5810 JOURNAL OF CLIMATE VOLUME 26

Evaluation of CMIP3 and CMIP5 Stress Using Satellite Measurements and Products

TONG LEE,DUANE E. WALISER,JUI-LIN F. LI,FELIX W. LANDERER, AND MICHELLE M. GIERACH Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

(Manuscript received 31 July 2012, in final form 14 January 2013)

ABSTRACT

Wind stress measurements from the Quick Scatterometer (QuikSCAT) satellite and two atmospheric reanalysis products are used to evaluate the annual mean and seasonal cycle of wind stress simulated by phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). The ensemble CMIP3 and CMIP5 wind stresses are very similar to each other. Generally speaking, there is no significant im- provement of CMIP5 over CMIP3. The CMIP ensemble–average zonal wind stress has eastward biases at midlatitude westerly wind regions (308–508N and 308–508S, with CMIP being too strong by as much as 55%), westward biases in subtropical–tropical easterly wind regions (158–258N and 158–258S), and westward biases at high-latitude regions (poleward of 558S and 558N). These biases correspond to too strong anticyclonic (cy- clonic) wind stress curl over the subtropical (subpolar) ocean gyres, which would strengthen these gyres and influence oceanic meridional heat transport. In the equatorial zone, significant biases of CMIP wind exist in individual basins. In the equatorial Atlantic and Indian Oceans, CMIP ensemble zonal wind stresses are too weak and result in too small of an east–west gradient of sea level. In the equatorial Pacific Ocean, CMIP zonal wind stresses are too weak in the central and too strong in the western Pacific. These biases have important implications for the simulation of various modes of climate variability originating in the tropics. The CMIP as a whole overestimate the magnitude of seasonal variability by almost 50% when averaged over the entire global ocean. The biased wind stress in CMIP not only have implications for the simulated ocean circulation and climate variability but other air–sea fluxes as well.

1. Introduction Intercomparison Project (CMIP5) has released many ‘‘historical’’ simulations that encompass the twentieth The reliability of future climate projections using cli- century. The comparison of CMIP3 and CMIP5 in the mate models depends heavily on the fidelity of the cli- context of observations is an important initiative of the mate models. The latter can be assessed by evaluating WCRP that helps identify potential improvements and the ability of the climate models to simulate the present remaining issues in climate models that contribute to climate using available observations (e.g., Pierce et al. CMIP and the Intergovernmental Panel on Climate 2006; Gleckler et al. 2008; Waliser et al. 2009; Su et al. Change (IPCC) assessments (e.g., Gleckler et al. 2011). 2013). The World Climate Research Programme’s Examples of such studies include evaluating the fidelity of (WCRP) phase 3 of the Coupled Model Intercom- CMIP GCMs to represent water vapor and clouds (Jiang parison Project (CMIP3) (Meehl et al. 2007) has made et al. 2012), clouds and radiation (Li et al. 2012a,b; Li available a suite of twentieth-century simulations by vari- et al. 2013), and observed phenomena such as the ous coupled general circulation models (CGCMs) that structure of El Nino–Southern~ Oscillation (ENSO) (e.g., have facilitated many evaluation efforts (e.g., Guilyardi Kim and Yu 2012). 2006; Capotondi et al. 2006, 2012; Su et al. 2006; Yu and A key parameter of climate models that has not been Kim 2010a; Jamison and Kravtsov 2010; Kwok 2011; Li evaluated systematically across a suite of CMIP using et al. 2012a). Recently, phase 5 of the Coupled Model global observations is ocean surface wind stress. Here- after, we simply refer to this parameter as wind stress for the sake of convenience. Wind stress is an important Corresponding author address: Tong Lee, MS300-323, Jet Pro- pulsion Laboratory, California Institute of Technology, Pasadena, variable in the coupled climate system as it reflects the CA 91109. momentum flux between the ocean and atmosphere, E-mail: [email protected] which is a major forcing of ocean circulation. Direct

DOI: 10.1175/JCLI-D-12-00591.1

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TABLE 1. Model acronyms and expansions.

Ensemble Model acronym Model expansion CMIP3 CCCma (T47) Canadian Centre for Climate Modelling and Analysis CGCM (T47 resolution) CCCma (T63) Canadian Centre for Climate Modelling and Analysis CGCM (T63 resolution) CNRM-CM3 Centre National de Recherches Met eorologiques Coupled Global , version 3 CSIRO Mk3.5 Commonwealth Scientific and Industrial Research Organisation Mark, version 3.5 FGOALS-g.10 Flexible Global Ocean–Atmosphere–Land System Model gridpoint version 1.0 GFDL CM2.0 Geophysical Fluid Dynamics Laboratory Climate Model, version 2.0 GFDL CM 2.1 Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1 GISS-EH NASA Goddard Institute for Space Studies Model E-H GISS-ER NASA Goddard Institute for Space Studies Model E-R INM-CM3.0 Institute of Numerical Mathematics Coupled Model, version 3.0 IPSL-CM4 L’Institut Pierre-Simon Laplace Coupled Model, version 4 MIROC3.2 (highres) Model for Interdisciplinary Research on Climate, version 3.2 (high resolution) MIROC3.2 (medres) Model for Interdisciplinary Research on Climate, version 3.2 (medium resolution) MPI/ECHAM5 Max Planck Institute/ECHAM5 MRI CGCM2.3.2a Meteorological Research Institute Coupled General Circulation Model version 2.3.2a CCSM3.0 Community Climate System Model, version 3.0 HadCM3 Third climate configuration of the Met Office Unified Model HadGEM1 Hadley Centre Global Environment Model, version 1

