A multidecadal oscillation in the northeastern Pacific

CHEN Dong， WANG Hui-Jun， YANG Songand GAO Ya

aNansen-Zhu International Research Centre， Institute of Atmospheric Physics， Chinese Academy of Sciences， Beijing， China；bCollege of Earth Science， University of Chinese Academy of Sciences， Beijing， China；cClimate Change Research Center， Chinese Academy of Sciences， Beijing，China；dCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/International Joint Laboratory on Climate and Environment Change， Nanjing University of Information Science & Technology， Nanjing， China；eSchool of Atmospheric Sciences， Sun Yat-sen University， Guangzhou， China

A multidecadal oscillation in the northeastern Pacific

CHEN Donga，b，c， WANG Hui-Jund，c， YANG Songeand GAO Yaa，b，c

aNansen-Zhu International Research Centre， Institute of Atmospheric Physics， Chinese Academy of Sciences， Beijing， China；bCollege of Earth Science， University of Chinese Academy of Sciences， Beijing， China；cClimate Change Research Center， Chinese Academy of Sciences， Beijing，China；dCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/International Joint Laboratory on Climate and Environment Change， Nanjing University of Information Science & Technology， Nanjing， China；eSchool of Atmospheric Sciences， Sun Yat-sen University， Guangzhou， China

The internal modes of the North Pacifc can lead to climatic oscillations through ocean-atmosphere interactions and induce global climate responses. The best example is the Pacifc Decadal Oscillation，but this fails to explain many climate phenomena. Here， another multidecadal variability over the North Pacifc is described， found by analyzing reconstructed data covering the past 140 years. It is named the Pacifc Multidecadal Oscillation （PMO）， with anomalously high/low SSTs over the northeastern Pacifc， and a quasi-60-year cycle. Related to this low-frequency variability of SST，the global mean temperature and precipitation present signifcant interdecadal diferences. More importantly， the PMO index leads the global mean surface air temperature and SST by one to three years. The Arctic Oscillation pattern and atmospheric circulations are shown to change substantially with the transition of the PMO mode from positive to negative phases. This multidecadal oscillation improves the prospect for a long-term forecast of the global warming trend， since the PMO bears a remarkable relationship with global temperature.

ARTICLE HISTORY

Revised 6 April 2016

Accepted 22 April 2016

Climate change；multidecadal oscillation； sea surface temperature； Arctic oscillation； Pacifc

研究表明北太平洋海洋內(nèi)部模態(tài)能夠通過海氣相互作用導(dǎo)致氣候系統(tǒng)振蕩和全球氣候響應(yīng)。最好的例子就是太平洋年代際振蕩（PDO），但是以往的研究中發(fā)現(xiàn)很多氣候年代際變化現(xiàn)象用PDO沒法給與很好的解釋。本研究通過分析過去140年的資料發(fā)現(xiàn)北太平洋區(qū)域另外一個(gè)多年代際振蕩模態(tài)，并將其定義為太平洋多年代際振蕩（以下簡稱PMO），其表現(xiàn)為北太平洋白令海峽以南和阿拉斯加灣流區(qū)域海表面溫度存在60年左右周期的異常偏高或者異常偏低。隨著海溫這一低頻振蕩的發(fā)生，全球氣溫、降水都表現(xiàn)出顯著的年代際異常。更重要的是，PMO超前全球氣溫2-3年和海溫3-7年發(fā)生轉(zhuǎn)變。與此同時(shí)，北極濤動(dòng)形態(tài)和大氣環(huán)流隨著PMO正負(fù)位相的轉(zhuǎn)變也發(fā)生改變。由于PMO與全球溫度有著很好的關(guān)系，因此PMO的發(fā)現(xiàn)對全球變暖趨勢長時(shí)間尺度預(yù)報(bào)的改進(jìn)有著重要作用。

1. Introduction

The Pacific Decadal Oscillation （PDO） has been proposed in previous studies as the most significant interdecadal mode in the North Pacific （Mantua et al. 1997； Zhang，Wallace， and Battisti 1997）， having significant impacts on the North Pacific and global climate. Fluctuations in the PDO mostly occur over two periodicities: 50-70 and 15-25 years （Mantua and Hare 2002）. Independent studies have determined the 50-70-year climate oscillation from instrumental data over the North Pacific and North America （Minobe 1997）， and the landing of fish data over the Pacific （Hare 1996； Mantua et al. 1997）. Schlesinger and Ramankutty （1994） also indicated that the global climate system has an oscillation period of 65-70 years.

