and the Southwest United States, to further understand the associated formation mechanism related to the IPO and AMO, and to answer why there is an out-of-phase decadal precipitation pattern between North China and the Southwest United States through analysis of decadal ocean forcings.
Although many studies have investigated the contrasting relationship of precipitation variability between Asia and North America (e.g., Lau and Weng 2002; Lau et al. 2004; Li et al. 2005; Ma and Fu 2007; Hua et al. 2011; Zhao et al. 2011, 2016; Wang et al. 2014; Fang et al. 2014, 2015; Yang and Fu 2017; Zhu and Li 2016), the circulation mechanism for the seesaw relationship remains unclear. This is because that previous research has focused on inconsistent study areas (Zhao et al. 2011; 2016; Fang et al. 2015), time scales (Lau and Weng 2002; Lau et al. 2004; Fang et al. 2014; Wang et al. 2014), and precipitation-related variables (Li et al. 2005; Yang and Fu 2017). Additionally, for attribution of precipitation variability, more attention has been paid to the Asian–Pacific Oscillation (Zhao et al. 2011; Fang et al. 2015), East Asian subtropical monsoon heating (Zhu and Li 2016), Eurasian non-monsoon land heating (Zhao et al. 2016), and North Pacific sea surface temperature (SST) anomalies (Lau and Weng 2002; Lau et al. 2004), rather than to the IPO and AMO. We aim to address these shortcomings in this study.
The observed precipitation records are shorter than 150 years over the United States, and the quality of the records declines significantly backward in time. In China, most meteorological stations were established after the 1960s, and only few stations were available before the 1960s, resulting in only about 60 stations with approximately 100 years of precipitation observations. These datasets are just enough to cover one (or two) full cycles of the AMO and the IPO, which limits their utility for diagnosing robust regional climate impacts linked with the IPO and AMO. For instance, Newman et al. (2016) indicated that climate impacts correlated with the PDO are different from climate impacts that are predicted by the PDO because both climate impacts and the PDO may be driven by a common forcing function. It is difficult to distinguish these two relationships in observation. Atmospheric general circulation models (AGCMs) with prescribed observed or idealized SST patterns (e.g., Li et al. 2010; Hu et al. 2011; Dai 2013; Schubert et al. 2009) are effective tools to confirm or reject conclusions reached from observational analysis. Thus, in addition to observational and reanalysis data, a series of AGCM experiments designed by the U.S. Climate Variability and Predictability (CLIVAR) Drought Working Group (Schubert et al. 2009) are also employed in this study. As they are not fully coupled, their experiments are not suitable to disentangle air–sea interaction. Detailed physical mechanisms of the interdecadal oceanic modes and their potential influence to the seesaw relationship have not been addressed in this study, and further follow-up work is needed.
The rest of the paper is organized as follows. In section 2, we describe the data, AGCM experiments, and methods used in this study. The observed seesaw pattern and associated circulation are presented in section 3. The possible mechanisms are provided in section 4. A summary and discussion are given in section 5.
2. Data and methods
a. Data
The following datasets are used in this study:
Global merged monthly precipitation (based on gauge records) for 1850–2014 on a 2.5° latitude × 2.5° longitude grid obtained from Dai (2011, 2013). We focused on the period 1900–2014 because precipitation observations are sparse over China before around 1900.
Global land monthly precipitation for 1901–2015, with a high resolution of 0.5° latitude × 0.5° longitude, obtained from CRU TS4.0 (University of East Anglia Climatic Research Unit 2017).
Chinese long-term gridded precipitation data for 1900–2009 obtained from the China Meteorological Administration.
Global atmospheric reanalysis monthly data for circulation for 1948–2016 on a 2.5° latitude × 2.5° longitude grid, from the U.S. National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) (Kalnay et al. 1996).
Global gridded monthly SST for 1901–2016 on a 2° latitude × 2° longitude grid, from the National Oceanic and Atmospheric Administration Extended Reconstructed SST version 4 (ERSST v4; Huang et al. 2015).
The annual IPO index for 1920–2015, from Dai (2013), who defined the smoothed (by applying a 9-yr moving average twice) associated principal component of the second leading empirical orthogonal function (EOF) of the 3-yr moving averaged SST from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003), using data for 1920–2011 and 60°S–60°N.
The monthly AMO index for 1856–2016 (Enfield et al. 2001) downloaded from https://www.esrl.noaa.gov/psd/data/timeseries/AMO/.
b. AGCM experiments
The U.S. CLIVAR Drought Working Group initiated a series of global climate model experiments to address the physical mechanisms that link global SST variations to regional drought and flood (Schubert et al. 2009). Associated forcing SST fields for experiments were prescribed as follows:
The rotated empirical orthogonal function (REOF) method was applied to global annual-mean SST anomalies (Rayner et al. 2003) from 1901 to 2004 to obtain the leading patterns of SST anomalies.
The REOF2 (Fig. 1a) was scaled by ±2σ [σ is the standard deviation of the associated rotated principal component (RPC2; Fig. 1b)] and then superimposed onto the 1901–2004 monthly climatology, forming a forcing SST anomaly called the Pacific pattern.
Applying the method in step 2 to the REOF3 (Fig. 1c) yields the Atlantic pattern.
The second and third leading REOFs (°C) and associated time series RPCs (°C) of annual mean SST from 1901 to 2004 (Schubert et al. 2009). (a) The Pacific ENSO-like SSTA pattern of REOF2 and (b) associated RPC2 and the IPO index. (c) The Atlantic AMO SSTA pattern of REOF3 and (d) associated RPC3 and AMO index. The blue curves in (b) and (d) are smoothed time series obtained by applying a 9-yr moving average twice to the bars to emphasize the interdecadal variations. The same method is applied to the IPO and AMO indices. The terms r and p represent the correlation coefficients between the red and blue curves and the statistical significance level, respectively. The percentages indicate the corresponding explained variances.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
The second and third leading REOFs (°C) and associated time series RPCs (°C) of annual mean SST from 1901 to 2004 (Schubert et al. 2009). (a) The Pacific ENSO-like SSTA pattern of REOF2 and (b) associated RPC2 and the IPO index. (c) The Atlantic AMO SSTA pattern of REOF3 and (d) associated RPC3 and AMO index. The blue curves in (b) and (d) are smoothed time series obtained by applying a 9-yr moving average twice to the bars to emphasize the interdecadal variations. The same method is applied to the IPO and AMO indices. The terms r and p represent the correlation coefficients between the red and blue curves and the statistical significance level, respectively. The percentages indicate the corresponding explained variances.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
The second and third leading REOFs (°C) and associated time series RPCs (°C) of annual mean SST from 1901 to 2004 (Schubert et al. 2009). (a) The Pacific ENSO-like SSTA pattern of REOF2 and (b) associated RPC2 and the IPO index. (c) The Atlantic AMO SSTA pattern of REOF3 and (d) associated RPC3 and AMO index. The blue curves in (b) and (d) are smoothed time series obtained by applying a 9-yr moving average twice to the bars to emphasize the interdecadal variations. The same method is applied to the IPO and AMO indices. The terms r and p represent the correlation coefficients between the red and blue curves and the statistical significance level, respectively. The percentages indicate the corresponding explained variances.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
As shown in Fig. 1a, the Pacific pattern is analogous to the IPO-associated SST anomaly, and RPC2 is significantly correlated with the IPO index; thus, the Pacific pattern is deemed the IPO in this study. Likewise, the Atlantic pattern is deemed the AMO. Note that the prescribed SST fields vary monthly with the climatological (1901–2004) seasonal cycle but have no interannual or longer-term variability.
