1. Introduction
Understanding and predicting the global warming response of the extratropical eddy-driven jet streams (also called simply “jet” herein) is an active area of research (Collins et al. 2013; Christensen et al. 2013; Bony et al. 2015). Climate models robustly project that rising greenhouse gas concentrations will cause the jets to shift poleward in the annual and zonal mean in both hemispheres over the course of the twenty-first century (e.g., Kushner et al. 2001; Yin 2005; Kidston and Gerber 2010; Barnes and Polvani 2013; Simpson et al. 2014; Vallis et al. 2015; Collins et al. 2013, and references therein). The magnitude of these poleward jet shifts is uncertain, however: in coupled atmosphere–ocean simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012), intermodel differences in lower-tropospheric zonal wind changes and jet shifts are larger than the model-mean changes in both hemispheres (Fig. 1a; Table 1).
Annual-mean zonal-mean response of the zonal wind at 850 hPa to global warming in CMIP5 models across a hierarchy of setups. (a) Coupled atmosphere–ocean models in realistic setup with interactive SSTs. The wind change is the difference between years 1979–2005 of the historical simulations and years 2070–2099 of the representative concentration pathway 8.5 (RCP8.5) simulations, for which the radiative forcing at the end of the twenty-first century roughly corresponds to a quadrupling of CO2. (b) Realistic setup but with prescribed SSTs (atmosphere-only AMIP setup). Global warming is modeled by increasing SSTs by 4 K everywhere (amip4K − amip). (c) Atmosphere-only aquaplanet setup with prescribed SSTs and global warming mimicked by a likewise 4-K SST increase (aqua4K − aquaControl). For all plots, the thick black line shows the model-mean response. The models with the smallest and largest Southern Hemisphere jet shift are highlighted in blue and red, respectively. The numbers give the poleward shift of the Southern Hemisphere jet in degree latitude; for (c) the absolute value is given. If several realizations are available in the CMIP5 archive, the first is used.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Annual-mean zonal-mean response of the zonal wind at 850 hPa to global warming in CMIP5 models across a hierarchy of setups. (a) Coupled atmosphere–ocean models in realistic setup with interactive SSTs. The wind change is the difference between years 1979–2005 of the historical simulations and years 2070–2099 of the representative concentration pathway 8.5 (RCP8.5) simulations, for which the radiative forcing at the end of the twenty-first century roughly corresponds to a quadrupling of CO2. (b) Realistic setup but with prescribed SSTs (atmosphere-only AMIP setup). Global warming is modeled by increasing SSTs by 4 K everywhere (amip4K − amip). (c) Atmosphere-only aquaplanet setup with prescribed SSTs and global warming mimicked by a likewise 4-K SST increase (aqua4K − aquaControl). For all plots, the thick black line shows the model-mean response. The models with the smallest and largest Southern Hemisphere jet shift are highlighted in blue and red, respectively. The numbers give the poleward shift of the Southern Hemisphere jet in degree latitude; for (c) the absolute value is given. If several realizations are available in the CMIP5 archive, the first is used.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Annual-mean zonal-mean response of the zonal wind at 850 hPa to global warming in CMIP5 models across a hierarchy of setups. (a) Coupled atmosphere–ocean models in realistic setup with interactive SSTs. The wind change is the difference between years 1979–2005 of the historical simulations and years 2070–2099 of the representative concentration pathway 8.5 (RCP8.5) simulations, for which the radiative forcing at the end of the twenty-first century roughly corresponds to a quadrupling of CO2. (b) Realistic setup but with prescribed SSTs (atmosphere-only AMIP setup). Global warming is modeled by increasing SSTs by 4 K everywhere (amip4K − amip). (c) Atmosphere-only aquaplanet setup with prescribed SSTs and global warming mimicked by a likewise 4-K SST increase (aqua4K − aquaControl). For all plots, the thick black line shows the model-mean response. The models with the smallest and largest Southern Hemisphere jet shift are highlighted in blue and red, respectively. The numbers give the poleward shift of the Southern Hemisphere jet in degree latitude; for (c) the absolute value is given. If several realizations are available in the CMIP5 archive, the first is used.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Jet shifts in degrees latitude in CMIP5 models under global warming in the coupled, AMIP and aquaplanet simulations shown in Fig. 1. For the Southern Hemisphere (SH) jet, a negative value indicates a poleward shift; for the Northern Hemisphere (NH) jet, a positive value indicates a poleward shift. For the aquaplanet simulations, a positive value indicates a poleward shift. For IPSL-CM5A-LR and MPI-ESM-LR, the aquaplanet jet shift is calculated from the simulations performed in this study, which are longer compared to the CMIP5 archive and thus less affected by internal variability. (Acronym expansions are available online at http://www.ametsoc.org/PubsAcronymList.)
The jet is related to meridional and vertical atmospheric temperature gradients, and so any process that affects meridional and vertical gradients of atmospheric temperature has the potential to affect the jet. The jet position has been shown to be sensitive to ozone (Gillett and Thompson 2003; Arblaster and Meehl 2006; Polvani et al. 2011; McLandress et al. 2011), aerosols (Allen and Sherwood 2011; Allen et al. 2012; Rotstayn et al. 2014), high-latitude surface temperatures and sea ice (Deser et al. 2010; Kidston et al. 2011; Bader et al. 2013; Barnes and Screen 2015), and sea surface temperature (SST) gradients (Brayshaw et al. 2008; Chen et al. 2010). However, even when these factors are held constant, climate models show considerable spread in the magnitude of poleward jet shifts under global warming. This is clear from CMIP atmosphere-only (realistic continents) and aquaplanet (no continents; Medeiros et al. 2015) simulations in which SSTs are prescribed and global warming is mimicked by a uniform 4-K SST increase. Even though SST gradients do not change and models experience the same SST warming in these simulations, zonal wind changes and poleward jet shifts still differ by a factor of 2 between models. (Figs. 1b,c; Table 1). AMIP and aquaplanet setups with prescribed SSTs thus recover both the model robustness regarding the direction and the model nonrobustness regarding the magnitude of future jet shifts that are found in realistic coupled setups. They also show that model uncertainty in the jet response to global warming is largely independent of model uncertainty in climate sensitivity (Grise and Polvani 2014a; Vallis et al. 2015).
In addition to the above mentioned factors, radiative interactions of clouds play an important role for the extratropical circulation [see Ceppi and Hartmann (2015) for a review]. Previous model studies showed that cloud radiative interactions affect the position and strength of the jets in the present-day climate (Ceppi et al. 2012; Li et al. 2015) and investigated model deficiencies in the cloud–jet coupling (Grise and Polvani 2014b). By analyzing CMIP5 model ensembles, they also provided evidence that the cloud response to global warming modulates jet shifts by affecting SST gradients (Ceppi et al. 2014) and by demanding an increase in poleward energy transport (Shaw and Voigt 2016). Most recently, the idealized aquaplanet simulations with comprehensive climate models and prescribed clouds of Voigt and Shaw (2015) and Ceppi and Hartmann (2016) demonstrated that half or more of the poleward jet shift that occurs under global warming is caused by cloud radiative changes. The radiative coupling between clouds and the extratropical jets is considered one of the most important current challenges in climate science (Bony et al. 2015).
While previous work established the importance of cloud radiative changes for the jet response to global warming, the role of regional clouds for the jet response to global warming remains unclear. This is because Voigt and Shaw (2015) and Ceppi and Hartmann (2016) imposed cloud changes globally in their model simulations, and while they speculated about the role of regional cloud changes they did not study them explicitly. In Voigt and Shaw (2015) we speculated that the rise of tropical high-level clouds affects the jet position by heating the tropical upper-troposphere, but the extent to which this is indeed the case and if tropical high-level clouds dominate the cloud-induced jet shift in Voigt and Shaw (2015) remains unclear. Moreover, the longwave versus shortwave radiation decomposition used by Ceppi and Hartmann (2016) does not allow one to clearly distinguish the impact of regional cloud changes because of strong competitions between tropical high-level and high-latitude low-level longwave cloud radiative changes, as well as between high-latitude shortwave and longwave cloud radiative changes.
The aim of this paper is to understand the impact of regional cloud changes by extending our work in Voigt and Shaw (2015) with a suite of simulations in which regional cloud changes are imposed separately. Such an understanding is needed because the cloud response to global warming varies spatially and involves regionally distinct processes (Boucher et al. 2013, and references therein), including an upward shift of high-level clouds due to a rising tropopause and, in the tropics, the fixed-anvil temperature hypothesis (Hartmann and Larson 2002; Zelinka and Hartmann 2010), a poleward shift of midlatitude high-level clouds (Zelinka and Hartmann 2012; Zelinka et al. 2013), and an increase in the albedo of high-latitude low-level clouds due to phase changes from ice to liquid clouds (Ceppi et al. 2016; Wall and Hartmann 2015).
