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  • View in gallery

    Annual-mean (left) precipitation (mm day−1) and (right) 925-hPa winds (vectors; m s−1) and SST (shaded; K) in the (a),(b) observations, (c),(d) CNTL experiment, and (e),(f) CNTL minus observation. The dots in (e) and (f) indicate that the precipitation difference is significant at the 95% confidence level based on Student’s t test.

  • View in gallery

    Precipitation (shaded; mm day−1) in the (a)–(d) observations, (e)–(h) CNTL experiment, and (i)–(l) difference between observation and CNTL experiment during (left) DJF, (left center) MAM, (right center) JJA, and (right) SON. In (i)–(l), the dots indicate that the precipitation difference is significant at the 95% confidence level based on Student’s t test.

  • View in gallery

    As in Fig. 2, but for SST (shaded; K) and 925-hPa winds (vectors; m s−1).

  • View in gallery

    Month–longitude plots of SST (shaded; K), precipitation (white contours; mm day−1; starting from 1 mm day−1 with an interval of 1 mm day−1), and 925-hPa winds (vectors; m s−1) averaged over 5°–15°N in (a) the observations (SST from HadISST, precipitation from GPCP, and wind from NCEP1) and (b) the CNTL experiment. The North American monsoon region is roughly located between the two vertical purple lines.

  • View in gallery

    Month–longitude plots of SST (shaded; K), along with (a) surface wind speed (contour interval is 0.5 m s−1) from QuikSCAT and (b) latent heat flux from ERA5 (contour interval is 4 W m−2), showing the difference between the CNTL experiment and observations averaged over 5°–15°N. The North American monsoon region is roughly located between the two vertical green lines. In (a) and (b), the thick contour represents zero, the solid contours represent positive values, and the dashed contours represent negative values.

  • View in gallery

    Differences of 925-hPa wind (m s−1) between the atmospheric model of CESM1 (CAM5) and NCEP1 reanalysis during (a) DJF, (b) MAM, (c) JJA, (d) SON.

  • View in gallery

    Height–time plots of zonal wind (shaded; m s−1) and meridional wind (contours; m s−1) averaged over the northeastern Pacific (5°–15°N, 80°–120°W) in (a) NCEP1, (b) the CNTL experiment, and (c) the difference between CNTL experiment and NCEP1. The thick contour represents zero, the solid contours represent positive values, and the dashed contours represent negative values, with an interval of 0.5 m s−1.

  • View in gallery

    Differences of precipitation (shaded; mm day−1), SST (contours; K), and 925-hPa winds (vectors; m s−1) (a) between CNTL and observations (SST from HadISST, precipitation from GPCP, and wind from NCEP1) and (b) between the 1° and CNTL experiments, during MAM. The thick contour represents zero, the solid contours represent positive values, and the dashed contours represent negative values, with an interval of 0.3 K. The dots indicate that the precipitation difference is significant at the 95% confidence level based on Student’s t test.

  • View in gallery

    The difference of (top) SST (K) and (bottom) precipitation (mm day−1) between CNTL experiment and observation (blue bars), 1° and CNTL experiments (red bars), CMIP5 L-Model and observation (cyan bars), and CMIP5 H-Model and L-Model (orange bars) averaged over the northern ITCZ (0°–15°N, 80°–120°W) and southern ITCZ (15°S–0°, 80°–120°W) during (a),(e) MAM, (b),(f) JJA, (c),(g) SON, and (d),(h) DJF.

  • View in gallery

    Differences of precipitation (shaded; mm day−1), SST (contours; K), and 925-hPa winds (vectors; m s−1) between (left) the CMIP5 low-resolution model (L-Model) and the observations (SST from HadISST, precipitation from GPCP, and wind from NCEP1) and (right) the CMIP5 high-resolution model (H-Model) and L-Model during (a),(b) DJF, (c),(d) MAM, (e),(f) JJA and (g),(h) SON. The thick contour represents zero, the solid contours represent positive values, and the dashed contours represent negative values, with an interval of 0.3 K.

  • View in gallery

    Scatterplots between 925-hPa zonal wind bias and precipitation bias in 37 CMIP5 models (red and blue dots indicate L-Model and H-Model, respectively) over the northeastern Pacific (0°–15°N, 80°–120°W) during (a) MAM, (b) JJA, (c) SON and (d) DJF. The correlations between the two are 0.78, 0.81, 0.68, and 0.78 for MAM, JJA, SON, and DJF, respectively.

  • View in gallery

    Similar to Fig. 11, but between 925-hPa meridional wind bias and precipitation bias over the southeastern Pacific (15°S–0°, 80°–120°W). The correlations between the two are −0.64, −0.64, −0.69, and −0.77 for MAM, JJA, SON, and DJF, respectively.