We focused on nine baseline experiments, which are combinations of the Pacific (P) and Atlantic (A) patterns of cold (denoted by c; scaled by −2σ), neutral (n; zero anomaly), and warm (w; scaled by +2σ) anomalies (denoted by PxAy shown in Table 1, where x and y can be c, n, or w). These baseline experiments were performed by five AGCMs: the NASA Seasonal-to-Interannual Prediction Project AGCM (NSIPP1; Bacmeister et al. 2000), the Global Forecast System AGCM (GFS; Campana and Caplan 2005), the Geophysical Fluid Dynamics Laboratory AGCM (GFDL; Delworth et al. 2006), the Community Atmospheric Model (CAM3.5; Chen et al. 2010), and the Community Climate Model (CCM3; Kiehl et al. 1998). All simulations ran for 50 years, except the GFS (35 years). Additional experiments (performed by only GFDL and NSIPP1) forced by the tropical SST component of the Pacific pattern (Fig. 1a, blue box) were also analyzed. For more information and an overview of the U.S. CLIVAR Drought Working Group, please see Schubert et al. (2009).
The combinations of Pacific (P) and Atlantic (A) SST anomaly patterns used to force the AGCMs. Here the letter w refers to the warm phase of the pattern (scaled by +2σ), n denotes neutral (zero anomaly), and c refers to the cold phase (scaled by −2σ). The PnAn experiment denotes the control run forced with the annually varying climatological SST (Schubert et al. 2009).
c. Methods
We focused on interdecadal precipitation variations and used a 9-yr moving average twice, which is equivalent to a 17-yr low-pass filter with unequal weights, to remove short-term variations. Regression analysis and epoch composites were employed to depict the spatial patterns of anomalous SST and atmospheric circulation associated with the precipitation and the IPO. The threshold of ±0.5σ of the IPO index was used to define the IPO cold and warm phases. The long-term trend in global SST was removed by regressing the SST with the time series of global annual mean SST, while the long-term trend in other variables was removed using a linear least squares fit. The Student’s t test (Wilks 2005, 138–140) was used to test the difference of mean during different composite periods and detect the statistical significance of linear regression coefficients and Pearson correlation coefficients, after adjusting the degrees of freedom (see the online supplemental material). In addition, simulations from all five AGCMs were averaged to obtain an ensemble mean, which often reduces model uncertainties introduced by subgrid-scale parameterizations and produces signals closer to observations (Rowell 1998; Wang et al. 2005; Mo et al. 2009).
3. Interdecadal seesaw and associated circulations
Figure 2a shows detrended and smoothed regional average precipitation anomalies over North China and the Southwest United States, as well as the IPO and AMO indices. Precipitation variations over North China are significantly negatively correlated with those over the Southwest United States, with wet (dry) conditions over North China aligning with dry (wet) conditions over the Southwest United States, implying an apparent interdecadal seesaw pattern.
(a) Detrended monthly precipitation anomalies over North China (110°–118°E, 34°–42.5°N) and the Southwest United States (105°–120°W, 30°–40°N) and the IPO and AMO indices, obtained by applying a 109-month moving average twice. Observed precipitation anomalies over North China are referred to as “North China_station.” Black dotted lines represent ±0.5σ of the IPO index. (b) Annual mean precipitation anomaly percentage (%) over North China and the Southwest United States during different periods: cold IPO (IPO−), cold IPO plus warm AMO (IPO−AMO+), cold IPO plus cold AMO (IPO−AMO−), warm IPO (IPO+), warm IPO plus cold AMO (IPO+AMO−), and warm IPO plus warm AMO (IPO+AMO+), with respect to the climatology from 1920 to 2014. Statistically significant anomalies at the 5% level are indicated by black triangles.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
(a) Detrended monthly precipitation anomalies over North China (110°–118°E, 34°–42.5°N) and the Southwest United States (105°–120°W, 30°–40°N) and the IPO and AMO indices, obtained by applying a 109-month moving average twice. Observed precipitation anomalies over North China are referred to as “North China_station.” Black dotted lines represent ±0.5σ of the IPO index. (b) Annual mean precipitation anomaly percentage (%) over North China and the Southwest United States during different periods: cold IPO (IPO−), cold IPO plus warm AMO (IPO−AMO+), cold IPO plus cold AMO (IPO−AMO−), warm IPO (IPO+), warm IPO plus cold AMO (IPO+AMO−), and warm IPO plus warm AMO (IPO+AMO+), with respect to the climatology from 1920 to 2014. Statistically significant anomalies at the 5% level are indicated by black triangles.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
(a) Detrended monthly precipitation anomalies over North China (110°–118°E, 34°–42.5°N) and the Southwest United States (105°–120°W, 30°–40°N) and the IPO and AMO indices, obtained by applying a 109-month moving average twice. Observed precipitation anomalies over North China are referred to as “North China_station.” Black dotted lines represent ±0.5σ of the IPO index. (b) Annual mean precipitation anomaly percentage (%) over North China and the Southwest United States during different periods: cold IPO (IPO−), cold IPO plus warm AMO (IPO−AMO+), cold IPO plus cold AMO (IPO−AMO−), warm IPO (IPO+), warm IPO plus cold AMO (IPO+AMO−), and warm IPO plus warm AMO (IPO+AMO+), with respect to the climatology from 1920 to 2014. Statistically significant anomalies at the 5% level are indicated by black triangles.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Note that a significant positive correlation is found between Southwest U.S. precipitation and the IPO index, with the maximum correlation (r = 0.90) at zero lag, implying that they vary in phase. North China precipitation is significantly anticorrelated with the IPO index at zero lag, suggesting they vary out of phase. This means that the IPO may be associated with the interdecadal seesaw pattern, with the warm IPO phase aligned with below-normal precipitation over North China but above-normal precipitation over the Southwest United States and the cold IPO phase aligned with wet over North China but dry over the Southwest United States (Fig. 2b). It is also noted that the maximum negative correlation (r = −0.62) occurs when the IPO leads North China precipitation by about 8 years (see Fig. S2 in the supplemental material). The 8-yr lagged anticorrelation could be one reason why the PDO trend during 1971–2012 is nearly zero; however, there is a significant decline in North China precipitation mentioned by Zhou et al. (2013).
Unlike the IPO, the AMO has an insignificant positive correlation with North China precipitation at zero lag, implying that the AMO’s contemporaneous influence on precipitation is smaller than the IPO’s. The maximum positive correlation (r = 0.85) is found when the AMO leads precipitation by about 15 years, or approximately one-quarter of an AMO cycle. The AMO has a significant anticorrelation with precipitation over the Southwest United States, with maximum negative correlation (r = −0.81) when the AMO leads precipitation by about 11 years. This implies that the AMO has the opposite effect on the interdecadal seesaw with respect to the IPO. Additionally, the AMO can only strengthen (weaken) the magnitude of the IPO-induced precipitation anomaly when the AMO is out of phase (in phase) with the IPO (Fig. 2b).
We also performed the same analysis using the CRU TS4.0 precipitation data. The results (see Fig. S1) are analogous to those shown in Fig. 2a using the Dai precipitation data, indicating that the relationships are robust and not sensitive to the choice of data.
Given that the IPO may play a dominant role in the interdecadal seesaw pattern, the IPO phase composite anomalies of SST and precipitation over North China and the Southwest United States are shown in Fig. 3. During the first cold IPO phase (1947–72), precipitation shows a north–south dipole, with above-normal precipitation over North China but below-normal precipitation over South China (Fig. 3a). Meanwhile, most of the contiguous United States receives below-normal precipitation except parts of the Northwest and Southeast, where precipitation increases (Fig. 3b). During the warm IPO phase (1977–98), the spatial pattern of precipitation anomalies resembles that associated with the first cold IPO phase, but with opposite sign. However, note that the two cold IPO phases have distinct precipitation anomalies. For the recent cold phase (2003–14), above- and below-normal precipitation is expected over North China and the Southwest United States, respectively. However, precipitation decreases in both regions. A comparison of the SST anomalies associated with the two cold IPO phases (Figs. 3c and 3i) shows opposite-signed SST anomalies, except in the North Pacific. This implies that other interdecadal SST modes (e.g., the AMO) may influence regional precipitation anomalies; over North China, their influences tend to be comparable to that of the IPO, but over the Southwest United States they are relatively smaller. In addition, the 8-yr lag anticorrelation between precipitation over North China and the IPO for 1920–2014 is similar to that for 1920–2000 (Fig. S3), suggesting that the lag anticorrelation is robust. Therefore, the 8-yr lag could be another reason for the negative precipitation anomaly in North China during the recent IPO cold phase, which has thus far existed for only approximately a quarter of a traditional IPO cycle.