We use the same two CMIP5 models in prescribed-SST aquaplanet setup as in Voigt and Shaw (2015). The prescribed-SST aquaplanet setup is chosen because it replicates the model spread in jet shifts found in realistic setups, it provides an idealized framework to study the fundamental processes that couple clouds and the circulation, and it allows us to focus on how clouds impact the circulation by directly affecting the atmospheric energy balance without changing SSTs. The atmospheric impact operates mostly via longwave radiation and, as we will show, is dominated by high-level clouds. The impact of clouds on shortwave radiation and the surface energy balance requires interactive SST simulations. While there is no doubt that shortwave and surface-mediated cloud impacts are important (Ceppi and Hartmann 2016), our focus on atmospheric cloud impacts is a step toward understanding the cloud–circulation coupling across the full model hierarchy from idealized models to full Earth system models (Held 2005, 2014; Blackburn and Hoskins 2013).
The paper is organized as follows. In section 2 we quantify the impact of tropical, midlatitude, and high-latitude cloud changes on the jet position in two comprehensive CMIP5 aquaplanet models by means of the cloud and water vapor locking technique, which we also used in Voigt and Shaw (2015). In section 3 we use a dry model that we perturb with the radiative forcing from the cloud changes simulated by the comprehensive models. The dry simulations allow us to link the impact of regional cloud changes to dry dynamics and meridional and vertical temperature gradients. In section 4 we provide evidence that model spread in regional cloud changes contributes to model spread in jet shifts in the CMIP5 aquaplanet ensemble. In section 5 we investigate to what extent regional cloud changes occur independently of the circulation changes and discuss the impact that surface cloud radiative effects might have on the jet if they were allowed to affect SSTs. The paper finishes with conclusions in section 6.
2. Impact of regional cloud changes on the jet response to global warming
As in Voigt and Shaw (2015) we use aquaplanet simulations with the CMIP5 models MPI-ESM (Stevens et al. 2013) and IPSL-CM5A (Dufresne et al. 2013)1 with clouds prescribed to the radiative transfer calculation through the cloud and water vapor locking technique. While Voigt and Shaw (2015) imposed global cloud changes, we here assess the role of regional cloud changes. All simulations presented in this section and the following sections apply the CMIP5 aquaplanet setup with prescribed SSTs that are zonally uniform and constant in time. The control simulation, aquaControl, uses the qobs SST profile of Neale and Hoskins (2000). In the aqua4K simulation, global warming is mimicked by increasing SSTs everywhere by 4 K. There is no seasonal cycle in insolation (permanent equinox conditions) and no sea ice. The simulations are run for at least 20 years; to remove spinup effects the first year is excluded from the analysis. In the aquaplanet setup the Northern and Southern Hemisphere are statistically identical, and so results are averaged over both hemispheres. The aquaplanet circulation is narrower compared to actual Earth, with a jet position at around 37° latitude (instead of 45°–50° latitude) and a Hadley cell edge at around 26° latitude (instead of 30°–35° latitude) (Medeiros et al. 2015). Despite being narrower the aquaplanet circulation resembles the Southern Hemisphere circulation during DJF in that the barotropic extratropical jet and the upper-level baroclinic subtropical jet are merged, and the positions of the extratropical jet and the Hadley cell edge are closely coupled (Kang and Polvani 2011; Shaw and Voigt 2016). Because of the jet–Hadley cell coupling our results also apply to the width of the tropics, as we will show at the end of the section.
We quantify the impact of cloud radiative changes on the jet through the cloud and water vapor locking technique. The technique prescribes the radiative properties of clouds and water vapor in the models’ radiative transfer schemes and dates back to at least Wetherald and Manabe (1988), who used it to understand the impact of clouds on climate sensitivity (see also, e.g., Schneider et al. 1999; Langen et al. 2012; Mauritsen et al. 2013). The radiative properties of clouds and water vapor are diagnosed in the aquaControl and aqua4K simulations and saved at each radiation time step (every 2 h in MPI-ESM and every hour in IPSL-CM5A), level, and latitude and longitude. Then, aquaControl is repeated with the radiative properties of clouds imposed from aqua4K in selected regions of the atmosphere and from aquaControl everywhere else. Throughout the paper we will refer to these simulations as locked simulations. All locked simulations use aquaControl SSTs. To isolate the effect of cloud changes from the effect of increased water vapor (Joshi et al. 2006; Maycock et al. 2013; Voigt and Shaw 2015), water vapor is locked to aquaControl in the radiation code, even though some of the cloud changes simulated in aqua4K likely rely on radiative water vapor changes (Mauritsen et al. 2013). The locking technique only affects the radiative transfer scheme. In particular, moist convection and latent heating are unconstrained and their temperature tendencies are fully consistent with the simulated state of the atmosphere, in contrast to the radiative temperature tendencies that are based on the externally prescribed instead of the actually simulated clouds and water vapor. It is worth noting that the locking technique turns the comprehensive MPI-ESM and IPSL-CM5A models into moist-idealized models similar to those of Frierson et al. (2006) and O’Gorman and Schneider (2008) that include representations for longwave radiation, moist convection and latent heating but do not include radiative feedbacks from clouds and water vapor.
The response of cloud fraction to global warming is shown in Figs. 2a and 2b (aqua4K minus aquaControl). High-level clouds shift upward at all latitudes as one expects from a rising tropopause. In the tropics the cloud rise is consistent with the hypothesis of a fixed or proportionately higher cloud anvil temperature (FAT or PHAT; Hartmann and Larson 2002; Zelinka and Hartmann 2010), which predicts an increase in the height of tropical high-level clouds under global warming because of a well-established thermodynamic constraint that tropical deep convection detrains at a nearly fixed atmospheric temperature (Kuang and Hartmann 2007; Zelinka and Hartmann 2010; Popke et al. 2013). Cloud fraction decreases in the midlatitudes around 35° throughout the free troposphere. This is consistent with an expansion of the tropical belt and the subtropical dry zones, which reduces cloud fraction via anomalous subsidence, and a poleward jet shift, which reduces cloud fraction via a poleward shift of high-level storm track clouds. The rise of high-level clouds and the reduction in midlatitude cloud fraction throughout most of the troposphere are robust model features in the idealized aquaplanet setup (see section 4) as well as in realistic coupled setups (Zelinka et al. 2013). In the aquaplanet setup models differ, however, in the response of low-level clouds (Figs. 2a,b), which reflects known difficulties of global climate models to simulate low-latitude (Vial et al. 2013; Sherwood et al. 2014) and high-latitude (Grise and Polvani 2014b) boundary layer clouds. In high latitudes, low-level cloud fraction increases in MPI-ESM, but changes are more complex in IPSL-CM5A, with a cloud fraction increase at 900 hPa and a decrease below and above. High-latitude low-level clouds are a mixture of liquid and ice, and so the intermodel differences illustrate the challenge to accurately represent mixed-phase clouds and their governing microphysical processes in global climate models (e.g., the Bergeron–Findeisen process) (Klaus et al. 2012, 2016; Pithan et al. 2014; McCoy et al. 2015; Ceppi et al. 2016).
