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    Atmospheric and hydrospheric sciences


    Comparison of regional characteristics of land precipitation climatology projected by an MRI-AGCM multi-cumulus scheme and multi-SST ensemble with CMIP5 multi-model ensemble projections

    Ito R, Nakaegawa T, Takayabu I

    Regional precipitation, Projection uncertainty, MRI-AGCM, CMIP5, Future climate change

    (a) Model performance ranked using Taylor’s skill score for annual precipitation. The x-axis denotes the model rank within the set of 55 models analyzed and the y-axis denotes the model rank averaged over all 26 regions. (b) Uncertainty estimates for future change projections of regional land mean precipitation in 2080–2099 with respect to 1984–2003 for the RCP8.5 scenario (% K–1). Gray plots indicate all members of the CMIP5 AOGCMs. Yellow and green plots indicate the members in the CMIP5 AOGCMs with the highest 12 skill scores for global precipitation (CMIP5high) and for regional land precipitation, respectively. Red (blue) plots indicate all members of 60-km (20-km) resolution MRI-AGCM3.2. (c–d) Fraction of the uncertainty range from the projections with the 60-km resolution MRI-AGCM3.2 relative to the range from (c) CMIP5All and (d) CMIP5high.

    Ensembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.