Bias and Uncertainty of the Relationship between AO and Winter Synoptic Temperature Variability over the Northern Hemisphere under Present and Future Climate

Yuntao Jian aGuangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, CMA, Guangzhou, China
eGuy Carpenter Asia-Pacific Climate Impact Centre, Center for Ocean Research in Hong Kong and Macau (CORE), School of Energy and Environment, City University of Hong Kong, Hong Kong, China
gSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

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Marco Y. T. Leung bSchool of Ocean Science, Sun Yat-Sen University, Zhuhai, China
gSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

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Ruhua Zhang eGuy Carpenter Asia-Pacific Climate Impact Centre, Center for Ocean Research in Hong Kong and Macau (CORE), School of Energy and Environment, City University of Hong Kong, Hong Kong, China

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Wen Zhou cDepartment of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, China
eGuy Carpenter Asia-Pacific Climate Impact Centre, Center for Ocean Research in Hong Kong and Macau (CORE), School of Energy and Environment, City University of Hong Kong, Hong Kong, China

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Maoqiu Jian dSchool of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and Center for Monsoon and Environment Research, Sun Yat-Sen University, Zhuhai, China
gSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

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Song Yang dSchool of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, and Center for Monsoon and Environment Research, Sun Yat-Sen University, Zhuhai, China
gSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

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Yerong Feng aGuangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, CMA, Guangzhou, China

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Banglin Zhang aGuangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, CMA, Guangzhou, China
fCollege of Atmospheric Science, Lanzhou University, Lanzhou, China
gSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

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Abstract

In this study, the relationship between AO and winter synoptic temperature variability (STV) over the Northern Hemisphere is examined in 34 CMIP5/CMIP6 model outputs. With significant model bias around the North Pacific and North Atlantic, most models fail to capture the correct AO–STV pattern in historical simulations compared to observations. To investigate the bias of AO–STV relationship simulations, AO-related processes for the connection between AO and winter STV are examined in high pattern score (HPS) models and low pattern score (LPS) models, respectively. Furthermore, the bias of AO impact can be traced back to AO pattern simulations. On the one hand, compared to observations, HPS models can overall capture the intensity in the North Pacific and North Atlantic center of AO. On the other hand, LPS models tend to overestimate the North Pacific center and underestimate the North Atlantic center. In addition, similar to historical simulations, a robust AO–STV relationship can still be found over the Northern Hemisphere in future projections based on HPS models. Meanwhile, the uncertainty of the projected AO–STV relationship in the multimodel ensemble is confined mainly to the North Pacific, consistent with the large diversity of intensity over the North Pacific center of AO, which is related to the uncertainty of the relationship between AO and regional mode variability.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Banglin Zhang, zhangbl@gd121.cn

Abstract

In this study, the relationship between AO and winter synoptic temperature variability (STV) over the Northern Hemisphere is examined in 34 CMIP5/CMIP6 model outputs. With significant model bias around the North Pacific and North Atlantic, most models fail to capture the correct AO–STV pattern in historical simulations compared to observations. To investigate the bias of AO–STV relationship simulations, AO-related processes for the connection between AO and winter STV are examined in high pattern score (HPS) models and low pattern score (LPS) models, respectively. Furthermore, the bias of AO impact can be traced back to AO pattern simulations. On the one hand, compared to observations, HPS models can overall capture the intensity in the North Pacific and North Atlantic center of AO. On the other hand, LPS models tend to overestimate the North Pacific center and underestimate the North Atlantic center. In addition, similar to historical simulations, a robust AO–STV relationship can still be found over the Northern Hemisphere in future projections based on HPS models. Meanwhile, the uncertainty of the projected AO–STV relationship in the multimodel ensemble is confined mainly to the North Pacific, consistent with the large diversity of intensity over the North Pacific center of AO, which is related to the uncertainty of the relationship between AO and regional mode variability.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Banglin Zhang, zhangbl@gd121.cn

1. Introduction

The Arctic Oscillation [AO, also known as the Northern Annular Mode (NAM)] is commonly characterized by sea level pressure (SLP), representing a zonally symmetric, meridional seesaw of atmospheric mass between the Arctic and midlatitudes of the Northern Hemisphere (Thompson and Wallace 1998; Gong and Wang 1999; Thompson and Wallace 2000). Since AO is the dominant mode of atmospheric internal variability over the extratropical Northern Hemisphere, which modulates anomalous atmospheric circulation patterns, it has a huge impact on weather and climate over the Northern Hemisphere, especially for winter temperature. Many previous studies have discovered a linkage between AO and the magnitude of seasonal or monthly temperature over the Northern Hemisphere during boreal winter (Thompson and Wallace 2001; Wettstein and Mearns 2002; Wu and Wang 2002; Gong and Wang 2003; Gong et al. 2006; Wang et al. 2005; Park et al. 2010; Zuo et al. 2015; Dai and Tan 2017; He et al. 2017, 2019; Luo et al. 2019, 2020; Kryjov 2021).