The IPO phase composite anomaly (relative to 1900–2014) maps of the detrended precipitation (mm day−1) over (left) North China and (center) the Southwest United States, and (right) annual mean SST (°C), for the (a)–(c) cold (1947–72), (d)–(f) warm (1977–98), and (g)–(i) cold (2003–14) epochs. The boundaries of North China and the Southwest United States are outlined in blue boxes. The dotted areas represent statistically significant anomalies at the 5% level.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
The IPO phase composite anomaly (relative to 1900–2014) maps of the detrended precipitation (mm day−1) over (left) North China and (center) the Southwest United States, and (right) annual mean SST (°C), for the (a)–(c) cold (1947–72), (d)–(f) warm (1977–98), and (g)–(i) cold (2003–14) epochs. The boundaries of North China and the Southwest United States are outlined in blue boxes. The dotted areas represent statistically significant anomalies at the 5% level.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
The IPO phase composite anomaly (relative to 1900–2014) maps of the detrended precipitation (mm day−1) over (left) North China and (center) the Southwest United States, and (right) annual mean SST (°C), for the (a)–(c) cold (1947–72), (d)–(f) warm (1977–98), and (g)–(i) cold (2003–14) epochs. The boundaries of North China and the Southwest United States are outlined in blue boxes. The dotted areas represent statistically significant anomalies at the 5% level.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
To explore global large-scale atmospheric circulation anomalies associated with increased precipitation over North China and the Southwest United States, some key circulation variables as well as SST were separately regressed onto the smoothed regional mean precipitation anomalies over the two regions (Fig. 4).
Regression maps of the detrended monthly (a),(b) SST (°C; shaded) and SLP (hPa; contours) and (c),(d) 200-hPa geopotential height Z ( m; contours) and 850-hPa wind (UV; m s−1; vectors) regressed onto the detrended monthly precipitation anomalies over North China and the Southwest United States. The SST, circulation variables, and precipitation are smoothed by applying a 109-month moving average twice. Only statistically significant (p < 0.05) regression coefficients of 850-hPa wind are illustrated. The dotted and shaded areas represent significant (p < 0.05) regression coefficients of SST and 200-hPa geopotential height, respectively.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Regression maps of the detrended monthly (a),(b) SST (°C; shaded) and SLP (hPa; contours) and (c),(d) 200-hPa geopotential height Z ( m; contours) and 850-hPa wind (UV; m s−1; vectors) regressed onto the detrended monthly precipitation anomalies over North China and the Southwest United States. The SST, circulation variables, and precipitation are smoothed by applying a 109-month moving average twice. Only statistically significant (p < 0.05) regression coefficients of 850-hPa wind are illustrated. The dotted and shaded areas represent significant (p < 0.05) regression coefficients of SST and 200-hPa geopotential height, respectively.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Regression maps of the detrended monthly (a),(b) SST (°C; shaded) and SLP (hPa; contours) and (c),(d) 200-hPa geopotential height Z ( m; contours) and 850-hPa wind (UV; m s−1; vectors) regressed onto the detrended monthly precipitation anomalies over North China and the Southwest United States. The SST, circulation variables, and precipitation are smoothed by applying a 109-month moving average twice. Only statistically significant (p < 0.05) regression coefficients of 850-hPa wind are illustrated. The dotted and shaded areas represent significant (p < 0.05) regression coefficients of SST and 200-hPa geopotential height, respectively.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Associated with increased precipitation over North China, the predominant SST feature is a basinwide warming in the northern North Pacific and cooling in the tropical eastern Pacific (Fig. 4a), which resembles the IPO cold phase (Fig. 3c). There is an anomalously low sea level pressure (SLP) over China and Mongolia, whereas an anomalously high SLP occupies the entire northern North Pacific. A positive height anomaly in the upper troposphere is also found over the northern North Pacific, suggesting that anomalous high pressure has an equivalent barotropic structure (Fig. 4c). It is associated with an anomalous anticyclone extending from west coast of North America to the east coast of Asia. The lower-tropospheric circulation over East China features anomalous southwesterlies, indicating an enhanced EASM circulation. These circulation anomalies favor water vapor transport from the tropics and convergence into North China, associated with increased precipitation over North China but reduced precipitation over South China.
Compared with North China, increased precipitation over the Southwest United States is associated with opposite-signed anomalies in SST and circulation. The spatial pattern of SST anomalies is analogous to the warm IPO phase (Fig. 3f). The northern North Pacific is also dominated by an equivalent barotropic structure, but it is an anomalous low. The associated anomalous cyclone induces anomalous southwesterlies and increased precipitation over the Southwest United States (Fig. 4d). Meanwhile, anomalous northerlies are found over East China, implying a weakened EASM and reduced precipitation over North China.
4. Possible formation mechanism of the interdecadal seesaw
a. Impact of the IPO
Illustrated in Fig. 5 are multimodel ensemble mean anomalies of regional mean annual and seasonal precipitation responses in the eight AGCM experiments over North China and the Southwest United States. In PwAn (the Pacific warm SST anomaly and Atlantic SST in neutral condition), below-normal precipitation occurs over North China, whereas the Southwest United States receives above-normal precipitation, implying a seesaw pattern. The seesaw pattern reverses sign in PcAn (i.e., cold Pacific and neutral Atlantic; see Table 1 for all variants). The AMO also contributes to the seesaw pattern, but has the opposite influence, with wet (dry) anomalies over North China and dry (wet) anomalies over the Southwest United States associated with PnAw (PnAc). With respect to the annual-mean precipitation in PnAn, the IPO causes precipitation variations of approximately 6.4% and 39.8% of the mean over North China and the Southwest United States, respectively, while the AMO only induces variations of approximately 4.1% and 13.5% of the mean. These results suggest that the IPO is the primary influence on the seesaw pattern; its modulation tends to be intensified when the AMO is in the opposite phase, but weakened when the AMO is in the same phase. Besides, note that the intermodel consensus is much stronger for the precipitation responses over the Southwest United States than that over North China, implying that the linkage between the IPO (AMO) and precipitation variations is stronger over the Southwest United States, whereas there are some uncertainties over North China.
Multimodel ensemble mean anomalies of annual and seasonal precipitation (mm day−1) over (a) North China and (b) the Southwest United States relative to the control run (PnAn). Error bars indicate the intermodel range. Here, letters P and A denote the Pacific (IPO) and Atlantic (AMO) SST anomaly patterns; letters w, n, and c refer to the warm, neutral, and cold phases, respectively (Table 1).
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean anomalies of annual and seasonal precipitation (mm day−1) over (a) North China and (b) the Southwest United States relative to the control run (PnAn). Error bars indicate the intermodel range. Here, letters P and A denote the Pacific (IPO) and Atlantic (AMO) SST anomaly patterns; letters w, n, and c refer to the warm, neutral, and cold phases, respectively (Table 1).
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean anomalies of annual and seasonal precipitation (mm day−1) over (a) North China and (b) the Southwest United States relative to the control run (PnAn). Error bars indicate the intermodel range. Here, letters P and A denote the Pacific (IPO) and Atlantic (AMO) SST anomaly patterns; letters w, n, and c refer to the warm, neutral, and cold phases, respectively (Table 1).
