1. Introduction
Climate models robustly simulate poleward shifts of the midlatitude eddy-driven jet and storm track as the planet warms because of increased radiative forcing by carbon dioxide (Collins et al. 2013). Details of this shift vary across models, hemisphere, basin, and season (Barnes and Polvani 2013; Grise and Polvani 2016), however, and the theoretical reasons for the response remain debated (Simpson et al. 2014). It is reasonable to expect that poleward jet shifts will be accompanied by poleward shifts of the attendant midlatitude maxima in cloud coverage. This has led to a long-standing expectation that poleward jet shifts that accompany global warming will induce a positive cloud feedback from clouds shifting toward regions with less incoming solar radiation at the top of the atmosphere, weakening their overall shortwave cooling effect on the planet (Bony et al. 2006; Boucher et al. 2013). Indeed, global climate model (GCM) simulations of global warming produce a strong and robustly positive cloud feedback on the equatorward side of the midlatitude jets in both hemispheres that is highly suggestive of such a feedback (Zelinka et al. 2016). Moreover, recent studies have identified poleward shifts of cloud maxima in long-term cloud records that are often attributed to poleward shifts of the large-scale circulation (Bender et al. 2012; Eastman and Warren 2013; Marvel et al. 2015; Norris et al. 2016). Hence, the IPCC Fifth Assessment Report concluded that “model simulations, physical understanding and observations thus provide medium confidence that poleward shifts of cloud distributions will contribute to positive feedback, but by an uncertain amount” (Boucher et al. 2013, p. 589).
The notion that poleward jet shifts should be accompanied by poleward shifts in total cloud cover and a positive radiative feedback has, however, been recently called into question [see review by Ceppi and Hartmann (2015)]. While some models exhibit substantial positive shortwave (SW) cloud radiative effect (CRE)1 anomalies averaged over the SH midlatitudes associated with poleward jet shifts (Grise et al. 2013), others do not (Kay et al. 2014; Wall and Hartmann 2015). Regardless, in all models, estimates of the CRE anomalies induced solely by poleward jet shifts under global warming are much smaller than the total cloud radiative response to warming at midlatitudes (Kay et al. 2014; Ceppi and Hartmann 2015; Wall and Hartmann 2015).The magnitude of cloud-induced SW heating in response to poleward jet shifts under unforced climate variability is, however, highly correlated across models with that in response to rapid poleward jet shifts from CO2 quadrupling (Grise and Polvani 2014). In general, the models with large SW heating anomalies at midlatitudes in response to interannual poleward jet shifts are in worse agreement with observations, which exhibit a relatively muted radiative response (Grise and Polvani 2014; Ceppi and Hartmann 2015; Tselioudis et al. 2016). Together, these results suggest that the impact of jet shifts on CRE due to either a rapid adjustment to CO2 or to global warming is small.
The observed cloud response to jet shifts appears to be more complicated than a simple poleward shift leading to net radiative heating. Whereas midlevel and high clouds—which are tightly coupled to ascent in the storm tracks (Ceppi and Hartmann 2015; Grise and Polvani 2014; Li et al. 2014a,b)—do indeed move poleward with the jet, the net radiative effect of this shift is small. This is because of the close compensation between longwave and shortwave radiative effects for upper-level cloud anomalies, and because of the extensive underlying low-level clouds, which do not shift (Tselioudis et al. 2016) but instead tend to increase in regions vacated by upper-level clouds that are characterized by anomalous descent and increased low-level stability (Grise and Medeiros 2016). In unforced simulations of CAM5, Kay et al. (2014) find little change in absorbed solar radiation or in cloud liquid water path in response to month-to-month variability in SH jet latitude. They note that radiatively relevant low-level liquid clouds are dependent on temperature and lower-tropospheric stability, which can vary independently of jet location. Wall and Hartmann (2015) attribute the varied midlatitude jet shift–induced SW CRE responses across three aquaplanet models to differences in the response of low-level liquid clouds, which they trace to differences in their shallow convection and moist turbulence parameterizations. All of these results highlight the important role of low clouds in determining the overall cloud–radiative response to poleward jet shifts, and largely preventing the simple “drawing back of the curtains” response from being realized.
Grise and Medeiros (2016) explored the varied causes of cloud and radiation anomalies accompanying variations in the location of the midlatitude jet in both observations and models. Central to their analysis was a targeted assessment of the individual meteorological fields that change with the jet location, with a focus primarily on midtropospheric vertical motion (
However, many of the low-cloud and/or SW CRE anomalies highlighted in that study were not spatially coincident with either EIS or 
Our study seeks to better understand and quantify the role of various meteorological fields in controlling the low-cloud-cover response to interannual fluctuations in jet latitude over the North Pacific. While we have performed the analysis over other basins, we focus on the North Pacific here because it is the basin in which low-cloud-cover anomalies were arguably least well explained by EIS and 
2. Datasets
We consider monthly resolved data only over the oceans that are gridded via linear interpolation to a common 2° latitude by 2° longitude grid. All observational datasets are listed in Table 1, along with the period of their records that is utilized in this study.
Observational datasets and their analyzed time periods.
a. Observations
1) TOA radiative fluxes
TOA radiative fluxes come from the CERES Energy Balanced and Filled (EBAF) product, edition 4.0 (Loeb et al. 2018). CERES instruments fly on the Terra and Aqua satellites and measure filtered radiances in the shortwave (0.3–5 μm), total (0.3–200 μm), and infrared window (8–12 μm) regions. Unfiltered radiances are determined following Loeb et al. (2001). For the EBAF product, SW and longwave (LW) TOA fluxes are adjusted within their range of uncertainty to ensure TOA fluxes satisfy global-mean energy budget constraints (Loeb et al. 2009).