Impact of global and regional cloud changes on the jet. (a),(b) Change in cloud fraction under global warming, (c),(d) the resulting cloud radiative forcing [Eq. (1)], and (e),(f) the response of the 850-hPa zonal wind to the cloud change in locked simulations for (top) MPI-ESM and (bottom) IPSL-CM5A. The black lines in (a)–(d) are the control tropopause. For (e) and (f), the zonal wind change in response to the global cloud change is in black; the colored lines are for the tropical (0°–20°, red), midlatitude (20°–50°, blue), and high-latitude (50°–90°, green) cloud changes. For (e), the dashed lines show the wind change from tropical cloud changes above 500 hPa (red dashed), midlatitude cloud changes above 600 hPa (blue dashed), and high-latitude cloud changes below 600 hPa (green dashed). The numbers give the jet shifts (° lat); the values for the dashed lines are given in the parentheses. The gray vertical bars indicate the control jet position.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Impact of global and regional cloud changes on the jet. (a),(b) Change in cloud fraction under global warming, (c),(d) the resulting cloud radiative forcing [Eq. (1)], and (e),(f) the response of the 850-hPa zonal wind to the cloud change in locked simulations for (top) MPI-ESM and (bottom) IPSL-CM5A. The black lines in (a)–(d) are the control tropopause. For (e) and (f), the zonal wind change in response to the global cloud change is in black; the colored lines are for the tropical (0°–20°, red), midlatitude (20°–50°, blue), and high-latitude (50°–90°, green) cloud changes. For (e), the dashed lines show the wind change from tropical cloud changes above 500 hPa (red dashed), midlatitude cloud changes above 600 hPa (blue dashed), and high-latitude cloud changes below 600 hPa (green dashed). The numbers give the jet shifts (° lat); the values for the dashed lines are given in the parentheses. The gray vertical bars indicate the control jet position.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Impact of global and regional cloud changes on the jet. (a),(b) Change in cloud fraction under global warming, (c),(d) the resulting cloud radiative forcing [Eq. (1)], and (e),(f) the response of the 850-hPa zonal wind to the cloud change in locked simulations for (top) MPI-ESM and (bottom) IPSL-CM5A. The black lines in (a)–(d) are the control tropopause. For (e) and (f), the zonal wind change in response to the global cloud change is in black; the colored lines are for the tropical (0°–20°, red), midlatitude (20°–50°, blue), and high-latitude (50°–90°, green) cloud changes. For (e), the dashed lines show the wind change from tropical cloud changes above 500 hPa (red dashed), midlatitude cloud changes above 600 hPa (blue dashed), and high-latitude cloud changes below 600 hPa (green dashed). The numbers give the jet shifts (° lat); the values for the dashed lines are given in the parentheses. The gray vertical bars indicate the control jet position.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Global cloud changes have a strong impact on the jet position, as shown previously in Voigt and Shaw (2015). When global cloud changes are imposed via the locking method the jet shifts poleward by 2.3° in MPI-ESM and 1.8° in IPSL-CM5A (Figs. 2e and 2f, black lines). The cloud-induced jet shift is almost as large as the models’ total jet shift under global warming (70% of the total jet shift in MPI-ESM, 65% in IPSL-CM5A) and the 2.8° latitude model-mean jet shift of the CMIP5 aquaplanet ensemble (cf. Table 1).2 This highlights the important role that clouds can play for the response of the jet to climate change.
The radiative forcing is shown in Figs. 2c and 2d. It has a complex spatial pattern, but its qualitative aspects can be understood from changes in cloud fraction. High-level clouds warm at their base and cool at their top (Slingo and Slingo 1988; Li et al. 2015). An upward shift of high-level clouds therefore leads to a tripole of radiative forcing: radiative cooling at the old cloud base where cloud fraction decreases, radiative heating above at the new cloud base where cloud fraction also decreases, and radiative cooling even farther above at the new cloud top where cloud fraction increases. For high-latitude low-level clouds, an increase in cloud fraction enhances the layer’s longwave radiative emissivity and leads to radiative cooling, while a decrease in cloud fraction leads to radiative heating. As a result, cloud fraction change is a good predictor of radiative forcing. Regions of increased cloud fraction tend to experience radiative cooling, and regions of decreased cloud fraction tend to experience radiative heating (Figs. 2a–d and 3), although it is worth noting that the link between cloud fraction change and radiative forcing is weak in the tropical upper troposphere in MPI-ESM, indicating that changes in cloud ice also play a role there.
Local radiative forcing from the global warming response of clouds as a function of the local cloud fraction change in (left) MPI-ESM and (right) IPSL-CM5A. The cloud fraction change is a good predictor of the radiative forcing: a reduction in cloud fraction tends to lead to radiative heating, whereas an increase in cloud fraction tends to lead to radiative cooling. Each cross corresponds to one of the models’ latitude–pressure grid boxes belonging to the upper-level tropics (red), upper-level midlatitudes (blue), or lower-level high latitudes (green). The solid lines are linear fits to all grid boxes belonging to the same region.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Local radiative forcing from the global warming response of clouds as a function of the local cloud fraction change in (left) MPI-ESM and (right) IPSL-CM5A. The cloud fraction change is a good predictor of the radiative forcing: a reduction in cloud fraction tends to lead to radiative heating, whereas an increase in cloud fraction tends to lead to radiative cooling. Each cross corresponds to one of the models’ latitude–pressure grid boxes belonging to the upper-level tropics (red), upper-level midlatitudes (blue), or lower-level high latitudes (green). The solid lines are linear fits to all grid boxes belonging to the same region.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Local radiative forcing from the global warming response of clouds as a function of the local cloud fraction change in (left) MPI-ESM and (right) IPSL-CM5A. The cloud fraction change is a good predictor of the radiative forcing: a reduction in cloud fraction tends to lead to radiative heating, whereas an increase in cloud fraction tends to lead to radiative cooling. Each cross corresponds to one of the models’ latitude–pressure grid boxes belonging to the upper-level tropics (red), upper-level midlatitudes (blue), or lower-level high latitudes (green). The solid lines are linear fits to all grid boxes belonging to the same region.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
The radiative forcing is dominated by three main regional components (Figs. 2c,d) that arise from the spatial patterns of the control cloud fraction and the cloud fraction change. In the tropics (defined here as 0°–20°) and midlatitudes (20°–50°) the upward shift of high-level clouds heats the upper troposphere below the tropopause. We separate tropical and midlatitude cloud changes at 20° latitude because the aquaplanet has a narrower circulation than actual Earth (see above; see also Medeiros et al. 2015) (recall that the jet is positioned around 37° latitude in both hemispheres) and because high-level cloud fraction and radiative forcing have a local minimum at 20° latitude (Figs. 2a–d). The tropical and midlatitude upper-tropospheric radiative forcing is similar between MPI-ESM and IPSL-CM5A because the cloud fraction changes are similar. The radiative forcing is also large in the lower troposphere, particularly in high latitudes, but differs between the two models. In MPI-ESM the increase in low-level cloud fraction leads to strong radiative cooling near the surface. In contrast, the spatially complex cloud fraction change in IPSL-CM5A leads to a complicated radiative forcing with nearly zero average over the high-latitude lower troposphere (50°–90° latitude and 700–1000 hPa).
Based on the three main regional components of the radiative forcing and in order to assess the role of regional cloud changes, we perform simulations in which tropical (0°–20°), midlatitude (20°–50°), and high-latitude (50°–90°) cloud changes are imposed separately. Tropical cloud changes contribute one-third to the total cloud-induced jet shift. They lead to a poleward jet shift of +0.8° in MPI-ESM and +0.6° in IPSL-CM5A (Figs. 2e and 2f, red solid lines). Their radiative forcing peaks in the upper troposphere, which suggests that high-level cloud changes play the dominant role. Indeed, an MPI-ESM simulation in which the tropical cloud changes are imposed only above 500 hPa exhibits a very similar poleward jet shift (+0.7°; Figs. 2e,f, red dashed line). This shows that high-level clouds dominate the jet impact of tropical cloud changes.
Midlatitude cloud changes have as strong an impact as tropical cloud changes and lead to a poleward jet shift (+0.8° in MPI-ESM and +1.1° in IPSL-CM5A in Figs. 2e and 2f, respectively, blue solid lines). To isolate the role of high-level cloud changes, we run an MPI-ESM simulation in which the midlatitude radiative forcing between 20° and 50° and above 600 hPa is added to the model’s temperature equation as a diabatic heating term.3 In this simulation the jet shifts poleward by +1.0°, that is, as much as in response to all midlatitude cloud changes (Figs. 2e,f; blue dashed line). This demonstrates that high-level clouds are key to the jet impact of midlatitude cloud changes.
High-latitude cloud changes lead to a +0.3° poleward jet shift in MPI-ESM but have no impact on the jet position in IPSL-CM5A (Figs. 2e,f, green solid lines). An MPI-ESM simulation in which the radiative forcing below 600 hPa is imposed to the model, analogous to the approach for midlatitude high-level cloud changes, shows that the poleward jet shift in MPI-ESM results from the strong increase in low cloud fraction and the associated radiative cooling of the lower troposphere (Fig. 2e, green dashed line). High-latitude cloud changes cause no jet shift in IPSL-CM5A, consistent with the fact that their cloud fraction change and radiative forcing averaged over the lower troposphere are close to zero. Thus, the impact of high-latitude cloud changes is model dependent because the warming response of these clouds is model dependent.
In addition to their impact on the jet stream, cloud radiative changes also lead to an expansion of the tropical belt (Table 2). In the locked MPI-ESM and IPSL-CM5A simulations, a cloud-induced poleward jet shift is always associated with a poleward movement of the Hadley cell. Coordinated changes in the jet stream and Hadley cell are also seen in the CMIP5 aquaplanet model ensemble (Table 2; Medeiros et al. 2015; Shaw and Voigt 2016) and in realistic model setups in the Southern Hemisphere during austral summer (DJF) (Kang and Polvani 2011; Polvani et al. 2011). Thus, the impact of cloud radiative changes on the jet position applies equally to the width of the Hadley cell and the tropical belt.
