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Latent heating modifies the jet stream by modifying the vertical geostrophic wind shear, thereby altering the potential for baroclinic development. Hence, correctly representing diabatic effects is important for modelling the mid-latitude atmospheric circulation and variability. However, the direct effects of diabatic heating remain poorly understood. For example, there is no consensus on the effect of latent heating on the cross-jet temperature contrast. We show that this disagreement is attributable to the choice of spatio-temporal averaging. Jet representations relying on averaged wind tend to have the strongest latent heating on the cold flank of the jet, thus weakening the cross-jet temperature contrast. In contrast, jet representations reflecting the two-dimensional instantaneous wind field have the strongest latent heating on the warm flank of the jet. Furthermore, we show that latent heating primarily occurs on the warm flank of poleward directed instantaneous jets, which is the case for all storm tracks and seasons. [ABSTRACT FROM AUTHOR]

Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. "Cloud controlling factor" (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback. [ABSTRACT FROM AUTHOR]

Latent heating modifies the jet stream by modifying the vertical geostrophic wind shear, thereby altering the potential for baroclinic development. Hence, correctly representing diabatic effects is important for modelling the mid-latitude atmospheric circulation and variability. Yet, the direct effects of diabatic heating remain poorly understood. For example, there is no consensus on the effect of latent heating on the cross-jet temperature contrast. We show that this disagreement is attributable to the choice of spatio-temporal filtering. Jet representations relying on filtered wind tend to have the strongest latent heating on the cold flank of the jet, thus weakening the cross-jet temperature contrast. In contrast, jet representations reflecting the two-dimensional instantaneous wind field have the strongest latent heating on the warm flank of the jet. Furthermore, we show that latent heating primarily occurs on the warm flank of poleward directed instantaneous jets, which is the case for all storm tracks and seasons. [ABSTRACT FROM AUTHOR]

Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. "Cloud Controlling Factor" (CCF) analysis derives relationships between large-scale meteorological drivers and cloud-radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear. All combinations of tested CCFs predict historical, monthly variability well for most locations at grid-cell scales. Differences between configurations for predicting globally-aggregated radiative anomalies are more pronounced, where configurations including SUT outperform others. We show that for predicting local, historical anomalies, spatial domain size is more important than the selection of CCFs, finding an important discrepancy between optimal domain sizes for local and globally-aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks, and thus deduce that inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback. [ABSTRACT FROM AUTHOR]

Most current methods for evaluating climate feedbacks utilise variation with time in Earth's energy balance and surface temperatures, either from observations or Earth system model perturbation experiments. This study presents a new semi-empirical evaluation of Earth's climate feedbacks at equilibrium, constrained instead by variation with latitude in recent mean climatology. Latitudinally binned surface temperature and outgoing radiation climatology provides a first order net climate feedback estimate λ= -1.3±0.1 Wm-2 K-1, but this does not isolate the temperature influence on outgoing radiation from other factors. To isolate the surface temperature influence: First, we derive approximated functional relations for outgoing shortwave and longwave radiation in terms of surface temperature, surface relative humidity, fractional cloud amount, tropopause height and incident solar radiation. Second, we use observations of current zonal-mean climatology to constrain the relations and apply calculus to evaluate non-cloud climate feedbacks with latitude, including the Planck, water vapour-lapse rate and surface albedo. Our novel climatology-based evaluations of climate feedbacks weighted by the recent warming pattern, when combined with a recent estimate of cloud feedback from multiple lines of evidence, implies a global mean total net climate feedback λ= -1.1 (-0.8 to -1.4 at 66 % range) Wm-2 K-1 consistent with recent assessments of the literature. Our latitudinal method to constrain non-cloud climate feedback is independent of previous temporal approaches, using different observational lines of evidence, and so our method complements existing methods to help constrain climate feedback and climate sensitivity. [ABSTRACT FROM AUTHOR]