Response of the North Pacific Oscillation to global warming in the models of the Intergovernmental Panel on Climate Change Fourth Assessment Report*

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1 Journal of Oceanology and Limnology Vol. o., P. -, Response of the orth Pacific Oscillation to global warming in the models of the Intergovernmental Panel on Climate Change Fourth Assessment Report* CHE Zheng ( 陈峥 ) **, GA Bolan ( 甘波澜 ), WU Lixin ( 吴立新 ) Physical Oceanography Laboratory/CIMST, Ocean University of China, and Qingdao ational Laboratory for Marine Science and Technology, Qingdao, China Received Jan., ; accepted in principle Apr., ; accepted for publication Apr., Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer ature Abstract Based on of the climate models from phase of the Coupled Model Intercomparison Project, we investigate the ability of the models to reproduce the spatiotemporal features of the wintertime orth Pacific Oscillation (PO), which is the second most important factor determining the wintertime sea level pressure field in simulations of the pre-industrial control climate, and evaluate the PO response to the future most reasonable global warming scenario (the AB scenario). We reveal that while most models simulate the geographic distribution and amplitude of the PO pattern satisfactorily, only models capture both features well. However, the temporal variability of the simulated PO could not be significantly correlated with the observations. Further analysis indicates the weakened PO intensity for a scenario of strong global warming is attributable to the reduced lower-tropospheric baroclinicity at mid-latitudes, which is anticipated to disrupt large-scale and low-frequency atmospheric variability, resulting in the diminished transfer of energy to the PO, together with its northward shift. Keyword : orth Pacific Oscillation (PO); greenhouse gas warming; CMIP climate models; atmospheric baroclinicity; climate change; empirical orthogonal function ITRODUCTIO As an important teleconnection in the orthern Hemisphere, the orth Pacific Oscillation (PO) is a prominent mode of planetary-scale atmospheric variability of sea level pressure (SLP) over the orth Pacific. The PO features a meridional dipole, which, in its positive (negative) phase, has a zonally elongated band of above (below) normal SLP in the subtropics centered near Hawaii, and a center of below (above) normal SLP over Alaska at mid and high latitudes (Walker and Bliss, ). The PO is also a SLP signature of the west Pacific teleconnection pattern (Wallace and Gutzler, ) of upper-level geopotential height (Linkin and igam, ). The PO is prominent in the boreal wintertime, shares a close relationship with the orth American hydroclimate, and is a significant influence on the strength of the Asian-Pacific jet stream and Pacific storm tracks. For example, a northward shift and elongation of the jet stream is linked to the positive phase of the PO (e.g., Linkin and igam, ; Wettstein and Wallace, ). The PO also modulates the variation of storm tracks in association with an anomalous jet strength, such as a downstream intensification and zonal shift of storm-track activity (Lau, ; Rogers, ; Wettstein and Wallace, ). As a result, the PO is believed to impact the orth American surface climate, including the air temperature, precipitation, and the marginal ice zone in the western Bering Sea, especially in winter (e.g., igam, ; Linkin and igam, ). The PO provides the atmospheric forcing of the orth Pacific Gyre oscillation (e.g., Di Lorenzo et al., * Supported by the China ational Global Change Major Research Project (o. CB), the ational Science Foundation of China (SFC) Key Project (o. ), the SFC (os., ), and the SFC Major Project (o. ) ** Corresponding author: chenzhengouc@.com

2 J. OCEAOL. LIMOL., (), Vol. ; Chhak et al., ), which is the second leading mode of the sea surface temperature and sea surface height fields in the orth Pacific, and connects climate variability in the tropical and extratropical Pacific. For instance, El iño Modoki events in winter excite low-frequency variations of the southern lobe of the PO, which when integrated yields the oceanic orth Pacific gyre oscillation (Di Lorenzo et al., ). Variation of the wintertime PO helps initiate the El iño Modoki in the following year via the seasonal footprinting mechanism (Vimont et al., ; Ashok et al., ; Yu and Kim, ). To summarize, as long-term spatiotemporal shifts of the PO significantly affect the orth Pacific and orth American hydroclimate, it is worthwhile to comprehensively evaluate the ability of coupled climate models to reproduce the PO in past climates, and to explore future