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The Impacts of Horizontal Resolution on the Seasonally Dependent Biases of the Northeastern Pacific ITCZ in Coupled Climate Models

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  • 1 Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
  • 2 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
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ABSTRACT

The double-ITCZ bias has puzzled the climate modeling community for more than two decades. Here we show that, over the northeastern Pacific Ocean, precipitation and sea surface temperature (SST) biases are seasonally dependent in the NCAR CESM1 and 37 CMIP5 models, with positive biases during boreal summer–autumn and negative biases during boreal winter–spring, although the easterly wind bias persists year round. This seasonally dependent bias is found to be caused by the model’s failure to reproduce the climatological seasonal wind reversal of the North American monsoon. During winter–spring, the observed easterly wind dominates, so the simulated stronger wind speed enhances surface evaporation and lowers SST. It is opposite when the observed wind turns to westerly during summer–autumn. An easterly wind bias, mainly evident in the lower troposphere, also occurs in the atmospheric model when the observed SST is prescribed, suggesting that it is of atmospheric origin. When the atmospheric model resolution is doubled in the CESM1, both SST and precipitation are improved in association with the reduced easterly wind bias. During boreal spring, when the double-ITCZ bias is most significant, the northern and southern ITCZ can be improved by 29.0% and 18.8%, respectively, by increasing the horizontal resolution in the CESM1. When dividing the 37 CMIP5 models into two groups on the basis of their horizontal resolutions, it is found that both the seasonally dependent biases over the northeastern Pacific and year-round biases over the southeastern Pacific are reduced substantially in the higher-resolution models, with improvement of ~30% in both regions during boreal spring. Close relationships between wind and precipitation biases over the northeastern and southeastern Pacific are also found among CMIP5 models.

© 2020 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: Fengfei Song, fengfei.song@pnnl.gov

ABSTRACT

The double-ITCZ bias has puzzled the climate modeling community for more than two decades. Here we show that, over the northeastern Pacific Ocean, precipitation and sea surface temperature (SST) biases are seasonally dependent in the NCAR CESM1 and 37 CMIP5 models, with positive biases during boreal summer–autumn and negative biases during boreal winter–spring, although the easterly wind bias persists year round. This seasonally dependent bias is found to be caused by the model’s failure to reproduce the climatological seasonal wind reversal of the North American monsoon. During winter–spring, the observed easterly wind dominates, so the simulated stronger wind speed enhances surface evaporation and lowers SST. It is opposite when the observed wind turns to westerly during summer–autumn. An easterly wind bias, mainly evident in the lower troposphere, also occurs in the atmospheric model when the observed SST is prescribed, suggesting that it is of atmospheric origin. When the atmospheric model resolution is doubled in the CESM1, both SST and precipitation are improved in association with the reduced easterly wind bias. During boreal spring, when the double-ITCZ bias is most significant, the northern and southern ITCZ can be improved by 29.0% and 18.8%, respectively, by increasing the horizontal resolution in the CESM1. When dividing the 37 CMIP5 models into two groups on the basis of their horizontal resolutions, it is found that both the seasonally dependent biases over the northeastern Pacific and year-round biases over the southeastern Pacific are reduced substantially in the higher-resolution models, with improvement of ~30% in both regions during boreal spring. Close relationships between wind and precipitation biases over the northeastern and southeastern Pacific are also found among CMIP5 models.

© 2020 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: Fengfei Song, fengfei.song@pnnl.gov

1. Introduction

The eastern Pacific Ocean is a key region for the climate, since it is the only region on Earth where the intertropical convergence zone (ITCZ) is observed on both sides of the equator only during boreal spring (Zhang 2001). The climate modeling community has attempted to reproduce the observed ITCZ for more than 20 years (Mechoso et al. 1995; Lin 2007; de Szoeke and Xie 2008), but even the latest models still fail to depict this phenomenon well (Oueslati and Bellon 2015; Song and Zhang 2016; Zhang et al. 2015). They often underestimate the northern ITCZ and overestimate the southern ITCZ during boreal spring, leading to the double-ITCZ bias evident in the annual mean. The double-ITCZ bias impairs the credability of applying climate models to climate variability, prediction, and projection studies (e.g., Zhou and Xie 2015; Tian 2015).

Previous studies have identified several possible causes for the double-ITCZ bias. Because the ITCZ is of convective origin, the convection parameterization scheme is often blamed (e.g., Bacmeister et al. 2006; Bellucci et al. 2010; Chikira 2010; Hirota et al. 2011; Oueslati and Bellon 2013; Song and Zhang 2009, 2018; Zhang and Wang 2006; Zhang and Song 2010). For example, Oueslati and Bellon (2013) increased the lateral entrainment rate in the convection scheme of CNRM-CM5 (the expansions of model acronyms used in this paper can be found at https://www.ametsoc.org/PubsAcronymList) and found the southern side of the double ITCZ over the southeastern Pacific is reduced and the South Pacific convergence zone (SPCZ) is also improved. Song and Zhang (2018) adopted a revision of the default Zhang–McFarlane scheme (Zhang and McFarlane 1995) in CESM1.2.1, including dCAPE-based [dCAPE denotes convective available potential energy (CAPE) generation rate from large-scale forcing in the free troposphere] trigger function and closure instead of the original CAPE-based ones, and a spectral cloud model instead of the original bulk cloud model. After these improvements, they found that the double-ITCZ bias can be largely eliminated in all seasons in the CESM1.2.1.