Poleward jet shifts and expansion of the tropical belt in response to global and regional cloud changes, and the ratio between the two. The tropical expansion is measured as the poleward shift of the Hadley cell edge, which is defined as the subtropical latitude at which the mass streamfunction at 500 hPa crosses zero. For comparison, the model-mean global warming changes in the CMIP5 aquaplanet ensemble are 2.8°, 0.8°, and 0.5.
Table 2 summarizes the jet and Hadley cell shifts in response to global and regional cloud changes. The circulation shifts due to the regional cloud changes are approximately linearly additive; that is, they sum up to nearly the circulation shift that we find for global cloud changes for which all cloud changes are imposed simultaneously. In summary, tropical and midlatitude cloud changes contribute about equally to the jet shift and Hadley cell expansion in both models, whereas the impact of high-latitude cloud changes differs between the models.
3. Understanding the jet response to regional cloud radiative changes using dry dynamics
Cloud radiative changes impact the jet via atmospheric temperatures, which are connected to zonal winds through thermal wind balance and the Eady growth rate (see below). In this section we study the connection of the jet shift with the regional radiative forcing and regional atmospheric temperature changes by comparing the locked simulations of the previous section to simulations with a dry dynamical core. Dry dynamical core simulations have shown that 1) tropical upper-tropospheric warming shifts the jet poleward (Butler et al. 2010, 2011; Lu et al. 2014), 2) midlatitude upper-tropospheric warming shifts the jet poleward (Fig. 9 of Lorenz and DeWeaver 2007), and 3) polar lower-tropospheric warming shifts the jet equatorward (Butler et al. 2010). Such temperature changes arise in global warming simulations due to increased specific humidity and latent heating (for cases 1 and 2) and amplified warming in the Arctic (for case 3). Importantly, the radiative forcing from cloud changes peaks in the exact same three regions. This suggests that cloud changes trigger atmospheric temperature changes similar to those expected under global warming, and that the cloud impact on the jet can be understood from previous work on the circulation response to localized temperature changes. In this section we probe this idea through simulations with a dry dynamical core that models the response of the dry circulation to the radiative forcing from cloud changes in the absence of diabatic processes.
The temperature and zonal wind in the control simulations of DRY (no radiative forcing from cloud changes) and MPI-ESM are compared in Fig. 4. The extratropical jet stream in DRY is at nearly the same latitude as in MPI-ESM (36° in DRY and 37° in MPI-ESM). Consistent with other dry dynamical cores DRY has a relatively weak Hadley circulation and weak subtropical and extratropical jets that, as in MPI-ESM, are merged. The tropopause is somewhat lower in the tropics and has a less steep meridional slope in midlatitudes compared to MPI-ESM. The stratospheric circulation in DRY is quiescent since Teq is constant in the stratosphere [see Fig. 1a of Held and Suarez (1994)]. The differences in the control circulation, and the fact that the temperature response in DRY depends on the relaxation time scale kT, which is a free parameter, complicate a quantitative comparison between the jet shifts simulated by DRY and MPI-ESM. However, our goal is rather to establish a qualitative link between moist aquaplanet simulations with comprehensive models and previous work on dry dynamics.
Comparison of the time-mean zonal-mean control circulation in (a) MPI-ESM and (b) DRY. The zonal wind is shown with black contours (m s−1) and the temperature with colored shading (K).
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Comparison of the time-mean zonal-mean control circulation in (a) MPI-ESM and (b) DRY. The zonal wind is shown with black contours (m s−1) and the temperature with colored shading (K).
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Comparison of the time-mean zonal-mean control circulation in (a) MPI-ESM and (b) DRY. The zonal wind is shown with black contours (m s−1) and the temperature with colored shading (K).
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Cloud changes have the same qualitative impact on the jet in DRY as in MPI-ESM (Fig. 5). When forced with global cloud changes, DRY simulates a poleward jet shift. DRY also captures the relative importance of tropical, midlatitude, and high-latitude cloud changes found in MPI-ESM, with tropical and midlatitude cloud changes respectively having an about equal and larger impact than high-latitude cloud changes. The jet shift is about 3 times larger in DRY than in MPI-ESM. We will show below that this is because atmospheric temperatures respond more strongly in DRY than in MPI-ESM, for which convection, boundary layer processes, and fixed SSTs compensate some of the radiative forcing.
Response of the 850-hPa zonal wind in the dry model to the global (black) and regional cloud radiative changes (colors). As in Fig. 2, the cloud-induced jet shift is decomposed into contributions from tropical (red), midlatitude (blue), and high-latitude (green) cloud changes. The numbers give the jet shift. The gray vertical bar indicates the control jet position.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Response of the 850-hPa zonal wind in the dry model to the global (black) and regional cloud radiative changes (colors). As in Fig. 2, the cloud-induced jet shift is decomposed into contributions from tropical (red), midlatitude (blue), and high-latitude (green) cloud changes. The numbers give the jet shift. The gray vertical bar indicates the control jet position.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Response of the 850-hPa zonal wind in the dry model to the global (black) and regional cloud radiative changes (colors). As in Fig. 2, the cloud-induced jet shift is decomposed into contributions from tropical (red), midlatitude (blue), and high-latitude (green) cloud changes. The numbers give the jet shift. The gray vertical bar indicates the control jet position.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Response of temperature (colored shading) and zonal wind (black contours, with positive values in solid) to global and regional cloud radiative changes in (a)–(d) MPI-ESM, (e)–(h) DRY, and (i)–(l) IPSL-CM5A. The zonal wind contour interval is 0.6 m s−1 for MPI-ESM and IPSL-CM5A and 1.8 m s−1 for DRY. The thick black line is the control tropopause.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Response of temperature (colored shading) and zonal wind (black contours, with positive values in solid) to global and regional cloud radiative changes in (a)–(d) MPI-ESM, (e)–(h) DRY, and (i)–(l) IPSL-CM5A. The zonal wind contour interval is 0.6 m s−1 for MPI-ESM and IPSL-CM5A and 1.8 m s−1 for DRY. The thick black line is the control tropopause.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Response of temperature (colored shading) and zonal wind (black contours, with positive values in solid) to global and regional cloud radiative changes in (a)–(d) MPI-ESM, (e)–(h) DRY, and (i)–(l) IPSL-CM5A. The zonal wind contour interval is 0.6 m s−1 for MPI-ESM and IPSL-CM5A and 1.8 m s−1 for DRY. The thick black line is the control tropopause.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Relation between regional temperature changes and the jet shift for (a) tropical, (b) midlatitude, and (c) high-latitude cloud changes. The temperature change is averaged over the regions indicated by the green boxes in Figs. 6a–d (tropics: 0°–20°, 100–300 hPa; midlatitudes: 20°–50°, 225–500 hPa; and high latitudes: 50°–90°, 700–950 hPa). For DRY, the filled squares show the response to the cloud radiative forcing at its actual magnitude, and the open squares show the response to cloud radiative forcing multiplied by 3/4, 1/2, 1/3, 1/4, 1/5, and 1/8. The gray line fits DRY simulations and is obtained from an ordinary least squares linear regression through zero.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Relation between regional temperature changes and the jet shift for (a) tropical, (b) midlatitude, and (c) high-latitude cloud changes. The temperature change is averaged over the regions indicated by the green boxes in Figs. 6a–d (tropics: 0°–20°, 100–300 hPa; midlatitudes: 20°–50°, 225–500 hPa; and high latitudes: 50°–90°, 700–950 hPa). For DRY, the filled squares show the response to the cloud radiative forcing at its actual magnitude, and the open squares show the response to cloud radiative forcing multiplied by 3/4, 1/2, 1/3, 1/4, 1/5, and 1/8. The gray line fits DRY simulations and is obtained from an ordinary least squares linear regression through zero.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Relation between regional temperature changes and the jet shift for (a) tropical, (b) midlatitude, and (c) high-latitude cloud changes. The temperature change is averaged over the regions indicated by the green boxes in Figs. 6a–d (tropics: 0°–20°, 100–300 hPa; midlatitudes: 20°–50°, 225–500 hPa; and high latitudes: 50°–90°, 700–950 hPa). For DRY, the filled squares show the response to the cloud radiative forcing at its actual magnitude, and the open squares show the response to cloud radiative forcing multiplied by 3/4, 1/2, 1/3, 1/4, 1/5, and 1/8. The gray line fits DRY simulations and is obtained from an ordinary least squares linear regression through zero.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Response of the vertically averaged Eady growth rate 
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Response of the vertically averaged Eady growth rate
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Response of the vertically averaged Eady growth rate
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Global cloud changes lead to substantial temperature changes above 600 hPa, with tropical and midlatitude upper-level warming in MPI-ESM, DRY, and IPSL-CM5A (Figs. 6a,e,i). DRY does not capture the high-latitude upper-level cooling seen in MPI-ESM and IPSL-CM5A. The cooling is not driven by radiative forcing but rather is a dynamic response of the poleward jet shift; its absence in DRY is likely related to DRY’s quiescent stratospheric circulation. Global cloud changes lead to a decrease of 
The upper-level warming in the tropics is generated by tropical cloud changes, namely the upward shift of tropical high-level clouds, and is a direct radiative response to the tropical radiative forcing (Figs. 6b,f,j). While the tropical radiative forcing has spatial structure—it strongly peaks close to the equator in MPI-ESM and DRY—the warming is nearly uniform throughout the tropical upper troposphere because of the inability of the latter to maintain large horizontal temperature gradients (Charney 1963; Sobel et al. 2001). The warming extends farther into the midlatitudes up to 40° and down to lower levels around 25°, which has been shown to be related to the mean meridional circulation and eddies (Wu et al. 2012; Lu et al. 2014). The warming resembles the temperature effect of increased latent heating under global warming, with the distinction that there is no warming at lower levels. Consistent with previous dry model studies (Mbengue and Schneider 2013; Butler et al. 2010; Lu et al. 2014) and the scaling argument of Held (2000), the tropical upper-level warming leads to an expansion of the Hadley cell and a poleward jet shift. The role of the tropical upper-level warming for the jet shift is confirmed in additional simulations with DRY in which the tropical radiative forcing is decreased. These simulations show a linear relation between the warming magnitude and the jet shift (Fig. 7a). The tropical radiative forcing and the resulting tropical upper-level warming impact both the meridional temperature gradient and tropical vertical stability, which are both needed to explain the Eady growth rate response to tropical cloud changes (Fig. 8c).
