Biologically produced dimethylsulfide (DMS) is an important source of sulfur to the marine atmosphere that may affect cloud formation and properties. DMS is involved in a complex set of biochemical transformations and ecological exchanges so its global distribution is influenced by numerous factors, including oxidative stress from UV radiation. We re‐examine correlations between global surface DMS concentrations and mixed layer solar radiation dose (SRD), and find that SRD accounts for only a very small fraction (14%) of total variance in DMS measurements when using minimal aggregation methods. Moreover this relationship arises in part from the fact that when mixed layers deepen, both SRD and DMS decrease. When we control for this confounding effect, the correlation between DMS and SRD is reduced even further. These results indicate that factors other than solar irradiance play a leading role in determining global DMS emissions.
In this study, we examine marine low cloud cover variability in the Southeast Pacific and its association with lower-tropospheric stability (LTS) across a spectrum of timescales. On both daily and interannual timescales, LTS and low cloud amount are very well correlated in austral summer (DJF). Meanwhile in winter (JJA), when ambient LTS increases, the LTS–low cloud relationship substantially weakens. The DJF LTS–low cloud relationship also weakens in years with unusually large ambient LTS values. These are generally strong El Niño years, in which DJF LTS values are comparable to those typically found in JJA. Thus the LTS–low cloud relationship is strongly modulated by the seasonal cycle and the ENSO phenomenon. We also investigate the origin of LTS anomalies closely associated with low cloud variability during austral summer. We find that the ocean and atmosphere are independently involved in generating anomalies in LTS and hence variability in the Southeast Pacific low cloud deck. This highlights the importance of the physical (as opposed to chemical) component of the climate system in generating internal variability in low cloud cover. It also illustrates the coupled nature of the climate system in this region, and raises the possibility of cloud feedbacks related to LTS. We conclude by addressing the implications of the LTS–low cloud relationship in the Southeast Pacific for low cloud feedbacks in anthropogenic climate change.
The performance of five boundary layer parameterizations in the Weather Research and Forecasting Model is examined for marine boundary layer cloud regions running in single-column mode. Most parameterizations show a poor agreement of the vertical boundary layer structure when compared with large-eddy simulation models. These comparisons against large-eddy simulation show that a parameterization based on the eddy-diffusivity/mass-flux approach provides a better performance. The results also illustrate the key role of boundary layer parameterizations in model performance.
In 36 climate change simulations associated with phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), changes in marine low cloud cover (LCC) exhibit a large spread, and may be either positive or negative. Here we develop a heuristic model to understand the source of the spread. The model’s premise is that simulated LCC changes can be interpreted as a linear combination of contributions from factors shaping the clouds’ large-scale environment. We focus primarily on two factors—the strength of the inversion capping the atmospheric boundary layer (measured by the estimated inversion strength, EIS) and sea surface temperature (SST). For a given global model, the respective contributions of EIS and SST are computed. This is done by multiplying (1) the current-climate’s sensitivity of LCC to EIS or SST variations, by (2) the climate-change signal in EIS or SST. The remaining LCC changes are then attributed to changes in greenhouse gas and aerosol concentrations, and other environmental factors. The heuristic model is remarkably skillful. Its SST term dominates, accounting for nearly two-thirds of the intermodel variance of LCC changes in CMIP3 models, and about half in CMIP5 models. Of the two factors governing the SST term (the SST increase and the sensitivity of LCC to SST perturbations), the SST sensitivity drives the spread in the SST term and hence the spread in the overall LCC changes. This sensitivity varies a great deal from model to model and is strongly linked to the types of cloud and boundary layer parameterizations used in the models. EIS and SST sensitivities are also estimated using observational cloud and meteorological data. The observed sensitivities are generally consistent with the majority of models as well as expectations from prior research. Based on the observed sensitivities and the relative magnitudes of simulated EIS and SST changes (which we argue are also physically reasonable), the heuristic model predicts LCC will decrease over the 21st-century. However, to place a strong constraint, for example on the magnitude of the LCC decrease, will require longer observational records and a careful assessment of other environmental factors producing LCC changes. Meanwhile, addressing biases in simulated EIS and SST sensitivities will clearly be an important step towards reducing intermodel spread in simulated LCC changes.
