Evaluation of a component of the cloud response to climate change in an intercomparison of climate models

Most of the uncertainty in the climate sensitivity of contemporary general circulation models (GCMs) is believed to be connected with differences in the simulated radiative feedback from clouds. Traditional methods of evaluating clouds in GCMs compare time–mean geographical cloud fields or aspects of present-day cloud variability, with observational data. In both cases a hypothetical assumption is made that the quantity evaluated is relevant for the mean climate change response. Nine GCMs (atmosphere models coupled to mixed-layer ocean models) from the CFMIP and CMIP model comparison projects are used in this study to demonstrate a common relationship between the mean cloud response to climate change and present-day variability. Although atmosphere–mixed-layer ocean models are used here, the results are found to be equally applicable to transient coupled model simulations. When changes in cloud radiative forcing (CRF) are composited by changes in vertical velocity and saturated lower tropospheric stability, a component of the local mean climate change response can be related to present-day variability in all of the GCMs. This suggests that the relationship is not model specific and might be relevant in the real world. In this case, evaluation within the proposed compositing framework is a direct evaluation of a component of the cloud response to climate change. None of the models studied are found to be clearly superior or deficient when evaluated, but a couple appear to perform well on several relevant metrics. Whilst some broad similarities can be identified between the 60°N–60°S mean change in CRF to increased CO₂ and that predicted from present-day variability, the two cannot be quantitatively constrained based on changes in vertical velocity and stability alone. Hence other processes also contribute to the global mean cloud response to climate change.     

Temperature scaling pattern dependence on representative concentration pathway emission scenarios A letter

To preserve consistency among developed emission scenarios, the scenarios used in climate modeling, and the climate scenarios available for impact research, the pattern scaling technique is useful technique. The basic assumption of pattern scaling is that the spatial response pattern per 1 K increase in the global mean surface air temperature (SAT) (scaling pattern) is the same among emission scenarios, but this assumption requires further validation. We therefore investigated the dependence of the scaling pattern of the annual mean SAT on GHGs emission scenarios of representative concentration pathways (RCP) and the causes of that dependence using the Model for Interdisciplinary research on Climate 5 developed by Japanese research community. In particular, we focused on the relationships of the dependency with effects of aerosols and Atlantic meridional overturning circulation. We found significant dependencies of the scaling pattern on emission scenarios at middle and high latitudes of the Northern Hemisphere, with differences of >15 % over parts of East Asia, North America, and Europe. Impact researchers should take into account those dependencies that seriously affect their research. The mid-latitude dependence is caused by differences in sulfate aerosol emissions per 1 K increase in the global mean SAT, and the high-latitude dependence is mainly caused by nonlinear responses of sea ice and ocean heat transport to global warming. Long-term trends in land-use and land-cover changes did not significantly affect the scaling pattern of annual mean SAT, but they might have an effect at different timescales.     

Emission scenario dependencies in climate change assessments of the hydrological cycle

Anthropogenic global warming will lead to changes in the global hydrological cycle. The uncertainty in precipitation sensitivity per 1 K of global warming across coupled atmosphere-ocean general circulation models (AOGCMs) has been actively examined. On the other hand, the uncertainty in precipitation sensitivity in different emission scenarios of greenhouse gases (GHGs) and aerosols has received little attention. Here we show a robust emission-scenario dependency (ESD); smaller global precipitation sensitivities occur in higher GHG and aerosol emission scenarios. Although previous studies have applied this ESD to the multi-AOGCM mean, our surprising finding is that current AOGCMs all have the common ESD in the same direction. Different aerosol emissions lead to this ESD. The implications of the ESD of precipitation sensitivity extend far beyond climate analyses. As we show, the ESD potentially propagates into considerable biases in impact assessments of the hydrological cycle via a widely used technique, so-called pattern scaling. Since pattern scaling is essential to conducting parallel analyses across climate, impact, adaptation and mitigation scenarios in the next report from the Intergovernmental Panel on Climate Change, more attention should be paid to the ESD of precipitation sensitivity.     

A PDF-based hybrid prognostic cloud scheme for general circulation models

A new cloud parameterization based on prognostic equations for the subgrid-scale fluctuations in temperature and total water content is introduced for global climate models. The proposed scheme, called hybrid prognostic cloud (HPC) parameterization, employs simple probability density functions (PDFs) to the horizontal subgrid-scale inhomogeneity, allowing them to vary in shape in response to small-scale processes such as cumulus detrainment and turbulent mixing. Simple tests indicate that the HPC scheme is highly favorable as compared to a diagnostic scheme in terms of the cloud fraction and cloud water content under either uniform or non-uniform forcing. The relevance of the HPC scheme is investigated by implementing it in an atmospheric component model of the climate model MIROC with a coarse resolution of T42. A comparison of the short-term integrations between the T42 model and a global cloud resolving model (GCRM) reveals that the HPC scheme can reproduce, to a certain degree, the subgrid-scale variance and skewness of temperature and total water content simulated in the GCRM. It is also found that the HPC scheme significantly alters the climatological distributions in cloud cover, precipitation, and moisture, which are all improved from the model using a conventional diagnostic cloud scheme.