CMIP5 GISS-E2H Goddard Institute for Space Studies Model E, coupled with the HYCOM ocean model GISS-E2-R Goddard Institute for Space Studies Model E, coupled with the Russell ocean model INM-CM4 Institute of Numerical Mathematics Coupled Model, version 4.0 IPSL-CM5A-LR L’Institut Pierre-Simon Laplace Coupled Model, version 5, coupled with NEMO, low resolution MIROC4h Model for Interdisciplinary Research on Climate, version 4 (high resolution) MIROC5 Model for Interdisciplinary Research on Climate, version 5 MIROC-ESM-CHEM Model for Interdisciplinary Research on Climate, Earth System Model, Chemistry Coupled MIROC-ESM Model for Interdisciplinary Research on Climate, Earth System Model MPI-ESM-LR Max Planck Institute Earth System Model, low resolution MRI-CGCM3 Meteorological Research Institute Coupled Atmosphere–Ocean General Circulation Model, version 3 NorESM1-M Norwegian Earth System Model, version 1 (intermediate resolution)

observations of wind stress are extremely sparse. Satel- mean and seasonal cycle. The climatological state of lite scatterometers such as the Quick Scatterometer CMIP has strong implications to the simulated natural (QuikSCAT) provided a decade (from mid-1999 to late variability and probably to climate change projections 2009) of global measurements of wind stress with unprec- based on these models. Therefore, the present study is a edented spatial and temporal sampling. In the present necessary first step toward further evaluation for the study, we use wind stress measurements from QuikSCAT variability on other time scales (e.g., intraseasonal, inter- and auxiliary atmospheric reanalysis products to evaluate annual, and decadal variability) and for climate change CMIP3 and CMIP5 and discuss the implications of the projection. biases in CMIP wind stress to the simulation of ocean circulation and natural climate variability. 2. CMIP and reference datasets The evaluation of the CMIP wind stress can also help understand the positive aspects and limitations of other We analyzed the wind stress from the twentieth- wind-dependent ocean–atmosphere fluxes (e.g., latent century simulations of 18 models in CMIP3 and from the heat flux) simulated by CMIP. It is also relevant to the historical simulations of 11 models in CMIP5 that are assessment of the simulated state of the ocean by CMIP, available at the beginning of our analysis (the model such as the structure of sea surface temperatures (SST), acronyms and expansions used in CMIP3 and CMIP5 pycnocline and sea level, horizontal and meridional are provided in Table 1). The CMIP3 twentieth-century circulation, and meridional transport of heat and fresh- simulations end in 2000. Those for the CMIP5 historical water, which all have significant dependence on wind run extend beyond 2000. We choose to analyze a common stress. This study focuses on the evaluation of the wind 3-decade period for CMIP3 and CMIP5 from 1970 to stress climatology from CMIP, including the annual 1999. This period corresponds to the era with more robust

Unauthenticated | Downloaded 10/10/21 09:24 PM UTC 5812 JOURNAL OF CLIMATE VOLUME 26 observation methods and sampling of ocean surface wind. observations (e.g., Mestas-Nunez~ et al. 1994; Chelton Consequently, atmospheric reanalysis products that use and Frielich 2005). these modern-era observations are more reliable. CMIP3 For the sake of convenience, we refer to the two re- and CMIP5 simulations for the period of 1970–99 are used analysis products as NCEP-1 and ERA-Interim. The to produce the respective monthly climatology of wind NCEP-1 product encompasses the period from 1948 to stressandmappedonacommon28328 grid. the present while the ERA-Interim is from 1978 to the The observational reference dataset used in this study present. For NCEP-1, the climatology was computed for is based on satellite scatterometer measurements derived the period of 1970–99, the same period over which the from the QuikSCAT mission of the National Aeronautics CMIP climatologies were computed. For ERA-Interim, and Space Administration (NASA). Launched in June the wind stress climatology was computed for the period 1999, QuikSCAT provided over a decade of wind stress of 1978–2011. There are about half a dozen other at- measurements (until November 2009). QuikSCAT ob- mospheric reanalysis products available from NCEP, servations have revolutionized our capability to estimate ECMWF, the Japan Meteorological Agency (JMA), the dynamical forcing of the ocean from basin to meso- and the NASA Global Modeling and Assimilation Office scale and to study the related air–sea interaction pro- (GMAO) that we have not analyzed. It is not our ob- cesses (e.g., McPhaden and Zhang 2002; Chelton et al. jective to perform a systematic evaluation of atmospheric 2004; Lee and McPhaden 2008; also cf. review articles by reanalysis products using QuikSCAT data. We included Liu 2002; Chelton and Xie 2010; Lee et al. 2010; Bourassa NCEP-1 and ERA-Interim just as auxiliary reference et al. 2010, and references therein). The Scatterometer products primarily because of the limited period of the Climatology of Ocean (SCOW) based on QuikSCAT observations. Moreover, our results show QuikSCAT measurements (Risien and Chelton 2008) is that many of the biases of CMIP wind stress relative to used in this study to evaluate the climatology of CMIP. QuikSCAT are similar to the biases relative to the two This dataset is available online (from http://cioss.coas. reanalysis products. The climatology from atmospheric oregonstate.edu/scow/). Satellite scatterometer observa- reanalysis products were interpolated and the QuikSCAT tions of wind stress are also available for the 1990s from climatology (originally on a 0.5830.58 grid) was bin European Remote Sensing Satellite-1 (ERS-1)andERS-2. averaged to the same 28328 grid as CMIP to facilitate We used the QuikSCAT data because the SeaWinds in- the comparison. strument on QuikSCAT is a more advanced scatterometer Even though the QuikSCAT climatology is based on and QuikSCAT has a much better sampling (covering observations primarily in the 2000s and the CMIP simu- 90% of the global ocean every day). Moreover, a care- lations are for the late twentieth century, the comparison fully produced QuikSCAT wind stress climatology (i.e., is still relevant because the CMIP internal variability does SCOW) is available and has been widely used for various not match that observed in real time. The only real-time scientific investigations and evaluations of climate models information in the CMIP simulations is the radiative and and atmospheric reanalysis products (e.g., Slingo et al. aerosol forcing. These forcings are not expected to cause 2009; Kanzow et al. 2010; Roquet et al. 2011; Xue et al. a major difference in climatology on decadal time scales. 2011; Johnson et al. 2012). The utility of the atmospheric reanalysis products also In addition to QuikSCAT measurements, we also helps to address the potential issue of the dependence of use two atmospheric reanalysis products as additional climatology on natural decadal variability. We found reference. These are the National Centers for Environ- that the differences of the climatology for the atmo- mental Prediction–National Center for Atmospheric spheric reanalysis products between the entire periods Research (NCEP–NCAR) Global Reanalysis 1 (NCEP-1; of the products and the QuikSCAT period are much Kalnay et al. 1996) and the European Centre for Medium- smaller than the difference in climatology between the Range Weather Forecast (ECMWF) Interim Re-Analysis CMIP and the reanalysis products. This justifies the (ERA-Interim; Dee et al. 2011). The atmospheric re- utility of the QuikSCAT climatology to evaluate CMIP. analysis products assimilate different types of atmo- spheric observations into the underlying atmospheric 3. Results models. So the wind stress estimates from these products can be used as auxiliary reference products for evaluating The annual-mean zonal wind stress from QuikSCAT, CMIP. However, we are mindful about the fact that the NCEP-1, ERA-Interim, the ensemble average of the 18 wind fields based on atmospheric CMIP3 models, and the ensemble average of the 11 CMIP5 products are subject to the limitations of the underlying models are shown in Figs. 1a,b,d,f,h. The differences atmospheric models and the data assimilation methods between the reanalysis or CMIP ensemble averages and and have themselves various biases relative to satellite QuikSCAT are presented in Figs. 1c,e,g,i. Note that the