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Generally, the AGCMs in this study simulate well the observed contrasting precipitation variability between North China and the Southwest United States, as well as the dominant role of the IPO in driving this contrast. The precipitation response over the Southwest United States to the IPO is much larger than that over North China. This is consistent with the work of Dong and Dai (2015), who found a robust precipitation response over the Southwest United States for two cold IPO phases in a CanAM4 simulation forced by observed SSTs from 1950 to 2009, whereas the precipitation response was inconsistent over North China. Additionally, the similarity between the precipitation responses in the annual and seasonal averages, especially over the Southwest United States, may be related to imposing the annual-mean IPO or AMO SST anomaly onto the monthly climatology (Schubert et al. 2009).
To clarify the impact of the IPO on the seesaw, Fig. 6 shows the annual-mean atmospheric circulation response to PwAn and PcAn. In PwAn, the predominant feature of the SLP (Fig. 6a) and height responses at 500 hPa (Fig. 6c) is a negative anomaly occupying the entire northern North Pacific, implying a deepened Aleutian low. This is accompanied by a large-scale cyclonic anomaly extending from the west coast of North America to northern China. Meanwhile, there is an anticyclonic anomaly centered over the northwest Pacific. Combined, these circulations induce anomalous northwesterlies over North China and southwesterlies over both the Southwest United States and South China, and a belt of anomalous moisture transport from the Bay of Bengal, South China Sea, and the subtropical Pacific to the west coast of North America (Fig. 6e). This increased moisture transport is associated with a belt of increased precipitation, including the Southwest United States and South China (Fig. 6g). Less precipitation falls over North China, related to the increase in moisture convergence over South China associated with the weakened EASM (Yang et al. 2017b). The simulated circulation patterns resemble those related to increased precipitation over the Southwest United States in observations (Figs. 4b and 4d). In PcAn (right column in Fig. 6), the spatial patterns of the circulation responses are analogous to those in PwAn but with opposite signs, as well as those related to increased precipitation over North China in observations (Figs. 4a and 4c).
Multimodel ensemble mean anomalies of annual mean (a),(b) SLP (hPa; shaded) and its climatology in PnAn (hPa; contours), (c),(d) 500-hPa geopotential height Z (m; shaded) and 850-hPa wind (UV; m s−1; vectors), (e),(f) column-integrated moisture divergence (mm day−1; shaded) and moisture flux (g cm−1 s−1; vectors), and (g),(h) precipitation (Pre; mm day−1; shaded), for (left) PwAn and (right) PcAn. Note that an unequal contour interval is used to highlight the response over land. Stippling indicates areas where more than four of the five AGCMs agree on the sign.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean anomalies of annual mean (a),(b) SLP (hPa; shaded) and its climatology in PnAn (hPa; contours), (c),(d) 500-hPa geopotential height Z (m; shaded) and 850-hPa wind (UV; m s−1; vectors), (e),(f) column-integrated moisture divergence (mm day−1; shaded) and moisture flux (g cm−1 s−1; vectors), and (g),(h) precipitation (Pre; mm day−1; shaded), for (left) PwAn and (right) PcAn. Note that an unequal contour interval is used to highlight the response over land. Stippling indicates areas where more than four of the five AGCMs agree on the sign.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean anomalies of annual mean (a),(b) SLP (hPa; shaded) and its climatology in PnAn (hPa; contours), (c),(d) 500-hPa geopotential height Z (m; shaded) and 850-hPa wind (UV; m s−1; vectors), (e),(f) column-integrated moisture divergence (mm day−1; shaded) and moisture flux (g cm−1 s−1; vectors), and (g),(h) precipitation (Pre; mm day−1; shaded), for (left) PwAn and (right) PcAn. Note that an unequal contour interval is used to highlight the response over land. Stippling indicates areas where more than four of the five AGCMs agree on the sign.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Both observations and simulations suggest that the IPO-induced height anomaly over the northern North Pacific is critical in driving the seesaw pattern, via opposing wind and moisture anomalies on its eastern and western sides. Next, we consider the possible mechanisms for this height anomaly.
In PwAn, warm SST anomalies over the equatorial central-eastern Pacific warm the tropical troposphere through enhanced surface heat fluxes from the ocean to the atmosphere (Fig. 7a). Meanwhile, a zonal belt of extensive cooling exists over approximately 30°–60°N, which may be associated with changes in the eddy-driven mean meridional circulation (Seager et al. 2003). Then, the meridional temperature gradient (MTG), computed as the temperature difference from south to north, strengthens over the region 15°–45°N, collocated with enhanced westerlies in the upper troposphere via thermal wind balance. In the tropics and high latitudes, the weakened MTG is associated with anomalous easterlies. Associated with these wind anomalies is a dipole pattern of relative vorticity changes at 200 hPa over the North Pacific, with a cyclone over the northern North Pacific and an anticyclone over the southern North Pacific, with a dividing line at approximately 40°N (Fig. 7c). Consequently, an anomalous upper-troposphere low pressure center forms over the northern North Pacific.
Multimodel ensemble mean anomalies of annual mean (a),(b) air temperature T ( from the surface to 200 hPa; contours; K) and 200-hPa zonal wind U ( shaded; m s−1); wind (UV; m s−1; vectors) and relative vorticity (scaled by 1 × 105 s−1; shaded) at (c),(d) 200 and (e),(f) 850 hPa; (g),(h) meridional V (m s−1; vectors) and vertical W (10−3 m s−1; shaded) circulation, and (i),(j) SST (scaled by 2°C) and precipitation (Pre; mm day−1) over the Pacific (average over 150°–120°W), for (left) PwAn and (right) PcAn. For convenience in quantitatively comparing the precipitation responses in different experiments, the zonal-mean precipitation anomalies in experiments with the warm (Pw) and cold IPO (Pc), as well as PnAc and PnAw, are illustrated in (i) and (j), respectively.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean anomalies of annual mean (a),(b) air temperature T ( from the surface to 200 hPa; contours; K) and 200-hPa zonal wind U ( shaded; m s−1); wind (UV; m s−1; vectors) and relative vorticity (scaled by 1 × 105 s−1; shaded) at (c),(d) 200 and (e),(f) 850 hPa; (g),(h) meridional V (m s−1; vectors) and vertical W (10−3 m s−1; shaded) circulation, and (i),(j) SST (scaled by 2°C) and precipitation (Pre; mm day−1) over the Pacific (average over 150°–120°W), for (left) PwAn and (right) PcAn. For convenience in quantitatively comparing the precipitation responses in different experiments, the zonal-mean precipitation anomalies in experiments with the warm (Pw) and cold IPO (Pc), as well as PnAc and PnAw, are illustrated in (i) and (j), respectively.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean anomalies of annual mean (a),(b) air temperature T ( from the surface to 200 hPa; contours; K) and 200-hPa zonal wind U ( shaded; m s−1); wind (UV; m s−1; vectors) and relative vorticity (scaled by 1 × 105 s−1; shaded) at (c),(d) 200 and (e),(f) 850 hPa; (g),(h) meridional V (m s−1; vectors) and vertical W (10−3 m s−1; shaded) circulation, and (i),(j) SST (scaled by 2°C) and precipitation (Pre; mm day−1) over the Pacific (average over 150°–120°W), for (left) PwAn and (right) PcAn. For convenience in quantitatively comparing the precipitation responses in different experiments, the zonal-mean precipitation anomalies in experiments with the warm (Pw) and cold IPO (Pc), as well as PnAc and PnAw, are illustrated in (i) and (j), respectively.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
On the other hand, the mean meridional circulation over the North Pacific changes in response to the increased atmospheric heating over the tropical ocean. The thermally direct Hadley cell is strengthened by enhanced tropical convection (Fig. 7g), itself due to enhanced surface evaporation and low-level moisture convergence. The strengthened descending motion in the subsiding branch of the Hadley cell, centered at approximately 20°N, aligns with an anomalous anticyclone at 850 hPa (Fig. 7e). In the lower troposphere, the northward flow from the subtropical anomalous anticyclone converges in the midlatitudes and enhances the ascending branch of the Ferrel cell, forming an anomalous cyclone over the northern North Pacific. Therefore, in PwAn, a barotropic low pressure anomaly extends across the northern North Pacific, which is clearly related to the planetary-scale response of the atmosphere to the tropical warm SST anomalies.