In addition to the direct broadband radiative fluxes observed at the TOA by CERES, we make use of TOA fluxes from the International Satellite Cloud Climatology Project (ISCCP)-FD product (Zhang et al. 2004). Rather than being directly observed, these radiative fluxes are computed using a radiative transfer model with cloud, surface, and atmospheric properties collected by weather satellites used as input. Throughout the paper, TOA radiation anomalies are defined such that positive values indicate anomalous planetary radiative heating.
2) Meteorological fields
Meteorological data come from ERA-Interim (Dee et al. 2011). We use 2-m temperature, sea level pressure, surface relative humidity, 1000- and 850-hPa winds, 700-hPa vertical pressure velocity, and 1000- and 700-hPa temperature in our analysis. Sea surface temperature (SST) data come from the NOAA Optimum Interpolation (OI) SST dataset, version 2 (Reynolds et al. 2002). We use ship-based measurements of air–sea differences in temperature and saturation-specific humidity from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) 2° enhanced product, release 3.0.1 (Freeman et al. 2017). Ocean-to-atmosphere latent and sensible heat fluxes come from the National Oceanography Centre, Southampton (NOCS), surface flux dataset v2.0. These fluxes are derived from ICOADS v2.4 ship measurements (Berry and Kent 2009, 2011).
3) Cloud fraction
The ISCCP cloud dataset is composed of data collected by a suite of geostationary and polar-orbiting weather satellites. We use the 3-hourly (D1) dataset to create monthly joint histograms of cloud fraction in seven cloud-top pressure and six cloud optical depth bins following Rossow and Schiffer (1999). As is done with the GCM-oriented ISCCP product (Pincus et al. 2012), we include only daytime observations and compute averages over the entire month at once rather than first averaging each of the eight daily observation times together. Thus the dataset is consistent with that produced by the ISCCP simulator.
We use histograms of cloud fraction from the Pathfinder Atmospheres–Extended (PATMOS-x) dataset. These data come from all five channels of the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the polar-orbiting platforms of NOAA and EUMETSAT. Cloud detection is based on six Bayesian classifiers derived from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO; Heidinger et al. 2012), and the retrieval is based on an optimal estimation approach (Heidinger and Pavolonis 2009). Cloud-top pressure and cloud emissivity are retrieved using two IR channels at all times of day, and cloud optical depth is obtained from solar channels during daytime (Walther and Heidinger 2012; Heidinger et al. 2012, 2014). We make use of the 1330 UTC NOAA PATMOS-x dataset provided by the GEWEX cloud assessment (Stubenrauch et al. 2013).
We use joint histograms of cloud fraction as a function of seven optical depth and seven cloud-top pressure bins from the Moderate Resolution Imaging Spectroradiometer (MODIS; Salomonson et al. 1989). MODIS is a whisk-broom scanning radiometer with 36 channels covering 0.42–14.24 μm, flying on both the Terra and Aqua spacecrafts. We use the L3 gridded cloud product (MYD08_M3) from the latest MODIS release (Collection 6), which is described in detail in Baum et al. (2012). To avoid radiometric calibration drift issues identified by Yue et al. (2017), we only make use of data from the Aqua MODIS instrument. To distinguish this dataset, which is produced by the MODIS Atmosphere Science Team (MAST), from the CERES-MODIS+GEO product described below, we hereafter refer to it as “MAST-MODIS.”
The CERES CldTypHist Ed4A product provides MODIS and geostationary satellite cloud properties stratified by cloud pressure and optical depth (Wielicki et al. 1996). The product utilizes Terra and Aqua MODIS cloud properties that are based on the CERES cloud working group SYN1deg Ed4A retrievals (Minnis et al. 2011), supplemented with 1-hourly geostationary cloud retrievals equatorward of 60° latitude to provide diurnal coverage. Cloud properties are aggregated into the three cloud-top pressure and three optical depth bins commonly reported in ISCCP-D2 cloud products and are reported separately for liquid and ice clouds, which we sum to get the total cloud fraction. We will hereafter refer to this dataset as “CERES-MODIS+GEO.”
We use cloud fraction derived from measurements taken by the Advanced Along-Track Scanning Radiometer (AATSR) on EnviSat, which is available from July 2002 through April 2012. These data are provided by the European Space Agency Climate Change Initiative (CCI; Hollmann et al. 2013), and contain cloud fraction derived using the Community Cloud retrieval for Climate framework (Sus et al. 2018; McGarragh et al. 2018) for low, midlevel, and high clouds, with bin boundaries at 680 and 440 hPa. More details on the dataset can be found in Stengel et al. (2017).
We make use of effective cloud fraction—the product of cloud fraction and emissivity—from the Atmospheric Infrared Sounder (AIRS). The AIRS instrument suite is composed of a hyperspectral infrared instrument with 2378 channels spanning 3.7 to 15 μm and the Advanced Microwave Sounding Unit flying on board Aqua (Aumann et al. 2003; Chahine et al. 2006). Version 6 of the AIRS product (Kahn et al. 2014) provides the effective cloud fraction for low, midlevel, and high clouds, with bin boundaries at 680 and 440 hPa. We average data from ascending and descending orbits into a single monthly mean field.