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22 FIG. 1. Annual-mean zonal wind stress (N m ) from the (a) QuikSCAT, (b) NCEP-1, (d) ERA-Interim, (f) CMIP3 ensemble average, and (h) the CMIP5 ensemble average. (c),(e),(g),(i) The difference of the latter four from QuikSCAT. maximum color scale of the zonal wind stress difference There are systematic biases of CMIP zonal wind at 2 (0.1 N m 2) is smaller than that for the zonal wind stress different latitude ranges. The most conspicuous differ- 2 itself (0.25 N m 2). The differences between the re- ences are at midlatitude westerly wind regions, with CMIP analysis products and QuikSCAT are generally smaller eastward wind stress being too strong. This is shown by than those between CMIP ensemble averages and the positive differences in Figs. 1g,i at midlatitudes. At QuikSCAT. This is understandable because the re- subtropical–tropical transition latitudes where the pre- analysis products assimilate atmospheric observations. dominant wind is easterly, the CMIP westward wind The differences between the atmospheric reanalysis prod- stresses are too strong (negative differences from ucts and QuikSCAT are primarily not because of the dif- QuikSCAT). At high-latitude regions, CMIP zonal wind ference in periods over which the respective climatology stresses tend to have a westward bias (negative differences was computed. We have analyzed the NCEP-1 and ERA- from QuikSCAT). The mid- and high-latitude biases in Interim products for the QuikSCAT period and found CMIP wind stress climatology are related to the equator- the dominant differences from QuikSCAT to be similar. ward bias in the positions of midlatitude jets simulated by

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FIG. 2. As in Fig. 1, but for meridional wind stress.

CMIP3 and CMIP5. The equatorward position bias of the the meridional overturning circulation carries little heat austral midlatitude jet in CMIP3 twentieth-century simu- transport because the vertical temperature gradient is lations has been noted previously (e.g., Kidston and small at these latitudes (Lee et al. 2010). Therefore, in- Gerber 2010). The eastward bias at midlatitudes and accurate simulation of the strength of horizontal gyres westward bias at subtropical–tropical transition latitudes because of biased wind stress would affect these me- and at high latitudes correspond to too strong an anticy- ridional transports. Over the Southern Ocean, the bi- clonic wind stress curl over the subtropical ocean gyres ased wind stress and wind stress curl may influence the and too strong a cyclonic wind stress curl over the sub- structure of the Antarctic Circumpolar Current (ACC), polar ocean gyres. These have the effect of increasing the meridional overturning circulation in the region, and the strength of both the subtropical and subpolar gyres. surface heat flux and water mass formation. The Southern Horizontal gyre circulations contribute to meridional Ocean is believed to be an important sink for atmospheric heat transport, especially in high-latitude oceans where carbon dioxide (CO2). The biased CMIP wind over this

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22 FIG. 3. (a) Annual-mean zonal wind stress (N m ) from QuikSCAT and (b)–(s) the difference between CMIP3 models and QuikSCAT.

region thus has significant implications to CO2 flux in the Pacific influenced the magnitude of ENSO variability in region as well. a simple coupled model. Whether the biased meridional Figure 2 is the counterpart of Fig. 1 but for meridional wind in the eastern equatorial Pacific Ocean in CMIP wind stress. Note that the maximum color scale of the would affect the simulated behavior of ENSO warrants 2 meridional wind stress difference (0.05 N m 2) is smaller a further investigation. 2 than that for the zonal wind stress itself (0.01 N m 2). In The biases of CMIP ensemble averages relative to the North Pacific and Atlantic Oceans, CMIP has sub- QuikSCAT discussed earlier are representative of many stantially stronger midlatitude northward meridional CMIP models. To illustrate this, the differences between wind stress (positive difference from QuikSCAT) and the annual-mean zonal wind stress of individual CMIP3 somewhat stronger southward meridional wind stress models are shown in Fig. 3b–s. The QuikSCAT data are in subtropical–tropical latitudes (negative differences shown in Fig. 3a as a reference. Figure 4 is a similar from QuikSCAT). In the Southern Ocean, the merid- representation to Fig. 3 but showing the differences of ional wind stress in CMIP is generally more negative individual CMIP5 models from QuikSCAT. In Figs. 3 than QuikSCAT (more so in CMIP3 than CMIP5). In and 4, the maximum color scale for the differences be- the eastern equatorial Pacific Ocean, the negative tween individual CMIP models and QuikSCAT are the CMIP–QuikSCAT difference (blue color in Figs. 2g,i) same as that for QuikSCAT wind stress itself. This is indicates that the northward component of the south- a different presentation of color scale from Fig. 1 because easterly trade wind is too weak in CMIP. Perigaud et al. the differences between individual CMIP models and (1997) found that meridional wind in the equatorial QuikSCAT shown in Figs. 3 and 4 are generally larger