Collocated with vertical velocity anomalies (upward–downward–upward from south to north; Fig. 7g) over the North Pacific, there is above-normal precipitation in the tropics (10°S–10°N), below-normal precipitation in the subtropics (10°–30°N), and above-normal precipitation in the midlatitudes (30°–50°N; Fig. 7i). Note that the above-normal precipitation overlies cold SST anomalies across the northern North Pacific. The opposing signs of the precipitation and SST anomalies suggest that the increased precipitation is forced remotely, not by local SST anomalies (Wu and Kirtman 2007), strengthening the conclusion that the negative height anomaly over the northern North Pacific is a response to the IPO-associated tropical warm SST anomalies via the atmospheric bridge.
Then, a question naturally arises: Does the tropical SST component of the IPO induce the height anomaly over the northern North Pacific? To answer the question, additional experiments (performed by only GFDL and NSIPP1) forced by the tropical SST component of the Pacific pattern were also analyzed and compared with the baseline experiments.
Overall, circulation responses in AGCMs forced by the tropical SST anomaly only (abbreviated TP in experiment names) support the above finding (Fig. 8). Like PwAn, TPwAn also induces a tripole pattern of temperature anomalies over the North Pacific (warm–cold–warm from south to north), which is associated with a tripole structure of zonal wind anomalies (easterlies–westerlies–easterlies), due to the MTG changes. These favor a dipole pattern of relative vorticity shear in the upper troposphere, with positive vorticity over the northern North Pacific and negative vorticity over south of it. The former is associated with a barotropic low pressure anomaly, accompanied by a large-scale cyclonic flow over the northern North Pacific and adjacent regions in the lower troposphere. Consequently, the seesaw pattern forms (Fig. 9 and Fig. S5), characterized by dry over North China and wet over the Southwest United States.
Anomalies of annual mean (a),(b) air temperature T ( from the surface to 200 hPa; contours; K) and 200-hPa zonal wind U ( shaded; m s−1); wind (UV; m s−1; vectors) and relative vorticity (scaled by 1 × 105 s−1; shaded) at (c),(d) 200 and (e),(f) 850 hPa for TPwAn in (left) GFDL and (right) NSIPP1.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Anomalies of annual mean (a),(b) air temperature T ( from the surface to 200 hPa; contours; K) and 200-hPa zonal wind U ( shaded; m s−1); wind (UV; m s−1; vectors) and relative vorticity (scaled by 1 × 105 s−1; shaded) at (c),(d) 200 and (e),(f) 850 hPa for TPwAn in (left) GFDL and (right) NSIPP1.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Anomalies of annual mean (a),(b) air temperature T ( from the surface to 200 hPa; contours; K) and 200-hPa zonal wind U ( shaded; m s−1); wind (UV; m s−1; vectors) and relative vorticity (scaled by 1 × 105 s−1; shaded) at (c),(d) 200 and (e),(f) 850 hPa for TPwAn in (left) GFDL and (right) NSIPP1.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Responses of annual mean precipitation (mm day−1; shaded) to the Pacific pattern (PwAn and PcAn) and its tropical SST component (TPwAn and TPcAn) in GFDL. The dotted areas represent statistically significant anomalies at the 5% level.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Responses of annual mean precipitation (mm day−1; shaded) to the Pacific pattern (PwAn and PcAn) and its tropical SST component (TPwAn and TPcAn) in GFDL. The dotted areas represent statistically significant anomalies at the 5% level.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Responses of annual mean precipitation (mm day−1; shaded) to the Pacific pattern (PwAn and PcAn) and its tropical SST component (TPwAn and TPcAn) in GFDL. The dotted areas represent statistically significant anomalies at the 5% level.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
The atmospheric processes associated with PcAn (right column in Fig. 7) and TPcAn (Fig. S4) resemble those associated with PwAn and TPwAn, respectively, but with the opposite sign. A barotropic high pressure anomaly extends across the northern North Pacific in response to the IPO-associated tropical cold SST anomalies.
b. Impact of the AMO
Both observations (Fig. 2b) and simulations (Fig. 5) suggest that compared with the IPO, the AMO plays an opposite and secondary role in the contrasting precipitation variability between North China and the Southwest United States. This finding is also supported by the height response over the northern North Pacific, which is a critical circulation system associated with the seesaw pattern. As clearly shown in Fig. 10, opposite pressure anomalies are associated to the IPO and AMO. In addition, the magnitude of the AMO-induced height anomaly is only approximately one-third to one-half that induced by the IPO, suggesting that the IPO plays a dominant role. The IPO-induced height anomaly is intensified when the AMO in the opposite phase (PwAc and PcAw), whereas it is slightly weakened when the AMO in the same phase (PwAw and PcAc).
Multimodel ensemble mean anomalies of annual mean geopotential height (m) over the northern North Pacific (30°–65°N, 160°–140°W) from the surface to 70 hPa.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean anomalies of annual mean geopotential height (m) over the northern North Pacific (30°–65°N, 160°–140°W) from the surface to 70 hPa.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean anomalies of annual mean geopotential height (m) over the northern North Pacific (30°–65°N, 160°–140°W) from the surface to 70 hPa.
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
How does the AMO affect the IPO? It has been suggested that the impact of the AMO on the IPO could be through two pathways: 1) the midlatitude atmosphere process, whereby the AMO could modify the atmospheric anomalies over Eurasia and the northern North Pacific via downstream energy propagation at middle to high latitudes (e.g., Si and Ding 2016), or 2) the tropical atmosphere process, whereby the AMO could generate a transbasin SLP seesaw, characterized by opposite SLP trends in the Pacific and the Indo-Atlantic region, and further change SST over the eastern tropical Pacific via a modification of the Walker circulation involving low-level wind anomalies (e.g., McGregor et al. 2014). No matter which pathway is used, the warm (cold) AMO phase tends to induce the cold (warm) IPO phase. This is supported because there exists a significant lag anticorrelation between the AMO and the IPO indices with the AMO index leading by approximately 11–12 years (Wu et al. 2011; Chylek et al. 2014).
In the CLIVAR AGCMs experiments, the AMO may affect the modulation of the IPO on circulation and precipitation through the first pathway. PnAw (PnAc) drives an eastward-propagating wave train over Eurasia and then induces a barotropic high (low) pressure anomaly over the northern North Pacific [see Fig. 6 in Yang et al. (2017b); also see our Fig. S6], which is consistent with the height anomaly associated to PcAn (PwAn). Consequently, circulation and precipitation responses to PnAw (PnAc) closely resemble those associated with PcAn (PwAn), although with smaller magnitude. When the AMO exists in combination with the IPO in opposite phases, as in PwAc and PcAw (Fig. S7), precipitation and circulation responses resemble those in PwAn and PcAn (Fig. 6), respectively, but the strengthened height anomaly over the northern North Pacific is associated with larger anomalies of wind, moisture flux, and precipitation (Fig. 11; see also Fig. S8).
Multimodel ensemble mean differences (left) between PwAc and PwAn and (right) between PcAw and PcAn: (a),(b) SLP (hPa; shaded) and SLP climatology in PwAn (hPa; contours); (c),(d) 500-hPa geopotential height Z (m; shaded) and 850-hPa wind (UV; m s−1; vectors); (e),(f) column-integrated moisture divergence (mm day−1; shaded) and moisture flux (g cm−1 s−1; vectors); and (g),(h) precipitation (Pre; mm day−1; shaded).