We use cloud fraction retrieved by the Multiangle Imaging SpectroRadiometer (MISR) on board Terra (Diner et al. 2005). MISR consists of nine cameras pointed at different view angles collecting radiances in four narrow spectral bands located at 443, 555, 670, and 865 nm. We use the level 3 CFMIP-Obs MISR cloud observations for climate model evaluation product, version 6 (Marchand et al. 2010; Marchand and Ackerman 2010), which provides joint histograms of cloud fraction as a function of 15 cloud-top height (CTH) and seven cloud optical depth bins. Following Marchand (2013), we separate high, midlevel, and low clouds using CTH boundaries of 3 and 7 km.
Finally, we use cloud fraction retrieved from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, which is a two-wavelength (532 and 1064 nm) polarization lidar (Winker et al. 2009) on the CALIPSO satellite. We use the “MapLowMidHigh” cloud fraction field provided as part of the GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) v3.0 prepared for the Observations for Model Intercomparisons Project (obs4MIPs; Chepfer et al. 2010), which contains low (below 3.2 km), midlevel (3.2 to 6.5 km), and high (above 6.5 km) cloud fractions. These fields are fully consistent with those produced by the CALIPSO simulator utilized in climate models.
b. Global climate models
We make use of unforced preindustrial control (“piControl”) simulations from 35 global climate models taking part in CMIP5 (Taylor et al. 2012). We consider all of the same quantities in GCMs as we do in observations, but cloud fraction diagnostics from climate models are in general not directly comparable to cloud fraction retrieved from satellite sensors. Therefore we use two approaches to facilitate model–observation comparison of low-cloud coverage. First, for the GCMs that have implemented the CFMIP Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), we use output from the ISCCP simulator (Klein and Jakob 1999; Webb et al. 2001) and from the CALIPSO simulator (Chepfer et al. 2008). ISCCP simulator-derived low-cloud cover, LCCI, is computed from the “clisccp” histograms as described above, while that from the CALIPSO simulator, LCCC, is reported directly (“cllcalipso”). Low-cloud coverage from these can be compared directly to their observational counterparts.
Second, we derive a proxy for LCC from the standard vertical profile of monthly mean cloud fraction diagnosed by each model (“cl”) as the maximum of cloud fraction between the surface and 680 hPa at each grid point (Noda and Satoh 2014; Zhou et al. 2015). This field is available from all models, regardless of whether they implemented COSP. We refer to this as 
Because COSP output is usually available for at least 40 years, often broken up into two noncontiguous 20-yr periods, we use model output from the period of time containing COSP output. For models without COSP output, we use the final 40 years of the piControl run. Table 2 lists the models used and the duration of COSP output utilized, if applicable.
Global climate models used in this study. Availability of COSP output is noted. The “r1i1p1” ensemble member of each model’s preindustrial control run is used. For CCSM4, we use the “r3i1p1” member, as it is the only member for which COSP output is available.
3. Methodology
In this study, we attempt to understand and explain the cloud and net TOA radiation response to interannual fluctuations in the latitude of the midlatitude eddy-driven jet over the North Pacific Ocean, defined as spanning from 135°E to 125°W. We focus primarily on the impact of clouds on net TOA radiation rather than its LW and SW components because we are motivated by the implications of a potential jet shift–related cloud feedback for climate sensitivity, which is more unambiguously related to the net planetary energy budget. Because nonlow clouds tend to have counteracting LW and SW radiative effects, this leads inevitably to a focus on the response of low clouds to jet shifts.
To derive these responses, we perform ordinary least squares linear regression of interannual anomalies in radiation, clouds, and relevant meteorological fields on interannual anomalies in jet latitude. We will hereafter refer to these as “responses to poleward jet shifts,” with the acknowledgment that this is shorthand for the typical anomalies present when the midlatitude jet is in an anomalously poleward position on the interannual time scale. We focus in this paper on responses computed using all months of the year, but discuss seasonal dependence of the results below.
We compute the jet latitude for the North Pacific basin by first locating the latitude at which the zonal-mean 850-hPa zonal wind within the basin reaches its maximum value. Then we identify the latitude of maximum wind speed at a resolution of 0.01° using a cubic spline fit following Barnes and Polvani (2013). The interannual standard deviation of North Pacific jet latitude is 3.0° over the period 1982–2016. Therefore, all responses to a 1° jet shift shown below should be multiplied by 3 to estimate the response to a typical fluctuation in jet location on interannual time scales.
In addition to the standard meteorological fields noted in the previous section, there are two environmental fields that we derive. First, to measure the strength of lower-tropospheric atmospheric stability, we compute the estimated inversion strength (EIS; Wood and Bretherton 2006). This is expected to have an important control on LCC because a stronger capping inversion opposes entrainment of drier free-tropospheric air, thereby favoring a moister and cloudier boundary layer (Klein and Hartmann 1993; Wood and Bretherton 2006).
Second, we estimate surface temperature advection (
Surface temperature advection derived as the product of monthly resolved winds and SST gradients may not fully capture the true monthly fluctuations in surface buoyancy flux because of substantial submonthly variability in winds and air–sea temperature differences at mid- and high latitudes (Miyamoto et al. 2018). As an independent check that 
Usage of SST gradients in estimating 
In all analyses, we remove the climatological annual cycle and any long-term trend before regressing any field on the jet latitude. Both the annual cycle and the long-term trend (estimated using ordinary least squares regression) are computed for the period of overlap between the jet latitude time series and the field of interest. Regression slopes that are statistically significant at the 95% confidence level are determined using the two-tailed Student’s t test, with degrees of freedom that account for temporal autocorrelation in each dataset (Bretherton et al. 1999).
4. Results
a. Why do observed interannual jet shifts lead to small basin-mean net CRE responses?