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22 FIG. 4. (a) Annual-mean zonal wind stress (N m ) from QuikSCAT and (b)–(i) the difference between CMIP5 models and QuikSCAT. than that between the CMIP ensemble and QuikSCAT westerly wind regions (308–508N and 308–508S) and shown in Fig. 1. The main characteristics of the model– westward biases at subtropical–tropical easterly wind data differences in CMIP ensemble averages discussed regions (158–258N and 158–258S) and at high-latitude earlier (in reference to Figs. 1g,i) can be identified in regions (poleward of 558S and 558N). To provide some many CMIP models (especially in terms of the positive quantitative description of the maximum biases of model–data difference in midlatitude regions). annual-mean zonal wind stress at different latitudes, the Since the model–data differences in zonal wind stress differences between the mean of ensemble averages shown above tend to be more or less zonally oriented, for CMIP3 or CMIP5 and QuikSCAT are shown in we present a comparison of the zonally averaged zonal Fig. 5c. The largest positive bias at midlatitude west- wind stress in Fig. 5. The QuikSCAT, NCEP-1, and erly wind regime occurs near 398Nand398S, with CMIP ERA-Interim products are shown as black solid, black being stronger than QuikSCAT by approximately 55% dashed, and black dashed–dotted curves. The thick red (averaged between 418N and 418S). The largest negative curves denote CMIP ensemble averages. The thin color bias for the subtropical–tropical easterly wind regime curves correspond to the individual CMIP models. The occurs near 198N and 198S, with CMIP being too strong color curves in the upper and lower panels represent by 26%. The largest negative bias in the high-latitude CMIP3 and CMIP5, respectively. One could readily Southern Ocean occurs at 618S, with CMIP being too identify the major biases of CMIP zonal wind stress weak by 27%. In the high-latitude northern oceans, the discussed earlier: the eastward biases at midlatitude largest difference occurs at 638N with CMIP being 668%

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FIG. 5. Comparison of zonally averaged annual-mean zonal wind stress for (a) CMIP3 and (b) CMIP5 with the reference datasets. The thin color curves represent individual CMIP models. Their ensemble averages are represented by the thick red curves. QuikSCAT, NCEP-1, and ERA-Interim are denoted by the black solid, black dashed, and black dashed–dotted curves. (c) The difference between CMIP zonal wind (the CMIP3 and CMIP5 ensemble averages) and QuikSCAT.

too strong (because QuikSCAT is very small). At these figure). Figure 7a consolidates Fig. 6 by plotting only latitudes, CMIP ensemble zonal wind stress generally QuikSCAT and the CMIP3 and CMIP5 ensemble aver- has an opposite sign from QuikSCAT. ages to better illustrate the similarity and difference be- Near the equator, there is little model–data difference tween CMIP3 and CMIP5. The CMIP zonal wind stresses in the zonally averaged zonal wind stress when integrating over the equatorial Indian and Atlantic Oceans are both over all ocean basins. This apparent consistency is actually weaker than QuikSCAT (and the reanalysis products). misleading. As we show in the following, there are sub- In the equatorial Pacific, CMIP ensemble–average zonal stantial biases in the individual oceans that tend to cancel wind stresses are fairly close to QuikSCAT in the east. out when integrated over all the basins. Figure 6 presents However, they are weaker than QuikSCAT in the cen- the equatorial zonal wind stress averaged between 28S tral Pacific and stronger than QuikSCAT in the western and 28N as a function of longitude for individual CMIP Pacific. When integrated zonally, there is partial com- models (thin color curves), CMIP3 or CMIP5 ensemble pensation of the model–data differences between the average (thick red curves), and QuikSCAT and the two central and western equatorial Pacific and between the reanalysis products (thick black curves). From the figure, equatorial Atlantic and Indian Oceans. That explains why the CMIP3 and CMIP5 ensemble averages look surpris- there is such little model–data difference in global zonal– ingly similar (in part because of the vertical scale of the average zonal wind stress near the equator (Fig. 5).

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FIG. 6. Annual-mean equatorial zonal wind stress (averaged between 28S and 28N) from QuikSCAT (black solid), NCEP-1 (black dashed), ERA-Interim (black dashed–dotted), and the ensemble average (thick red) and individual CMIP models (thin color curves). The color curves represent (a) CMIP3 and (b) CMIP5 models.

The biased CMIP zonal wind stresses in the equatorial averages also affects the zonal gradient of sea level (and oceans are expected to affect the zonal structure (e.g., thus pycnocline). They include most of the CMIP5 east–west slope) of the equatorial sea level or pycnocline models analyzed in the present study. Despite the fact in individual basins. In the equatorial Indian Ocean, the that the CMIP5 models are not identical, the biased positive zonal wind stress causes sea level (pycnocline) to ensemble-average sea level can be explained by the tilt upward (downward) toward the east. The opposite is biased zonal wind stress in the equatorial zone. true for the Atlantic Ocean. The weak equatorial zonal Equatorial zonal wind stress and sea level (pycno- wind stresses in CMIP3 and CMIP5 for these two oceans cline) structure are important to various climate modes imply that the zonal tilts of sea level or pycnocline in originating in the tropics as well as their interaction these basins would be too weak. This is demonstrated in with multidecadal variability and climate change. For Fig. 7b, which compares the longitudinal variation of sea instance, the too weak zonal wind stress and too flat level (SSH) from CMIP3 and CMIP5 ensemble averages pycnocline in the Indian and Atlantic Oceans would with the observation-based estimate from Maximenko affect the simulated behavior of the Indian Ocean zonal/ et al. (2009). The (zonal) sea level anomalies for indi- dipole mode and Atlantic Nino.~ For instance, some vidual basins were referenced to the equatorial average CGCMs have difficulties capturing Atlantic Nino~ be- (28S–28N) for each basin both for the observation and for cause of a too weak wind stress and too flat thermocline the CMIP models. This removes the bias of equatorial (Deser et al. 2006; Richter et al. 2012), which are be- zonal–average sea level from individual models in in- lieved to be related to the biased zonal wind and sea dividual basins and isolates the zonal variation. For the level (pycnocline) slope in the equatorial Atlantic. Cai Indian Ocean Basin and Atlantic Ocean basin where the and Cowen (2013) found that CMIP3 and CMIP5 CMIP equatorial zonal wind stresses are too weak, the models overestimated the amplitude of Indian Ocean CMIP sea level slopes are also consistently too weak. For dipole because the pycnocline depth in the eastern the Pacific, the too strong (weak) zonal wind stress in the equatorial Indian Ocean is too shallow owing to the western (central) part of the basin in CMIP ensemble weak equatorial zonal wind. The biases in the equatorial

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FIG. 7. Annual-mean equatorial (a) zonal wind stress and (b) SSH anomalies averaged be- tween 28S and 28N. The SSH anomalies were referenced to zonal averages of the respective ocean basin for observation and each CMIP model before the ensemble average was computed. The wind and SSH observations are QuikSCAT and the mean dynamic topography is from Maximenko et al. (2009).