changes in a warming environment. Since these issues have not been fully addressed, we revisit the assessment of PO spatiotemporal features in climate simulations of the th century, and further investigate potential changes in future warming simulations based on the global coupled models participating in the Coupled Model Intercomparison Project phase (CMIP). Below, Section describes the observational datasets, multi-model outputs, and analysis methods. Section compares the observed spatiotemporal features of the PO in the th century with modeling results, and shows changes in the PO resulting from global warming. In Section, we investigate the possible mechanism driving the significant response of the PO to global warming, with a focus on the influence of atmospheric baroclinicity. A summary and discussion are found in Section. MATERIAL AD METHOD. Observation The SLP data taken from the ensemble-mean fields of th Century Reanalysis dataset version (CRv) is used for the observational analyses (Compo et al., ). The dataset contains monthly mean values from to at a resolution of on latitudelongitude grids.. Multi-model outputs We evaluate climate data from the coupled climate models of the CMIP, which was used in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR; Solomon et al., ) (Table ). Our analysis is based on outputs from three sets of simulations: ) the pre-industrial control simulation (PI: CO concentration stabilizes at - ) representing the unforced natural variability of the climate system; ) the th Century Climate in Coupled Models (CM) experiment, which incorporate anthropogenic and natural forcings from the observed atmospheric change in composition during the th century; ) the Special Report on Emission Scenarios (SRES) AB scenario, representing the most likely greenhouse gas emissions (CO concentration reaches - at the end of the st century and stabilizes afterward) of the global warming scenario in the st nd centuries. In Section, we use the SLP output of models from the CM simulation to evaluate the simulation of the spatiotemporal features of the PO in the th century. The models simulating both the geographic distribution and amplitude of PO variability relatively well are chosen to further investigate the response of the PO to global warming. Here, we compare model statistics from the SRES-AB scenario with PI runs. In Section, we use the additional output of air temperature ( T a ) to study atmospheric baroclinicity. Monthly-mean atmospheric fields are interpolated to a.. grid for both observational and multi-model data to facilitate comparisons between models of different spatial resolutions (Table ). To control temporal variables, we chose a -year period for both the PI control period ( ) and SRES-AB ( ) scenario when CO levels stabilize (selecting the time that the models are running stably). Only one member (run) of each model is used.. Methodology of constructing PO pattern and its intensity The primary method of constructing the principal component of SLP anomalies is empirical orthogonal function (EOF) analysis, which is defined by eigenvectors of the cross-covariance matrix between grid points (Lorenz, ), and aims to find uncorrelated linear combinations of different variables that explain maximum variance. Thus, a set of orthogonal spatial patterns and a set of associated uncorrelated time series or principal components are constructed when the spatiotemporal meteorological field is given (Hannachi et al., ). The PO index is defined as the second leading

3 o. CHE et al.: Response of PO to global warming Table List of the IPCC AR models analyzed, along with originating group, country and atmospheric spatial resolution of each model Originating group, country Model name Atmospheric-spatial resolution (lon lat) Bjerknes Centre for Climate Research, orway BCCR-BCM. T, L Canadian Centre for Climate Modeling and Analysis, Canada CGCM-.-T T, L Canadian Centre for Climate Modeling and Analysis, Canada CGCM-.-T T, L Météo-France/Centre ational de Recherches Météorologiques, France CRM-CM T, L Commonwealth Scientific and Industrial Research Organisation, Australia CSIRO. T, L Geophysical Fluid Dynamics Laboratory, United States of America GFDL-CM.., L Geophysical Fluid Dynamics Laboratory, United States of America GFDL-CM.., L Goddard Institute for Space Studies, United States of America GISS-AOM, L Goddard Institute for Space Studies, United States of America GISS-EH, L Goddard Institute for Space Studies, United States of America GISS-ER, L Institute of Atmospheric Physics, China IAP-FGOALS-g. T, L Institute of umerical Mathematics, Russia IM-CM., L Institute Pierre Simon Laplace (IPSL), France IPSL-CM.., L