In response to a 1° poleward shift of the jet, net cloud radiative effect, which is negative on average throughout the basin, weakens in the northwestern part of the basin and strengthens in the southeastern part of the basin, especially just off the west coast of North America (Fig. 1a). Results derived using ISCCP-FD data are in qualitative agreement with those shown from CERES (appendix A and Fig. A1a). Rather than exhibiting a north–south dipole with a large positive CRE anomaly to the south and a smaller negative net CRE anomaly to the north, as would be expected if clouds simply shifted poleward with the jet, the anomalous structure is very zonally asymmetric and does not show any obvious correspondence to the anomalous 850-hPa zonal wind pattern, which is overlain in contours.
Anomalies in (a) CERES net CRE and in TOA net radiation induced by (b) all clouds, (c) nonlow clouds, and (d) low clouds in response to a 1° poleward jet shift. Anomalies in (b)–(d) are derived using cloud radiative kernels and MAST-MODIS cloud fraction histogram anomalies. Stippling indicates regression slopes that are statistically significant at the 95% confidence level. Overlain in (a)–(d) in contours are anomalies in the 850-hPa zonal wind, with a contour interval of 0.2 m s−1 and the zero contour in bold. (e) Response of the zonal-mean quantities to a 1° poleward jet shift, with statistically significant (at 95% confidence) responses indicated by filled circles. Shading represents the 2σ uncertainty in zonal-mean net CRE regression slope. Other slope uncertainties are omitted for clarity. (f) Amount and optical depth components of the low-cloud-induced radiation anomalies. Shading represents the 2σ uncertainty in zonal-mean regression slopes. All uncertainties are estimated using degrees of freedom that account for temporal autocorrelation.
Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0114.1
Given that CRE anomalies in this region can be caused by changes in both the amount and optical depth of clouds at all vertical levels, and that the relative importance of variations in environmental factors likely differs depending on the cloud type and property change of interest (Norris and Iacobellis 2005; Li et al. 2014a; Terai et al. 2016; Ceppi et al. 2016), it is important to determine which cloud types and properties are causing these observed CRE anomalies. To do so, we compute cloud-induced radiation anomalies using cloud radiative kernels and anomalies in cloud fraction—both as functions of cloud-top pressure and visible optical depth—and break down their contributions from changes in amount, altitude, and optical depth, for both low- (CTP > 680 hPa) and free-tropospheric (CTP ≤ 680 hPa) clouds (Zelinka et al. 2012a,b, 2016). Cloud-induced radiation anomalies computed using kernels applied to MAST-MODIS cloud fraction anomalies (Fig. 1b) agree closely with the CERES net CRE anomalies (Fig. 1a), indicating that CRE anomalies are caused primarily by anomalies in clouds rather than in noncloud fields. With the exception of the negative net CRE anomalies at 10°N near the date line, which are primarily due to free-tropospheric clouds (Fig. 1c), net CRE anomalies over the vast majority of the basin are primarily driven by low-cloud anomalies (Fig. 1d).
Zonally averaging across the North Pacific basin, there are small but statistically significant negative net CRE anomalies on the equatorward side of the climatological jet and small but statistically significant positive anomalies on the poleward side of the climatological jet (Fig. 1e). Hence, not only do low-cloud-induced radiation anomalies strongly oppose those of upper-level clouds, they dominate the overall net radiative signature of the jet shift, leading to a north–south dipole that is opposite to what would be expected if the cloud field simply shifted poleward. Breaking the low-cloud component down even further reveals that negative radiative anomalies on the equatorward side of the jet are due to local increases in low-cloud amount, and positive anomalies on the poleward side are due to decreases in low-cloud amount (Fig. 1f). Low-cloud optical depth changes in response to a jet shift are generally indistinguishable from zero in the zonal mean.
In summary, the large northwest–southeast dipole in the net CRE response to a poleward jet shift, which averages out to a near-zero basinwide net radiation anomaly (−0.02 and 0.01 W m−2 for CERES-EBAF and ISCCP-FD, respectively), is primarily driven by low-cloud amount changes. Hereafter, we will focus on understanding the causes of these low-cloud anomalies, particularly the increase in low clouds in the eastern part of the basin.
b. How robust is the low-cloud response to jet shifts?
We establish the robustness of these cloud anomalies across datasets that span different time periods and rely on different sensors (passive vs active), orbital configuration (polar-orbiting vs geostationary), retrieval algorithms, and spectral channels (IR only, visible only, and combined IR visible). In Fig. 2 we show the LCC response to a 1° poleward jet shift from all eight cloud datasets. LCC from passive retrievals has been adjusted to account for obscuration effects following Eq. (1).
Low-cloud-cover anomalies in response to a 1° poleward jet shift observed by (a) ISCCP, (b) PATMOS-x, (c) MAST-MODIS, (d) CERES-MODIS+GEO, (e) AATSR, (f) AIRS, (g) MISR, and (h) CALIPSO-GOCCP. Stippling indicates regression slopes that are statistically significant at the 95% confidence level. Overlain in black contours are anomalies in meridional 
Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0114.1
LCC anomalies are in excellent qualitative agreement across these eight observational datasets, and indicate a robust LCC increase across a large swath of the eastern North Pacific and a robust decrease in the northwestern portion of the basin. Rather than simply being revealed by poleward-shifted high clouds vacating the overlying atmosphere, these robust increases in obscuration-adjusted LCC occur over a broader region than is implied by the direct satellite view of low clouds. Despite the much shorter record, the active satellite measurements from CALIPSO qualitatively confirm the pattern of LCC anomalies seen in the passive sensors (Fig. 2h).