Pacific also have implications to the behavior of inter- To further illustrate the overall agreement of the spatial annual variability. In particular, the biased zonal wind structure of the annual-mean wind stress among CMIP stress structure in the central and western equatorial models, QuikSCAT data, and the reanalysis products, Pacific may affect the behavior of the anomalous warm- we present the Taylor diagram (Taylor 2001) of the an- ing events that occur in the central equatorial Pacific nual-mean wind stress for CMIP3 (Fig. 8) and CMIP5 Ocean also known as the central Pacific El Nino~ (Kao and (Fig. 9). In these diagrams, QuikSCAT is used as the Yu 2009; Yu and Kim 2010b; Lee and McPhaden 2010), reference dataset (indicated by ‘‘OBS’’ in Figs. 8, 9). warm-pool El Nino~ (Kug et al. 2009), date line El Nino~ The statistics presented in the diagram, the normalized (Larkin and Harrison 2005), or El Nino~ Modoki (Ashok spatial standard deviation (along the radius) for each et al. 2007). The NCEP-1 equatorial zonal wind stress product and the spatial correlation with the QuikSCAT is generally weaker than that of QuikSCAT and ERA- data (along the arc), are average statistics for zonal and Interim in much of the Indian, Pacific, and Atlantic meridional wind stress. For both figures, the four panels Oceans. The weaker NCEP-1 equatorial easterly wind show the Taylor diagrams for the global ocean, 308S– was also reported by Wittenberg (2004) in comparison 308N, north of 308N, and south of 308S. For both CMIP3 with the in situ–based Florida State University (FSU) and CMIP5, the models are more consistent among one wind product. another at low latitudes (308S–308N) than at higher

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FIG. 8. Taylor diagrams of annual-mean wind stress for global, tropics, northern, and southern latitudes according to CMIP3 models. The QuikSCAT, NCEP-1, ERA-Interim, and CMIP3 ensemble–average climatology are denoted by OBS, N, E, and C, respectively. Individual CMIP models are shown by red dots that have no labels. latitudes, as indicated by the tighter clustering of the We next investigate the representation of seasonal dots in Figs. 8b and 9b than in Figs. 8c,d and 9c,d. The anomalies by CMIP models (with reference to their consistency of the CMIP models with QuikSCAT data respective annual mean). Comparison of the seasonal and the reanalysis products are also generally better at anomalies of CMIP3 and CMIP5 models with QuikSCAT the 308S–308N latitudes than at higher latitudes. This is data suggest that many models have a tendency to over- evident from the generally smaller distance from the estimate the overall magnitude of the seasonal anomalies. model dots to QuikSCAT and to the reanalysis prod- An example is shown in Fig. 10 for October anomalies ucts denoted by OBS, N, and E. Also evident from the of zonal wind stress where many of the CMIP3 models Taylor diagrams is that the ensemble averages of CMIP show more red and blue colors (larger anomalies) than models C in Figs. 8 and 9 are generally closer to the QuikSCAT. The overestimation of the magnitude of sea- observation than most of the individual CMIP models. sonal anomalies in CMIP is more evident during the

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FIG. 9. As in Fig. 8, but for CMIP5 models.

spring and fall (e.g., April or October) than during winter overall fairly similar to each other as a whole, especially and summer (e.g., January and July), regardless of the in terms of the ensemble-average wind stress (e.g., Figs. 1, hemisphere (not shown). To further illustrate the model– 2). The biases relative to QuikSCAT are similar in many data comparison of the seasonal temporal changes, we CMIP3 and CMIP5 models (e.g., Figs. 3–7, 11). Using present the Taylor diagrams for the temporal vari- the Taylor diagrams as a metric, CMIP5 appears to be ability of seasonal anomalies, averaged over the global marginally better than CMIP3 in the representation of ocean and averaged between the statistics of zonal and the structure of annual-mean wind stress (Fig. 8a versus meridional wind stress (Fig. 11). The magnitude of the Fig. 9a). This also appears to be the case for the repre- seasonal anomalies of the CMIP3 and CMIP5 models sentation of the seasonal cycle (Fig. 11a versus Fig. 11b), (temporal standard deviation) as a whole is almost 1.5 where the standard deviation of the CMIP5 models as times larger than that of the QuikSCAT data or the re- awholeiscloseto1.5whilethatofCMIP3islargerthan analysis products. 1.5; the correlation of the CMIP5 models with QuikSCAT The diagnostics of the CMIP3 and CMIP5 wind stress cluster around 0.7, while those for CMIP3 are mostly less presented above indicate that CMIP3 and CMIP5 are than 0.7. The standard deviation and correlation for the