Besides convection parameterization, the effects of other common biases among models on the double ITCZ also receive much attention [see a recent review paper by Zhang et al. (2019)], such as the southeastern Pacific warm bias (Ma et al. 1996; Yu and Mechoso 1999; Dai et al. 2003, 2005; Song and Zhang 2016), northeastern Pacific cold bias (de Szoeke and Xie 2008), tropical North Atlantic cold bias (Song and Zhang 2017), and Southern Ocean cloud bias (Hwang and Frierson 2013). Xiang et al. (2018) performed two simulations with the top-of-the-atmosphere shortwave radiative heating prescribed in either the Southern Ocean (50°–80°S) or southern tropics (0°–20°S). They concluded that the response of the ITCZ location to the southern tropical forcing is 2 times as strong as the Southern Ocean forcing. When prescribing the observed southeastern Pacific and tropical North Atlantic Ocean SST in the National Center for Atmospheric Research (NCAR) CESM1 model, Song and Zhang (2017) found that both northern and southern ITCZs during boreal spring are improved significantly but that the cold bias in the northeastern Pacific still exists. It seems that the northeastern Pacific cold bias during boreal spring is not related to southeastern Pacific and/or tropical North Atlantic SST biases, but its origin is still unclear. One clue is that the northeastern Pacific is located in the oceanic part of the North American monsoon system (Wang and Ding 2008), which is a circulation system that brings abundant summer rainfall to vast areas of southwestern North America (Adams and Comrie 1997; Higgins et al. 1997). Previous studies argue that the North American monsoon is driven by the northward migration of the ITCZ, but whether biases in the North American monsoon, which are common in current climate models (Liang et al. 2008; Geil et al. 2013; Pascale et al. 2017), affect the northern ITCZ has not been investigated.

In this study, we investigate the seasonal cycle of the northeastern Pacific cold bias and ITCZ bias in the NCAR CESM1.2.2 to obtain a better view of the physical processes. Moreover, we investigate the possible effects of the North American monsoon bias on the northern ITCZ bias. We point out that the northeastern Pacific cold bias is closely related to the failure of the model to reproduce the seasonal reversal of North American monsoon circulation. When the model resolution is increased, the seasonal reversal of North American monsoon circulation is better captured. Consequently, both the northeastern Pacific cold bias and ITCZ bias are reduced. Last, we point out that these seasonally dependent biases are quite universal among phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012) coupled climate models. By dividing CMIP5 models into two groups on the basis of their horizontal resolution, we also investigate the influence of resolution on the seasonally dependent biases in CMIP5 models. The rest of the study is organized as follows: section 2 describes the model experiments and observational datasets, section 3 shows the main results, and the summary is given in section 4.

2. Data and model experiments

In this study, we conducted two experiments to investigate the eastern Pacific ITCZ bias using NCAR CESM1.2.2 (see http://www.cesm.ucar.edu/models/cesm1.2/ for model details). The first one, the control (CNTL) run, is a freely coupled simulation, with the external forcing set at the year-2000 level. The atmospheric model resolution is f19 (1.9° latitude × 2.5° longitude) and the ocean model resolution is g16 (1° latitude × 1° longitude). The other is a 1° simulation (referred to as 1° run), which is the same as the first one, except the atmospheric model resolution is increased to f09 (0.9° latitude × 1.25° longitude). Both experiments are initialized in a “warm start” mode, in which the oceanic state is already steady, with default restart files provided by NCAR, as we did in previous experiments (Song and Zhang 2016, 2017). These two simulations are run for 15 years; the first two years are regarded as spinup, and the last 13 years are used for analysis. To examine whether the easterly wind bias is of atmospheric origin, we also use an Atmospheric Model Intercomparison Project (AMIP)-type experiment, in which the observed SST and sea ice are prescribed as the boundary forcing of CESM1 (CAM5) (with horizontal resolution of f09), that is in the CMIP5 archive (http://www.ipcc-data.org/sim/gcm_monthly/AR5/Reference-Archive.html). Last, we also use precipitation, SST, and 925-hPa zonal wind of historical simulations from 37 CMIP5 models to examine whether the seasonally dependent biases found in CESM1 are also common in other models (see Table 1 for model details).

Table 1.

The historical simulations of 37 CMIP5 models used in this study, with the number of grid points (latitude × longitude) listed in the parentheses. The H and L indicate that the model belongs to high- and low-resolution model group (H-Model and L-Model), respectively. Model name expansions can be found online (https://www.ametsoc.org/PubsAcronymList).

Table 1.