Previous studies have shown three apparent changes（in the 1920s， 1940s and 1970s） that have been labeled as regime shifts； and most studies （Mantua et al. 1997；Schlesinger and Ramankutty 1994） have attributed the regime shifts to the PDO and its impact on marine climate variability. However， the phases of related indices （e.g. the atmospheric circulation index （ACI） and global air temperature presented in Figure 1 of Chavez et al. （2003）， as well as the Washington streamfow and Pacifc salmon catch records in Figure 5 of Mantua et al. （1997）） do not match the PDO， especially from 1900 to 1945. During this period，changes occurred in the phases of all the related indices from 1900-1924 to 1925-1947， but the phase of the PDO was the same during 1900-1945. The PDO is related to the overlying Aleutian low； however， the same distinction can also be seen between the North Pacifc Index （NPI），which depicts changes in the intensity of the Aleutian low （Trenberth and Hurrell 1994）， and the PDO （Mantua et al. 1997）. According to Minobe （1997） and Klyashtorin（1998）， another important regime shift in the related indices occurred in the 1890s， but the PDO did not change at this time. The landings of sardines vary synchronously of Japan， California， Peru， and Chile （Chavez et al. 2003）. However， it remains unclear as to why sardines increase of Japan when the local waters are cool and become more productive， while they increase of California and Peru when those regions are warm and become less productive （Chavez et al. 2003； McFarlane et al. 2002）. All of these questions have not yet been answered.

Figure 1.The （a） second EOF mode of the non-fltered winter SSTs， during 1871-2010， and （b） its PC （solid black line）， as well as the lowpass fltered results （red line）.

To investigate whether there is another multidecadal mode over the North Pacifc that can explain these regime shifts， we examine SST data using EOF analysis， and fnd that the second mode has a certain multidecadal signal. This mode is similar to the North Pacifc Gyre Oscillation（NPGO）， or Victoria mode， at interannual timescales （Bond et al. 2003； Di Lorenzo et al. 2008）， which possess no such multidecadal oscillation characteristics. Further analysis reveals that the multidecadal signal comes from the area north of 50°N over the North Pacifc， rather than NPGO region； and then we defne the Pacifc Multidecadal Oscillation （PMO） region and index. In terms of the role of the PMO at multidecadal timescales in global climate change， this remains largely unknown. After proving the existence of the PMO， this study focuses on the impacts of the PMO on the global climate system， and the global atmosphere and SSTs.

2. Datasets and methods

The datasets used in the current study include ERSST.v3b，for the period 1871-2010， and with a horizontal resolution of 2° latitude/longitude （Smith and Reynolds 2003，2004； Smith et al. 2008）. The monthly mean SLP data for1871-2010 are from the Hadley Centre website （http:// www.esrl.noaa.gov/psd/data/gridded/data.hadslp2.html），with a horizontal resolution of 5° latitude/longitude （Allan and Ansell 2006）. The monthly mean geopotential height，air temperature at 2 m， and precipitation rates， are from the twentieth century reanalysis V2 （Compo et al. 2011），provided by NOAA. Surface air temperatures （CRUTEM3v）are also from NOAA （http://www.esrl.noaa.gov/psd/）. The monthly mean SSTs from the Hadley Centre （HADSST，Rayner et al. 2003）， NOAA （KAPSST， Kaplan et al. 1998） and COBESST， Ishii et al. 2005； Hirahara， Ishii， and Fukuda 2014））and the ECMWF （ERASST， Poli et al. 2013）， are also used to support our results， as are near-surface temperatures from the CRU （Harris et al. 2014）.

Several climate indices are also used in this study，including: （1） The ACI， also known as the Vangengeim-Girs index， calculated based on atmospheric activity in the Atlantic-Eurasia region （Girs 1974； Hoy et al. 2013；Klyashtorin 1998）； （2） The NPI， defned by Trenberth and Hurrell （1994） as an area-weighted SLP average over （30-65°N， 160°E-140°W）； （3） The southern oscillation index（SOI）， defned as the leading principal component （PC） of winter SLP anomalies over （40°S-20°N， 90°E-65°W） （this SOI is similar to that defned by Trenberth （1984）， as the Tahiti pressure minus the Darwin pressure， and the correlation coefcient between the two is as high as 0.81 during 1871-2003）； （4） The eastern Pacifc wave train （EPW），defned as the volume average of the seasonal vertical wave activity fux in the domain （30-60°N， 170-120°W；950-500 hPa）. This defnition of the EPW describes the strength of both the vertical and horizontal fuxes well（Sun and Tan 2013）.

In order to obscure the greenhouse warming signal，we assume that the relationship between greenhouse gas （GHG） concentrations and global temperature is linear. We remove the global warming signal of CO2from the surface air temperatures and SSTs using the method of An （2003） and Chen et al. （2015）. The remaining temperature changes can be regarded as containing no global warming signal and are subjected to further analysis. The formula employed to remove the global warming signal is