In fact, temperature fluctuation during winter is also worthy of investigation, as it plays an important role in agriculture, human disease transmission, energy demand estimation, and air pollution transport (Yin et al. 1996; Yoshikado and Tsuchida 1996; Ikram et al. 2015; Kim and Lee 2019; Xu et al. 2020). Therefore, we are motivated to discover the factors influencing temperature variability, which will help improve the short-term predictability of winter climate. Several recent studies have found that winter synoptic temperature variability (STV) over the Asian–Pacific–American region can be influenced by El Niño–Southern Oscillation (ENSO), which is the dominant mode of sea surface temperature (SST) variation in the tropical Pacific (Trenberth 1997; McPhaden et al. 2006). These studies have indicated that El Niño (La Niña) events can induce a Rossby wave train across the Asian–Pacific–American region, enhancing (weakening) the meridional temperature gradient, which provides favorable (unfavorable) mean-flow conditions for supporting extratropical eddy development, thus resulting in larger (smaller) winter STV with a stronger (weaker) eddy growth rate in the midlatitudes (Schneider et al. 2015; Leung and Zhou 2016; Ren et al. 2020; Jian et al. 2021a,b; Luo et al. 2021). Recently, based on numerical experiment, Lutsko et al. (2019) suggested that large-scale orography may also play a role in influencing winter synoptic temperature variability by modulating the temperature gradient.

Except for tropical SST forcing, since AO is the dominant mode of internal atmospheric variability over the Northern Hemisphere, the relationship between AO and winter STV over the Northern Hemisphere also needs to be considered. Previous studies have found that AO can influence winter daily temperature variance over East Asia by modulating the high-frequency fluctuation of the Siberian high, which is closely associated with the outbreak of cold surge events (Gong et al. 2004; Gong and Ho 2004). Furthermore, Rudeva and Simmonds (2021) revealed that the relationship between winter temperature extreme events and high-frequency wave propagation from tropics and Arctic. Based on data from 542 meteorological stations, S. Chen et al. (2013) suggested that winter extreme cold days over the northern part of eastern China are closely related to AO, which is similar to the results of You et al. (2013). Moreover, Wettstein and Mearns (2002) discovered that the variance of daily extreme temperature over the Canada and northeastern United States is also related to the North Atlantic–Arctic Oscillation (NAO–AO; Thompson and Wallace 1998; Thompson et al. 2000), indicating that the extreme temperature variances tend to be stronger (weaker) when the NAO–AO index is high (low). Higgins et al. (2002) also revealed that the negative (positive) phase of AO is related to less (more) extreme warm and more (fewer) cold days over the conterminous United States during wintertime. Furthermore, Griffiths and Bradley (2007) indicated that AO can be a better predictor than ENSO of winter daily temperature extremes over the northeastern United States during 1926–2000. In addition, a relationship between AO and winter temperature variability can be found in other regions over the Northern Hemisphere (Shabbar and Bonsal 2004; Griffiths and Bradley 2007; Woo et al. 2012). On the other hand, under the global warming trend of recent decades, some studies have discovered that long-term changes in the relationship between AO and winter temperature variability are unstable and have shown fluctuations in recent decades (Woo et al. 2012; S. Chen et al. 2013; Jian et al. 2021b), suggesting that the AO–STV relationship may change in a warmer climate in the future.

However, due to limited data, one effective way to investigate the AO–STV relationship in the future climate is to use climate models for future projections. Currently, the outputs from state-of-the-art climate models in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012; CMIP6; Eyring et al. 2016) are widely used to examine robust relationships in observations in the present day and project possible changes in climate systems. Compared to CMIP3 (Meehl et al. 2007), the feature of AO simulation in CMIP5/CMIP6 is overall improved (Zuo et al. 2013; Cusinato et al. 2021). Although many previous studies have examined the performance of climate models in simulating AO impact on the magnitude of winter mean temperature over the Northern Hemisphere (Wang et al. 2013; Guo et al. 2016; Li et al. 2018; Miao et al. 2020), few studies have checked out the relationship between AO and temperature fluctuation during winter based on the CMIP5/CMIP6 models in current and future climate. The performance of climate models in reproducing the AO–STV relationship in historical simulation is still unclear. Meanwhile, some recent studies pointed out that the climate simulation in high-latitudes seems do not improve significantly from CMIP5 to CMIP6 (Davy and Outten 2020; Luo et al. 2021), which also motivate us to compare the ability of climate models in the new version (CMIP6) to their last generation (CMIP5) on the simulated AO–STV relationship, providing implications for model improvement.

