A comprehensive view on climate change: coupling of Earth system and integrated assessment models

There are several reasons to strengthen the cooperation between the integrated assessment (IA) and earth system (ES) modeling teams in order to better understand the joint development of environmental and human systems. This cooperation can take many different forms, ranging from information exchange between research communities to fully coupled modeling approaches. Here, we discuss the strengths and weaknesses of different approaches and try to establish some guidelines for their applicability, based mainly on the type of interaction between the model components (including the role of feedback), possibilities for simplification and the importance of uncertainty. We also discuss several important areas of joint IA–ES research, such as land use/land cover dynamics and the interaction between climate change and air pollution, and indicate the type of collaboration that seems to be most appropriate in each case. We find that full coupling of IA–ES models might not always be the most desirable form of cooperation, since in some cases the direct feedbacks between IA and ES may be too weak or subject to considerable process or scenario uncertainty. However, when local processes are important, it could be important to consider full integration. By encouraging cooperation between the IA and ES communities in the future more consistent insights can be developed

A comprehensive view on climate change: Coupling of earth system and integrated assessment models

There are several reasons to strengthen the cooperation between the integrated assessment (IA) and earth system (ES) modeling teams in order to better understand the joint development of environmental and human systems. This cooperation can take many different forms, ranging from information exchange between research communities to fully coupled modeling approaches. Here, we discuss the strengths and weaknesses of different approaches and try to establish some guidelines for their applicability, based mainly on the type of interaction between the model components (including the role of feedback), possibilities for simplification and the importance of uncertainty. We also discuss several important areas of joint IAES research, such as land use/land cover dynamics and the interaction between climate change and air pollution, and indicate the type of collaboration that seems to be most appropriate in each case. We find that full coupling of IAES models might not always be the most desirable form of cooperation, since in some cases the direct feedbacks between IA and ES may be too weak or subject to considerable process or scenario uncertainty. However, when local processes are important, it could be important to consider full integration. By encouraging cooperation between the IA and ES communities in the future more consistent insights can be developed

A comprehensive view on climate change: coupling of Earth system and integrated assessment models

There are several reasons to strengthen the cooperation between the integrated assessment (IA) and earth system (ES) modeling teams in order to better understand the joint development of environmental and human systems. This cooperation can take many different forms, ranging from information exchange between research communities to fully coupled modeling approaches. Here, we discuss the strengths and weaknesses of different approaches and try to establish some guidelines for their applicability, based mainly on the type of interaction between the model components (including the role of feedback), possibilities for simplification and the importance of uncertainty. We also discuss several important areas of joint IA–ES research, such as land use/land cover dynamics and the interaction between climate change and air pollution, and indicate the type of collaboration that seems to be most appropriate in each case. We find that full coupling of IA–ES models might not always be the most desirable form of cooperation, since in some cases the direct feedbacks between IA and ES may be too weak or subject to considerable process or scenario uncertainty. However, when local processes are important, it could be important to consider full integration. By encouraging cooperation between the IA and ES communities in the future more consistent insights can be developed

Stage-specific, Nonlinear Surface Ozone Damage to Rice Production in China

China is one of the most heavily polluted nations and is also the largest agricultural producer. There are relatively few studies measuring the effects of pollution on crop yields in China, and most are based on experiments or simulation methods. We use observational data to study the impact of increased air pollution (surface ozone) on rice yields in Southeast China. We examine nonlinearities in the relationship between rice yields and ozone concentrations and find that an additional day with a maximum ozone concentration greater than 120 ppb is associated with a yield loss of 1.12% ± 0.83% relative to a day with maximum ozone concentration less than 60 ppb. We find that increases in mean ozone concentrations, SUM60, and AOT40 during panicle formation are associated with statistically significant yield losses, whereas such increases before and after panicle formation are not. We conclude that heightened surface ozone levels will potentially lead to reductions in rice yields that are large enough to have implications for the global rice market

The Main Pollutants and Their Impacts on Agriculture, Ecosystems and Health

International audienc