We used the following observational and reanalysis datasets as the observational reference to gauge model performance: 1) precipitation from the Global Precipitation Climatology Project (GPCP; Adler et al. 2003), 2) SST from HadISST for 1994–2006, 3) zonal and meridional winds at different vertical levels from the National Centers for Environmental Prediction (NCEP)–NCAR reanalysis project (NCEP1; Kalnay et al. 1996) for 1994–2006, 4) surface wind speed from Quick Scatterometer (QuikSCAT; http://www.remss.com) satellite retrievals for 2000–08, and 5) surface latent heat flux from ERA5 (C3S 2017). We have also compared the wind fields between NCEP1 and ERA5 and found that our results remain almost the same.

3. Results

A first look at the annual-mean simulation in the CNTL experiment shows a clear double-ITCZ bias in the eastern Pacific, as compared with one single ITCZ band in the observations (Figs. 1a,c). This corresponds to more precipitation and warmer SST south of the equator in the model (Figs. 1e,f). North of the equator, there is slightly less precipitation over the northeastern Pacific in the model, while the SST bias is not evident, suggesting that the northeastern Pacific cold bias previously found (de Szoeke and Xie 2008) is not a year-round phenomenon in the CESM1. Moreover, the annual-mean wind in the northeastern Pacific is quite weak in observations (Fig. 1b), but it shows strong northeasterly wind in the model (Fig. 1d). Hence, there are stronger easterly wind biases in the region blowing across Central America from the tropical Atlantic Ocean (Fig. 1f).

Fig. 1.
Fig. 1.

Annual-mean (left) precipitation (mm day−1) and (right) 925-hPa winds (vectors; m s−1) and SST (shaded; K) in the (a),(b) observations, (c),(d) CNTL experiment, and (e),(f) CNTL minus observation. The dots in (e) and (f) indicate that the precipitation difference is significant at the 95% confidence level based on Student’s t test.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

As pointed out previously, the double-ITCZ bias is most evident in boreal spring (Oueslati and Bellon 2013; Song and Zhang 2016, 2017), so it is natural to look at its seasonal cycle (Fig. 2). Among the four seasons, double ITCZs only appear in spring [March–May (MAM)] in the observation (Fig. 2b), but they are evident in both boreal winter [December–February (DJF)] and spring in the model (Figs. 2e,f). Even in spring the northern ITCZ is still stronger than the southern one in the observation, but the southern ITCZ is stronger than the northern one during both winter and spring in the model. Another prominent feature in the observation is that during DJF and MAM the easterly wind dominates the northeastern Pacific (Figs. 3a,b), while it changes to westerly wind during June–August (JJA) and September–November (SON). This is because the northeastern Pacific is the oceanic part of the North American monsoon system (Wang and Ding 2008) with a clear wind reversal in the year. Although the model can reproduce the easterly wind well outside the northeastern Pacific, it cannot reproduce the seasonal reversal of wind over this region, exhibiting a year-round easterly wind bias (Figs. 3i–l). The precipitation and SST biases in the southeastern Pacific are persistent year-round, with excessive precipitation and warmer SST in all four seasons. However, although the easterly wind bias is persistent year-round over the northeastern Pacific, precipitation and SST biases are seasonally dependent. The deficient precipitation and colder SST is evident in spring (MAM), while excessive precipitation and warmer SST appear in autumn (SON). This seasonally dependent bias is more evident when we look at the seasonal cycle of 925-hPa winds, precipitation, and SST averaged over 5°–15°N (Fig. 4). In the observation, the easterly wind is dominant year-round except in the North American monsoon region (Fig. 4a), where the easterly wind is evident from January to April, then replaced by the westerly wind from June to October, and finally comes back at the end of the year. This seasonal reversal of wind is the nature of monsoon, which produces more rainfall during the westerly wind regime and less rainfall during the easterly wind regime over the northeastern Pacific. The model reproduces the easterly wind well outside the North American monsoon region, but it fails to reproduce the seasonal reversal of wind over the monsoon region (Fig. 4b). Instead, the year-round easterly wind dominates the whole region, along with a much drier winter–spring and wetter summer–autumn relative to the observations.

Fig. 2.
Fig. 2.

Precipitation (shaded; mm day−1) in the (a)–(d) observations, (e)–(h) CNTL experiment, and (i)–(l) difference between observation and CNTL experiment during (left) DJF, (left center) MAM, (right center) JJA, and (right) SON. In (i)–(l), the dots indicate that the precipitation difference is significant at the 95% confidence level based on Student’s t test.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for SST (shaded; K) and 925-hPa winds (vectors; m s−1).

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

Fig. 4.
Fig. 4.