In all cases, a strong spatial correspondence is apparent between increased LCC and anomalous meridional surface cold advection, which is overlain in contours. The boundary between anomalous cold and warm advection (bold contour) roughly demarcates the boundary between positive and negative LCC anomalies. This relationship will be explored further below.
ISCCP often erroneously assigns clouds to midlevels when optically thin high clouds are present above low clouds (Marchand et al. 2010; Mace et al. 2011) or when low clouds are present under strong temperature inversions (Garay et al. 2008). However, we find that ISCCP LCC anomalies are in excellent agreement with anomalies in obscuration-adjusted low plus midlevel cloud cover (not shown), indicating that ISCCP is not underestimating the true LCC response.
c. How does the relevant large-scale meteorology respond to jet shifts?
Several meteorological fields that have been demonstrated to impact LCC exhibit regional anomalies in response to a poleward jet shift (Fig. 3). A poleward jet shift is associated with anomalously high sea level pressure throughout most of the basin, with maximum anomalies exceeding 1 hPa near about 45°N, 160°W (Fig. 3a). This is consistent with a poleward shift of the subtropical high as the circulation retreats poleward. Associated with this are anomalous anticyclonic surface winds, with anomalous northerlies in the southeastern part of the domain and anomalous southerlies in the northwestern part of the domain (Fig. 3c). These ERA-Interim wind anomalies are confirmed in ICOADS data (not shown).
Anomalies in (a) sea level pressure, surface (b) zonal and (c) meridional wind, (d) EIS, temperature advection by the (e) total and (f) meridional surface winds, (g) 
Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0114.1
We find that interannual variations in surface temperature advection north of 20°N are almost entirely driven by variations in winds rather than variations in SST gradients (not shown). Therefore, the aforementioned pattern of anomalous meridional flow is reflected in the pattern of anomalous surface temperature advection (Fig. 3e), which is largely set by its meridional component (Fig. 3f). Specifically, anomalous cold advection occurs in a triangular region bounded on the east by the North American coast and on the west by a southwest-to-northeast line passing through the center of the anomalous anticyclone. Anomalous cold advection is particularly pronounced west of California at around 40°N, 135°W. Anomalous warm advection is maximized along the Kuroshio, where anomalous southerlies pass over a sharp meridional SST gradient. Despite the fact that zonal wind anomalies are larger than meridional wind anomalies (Figs. 3b,c), 
Associated with anomalous cold advection in the eastern part of the basin are anomalous sensible and latent heat fluxes from ocean to atmosphere (Figs. 3h,i), as expected from studies of cold-air outbreaks (Bunker 1960; Grossman and Betts 1990; Brummer 1996). These anomalous fluxes are driven by large air–sea differences in temperature and saturation-specific humidity (Figs. 3k,l) as relatively cold and dry air passes southward over the relatively warm ocean surface in this region. The opposite is the case over the Kuroshio region, where the typically large sea-to-air fluxes are reduced by anomalously warm and moist poleward-flowing air. These results imply that 
Other relevant meteorological factors are perturbed when the jet shifts poleward. As highlighted in Grise and Medeiros (2016), EIS anomalies exhibit a north–south tripole across most of the North Pacific basin, with increased stability between about 30° and 55°N across the basin and decreased stability south of 30°N in the central part of the basin (Fig. 3d). These EIS anomalies are closely matched by 700-hPa temperature anomalies throughout most of the basin (not shown). Anomalously high stability also extends along the entire coast of North America from 20° to 50°N despite the presence of negative 700-hPa temperature anomalies. EIS increases in this region are caused by locally negative SST anomalies (Fig. 3j). A region of anomalous descent occurs in the northeastern Pacific, surrounded by regions of weaker anomalous ascent (Fig. 3g).
d. What are the key environmental factors driving the low-cloud response to jet shifts?
Having considered two-variate and three-variate regression models including all possible combinations of 10 commonly used low-cloud-controlling factors, we found that a bivariate model containing only EIS and 
Clouds at a given location are affected by upwind environmental conditions owing to the lag of up to several days between anomalies in environmental conditions and boundary layer properties (Klein et al. 1995). In some regions where the wind is steady, including the eastern North Pacific, this will be a location about 300–500 km away. To better capture the nonlocal effect of meteorology on clouds, we perform multiple linear regression analysis on fields that have been averaged into 10° latitude–longitude boxes rather than at the data’s native resolution.
If predictor variables are too strongly correlated with each other, they will not add skill to the regression model and are considered redundant. In such a situation of substantial multicollinearity, the derived sensitivity of LCC to individual predictors may change erratically in response to small changes in the model or the dataset, possibly giving invalid results about individual predictors. Variance inflation factors (VIFs) quantify how much larger the variance of an estimated regression coefficient is compared with what it would be if that variable were uncorrelated with the other model predictors. VIFs exceeding 5 or 10 are commonly considered to indicate that substantial multicollinearity is present. In all results shown below, VIFs rarely exceed 3 in any grid box (not shown). The relative stability of our regression coefficients across satellite datasets further supports their robustness.
In Figs. 4a and 4d, we show the ISCCP LCC sensitivity to the two cloud-controlling factors in our regression model, EIS and 
Sensitivity of ISCCP low-cloud cover to a standard deviation anomaly in (a) EIS and (d) 
Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0114.1
LCC increases with EIS at nearly every grid box, though the sensitivities are not statistically significant over much of the southern part and extreme northern part of the basin (Fig. 4a). This is consistent with Naud et al. (2016), who found that the climatological correlation between LCC and EIS for undisturbed and weakly subsiding regions in the tropics and midlatitudes (Wood and Bretherton 2006) also holds in post-cold-frontal regimes with strong subsidence and highly disturbed dynamical conditions.