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean differences (left) between PwAc and PwAn and (right) between PcAw and PcAn: (a),(b) SLP (hPa; shaded) and SLP climatology in PwAn (hPa; contours); (c),(d) 500-hPa geopotential height Z (m; shaded) and 850-hPa wind (UV; m s−1; vectors); (e),(f) column-integrated moisture divergence (mm day−1; shaded) and moisture flux (g cm−1 s−1; vectors); and (g),(h) precipitation (Pre; mm day−1; shaded).
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
Multimodel ensemble mean differences (left) between PwAc and PwAn and (right) between PcAw and PcAn: (a),(b) SLP (hPa; shaded) and SLP climatology in PwAn (hPa; contours); (c),(d) 500-hPa geopotential height Z (m; shaded) and 850-hPa wind (UV; m s−1; vectors); (e),(f) column-integrated moisture divergence (mm day−1; shaded) and moisture flux (g cm−1 s−1; vectors); and (g),(h) precipitation (Pre; mm day−1; shaded).
Citation: Journal of Climate 32, 10; 10.1175/JCLI-D-18-0082.1
5. Conclusions and discussion
Using data from observations, reanalysis, and a set of AGCMs simulations forced by idealized IPO- and AMO-associated SST anomalies, we compared the interdecadal precipitation variations over North China and the Southwest United States, as well as the associated circulations. Our main findings are summarized as follows.
First, on interdecadal scales, precipitation variations over North China and the Southwest United States are anticorrelated, implying a seesaw pattern. The IPO could play a dominant role in driving the seesaw pattern, with the warm IPO phase associated with less precipitation over North China but more over the Southwest United States; the cold phase is associated with the opposite-signed pattern. The AMO contributes oppositely and less. The IPO-induced precipitation anomaly intensifies when the AMO is in the opposite phase, whereas it is slightly weakened when the AMO in the same phase. The IPO has a much stronger influence on precipitation variations over the Southwest United States than that over North China. AGCM simulations from the U.S. CLIVAR Drought Working Group capture these relationships well.
Second, for the IPO, the large-scale lower-tropospheric cyclonic or anticyclonic circulation anomaly across the northern North Pacific is the predominant factor in driving the seesaw pattern. During the warm IPO phase, North China and the Southwest United States, which are located at the western and eastern parts of the deepened Aleutian low, experience anomalous northwesterlies and southwesterlies and receive below-normal and above-normal precipitation, respectively. Opposite-signed circulation anomalies are associated with the cold IPO phase.
Third, the height anomaly over the northern North Pacific, which exhibits an equivalent barotropic structure, is part of the planetary-scale atmospheric response to the IPO-associated tropical SST anomaly. For instance, the warm IPO phase induces a tripole pattern of temperature anomalies over the North Pacific (warm–cold–warm from south to north) that strengthens the midlatitude meridional temperature gradient but weakens it in the tropics and high latitudes. By thermal wind balance, these are accompanied by a tripole structure of the zonal wind anomaly (easterlies–westerlies–easterlies) that favors a dipole pattern of upper-tropospheric relative vorticity shear, with positive vorticity over the northern North Pacific and negative vorticity to the south. Consequently, a barotropic cyclonic anomaly extends across the northern North Pacific.
Given the association between the IPO (AMO) phase and interdecadal precipitation variations over North China and the Southwest United States, improved prediction of the IPO (AMO) could provide a potential predictability of drought-related water stresses over the two regions, although there are still many uncertainties in our understanding and prediction of these interdecadal oceanic modes. If the phase of the IPO turns positive, as it was projected to in late 2014 (Meehl et al. 2016), North China may experience anomalously decreased precipitation, whereas precipitation may be enhanced across much of North America, including the Southwest United States. However, we caution that this analysis considers the IPO and AMO only, and neglects other processes, such as anthropogenic greenhouse gases and aerosols, which may also induce interdecadal precipitation variability, either through a direct effect on precipitation or an indirect influence on the magnitude or period of the IPO and AMO (e.g., Dong et al. 2014; Smith et al. 2016; Liguori and Di Lorenzo 2018).
The observed seesaw pattern is the result of air–sea interactions, although the SST may play a more active role than the atmosphere. However, AGCM simulations used in this study cannot reflect the feedback of the atmosphere to the SST, which may lead to erroneous attribution conclusions for decadal-scale circulation and precipitation variability, especially in East Asia (Dong et al. 2017). Additionally, the AGCM simulations show only the steady-state response, which cannot distinguish which circulation feature occurs first in response to the tropical SST anomaly, and thus may cause uncertainty in the potential pathway described here. The Pacific SST forcing (Fig. 1a) used in the AGCM simulations includes a same-signed SST anomaly in the Indian Ocean, which has been shown to contribute to precipitation variations over the United States (e.g., Hoerling and Kumar 2003) and North China. The role of the Indian Ocean in the anticorrelation of precipitation over North China and the Southwest United States is not clear. These issues require further investigation to verify the link between the interdecadal seesaw pattern and the IPO and AMO.
Acknowledgments
This study is jointly sponsored by the National Key R&D Program of China (2016YFA0600404), the National Natural Science Foundation of China (41530532, 41705073), the China Special Fund for Meteorological Research in the Public Interest (GYHY201106028, GYHY201506001-1), and the Jiangsu Collaborative Innovation Center for Climate Change. Nicholas P. Klingaman was funded by an Independent Research Fellowship from the UK Natural Environment Research Council (NE/L010976/1). Peili Wu was supported by the UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Services Partnership (CSSP), as part of the Newton Fund.
REFERENCES
Bacmeister, J., P. J. Pegion, S. D. Schubert, and M. J. Suarez, 2000: An atlas of seasonal means simulated by the NSIPP 1 atmospheric GCM. NASA Tech. Memo. 104606, Vol. 17, 194 pp., https://archive.org/details/nasa_techdoc_20000110228.
Campana, K., and P. Caplan, Eds., 2005: Technical procedure bulletin for the T382 Global Forecast System. NOAA/NCEP/EMC, https://www.emc.ncep.noaa.gov/gc_wmb/Documentation/TPBoct05/T382.TPB.FINAL.htm.
Chen, H., T. Zhou, R. B. Neale, X. Wu, and G. J. Zhang, 2010: Performance of the new NCAR CAM3.5 in East Asian summer monsoon simulations: Sensitivity to modifications of the convection scheme. J. Climate, 23, 3657–3675, https://doi.org/10.1175/2010JCLI3022.1.
Chylek, P., M. K. Dubey, G. Lesins, J. N. Li, and N. Hengartner, 2014: Imprint of the Atlantic multi-decadal oscillation and Pacific decadal oscillation on southwestern US climate: Past, present, and future. Climate Dyn., 43, 119–129, https://doi.org/10.1007/s00382-013-1933-3.
Dai, A., 2011: Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008. J. Geophys. Res., 116, D12115, https://doi.org/10.1029/2010JD015541.
Dai, A., 2013: The influence of the inter-decadal Pacific oscillation on US precipitation during 1923–2010. Climate Dyn., 41, 633–646, https://doi.org/10.1007/s00382-012-1446-5.
Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J. Climate, 19, 643–674, https://doi.org/10.1175/JCLI3629.1.
Di Lorenzo, E., G. Liguori, N. Schneider, J. C. Furtado, B. T. Anderson, and M. A. Alexander, 2015: ENSO and meridional modes: A null hypothesis for Pacific climate variability. Geophys. Res. Lett., 42, 9440–9448, https://doi.org/10.1002/2015GL066281.
Dong, B., and A. Dai, 2015: The influence of the interdecadal Pacific oscillation on temperature and precipitation over the globe. Climate Dyn., 45, 2667–2681, https://doi.org/10.1007/s00382-015-2500-x.
