Climate Feedbacks and Emergent Constraints

Projected 21st-century changes in marine low-cloud cover (LCC) are known to exhibit a large spread across general circulation models (GCMs), with the sign of the change being either positive or negative. In a previous study, we developed a two-variable heuristic model to interpret LCC changes in 36 climate simulations from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). These two variables are: the tropical inversion strength (measured by the estimated inversion strength, EIS) and sea surface temperature (SST). Simulated 21st-century LCC changes in GCMs were approximated by the sum of EIS- and SST-induced LCC changes, where EIS- or SST-induced LCC changes are computed by multiplying the current climate's sensitivity of LCC to interannual EIS or SST variations, by (b) changes in EIS or SST over the 21st-century. The main goal of this paper is to elucidate the processes underlying LCC sensitivities to EIS and SST variations, and develop a method to constrain them observationally.lowing an abrupt increase in atmospheric carbon dioxide (CO2).

The process driving LCC sensitivity to EIS variations is well-understood. A stronger inversion suppresses mixing of boundary layer air with drier free-tropospheric air, leading to a shallower, moister and cloudier boundary layer. There is no strong consensus on the physical process underlying LCC sensitivity to SST variations, and SST may be a surrogate for multiple processes. A central goal of this study is to expand the two-variable heuristic model to include more variables, and shed light on the most important processes driving the SST sensitivity in GCMs. The additional variables included in a seven-variable heuristic model are: surface latent heat (LHF), the moisture contrast (δq) between the boundary layer and free troposphere, relative humidity in the free-troposphere (RH), vertical velocity at 700 hPa (Ω700), surface wind speed (Us), and horizontal temperature advection at the surface (Tadv). Terms associated with EIS, δq and LHF emerge as dominant (Fig.~1). The δq term contributes to an LCC decrease. The LHF term assumes large, negative values in many GCMs, but small, positive values are also seen. When combined, terms associated with LHF and δq successfully recover LCC changes associated with SST in the two-variable heuristic model. Terms associated with RH, Ω700, Us and Tadv are nearly negligible.

These results imply that the conventional interpretation of the simulated EIS sensitivity is correct, and that GCMs with EIS slopes outside observational bounds may have unrealistic EIS-LCC physics. These results also suggest a GCM’s SST sensitivity can be understood either as direct responses to temperature or as a combined response to changes in LHF and δq. Because LHF and δq sensitivities are not easily observed, we focus on GCM agreement with observed SST sensitivities. Satellite observed EIS and SST slopes and associated 5-95% confidence ranges are shown in Fig. 2. In about 75% of CMIP3 models and half of CMIP5 models, the EIS slope is less than observed. Similarly, the SST slope is smaller in magnitude than observed in about half the CMIP models. In only seven models are both EIS and SST slopes consistent with observed. These models give a range for LCC changes, -2 to -4% under the scenario A1B and -3 to -7% under RCP8.5. These predictions support a positive marine LCC feedback, albeit one with a large uncertainty range that includes both small and large 21st-century LCC decreases. Based on this study and others, we conclude that a negative marine LCC feedback is unlikely to be a credible response to anthropogenic forcing, and GCMs with a negative marine LCC feedback may systematically underestimate equilibrium climate sensitivity.

Figure 2: (Click to enlarge.) An observed constraint on EIS and SST slopes. (a) Scatterplot of the EIS slope vs. the SST slope in 36 CMIP3 and CMIP5 models. Both EIS and SST slopes are regional averages over the five main low cloud regions. The associated 5-95% confidence ranges are represented by the whiskers. Estimates of observed EIS and SST slopes are shown as the short-dashed lines, and the associated 5-95% confidence ranges are represented by the solid lines. The area within the confidence ranges is shaded in gray. The long-dashed line (y=-2.5x) is composed of all pairs of EIS and SST slopes corresponding to zero LCC changes according to Eq. (1), if the residual term is disregarded and the proportionality of SST change to EIS change is 2.5. This proportionality is typical for simulated SST and EIS changes in GCMs. Most models on the right side of the line exhibit an LCC increase, suggestive of a dominant role by EIS, while most models on the left side of the line exhibit an LCC decrease, suggestive of a dominant role by SST. (b) Simulated 21st-century LCC changes averaged over the five main low cloud regions in 18 CMIP3 models. (c) As in (b) but for 18 CMIP5 models. Models with EIS and SST slopes consistent with observed EIS and SST slopes are marked with the symbol “X” in (b) and (c). This is determined by whether or not there is discernable overlap between the confidence ranges of observed and simulated EIS and SST slopes. In each diagram, models are color-coded by the signs of simulated LCC changes, orange for positive and purple for negative.

Download the publication (Qu et al. 2015) describing these results in more detail. This study was conducted as part of a joint project between UCLA and Lawrence Livermore National Laboratory, funded by the US Department of Energy.

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