Midlatitude cloud changes warm the midlatitude upper troposphere through reduced cloud fraction (Figs. 6c,g,k; cf. section 2). Although their temperature impact is much smaller compared to tropical cloud changes, they cause a similar jet shift. This can be understood from the midlatitude upper-level warming and its effect on the Eady growth rate, whose response is dominated by changes in the meridional temperature gradient (Fig. 8d). Occurring right at the jet position, the warming decreases the meridional temperature gradient on the jet’s equatorward flank and increases it on the jet’s poleward flank and, despite being small, is able to create a large change in the meridional temperature gradient and Eady growth rate. This makes the midlatitude upper-level warming very effective in shifting the jet poleward. The importance of the midlatitude upper-level warming is confirmed in DRY sensitivity simulations in which the midlatitude radiative forcing is decreased. These show a linear relation between the warming and the jet shift (Fig. 7b), consistent with the dry model results of Lorenz and DeWeaver (2007) (see their Fig. 7).
High-latitude cloud changes shift the jet poleward in MPI-ESM and DRY by cooling the high-latitude lower troposphere through increased low cloud fraction and enhanced emission of longwave irradiance (Figs. 6d,h). The high-latitude low-level cooling increases the meridional temperature gradient poleward of the jet. This leads to an increase in the Eady growth rate in high latitudes (Fig. 8e) and thus a poleward jet shift, consistent with previous dry studies (Butler et al. 2010, 2011; Yuval and Kaspi 2016). DRY simulations with decreased high-latitude radiative forcing confirm the relationship between the cooling of the high-latitude lower troposphere and the jet shift (Fig. 7c).
Why does DRY consistently show larger jet shifts than MPI-ESM even though both models experience the same radiative forcing? In both DRY and MPI-ESM the radiative forcing can be compensated radiatively by a local temperature change and dynamically by a change in the energy advected by the circulation. MPI-ESM, however, also takes into account small-scale processes other than radiation. In combination with the fact that the fixed SSTs constrain near-surface temperatures and provide an infinite source of energy at the surface in MPI-ESM (but not in DRY), these processes respond to the radiative forcing in a way as to damp the temperature change. Specifically, the tropical upper-level radiative heating stabilizes the tropical troposphere, which in MPI-ESM leads to a decrease in moist convection and anomalous moist convective cooling (compared to the control climate). Similarly, the high-latitude low-level radiative cooling in MPI-ESM destabilizes the boundary layer and enhances vertical mixing of latent and sensible energy from the surface into the boundary layer, which works against the radiative cooling. As a result, temperature changes, and hence jet shifts, are much smaller in MPI-ESM than DRY. This illustrates that the interaction of cloud radiative changes with other small-scale processes plays an important role for the magnitude of the circulation response.
In summary, the jet response to regional cloud changes can be captured qualitatively in a dry dynamical core, provided the dry model is perturbed with the radiative forcing from cloud changes obtained from a model with comprehensive representation of cloud processes. The section also showed that the jet shift due to regional cloud changes can be related to temperature changes in the exact same regions in which the cloud changes create a radiative forcing. In the next section, we will use the relationships between regional cloud changes, regional temperature changes, and jet shifts to understand how clouds impact the jet in global warming simulations with 10 CMIP5 aquaplanet models.
4. Jet shifts and cloud changes in the CMIP5 aquaplanet simulations of global warming
The strong impact of regional cloud changes on the jet suggests that model differences in the cloud response to global warming contribute to model differences in jet shifts. Previous work indeed found that in coupled simulations with interactive SSTs, model differences in shortwave cloud radiative changes lead to model differences in Southern Hemisphere jet shifts (Ceppi et al. 2014). Here we investigate whether atmospheric cloud changes also contribute to model differences in jet shifts. To this end we analyze the prescribed-SST CMIP5 aquaplanet ensemble, and more precisely the aquaControl and aqua4K simulations from 10 comprehensive climate models (Table 1). In these simulations, clouds and water vapor are free and change together with the imposed 4-K surface warming.
The CMIP5 aquaplanet setup is described in section 2. The aquaplanet simulations provided in the CMIP5 archive are run for at least five years; to remove spinup effects we exclude the first year from the analysis. Since the Northern and Southern Hemisphere are statistically identical, results are averaged over both hemispheres. The CMIP5 archive provides cloud fraction, cloud ice, and cloud water on model levels. We interpolate these fields to the 17 CMIP5 standard pressure levels using the model-specific surface pressure and vertical grid coefficients that we extracted from the CMIP5 archive. For the HadGEM2-A model, cloud fields are archived on height levels, which we interpolate to pressure levels using the geopotential height data on pressure levels.
All models exhibit a poleward jet shift in response to global warming. The model-mean shift is +2.8° latitude. However, the jet shift differs considerably in magnitude between models. The most sensitive model shifts the jet by twice as much as the least sensitive model (Fig. 1c; Table 1). The jet shift is not correlated with the control jet position (not shown).
The models simulate substantial cloud changes in response to global warming (Fig. 9a). In all models cloud fraction shifts upward in the tropics near the tropopause, together with cloud ice (not shown), decreases around 35° latitude in the troposphere, and increases in the mid- and high latitudes above 400 hPa. Despite this qualitative consensus between models, there are large differences in the magnitude of the cloud fraction changes, and there are additional cloud fraction changes that are large in some models but absent or of different sign in other models (Fig. 9b). In particular, models differ in the cloud fraction response of tropical high-level clouds (model spread of 20% in absolute units) and midlatitude high-level clouds (model spread of 10%). Models further differ in the response of high-latitude low-level clouds, with some models simulating a cloud fraction increase, some a decrease, and some an increase close to the surface and a decrease above. Intriguingly, the changes in cloud fraction differ between models in the exact same tropical, midlatitude, and high-latitude regions that we found important to the jet shift in sections 2 and 3.