At every grid box, LCC increases with increasing cold advection (Fig. 4d), as has been highlighted by previous authors (Deser et al. 1993; Klein et al. 1995; Norris 1998a,b; Mansbach and Norris 2007; Myers and Norris 2015; Seethala et al. 2015; Fletcher et al. 2016). The derived sensitivities are statistically significant at nearly every location. As noted above, the reasons for this relationship involve enhanced sea-to-air sensible and latent heat fluxes when colder, drier air passes over relatively warmer water, favoring low-cloud formation. The formation of fog under conditions of strong surface warm advection, which would give a relationship of the opposite sign, is apparently not manifest in calculations performed on these spatial scales.
Black contours overlain in Figs. 4a and 4d are the actual responses of each cloud-controlling factor to a 1° poleward shift of the jet. Since the responses are normalized by the standard deviations of their entire time series in each box, they are expressed in units of standard deviation σ. When multiplied by the previously discussed LCC sensitivities, they produce estimates of the contribution of each cloud-controlling factor to the actual LCC response (Figs. 4b,e).
As noted above, EIS anomalies exhibit a north–south tripole in the North Pacific, with increased stability north of about 30°N and decreased stability south of 30°N (Fig. 4a). The positive and negative EIS anomalies are comparable in size but the positive anomalies occur in a region in which LCC is more strongly sensitive to a standardized change in EIS. Therefore, the EIS-induced LCC increases north of 30°N are larger than the EIS-induced LCC decreases south of 30°N (Fig. 4b). The EIS-induced LCC patterns are quite different from the actual LCC anomalies in response to the jet shift (Fig. 4g), with a pattern correlation of 0.03.
Anomalous cold advection over all but the northwestern portion of the North Pacific (Fig. 4d) induces large increases in LCC in these regions, particularly in the region between 20°–40°N and 160°–130°W, where both the cold advection anomaly and the LCC sensitivity to cold advection are large (Fig. 4e). Decreases in LCC in the northwestern part of the domain are also attributable to increased warm advection (Fig. 4e). This pattern of LCC anomalies is highly correlated (r = 0.86) with the actual LCC anomalies shown in Fig. 4g, and indicates that the 
The connections observed here between the poleward-shifted jet, strengthened subtropical anticyclone, enhanced cold advection, and larger low-cloud fraction are consistent with the findings from a nearly 25-yr ship-based weather station record in the northeast Pacific at 30°N, 140°W (Klein et al. 1995). In that study, it was found that a stronger subtropical high favored increased low-level cloud amount at the observing station because of increased incidence and strength of cold advection and decreased incidence of midlatitude cyclones that could disrupt boundary layer processes.
The sum of both individual components of the multiple linear regression model is shown in Fig. 4h. Though imperfect, it largely reproduces the actual LCC response (Fig. 4g), both in terms of the spatial pattern (r = 0.81) and the magnitude of the anomalies. The same is true for all cloud datasets considered, with pattern correlations ranging from 0.69 to 0.85 across datasets (not shown). The regression model appears to better capture the increase in LCC in the eastern part of the domain than the decrease in LCC in the western part of the domain.
Over most grid boxes of the North Pacific, more interannual variance in LCC is explained by 
e. Do climate models capture the observed response of clouds and meteorology to jet shifts?
To further quantify the level of agreement between the actual cloud response and those predicted by the regression model, and to compare these quantities across observational datasets and with climate models, we compute average LCC sensitivities and responses over the red box region. For all results shown below, anomalies in the predictors and LCC are first averaged over this domain before regression slopes are computed. For both the observations and GCMs, cloud-controlling factors are first normalized by the standard deviation of the observed deseasonalized time series in the red box region. This allows the LCC sensitivities and the anomalies of the predictors to be directly compared across predictors, observational datasets, and models, as they are expressed with respect to a common 1σ observed interannual anomaly in each of the two predictors. Unless otherwise noted, model-based results will refer to 
First, we evaluate the ability of models to capture the observed sensitivity of LCC to the two cloud-controlling factors (Fig. 5a). In agreement with observations, most models show a statistically significant increase of LCC with increasing EIS when holding 
Quantities averaged over the red box region of the North Pacific, for both the piControl runs of 35 GCMs and eight satellite datasets. GCM results are summarized in the box-and-whisker plots, in which whiskers extend from the minimum to the maximum, the box extends from the 25th to 75th percentile, the dashed blue line represents the median, and the red symbol represents the mean. Three boxes are provided in (a) and (c), for results derived using three estimates of LCC (star: 
Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0114.1
All observational datasets agree on a decrease of LCC with increases in 
We now turn to the response of these three cloud-controlling factors to a 1° poleward jet shift (Fig. 5b). Multiple observational estimates of the meteorological response are provided, differing only because the period of analysis varies with the satellite dataset. Whereas the sensitivity of LCC to EIS and 
Box-mean 
LCC anomalies attributable to EIS anomalies plotted against those attributable to 
Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0114.1
As discussed previously, the cold advection–driven increase in LCC is the largest contributor to observed LCC increases in this region (Fig. 5c, column 2). In all but three observational datasets (AIRS, MAST-MODIS, and GOCCP), the 
Further evidence that GCMs capture the observed large-scale meteorological responses with much greater fidelity than they capture the cloud and radiation responses is provided in Fig. 7. These so-called Taylor diagrams (Taylor 2001) indicate that most models’ meteorological responses have pattern correlations with the observed responses exceeding 0.6, whereas most models’ net CRE and LCC responses have pattern correlations well below 0.5. The spatial variance of the multimodel mean EIS and 
Taylor diagram comparing modeled and observed responses of (a) EIS, (b) 
Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0114.1
Consistent with the model biases in net CRE, models systematically underestimate the spatial variance of observed ISCCP LCC anomalies and have very small pattern correlations with them (Fig. 7d). Similar results are seen for the 
In summary, we conclude that GCMs systematically underestimate the LCC increase in this region in response to a poleward jet shift because they underestimate contributions from increases in cold advection and, to a lesser extent, EIS. Because the models generally closely capture the observed responses of EIS and 
Actual LCC anomalies in the red box region plotted against LCC anomalies predicted by the multilinear regression model, for both models (blue symbols) and observations (black symbols). Observational cloud datasets are indicated with the same symbols as in Fig. 5. Error bars represent the 95% confidence intervals.
Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0114.1
5. Conclusions and discussion
Poleward jet shifts associated with interannual climate variability result in negligible net cloud radiative effects averaged over the North Pacific basin. In stark contrast to what would be expected if the total cloud field shifted poleward along with the jet toward regions of reduced insolation, observed jet shifts are characterized by zonally asymmetric net CRE anomalies, with cloud-induced cooling in the southeastern part of the basin countered by cloud-induced warming to the northwest. These CRE anomalies are primarily caused by anomalies in the coverage of low-level clouds. Specifically, low clouds dramatically increase over a broad expanse of the eastern subtropical and midlatitude North Pacific when the jet is anomalously poleward. These low-cloud-cover anomalies are robust across eight satellite datasets that span different time periods and are derived using diverse retrieval algorithms applied to measurements from both active and passive sensors across a range of spectral channels aboard both polar-orbiting and geostationary satellites.
We find that the low-cloud increases are primarily caused by enhanced cold advection in the eastern Pacific set up by anomalous northerly winds associated with an anomalous anticyclonic circulation spanning the Pacific basin. Increases in low-cloud cover are favored by enhanced cold advection because it drives enhanced turbulent fluxes from the ocean into the boundary layer as relatively cold and dry air flows southward over relatively warmer water. Increases in EIS play a secondary role in increasing low-cloud coverage. Models closely capture the large-scale dynamical response of the atmosphere to interannual variations in jet latitude, but the sensitivity of models’ LCC to cold advection is systematically too weak, leading to a systematic underestimate of the LCC increase. These biases are likely caused by parameterized physics in the models and may also be relevant to the representation of cloud feedbacks in GCMs under global warming. However, the component of the global warming response of low clouds attributable to 
Our results lead to an apparently different conclusion than those of Grise and Medeiros (2016), who highlighted the roles of EIS and 
In this study we have focused on the response of clouds to the jet location over the North Pacific Ocean in all months of the year, mainly to maximize the sample size. One can imagine that the results may vary with season and may be different in other ocean basins. The results shown in this study do not qualitatively change if the DJF season is considered in isolation, as that is the season with the largest increases in eastern North Pacific low-cloud cover and the one that apparently dominates the full response using all months (Fig. 10 of Grise and Medeiros 2016). LCC responses computed for the JJA season in isolation are much smaller in magnitude and less spatially coherent than shown in Fig. 2, and the meteorological responses are considerably less dramatic than those shown in Fig. 3 (not shown). Maps of the sensitivity of LCC to EIS and 
One may also expect midlatitude clouds to respond differently to changes in jet strength, frequency or strength of midlatitude cyclones, and shifts of the Hadley cell edge. Indeed, Tselioudis et al. (2016) argue that poleward shifts in the Hadley cell edge are the more important driver of midlatitude radiative feedbacks than poleward jet shifts because they are in general more effective at inducing poleward cloud shifts and clearing out the subtropics. How and why shifts of the Hadley cell edge affect low clouds differently and independently from concurrent poleward jet shifts remains to be understood, and would benefit from considering the responses of cloud-controlling factors. This is especially poignant given the apparent dependence of clouds, radiation, and even climate sensitivity on the location of the Hadley cell edge in GCMs (Lipat et al. 2017).
As noted in the introduction, observational analyses using long-term satellite and ground-based cloud observations indicate decreasing midlatitude cloud-cover trends, especially on the equatorward side of the climatological jet (Bender et al. 2012; Eastman and Warren 2013; Marvel et al. 2015; Norris et al. 2016). These studies commonly attribute these cloud trends to poleward circulation shifts without explicitly establishing the linkages between cloud properties and large-scale dynamics. Assuming the observational trends are real, then they need to be reconciled with the increasing evidence that poleward jet shifts are not responsible for reductions in total cloud cover at midlatitudes. Interpreting cloud changes through the lens of how their thermodynamic controls are changing seems to be a more useful approach than assessing their relatively indirect dependence on features of the large-scale circulation.
Because our study was motivated by a potential jet shift–induced cloud feedback and its possible implications for climate sensitivity, we have focused solely on understanding the impact of jet shift–induced low-cloud responses on top-of-the-atmosphere net radiation. Having confirmed previous findings that cloud responses to interannual jet shifts result in negligible perturbations to the planetary energy balance, it remains an open question how the very substantial regional cloud anomalies—including midlevel- and high-cloud anomalies that were not discussed here—may induce gradients in surface radiation and atmospheric radiative cooling, thereby affecting baroclinicity and potentially feeding back on jet strength and/or location. Observational analyses are needed to complement the growing body of GCM-based inferences on this topic (Ceppi et al. 2012, 2014; Li et al. 2015; Voigt and Shaw 2015; Ceppi and Hartmann 2016), especially in light of the model response errors highlighted in this study.
