Comparison of Monthly Temperature Extremes Simulated by CMIP3 and CMIP5 Models

The extreme monthly-mean temperatures simulated by 28 models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated and compared with those from 24 models in the third phase of the Coupled Model Intercomparison Project (CMIP3). Comparisons with observations and reanalyses indicate that the models from both CMIP3 and CMIP5 perform well in simulating temperature extremes, which are expressed as 20-yr return values. When the climatological annual cycle is removed, the ensemble spread in CMIP5 is smaller than that in CMIP3. Benefitting from a higher resolution, the CMIP5 models perform better at simulating extreme temperatures on the local gridcell scale. The CMIP5 representative concentration pathway (RCP4.5) and CMIP3 B1 experiments project a similar change pattern in the near future for both warm and cold extremes, and the pattern is in agreement with that of the seasonal extremes. By the late twenty-first century, the changes in monthly temperature extremes projected under the three CMIP3 (B1, A1B, and A2) and two CMIP5 (RCP4.5 and RCP8.5) scenarios generally follow the changes in climatological annual cycles, which is consistent with previous studies on daily extremes. Compared with the CMIP3 ensemble, the CMIP5 ensemble shows a larger intermodel uncertainty with regard to the change in cold extremes in snow-covered regions. Enhanced changes in extreme temperatures that exceed the global mean warming are found in regions where the retreat of snow (or the soil moisture feedback effect) plays an important role, confirming the findings for daily temperature extremes.

An efficient method to generate a perturbed parameter ensemble of a fully coupled AOGCM without flux-adjustment

We present a simple method to generate a perturbed parameter ensemble (PPE) of a fully-coupled atmosphere-ocean general circulation model (AOGCM), HadCM3, without requiring flux-adjustment. The aim was to produce an ensemble that samples parametric uncertainty in some key variables and displays a similar range of behavior as seen in multi-model ensembles (MMEs). Six atmospheric parameters, a sea-ice parameter and an ocean parameter were jointly perturbed within a reasonable range to generate an initial group of 200 members. To screen out implausible ensemble members, 20 yr pre-industrial control simulations were run and members whose temperature response to the parameter perturbations was projected to be outside the range of 13.6 ± 2°C, i.e. near to the observed pre-industrial global mean, were discarded. 21 members, including the standard unperturbed model, were accepted, covering almost the entire span of the eight parameters, challenging the argument that without flux-adjustment parameter ranges would be unduly restricted. This ensemble was used in 3 experiments; a 800 yr pre-industrial, a 150 yr quadrupled CO2, and a 150 yr 1% CO2 rise per annum simulation. The behavior of the PPE for the pre-industrial control compared well to the CMIP3 ensemble for a number of surface and atmospheric column variables with the exception of a few members in the Tropics. However, we find that members of the PPE with low values of the entrainment rate coefficient show very large increases in upper tropospheric and stratospheric water vapor concentrations in response to elevated CO2 and some show implausibly high climate sensitivities, and as such some of these members will be excluded from future experiments with this ensemble. The outcome of this study is a PPE of a fully-coupled AOGCM which samples parametric uncertainty with a range of behavior similar to the CMIP3 ensemble and a simple methodology which would be applicable to other GCMs.