Month–longitude plots of SST (shaded; K), precipitation (white contours; mm day−1; starting from 1 mm day−1 with an interval of 1 mm day−1), and 925-hPa winds (vectors; m s−1) averaged over 5°–15°N in (a) the observations (SST from HadISST, precipitation from GPCP, and wind from NCEP1) and (b) the CNTL experiment. The North American monsoon region is roughly located between the two vertical purple lines.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

How to reconcile the year-round wind bias and seasonally dependent precipitation/SST bias over the northeastern Pacific? As shown above, during winter and spring, the northeastern Pacific is occupied by easterly wind, but it changes to westerly wind during summer and autumn. However, the model cannot capture this seasonal reversal well, with easterly winds occurring almost the whole year. This kind of North American monsoon bias is quite common among models and has been pointed out by several previous studies (Liang et al. 2008; Geil et al. 2013; Pascale et al. 2017). Here, we propose that the eastern Pacific ITCZ bias can be partly explained by the North American monsoon bias. To show this, we plot the seasonal evolution of surface wind speed and SST biases in Fig. 5. As shown in Figs. 3 and 4, the climatological wind field in the observation changes from winter–spring easterly to summer–autumn westerly. When the year-round easterly wind bias over the northeastern Pacific in the model is superposed to the observed winds, the wind speed in the model is larger than observed during winter–spring and smaller during summer–autumn over the northeastern Pacific (contours in Fig. 5a). The wind speed bias is similar when we use the reanalysis data from ERA5 (not shown). Following the wind speed bias and the corresponding latent heat flux biases (contours in Fig. 5b), which contribute the most to the net surface heat flux (not shown), the SST bias flips the sign from cold bias in winter–spring to warm bias in summer–autumn (shadings in Fig. 5). Note that we mainly focus on latent heat flux here as it shows similar seasonally dependent bias to SST. As shown in Song and Zhang (2019), which analyzed the mixed layer heat budget in the northern ITCZ region, the surface flux term is the dominant term in the seasonal evolution of SST, although the upwelling term is also important from October to January. The fact that the wind speed and latent heat flux biases lead the SST bias confirms the role of wind–evaporation–SST (WES) feedback (Xie and Philander 1994). This WES feedback occurs not just in the northeastern Pacific. When this northeasterly wind anomaly crosses the equator, it changes the direction into northwesterly wind as a result of the Coriolis effect and reduces the prevalent southeasterly wind in the southeastern Pacific and warms the SST there. The warmed SST will further enhance the northwesterly wind over the southeastern Pacific as a Gill-type response (Gill 1980). Hence, the year-round easterly bias over the northeastern Pacific also contributes to the year-round warm SST bias over the southeastern Pacific. Over the North Atlantic, the cold bias persists throughout the year in the model (Fig. 5), which is thought to be related to the weaker Atlantic meridional overturning circulation transporting much less energy to the north and some other factors (Wang et al. 2014). Zhang and Delworth (2005) suggested that this North Atlantic cooling can cause a divergent wind field and induce easterly wind from the Atlantic to the Pacific, but Song and Zhang (2017), by prescribing the observed SST in the tropical Atlantic using the coupled CESM1, found that this effect was very weak (see their Fig. 4d).

Fig. 5.
Fig. 5.

Month–longitude plots of SST (shaded; K), along with (a) surface wind speed (contour interval is 0.5 m s−1) from QuikSCAT and (b) latent heat flux from ERA5 (contour interval is 4 W m−2), showing the difference between the CNTL experiment and observations averaged over 5°–15°N. The North American monsoon region is roughly located between the two vertical green lines. In (a) and (b), the thick contour represents zero, the solid contours represent positive values, and the dashed contours represent negative values.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

Although the WES feedback establishes a link between wind and SST, it cannot tell us their causality in the coupled simulation. The fact that wind leads SST gives us an indicator that wind may be the reason for the SST change. To further check the causality link, we further examined the wind simulation in the atmospheric model of CESM1 (CAM5) in which SST is prescribed (Fig. 6). We can see that the easterly wind bias in the atmospheric model is evident in all four seasons, resembling the wind bias in the coupled models but with weaker magnitude (Figs. 3i–l vs Fig. 6). Since SST in the atmospheric model is the same as observed, the wind bias should come from the atmospheric processes. Hence, it demonstrates that the wind bias, originated from the atmospheric model, causes the SST bias via the WES feedback when the atmospheric model is coupled to the ocean model.

Fig. 6.
Fig. 6.

Differences of 925-hPa wind (m s−1) between the atmospheric model of CESM1 (CAM5) and NCEP1 reanalysis during (a) DJF, (b) MAM, (c) JJA, (d) SON.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

As the low-level wind bias plays an important role in the SST and precipitation biases over the eastern Pacific, it is natural to search for the origin of the wind bias. For this, we show the seasonal evolution of zonal and meridional winds over the northeastern Pacific (Fig. 7). The observed seasonal reversal of wind occurs in both the upper level (above 400 hPa) and low level (below 850 hPa), reflecting the seasonal reversal of the overturning circulation of the monsoon. The low-level southwesterly wind is evident from June to November below 850 hPa in the observation (Fig. 7a) but not in the model (Fig. 7b). In the model the southerly component can occur from July to November with much weaker magnitude, but the westerly component is absent. When taking a difference (Fig. 7c), the low-level easterly and northerly wind biases are evident throughout the year, quite independent of time. This time-independent low-level wind bias reminds us of the possible effects of orography, since terrain-complex Central America is located just east of the northeastern Pacific. There are many mountains in Central America higher than 1500 m (about 850 hPa), but they often cover a narrow range and separate the Pacific and Atlantic Oceans (Pascale et al. 2017). The coarse model resolution often smooths out this elevation difference between mountains and oceans, resulting in much shallower elevation in the model, which allows the year-round easterly trade wind in the North Atlantic to cross the isthmus and affect SST/precipitation over the eastern Pacific. Hence, the coarse model resolution may play a role in the year-round low-level wind bias.