Dong, B. W., R. T. Sutton, L. Shaffrey, and N. P. Klingaman, 2017: Attribution of forced decadal climate change in coupled and uncoupled ocean–atmosphere model experiments. J. Climate, 30, 6203–6223, https://doi.org/10.1175/JCLI-D-16-0578.1.
Dong, L., T. Zhou, and X. Chen, 2014: Changes of Pacific decadal variability in the twentieth century driven by internal variability, greenhouse gases, and aerosols. Geophys. Res. Lett., 41, 8570–8577, https://doi.org/10.1002/2014GL062269.
Enfield, D. B., A. M. Mestas-Nuñez, and P. J. Trimble, 2001: The Atlantic multidecadal oscillation and its relationship to precipitation and river flows in the continental U.S. Geophys. Res. Lett., 28, 2077–2080, https://doi.org/10.1029/2000GL012745.
Fang, K., D. Chen, J. Li, and H. Seppä, 2014: Covarying hydroclimate patterns between monsoonal Asia and North America over the past 600 years. J. Climate, 27, 8017–8033, https://doi.org/10.1175/JCLI-D-13-00364.1.
Fang, K., H. Seppä, and D. Chen, 2015: Interdecadal hydroclimate teleconnections between Asia and North America over the past 600 years. Climate Dyn., 44, 1777–1787, https://doi.org/10.1007/s00382-014-2266-6.
Farneti, R., F. Molteni, and F. Kucharski, 2014: Pacific interdecadal variability driven by tropical–extratropical interactions. Climate Dyn., 42, 3337–3355, https://doi.org/10.1007/s00382-013-1906-6.
Feng, S., Q. Hu, and R. J. Oglesby, 2011: Influence of Atlantic sea surface temperatures on persistent drought in North America. Climate Dyn., 37, 569–586, https://doi.org/10.1007/s00382-010-0835-x.
Henley, B. J., 2017: Pacific decadal climate variability: Indices, patterns and tropical-extratropical interactions. Global Planet. Change, 155, 42–55, https://doi.org/10.1016/j.gloplacha.2017.06.004.
Henley, B. J., and Coauthors, 2017: Spatial and temporal agreement in climate model simulations of the interdecadal Pacific oscillation. Environ. Res. Lett., 12, 044011, https://doi.org/10.1088/1748-9326/aa5cc8.
Hoerling, M., and A. Kumar, 2003: The perfect ocean for drought. Science, 299, 691–694, https://doi.org/10.1126/science.1079053.
Hu, Q., S. Feng, and R. J. Oglesby, 2011: Variations in North American summer precipitation driven by the Atlantic multidecadal oscillation. J. Climate, 24, 5555–5570, https://doi.org/10.1175/2011JCLI4060.1.
Hua, L., Z. Ma, and L. Zhong, 2011: A comparative analysis of primary and extreme characteristics of dry or wet status between Asia and North America. Adv. Atmos. Sci., 28, 352–362, https://doi.org/10.1007/s00376-010-9230-0.
Huang, B., and Coauthors, 2015: Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4). Part I: Upgrades and intercomparisons. J. Climate, 28, 911–930, https://doi.org/10.1175/JCLI-D-14-00006.1.
Huang, J., and Coauthors, 2017: Dryland climate change: Recent progress and challenges. Rev. Geophys., 55, 719–778, https://doi.org/10.1002/2016RG000550.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Kang, S., and E. A. B. Eltahir, 2018: North China Plain threatened by deadly heatwaves due to climate change and irrigation. Nat. Commun., 9, 2894, https://doi.org/10.1038/s41467-018-05252-y.
Kerr, R. A., 2000: A North Atlantic climate pacemaker for the centuries. Science, 288, 1984–1986, https://doi.org/10.1126/science.288.5473.1984.
Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, D. L. Williamson, and P. J. Rasch, 1998: The National Center for Atmospheric Research Community Climate Model: CCM3. J. Climate, 11, 1131–1149, https://doi.org/10.1175/1520-0442(1998)011<1131:TNCFAR>2.0.CO;2.
Lau, K., and H. Weng, 2002: Recurrent teleconnection patterns linking summertime precipitation variability over East Asia and North America. J. Meteor. Soc. Japan, 80, 1309–1324, https://doi.org/10.2151/jmsj.80.1309.
Lau, K., J. Lee, K. Kim, and I. Kang, 2004: The North Pacific as a regulator of summertime climate over Eurasia and North America. J. Climate, 17, 819–833, https://doi.org/10.1175/1520-0442(2004)017<0819:TNPAAR>2.0.CO;2.
Li, H., A. Dai, T. Zhou, and J. Lu, 2010: Responses of East Asian summer monsoon to historical SST and atmospheric forcing during 1950–2000. Climate Dyn., 34, 501–514, https://doi.org/10.1007/s00382-008-0482-7.
Li, Q., S. Yang, V. E. Kousky, R. W. Higgins, K. M. Lau, and P. Xie, 2005: Features of cross-Pacific climate shown in the variability of China and US precipitation. Int. J. Climatol., 25, 1675–1696, https://doi.org/10.1002/joc.1271.
Li, S., and G. T. Bates, 2007: Influence of the Atlantic multidecadal oscillation on the winter climate of East China. Adv. Atmos. Sci., 24, 126–135, https://doi.org/10.1007/s00376-007-0126-6.
Liguori, G., and E. Di Lorenzo, 2018: Meridional modes and increasing Pacific decadal variability under anthropogenic forcing. Geophys. Res. Lett., 45, 983–991, https://doi.org/10.1002/2017GL076548.
Lu, R., B. Dong, and H. Ding, 2006: Impact of the Atlantic multidecadal oscillation on the Asian summer monsoon. Geophys. Res. Lett., 33, L24701, https://doi.org/10.1029/2006GL027655.
Ma, Z., 2007: The interdecadal trend and shift of dry/wet over the central part of North China and their relationship to the Pacific decadal oscillation (PDO). Chin. Sci. Bull., 52, 2130–2139, https://doi.org/10.1007/s11434-007-0284-z.
Ma, Z., and L. Shao, 2006: Relationship between dry/wet variation and the Pacific decadal oscillation (PDO) in northern China during the last 100 years (in Chinese). Chin. J. Atmos. Sci., 30, 464–474.
Ma, Z., and C. Fu, 2007: Global aridification in the second half of the 20th century and its relationship to large-scale climate background. Sci. China, 50D, 776–788, https://doi.org/10.1007/s11430-007-0036-6.
Mantua, N. J., and S. R. Hare, 2002: The Pacific decadal oscillation. J. Oceanogr., 58, 35–44, https://doi.org/10.1023/A:1015820616384.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 1069–1080, https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.
McCabe, G. J., M. A. Palecki, and J. L. Betancourt, 2004: Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States. Proc. Natl. Acad. Sci. USA, 101, 4136–4141, https://doi.org/10.1073/pnas.0306738101.
McGregor, S., A. Timmermann, M. F. Stuecker, M. H. England, M. Merrifield, F.-F. Jin, and Y. Chikamoto, 2014: Recent Walker circulation strengthening and Pacific cooling amplified by Atlantic warming. Nat. Climate Change, 4, 888–892, https://doi.org/10.1038/nclimate2330.
Meehl, G. A., and A. Hu, 2006: Megadroughts in the Indian monsoon region and southwest North America and a mechanism for associated multidecadal Pacific sea surface temperature anomalies. J. Climate, 19, 1605–1623, https://doi.org/10.1175/JCLI3675.1.
Meehl, G. A., A. Hu, and H. Teng, 2016: Initialized decadal prediction for transition to positive phase of the interdecadal Pacific oscillation. Nat. Commun., 7, 11718, https://doi.org/10.1038/ncomms11718.
Mo, K. C., J.-K. E. Schemm, and S.-H. Yoo, 2009: Influence of ENSO and the Atlantic multidecadal oscillation on drought over the United States. J. Climate, 22, 5962–5982, https://doi.org/10.1175/2009JCLI2966.1.