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FIG. 10. October anomaly of zonal wind stress from (a) QuikSCAT and (b)–(s) CMIP3, with reference to the respective annual mean. ensemble average of CMIP5 are also slightly better than of the global oceans (e.g., Fig. 5) suggests that the effect of those for CMIP3 (cf. the distance of the C dots to the not accounting for the moving ocean currents may be mi- reference datasets OBS, N, and E). Another feature re- nor in comparison to the fundamental biases in the CMIP vealed by Fig. 11 is that the ensemble averages of CMIP models. models are distinctively closer to observed seasonal wind stress anomalies than any individual CMIP model. This is 4. Concluding remarks reflected by the smaller distances of the C dots to the reference datasets (OBS, N, and E) than the distances We have used QuikSCAT satellite scatterometer between individual model dots and the reference data- measurements and NCEP-1 and ERA-Interim products sets. There are less CMIP5 models (11) analyzed than to evaluate the annual mean and seasonal cycle of wind CMIP3 models (18). The statistics based on a future stress simulated by 18 CMIP3 and 11 CMIP5 models. comparison using the same families of models from The ensemble CMIP3 and CMIP5 wind stresses are CMIP3andCMIP5wouldbemorerigorous. found to be fairly similar to each other and thus similar Wind forcing acts on the ocean currents. The wind in terms of their differences from QuikSCAT and the stress observed by QuikSCAT reflects the momentum reanalysis products. Generally speaking, there is a lack transfer between the atmosphere and the moving ocean of significant improvement of CMIP5 over CMIP3. surface (i.e., ‘‘relative wind,’’ as opposed to the ‘‘absolute There are systematic biases of CMIP zonal wind stress at wind’’ reference to the stationary continent). However, the different latitude ranges. The CMIP annual-mean zonal wind stress from CMIP and reanalysis products do not wind stress has eastward biases at the midlatitude (308– take into account the ocean current. In regions with a 508Nand308–508S) regions where the predominant winds strong ocean current such as the ACC, this effect could are westerly, westward biases in subtropical–tropical be significant (e.g., Duhaut and Straub 2006). The fact transition latitudes (158–258Nand158–258S) where the that the reanalysis products are more similar to predominant winds are easterly, and westward biases at QuikSCAT than CMIP ensemble averages over much high-latitude regions (poleward of 558Sand558N). At

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FIG. 11. Taylor diagrams of seasonal temporal anomalies of wind stress for the global ocean for (a) CMIP3 and (b) CMIP5. The QuikSCAT, NCEP-1, ERA-Interim, and CMIP ensemble–average climatology are denoted by OBS, N, E, and C, respectively. Individual CMIP models are shown by red dots that have no labels.

midlatitude westerly (subtropical–tropical easterly) re- The CMIP models tend to overestimate the magni- gions, CMIP zonal wind stress is stronger than QuikSCAT tude of seasonal variability, especially during spring and by approximately 55% (26%). At the high-latitude autumn. The standard deviation of seasonal variability Southern Ocean, CMIP zonal wind stress is weaker than averaged over the global ocean from CMIP models as QuikSCAT by about 27%. These biases correspond to a whole is almost 50% larger than that observed by too strong anticyclonic wind stress curl over the sub- QuikSCAT and inferred from the reanalysis products. tropical ocean gyres and too strong cyclonic wind stress The biases in CMIP wind stress climatology are not curl over the subpolar ocean gyres, which would make the sensitive to the decades over which the climatology was subtropical and subpolar gyres too intense and influence defined for CMIP models and for the reference datasets oceanic meridional heat transport. The biased wind stress (see appendix). The cause of the biases in CMIP wind over the high-latitude Southern Ocean would affect var- stress climatology needs to be examined. Our findings ious aspects of the Southern Ocean circulation. also prompt investigations of the consequences of the When integrated zonally across all oceans, the equato- biased CMIP wind stress on the simulated ocean circu- rial zonal wind stresses are relatively close to QuikSCAT. lation, meridional transports of properties (e.g., heat), However, significant biases exist in individual basins. In the representation of interannual and decadal variability the equatorial Atlantic and Indian Oceans, CMIP en- that could be affected by the climatological state, and semble zonal wind stresses are both too weak. Consis- other air–sea fluxes (e.g., latent heat and CO2). The tent with these wind biases, the zonal slope of sea level potential effects of the biased climatological state on from CMIP3 and CMIP5 ensemble averages are both climate projection also need to be assessed. too small in these basins, implying the same for zonal slope of the pycnocline. In the equatorial Pacific, CMIP Acknowledgments. We acknowledge the GCM mod- zonal wind stresses are fairly close to QuikSCAT in the eling groups, the Program for Climate Model Diagnosis east but are too weak in the central Pacific and too and Intercomparison (PCMDI), and the WCRP’s Work- strong in the western Pacific. These biases in equatorial ing Group on Coupled Modeling for their roles in making zonal wind stress are expected to affect the simulation available the WCRP CMIP3 and CMIP5 multimodel of climate modes originating in the tropics such as datasets. Support of these data sets is provided by the the Indian Ocean zonal/dipole modes, Atlantic Nino,~ Office of Science, U.S. Department of Energy. This and ENSO (especially the so-called central Pacific research was carried out in part at the Jet Propulsion El Nino).~ Laboratory, California Institute of Technology, under

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FIG. A1. Annual-mean (a) zonally averaged zonal wind stress and (b) equatorial zonal wind stress for CMIP5 computed over the period of 1970–99 (red curve) and over 1980–2005 (blue dashed curve) and for the three reference datasets (QuikSCAT, NCEP-1, and ERS-1) com- puted over different decades as indicated in the legend.

a contract with the National Aeronautics and Space climatology defined over different periods. Therefore, Administration. the 30-yr climatology used in this study is representative of the climatology over a much longer period (i.e., not APPENDIX subject to any significant effect of decadal variability). Figure A2 presents a comparison of CMIP5, NCEP-1, and ERA-Interim climatology for the common period of Uncertainties due to Decadal and Diurnal Variability 1978–2005. This figure is very similar to Fig. A1, where the A possible concern of this study might be how decadal climatology was defined over different periods. The find- variability affects our conclusions given the dissimilar ings remain the same: for example, CMIP5 zonal wind periods for climatologies (i.e., 1970–99 for NCEP-1 and stress has westward biases at midlatitudes and eastward CMIP; 1978–2011 for ERA-Interim; and 1999–2009 for biases at high latitudes and at subtropical–tropical lati- QuikSCAT). We performed further analysis of clima- tudes,andCMIP5zonalwindstressesaretooweakinthe tologies defined over different periods and found that equatorial Indian and Atlantic Oceans and too strong in our conclusions were not affected. the western equatorial Pacific Ocean. From Figs. A1 and We first examined the sensitivity of CMIP5 climatology A2, it is seen that these biases in CMIP wind stress rel- to the period over which the climatology is defined. The ative to all three reference datasets are similar. climatology for 1970–99 as presented in the paper is ex- Another concern of this study might be the sampling tremely similar to that computed over a 1.5-century (1850– error in the QuikSCAT data. QuikSCAT, sampling ap- 2005) period (Fig. A1, red and blue curves). Figure A1 is proximately 90% of the World Ocean on a daily basis, is taken from Figs. 5b and 6b without the individual CMIP insufficient to capture diurnal variability of the wind field. The models (the thin color curves) and with the addition of magnitude of the bias of time mean QuikSCAT wind stress the 1.5-century CMIP5 climatology (the blue dashed caused by the limited sampling (e.g., Fig. 2 in Lee et al. 2008; curve). The differences of CMIP5 from the reference Guan et al. 2013) is several times smaller than the differences datasets are much larger than the differences in CMIP5 between CMIP and QuikSCAT shown in this study.

