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

Abstract

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.

Atmospheric Rivers Bring More Frequent and Intense Extreme Rainfall Events Over East Asia Under Global Warming

“…The AGCM used in the d4PDF is the Meteorological Research Institute AGCM version 3.2 (MRI‐AGCM3.2; Mizuta et al., 2012) with 60‐km horizontal resolution and 64 vertical layers. This model realistically simulates rainfall (Ito et al., 2020) and AR activity (Kamae, Mei, et al., 2017) over East Asia. For the past simulations, the AGCM was driven by historical radiative forcing (greenhouse gases, aerosols, and ozone), sea surface temperature (SST) and sea ice (Hirahara et al., 2014) for 1951–2010 (PAST simulations hereafter).…”

Preface for “Projection and impact assessment of global change”

“…Projection data generated by global climate models such as the CMIP6 models can be applied, e.g., to evaluate the global geographic distribution of terrestrial precipitation change with uncertainty, as in the work of Ito et al (2020b), and to elucidate the processes of sea level change associated with heat absorption and transport in the North Pacific Ocean, as presented by Suzuki and Tatebe (2020). Wang and Zhang (2020) utilize projection data to estimate future changes in wetness over Asia, which has a sub-continental spatial scale so that even relatively low-resolution data can be used as is.…”

Evaluating the water cycle over CONUS at the watershed scale for the Energy Exascale Earth System Model version 1 (E3SMv1) across resolutions

“…Similar scales of resolution (on the order of tens of kilometers) have found improvements to precipitation (e.g. Schiemann et al, 2018;Demory et al, 2020), though these are not uniform (Ito et al, 2020). Ajibola et al (2020) found that increasing resolution to roughly quarter or half degree grid spacing showed no reliable improvement in rainfall over West Africa.…”