Newman, M., and Coauthors, 2016: The Pacific decadal oscillation, revisited. J. Climate, 29, 4399–4427, https://doi.org/10.1175/JCLI-D-15-0508.1.
Power, S., T. Casey, C. Folland, A. Colman, and V. Mehta, 1999: Inter-decadal modulation of the impact of ENSO on Australia. Climate Dyn., 15, 319–324, https://doi.org/10.1007/s003820050284.
Qian, C., and T. Zhou, 2014: Multidecadal variability of North China aridity and its relationship to PDO during 1900–2010. J. Climate, 27, 1210–1222, https://doi.org/10.1175/JCLI-D-13-00235.1.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.
Rowell, D. P., 1998: Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J. Climate, 11, 109–120, https://doi.org/10.1175/1520-0442(1998)011<0109:APSPWA>2.0.CO;2.
Schubert, S., and Coauthors, 2009: A U.S. CLIVAR project to assess and compare the responses of global climate models to drought-related SST forcing patterns: Overview and results. J. Climate, 22, 5251–5272, https://doi.org/10.1175/2009JCLI3060.1.
Seager, R., N. Harnik, and Y. Kushnir, 2003: Mechanisms of hemispherically symmetric climate variability. J. Climate, 16, 2960–2978, https://doi.org/10.1175/1520-0442(2003)016<2960:MOHSCV>2.0.CO;2.
Si, D., and Y. Ding, 2016: Oceanic forcings of the interdecadal variability in East Asian summer rainfall. J. Climate, 29, 7633–7649, https://doi.org/10.1175/JCLI-D-15-0792.1.
Si, D., and A. Hu, 2017: Internally generated and externally forced multidecadal oceanic modes and their influence on the summer rainfall over East Asia. J. Climate, 30, 8299–8316, https://doi.org/10.1175/JCLI-D-17-0065.1.
Smith, D. M., and Coauthors, 2016: Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown. Nat. Climate Change, 6, 936–940, https://doi.org/10.1038/nclimate3058.
Sutton, R. T., and D. L. Hodson, 2005: Atlantic Ocean forcing of North American and European summer climate. Science, 309, 115–118, https://doi.org/10.1126/science.1109496.
Sutton, R. T., and D. L. Hodson, 2007: Climate response to basin-scale warming and cooling of the North Atlantic Ocean. J. Climate, 20, 891–907, https://doi.org/10.1175/JCLI4038.1.
University of East Anglia Climatic Research Unit, 2017: CRU TS4.00: Climatic Research Unit (CRU) Time-Series (TS) version 4.00 of high resolution gridded data of month-by-month variation in climate (Jan. 1901–Dec. 2015). Centre for Environmental Data Analysis, accessed September 2017, https://doi.org/10.5285/edf8febfdaad48abb2cbaf7d7e846a86.
Vellinga, M., and P. Wu, 2004: Low-latitude freshwater influence on centennial variability of the Atlantic thermohaline circulation. J. Climate, 17, 4498–4511, https://doi.org/10.1175/3219.1.
Wang, B., Q. H. Ding, X. H. Fu, I. S. Kang, K. Jin, J. Shukla, and F. Doblas-Reyes, 2005: Fundamental challenge in simulation and prediction of summer monsoon precipitation. Geophys. Res. Lett., 32, L15711, https://doi.org/10.1029/2005GL022734.
Wang, F., S. Yang, W. Higgins, Q. Li, and Z. Zuo, 2014: Long-term changes in total and extreme precipitation over China and the United States and their links to oceanic-atmospheric features. Int. J. Climatol., 34, 286–302, https://doi.org/10.1002/joc.3685.
Wilks, D. S., 2005: Statistical Methods in the Atmospheric Sciences. 2nd ed. Elsevier, 648 pp.
Wu, P., N. Christidis, and P. Stott, 2013: Anthropogenic impact on Earth’s hydrological cycle. Nat. Climate Change, 3, 807–810, https://doi.org/10.1038/nclimate1932.
Wu, P., J. Ridley, A. Pardaens, R. Leavine, and J. Lowe, 2015: The reversibility of CO2 induced climate change. Climate Dyn., 45, 745–754, https://doi.org/10.1007/s00382-014-2302-6.
Wu, R., and B. P. Kirtman, 2007: Regimes of seasonal air–sea interaction and implications for performance of forced simulations. Climate Dyn., 29, 393–410, https://doi.org/10.1007/s00382-007-0246-9.
Wu, S., Z. Liu, R. Zhang, and T. L. Delworth, 2011: On the observed relationship between the Pacific decadal oscillation and the Atlantic multi-decadal oscillation. J. Oceanogr., 67, 27–35, https://doi.org/10.1007/s10872-011-0003-x.
Yang, L., and Z. Fu, 2017: Out-phased decadal precipitation regime shift in China and the United States. Theor. Appl. Climatol., 130, 535–544, https://doi.org/10.1007/s00704-016-1907-6.
Yang, Q., Z. Ma, and B. Xu, 2017a: Modulation of monthly precipitation patterns over East China by the Pacific decadal oscillation. Climatic Change, 144, 405–417, https://doi.org/10.1007/s10584-016-1662-9.
Yang, Q., Z. Ma, X. Fan, Z.-L. Yang, Z. Xu, and P. Wu, 2017b: Decadal modulation of precipitation patterns over eastern China by sea surface temperature anomalies. J. Climate, 30, 7017–7033, https://doi.org/10.1175/JCLI-D-16-0793.1.
Zhang, L., P. Wu, and T. Zhou, 2017: Aerosol forcing of extreme summer drought over north China. Environ. Res. Lett., 12, 034020, https://doi.org/10.1088/1748-9326/aa5fb3.
Zhang, Y., J. M. Wallace, and D. S. Battisti, 1997: ENSO-like interdecadal variability: 1900–93. J. Climate, 10, 1004–1020, https://doi.org/10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2.
Zhao, P., S. Yang, H. Wang, and Q. Zhang, 2011: Interdecadal relationships between the Asian–Pacific oscillation and summer climate anomalies over Asia, North Pacific, and North America during a recent 100 years. J. Climate, 24, 4793–4799, https://doi.org/10.1175/JCLI-D-11-00054.1.
Zhao, P., and Coauthors, 2016: Summer precipitation anomalies in Asia and North America induced by Eurasian non-monsoon land heating versus ENSO. Sci. Rep., 6, 21346, https://doi.org/10.1038/srep21346.
Zhou, T., F. Song, R. Lin, X. Chen, and X. Chen, 2013: The 2012 North China floods: Explaining an extreme precipitation event in the context of a longer-term drying tendency [in “Explaining Extreme Events of 2012 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 94, S49–S51, https://doi.org/10.1175/BAMS-D-13-00085.1.
Zhu, Y., H. Wang, W. Zhou, and J. Ma, 2011: Recent changes in the summer precipitation pattern in East China and the background circulation. Climate Dyn., 36, 1463–1473, https://doi.org/10.1007/s00382-010-0852-9.
Zhu, Y., H. Wang, J. Ma, T. Wang, and J. Sun, 2015: Contribution of the phase transition of Pacific decadal oscillation to the late 1990s’ shift in East China summer precipitation. J. Geophys. Res. Atmos., 120, 8817–8827, https://doi.org/10.1002/2015JD023545.
Zhu, Y., T. Wang, and J. Ma, 2016: Influence of internal decadal variability on the summer precipitation in eastern China as simulated by CCSM4. Adv. Atmos. Sci., 33, 706–714, https://doi.org/10.1007/s00376-016-5269-x.
Zhu, Z., and T. Li, 2016: A new paradigm for continental U.S. summer rainfall variability: Asia–North America teleconnection. J. Climate, 29, 7313–7327, https://doi.org/10.1175/JCLI-D-16-0137.1.