Moreover, some previous studies have pointed out that many climate models fail to simulate the correct AO pattern compared to observations (Miller et al. 2006; Stoner et al. 2009; Zuo et al. 2013; Gong et al. 2019), and many climate models tend to underestimate (overestimate) the intensity of the North Atlantic (North Pacific) centers, the two main centers of the AO in the midlatitudes. Based on 32 CMIP5 models, Gong et al. (2017) further investigated the possible cause of the bias of the North Pacific center in a historical multimodel ensemble, indicating that a too-strong connection between AO and the regional mode over the North Pacific [North Pacific dominant Mode (NPM)] was related to the excessively strong North Pacific center of AO. As mentioned above, although the AO pattern bias in climate models has been indicated in many previous studies, the climate impact of such bias is still unknown. Whether the model bias in simulating the AO–STV relationship can be traced back to AO pattern bias is also worth investigating.

In this study, we first aim to examine the relationship between AO and winter STV over the Northern Hemisphere based on the outputs of CMIP5/CMIP6 historical simulations and analyze the possible cause of model bias. Then we try to project the AO–STV relationship by using the models with good performance in the historical simulation, discussing the influence of AO on winter STV over the Northern Hemisphere in the future climate, which will provide implications for our selection of climate predictors for seasonal forecasts. The rest of this paper is organized as follows. Section 2 describes the model output, data, and methodologies used in this study. Section 3 examines the relationship between AO and winter STV over the Northern Hemisphere in the historical simulations. Section 4 investigates the possible causes of model bias in the AO–STV relationship among the historical multimodel ensemble. Section 5 projects the AO–STV relationship in the future climate and analyzes its uncertainty. Finally, a summary and discussion are provided in section 6.

2. Models, datasets, and methodologies

a. Models and datasets

To examine the model performance on simulating the AO–STV relationship, we use 18 CMIP5 models and 16 CMIP6 models in both a historical experiment (1950–2005) as present climate and a scenario experiment (2016–2100) under a high-level greenhouse gas emission scenario in CMIP5 (RCP8.5) and CMIP6 (SSP5-8.5) as future climate, including monthly and daily variables of SLP, air temperature, horizontal winds, and geopotential height (Taylor et al. 2012; Eyring et al. 2016). Model details are shown in Table 1. For comparison, we use the daily and monthly mean output datasets from the Japanese 55-year Reanalysis (JRA55) with a horizontal resolution of 1.25° latitude × 1.25° longitude (Kobayashi et al. 2015), including SLP, geopotential height, and near-surface air temperature. To compare the results between observations and model outputs, we interpolate all data onto a uniform horizontal grid with a resolution of 2.5° latitude × 2.5° longitude. For the mean of the multimodel ensemble (MME), we here simply define it as the equal weight average of model results. The reanalysis datasets cover the period from December 1960 to February 2020. In this study, winter-mean values are considered as the average of monthly means of December, January, and February.

Table 1

List of 18 CMIP5 and 16 CMIP6 model names and their horizontal resolution (grids in longitude × grids in latitude) used in this study.

Table 1

b. Methodologies

Similar to Li and Wang (2003), we here define the AO index (AOI) as the SLP difference between the mid and high-latitude annular belt of action over the Northern Hemisphere:
AOI=P35°NP65°N,
where P35°N and P65°N represent the normalized zonal-mean winter SLP anomalies at 35° and 65°N, respectively. To investigate winter STV, we first obtain the synoptic temperature with high-frequency variation less than 10 days of daily mean near-surface temperature in each winter by using a Lanczos filter (Duchon 1979; Leung and Zhou 2016; Jian et al. 2021a,b). Then we use the standard deviation of synoptic temperature during winter to represent winter STV. To avoid the impact of long-term linear trend, we remove the linear trend for all data used in this study. To examine the significance of the results in regression coefficients, correlation coefficients, and composite analysis, we applied the Student’s t test in this study.
To quantify the intensification of extratropical eddies, the maximum Eady growth rate is used to represent the intensity of atmospheric baroclinicity instability (Eady 1949; Lindzen and Farrell 1980; Simmonds and Lim 2009; Leung and Zhou 2016). Following Simmonds and Lim (2009), the maximum Eady growth rate (σE) is calculated by the formula:
σE=0.3098|f||u(z)z|N,
where N is the Brunt–Väisälä frequency [N2=(g/θ)(θ/z)], g is the gravitational acceleration, θ is the potential temperature, f is the Coriolis parameter, and u(z) is the vertical profile of the zonal wind component.

