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Climate models exhibit significant biases in simulating present‐day tropical Pacific sea surface temperature (SST) patterns, particularly the zonal SST gradient, which may contribute to uncertainties in precipitation projections over mid‐latitude populated regions. Biases in the simulated tropical Pacific SST gradient across CMIP6 models significantly influence present‐day and future winter precipitation over South America through a stationary wave pattern resembling the Pacific–South American (PSA‐2) mode. Models with a weaker‐than‐observed SST gradient simulate a deeper trough east of South America, resulting in stronger wetting trends over northern Argentina. Applying observational constraints reduces uncertainties in projected precipitation trends by approximately 31%. For Tasmania and New Zealand, SST gradient biases impact the simulation of present‐day winter precipitation, but are not well correlated with future precipitation projections. Our findings highlight the critical need to accurately represent the tropical Pacific SST gradient and its associated atmospheric circulation features for reliable regional climate simulation. 

This study investigates how clouds and their atmospheric radiative effects respond to meridional shifts in the Southern Hemisphere (SH) mid‐latitude jet, represented by the Southern Annular Mode (SAM), using reanalysis data, CloudSat/CALIPSO observations, and CMIP6 models. Consistent with previous studies, poleward jet shifts displace storm‐track clouds, creating lower tropospheric radiative heating anomalies poleward of the mean jet latitude and cooling anomalies on the equatorward side of the mean jet latitude where large‐scale subsidence increases low cloud fraction. Whether these radiative heating anomalies can affect SAM persistence is also investigated in CMIP6 models. If observed sea surface temperatures are prescribed, models that simulate low cloud responses more realistically show less SAM persistence, aligning more closely with observations. Our results based on CMIP6 models agree with a recent idealized modeling study and suggest that atmospheric cloud radiative heating anomalies, induced by the poleward jet shift, contribute to a reduction in SAM persistence. 

Two common methods used to develop a process-level understanding of global cloud cover are 1) analyzing large-scale meteorological variables (cloud controlling factors) associated with cloud variability and 2) classifying cloud types using clustering algorithms applied to satellite data, such as the International Satellite Cloud Climatology Project (ISCCP) weather states. The cloud controlling factor method is advantageous to apply to climate models, as it does not rely on cloud parameterizations or the availability of satellite simulator output. The purpose of this study is to document the relationship between cloud controlling factors and the ISCCP weather states in the observational record, providing a benchmark for the application of cloud controlling factors to study individual cloud types in future studies. Most ISCCP weather states are linked to distinct dynamical regimes characterized by unique combinations of six cloud controlling factors. These relationships are present in both the long-term mean climatology and daily-to-monthly climate variability. For example, deep convective and midlatitude storm clouds dominate ascending regions. In descending regions, shallow cumulus is more frequent in regimes characterized by weak boundary layer temperature inversions [estimated inversion strength (EIS)] and strong subsidence, and stratocumulus is more frequent in regimes with larger values of EIS, weaker subsidence, and relatively weak near-surface cold advection. Midlevel clouds are prominent in descending regions with strong cold advection. Overall, the results of this study suggest promise in using cloud controlling factors to identify dynamical regimes where individual cloud types are more or less likely and to understand the physical processes responsible for the transitions among them. 

Anthropogenically forced climate change signals are emerging from the noise of internal variability in observations, and the impacts on society are growing. For decades, Climate or Earth System Models have been predicting how these climate change signals will unfold. While challenges remain, given the growing forced trends and the lengthening observational record, the climate science community is now in a position to confront the signals, as represented by historical trends, in models with observations. This review covers the state of the science on the ability of models to represent historical trends in the climate system. It also outlines robust procedures that should be used when comparing modeled and observed trends and how to move beyond quantification into understanding. Finally, this review discusses cutting-edge methods for identifying sources of discrepancies and the importance of future confrontations. 

Abstract An effective method to understand cloud processes and to assess the fidelity with which they are represented in climate models is the cloud controlling factor framework, in which cloud properties are linked with variations in large-scale dynamical and thermodynamical variables. This study examines how midlatitude cloud radiative effects (CRE) over oceans co-vary with four cloud controlling factors: mid-tropospheric vertical velocity, estimated inversion strength (EIS), near-surface temperature advection, and sea surface temperature (SST), and assesses their representation in CMIP6 models with respect to observations and CMIP5 models. CMIP5 and CMIP6 models overestimate the sensitivity of midlatitude CRE to perturbations in vertical velocity, and underestimate the sensitivity of midlatitude shortwave CRE to perturbations in EIS and temperature advection. The largest improvement in CMIP6 models is a reduced sensitivity of CRE to vertical velocity perturbations. As in CMIP5 models, many CMIP6 models simulate a shortwave cloud radiative warming effect associated with a poleward shift in the Southern Hemisphere (SH) midlatitude jet stream, an effect not present in observations. This bias arises because most models’ shortwave CRE are too sensitive to vertical velocity perturbations and not sensitive enough to EIS perturbations, and because most models overestimate the SST anomalies associated with SH jet shifts. The presence of this bias directly impacts the transient surface temperature response to increasing greenhouse gases over the Southern Ocean, but not the global-mean surface temperature. Instead, the models’ climate sensitivity is correlated with their shortwave CRE sensitivity to surface temperature advection perturbations near 40°S, with models with more realistic values of temperature advection sensitivity generally having higher climate sensitivity.