3. Characteristics of the PMO

Consistent with previous studies （Mantua et al. 1997；Zhang， Wallace， and Battisti 1997）， the frst EOF mode -by applying the EOF to the winter SST anomalies （SSTAs）over the North Pacifc （20-65°N， 110°E-110°W） - is the PDO， which explains 22.2% of the total variance. The second EOF mode is similar to the Victoria mode， or NPGO（Bond et al. 2003； Di Lorenzo et al. 2008； Liu 2012）， accounting for 17% of the total variance of the winter SSTAs. The global warming signal has been removed from the computation. Both the second EOF （EOF2） mode and the PC related to EOF2 （PC2） are shown in Figure 1. Following the low-pass fltered result of PC2， a number of multidecadal signals are found. Using the high-pass fltered data and low-pass fltered data of PC2 separately to perform correlation analyses with SST， we fnd that the most dominant interdecadal signal is from the area north of 50°N， and the most dominant interannual signal is from the area south of 50°N over the North Pacifc； namely， the NPGO region（Figure 2）. The pattern in Figure 2（a） exhibits a northeastsouthwest-oriented dipole， which is very similar to the SSTA regression pattern associated with the NPGO index（Di Lorenzo et al. 2008）. And this also proves that the interannual signal is the NPGO. However， the distribution of correlation coefcients between the low-pass fltered PC2 and SST is diferent to the NPGO pattern， which indicates the interdecadal signal is not the NPGO. Considering the fact that the NPGO has no such long-term cycle about 60 years （Di Lorenzo et al. 2008）， and the regions of this interdecadal signal and the NPGO are diferent from one another， we defne this multidecadal signal as the PMO. And the most relevant areas （（50-65°N， 160°E-145°W）and （40-65°N， 145-130°W）） of the low-frequency signal are together defned as the PMO region. The PMO index is then defned as the area-averaged SSTA over the PMO region. Furthermore， from the time-latitude cross section of the 160°E-140°W zonally averaged SSTA， we reach the same conclusion that the multidecadal signal of the North Pacifc SSTA lies in the change of SST in the area north of 50°N （Figure 3）， which difers from the NPGO.

Figure 2.（a） Correlation coefcients between the high-pass fltered data of PC2 and SSTs during 1871-2010. （b） As in （a） but for the lowpass data of PC2 and SSTs. The blue dashed line indicates the grid line of 50°N. The black boxes in （b） together comprise the PMO region.

To further distinguish the PMO from the NPGO， correlation analysis is separately performed between the NPGO index （Di Lorenzo et al. 2008， http://www.o3d.org/ npgo/npgo.php） and the PMO index and PC2. The results show that only PC2 bears a signifcant relationship with the NPGO during 1950-2010 （correlation coefcient of -0.6）， while between the PMO and NPGO the value is -0.08 （fgures omitted）. Furthermore， we analyze the differences in the relationships of the PMO and NPGO with atmospheric circulations. The regression maps of PMO and PC2 with geopotential height and horizontal wind（850 hPa） are shown in Figure S1， revealing a signifcant diference between them. The former exhibits a northeast-southwest-oriented wave train， while the latter is a north-south-oriented dipole that is very similar to the regression map of the NPGO index and SLP， in which the NPGO index defned by Di Lorenzo et al. （2008）. All the diferences demonstrate that the PMO and NPGO are two diferent modes.

We also examine the low-pass fltered SSTs of four other datasets over the northeastern Pacifc by calculating the area-average of the PMO region. The results obtained from these four datasets are very similar to the PMO index of ERSST.v3b （Figure S2）. All of the datasets yield the same PMO over the northern Pacifc， which indicates that the PMO is a real mode in the North Pacifc and primarily depicts the variation in SSTs in these regions. The center of the PMO mode is located in the northeastern Pacifc， near the Alaska Current and Bering Strait. It can be seen from the corresponding time series that the PMO has a quasi-60-year oscillation during 1871-2010， which appears signifcant in the power spectrum of the PMO index （Figure 4）. Roughly， the periods of 1871-1897， 1923-1948， and 1979-2003 were positive phases， and 1898-1922 and 1949-1978 were negative phases.

The PDO index is defned as the corresponding time series of the frst mode （Figure 5（b）） using the method of Mantua et al. （1997）. Lead-lag correlation analysis，between the PMO and PDO， yields no signifcant relationship， suggesting that the PMO and PDO are two relatively independent modes over the North Pacifc. Large diferences in periodicity exist between the PMO index and PDO index （Figures 5（a） and （b））. The former has only one relatively stable cycle， of around 60 years， while fuctuationsin the PDO mostly occur over two periodicities: one of 50-70 years， and the other about 15-25 years. These features are consistent with the results of Mantua and Hare（2002）. The diference between the cycles also proves that the PDO and PMO are two independent modes.

Figure 3.Time-latitude cross section of the 160°E-140°W zonally averaged SSTA using （a） the original data and （b） the low-pass fltered data， in which the global warming signals have been removed from the SST data.

Figure 4.Power spectrum of the PMO index shown in Figure 5（a）（line-flled）.

Importantly， previous studies （Gershunov and Barnett 1998） have documented that the PDO and ENSO are closely related to one another. They can result in similar climate patterns in the North Pacifc （Mantua and Hare 2002）. In its positive （negative） phase， the PDO is able to enhance canonical El Ni?o （La Ni?a） events （also known as the eastern Pacifc （EP） pattern， i.e. EP-type ENSO） （Gershunov and Barnett 1998）. Conversely， the PMO bears no signifcant relationship with EP-type ENSO events； and the patterns related to ENSO and the PMO are totally diferent， as shown by the correlation between the PMO index and SSTs （Figure S3a）， in which there are no large areas of signifcance in the relationship （like there are for the PDO） over the eastern tropical Pacifc （Gershunov and Barnett 1998； Mantua and Hare 2002）. Figure S3a shows that the PMO has a signifcant efect on global SSTs， especially over the west coast of North and South America， the North Atlantic， and the tropical western Pacifc. Under the modulating efects of the PMO， the global SSTs present a near uniform distribution pattern， and this may be the reason why the landingsof sardines vary synchronously of Japan， California， Peru，and Chile， which cannot be explained by the PDO （Chavez et al. 2003）. During the warm （cool） periods of the PMO，the global SSTs increase （decrease）， so the interdecadal variability of the landings of sardines is consistent with the PMO over these four places.

Figure 5.The （a） PMO index and （b） PDO index defned as low-pass fltered time series associated with the frst EOF mode of the winter SST data over the North Pacifc （north of 20°N） for 1871-2010.

4. Evidence of the PMO

The PMO has a strong periodicity， with a cycle of around 60 years. For 25-30 years， the North Pacifc SSTs are warmer than average， and in the following 25-30 years they shift to being cooler than average. Minobe （1997） analyzed instrumental data and found that there was a 50-70-year oscillation over the North Pacifc and North America （see Figure 1 of Minobe （1997））. It is known， however， that the speed of atmospheric signal dispersion is very fast. As such， the signal generally cannot sustain for 50-70 years. Instead， the ocean has long ‘memory' characteristics， and the cycle of 50-70 years may be a result of air-sea interaction. Furthermore， the interdecadal variability of temperature over the west coast of North America fts the PMO very well （Minobe 1997）. These phenomena prove， to some extent， that the PMO does exist， and has a relationship with climatic oscillations， including the spring air temperatures in western North America， the spring SSTs in the eastern North Pacifc， the winter-spring SLPs in the central North Pacifc， the spring-summer SSTs at Enoshima， Japan，and the annual mean SSTs in the Indian Ocean Maritime Continent.

Figure 6.Similarities between the （a） PMO index and other climate indices during June-August 1871-2010: （b） ACI； （c） SOI； （d） NPI； （e）Northern Hemisphere precipitation index （PRT-JJAI）.

There is increasing evidence that marine climate variability can strongly afect populations of Pacifc salmon （Beamish and Bouillon 1993； Francis et al. 1998）. Furthermore， the landing of salmon also fuctuates markedly - on the interdecadal timescale， with a cycle of about 60 years （Hare 1996； Mantua et al. 1997）. Previous studies （Chavez et al. 2003； Mantua et al. 1997） suggest that the PDO might be the reason for the fuctuations in the landings of these fsh， modulating the populations by changing large-scale atmospheric and oceanic patterns. Further examination （see Figures 5 and 6 of Mantua et al.（1997）， and Figure 1 of Chavez et al. （2003）） indicates that the phases of the indices and records do not match with the PDO， especially from 1900 to 1945. However， they do match the PMO very well， which demonstrates that the PMO is another key internal mode， as well as the PDO，and can modulate large-scale atmospheric and oceanic patterns.

Other climate indices， including changes in northern hemispheric summer precipitation， also present the same oscillation as the PMO （Figure 6）. Based on analysis of the ACI （the anomalies of which show the relative dominance of zonal （positive anomalies） and meridional （negative anomalies） transport in the Atlantic-Eurasia region）， SOI（commonly used to monitor the seesaw of surface air pressure over the eastern and western tropical Pacifc）， NPI（mainly refects the change in the Aleutian Low）， and summer precipitation index， it is interesting to see that， when the PMO varies from one polarity to another， coinciding with this， all of these indices also modify from one phase to another. This further proves that the PMO is indeed a multidecadal mode that is independent from the PDO over the North Pacifc. Moreover， it has important relationships with several key indices and records of climate change.

Figure 7.Explanation of （a） global mean land-ocean temperature（solid black line） and （b） global SST （solid black line） by the PMO in conjunction with the greenhouse efect.

5. PMO effects and possible mechanisms

Several studies have shown the impacts of internal and external factors of oscillations， such as the Atlantic Multidecadal Oscillation （AMO） （Delworth and Knutson 2000； Mann， Bradley， and Hughes 1999； Schlesinger and Ramankutty 1994） and solar radiation oscillation （Beer，Mende， and Stellmacher 2000； Marcus， Ghil， and Ide 1999；Tett et al. 1999）， on near-surface temperatures. Recent studies （Dai et al. 2015； Tollefson 2014） indicate that the PDO and Interdecadal Pacifc Oscillation （IPO） may explain the recent hiatus in global warming， demonstrating that oceans play an essential role in the interdecadal fuctuation of global temperature. Figure S4 shows that the fuctuations of the PMO and global temperature are remarkably similar. Over the past century， the primary cause of global warming has been GHG emissions， produced by human activity. However， despite the continuous increase in GHGs，periods of both increasing （20-30 years） and decreasing global temperatures have occurred. This means that other factors must also change global temperature， and the latest curve is afected simultaneously by all factors.

After the PDO and AMO， the PMO may be another important internal factor that can modulate near-surface temperatures， especially when （before the 1920s） the PDO and IPO indices do not match the global temperature curves. Figure 7 shows that the variation in global temperature may be explained by the PMO in conjunction with the greenhouse efect. When the PMO changes from a positive to negative phase， the cooling efect of the PMO is superimposed on the warming efect of GHGs. As a result，the increase in global temperature becomes slower； the efect of the PMO may even be more dominant than the GHG efect， and cause global temperatures to decrease. In contrast， when the PMO turns from a negative to a positive phase， the warming associated with the PMO is superimposed on the warming efect of GHGs， and warming then accelerates.

After 2000， the PMO turned to a negative phase， and thus there has been a lull in global warming in recent years. Furthermore， according to the cycle of the PMO， it may be anticipated that the lull in global warming will continue to around 2，025. After this point， the global temperature will probably increase again， because of a change of the PMO into a positive phase. Moreover， using the PMO index to perform a lead-lag correlation with the low-pass fltered global-mean near-surface air temperature （AIR）， SSTs， and the combined SST + AIR index， we fnd that the PMO leads all of these indices by between one to three years （Figure S4b）. This makes the PMO more important to global climate change. Also， based on lead-lag correlations between the PMO and regional SST indices （Figure S3c）， similar results are obtained； the PMO leads global SSTs.

The Arctic Oscillation （AO） and ENSO are among the most important climate systems over the Northern Hemisphere in winter （December-February）. The question therefore arises as to whether the PMO can infuence the AO and ENSO systems. Figure 8 shows the correlations between the AO and northern hemispheric 850-hPa geopotential height， during fve diferent positive and negative phases of the PMO （1871-1897， 1923-1948， 1979-2003，1898-1922， and 1949-1978）. The results show that， during the three PMO positive phases， the distributions of geopotential height anomalies related to the AO are zonally symmetric. In the meantime， the AO infuences not only the North Atlantic but also the North Pacifc. In contrast，the AO has little infuence over the North Pacifc during the two negative phases of the PMO.

The relationships of the AO with horizontal wind and air temperature are also diferent between the positive and negative phases of the PMO （fgures omitted）. Sun and Tan （2013） demonstrated that the intensity of the EPW fux could change the traditional AO pattern. Both the Pacifc center and the Atlantic center of the traditional AO pattern were present when the EPW index was high.However， the Pacifc center disappeared when the EPW index was low， and subsequently the AO presented a North Atlantic Oscillation pattern. This feature indicates that the AO exhibits both an asymmetric and a symmetric pattern，consistent with our results.

Figure 8.Correlation coefcients between the AO and northern hemispheric geopotential height at 850 hPa， during diferent phases of the PMO: （a） 1871-1897； （b） 1923-1948； （c） 1979-2003； （d） 1898-1922； （e） 1949-1978.

Figure S5 shows the climatological distribution of the northern hemispheric 3D stationary wave activity fux（Plumb 1985） and EPW index （Sun and Tan 2013）. The results show that remarkable interdecadal variance of the EPW index exists， and it consistently changes between positive and negative phases with the PMO cycle. Therefore， a possible mechanism through which the PMO infuences the AO pattern can be proposed. During positive PMO phases， the northeastern Pacifc SSTs are warmer than average. This generates strong EPW， which in turn infuences the Aleutian low-Icelandic low （AL-IL） seesaw， and the AO pattern. During negative PMO phases， the northeastern Pacifc SSTs are cooler than average， which causes a weak EPW. The Pacifc center of the traditional AO pattern thus disappears， when the AL-IL seesaw is absent.

Canonical El Ni?o and El Ni?o Modoki are two coupled ocean-atmosphere phenomena in the tropical Pacifc（Ashok et al. 2007； Rasmusson and Carpenter 1982）. The anomalous SST patterns related to the two diferent types of El Ni?o are diferent. Ashok et al. （2007） showed that El Ni?o Modoki is represented by the second EOF mode of SSTs， and explained that the recently more frequent El Ni?o Modoki might be caused by the fatter thermocline in the tropical Pacifc. As shown in Figure S3a， a positive PMO phase can result in warm SSTs near the eastern and western edges of the tropical Pacifc， but cool SSTs in the central tropical Pacifc. These changes modify SST gradients and， subsequently， the thermocline. Therefore， it can be speculated that the PMO is able to modulate El Ni?o Modoki events， by exerting a change in the thermocline.

Figure 9 shows the second EOF modes of the SSTs in 110°E-70°W and 30°S-30°N， during the diferent phases of the PMO， corresponding to the three warm phases （a，b， and c） and the two cool phases （d and e） of the PMO. It is shown that the El Ni?o Modoki mode appears on the second EOF mode during the positive phases only of the PMO. The possible mechanism underpinning this phenomenon is that， when the PMO is positive， the SSTs in the eastern tropical Pacifc and of the coast of South America are warmer than average， and subsequently the Southern Oscillation decreases， weakening the Walker Circulationand the tropical easterly fow. The ofshore current and upwelling then weaken， due to the lessened easterly fow in the lower troposphere. In this case， exchanges between the deep waters and surface waters become more difcult than usual. On the other hand， exchanges in the central tropical Pacifc are much easier. The above discussion probably explains why El Ni?o Modoki events have recently become more frequent. In contrast， when the PMO is negative， El Ni?o Modoki events hardly ever occur.

Figure 9.The second EOF mode of winter tropical Pacifc SSTs during diferent phases of the PMO: （a） 1871-1897； （b） 1923-1948； （c）1979-2003； （d） 1898-1922； （e） 1949-1978. Panels （a-c） are three positive phases of the PMO， and （d， e） two negative phases.

6. Discussion

This paper examines various aspects of the PMO. The PMO has a strong periodicity， with a cycle of around 60 years，and is fundamentally diferent to the PDO. There have been three positive and two negative phases of the PMO during 1871-2010. Currently， the PMO lies in a negative phase，which started around 2003 and may persist in total for about 25-30 years， before changing into a positive phase around 2，025. The PMO has a signifcant efect on global SSTs， especially of of North and South America， in the North Atlantic， and in the western tropical Pacifc. Under the modulating efect of the PMO， global SSTs present a near uniform distribution pattern. During PMO positive phases， global SSTs are higher than average. At the same time， there is more rainfall in summer in the Northern Hemisphere， the Aleutian low strengthens， the Southern Oscillation weakens， and zonal atmospheric transport is relatively dominant in the Atlantic-Eurasia region. Conversely， during negative phases of the PMO， the opposite features appear.

The relationship of the PMO with the AO and ENSO is examined during boreal winter. It seems that the AO and ENSO can also be infuenced by the PMO； during warm PMO phases， the AO infuences both the North Atlantic and the North Pacifc area. Meanwhile， El Ni?o Modoki events become more frequent. During cool PMO phases，however， the AO exerts a signifcant infuence over the North Atlantic only， and El Ni?o Modoki events hardly ever occur.

Both the PMO （as a natural internal oscillation） and GHGs exert efects on global temperature. When the phase of the PMO changes from negative to positive， the increase in temperature related to the PMO and the efects of GHGsare signifcant. In contrast， when the PMO changes from a positive phase to a negative phase， the cooling efects related to the PMO phase ofset global warming due to the GHGs. Of greater importance， generally， changes in global air temperature and SST lag the PMO by between one to three years.

While the causes of the PMO are not fully understood at present， two possibilities are proposed: （1） external oscillatory forcing， such as a variation in the solar constant；and （2） an internal oscillation of the atmosphere-ocean system. However， comparison between the PMO index and the sunspot cycle during the last century reveals no signifcant relationship. Therefore， the internal oscillation of the atmosphere-ocean system is the more likely cause.

Issues related to the global warming hiatus in the last decade have received a great deal of attention； however，the reasons behind the phenomenon are still unclear. The AMOC （Atlantic Meridional Overturning Circulation）has been identifed as a factor， according to Smith et al.（2007） and Keenlyside et al. （2008）. In contrast， Tollefson（2014） and Dai et al. （2015） claim that the pause is caused by change in the PDO and IPO， from a positive phase to a negative phase. The current study shows that the hiatus in global warming that has occurred in the last decade may be related to the infuence of the PMO. If so， this pause in global warming may persist until 2025， after which apparent warming may return. It should be noted that all the results presented in this paper are statistical， and that sensitivity experiments using coupled models are needed to confrm the fndings.

Acknowledgements

We wish to acknowledge Dabang JIANG and Wenju CAI for their scientifc comments and suggestions about this work. We also thank the editor and two anonymous reviewers for their helpful comments.

Disclosure statement

No potential confict of interest was reported by the authors.

Funding

This research was supported by the National Natural Science Foundation of China ［grant number 41421004］， ［grant number 41130103］.

Notes on contributors

CHEN Dong is a PhD candidate at the Institute of Atmospheric Physics （IAP）， Chinese Academy of Sciences （CAS）. His main research interests are interdecadal variability， climate change，climate prediction. His recent publications include papers in

Atmospheric and Oceanic Science Letters， International Journal of Climatology， Journal of Climate， and other journals.

WANG Hui-Jun is a professor at Nanjing University of Information Science & Technology. His main research interests are Paleoclimate， East Asian monsoon， climate change， climate prediction. His recent publications include papers in Climate Dynamics， Atmospheric and Oceanic Science Letters， International Journal of Climatology， Journal of Climate， Journal of Geophysical Research-Atmospheres， Science China: Earth Sciences， Advances in Atmospheric Sciences， and other journals.

YANG Song is a professor at Sun Yat-sen University. His main research interests are climate predictability， air-sea interaction，global monsoon， the response and feedback of regional climate to global climate change. His recent publications include papers in Climate Dynamics， Nature Communications， Journal of Climate，Atmospheric and Oceanic Science Letters， Journal of Geophysical Research， and other journals.

GAO Ya is an assistant prefessor at IAP， CAS. Her main research interests are East Asian monsoon variability， climate change， climate prediction. Her recent publications include papers in International Journal of Climatology， Journal of Climate， Journal of Geophysical Research， Science China: Earth Sciences， and other journals.

References

Allan， R.， and T. Ansell. 2006. “A New Globally Complete Monthly Historical Gridded Mean Sea Level Pressure Dataset（HadSLP2）: 1850-2004.” Journal of Climate 19: 5816-5842.

An， S. I. 2003. “Conditional Maximum Covariance Analysis and Its Application to the Tropical Indian Ocean SST and Surface Wind Stress Anomalies.” Journal of Climate 16: 2932-2938.

Ashok， K.， S. K. Behera， S. A. Rao， H. Weng， and T. El Yamagata. 2007. “El Ni?o Modoki and Its Possible Teleconnection.”Journal of Geophysical Research 112: C11007. doi:http:// dx.doi.org/10.1029/2006JC003798.

Beamish， R. J.， and D. R. Bouillon. 1993. “Pacifc Salmon Production Trends in Relation to Climate.” Canadian Journal of Fisheries and Aquatic Sciences 50: 1002-1016.

Beer， J.， W. Mende， and R. Stellmacher. 2000. “The Role of the Sun in Climate Forcing.” Quaternary Science Reviews 19: 403-415. Bond， N. A.， J. E. Overland， M. Spillane， and P. Stabeno. 2003.“Recent Shifts in the State of the North Pacifc.” Geophysical Research Letters 30 （23）: 2183. doi:http://dx.doi.org/10.1029/ 2003GL018597.

Chavez， F. P.， J. Ryan， S. E. Lluch-Cota， and M. ?iquen. 2003. “From Anchovies to Sardines and Back: Multidecadal Change in the Pacifc Ocean.” Science 299: 217-221.

Chen， D.， H. Wang， J. Liu， and G. Li. 2015. “Why the Spring North Pacifc Oscillation is a Predictor of Typhoon Activity over the Western North Pacifc.” International Journal of Climatology 35: 3353-3361.

Compo， G. P.， J. S. Whitaker， P. D. Sardeshmukh， N. Matsui， R. J. Allan， X. Yin， B. E. Gleason， et al. 2011. “The Twentieth Century Reanalysis Project.” Quarterly Journal of the Royal Meteorological Society 137: 1-28.

Dai， A.， J. C. Fyfe， S. P. Xie， and X. Dai. 2015. “Decadal Modulation of Global Surface Temperature by Internal Climate Variability.”Nature Climate Change 5: 555-559.

Delworth， T. L.， and T. R. Knutson. 2000. “Simulation of Early 20th Century Global Warming.” Science 287: 2246-2250.

Di Lorenzo， E.， N. Schneider， K. M. Cobb， P. J. Franks， K. Chhak，A. J. Miller， J. C. McWilliams， et al. 2008. “North Pacifc Gyre Oscillation Links Ocean Climate and Ecosystem Change.”Geophysical Research Letters 35: L08607. doi:http://dx.doi. org/10.1029/2007GL032838.

Francis， R. C.， S. R. Hare， A. B. Hollowed， and W. S. Wooster. 1998.“Efects of Interdecadal Climate Variability on the Oceanic Ecosystems of the NE Pacifc.” Fisheries Oceanography 7: 1-21.

Gershunov， A.， and T. P. Barnett. 1998. “Interdecadal Modulation of ENSO Teleconnections.” Bulletin of the American Meteorological Society 79: 2715-2725.

Girs， A. A. 1974. “Makrocirkuljacionnyj metod dolgosro?nyh meteorologi?eskih prognozov.” ［Macro-circulation Method of Long-term Meteorological Forecasts.］ Gidrometeotzdat，Leningrad 488 pp； in Russian.

Hare， S. R. 1996. “Low Frequency Climate Variability and Salmon Production”. PhD diss.，. University of Washington， Seattle， WA，306 pp.

Harris， I.， P. D. Jones， T. J. Osborn， and D. H. Lister. 2014. “Updated High-resolution Grids of Monthly Climatic Observations -The CRU TS3.10 Dataset.” International Journal of Climatology 34: 623-642.

Hirahara， S.， M. Ishii， and Y. Fukuda. 2014. “Centennial-scale Sea Surface Temperature Analysis and Its Uncertainty.” Journal of Climate 27 （1）: 57-75.

Hoy， A.， J. Jaagus， M. Sepp， and J. Matschullat. 2013. “Spatial Response of Two European Atmospheric Circulation Classifcations （Data 1901-2010）.” Theoretical and Applied Climatology 112: 73-88.

Ishii， M.， A. Shouji， S. Sugimoto， and T. Matsumoto. 2005.“Objective Analyses of Sea-surface Temperature and Marine Meteorological Variables for the 20th Century Using ICOADS and the Kobe Collection.” International Journal of Climatology 25 （7）: 865-879.

Kaplan， A.， M. Cane， Y. Kushnir， A. Clement， M. Blumenthal，and B. Rajagopalan. 1998. “Analyses of Global Sea Surface Temperature 1856-1991.” Journal of Geophysical Research: Oceans 103: 18567-18589.

Keenlyside， N. S.， M. Latif， J. Jungclaus， L. Kornblueh， and E. Roeckner. 2008. “Advancing Decadal-scale Climate Prediction in the North Atlantic Sector.” Nature 453: 84-88.

Klyashtorin， L. B. 1998. “Long-term Climate Change and Main Commercial Fish Production in the Atlantic and Pacifc.”Fisheries Research 37: 115-125.

Liu， Z. 2012. “Dynamics of Interdecadal Climate Variability: A Historical Perspective.” Journal of Climate 25: 1963-1995.

Mann， M. E.， R. S. Bradley， and M. K. Hughes. 1999. “Northern Hemisphere Temperatures during the past Millennium: Inferences， Uncertainties， and Limitations.” Geophysical Research Letters 26: 759-762.

Mantua， N. J.， and S. R. Hare. 2002. “The Pacifc Decadal Oscillation.” Journal of Oceanography 58: 35-44.

Mantua， N. J.， S. R. Hare， Y. Zhang， J. M. Wallace， and R. C. Francis. 1997. “A Pacifc Interdecadal Climate Oscillation with Impacts on Salmon Production.” Bulletin of the American Meteorological Society 78: 1069-1079.

Marcus， S. L.， M. Ghil， and K. Ide. 1999. “Models of Solar Irradiance Variability and the Instrumental Temperature Record.”Geophysical Research Letters 26: 1449-1452.

McFarlane， G. A.， P. E. Smith， T. R. Baumgartner， and J. R. Hunter. 2002. “Climate Variability and Pacifc Sardine Populations and Fisheries.” American Fisheries Society Symposium 32: 195-214.

Minobe， S. 1997. “A 50-70 Year Climatic Oscillation over the North Pacifc and North America.” Geophysical Research Letters 24: 683-686.

Plumb， R. A. 1985. “On the Three-dimensional Propagation of Stationary Waves.” Journal of the Atmospheric Sciences 42: 217-229.

Poli， P.， H. Hersbach， D. Tan， D. Dee， J.-N. Thépaut， A. Simmons，C. Peubey， et al. 2013. The Data Assimilation System and Initial Performance Evaluation of the ECMWF Pilot Reanalysis of the 20th-century Assimilating Surface Observations Only （ERA-20C）. ERA Report Series No. 14， ECMWF， 59 pp. http://www. ecmwf.int/sites/default/files/elibrary/2013/11699-dataassimilation-system-and-initial-performance-evaluationecmwf-pilot-reanalysis-20th.pdf.

Rasmusson， E. M.， and T. H. Carpenter. 1982. “Variation in Tropical Sea Surface Temperature and Surface Wind Fields Associated with Southern Oscillation/El Nino.” Monthly Weather Review 110: 354-384.

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.”Journal of Geophysical Research 108 （D14）: 4407. doi:http:// dx.doi.org/10.1029/2002JD002670.

Schlesinger， M. E.， and N. Ramankutty. 1994. “An Oscillation in the Global Climate System of Period 65-70 Years.” Nature 367: 723-726.

Smith， T. M.， and R. W. Reynolds. 2003. “Extended Reconstruction of Global Sea Surface Temperatures Based on COADS Data（1854-1997）.” Journal of Climate 16: 1495-1510.

Smith， T. M.， and R. W. Reynolds. 2004. “Improved Extended Reconstruction of SST （1854-1997）.” Journal of Climate 17: 2466-2477.

Smith， D. M.， S. Cusack， A. W. Colman， C. K. Folland， G. R. Harris，and J. M. Murphy. 2007. “Improved Surface Temperature Prediction for the Coming Decade from a Global Climate Model.” Science 317: 796-799.

Smith， T. M.， R. W. Reynolds， T. C. Peterson， and J. Lawrimore. 2008. “Improvements to NOAA's Historical Merged Land-Ocean Surface Temperature Analysis （1880-2006）.” Journal of Climate 21: 2283-2296.

Sun， J.， and B. K. Tan. 2013. “Mechanism of the Wintertime Aleutian Low-Icelandic Low Seesaw.” Geophysical Research Letters 40: 4103-4108.

Tett， S. F.， P. A. Stott， M. R. Allen， W. J. Ingram， and J. F. Mitchell. 1999. “Causes of Earth's near-surface Temperature Change in the Twentieth Century.” Nature 399: 569-572.

Tollefson， J. 2014. “The Case of the Missing Heat.” Nature 505: 276-278.

Trauth， M. H.， R. Gebbers， N. Marwan， and E. Sillmann. 2007. MATLAB Recipes for Earth Sciences （Vol. 34）. Berlin: Springer.

Trenberth， K. E. 1984. “Signal versus Noise in the Southern Oscillation.” Monthly Weather Review 112: 326-332.

Trenberth， K. E.， and J. W. Hurrell. 1994. “Decadal Atmosphereocean Variations in the Pacifc.” Climate Dynamics 9: 303-319.

Zhang， Y.， J. M. Wallace， and D. S. Battisti. 1997. “ENSO-like Interdecadal Variability: 1900-1993.” Journal of Climate 10: 1004-1020.

氣候變化； 多年代際振蕩；海表面溫度； 北極濤動(dòng)； 太平洋

23 November 2015

CONTACT CHEN Dong chend@mail.iap.ac.cn

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