3. Model performance in simulating the relationship between AO and winter STV over the Northern Hemisphere in the historical simulation

Figure 1 shows the correlation map between AOI and winter STV over the Northern Hemisphere in observations (1960–2020) and 18 CMIP5 models (1951–2005). In the observations, AO is significantly related to the winter STV in the midlatitudes over the Northern Hemisphere, such as over Eurasia, the North Pacific, North America, and North Atlantic, which is similar to previous studies (Higgins et al. 2002; Wettstein and Mearns 2002; Gong et al. 2004; Yang et al. 2018). To quantify model performance in simulating the AO–STV relationship over the Northern Hemisphere, similar to Jian et al. (2021b), we designed a pattern score (PSAO–STV). We first calculated AOI–STV correlation maps for the models and observations, respectively. We then obtained the spatial correlation coefficients of the two correlation maps between the models and observations in the midlatitudes over the Northern Hemisphere (0°–360°, 20°–70°N), representing PSAO–STV.

Fig. 1.
Fig. 1.

Correlation map between the Arctic Oscillation index (AOI) and winter synoptic temperature variability (STV) for (a) observations and (b)–(s) 18 CMIP5 models. The score of model performance (PSAO–STV) is shown in each panel (at the top right). The black solid (dashed) lines indicate the regression values between AOI and winter STV at ±0.07, respectively. Stippling indicates values of exceeding the 0.05 significance level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

Unfortunately, compared to observations (Fig. 1a), most CMIP5 models (Figs. 1b–s) do not well reproduce the pattern of the AO–STV relationship over the Northern Hemisphere; only a few models can well capture the correct pattern with high PSAO–STV. Similarly, as Fig. 2 shows, the overall performance for AO–STV relationship simulations in CMIP6 (Figs. 2b–q) seems close to CMIP5, showing an average PSAO–STV of the multimodel ensemble in CMIP6 and CMIP5 of 0.57 and 0.53, respectively. In addition, some models with high PSAO–STV in CMIP6 (e.g., CNRM-CM6-1, PSAO–STV:0.69; GFDL-CM4, PSAO–STV: 0.68; MIROC6, PSAO–STV: 0.65) produce similar results to their last generation in CMIP5 (CNRM-CM5, PSAO–STV:0.66; GFDL-CM3, PSAO–STV: 0.63; MIROC5 PSAO–STV: 0.64), indicating that the ability of CMIP6 models to simulate the AO–STV relationship has not been significantly improved compared to CMIP5 on the whole, which is also similar to some previous results on comparing the evaluations of mid- to high-latitude climate between CMIP5 and CMIP6 (Davy and Outten 2020; Luo et al. 2021). Meanwhile, although some models in CMIP6 (e.g., HadGEM3-GC31-MM, CNRM-CM6-1, GFDL-CM4) show an excellent simulated AO–STV pattern with very high PSAO–STV among the CMIP5/6 model ensemble, more than half of the models fail to reproduce the correct AO–STV relationship over the Northern Hemisphere, which limits the predictability of AO-related winter STV over the Northern Hemisphere based on the CMIP5/CMIP6 multimodel ensemble, suggesting that the source of the bias in the simulated AO–STV relationship needs to be further investigated.

Fig. 2.
Fig. 2.

As in Fig. 1, but for the results of 16 CMIP6 models.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

4. Bias analysis for the AO–STV relationship in the historical simulation

As some previous studies have shown, winter STV is directly related to the intensification of extratropical eddy development, which is closely associated with atmospheric baroclinicity. Therefore, climate factors can affect winter STV by changing the lower-level meridional temperature gradient in the midlatitudes, which provides the mean-flow condition for baroclinicity instability, supporting extratropical eddy development (Leung and Zhou 2016; Yang et al. 2018; Ren et al. 2020; Jian et al. 2021a,b). Similarly, here we will also examine the possible source of bias for AO–STV simulations in CMIP5/CMIP6 based on the above mechanism.

To investigate model bias in simulating the AO–STV relationship among the CMIP5/6 multimodel ensemble, we choose five high pattern score (HPS) models (CNRM-CM5, CNRM-CM6-1, GFDL-CM4, HadGEM3-GC31-MM, MIROC6) with PSAO–STV above 0.65, and six low pattern score (LPS) models (BCC-CSM1-1, BCC-CSM1-1-M, CanESM2, MRI-ESM2-0, CanESM5, MPI-ESM1-2-LR) with PSAO–STV below 0.5, respectively. Figures 3b and 3c display the composited pattern of the AO–STV relationship in the HPS and LPS models, respectively. Compared to observations (Fig. 3a), the MME of the HPS models (Fig. 3b) can overall well capture the AO–STV relationship in both spatial distribution and magnitude, with a PSAO–STV of 0.81. On the other hand, with a lower PSAO–STV of 0.53, the MME of the LPS models (Fig. 3c) suffers more bias in simulating the AO–STV relationship, showing a significant difference around the North Pacific and North Atlantic between these two groups (Fig. 3d). We perform bias analysis on the AO–STV simulations based on the comparison of these two groups in the following discussion.

Fig. 3.
Fig. 3.

