Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments

Abstract

In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10–30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, downscaling emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in downscaling methods significantly affect climate variables such as temperature and precipitation. We tested two downscaling methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The downscaled emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the downscaled spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two downscaling methods.

Global advanced bioenergy potential under environmental protection policies and societal transformation measures

“…AIM/CGE is a recursive dynamic general equilibrium model containing 17 regions globally and 42 industrial classifications including 10 agricultural sectors (Fujimori, Masui, & Yuzuru, ). It has been widely adopted for various studies on climate change (e.g., Fujimori, Abe, et al, ; Fujimori, Hasegawa, et al, ; Fujimori, Kainuma, Masui, Hasegawa, & Dai, ; Hasegawa et al, ; Hasegawa, Fujimori, Shin, et al, ;Hasegawa, Fujimori, Takahashi, & Masui, Hasegawa, Fujimori, Takahashi, Yokohata, & Masui, , Hasegawa et al, ; ; Hasegawa et al, ). Production sectors in AIM/CGE are characterized by profit maximization under multi‐nested constant elasticity substitution (CES) functions.…”

“…This SSP assumes that current trends will continue for social, economic, and technological development. No climate change impact and mitigation policies are involved, and the model parameters are based on Fujimori, Abe, et al ().…”

Gridded emissions and land-use data for 2005–2100 under diverse socioeconomic and climate mitigation scenarios

“…Then, the GDP distributions were allocated based on the populations considering several geographical constraints, including mountains, waterbodies, and urban sprawl. These methods are summary versions of descriptions in our related work 12 .…”

“…To this, although we applied similar methodology to that described in Fujimori et al 12 and Hasegawa et al 13 for emissions and land-use respectively, our paper has different goals from these two studies, which derived methodological implications of climate simulation outcomes and land-use model integrations, respectively. The aims of this paper are as follows.…”

Going Global to Local: Connecting Top-Down Accounting and Local Impacts, A Methodological Review of Spatially Explicit Input–Output Approaches

“…Future options for SIO models may include integration with other environmental assessment models including technology-rich Integrated Assessment Models (IAMs), such as IMAGE, 150 GCAM, 151 AIM/CGE, 152 MESSAGE, 153 REMIND. 154 Generally, IAMs use macroeconomic models to downscale the world spatially into 10–30 aggregated regions, 155 after which they are coupled to earth system models or environmental data using spatially explicit models. The environmental impacts are then downscaled to that resolution (Figure 2).…”