Differences in simulations of tropical marine low‐cloud cover (LCC) feedback are sources of significant spread in temperature responses of climate models to anthropogenic forcing. Here we show that in models the feedback is mainly driven by three large‐scale changes—a strengthening tropical inversion, increasing surface latent heat flux, and an increasing vertical moisture gradient. Variations in the LCC response to these changes alone account for most of the spread in model‐projected 21st century LCC changes. A methodology is devised to constrain the LCC response observationally using sea surface temperature (SST) as a surrogate for the latent heat flux and moisture gradient. In models where the current climate's LCC sensitivities to inversion strength and SST variations are consistent with observed, LCC decreases systematically, which would increase absorption of solar radiation. These results support a positive LCC feedback. Correcting biases in the sensitivities will be an important step toward more credible simulation of cloud feedbacks.
Emergent constraints are physically explainable empirical relationships between characteristics of the current climate and long-term climate prediction that emerge in collections of climate model simulations. With the prospect of constraining long-term climate prediction, scientists have recently uncovered several emergent constraints related to long-term cloud feedbacks. We review these proposed emergent constraints, many of which involve the behavior of low-level clouds, and discuss criteria to assess their credibility. With further research, some of the cases we review may eventually become confirmed emergent constraints, provided they are accompanied by credible physical explanations. Because confirmed emergent constraints identify a source of model error that projects onto climate predictions, they deserve extra attention from those developing climate models and climate observations. While a systematic bias cannot be ruled out, it is noteworthy that the promising emergent constraints suggest larger cloud feedback and hence climate sensitivity.
How tropical low clouds change with climate remains the dominant source of uncertainty in global warming projections. An analysis of an ensemble of CMIP5 climate models reveals that a significant part of the spread in the models’ climate sensitivity can be accounted by differences in the climatological shallowness of tropical low clouds in weak-subsidence regimes: models with shallower low clouds in weak-subsidence regimes tend to have a higher climate sensitivity than models with deeper low clouds. The dynamical mechanisms responsible for the model differences are analyzed. Competing effects of parameterized boundary-layer turbulence and shallow convection are found to be essential. Boundary-layer turbulence and shallow convection are typically represented by distinct parameterization schemes in current models—parameterization schemes that often produce opposing effects on low clouds. Convective drying of the boundary layer tends to deepen low clouds and reduce the cloud fraction at the lowest levels; turbulent moistening tends to make low clouds more shallow but affects the low-cloud fraction less. The relative importance different models assign to these opposing mechanisms contributes to the spread of the climatological shallowness of low clouds and thus to the spread of low-cloud changes under global warming.
In this study, we evaluate the ability of the Weather Research and Forecasting model to simulate surface energy fluxes in the southeast Pacific stratocumulus region. A total of 18 simulations is performed for the period of October to November 2008, with various combinations of boundary layer, microphysics, and cumulus schemes. Simulated surface energy fluxes are compared to those measured during VOCALS-REx. Using a process-based model evaluation, errors in surface fluxes are attributed to errors in cloud properties. Net surface flux errors are mostly traceable to errors in cloud liquid water path (LWPcld), which produce biases in downward shortwave radiation. Two mechanisms controlling LWPcld are diagnosed. One involves microphysics schemes, which control LWPcld through the production of raindrops. The second mechanism involves boundary layer and cumulus schemes, which control moisture available for cloud by regulating boundary layer height. In this study, we demonstrate that when parameterizations are appropriately chosen, the stratocumulus deck and the related surface energy fluxes are reasonably well represented. In the most realistic experiments, the net surface flux is underestimated by about 10 W m−2. This remaining low bias is due to a systematic overestimation of the total surface cooling due to sensible and latent heat fluxes in our simulations. There does not appear to be a single physical reason for this bias. Finally, our results also suggest that inaccurate representation of boundary layer height is an important factor limiting further gains in model realism.
The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m−2 K−1 (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.