Correlation map between AOI and winter STV over the Northern Hemisphere at present climate in (a) observations, (b) MME of HPS models, (c) MME of LPS models, and (d) the difference between (c) and (b). Stippling in (a)–(c) indicates the values exceeding the 0.05 significance level based on one sample t test. Stippling in (d) indicates the differences between (c) and (b) exceeding the 0.05 significance level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

A recent study examined the relationship between the regional winter STV and Eady growth rate in the midlatitudes based on a multimodel ensemble, showing a robust relationship between the intensity of winter STV and the development of extratropical eddies that can be found in climate models compared to observations (Jian et al. 2021a). Therefore, we turn our attention to investigate the relationship between AO and extratropical eddy development. Figure 4 shows a composited regression map between AOI and Eady growth rate and low-level meridional wind synoptic variability at 850 hPa in HPS and LPS models, respectively. As shown in Figs. 4a–d, when AO is in its positive (negative) phase, the Eady growth rate is stronger (weaker) in the midlatitudes over the Northern Hemisphere (Figs. 4a,c), which is physically consistent with the larger (smaller) AO-related low-level meridional wind synoptic variability along the midlatitudes (Figs. 4a,c). In other words, along with the stronger (weaker) intensification of extratropical eddies, the exchange of cold/warm air between north and south increases (decreases) via the larger (smaller) meridional wind variation, resulting in the larger (smaller) synoptic temperature fluctuation. Although the overall pattern of the AO-related Eady growth rate in LPS models is similar to HPS models, compared to HPS models, the intensity of their relationships is stronger over Europe and Alaska and weaker over the North Atlantic (Fig. 4e), resulting in a similar difference downstream of the AO-related synoptic meridional wind variability between LPS and HPS models (Fig. 4f).

Fig. 4.
Fig. 4.

Regression map between AOI and (a) 850-hPa Eady growth rate (EGR, day−1) and (b) 850-hPa synoptic meridional wind variability for MME of HPS models. (c),(d) As in (a) and (b), but for MME of LPS models. (e),(f) The MME difference between HPS and LPS models, respectively. Stippling in (a)–(d) indicates the MME exceeding the 0.05 significance level based on one sample t test. Stippling in (e) and (f) indicates their differences exceeding the 0.05 significance level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

Furthermore, since the Eady growth rate depends on the vertical variation of the potential temperature (N) and meridional temperature gradient, similar to some recent studies (Jian et al. 2021b; Simmonds and Li 2021), we will discuss their relative role in the connection with AO separately, which is helpful for us to understand the physic process of AO impact on Eady growth rate in detail. On the one hand, as shown in Fig. 5, AO is not related to N over the area of the AO-related Eady growth rate in either HPS or LPS models (Figs. 5a,c). On the other hand, AO-related horizontal wind anomalies in both model groups induce warm (cold) air transport in high (low) latitude via advection, which cause the anomalous temperature dipole pattern in the midlatitudes, resulting in a weaker meridional temperature gradient over Europe. Similarly, the opposite results can also be found over the North Pacific and North Atlantic. As a result, AO is significantly correlated with the low-level anomalous meridional temperature gradients in the midlatitudes over the Northern Hemisphere (Figs. 5b,d), which well matches the significant area of AO-related Eady growth rate (Figs. 4a,c), suggesting that AO can modulate the Eady growth rate via a temperature gradient in the midlatitudes. In addition, compared to HPS models, the AO-related meridional temperature gradient in LPS models is stronger over Europe and weaker over the North Pacific and North Atlantic (Fig. 5f), which is consistent with the stronger (weaker) Eady growth rate over Europe (North Pacific and North Atlantic) (Fig. 4e).

Fig. 5.
Fig. 5.

Spatial pattern of the regression coefficients between AOI and (a) winter Brunt–Väisälä frequency (N; s−1), (b) temperature anomalies at 700 hPa (shaded; °C), and horizontal wind anomalies at 700 hPa (vectors; m s−1) for MME of HPS models. (c),(d) As in (a) and (b), but for MME of LPS models. (e),(f) The MME difference between (c) and (a), and (d) and (b), respectively. The purple contours in (b) and (d) represent the winter climatological temperature at 700 hPa (contour interval: 5°C). Only the values exceeding the 0.05 significance level are plotted. Stippling in (f) indicates differences in the anomalous meridional temperature gradient between LPS and HPS models exceeding the 0.05 significance level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

