Role of H2O and CO2 Ices in Martian Climate Changes

In order to study the stability of martian climate, we constructed a two-dimensional (horizontal-vertical) energy balance model. The long-term CO₂ mass exchange process between the atmosphere and CO₂ ice caps is investigated with particular attention to the effect of planetary ice distribution on the climate stability. Our model calculation suggests that high atmospheric pressure presumed for past Mars would be unstabilized if H₂O ice widely prevailed. As a result, a cold climate state might have been achieved by the condensation of atmospheric CO₂ onto ice caps. On the other hand, the low atmospheric pressure, which is buffered by the CO₂ ice cap and likely close to the present pressure, would be unstabilized if the CO₂ ice albedo decreased. This may have led the climate into a warm state with high atmospheric pressure owing to complete evaporation of CO₂ ice cap. Through the albedo feedback mechanisms of H₂O and CO₂ ices in the atmosphere-ice cap system, Mars may have experienced warm and cold climates episodically in its history.     

Perturbed physics ensemble using the MIROC5 coupled atmosphere–ocean GCM without flux corrections: experimental design and results

In this study, we constructed a perturbed physics ensemble (PPE) for the MIROC5 coupled atmosphere–ocean general circulation model (CGCM) to investigate the parametric uncertainty of climate sensitivity (CS). Previous studies of PPEs have mainly used the atmosphere-slab ocean models. A few PPE studies using a CGCM applied flux corrections, because perturbations in parameters can lead to large radiation imbalances at the top of the atmosphere and climate drifts. We developed a method to prevent climate drifts in PPE experiments using the MIROC5 CGCM without flux corrections. We simultaneously swept 10 parameters in atmosphere and surface schemes. The range of CS (estimated from our 35 ensemble members) was not wide (2.2–3.2?°C). The shortwave cloud feedback related to changes in middle-level cloud albedo dominated the variations in the total feedback. We found three performance metrics for the present climate simulations of middle-level cloud albedo, precipitation, and ENSO amplitude that systematically relate to the variations in shortwave cloud feedback in this PPE.     

Fast and slow timescales in the tropical low-cloud response to increasing CO2 in two climate models

To obtain physical insights into the response and feedback of low clouds (C _l ) to global warming, ensemble 4?×?CO₂ experiments were carried out with two climate models, the Model for Interdisciplinary Research on Climate (MIROC) versions 3.2 and 5. For quadrupling CO₂, tropical-mean C _l decreases, and hence, acts as positive feedback in MIROC3, whereas it increases and serves as negative feedback in MIROC5. Three time scales of tropical-mean C _l change were identified—an initial adjustment without change in the global-mean surface air temperature, a slow response emerging after 10–20?years, and a fast response in between. The two models share common features for the former two changes in which C _l decreases. The slow response reflects the variability of C _l associated with the El Ni?o-Southern Oscillation in the control integration, and may therefore be constrained by observations. However, the fast response is opposite in the two models and dominates the total response of C _l . Its sign is determined by a subtle residual of the C _l increase and decrease over the ascending and subsidence regions, respectively. The regional C _l increase is consistent with a more frequent occurrence of a stable condition, and vice versa, as measured by lower-tropospheric stability (LTS). The above frequency change in LTS is similarly found in six other climate models despite a large difference in both the mean and the changes in the low-cloud fraction for a given LTS. This suggests that the response of the thermodynamic constraint for C _l to increasing CO₂ concentrations is a robust part of the climate change.     

Reliability and importance of structural diversity of climate model ensembles

We investigate the performance of the newest generation multi-model ensemble (MME) from the Coupled Model Intercomparison Project (CMIP5). We compare the ensemble to the previous generation models (CMIP3) as well as several single model ensembles (SMEs), which are constructed by varying components of single models. These SMEs range from ensembles where parameter uncertainties are sampled (perturbed physics ensembles) through to an ensemble where a number of the physical schemes are switched (multi-physics ensemble). We focus on assessing reliability against present-day climatology with rank histograms, but also investigate the effective degrees of freedom (EDoF) of the fields of variables which makes the statistical test of reliability more rigorous, and consider the distances between the observation and ensemble members. We find that the features of the CMIP5 rank histograms, of general reliability on broad scales, are consistent with those of CMIP3, suggesting a similar level of performance for present-day climatology. The spread of MMEs tends towards being “over-dispersed” rather than “under-dispersed”. In general, the SMEs examined tend towards insufficient dispersion and the rank histogram analysis identifies them as being statistically distinguishable from many of the observations. The EDoFs of the MMEs are generally greater than those of SMEs, suggesting that structural changes lead to a characteristically richer range of model behaviours than is obtained with parametric/physical-scheme-switching ensembles. For distance measures, the observations and models ensemble members are similarly spaced from each other for MMEs, whereas for the SMEs, the observations are generally well outside the ensemble. We suggest that multi-model ensembles should represent an important component of uncertainty analysis.