Yasuhiro Ishizaki

Predicting future uncertainty constraints on global warming projections

Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by “current knowledge” of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.

Dependence of Precipitation Scaling Patterns on Emission Scenarios for Representative Concentration Pathways

AbstractPattern scaling is an efficient way to generate projections of regional climate change for various emission scenarios. This approach assumes that the spatial pattern of changes per degree of global warming (scaling pattern) is the same among emission scenarios. The hypothesis was tested for the scaling pattern of precipitation by focusing on the scenario dependence of aerosol scaling patterns. The scenario dependence of aerosol scaling patterns induced the scenario dependence of the surface shortwave radiation scaling pattern. The scenario dependence of the surface shortwave radiation scaling pattern over the ocean tended to induce the scenario dependence of evaporation scaling patterns. The scenario dependence of evaporation scaling patterns led to the scenario dependence of precipitation scaling patterns locally and downwind. Contrariwise, when the scenario dependence of aerosol scaling patterns occurred over land, the scenario dependence of surface shortwave radiation scaling patterns induced t...

Validation of precipitation over Japan during 1985–2004 simulated by three regional climate models and two multi-model ensemble means

We dynamically downscaled Japanese reanalysis data (JRA-25) for 60 regions of Japan using three regional climate models (RCMs): the Non-Hydrostatic Regional Climate Model (NHRCM), modified RAMS version 4.3 (NRAMS), and modified Weather Research and Forecasting model (TWRF). We validated their simulations of the precipitation climatology and interannual variations of summer and winter precipitation. We also validated precipitation for two multi-model ensemble means: the arithmetic ensemble mean (AEM) and an ensemble mean weighted according to model reliability. In the 60 regions NRAMS simulated both the winter and summer climatological precipitation better than JRA-25, and NHRCM simulated the wintertime precipitation better than JRA-25. TWRF, however, overestimated precipitation in the 60 regions in both the winter and summer, and NHRCM overestimated precipitation in the summer. The three RCMs simulated interannual variations, particularly summer precipitation, better than JRA-25. AEM simulated both climatological precipitation and interannual variations during the two seasons more realistically than JRA-25 and the three RCMs overall, but the best RCM was often superior to the AEM result. In contrast, the weighted ensemble mean skills were usually superior to those of the best RCM. Thus, both RCMs and multi-model ensemble means, especially multi-model ensemble means weighted according to model reliability, are powerful tools for simulating seasonal and interannual variability of precipitation in Japan under the current climate.

Validation of a Pattern Scaling Approach for Determining the Maximum Available Renewable Freshwater Resource

AbstractA pattern scaling approach allows projection of regional climate changes under a wide range of emission scenarios. A basic assumption of this approach is that the spatial response pattern to global warming (scaling pattern) is the same for all emission scenarios. Precipitation minus evapotranspiration (PME) over land can be considered to be a measure of the maximum available renewable freshwater resource, and estimation of PME is fundamentally important for the assessment of water resources. The authors assessed the basic assumption of pattern scaling for PME by the use of five global climate models. A significant scenario dependency (SD) of the scaling pattern of PME was found over some regions. This SD of the scaling pattern of PME was mainly due to the SD and the nonlinear response of large-scale atmospheric and oceanic changes. When the SD of the scaling pattern of PME is significant in a target area, projections of the impact of climate change need to carefully take into consideration the SD....