Therefore, all the above results demonstrate that the bias of the AO–STV relationship can be traced back to the AO impact on winter STV. However, whether the intensity or pattern simulation of AO may cause this bias is still unclear. Here we define AO intensity as the standard deviation of AOI in each model, while the AO pattern is obtained by regressing the winter-mean SLP anomalies onto winter AOI in the extratropical Northern Hemisphere (20°–90°N). Additionally, similar to PSAO–STV, we define the pattern score of the AO pattern (PSAO) by using the spatial correlation coefficients of the AO pattern between each model and observations, representing the model performance in simulating the AO pattern. To verify this hypothesis, as mentioned above, we examine the relationship between AO simulations and PSAO–STV among the multimodel ensembles in Fig. 6. As the figure shows, AO intensity is not related to PSAO–STV, with intermodel correlation coefficients of −0.06 (Fig. 6a). However, PSAO–STV is significantly correlated with PSAO, with intermodel correlation coefficients of 0.52, exceeding the 0.01 significance level based on the Student’s t test. Meanwhile, a significant relationship between PSAO–STV and the AO pattern simulation can be found in both the CMIP5 and CMIP6 ensembles, with inter-model correlation coefficients of 0.51 and 0.54, respectively, indicating that AO pattern uncertainty has an impact on the AO–STV relationship simulation in the two generations of climate models.

Fig. 6.
Fig. 6.

Scatterplots of (a) AO intensity vs the pattern score of the correlation map between AOI and winter STV (PSAO–STV) and (b) the pattern score of the AO pattern (PSAO) vs PSAO–STV among the 18 CMIP5 and 16 CMIP6 models in historical simulations. The intermodel correlation coefficient (R) among 34 models is shown in each panel.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

Since the bias of the AO–STV relationship is related to the AO pattern, we compare the AO pattern in the observations, HPS models, and LPS models in Fig. 7. Compared to observations (Fig. 7a), both HPS (Fig. 7b) and LPS models (Fig. 7c) can well capture two annular belts along the mid and high latitudes over the Northern Hemisphere, with two centers in the North Pacific and North Atlantic, respectively. But for the intensity of these two centers, only HPS models can simulate the correct amplitude of the AO pattern with a stronger center in the North Atlantic and a weaker center in the North Pacific, which is consistent with observations. In contrast, the LPS models fail to capture this feature and show a much weaker North Atlantic center and a stronger North Pacific center compared to HPS models (Fig. 7d). Moreover, we also examine the intermodel relationship between PSAO–STV and the AO pattern simulation (Fig. 7e). As the figure shows, a significant relationship can be found mainly in the North Pacific and North Atlantic, indicating that models with higher (lower) PSAO–STV tend to have a weaker (stronger) North Pacific center and a stronger (weaker) North Atlantic center. Therefore, due to the offset of the opposite bias between the North Atlantic and North Pacific, the relationship between PSAO-STV and the overall intensity of AO is insignificant among models (Fig. 6a). Meanwhile, it seems that the uncertainty in the AO–STV relationship in historical simulations depends largely on the bias of the North Atlantic center, which has higher correlation coefficients compared to other regions.

Fig. 7.
Fig. 7.

Distribution of the regression coefficients between winter-mean SLP anomalies and AOI in historical simulations for (a) observations, (b) HPS models, (c) LPS models, and (d) the difference between LPS and HPS models. (e) Distribution of intermodel correlation coefficients between PSAO–STV and the AO pattern. Stippling in (d) indicates the differences between (c) and (b) exceeding the 0.05 significance level. Only the correlation coefficients in (e) exceeding the 0.1 significance level are plotted.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

5. Uncertainty of the AO–STV relationship in the future projection

As the results show in section 3, HPS models can well capture the pattern of AO and its related winter STV over the Northern Hemisphere compared to observations, suggesting that the performance of HPS models in simulating the AO–STV relationship seems reliable, and these models could be used for future projection. Figure 8 shows the projected correlation map between AO and winter STV for HPS and LPS models under the RCP8.5 scenario (CMIP5) and SSP5-8.5 scenario (CMIP6), respectively. Similar to present-day simulations, the results of HPS models (Fig. 8a) show that AO is still well related to winter STV in the midlatitudes over the Northern Hemisphere in a warmer climate, with a negative (positive) relationship in Eurasia (North Pacific and North Atlantic). On the other hand, although LPS models also show a significant relationship between AOI and winter STV in the midlatitudes over the Northern Hemisphere in future projections (Fig. 8b), the AO–STV relationship in the midlatitudes around the North Pacific (subtropical North Pacific) tends to be overestimated (underestimated) compared to HPS models (Fig. 8c), suggesting that the uncertainty of the projected AO–STV relationship among multimodel ensembles is located mainly in the North Pacific.

Fig. 8.
Fig. 8.

