Ivy Tan

Observational constraints on mixed-phase clouds imply higher climate sensitivity

A more sensitive climate system How much global average temperature eventually will rise depends on the Equilibrium Climate Sensitivity (ECS), which relates atmospheric CO2 concentration to atmospheric temperature. For decades, ECS has been estimated to be between 2.0° and 4.6°C, with much of that uncertainty owing to the difficulty of establishing the effects of clouds on Earths energy budget. Tan et al. used satellite observations to constrain the radiative impact of mixed phase clouds. They conclude that ECS could be between 5.0° and 5.3°C—higher than suggested by most global climate models. Science, this issue p. 224 Weaknesses in cloud parameterizations may be causing global climate models to underestimate future warming. Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO2, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C. Clouds are among the leading causes of this uncertainty. Here we show that the ECS can be up to 1.3°C higher in simulations where mixed-phase clouds consisting of ice crystals and supercooled liquid droplets are constrained by global satellite observations. The higher ECS estimates are directly linked to a weakened cloud-phase feedback arising from a decreased cloud glaciation rate in a warmer climate. We point out the need for realistic representations of the supercooled liquid fraction in mixed-phase clouds in GCMs, given the sensitivity of the ECS to the cloud-phase feedback.

On the relationships among cloud cover, mixed-phase partitioning, and planetary albedo in GCMs

In this study, it is shown that CMIP5 global climate models (GCMs) that convert supercooled water to ice at relatively warm temperatures tend to have a greater mean-state cloud fraction and more negative cloud feedback in the middle and high latitude Southern Hemisphere. We investigate possible reasons for these relationships by analyzing the mixed-phase parameterizations in 26 GCMs. The atmospheric temperature where ice and liquid are equally prevalent (T5050) is used to characterize the mixed-phase parameterization in each GCM. Liquid clouds have a higher albedo than ice clouds, so, all else being equal, models with more supercooled liquid water would also have a higher planetary albedo. The lower cloud fraction in these models compensates the higher cloud reflectivity and results in clouds that reflect shortwave radiation (SW) in reasonable agreement with observations, but gives clouds that are too bright and too few. The temperature at which supercooled liquid can remain unfrozen is strongly anti-correlated with cloud fraction in the climate mean state across the model ensemble, but we know of no robust physical mechanism to explain this behavior, especially because this anti-correlation extends through the subtropics. A set of perturbed physics simulations with the Community Atmospheric Model Version 4 (CAM4) shows that, if its temperature-dependent phase partitioning is varied and the critical relative humidity for cloud formation in each model run is also tuned to bring reflected SW into agreement with observations, then cloud fraction increases and liquid water path (LWP) decreases with T5050, as in the CMIP5 ensemble.

Spaceborne lidar observations of the ice‐nucleating potential of dust, polluted dust, and smoke aerosols in mixed‐phase clouds

Previous laboratory studies and in situ measurements have shown that dust particles possess the ability to nucleate ice crystals, and smoke particles to some extent as well. Even with coatings of pollutants such as sulphate and nitrate on the surface of dust particles, it has been shown that polluted dust particles are still able to nucleate ice in the immersion, deposition, condensation, and contact freezing modes, albeit less efficiently than unpolluted dust. The ability of these aerosols to act as ice nuclei in the Earths atmosphere has important implications for the Earths radiative budget and hence global climate change. Here we determine the relationship between cloud thermodynamic phase and dust, polluted dust, and smoke aerosols individually by analyzing their vertical profiles over a ∼5 year period obtained by NASAs spaceborne lidar, Cloud-Aerosol Lidar with Orthogonal Polarization. We found that when comparing the effects of temperature and aerosols, temperature appears to have the dominant influence on supercooled liquid cloud fraction. Nonetheless, we found that aerosols still appear to exert a strong influence on supercooled liquid cloud fraction as suggested by the existence of negative temporal and spatial correlations between supercooled liquid cloud fraction and frequencies of dust aerosols from around the world, at the −10°C, −15°C, −20°C, and −25°C isotherms. Although smoke aerosol frequencies were also found to be negatively correlated with supercooled liquid cloud fraction, their correlations are weaker in comparison to those between dust frequencies and supercooled liquid cloud fraction. For the first time, we show this based on observations from space, which lends support to previous studies that dust and potentially smoke aerosols can globally alter supercooled liquid cloud fraction. Our results suggest that the ice-nucleating ability of these aerosols may have an indirect climatic impact that goes beyond the regional scale, by influencing cloud thermodynamic phase globally.