Global warming response of cloud fraction in 10 CMIP5 aquaplanet models. (a) Model-mean cloud fraction change in colored shading, with the model-mean control cloud fraction in solid contours (contour interval of 5%). (b) Model spread in the cloud fraction change, calculated as the difference between the maximum and the minimum change across the model ensemble at each latitude and level. The green boxes indicate the regions of 0°–20° and 40–125 hPa (tropics), 20°–50° and 225–500 hPa (midlatitudes), and 50°–90° and 700–950 hPa (high latitudes) over which the cloud fraction change in Fig. 10 is averaged.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Global warming response of cloud fraction in 10 CMIP5 aquaplanet models. (a) Model-mean cloud fraction change in colored shading, with the model-mean control cloud fraction in solid contours (contour interval of 5%). (b) Model spread in the cloud fraction change, calculated as the difference between the maximum and the minimum change across the model ensemble at each latitude and level. The green boxes indicate the regions of 0°–20° and 40–125 hPa (tropics), 20°–50° and 225–500 hPa (midlatitudes), and 50°–90° and 700–950 hPa (high latitudes) over which the cloud fraction change in Fig. 10 is averaged.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Global warming response of cloud fraction in 10 CMIP5 aquaplanet models. (a) Model-mean cloud fraction change in colored shading, with the model-mean control cloud fraction in solid contours (contour interval of 5%). (b) Model spread in the cloud fraction change, calculated as the difference between the maximum and the minimum change across the model ensemble at each latitude and level. The green boxes indicate the regions of 0°–20° and 40–125 hPa (tropics), 20°–50° and 225–500 hPa (midlatitudes), and 50°–90° and 700–950 hPa (high latitudes) over which the cloud fraction change in Fig. 10 is averaged.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Models with a larger jet shift show larger tropical upper-level warming (Fig. 10a), consistent with realistic model setups (Gerber and Son 2014). The warming differs by 4 K between models. This difference is about as large as the tropical upper-level warming that we found in response to tropical cloud changes in section 3 (length of red line in Fig. 10a), indicating that a substantial part of the model difference in warming might be due to model differences in tropical high-level cloud changes. Indeed, models with a larger warming also show a larger cloud fraction increase in the tropical upper troposphere–lower stratosphere (UTLS; between 0° and 20° latitude and at 40–125 hPa; Fig. 10b). This provides evidence that while all models shift their clouds upward, model differences in the cloud fraction increase in the tropical UTLS contribute to model differences in the tropical upper-level warming and thus the jet shift.
Relationship (left) between the jet shift and regional temperature changes and (right) between regional temperature and cloud fraction changes in response to global warming in the 10 CMIP5 aquaplanet models (numbers correspond to Table 1), showing (a),(b) tropical upper-tropospheric temperature and cloud changes, (c),(d) midlatitude upper-tropospheric changes, and (e),(f) high-latitude lower-tropospheric changes. Cloud changes are averaged over the green boxes indicated in Fig. 9; temperature changes are averaged over the same regions as in Fig. 7 (cf. green boxes in Fig. 6). The correlation coefficient r and the p values are also given. If the correlation is statistically significant at the 90% level (p < 0.1), the linear regression line is plotted. In (a),(c),(e), the horizontal lines at the bottom of the panels indicate the magnitude of regional temperature change that occurs in the locked simulations due to regional cloud changes (cf. Fig. 7), illustrating the potential of regional cloud changes to induce model spread in temperature changes and hence the jet shift.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Relationship (left) between the jet shift and regional temperature changes and (right) between regional temperature and cloud fraction changes in response to global warming in the 10 CMIP5 aquaplanet models (numbers correspond to Table 1), showing (a),(b) tropical upper-tropospheric temperature and cloud changes, (c),(d) midlatitude upper-tropospheric changes, and (e),(f) high-latitude lower-tropospheric changes. Cloud changes are averaged over the green boxes indicated in Fig. 9; temperature changes are averaged over the same regions as in Fig. 7 (cf. green boxes in Fig. 6). The correlation coefficient r and the p values are also given. If the correlation is statistically significant at the 90% level (p < 0.1), the linear regression line is plotted. In (a),(c),(e), the horizontal lines at the bottom of the panels indicate the magnitude of regional temperature change that occurs in the locked simulations due to regional cloud changes (cf. Fig. 7), illustrating the potential of regional cloud changes to induce model spread in temperature changes and hence the jet shift.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Relationship (left) between the jet shift and regional temperature changes and (right) between regional temperature and cloud fraction changes in response to global warming in the 10 CMIP5 aquaplanet models (numbers correspond to Table 1), showing (a),(b) tropical upper-tropospheric temperature and cloud changes, (c),(d) midlatitude upper-tropospheric changes, and (e),(f) high-latitude lower-tropospheric changes. Cloud changes are averaged over the green boxes indicated in Fig. 9; temperature changes are averaged over the same regions as in Fig. 7 (cf. green boxes in Fig. 6). The correlation coefficient r and the p values are also given. If the correlation is statistically significant at the 90% level (p < 0.1), the linear regression line is plotted. In (a),(c),(e), the horizontal lines at the bottom of the panels indicate the magnitude of regional temperature change that occurs in the locked simulations due to regional cloud changes (cf. Fig. 7), illustrating the potential of regional cloud changes to induce model spread in temperature changes and hence the jet shift.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
A similar argument holds for midlatitude high-level cloud changes. Models with a larger jet shift show a larger midlatitude upper-tropospheric warming (Fig. 10c). The model difference in the warming is comparable to (albeit weaker than) the warming that we have found in response to midlatitude cloud changes in section 3 (length of blue line in Fig. 10c). Across models the warming is negatively correlated with the decrease of cloud fraction in the midlatitude upper troposphere (between 20° and 50° and at 225–500 hPa; Fig. 10d). Because sections 2 and 3 showed that a decrease in midlatitude upper-tropospheric cloud fraction leads to a midlatitude upper-tropospheric warming and a poleward jet shift, the correlations provide evidence that model differences in the jet shift are partly caused by model differences in the global warming response of midlatitude high-level clouds.
Although high-latitude low-level cloud changes differ strongly between models, we do not find statistically significant correlations between the high-latitude lower-tropospheric cloud and temperature changes, or between the high-latitude lower-tropospheric temperature change and the jet shift (Figs. 10e,f). The use of prescribed SSTs constrains near-surface temperatures and limits the temperature impact of low cloud changes, which might explain the lack of a correlation. However, because high-latitude low-level clouds can impact the jet position as demonstrated in section 2 and because their response to global warming is highly model dependent, an improved representation of high-latitude low-level clouds in models still appears important for accurate jet shift predictions. This is certainly all the more so for simulations with interactive surface temperatures that allow for surface radiative impacts of cloud changes [see section 5 and also Ceppi and Hartmann (2016)].
In summary our analysis indicates that model differences in tropical and midlatitude high-level cloud changes are a substantial source of model differences in the jet response to global warming in the CMIP5 aquaplanet ensemble. We emphasize that while the cloud–jet correlations obtained from the CMIP5 aquaplanet ensemble would not be sufficient to draw this conclusion, the combination with our locked simulations of sections 2 and 3 gives us confidence in the causal connection between model differences in the regional cloud changes and the circulation shifts.
5. Discussion
a. Dynamic versus thermodynamic cloud changes
Our locked simulations of section 2 assume the cloud changes are known and treat them as a forcing to the circulation. However, the circulation sets the environment in which clouds form, and so it is likely that part of the cloud changes are caused by circulation changes. Circulation-induced cloud changes are often referred to as dynamic to separate them from so-called thermodynamic cloud changes that occur independently of the circulation. Separating dynamic from thermodynamic cloud changes helps to interpret the correlations between the jet shift and regional cloud changes found in the CMIP5 aquaplanet models in section 4. If regional cloud changes are predominantly caused by thermodynamics, the correlations indeed signal a model-dependent impact of regional cloud changes on the jet shift, and not simply an impact of the jet on clouds.
Modeling and observational studies have provided evidence that dynamic cloud controls are important in the midlatitude upper troposphere, with poleward shifts of the jet (or corresponding changes in the phase of the barotropic annular mode) and an expansion of the Hadley cell being associated with a poleward and upward shift of midlatitude high-level clouds (Bender et al. 2012; Grise and Polvani 2014b; Li and Thompson 2016; Tselioudis et al. 2016). Our simulations are consistent with such a circulation impact on midlatitude high-level clouds. Following previous studies we use internal variability and regress monthly-mean cloud fraction on the monthly-mean jet position in the aquaControl simulation. During months in which the jet is located anomalously poleward, midlatitude cloud fraction is reduced throughout most of the troposphere and enhanced near the tropopause (Fig. 11). This indicates that a poleward jet shift leads to a poleward and upward shift of midlatitude clouds. We obtain a very similar plot when we regress on the Hadley cell edge, which is unsurprising given the strong coupling of the Hadley cell edge with the jet position in the aquaplanet simulations. This suggests that some of the midlatitude high-level cloud changes that occur under global warming, in particular the reduction in midlatitude upper-tropospheric cloud fraction, are caused by the poleward jet shift and expansion of the Hadley cell (cf. Figs. 2c,d and 11).