Correlation map between AOI and winter STV in future projections of (a) MME of HPS models, (b) MME of LPS models, and (c) their difference, respectively. The black solid (dashed) lines represent the regression values between AOI and winter STV at ±0.07, respectively. Stippling in (a) and (b) indicates the correlation coefficients exceeding the 0.05 significance level based on one sample t test. Stippling in (c) indicates the differences between (b) and (a) exceeding the 0.05 significance level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

Since the bias of the AO–STV relationship is related to the AO pattern in the historical simulation, we also compare the projected AO pattern in HPS and LPS models to investigate the uncertainty of AO–STV in Fig. 9. As the figure shows (Fig. 9c), the main difference in the AO pattern between LPS (Fig. 9b) and HPS models (Fig. 9a) is over the North Pacific; LPS models show a larger amplitude of the North Pacific center of AO, which is consistent with the center of the intermodel uncertainty of the AO pattern being in the same region (Fig. 9d). Due to the large intermodel spread over the North Pacific, a possible linkage between the intensity of the North Pacific center of AO and the AO–STV relationship among the multimodel ensembles is examined in Fig. 10. Here we use the North Pacific index (NPI, average AO-related SLP anomalies over the North Pacific: 25°–50°N, 120°E–120°W) to represent the intensity of the North Pacific center. As the figure shows (Fig. 10a), an intermodel relationship between NPI and AO–STV can be found near the North Pacific, with significant positive (negative) correlation coefficients in the midlatitudes around the North Pacific (subtropical North Pacific), which is consistent with previous results (Fig. 8c), suggesting that the uncertainty of the projected AO–STV relationship is closely associated with the large diversity of the North Pacific center of AO. Meanwhile, the NPI-related temperature pattern among models also supports this result, with a stronger (weaker) temperature gradient in the midlatitude (subtropical) North Pacific, which provides favorable (unfavorable) conditions for extratropical eddy development, resulting in larger (smaller) winter STV.

Fig. 9.
Fig. 9.

Distribution of the regression coefficients between AOI and winter-mean SLP anomalies in future projections for (a) HPS models, (b) LPS models, and (c) the difference between LPS and HPS models. (d) Distribution of intermodel uncertainty of the AO pattern among 34 models, defined as the standard deviation of the AO pattern among these models. Stippling in (c) indicates the differences between (b) and (a) exceeding the 0.1 significance level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

Fig. 10.
Fig. 10.

Intermodel correlation map between the North Pacific index (NPI) and (a) correlation coefficients between AOI and winter STV (RAO–STV) and (b) AO-related temperature anomalies at 700 hPa in future projections among 34 models. Stippling in (a) indicates the correlation coefficients exceeding the 0.05 significance level. Only the correlation coefficients in (b) exceeding the 0.05 significance level are plotted. Stippling in (b) indicates the AO-related anomalous meridional temperature gradient at 700 hPa exceeding the 0.05 significance level.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

Since the diversity of the North Pacific center of intensity among the models plays a dominant role in the uncertainty of the projected AO–STV relationship, the possible cause of such diversity needs further investigation. In fact, Gong et al. (2017) recently pointed out that the excessive connection between AO and the North Pacific dominant mode [NPM, calculated by the first EOF mode of winter-mean SLP anomalies over the North Pacific (20°–65°N, 120°E–120°W)] contributes to the overestimation of the North Pacific center of AO in historical simulations. However, whether such a factor may still play a role in the uncertainty of future projections is unclear. Therefore, we examine the AO-NPM relationship based on the multimodel ensemble in Fig. 11a. As the figure shows, most models have a strong connection between AO and NPM, with a high intermodel correlation coefficient of 0.92, showing in both significant temporal [RAO–NPM(T)] and spatial correlation coefficients [RAO–NPM(S)]. The RAO–NPM(T) is calculated by the correlation between AOI and PC1 of NPM, while RAO–NPM(S) is obtained by the pattern correlation between AO-related SLP anomalies over the North Pacific (20°–65°N, 120°E–120°W) and SLP anomalies regressed onto PC1 of NPM over the North Pacific (20°–65°N, 120°E–120°W). Furthermore, the intermodel relationship between NPI and RAO–NPM(T) is significant, suggesting that the models with a stronger AO–NPM connection tend to have a stronger North Pacific center of AO (Fig. 11b). In other words, when AO occurs, SLP variability over the North Pacific is overestimated, which induces the regional pattern of AO to be similar to NPM over the North Pacific, resulting in a strong AO–NPM connection. Meanwhile, as Fig. 11c shows, the intermodel spatial correlation between RAO–NPM(T) and the AO pattern also supports this result, which indicates that the diversity of RAO–NPM(T) is associated with the intensity of AO-related SLP variability over the North Pacific, resulting in the main uncertainty of the AO pattern in future projection.

Fig. 11.
Fig. 11.

Scatterplots of the (a) temporal correlation coefficients between AO and NPM [RAO–NPM(T)] vs the spatial correlation coefficients between AO and NPM [RAO–NPM(S)], and (b) RAO–NPM(T) vs NPI among 18 CMIP5 and 16 CMIP6 models in future projections. The intermodel correlation coefficients (R) among 34 models are shown in each panel. (c) The intermodel correlation coefficients between RAO–NPM(T) and the AO pattern among the 34 models. Only the correlation coefficients in (c) exceeding the 0.1 significance level are plotted.