Sensitivity Study on the Influence of Cloud Microphysical Parameters on Mixed-Phase Cloud Thermodynamic Phase Partitioning in CAM5

AbstractThe influence of six CAM5.1 cloud microphysical parameters on the variance of phase partitioning in mixed-phase clouds is determined by application of a variance-based sensitivity analysis. The sensitivity analysis is based on a generalized linear model that assumes a polynomial relationship between the six parameters and the two-way interactions between them. The parameters, bounded such that they yield realistic cloud phase values, were selected by adopting a quasi–Monte Carlo sampling approach. The sensitivity analysis is applied globally, and to 20°-latitude-wide bands, and over the Southern Ocean at various mixed-phase cloud isotherms and reveals that the Wegener–Bergeron–Findeisen (WBF) time scale for the growth of ice crystals single-handedly accounts for the vast majority of the variance in cloud phase partitioning in mixed-phase clouds, while its interaction with the WBF time scale for the growth of snowflakes plays a secondary role. The fraction of dust aerosols active as ice nuclei in l...

Influence of cloud phase composition on climate feedbacks

The ratio of liquid water to ice in a cloud, largely controlled by the presence of ice nuclei and cloud temperature, alters cloud radiative effects. This study quantitatively examines how the liquid fraction of clouds influences various climate feedbacks using the NCAR Community Atmosphere Model (CAM). Climate feedback parameters were calculated using equilibrated temperature changes in response to increases in the atmospheric concentration of carbon dioxide in CAM Version 3.0 with a slab ocean model. Two sets of model experiments are designed such that cloud liquid fraction linearly decreases with a decrease in temperature down to −20°C (Experiment “C20”) and −40°C (Experiment “C40”). Thus, at the same subzero temperature, C20 yields fewer liquid droplets (and more ice crystals) than C40. Comparison of the results of experiments C20 and C40 reveals that experiment C20 is characterized by stronger cloud and temperature feedbacks in the tropics (30°N–30°S) (by 0.25 and −0.28 W m−2 K−1, respectively) but weaker cloud, temperature, and albedo feedbacks (by −0.20, 0.11, and −0.07 W m−2 K−1) in the extratropics. Compensation of these climate feedback changes leads to a net climate feedback change of ~7.28% of that of C40 in the model. These results suggest that adjustment of the cloud phase function affects all types of feedbacks (with the smallest effect on water vapor feedback). Although the net change in total climate feedback is small due to the cancellation of positive and negative individual feedback changes, some of the individual changes are relatively large. This illustrates the importance of the influence of cloud phase partitioning for all major climate feedbacks, and by extension, for future climate change predictions.

Cloud Phase Changes Induced by CO2 Warming—a Powerful yet Poorly Constrained Cloud-Climate Feedback

We review a cloud feedback mechanism that has so far been considered of secondary importance, despite a body of research suggesting that it represents a powerful climate feedback that can control the sign of the overall cloud feedback simulated in global climate models (GCMs). The feedback mechanism is associated with phase changes in clouds triggered by a warming atmosphere, which in turn yields optically thicker clouds. Output from the latest generation of GCMs suggest that this is the dominant cloud feedback at high latitudes, with obvious implications for climatically sensitive regions such as the Arctic and the Southern Ocean. Here, we present an overview of the relatively few modeling studies that have investigated this particular feedback mechanism to date, along with new results suggesting that the cloud-climate feedback simulated by a GCM can change dramatically depending on its cloud phase partitioning.

Chapter 10 – The Climatic Impact of Thermodynamic Phase Partitioning in Mixed-Phase Clouds

Abstract This chapter presents an extension of previous work on the impact of the supercooled liquid fraction (SLF) of mixed-phase clouds on equilibrium climate sensitivity (ECS) using a series of coupled climate simulations constrained by satellite observations. Simulations with vastly differing values of SLF were run with both present-day and doubled CO 2 concentrations. The ECS values were shown to increase monotonically with increasing SLF, and the increases in ECS were attributed to a progressive weakening of the cloud phase feedback. This study presents non-cloud feedbacks (Planck, water vapor, lapse rate, albedo) and cloud feedbacks computed using two different methods. In the global mean, the Planck feedback is the strongest non-cloud feedback followed by the water vapor feedback. Additional simulations run to gauge the influence of the model tuning required to avoid climate drift in the coupled simulations suggest that the results of previous work are robust to retuning.