Fig. 7.
Fig. 7.

Height–time plots of zonal wind (shaded; m s−1) and meridional wind (contours; m s−1) averaged over the northeastern Pacific (5°–15°N, 80°–120°W) in (a) NCEP1, (b) the CNTL experiment, and (c) the difference between CNTL experiment and NCEP1. The thick contour represents zero, the solid contours represent positive values, and the dashed contours represent negative values, with an interval of 0.5 m s−1.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

To confirm our hypothesis, we conducted a sensitivity experiment by doubling the atmospheric model resolution (0.9° latitude × 1.25° longitude), namely, the 1° run, using the same model with the same configuration, compared to the original 1.9° latitude × 2.5° longitude resolution CNTL run. The results show that increasing resolution reduces the low-level easterly wind bias over the northeastern Pacific by at least 0.5 m s−1 with the lowest reduction during MAM (not shown). As the ITCZ bias in spring is most significant in the model, we show precipitation, SST, and 925-hPa wind differences in spring between CNTL and observation and between the 1° run and CNTL run in Fig. 8. The northwesterly wind bias over the southeastern Pacific is reduced significantly in the 1° run, while the reduction of northeasterly wind bias over the northeastern Pacific seems less evident from the horizontal distribution. But averaged over the northeastern Pacific, the reduction of easterly wind bias can still reach 0.5 m s−1 during spring. Note that the wind change leads SST change by ~1–2 months over the northeastern Pacific as shown in Fig. 5. As such, the more evident wind improvement in the previous months also plays a role in the SST change during boreal spring. Consistent with wind improvement, SST and precipitation over the northeastern and southeastern Pacific is also improved considerably. SST is relatively warmer in the 1° run than in the CNTL run in the northeastern Pacific, extending from the Central American coast to ~160°W. Over the southeastern Pacific, SST warm bias in the CNTL run is also reduced. Consistent with the SST change, precipitation over the northeastern (southeastern) Pacific is enhanced (reduced) by increasing the model resolution. Another feature is the latitudinal contraction of ITCZ during spring by increasing resolution when we compare the precipitation difference between 1° and CNTL runs (Fig. 8b) with the mean-state precipitation in the CNTL run (Fig. 2f). Möbis and Stevens (2012) and Talib et al. (2018) suggest that deep convection is only initiated when a minimum boundary layer moist static energy is achieved. It seems that the increase of model resolution may decrease the latitude at which the minimum boundary layer moist static energy is achieved, which is also found in an aquaplanet simulation of Benedict et al. (2017). However, this is out of the scope of this study and deserves further investigation in the future.

Fig. 8.
Fig. 8.

Differences of precipitation (shaded; mm day−1), SST (contours; K), and 925-hPa winds (vectors; m s−1) (a) between CNTL and observations (SST from HadISST, precipitation from GPCP, and wind from NCEP1) and (b) between the 1° and CNTL experiments, during MAM. The thick contour represents zero, the solid contours represent positive values, and the dashed contours represent negative values, with an interval of 0.3 K. The dots indicate that the precipitation difference is significant at the 95% confidence level based on Student’s t test.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

We quantify precipitation and SST improvement over the northeastern and southeastern Pacific during all four seasons in the 1° run from CESM1.2.2 by comparing the blue and red bars in Fig. 9. Over the southern ITCZ, the warm SST bias and excessive precipitation bias exist in all four seasons and are consistently reduced by improving the resolution. Over the northern ITCZ, precipitation and SST biases are small during winter and summer but large and opposite between spring and autumn. By improving the model resolution, both the precipitation and SST biases over the northern ITCZ are also reduced substantially during spring and autumn. As double-ITCZ bias is most significant during spring, we pay particular attention to the improvement during this season below. Over the northern ITCZ, the cold SST bias can reach 1 K and the negative precipitation bias is 1.32 mm day−1 in the CNTL run during spring (Fig. 9a). By increasing the model resolution, the cold SST bias can be reduced by 0.17 K and precipitation can be enhanced by 0.39 mm day−1, both significant at the 1% level. In other words, during boreal spring, the cold SST bias and deficient precipitation over the northeastern Pacific is reduced by 17.0% and 29.5%, respectively. In the CNTL run, the warm bias in the southern ITCZ reaches 1.03 K and excessive precipitation bias is 3.78 mm day−1 during spring. Due to the improved monsoon simulation in the 1° run, the warm bias is reduced by 0.3 K (29.1%) and precipitation is reduced by 0.71 mm day−1 (18.8%), both significant at the 1% level. Here, by increasing the model resolution we confirm our hypothesis that the low-level wind bias associated with monsoon exerts significant influences on the ITCZ bias. By improving the monsoon simulation in the model at a higher resolution, the ITCZ bias is reduced considerably. However, we need to point out that increasing model resolution is just one way to improve the monsoon simulation (Pascale et al. 2017). As shown in many previous studies (e.g., Gochis et al. 2002; Liang et al. 2004), the North American monsoon is also sensitive to convection scheme used in the climate model. Song and Zhang (2019) showed that the convection bias over the Central American land also contributes to the low-level wind bias over the northeastern Pacific.