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FIG. A2. Annual-mean (a) zonally averaged zonal wind stress and (b) equatorial zonal wind stress for CMIP5, NCEP-1, and ERA-Interim computed over the common period of 1978–2005.

REFERENCES Deser, C., A. Capotondi, R. Saravanan, and A. S. Phillips, 2006: Tropical Pacific and Atlantic climate variability in CCSM3. Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, J. Climate, 19, 2451–2481. 2007: El Nino~ Modoki and its possible teleconnection. J. Geo- Duhaut, T. H. A., and D. N. Straub, 2006: Wind stress dependence phys. Res., 112, C11007, doi:10.1029/2006JC003798. on ocean surface velocity: Implications for mechanical en- Bourassa, M. A., S. T. Gille, D. L. Jackson, J. B. Roberts, and G. A. ergy input to ocean circulation. J. Phys. Oceanogr., 36, 202– Wick, 2010: Ocean winds and turbulent air-sea fluxes inferred 211. from remote sensing. Oceanography, 23, 36–51. Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance Cai, W., and T. Cowen, 2013: Why is the amplitude of the Indian metrics for climate models. J. Geophys. Res., 113, D06104, Ocean dipole overly large in CMIP3 and CMIP5 climate models? doi:10.1029/2007JD008972. Geophys.Res.Lett.,40, 1200–1205, doi:10.1002/grl.50208. ——, R. Ferraro, and D. Waliser, 2011: Better use of satellite data Capotondi, A., A. Wittenberg, and S. Masina, 2006: Spatial and in evaluating climate models. Eos, Trans. Amer. Geophys. temporal structure of tropical Pacific interannual variability in Union, 92, 172. 20th century coupled simulations. Ocean Modell., 15, 274–298, Guan, B., D. E. Waliser, J.-L. Li, and A. da Silva, 2013: Evaluating the doi:10.1016/j.ocemod.2006.02.004. impact of orbital sampling on satellite-climate model compari- ——, M. A. Alexander, N. A. Bond, E. N. Curchister, and J. D. sons. J. Geophys. Res., 118, doi:10.1029/2012JD018590, in press. Scott, 2012: Enhanced upper ocean stratification with climate Guilyardi, E., 2006: El Nino-mean state-seasonal cycle interac- change in the CMIP3 models. J. Geophys. Res., 117, C04031, tions in a multi-model ensemble. Climate Dyn., 26, 329–348, doi:10.1029/2011JC007409. doi:10.1007/s00382-005-0084-6. Chelton, D. B., and M. H. Frielich, 2005: Scatterometer-based as- Jamison, N., and S. Kravtsov, 2010: Decadal variations of North sessment of 10-m wind analysis from the operational ECMWF Atlantic sea surface temperature in observations and CMIP3 and NCEP numerical weather prediction models. Mon. Wea. simulations. J. Climate, 23, 4619–4636. Rev., 133, 409–429. Jiang, J. H., and Coauthors, 2012: Evaluation of cloud and water ——, and S.-P. Xie, 2010: Coupled ocean-atmospheric interaction vapor simulations in CMIP5 climate models using NASA at oceanic mesoscales. Oceanography, 23, 52–69. ‘‘A-Train’’ satellite observations. J. Geophys. Res., 117, D14105, ——, M. G. Schlax, M. H. Freilich, and R. F. Milliff, 2004: Satellite doi:10.1029/2011JD017237. measurements reveal persistent small-scale features in ocean Johnson, G. C., S. Schmidtko, and J. M. Lyman, 2012: Relative winds. Science, 303, 978–983, doi:10.1126/science.1091901. contributions of temperature and salinity to seasonal mixed Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: layer density changes and horizontal density gradients. Configuration and performance of the data assimilation J. Geophys. Res., 117, C04015, doi:10.1029/2011JC007651. system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/ Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re- qj.828. analysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.

Unauthenticated | Downloaded 10/10/21 09:24 PM UTC 5826 JOURNAL OF CLIMATE VOLUME 26