Citation: Journal of Climate 36, 10; 10.1175/JCLI-D-22-0230.1

6. Summary and discussion

a. Summary

In this study, we examine the relationship between AO and winter STV over the Northern Hemisphere based on 18 CMIP5 and 16 CMIP6 models, and we investigate the possible bias of the AO–STV relationship in historical simulations and analyze its uncertainty in future projections. For historical simulations, although a few models (CNRM-CM6-1, HadGEM3-GC31-MM, CNRM-CM5, GFDL-CM4, MIROC6) can well reproduce the AO–STV relationship over the Northern Hemisphere compared to observations, most models fail to simulate the correct pattern of the AO–STV relationship with significant bias around the North Pacific and North Atlantic. Meanwhile, compared to the last generation (CMIP5), the overall performance of CMIP6 models in simulating the AO–STV relationship has not improved significantly. To investigate the bias of the AO–STV relationship in historical simulations, we compare AO-related mean flow conditions for extratropical eddy development in HPS and LPS models. Compared to HPS models, the AO-related meridional temperature gradient in LPS models is stronger (weaker) in Europe (North Pacific and North Atlantic), which causes stronger (weaker) atmospheric baroclinic instability, resulting in more (less) intensified extratropical eddy development with a larger (smaller) Eady growth rate in the same area, which is closely associated with the intensity of winter STV. Therefore, the bias of the AO–STV relationship can be traced back to AO simulations. Furthermore, the bias of AO simulations is related mainly to the AO pattern rather than AO intensity. If we compare the AO pattern in the two model groups, LPS models tend to underestimate (overestimate) the AO-related SLP variability over the North Atlantic (North Pacific), while HPS models can simulate results similar to observations. In addition, based on the HPS models, an AO–STV relationship also exists over the Northern Hemisphere under a high-emission scenario in the future projection, which has a pattern similar to historical simulations, suggesting that AO could still be an important factor influencing winter STV over the Northern Hemisphere in a warmer climate, providing implications for our selection of climate predictors in the future. On the other hand, the uncertainty of the projected AO–STV relationship focuses mainly around the North Pacific, which is physically consistent with the large diversity in the North Pacific center of intensity of AO among the multimodel ensemble. Finally, we found that the overestimation of the projected AO pattern over the North Pacific is related to an excessively strong connection between AO and NPM, indicating that the variability of the regional variation mode may be included in AO-related SLP variability, suggesting that regional uncertainty still needs to be considered in future projections.

b. Discussion

In this study, we focus mainly on the linkage between AO and winter STV in climate models. But winter STV over the Northern Hemisphere is also modulated by other factors (e.g., ENSO; Leung and Zhou 2016; Jian et al. 2021a,b). Recently, Jian et al. (2021a) examined model performance in simulating the ENSO–STV relationship based on CMIP5/CMIP6, finding that some climate models can well capture this relationship compared to observations. Furthermore, some previous studies have also found a combined effect of ENSO and AO on winter temperature over the Northern Hemisphere (Shabbar and Bonsal 2004; Lim and Schubert 2011; S. Chen et al. 2013; W. Chen et al. 2013; Kim et al. 2021). In addition, based on reanalysis data, Jian et al. (2021b) also found that a decadal or interdecadal shift has occurred between the ENSO–STV relationship and the AO–STV relationship in recent decades, respectively. Therefore, since ENSO and AO are two important factors in winter STV, model performance in simulating the stability of the ENSO–STV and AO–STV relationships with long-term variation, as well as their relative contribution and combined impact on winter STV in historical simulations, needs to be investigated in future studies. Moreover, the stability of their relationship in a warmer climate is also worth examining, as it will provide implications for our selection of climate factors in predicting winter STV over the Northern Hemisphere.

Acknowledgments.

This work is supported by the Guangdong Province Introduction of Innovative R&D Team Project China (2019ZT08G669), the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), and CORE funding (CORE is a joint research center for ocean research between QNLM and HKUST).

Data availability statement.

Reanalysis datasets analyzed during the current study are available from NCAR/UCAR (https://rda.ucar.edu/). These datasets were derived from the following public domain resource: JRA55 (https://rda.ucar.edu/datasets/ds628.0/). CMIP5 project data are from the historical and RCP85 experiment, Department of Energy, Lawrence Livermore National Laboratory (https://esgf-node.llnl.gov/search/cmip5/). CMIP6 project data are from the historical and SSP5-8.5 experiment, Department of Energy, Lawrence Livermore National Laboratory (https://esgf-node.llnl.gov/search/cmip6/).

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