Fig. 9.
Fig. 9.

The difference of (top) SST (K) and (bottom) precipitation (mm day−1) between CNTL experiment and observation (blue bars), 1° and CNTL experiments (red bars), CMIP5 L-Model and observation (cyan bars), and CMIP5 H-Model and L-Model (orange bars) averaged over the northern ITCZ (0°–15°N, 80°–120°W) and southern ITCZ (15°S–0°, 80°–120°W) during (a),(e) MAM, (b),(f) JJA, (c),(g) SON, and (d),(h) DJF.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

To gauge whether the sensitivity of the ITCZ to model resolution is common in GCMs, we examine the seasonally dependent biases of precipitation and SST over the northeastern Pacific in CMIP5 models. To do so, we select the historical simulations of 37 CMIP5 models and divide them roughly equally into two groups [high-resolution model (H-Model) group and low-resolution model (L-Model) group] according to their horizontal resolution shown in Table 1. Figure 10 shows precipitation, SST, and 925-hPa wind biases in L-Model and possible improvements in H-Model. Consistent with our results from CESM1.2.2, over the northeastern Pacific, precipitation and SST biases in the CMIP5 L-Model are seasonally dependent, with positive precipitation and SST biases during boreal summer–autumn and negative precipitation and SST biases during boreal winter–spring, although the easterly wind bias persists year round. Compared to the L-Model, these seasonally dependent precipitation and SST biases are largely alleviated in the H-Model, with reduced easterly wind biases in all four seasons. We also quantify the improvements of precipitation and SST over the northeastern and southeastern Pacific in the CMIP5 H-Model by comparing cyan and orange bars in Fig. 9. It shows that the SST and precipitation biases in CMIP5 L-Model are very similar to those in the CNTL run. Relative to the L-Model, the precipitation and SST biases are also significantly reduced in the H-Model. During boreal spring, the negative SST and precipitation biases over the northeastern Pacific are reduced by 15.5% and 30.3%, respectively, in the H-Model. Over the southeastern Pacific, the corresponding improvements are 44.4% and 28.7%, respectively. This provides another piece of evidence supporting our findings in this study based on a single model. Further, the relationship between the zonal (meridional) wind bias and precipitation bias over the northeastern (southeastern) Pacific is shown in Fig. 11 (Fig. 12) for the four seasons. It is found that during summer, the observed westerly wind over the northeastern Pacific can only be reproduced by 8 CMIP5 models, all belonging to the H-Model group (Fig. 11b). The correlation coefficients between 925-hPa zonal wind and precipitation biases over the northeastern Pacific are very high (0.78, 0.81, 0.68, and 0.78 for MAM, JJA, SON, and DJF, respectively; all are significant at 1% level). Because most models during spring have negative precipitation bias over the northeastern Pacific, it suggests that the model with less easterly wind bias also has less precipitation bias. Over the southeastern Pacific, the correlation coefficients between 925-hPa meridional wind and precipitation biases can reach −0.64, −0.64, −0.69, and −0.77, all significant at 1% level. It means that the less northerly wind bias corresponds to less precipitation bias, as all models show excessive precipitation bias over the southeastern Pacific in all seasons. The close relationship between easterly wind over the northeastern Pacific (northerly wind over the southeastern Pacific) and local precipitation confirms the role of the WES feedback mechanism we propose based on CESM1.2.2. Another important feature shown in Figs. 11 and 12 is that the H-Model generally shows reduced easterly wind bias over the northeastern Pacific and northerly wind bias over the southeastern Pacific during boreal spring (MAM) relative to the L-Model, which is consistent with the reduced alternating ITCZ bias.

Fig. 10.
Fig. 10.

Differences of precipitation (shaded; mm day−1), SST (contours; K), and 925-hPa winds (vectors; m s−1) between (left) the CMIP5 low-resolution model (L-Model) and the observations (SST from HadISST, precipitation from GPCP, and wind from NCEP1) and (right) the CMIP5 high-resolution model (H-Model) and L-Model during (a),(b) DJF, (c),(d) MAM, (e),(f) JJA and (g),(h) SON. The thick contour represents zero, the solid contours represent positive values, and the dashed contours represent negative values, with an interval of 0.3 K.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

Fig. 11.
Fig. 11.