Kanzow, T., and Coauthors, 2010: Seasonal variability of the At- McPhaden, M. J., and D. Zhang, 2002: Slowdown of the meridional lantic meridional overturning circulation at 26.58N. J. Climate, overturning circulation in the upper Pacific Ocean. Nature, 23, 5678–5698. 415, 603–608, doi:10.1038/415603a. Kao, H.-Y., and J.-Y. Yu, 2009: Contrasting eastern-Pacific and Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. central-Pacific types of ENSO. J. Climate, 22, 615–632. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007: The WCRP Kidston, J., and E. P. Gerber, 2010: Intermodel variability of the CMIP3 multi-model dataset: A new era in climate change poleward shift of the austral jet stream in the CMIP3 in- research. Bull. Amer. Meteor. Soc., 88, 1383–1394. tegrations linked to biases in 20th century climatology. Geo- Mestas-Nunez,A.M.,D.B.Chelton,M.H.Frielich,andJ.G.~ phys. Res. Lett., 37, L09708, doi:10.1029/2010GL042873. Richman, 1994: An evaluation of ECMWF-based climatolog- Kim, S. T., and J.-Y. Yu, 2012: The two types of ENSO in ical wind stress fields. J. Phys. Oceanogr., 24, 1532–1549. CMIP5 models. Geophys. Res. Lett., 39, L11704, doi:10.1029/ Perigaud, C., S. E. Zebiak, F. Melin, J.-P. Boulanger, and 2012GL052006. B. Dewitte, 1997: On the role of meridional wind anomalies in Kug, J.-S., F.-F. Jin, and S.-I. An, 2009: Two types of El Nino~ a coupled model of ENSO. J. Climate, 10, 761–773. events: Cold tongue El Nino~ and warm pool El Nino.~ J. Cli- Pierce, D. W., T. P. Barnett, E. J. Fetzer, and P. J. Gleckler, 2006: mate, 22, 1499–1515. Three-dimensional tropospheric water vapor in coupled cli- Kwok, R., 2011: Observational assessment of Arctic Ocean sea ice mate models compared with observations from the AIRS motion, export, and thickness in CMIP3 climate simulations. satellite system. Geophys. Res. Lett., 33, L21701, doi:10.1029/ J. Geophys. Res., 116, C00D05, doi:10.1029/2011JC007004. 2006GL027060. Larkin, N. K., and D. E. Harrison, 2005: Global seasonal tem- Richter, I., S.-P. Xie, A. T. Wittenberg, and Y. Masumoto, 2012: perature and precipitation anomalies during El Nino~ autumn Tropical Atlantic biases and their relation to surface wind and winter. Geophys. Res. Lett., 32, L16705, doi:10.1029/ stress and terrestrial precipitation. Climate Dyn., 38, 985–1001, 2005GL022860. doi:10.1007/s00382-011-1038-9. Lee, T., and M. J. McPhaden, 2008: Decadal phase change in large- Risien, C. M., and D. B. Chelton, 2008: A global climatology of sur- scale sea level and winds in the Indo-Pacific region at the end of face wind and wind stress fields from eight years of QuikSCAT the 20th century. Geophys. Res. Lett., 35, L01605, doi:10.1029/ scatterometer data. J. Phys. Oceanogr., 38, 2379–2413. 2007GL032419. Roquet, F., C. Wunsch, and G. Madec, 2011: On the patterns of ——, and ——, 2010: Increasing intensity of El Nino in the central- wind-power input to the ocean circulation. J. Phys. Oceanogr., equatorial Pacific. Geophys. Res. Lett., 37, L14603, doi:10.1029/ 41, 2328–2342. 2010GL044007. Slingo, J., and Coauthors, 2009: Developing the next-generation ——, O. Wang, W. Tang, and W. T. Liu, 2008: Wind stress mea- climate system models: Challenges and achievements. Philos. surements from the QuikSCAT-SeaWinds scatterometer Trans. Roy. Soc., 367A, 815–831, doi:10.1098/rsta.2008.2007. tandem mission and the impacts on an ocean model. J. Geo- Su, H., D. E. Waliser, J. H. Jiang, J. Li, W. G. Read, J. W. Waters, phys. Res., 113, C12019, doi:10.1029/2008JC004855. and A. M. Tompkins, 2006: Relationships among upper tro- ——,S.Hakkinen,K.Kelly,B.Qiu,H.Bonekamp,andE.J. pospheric water vapor, clouds and SST: MLS observations, Lindstrom, 2010: Satellite observations of ocean circulation ECMWF analyses and GCM simulations. Geophys. Res. Lett., changes associated with climate variability. Oceanography, 23, 33, L22802, doi:10.1029/2006GL027582. 70–81. ——, and Coauthors, 2013: Diagnosis of regime-dependent cloud Li, J.-L. F., and Coauthors, 2012a: An observationally based eval- simulation errors in CMIP5 models using ‘‘A-Train’’ satellite uation of cloud ice water in CMIP3 and CMIP5 GCMs and observations and reanalysis data. J. Geophys. Res., 118, 2762– contemporary analyses. J. Geophys. Res., 117, D16105, 2780, doi:10.1029/2012JD018575. doi:10.1029/2012JD017640. Taylor, K. E., 2001: Summarizing multiple aspects of model perfor- ——, and Coauthors, 2012b: An observationally based evaluation mance in a single diagram. J. Geophys. Res., 106 (D7), 7183–7192. of cloud liquid water in CMIP3 and CMIP5 GCMs and con- Waliser, D. E., and Coauthors, 2009: Cloud ice: A climate model temporary analyses. J. Geophys. Res., 117, D16105, doi:10.1029/ challenge with signs and expectations of progress. J. Geophys. 2012JD017640. Res., 114, D00A21, doi:10.1029/2008JD010015. ——, D. E. Waliser, G. Stephens, S. Lee, T. L’Ecuyer, S. Kato, Wittenberg, A. T., 2004: Extended wind stress analyses for ENSO. N. Loeb, and H.-Y. Ma, 2013: Characterizing and understanding J. Climate, 17, 2526–2540. radiation budget biases in CMIP3/CMIP5 GCMs, contem- Xue, Y., B. Huang, Z.-Z. Hu, A. Kumar, C. Wen, D. Behringer, and porary GCM and reanalysis. J. Geophys. Res., 118, doi:10.1002/ S. Nadiga, 2011: An assessment of oceanic variability in the jgrd.50378, in press. NCEP climate forecast system reanalysis. Climate Dyn., 37, Liu, W. T., 2002: Progress in scatterometer application. J. Ocean- 2511–2529, doi:10.1007/s00382-010-0954-4. ogr., 58, 121–136. Yu, J.-Y., and S. T. Kim, 2010a: Identification of central-Pacific and Maximenko, N., P. Niiler, L. Centurioni, M.-H. Rio, O. Melnichenko, eastern-Pacific types of ENSO in CMIP3 models. Geophys. D. Chambers, V. Zlotnicki, and B. Galperin, 2009: Mean dynamic Res. Lett., 37, L08706, doi:10.1029/2010GL044082. topography derived from satellite and drifter buoy data using ——, and ——, 2010b: Three evolution patterns of central-Pacific three different techniques. J. Atmos. Oceanic Technol., 26, 1910– El Nino.~ Geophys. Res. Lett., 37, L08706, doi:10.1029/ 1919. 2010GL042810.

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