Regression between month-to-month variability in cloud fraction and jet position in the aquaControl simulations in which clouds are free for (left) MPI-ESM and (right) IPSL-CM5A. The colored shading shows the cloud fraction change per degree poleward jet shift. The thick black line is the tropopause, and the boxes at (left) indicate the averaging regions used in Table 3. The jet shift occurs together with a shift of the intertropical convergence zone, which leads to meridional displacements of tropical high-level clouds in MPI-ESM but no upward cloud rise (Table 3). Regions in which the regression is statistically significant at the 95% level (p value < 0.05) are stippled.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Regression between month-to-month variability in cloud fraction and jet position in the aquaControl simulations in which clouds are free for (left) MPI-ESM and (right) IPSL-CM5A. The colored shading shows the cloud fraction change per degree poleward jet shift. The thick black line is the tropopause, and the boxes at (left) indicate the averaging regions used in Table 3. The jet shift occurs together with a shift of the intertropical convergence zone, which leads to meridional displacements of tropical high-level clouds in MPI-ESM but no upward cloud rise (Table 3). Regions in which the regression is statistically significant at the 95% level (p value < 0.05) are stippled.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Regression between month-to-month variability in cloud fraction and jet position in the aquaControl simulations in which clouds are free for (left) MPI-ESM and (right) IPSL-CM5A. The colored shading shows the cloud fraction change per degree poleward jet shift. The thick black line is the tropopause, and the boxes at (left) indicate the averaging regions used in Table 3. The jet shift occurs together with a shift of the intertropical convergence zone, which leads to meridional displacements of tropical high-level clouds in MPI-ESM but no upward cloud rise (Table 3). Regions in which the regression is statistically significant at the 95% level (p value < 0.05) are stippled.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
According to this analysis the rise of tropical high-level clouds and the high-latitude low-level cloud changes occur independent of the circulation (Table 3), are almost exclusively generated by local thermodynamic changes (FAT or PHAT in the tropics, cloud microphysics in high latitudes), and can be considered a pure forcing to the jet. The circulation impact is larger in the midlatitude upper troposphere, where about 20% of the cloud fraction decrease in response to global warming can be attributed to the jet shift and the Hadley cell expansion (Table 3). Midlatitude high-level cloud changes are thus partly produced by the circulation change, creating the potential for two-way radiative interactions between midlatitude high-level clouds and the circulation (Li and Thompson 2016). Nevertheless, cloud changes are predominantly caused by thermodynamics also in the midlatitude upper troposphere.
Percentage of regional cloud fraction changes under global warming that is attributed to the poleward jet shift (top rows) and the Hadley cell expansion (bottom rows). The attribution is based on the cloud fraction change associated with internal variability in the jet position and Hadley cell edge [first term on rhs of Eq. (8)]. For high-latitude low-level clouds in IPSL-CM5A, the jet shift and Hadley cell expansion lead to an increase in area-mean cloud fraction, in contrast to global warming which leads to a small decrease. The jet shift thus works against the cloud fraction change in this region, and so no percentage is given.
Overall, the relatively weak impact of the circulation on the cloud changes implies that the correlations in the CMIP5 aquaplanet ensemble between jet shifts and tropical and midlatitude high-level cloud changes can be interpreted as to show that model differences in the global warming response of these clouds contribute to model differences in the jet shift. Future studies that use other measures to quantify dynamic controls on clouds, such as by changing the circulation via regional torques (Wall and Hartmann 2015), would be desirable, however, to obtain a more complete picture.
It is worth noting that regional cloud changes might not occur completely independent of each other. For example, tropical cloud changes might cause some of the midlatitude cloud changes by shifting the jet. This suggests that some fraction of the jet shift due to midlatitude cloud changes should actually be attributed to remote cloud changes in the tropics, but more work would be needed to quantify such indirect impacts.
b. Circulation impact of surface radiative forcing from cloud changes
Our study focuses on cloud radiative effects inside the atmosphere. Figure 12 shows the vertically integrated atmospheric radiative forcing from cloud changes. Cloud changes heat the atmosphere in the tropics and cool the atmosphere in the high latitudes. This demands additional poleward meridional energy transport and a poleward jet shift, as is discussed in detail in Shaw and Voigt (2016). The vertically integrated atmospheric radiative forcing thus provides some guidance about the impact of cloud changes on the jet position, even though it is unable to predict the poleward jet shift from midlatitude cloud changes, whose vertically integrated radiative forcing is close to zero.
Vertically integrated atmospheric radiative forcing (black) and surface radiative forcing in the shortwave (blue) and longwave (red) part of the electromagnetic spectrum from cloud changes for the (a) MPI-ESM and (b) IPSL-CM5A aquaplanet models. As in Fig. 2, the radiative forcing is diagnosed from a partial radiative perturbation calculation. The right y axis shows the surface temperature change that would result if the surface radiative forcing was entirely compensated by a change in surface upwelling longwave radiation [Eq. (9)]. For the figure the global-mean SST of 288 K is used as the control temperature in Eq. (9) to translate the radiative forcing into units of kelvin, but the SST perturbations imposed in section 5 use the local control SST.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Vertically integrated atmospheric radiative forcing (black) and surface radiative forcing in the shortwave (blue) and longwave (red) part of the electromagnetic spectrum from cloud changes for the (a) MPI-ESM and (b) IPSL-CM5A aquaplanet models. As in Fig. 2, the radiative forcing is diagnosed from a partial radiative perturbation calculation. The right y axis shows the surface temperature change that would result if the surface radiative forcing was entirely compensated by a change in surface upwelling longwave radiation [Eq. (9)]. For the figure the global-mean SST of 288 K is used as the control temperature in Eq. (9) to translate the radiative forcing into units of kelvin, but the SST perturbations imposed in section 5 use the local control SST.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Vertically integrated atmospheric radiative forcing (black) and surface radiative forcing in the shortwave (blue) and longwave (red) part of the electromagnetic spectrum from cloud changes for the (a) MPI-ESM and (b) IPSL-CM5A aquaplanet models. As in Fig. 2, the radiative forcing is diagnosed from a partial radiative perturbation calculation. The right y axis shows the surface temperature change that would result if the surface radiative forcing was entirely compensated by a change in surface upwelling longwave radiation [Eq. (9)]. For the figure the global-mean SST of 288 K is used as the control temperature in Eq. (9) to translate the radiative forcing into units of kelvin, but the SST perturbations imposed in section 5 use the local control SST.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Clouds also impact the surface energy balance. Previous work indicates that the surface impact of clouds is important for the circulation (Ceppi and Hartmann 2016; Shaw et al. 2015). The partial radiative perturbation calculations of section 2 provide not only the atmospheric but also the surface radiative forcing from cloud changes. The surface radiative forcing is included in Fig. 12 and shown separately for shortwave and longwave radiation. The surface radiative forcing differs substantially between the MPI-ESM and IPSL-CM5A models in the tropics and midlatitudes, reflecting differences in the intertropical convergence zone and the response of deep-convective ice clouds to global warming (Voigt and Shaw 2015). At high latitudes (i.e., poleward of 50°), both models predict a negative shortwave and a positive longwave surface radiative forcing (blue and red lines in Fig. 12), consistent with what Ceppi and Hartmann (2016) found in an interactive SST slab-ocean aquaplanet setup with the GFDL Atmospheric Model, version 2 (AM2). The negative shortwave surface radiative forcing results from an increase in cloud albedo due to a cloud-phase change from ice to liquid (clouds becoming “juicier” with warming; e.g., Ceppi et al. 2016) and an increase in total cloud cover.4 The positive longwave surface radiative forcing results from an increase in cloud fraction near the surface, which enhances the amount of downwelling longwave radiation and cools the lower atmosphere but warms the surface.
The shortwave surface radiative forcing translates into a high-latitude SST cooling of more than 6 K in MPI-ESM and 2 K in IPSL-CM5A. When the equivalent SST change is imposed to the models, the jet shifts poleward and the Hadley cell expands by several degrees (Table 4), consistent with the increase in the meridional temperature gradient. The longwave surface radiative forcing translates into a high-latitude SST warming of more than 4 K in MPI-ESM and almost 2 K in IPSL-CM5A. When the equivalent SST change is imposed to the models, the jet shifts equatorward and the Hadley cell narrows, even though the response is smaller compared to the shortwave surface radiative forcing (Table 4). These shortwave- and longwave-induced SST and circulation changes are qualitatively consistent with the interactive-SST slab-ocean aquaplanet simulations of Ceppi and Hartmann (2016) with the AM2 model.
Jet shifts and changes in Hadley cell edge in simulations in which SSTs are changed to take into account the surface radiative forcing from cloud changes in the shortwave and longwave radiation domain. To isolate the surface impact from the impact of the atmospheric radiative forcing, clouds and water vapor are locked to aquaControl values. Positive values indicate a poleward shift of the jet and Hadley cell edge.