Scatterplots between 925-hPa zonal wind bias and precipitation bias in 37 CMIP5 models (red and blue dots indicate L-Model and H-Model, respectively) over the northeastern Pacific (0°–15°N, 80°–120°W) during (a) MAM, (b) JJA, (c) SON and (d) DJF. The correlations between the two are 0.78, 0.81, 0.68, and 0.78 for MAM, JJA, SON, and DJF, respectively.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

Fig. 12.
Fig. 12.

Similar to Fig. 11, but between 925-hPa meridional wind bias and precipitation bias over the southeastern Pacific (15°S–0°, 80°–120°W). The correlations between the two are −0.64, −0.64, −0.69, and −0.77 for MAM, JJA, SON, and DJF, respectively.

Citation: Journal of Climate 33, 3; 10.1175/JCLI-D-19-0399.1

4. Conclusions

The double-ITCZ bias has puzzled the climate modeling community for more than two decades and many efforts have been devoted to reducing it. Here we propose that the low-level wind bias associated with the North American monsoon plays an important role in the spring ITCZ bias over the eastern Pacific based on CESM1.2.2 and 37 CMIP5 models. First, we found that SST and precipitation biases over the northeastern Pacific are seasonally dependent, although the low-level easterly wind bias persists around the year. In boreal winter and spring there is cold SST bias and negative precipitation bias over the northeastern Pacific. But during boreal summer and autumn the sign flips, with warm SST bias and positive precipitation bias. This seasonally dependent bias is caused by the failure of the model to reproduce the climatological seasonal wind reversal of the North American monsoon. During winter and spring, the climatological easterly wind dominates, so the easterly wind bias in the model leads to stronger wind speed in the model, which enhances the evaporation and lowers the SST through wind-evaporation-SST feedback. However, during summer and autumn as the climatological westerly wind dominates, the easterly wind bias reduces the wind speed, leading to sea surface warming and more precipitation. This northeasterly wind bias also contributes to the southeastern Pacific warming bias and excessive precipitation bias when it crosses the equator and changes into northwesterly wind, which reduces the year-round southeasterly wind and warms the SST there.

Second, it is found that the year-round easterly wind bias is due to the poor simulation of the North American monsoon in the model, partly because the coarse model resolution cannot depict the complex Central American mountains and smooths out the height difference between the narrow mountains and oceans. In the higher-resolution model simulation, it is found that the easterly wind bias is reduced year-round by at least 0.5 m s−1. Correspondingly, the simulated SST and precipitation are improved substantially in both northeastern and southeastern Pacific. During boreal spring when the ITCZ bias is most evident in the model, precipitation over the northern and southern ITCZ can be improved by 29% and 18.8%, respectively, due to the improvement of low-level wind simulation in the relatively higher-resolution simulation.

Last, we show that these seasonally dependent biases over the eastern Pacific are quite universal among the CMIP5 models; it is not just a CESM1 problem. By dividing 37 CMIP5 models into two groups according to their horizontal resolution, we find that not only the seasonally dependent biases over the northeastern Pacific, but also the persistent warming and excessive precipitation biases over the southeastern Pacific, are largely reduced in the higher-resolution models. Compared to the lower-resolution models, the SST and precipitation biases over the northeastern Pacific in boreal spring is reduced by 15.5% and 30.3%, respectively, in the higher-resolution models. Over the southeastern Pacific, the corresponding improvements are 44.4% and 28.7%, respectively. Moreover, the close relationships between 925-hPa zonal wind and precipitation over the northeastern Pacific, and between 925-hPa meridional wind and precipitation over the southeastern Pacific, are also found among models, confirming the mechanisms we propose based on CESM1.2.2.

This study suggests that low-level wind bias associated with the North American monsoon plays an important role in the eastern Pacific ITCZ bias and points to a new avenue to improve the ITCZ simulation. Increasing the horizontal resolution in the climate model is one way to improve the monsoon simulation. But the sensitivity experiment in this study only uses one climate model (NCAR CESM1) and the increased resolution (0.9° latitude × 1.25° longitude) is still not high enough to depict the complex Central American mountains accurately as shown in Pascale et al. (2017). We did show that these seasonally dependent biases are very common across CMIP5 models, most of which use very coarse model resolutions. The upcoming High-Resolution Model Intercomparison Project (HighResMIP; Haarsma et al. 2016) in the CMIP6 will provide high-resolution (~30 km) coupled model simulations from different model centers. It will provide a good opportunity for us to examine to what extent the enhanced model resolution can improve the ITCZ simulation.

Acknowledgments

This work was initiated when author Song was a postdoctoral researcher at the Scripps Institution of Oceanography. It was supported by the U.S. Department of Energy Office of Science Biological and Environmental Research Program (BER), under Awards DE-SC0019373 and DE-SC0016504, and by National Science Foundation Grant AGS-1549259. Song is also supported by BER as part of the Regional and Global Modeling and Analysis program. PNNL is operated for the Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830. The computational support for this work was provided by the NCAR Computational and Information Systems Laboratory. Fengfei Song thanks Dr. Xiaolong Chen for the helpful discussion. We thank the anonymous reviewers for their constructive comments that helped to improve the paper greatly.

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