Acknowledgments
The work of M.D.Z., S.A.K., C.Z., and A.M.D. was supported by the Regional and Global Climate Modeling Program of the Office of Science of the U.S. Department of Energy (DOE) and was performed under the auspices of the U.S. DOE by LLNL under contract DE-AC52-07NA27344. M.D.Z.’s work was additionally supported by the NASA New Investigator Program (NNH14AX83I). K.M.G. was supported by the National Science Foundation under Grant AGS-1522829. M.W.C. was supported by the European Space Agency Climate Change Initiative Cloud_cci (contract 4000109870/13/I-NB). 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 (listed in Table 2) for producing and making available their model output. For CMIP, the U.S. DOE’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank Tim Myers, Bernard Lipat, and three anonymous reviewers for their helpful comments. CERES-EBAF Ed4.0 and CldTypHist data were obtained from the NASA Langley Research Center CERES ordering tool at http://ceres.larc.nasa.gov. ISCCP-FD data were obtained from the NASA Goddard Institute for Space Studies (http://isccp.giss.nasa.gov/projects/flux.html). The NOAA Optimum Interpolation (OI) SST dataset is provided by NOAA/NCEI at https://www.ncdc.noaa.gov/oisst. National Oceanography Centre/University of Southampton (NOCS) Surface Flux Dataset v2.0 is provided by the Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory at http://rda.ucar.edu/datasets/ds260.3/. ICOADS data are provided by the NOAA/OAR/ESRL/PSD, Boulder, Colorado, from their website at http://www.esrl.noaa.gov/psd/. ERA-Interim data are provided by ECMWF at http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/. ISCCP-D1 data are available from https://eosweb.larc.nasa.gov/project/isccp/isccp_d1_table. PATMOS-x data are available from the GEWEX cloud assessment at http://climserv.ipsl.polytechnique.fr/gewexca/instruments/PATMOSX.html. MODIS Collection 6 data are accessible from https://modis-atmos.gsfc.nasa.gov/MOD06_L2/index.html. We thank Dr. Q. Yue for advice on the appropriate MODIS dataset. AATSR data are available from the Centre for Environmental Data Analysis (CEDA; http://www.ceda.ac.uk, ESA, 2014). AIRS data are provided by the NASA Goddard Earth Sciences Data Information and Services Center (GESDISC) at https://disc.gsfc.nasa.gov/. MISR data are available from http://climserv.ipsl.polytechnique.fr/cfmip-obs/. CALIPSO-GOCCP data are provided at http://climserv.ipsl.polytechnique.fr/cfmip-obs/.
APPENDIX A
Consistency of TOA Radiation Anomalies among Datasets
As a check on the net CRE anomalies and attribution to individual cloud types shown in Fig. 1 with independent datasets, we show in Fig. A1 the net CRE anomalies in response to a 1° jet shift derived from ISCCP-FD fluxes, along with the TOA net radiation anomalies induced by all clouds (Fig. Alb), nonlow clouds (Fig. A1c), and low clouds (Fig. A1d) derived using cloud radiative kernels applied to anomalous ISCCP cloud fraction histograms. The longer ISCCP dataset used in generating this figure allows for more robust statistics and results in smoother fields with anomalies of smaller amplitude than those shown in Fig. 1. However, the qualitative picture remains unchanged: A 1° poleward jet shift induces a northwest-to-southeast dipole in the net CRE anomalies (Fig. A1a) that is confirmed in the kernel calculation (Fig. A1b), with large negative anomalies in the southeast Pacific that are primarily caused by increases in low-cloud coverage rather than optical depth (Figs. A1d,f).
As in Fig. 1, but for (a) ISCCP-FD net CRE anomalies and TOA net radiation induced by (b) all clouds, (c) nonlow clouds, and (d) low clouds in response to a 1° poleward jet shift. Anomalies in (b)–(d) are derived using cloud radiative kernels and ISCCP cloud fraction histogram anomalies.
Citation: Journal of Climate 31, 19; 10.1175/JCLI-D-18-0114.1
Unlike in Figs. 1c and 1d, however, the region of dominant nonlow cloud contribution extends more broadly from 10° to 25°N between 180° and 120°W, and from 20° to 40°N between 120°E and 150°W (Figs. A1c,d). Unlike Fig. 1e, the zonal-mean ISCCP-FD net CRE anomalies are not statistically different from zero at any latitude (Fig. A1e). These quantitative differences persist if considering only the period September 2002–December 2009, when ISCCP, CERES, and MODIS datasets overlap, indicating that they are due to differences in the observational datasets rather than to different time periods sampled (not shown).
APPENDIX B
Choosing the Optimum Set of Predictors
We assess the variance in LCC in the northeast Pacific red box region (20°–50°N, 180°–120°W) explained by two- and three-variate regression models for all possible combinations of 10 predictors that are common in the low-cloud literature. Specifically, in addition to 
We find that the best possible two-variable model (which employs EIS and 
Despite explaining marginally more LCC variance by including 
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CRE quantifies the impact of clouds on the top-of-atmosphere (TOA) energy budget relative to a hypothetical cloud-free planet, and is computed as the difference in downwelling radiation at the top of the atmosphere between all- and clear-sky conditions (Charlock and Ramanathan 1985).