Center for Climate System Research, Japan MIROC-hires T, L Center for Climate System Research, Japan MIROC-medres T, L Meteorological Institute of the University of Bonn, Germany MIUBECHOG T, L Max Planck Institute for Meteorology, Germany MPI-ECHAM T, L Meteorological Research Institute, Japan MRI-CGCM.. T, L ational Center for Atmospheric Research, United States of America CAR-CCSM. T, L ational Center for Atmospheric Research, United States of America CAR-PCM T, L Hadley Centre for Climate Prediction and Research/Met Office, United Kingdom UKMO-HadCM.., L Hadley Centre for Climate Prediction and Research/Met Office, United Kingdom UKMO-HadGEM.., L principal component (PC) of the SLP anomalies in the region bounded by the area and E W (Linkin and igam, ), which is also used to bound regression analyses and other general research on the PO. For the PO pattern, monthly anomalies are calculated by removing the climatology and linear trend in -year windows. Then, the anomalies are smoothed with a -month temporal filter to obtain the December January February (DJF) value of the PO, which is generally persistent throughout the boreal winter months. Before the EOF analysis, the filtered fields are weighted by the square root of the cosine of latitude to account for the poleward decrease of grid area. The wintertime PO pattern is derived by regression of the monthly DJF anomaly field onto the normalized PC, which represents the typical PO amplitude corresponding to one standard deviation of the principal component from the EOF analysis. Based on the PO pattern, we define the PO intensity (PO i ) as a linear combination of centers of action, PO i = (-PO a i )+PO b i, () where PO a i (northern lobe) and PO b i (southern lobe) denote intensities of the two centers of the PO dipole, which are computed as a five-point mean (i.e., central maxima or minima plus four orthogonal values around the center point) of the central SLP anomaly value. A positive (negative) value of PO i represents its positive (negative) phase, and only positive PO i are used herein for a unified standard of PO intensity between the SRES-AB scenario and PI control simulations. Therefore, (-PO a i ) and PO b i in Eq. are all positive values. While Wallace and Gutzler () defined a West Pacific index using the -hpa geopotential height field corresponding to the PO at SLP with a similar vertical structure (Linkin and igam, ), the steady locations used to measure centers of the patterns could not be used here to evaluate all the models on a consistent basis. This is because of the inherent bias unique to each model, which results in a different location of the center of action depending on the particular model. Therefore, we track every center location and extreme anomaly

4 J. OCEAOL. LIMOL., (), Vol. Table Correlations ( R ) of PO patterns in each model with observations Model names R VC OBS.. BCCR-BCM... CCCMA-CGCM.-T.. CCCMA-CGCM.-T.. of the patterns to ensure PO i comparability between different models. Multi-model ensemble mean (MEM) statistics are calculated from the mean of the individual statistic analyzed. For example, the MEM pattern of the PO is computed as the mean of individual patterns (adjusted to the positive phase) derived by regression of the SLP anomaly field in DJF onto the normalized PO index from individual models. RESULT CRM-CM.. CSIRO-MK... GFDL-CM... GFDL-CM... GISS-AOM.. GISS-EH.. GISS-ER.. IAP-FGOALS-.g.. IM-CM... IPSL-CM.. MIROC-hires.. MIROC- medres.. MIUB-ECHO-G.. MPI-ECHAM.. MRI-CGCM.... CAR-CCSM... CAR-PCM.. UKMO-HadCM.. UKMO-HadGEM.. Ensemble mean. **** Selected ensemble mean. **** Values for R >. are in bold. ormalized variance contributions (i.e. actual variance contribution of each model divided by the observation) between. and. are in bold in the third column. We select those models (in bold) with both bold values of R and VC. The values of R for the ensemble mean patterns of all models (the ensemble mean) and the selected models in bold (the selected ensemble mean) are given at the bottom. Here, we analyze the PO response to global warming from simulations of the future climate, with a focus on changes in intensity (see Section. for the definition of PO intensity). First, models showing similar spatial patterns to observations are selected for assessment of the PO response to global warming. Second, the internal PO variability is tested to ensure the change is significant. Third, we inspect the changes of the PO in those models that represent the observed spatial features well.. Multi-model selection Given the relatively modest role of observed greenhouse gas and aerosol forcing, simulations of the climate of the th century are a significant challenge for models, because of the difficulty in simulating natural variability as a result of the sensitivity to the initial and boundary conditions, as well as the uncertainty with the parameterization schemes and the degree of atmosphere-ocean coupling. Consequently, the outputs from models vary, for which the accurate portrayal of key features of the low-frequency PO pattern is a first-order question. Therefore, we calculate the correlation coefficient R between the observed spatial pattern o and the spatial patterns of the models m for the CM simulation of the period (the same period as the observations), where R is defined as: R ( o o)( m m)/ n n o m, () n where o and m are mean values and σ o and σ m are the standard deviations of o and m, respectively. Table lists R and the normalized variance contributions (VC) (i.e. the actual variance contribution of the models divided by the observations) of the principal component of each of the IPCC AR models, showing the failure ( R <.) of the BCCR-BCM. ( R =.), GISS-EH ( R =.), IM-CM. ( R =.) and UKMO-HadGEM ( R =.) models to capture PO patterns sufficiently. As the VC is a standardized variance contribution, the VC of the observed PO pattern equals one; values much larger or smaller than one insufficiently capture the amplitude of PO variability. We find models with R >. and VC between. and. (CCCMA-CGCM.-T, CRM-CM, CSIRO-MK., GFDL-CM., GFDL-CM., IAP- FGOALS-.g, IPSL-CM, MIROC-hires, MIROCmedres, MIUB-ECHO-G, MPI-ECHAM, MRI- CGCM.., CAR-CCSM.) whose ensemble mean pattern (Fig.c) has a more similar spatial

5 o. CHE et al.: Response of PO to global warming E d a Observation b Ensemble mean of CMIP models c E W W W E E W W Principal components Of CMIP models models EM Corr. OBS=. models EM Corr. OBS=. W E Ensemble mean of selected models E W W W PC (normalized) Year Observation models EM+ selected models EM CMIP models Fig. (a) Positive phase of the observed PO (hpa) with dashed contours indicating negative values and solid contours positive values; as (b) but for the ensemble mean pattern of the IPCC-AR models for the CM experiment and (c) the ensemble mean pattern for the selected models; (d) normalized principal components of multiple models for the CM experiment and observations Black curve: PO time series of observations; red curve: all models (offset by +); blue curve: selected models (offset by -) for the ensemble mean time series; gray thin lines: time series of all IPCCAR models. structure and greater correlation (Table ) than that delivered by the ensemble of all models (Fig.b), as compared with observations (Fig.a). For the temporal component, we correlate the observational time series of the EOF analysis with each model s time series to find (not shown) no correlation ( R <., P >. for most models). Hence, while it is undeniable that temporal simulations of all IPCC-AR models are flawed, two models (CSIRO-Mk. and UKMO- HadGEM) do give results above the % confidence level; correlations nonetheless remain <.. Figure d shows normalized time series of the PO from models, along with the ensemble mean of models (offset by +), the ensemble mean of the selected models (offset by -), and observational indices, with correlations between the ensemble means and observations both <. ( P >.). Therefore, as the accuracy of the temporal simulation still needs improvement, our assessment of the PO response to global warming mainly depends on those models with a reasonable spatial correspondence with observations.. Unforced internal variability of the PO To test the significance of the changes in the PO variability as forced by global warming, the unforced internal variability in the PI simulation of each selected model is calculated. First, -year outputs of the PI run of each model (except MIROC-hires, which has only years in the PI simulation) are used to create forty -year-length sliding windows (i.e.,,,, ; where is the initial year of the PI run). Then, positive PO patterns are derived from these segments, which are considered independent of each other by assuming that the atmospheric states are uncorrelated. Indeed, no significant correlations of the time series between two consecutive segments are found upon further inspection. Finally, we compute the PO intensity in each pattern, and use the standard deviation of the intensities as the unforced internal variability of the PO.. Response of the PO to global warming In this section, we ascertain the PO response to a warmer environment from the global warming simulation (SRES AB). To facilitate comparisons between the SRES-AB scenario and the PI control simulation of the PO pattern, we present positive-

6 J. OCEAOL. LIMOL., (), Vol. E E E E E E E E E E CRM-CM-PI CRM-CM-AB GFDL-CM.-PI GFDL-CM.-AB GFDL-CM.-PI CCCMA-CGCM.-T-PI E CSIRO-MK.-PI E E E IAP-FGOALS-.g-PI W W W MIROC-hires-PI W W W W W W W W W W W W W W W W W W E E E E E E E E E E GFDL-CM.-AB IPSL-CM-PI IPSL-CM-AB MIROC-medres-PI MIROC-medres-AB MRI-CGCM..-PI CCCMA-CGCM.-T-AB MIUB-ECHO-G-PI E E E E MRI-CGCM..-AB MIUB-ECHO-G-AB CSIRO-MK.-AB MPI-ECHAM-PI MPI-ECHAM-AB IAP-FGOALS-.g-AB MIROC-hires-AB W W W W W W W W W W W W W W W W W W W W W E E E E E E E E E E E E W W W W W W W W W W W W W W W E E E E W W CAR-CCSM.-PI CAR-CCSM.-AB W W W E E E E E E E E W W W W W W W W W W W W W W W W Fig. From top to bottom and left to right, are PO dipole changes from most to least between the PI control and SRES- AB scenario for each of the selected models A positive phase of PO patterns for each model is used for comparison. Solid (dashed) contours stand for positive (negative) values of PO dipole. phase PO patterns of the selected models in Fig., with both PI and SRES-AB simulations using the method described in Section. Figure displays patterns of PO change by calculating the PO i difference in each model between the SRES-AB scenario and the PI control simulations, and then sequencing the models from the most change (largest percentage of the difference divided by PO i in the PI run) to the least change (smallest percentage). After global warming, five models (CRM-CM, MIROC-medres, GFDL- CM., MRI-CGCM.., and MIUB-ECHO-G) show decreases in intensity of both the northern and southern lobes of the PO dipole. Five other models

7 o. CHE et al.: Response of PO to global warming E E E E E E Ensemble mean-pi W Ensemble mean-ab W Ensemble mean-diff W W W W W W W Fig. As in Fig., but for MEM patterns (hpa) of the selected models, along with the anomaly (lower panel, hpa) between the SRES-AB scenario (middle panel) and PI control (upper panel) simulations (CCCMA-CGCM.-T, CSIRO-MK., MPI- ECHAM, IAP-FGOALS-.g, and CAR- CCSM.) reveal a reduced PO a and enhanced PO b. Two models (IPSL-CM and GFDL-CM.) present an increased PO a and weakened PO b. The MIROC-hires output is placed last because it shows little change in intensities of the PO dipole. Therefore, as of the selected models show a weakened PO a versus only of the selected models showing a weakened PO b, the PO a is considered more robust, which is consistent with an overall weakening of the PO. We also find a westward shifting of the southern lobe (PO b ) in the results of three models (IPSL-CM, GFDL-CM., MIROC-medres, and MPI-ECHAM), but an eastward shift for two models (GFDL-CM. and CCCMA-CGCM.-T). The remaining models indicate no zonal shift. The MEM patterns of the selected models for the PI control and SRES-AB scenario simulations are shown in the upper and middle panels of Fig., along with the anomaly (AB PI) in the lower panel, which reveals a weakened PO dipole. Figure depicts a summary of PO i differences between the SRES-AB scenario and the PI control from the patterns found in Fig., and represents the change is PO intensity resulting from global warming. The models are presented in the same order as Fig. from left to right. Error bars show the internal variability of each model in the PI simulation via the aforementioned method. Because there are only onehundred years in the PI data of the MIROC-hires model, we could not determine its internal variability. As this model shows little change between the SRES- AB scenario and the PI control simulations, it is considered as an outlier here. The percentages displayed below the bars are computed by dividing the difference by the PO i in the PI reference period. The PO i differences of the MEM patterns (Fig.) is also calculated, with the error bars showing results significant at the % confidence level based on a two-tailed Student s t -test of the models. The findings show that the PO weakens in of the selected models, with of the models having significant results after global warming, and decreasing by more than ~% in PO i. The variance contributions of EOF- in of the models decreases for the SRES-AB scenario with the exception of the MIROC-hires model, which increases by % from. to.. That a clear and robust weakening of the PO in response to global warming is evident motivates us to find the cause of this phenomenon. DISCUSSIO. A possible mechanism for the PO response to global warming The momentum flux resulting from baroclinic eddies has been shown to influence large-scale midlatitude stirring associated with the orth Atlantic Oscillation (Vallis et al., ), which has similarly been associated with the large-scale climate mode of the PO (Di Lorenzo et al,. Moreover, eddy formation is generally closely related to atmospheric

8 J. OCEAOL. LIMOL., (), Vol. PO i changes in selected models (AB PI)... PO i (hpa) % -.% -.% -.% -.% -.% -.% -.% -.% -.% -.% -.%.% -.% CRM-CM GFDL-CM. IPSL-CM GFDL-CM. MIROC-medres MIUB-ECHO-G CCCMA-CGCM.-T MRI-CGCM.. MPI-ECHAM CSIRO-MK. CAR-CCSM. IAP-FGOALS-.g Ensemble mean MIROC-hires Fig. Bar graph showing PO i differences between the SRES-AB scenario and the PI control simulations of the selected models, with the same sequence of models as Fig. The error bars represent the internal variability (see Section.) of each model, which is significant when the PO i changes its internal variability. The percentages show an PO i change (i.e., the difference divided by PO i for the PI reference) in each model. The percentage difference of the MEM pattern located at the tips of the error bars indicate the results of Student s t -test of the models listed for the % significance level. baroclinicity (akamura, ). Such eddies are important in determining atmospheric circulation patterns via their interaction with the time-mean flow, interactions among themselves, and latitudinal transport of momentum and heat (e.g., Hoskins et al., ; Lau, ; Branstator, ). To gauge the extent of PO weakening, we consider all the models giving significant PO weakening (CCCMA-CGCM.-T, CRM-CM, CSIRO-MK., GFDL-CM., GFDL-CM., IPSL- CM, MIROC-medres, MPI-ECHAM, and MRI- CGCM..) according to Fig. (i.e., an PO i change exceeding its internal variability) except for the MIUB-ECHO-G model, which does not have atmospheric pressure-level data. We transform center locations of the PO dipole from the selected models into statistics to ascertain the zonal range of PO a (PO b ) centers as. E (. E) to W (. W) in both simulations of the PI control and the SRES-AB scenario. We then use monthly T a data to compute atmospheric baroclinicity over a zonal mean ( E W, i.e., containing the zonal range of both centers of the PO) for the vertical (pressure levels: hpa, hpa, hpa and hpa) temperature difference between the SRESAB scenario and PI reference from to (units C), which are shown in Fig.. We exclude the IPSL-CM model, whose output contains numerous missing values in the selected longitudinal band to at the - hpa and -hpa levels. The mean vertical shear of the zonal geostrophic wind (akamura, ) is given by: U g T -, () z f T y where f is the Coriolis parameter at, g is the acceleration due to gravity, T is the air temperature, and U is the zonal wind. Here, we replace U and T with U, which is the zonal-wind difference between the SRES-AB scenario and the PI control simulations, i.e., U AB U PI, and, similarly, T = T AB T PI, respectively. The value of T in the Eq. is the mean air temperature in the selected area between certain vertical levels. Therefore, as g/f T is a constant, the sign of U/ z depends on T/ y. We see from all the models in Fig. that T > and T/ y> at mid-latitudes (i.e., the area between the two centers of the PO dipole from about to ) below the -hpa level, which results in

9 o. CHE et al.: Response of PO to global warming Ensemble mean - CRM-CM - - GFDL-CM. - MIROC-medres - - GFDL-CM. - - MRI-CGCM.. U' U U z z z CCCMA-CGCM.-T AB PI ( ), and, hence, there is a weakening of atmospheric baroclinicity in the results of the SRES-AB scenario. The local barotropic instability of a zonally varying background state has been shown to be an important energy source for eddies (e.g., Mak and Cai, ). Generally, eddy activity decreases at mid-latitudes of the orth Pacific, which transfers less vorticity and energy to the large-scale and low-frequency variability of the PO (Di Lorenzo et al, ). As the baroclinic - CSIRO-MK. - - energy conservation terms are dominant at lower levels where the background baroclinicity is strongest, vertical motion tends to have a maximum in the lower troposphere, which results in a substantial energy conversation term from eddy potential energy to eddy kinetic energy. Also, the potential energy conversion is mainly associated with the meridional temperature contrast (Cai et al., ). Returning to Fig. and taking the MEM of the T a difference as an example, the decrease of lower-tropospheric baroclinicity restricts eddy activities at the mid-latitudes of the orth Pacific, which leads to a weakening of the PO MPI-ECHAM Fig. Zonal mean ( E W) air temperature ( T a ) difference between the SRES-AB scenario and the PI control simulations from to ( x -axis) at -hpa, -hpa, -hpa and -hpa levels ( y -axis) (units: C) Center locations of the PO dipole in the PI control (SRES-AB scenario) are indicated by dashed (solid) straight lines. The MEM pattern is calculated by averaging the T a differences of the eight models.

10 J. OCEAOL. LIMOL., (), Vol. as a result of the diminished energy transfer. By marking the center positions of the PO dipole for the PI reference (SRES-AB scenario) with dashed (solid) lines, we see that the PO of most models shifts northward in the global-warming simulations. As mentioned above, the PO a according to the GFDL-CM. model is enhanced. From Fig., PO a centers in both the PI and SRES-AB simulations of that model are located directly in the area of increased atmospheric baroclinicity (i.e., U / z>), which then shift southward. The incremental PO b centers according to the CCCMA- CGCM.-T, CSIRO-MK. and MPI-ECHAM models are also situated in the enhanced baroclinicity area, but are also shifted northward. We hypothesize that the center of the northern lobe PO a is located in an area of weaker baroclinity gradient than that for the southern lobe PO b, thus leading to the northward movement of the PO. For further proof of the relationship between the weakening of the PO and the decrease in atmospheric baroclinicity, Fig. shows the linear correlation between the PO intensity and baroclinicity. We also calculate the negative mean meridional gradient of surface air temperature difference ( T / y) from to (the area containing the PO dipole) representing the baroclinity change in each model, because other parameters ( f, g, and T ) are constants in the Eq.. Then, we compare T / y with the PO i to find a robust correlation ( R =., P =.) between the two, demonstrating that the PO weakening is mainly caused by the decrease in baroclinity. Global warming weakens the PO dipole and shifts it further northward, which is mainly owing to the movement of PO a, and is caused by a decrease in lower-tropospheric baroclinity in the mid-latitude orth Pacific. Consequently, eddy activity is reduced, resulting in the transfer of less energy to the PO. COCLUSIO The second leading pattern of the SLP field in the boreal winter was explored with the help of CRv reanalysis data. A -year wintertime from to is extracted from monthly SLP data to reproduce the spatial structure of the PO pattern via EOF and regression analyses, which enables direct visual inspection of the PO for evaluation of model performance with respect to the observations. The CM simulations of the IPCC AR models are used to evaluate the reconstruction of the spatial PO pattern. By correlating each PO pattern PO' (hpa) Corr=. P= TA grandiet' - ( C/m) CCCMA-CGCM.-T CRM-CM CSIRO-MK. GFDL-CM. GFDL-CM. MIROC-medres MPI-ECHAM MRI-CGCM.. Fig. Scatter plot of negative mean meridional gradient of surface air temperature difference ( x -axis) from to versus changes in PO intensity under global warming ( y -axis) The blue line denotes the linear regression. The correlation between points is indicated in the upper part of the diagram. captured in the SLP anomaly field of each model, and comparing the variance contribution of each second mode of the EOF analysis with observations, we selected models with superior MEM patterns to the models together when compared with the observations. As there is little temporal correlation between the models and observations, we use the spatially generated PO structure for assessment of model capability. The PO patterns of both the PI reference and SRES-AB scenario simulations are used to evaluate the PO response to higher average global temperatures based on the selected models. To indicate the change in PO pattern resulting from global warming, we define the PO intensity PO i for determining the strength of the spatial pattern delivered by each model. The different PO i resulting from the SRES-AB scenario and PI control simulations lead us to conclude that the PO intensity both weakens and shifts northward as a result of global warming. The cause of the weakened PO is investigated through atmospheric baroclinicity, with eight models showing substantial reductions in baroclinicity during the SRES-AB scenario, particularly in the lower troposphere over the mid-latitude orth Pacific. A reduced baroclinicity in the lower troposphere transfers less energy and vorticity to the large and low-frequency scales of motion, thus leading to PO weakening. DATA AVAILABILITY STATEMET The th Century Reanalysis Version data analyzed here are provided by OAA/OAR/ESRL

11 o. CHE et al.: Response of PO to global warming PSD, Boulder, Colorado, USA, via their website at Data from the IPCC AR models are available from the World Data Centre Climate (WDCC) at the German Climate Computing Centre (DKRZ), via their website at monthly/sres_ar/index.html. ACKOWLEDGEMET The authors acknowledge various modeling groups for making their simulations available for analysis, as well as the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the CMIP/IPCC AR model output. References Ashok K, Behera S K, Rao S A, Weng H Y, Yamagata T.. El iño Modoki and its possible teleconnection. J. Geophys. Res., (C): C. Branstator G W.. Organization of storm track anomalies by recurring low-frequency circulation anomalies. J. Atmos. Sci., (): -. Cai M, Yang S, Van den Dool H M, Kousky V E.. Dynamical implications of the orientation of atmospheric eddies: a local energetics perspective. Tellus A : Dynamic Meteorology and Oceanography, (): -. Chhak K C, Di Lorenzo E, Schneider, Cummins P F.. Forcing of low-frequency ocean variability in the ortheast Pacific. J. Climate, (): -. Compo G P, Whitaker, J S, Sardeshmukh P D, Matsui, Allan R J, Yin X, Gleason B E, Vose R S, Rutledge G, Bessemoulin P, Brönnimann S, Brunet M, Crouthamel R I, Grant A, Groisman P Y, Jones P D, Kruk M C, Kruger A C, Marshall G J, Maugeri M, Mok H Y, ordli Ø, Ross T F, Trigo R M, Wang X L, Woodruff S D, Worley S J.. The twentieth century reanalysis project. Quart. J. Roy. Meteor. Soc., (): -. Di Lorenzo E, Cobb K M, Furtado J C, Schneider, Anderson B T, Bracco A, Alexander M A, Vimont D J.. Central Pacific El iño and decadal climate change in the orth Pacific Ocean. at. Geosci., (): -. Di Lorenzo E, Combes V, Keister J E, Strub P T, Thomas A C, Franks P J S, Ohman M D, Furtado J C, Bracco A, Bograd S J, Peterson W T, Schwing F B, Chiba S, Taguchi B, Hormazabal S, Parada C.. Synthesis of Pacific Ocean climate and ecosystem dynamics. Oceanography, (): -. Di Lorenzo E, Schneider, Cobb K M, Franks P J S, Chhak K, Miller A J, McWilliams J C, Bograd S J, Arango H, Curchitser E, Powell T M, Rivière P.. orth Pacific Gyre Oscillation links ocean climate and ecosystem change. Geophys. Res. Lett., (): L. Hannachi A, Jolliffe I T, Stephenson D B.. Empirical orthogonal functions and related techniques in atmospheric science: a review. Int. J. Climatol., (): -. Hoskins B J, James I, White G H.. The shape, propagation and mean-flow interaction of large-scale weather systems. J. Atmos. Sci., (): -. Lau C.. Variability of the observed midlatitude storm tracks in relation to low-frequency changes in the circulation pattern. J. Atmos. Sci., (): -. Linkin M E, igam S.. The orth Pacific Oscillation- West Pacific teleconnection pattern: mature-phase structure and winter impacts. J. Climate, (): -. Lorenz E.. Empirical orthogonal functions and statistical weather prediction. Department of Meteorology, Scientific Report o., MIT, Cambridge. p.-. Mak M, Cai M.. Local barotropic instability. J. Atmos. Sci., (): -. akamura H.. Midwinter suppression of baroclinic wave activity in the Pacific. J. Atmos. Sci., (): -. igam S.. Teleconnections. In : Holton J R, Pyle J A, Curry J A eds. Encyclopedia of Atmospheric Sciences. Academic Press, London. p. -. Rogers J C.. Patterns of low-frequency monthly sea level pressure variability (-) and associated wave cyclone frequencies. J. Climate, (): -. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K B, Tignor M, Miller H L.. Climate Change : the Physical Science Basis: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge UK. Vallis G K, Gerber E P, Kushner P J, Cash B A.. A mechanism and simple dynamical model of the orth Atlantic Oscillation and annular modes. J. Atmos. Sci., (): -. Vimont D J, Wallace J M, Battisti D S.. The seasonal footprinting mechanism in the Pacific: implications for ESO. J. Climate, (): -. Walker G T, Bliss E W.. World weather V. Mem. Roy. Meteor. Soc., : -. Wallace J M, Gutzler D S.. Teleconnections in the geopotential height field during the orthern Hemisphere winter. Mon. Wea. Rev., (): -. Wettstein J J, Wallace J M.. Observed patterns of monthto-month storm-track variability and their relationship to the background flow. J. Atmos. Sci., (): -. Yu J Y, Kim S T.. Relationships between extratropical sea level pressure variations and the central Pacific and eastern Pacific types of ESO. J. Climate, (): -.