While our approach to include surface impacts from cloud changes in a prescribed-SST framework is approximate, the simulations illustrate the competing impacts that cloud changes have on the circulation through their effects on the atmosphere and surface energy balances in the shortwave and longwave radiation domain. In particular, the simulations highlight the competition that arises for high-latitude cloud changes, with the longwave atmospheric and shortwave surface radiative forcing creating a poleward jet shift and the longwave surface radiative forcing creating an equatorward jet shift. This calls for future research to properly disentangle these competing impacts and their role for long-term climate change, when SSTs have time to respond, and circulation variability on shorter time scales, when the ocean thermal inertia damps the SST response to variations in air–sea fluxes.
6. Conclusions
Climate models robustly predict that global warming will shift the extratropical jet streams poleward in the annual and zonal mean. Such shifts are found across a hierarchy of model setups, ranging from realistic coupled setups, to realistic setups with prescribed SSTs, to idealized aquaplanet setups with prescribed SSTs. Yet, the magnitude of these projected shifts differs by several degrees between models across the same hierarchy. This motivates us to study how cloud radiative interactions, or more precisely their regional changes under global warming, impact jet shifts, and to what extent model differences in the regional cloud response to warming contributes to model differences in jet shifts. We make use of the aquaplanet setup as this provides an idealized framework to study the impact of clouds in the absence of other controls on the jet. We prescribe SSTs to focus on the radiative effects of clouds inside the atmosphere. We use the cloud-locking method in two comprehensive climate models to elucidate how regional cloud changes impact the jet via their impact on atmospheric temperatures. By doing so we complement previous work on the circulation response to global cloud changes (Voigt and Shaw 2015; Li et al. 2015; Ceppi and Hartmann 2016), and on surface temperature–mediated cloud impacts (Ceppi et al. 2012, 2014).
Our results can be summarized as follows:
The rise of tropical high-level clouds warms the tropical upper troposphere and leads to a poleward jet shift and an expansion of the Hadley circulation. The cloud rise results from a thermodynamic cloud control (i.e., the tendency of tropical high-level clouds to maintain their anvil temperature). It is qualitatively robust across climate models.
The rise and poleward shift of midlatitude high-level clouds warms the midlatitude upper troposphere below the tropopause and leads to a poleward jet shift and an expansion of the Hadley circulation. The impact of midlatitude high-level cloud changes is as large as the impact of tropical high-level cloud changes. While some of the midlatitude high-level cloud changes are caused by the circulation change itself, our analysis of internal cloud-circulation variability shows that they are dominated by thermodynamic changes. The midlatitude high-level cloud changes are qualitatively robust across models.
High-latitude low-level cloud changes have the potential to shift the jet poleward by cooling the high-latitude lower troposphere, provided their cloud fraction increases. They are, however, not robust across models, and their impact is smaller compared to tropical and midlatitude cloud changes. High-latitude low-level cloud changes strongly affect the surface energy balance. Their circulation impact would likely be larger in interactive-SST setups, consistent with Ceppi and Hartmann (2016).
Dry dynamical core simulations that are perturbed with the radiative forcing from the cloud changes qualitatively reproduce the cloud-induced jet shifts simulated in the comprehensive models. Although the dry simulations are too sensitive because of the lack of small-scale processes and because near-surface temperatures are not constrained to the prescribed SSTs, they allow us to clearly relate the jet response to regional temperature changes. An analysis of the Eady growth rate shows that meridional and vertical temperature changes both contribute to the jet response, with changes in meridional temperature gradients playing a larger role for mid- and high-latitude cloud changes.
An analysis of 10 CMIP5 aquaplanet models indicates that model differences in the response of tropical and midlatitude high-level clouds contribute to model differences in the jet response to global warming.
In this paper we have used the idealized aquaplanet framework with prescribed surface temperatures as a stepping stone to advance understanding of how clouds couple with the extratropical circulation of the atmosphere via their atmospheric impact, and what this implies for the circulation response to climate change. Future work is needed to better disentangle atmospheric and surface impacts of clouds on the circulation, as well as to study to what extent the aquaplanet results presented here and in other recent studies (Voigt and Shaw 2015; Ceppi and Hartmann 2016) hold in realistic model setups in which continents, topography, seasonal cycle, sea ice, and interactive SSTs can affect the extratropical circulation. This would allow for an assessment of the cloud–circulation coupling across a hierarchy of models, and thus for a better understanding of its role in the real climate system. Because the cloud–circulation coupling works not via the top-of-the-atmosphere energy balance but rather via the atmospheric and surface energy balances, clouds that have little impact on climate sensitivity (e.g., midlatitude high-level clouds) can have a large impact on the circulation. This highlights the importance of understanding the entire cloud spectrum and its response to climate change.
Acknowledgments
AV and TAS are supported by the David and Lucile Packard Foundation. TAS is also supported by NSF Award AGS-1255208 and the Alfred P. Sloan Foundation. We thank three anonymous reviewers for their comments. The model data in this study are available from AV upon request. We thank N. Henderson and H. Liu for help downloading the CMIP5 data, and G. Correa for administering the computing cluster of LDEO’s Ocean and Climate Physics Division, which was used for the simulations. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
APPENDIX
Vertically Restricted Cloud Changes Need to be Imposed as a Diabatic Heating Term outside of the Tropics
Cloud changes affect the circulation response to global warming through the radiative forcing that they create. The spatial pattern of the radiative forcing, which can be obtained by a partial radiative perturbation (PRP) calculation [Eq. (1)], motivated us to separate the circulation impact of regional cloud changes in section 2. Since the cloud changes and their radiative forcing not only vary with latitude but also with height, we further wanted to understand the impact of vertically restricted cloud changes (i.e., tropical high-level cloud changes, midlatitude high-level cloud changes, and high-latitude low-level cloud changes). To quantify the jet shift from tropical high-level cloud changes we imposed tropical clouds of the perturbed simulation (aqua4K) above 500 hPa and used clouds from the control simulation (aquaControl) below. To quantify the jet shift from midlatitude high-level cloud changes, however, we found it necessary to add the radiative forcing above 600 hPa as a diabatic heating term to the model’s temperature equation, instead of directly imposing the cloud changes. We also used this indirect approach for high-latitude low-level cloud changes. The reason for doing so is described below.
In the tropics, high- and low-level clouds are separated by a midtropospheric minimum in cloud fraction (Figs. 2a,d). The separation ensures that tropical cloud changes can be sliced vertically in the midtroposphere without creating a discontinuity in cloud fraction. Restricting the cloud change to above 500 hPa therefore recovers the upper-level radiative forcing. This is shown in Fig. A1 (center) that displays the radiative forcing from a PRP calculation with aqua4K clouds above 600 hPa and aquaControl clouds below. The calculation also demonstrates that the upper-tropospheric radiative forcing results from high-level cloud changes (Figs. A1, left and center).
Radiative forcing from cloud changes in MPI-ESM diagnosed from a partial radiative perturbation calculation, showing the radiative forcing (left) when clouds are changed at all levels, and when cloud changes are imposed only (center) above and (right) below 600 hPa.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Radiative forcing from cloud changes in MPI-ESM diagnosed from a partial radiative perturbation calculation, showing the radiative forcing (left) when clouds are changed at all levels, and when cloud changes are imposed only (center) above and (right) below 600 hPa.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
Radiative forcing from cloud changes in MPI-ESM diagnosed from a partial radiative perturbation calculation, showing the radiative forcing (left) when clouds are changed at all levels, and when cloud changes are imposed only (center) above and (right) below 600 hPa.
Citation: Journal of Climate 29, 23; 10.1175/JCLI-D-16-0140.1
A discontinuity in cloud fraction, however, occurs when one vertically slices mid- and high-latitude cloud changes, and this leads to a strong and unwanted radiative forcing at the slicing level. This is shown in Fig. A1 (center) and arises because clouds and cloud changes extend through the entire troposphere outside of the tropics (Figs. 2a,d). While Fig. A1 (center) confirms that the midlatitude upper-tropospheric radiative forcing is indeed due to changes in high-level clouds, it also shows that the radiative forcing cannot be isolated by prescribing aqua4K clouds above 600 hPa and aquaControl clouds below. A similar issue arises for high-latitude low-level cloud changes (Fig. A1, right). This makes it necessary to isolate the circulation impact of midlatitude high-level cloud changes and high-latitude low-level cloud changes by adding their radiative forcing as a diabatic heating term to the model’s temperature equation.
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