Antioxidants l-carnitine and d-methionine modulate neuronal activity through GABAergic inhibition

Article on antioxidants L-carnitine and D-methionine modulate neuronal activity through GABAergic inhibition

Accounting for radiative forcing from albedo change in future global land-use scenarios

We demonstrate the effectiveness of a new method for quantifying radiative forcing from land use and land cover change (LULCC) within an integrated assessment model, the Global Change Assessment Model (GCAM). The method relies on geographically differentiated estimates of radiative forcing from albedo change associated with major land cover transitions derived from the Community Earth System Model. We find that conversion of 1 km of woody vegetation (forest and shrublands) to non-woody vegetation (crops and grassland) yields between 0 and −0.71 nW/m of globally averaged radiative forcing determined by the vegetation characteristics, snow dynamics, and atmospheric radiation environment characteristic within each of 151 regions we consider globally. Across a set of scenarios designed to span a range of potential future LULCC, we find LULCC forcing ranging from −0.06 to −0.29 W/m by 2070 depending on assumptions regarding future crop yield growth and whether climate policy favors afforestation or bioenergy crops. Inclusion of this previously uncounted forcing in the policy targets driving future climate mitigation efforts leads to changes in fossil fuel emissions on the order of 1.5 PgC/yr by 2070 for a climate forcing limit of 4.5 Wm , corresponding to a 12–67 % change in fossil fuel emissions depending on the scenario. Scenarios with significant afforestation must compensate for albedo-induced warming through additional emissions reductions, and scenarios with significant deforestation need not mitigate as aggressively due to albedo-induced cooling. In all scenarios considered, inclusion of albedo forcing in policy targets increases forest and shrub cover globally. 2 2 2 −

Accounting for radiative forcing from albedo change in future global land-use scenarios

We demonstrate the effectiveness of a new method for quantifying radiative forcing from land use and land cover change (LULCC) within an integrated assessment model, the Global Change Assessment Model (GCAM). The method relies on geographically differentiated estimates of radiative forcing from albedo change associated with major land cover transitions derived from the Community Earth System Model. We find that conversion of 1 km2 of woody vegetation (forest and shrublands) to non-woody vegetation (crops and grassland) yields between 0 and −0.71 nW/m2 of globally averaged radiative forcing determined by the vegetation characteristics, snow dynamics, and atmospheric radiation environment characteristic within each of 151 regions we consider globally. Across a set of scenarios designed to span a range of potential future LULCC, we find LULCC forcing ranging from −0.06 to −0.29 W/m2 by 2070 depending on assumptions regarding future crop yield growth and whether climate policy favors afforestation or bioenergy crops. Inclusion of this previously uncounted forcing in the policy targets driving future climate mitigation efforts leads to changes in fossil fuel emissions on the order of 1.5 PgC/yr by 2070 for a climate forcing limit of 4.5 Wm−2, corresponding to a 12–67 % change in fossil fuel emissions depending on the scenario. Scenarios with significant afforestation must compensate for albedo-induced warming through additional emissions reductions, and scenarios with significant deforestation need not mitigate as aggressively due to albedo-induced cooling. In all scenarios considered, inclusion of albedo forcing in policy targets increases forest and shrub cover globally

Quantifying Human-Mediated Carbon Cycle Feedbacks

Changes in land and ocean carbon storage in response to elevated atmospheric carbon dioxide concentrations and associated climate change, known as the concentration-carbon and climate-carbon feedbacks, are principal controls on the response of the climate system to anthropogenic greenhouse gas emissions. Such feedbacks have typically been quantified in the context of natural ecosystems, but land management activities are also responsive to future atmospheric carbon and climate changes. Here we show that inclusion of such human-driven responses within an Earth system model shifts both the terrestrial concentration-carbon and climate-carbon feedbacks toward increased carbon storage. We introduce a conceptual framework for decomposing these changes into separate concentration-land cover, climate-land cover, and land cover-carbon effects, providing a parsimonious means to diagnose sources of variation across numerical models capable of estimating such feedbacks

Quantifying Human-Mediated Carbon Cycle Feedbacks

Changes in land and ocean carbon storage in response to elevated atmospheric carbon dioxide concentrations and associated climate change, known as the concentration-carbon and climate-carbon feedbacks, are principal controls on the response of the climate system to anthropogenic greenhouse gas emissions. Such feedbacks have typically been quantified in the context of natural ecosystems, but land management activities are also responsive to future atmospheric carbon and climate changes. Here we show that inclusion of such human-driven responses within an Earth system model shifts both the terrestrial concentration-carbon and climate-carbon feedbacks toward increased carbon storage. We introduce a conceptual framework for decomposing these changes into separate concentration-land cover, climate-land cover, and land cover-carbon effects, providing a parsimonious means to diagnose sources of variation across numerical models capable of estimating such feedbacks

The integrated Earth system model version 1: Formulation and functionality

The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human-Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. The iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human-Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper describes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community

The integrated Earth system model version 1: Formulation and functionality

The integrated Earth system model (iESM) has been developed as a new tool for projecting the joint human/climate system. The iESM is based upon coupling an integrated assessment model (IAM) and an Earth system model (ESM) into a common modeling infrastructure. IAMs are the primary tool for describing the human-Earth system, including the sources of global greenhouse gases (GHGs) and short-lived species (SLS), land use and land cover change (LULCC), and other resource-related drivers of anthropogenic climate change. ESMs are the primary scientific tools for examining the physical, chemical, and biogeochemical impacts of human-induced changes to the climate system. The iESM project integrates the economic and human-dimension modeling of an IAM and a fully coupled ESM within a single simulation system while maintaining the separability of each model if needed. Both IAM and ESM codes are developed and used by large communities and have been extensively applied in recent national and international climate assessments. By introducing heretofore-omitted feedbacks between natural and societal drivers, we can improve scientific understanding of the human-Earth system dynamics. Potential applications include studies of the interactions and feedbacks leading to the timing, scale, and geographic distribution of emissions trajectories and other human influences, corresponding climate effects, and the subsequent impacts of a changing climate on human and natural systems. This paper describes the formulation, requirements, implementation, testing, and resulting functionality of the first version of the iESM released to the global climate community

The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information

Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that finescale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region's climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions