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This paper summarizes the development process and main characteristics of the Representative Concentration Pathways (RCPs), a set of four new pathways developed for the climate modeling community as a basis for long-term and near-term modeling experiments. The four RCPs together span the range of year 2100 radiative forcing values found in the open literature, i.e. from 2.6 to 8.5 W/m 2 . The RCPs are the product of an innovative collaboration between integrated assessment modelers, climate modelers, terrestrial ecosystem modelers and emission inventory experts. The resulting product forms a comprehensive data set with high spatial and sectoral resolutions for the period extending to 2100. Land use and emissions of air pollutants and greenhouse gases are reported mostly at a 0.5 × 0.5 degree spatial resolution, with air pollutants also provided per sector (for well-mixed gases, a coarser resolution is used). The underlying integrated assessment model outputs for land use, atmospheric emissions and concentration data were harmonized across models and scenarios to ensure consistency with historical observations while preserving individual scenario trends. For most variables, the RCPs cover a wide range of the existing literature. The RCPs are supplemented with extensions (Extended Concentration Pathways, ECPs), which allow climate modeling experiments through the year 2300. The RCPs are an important development in climate research and provide a potential foundation for further research and assessment, including emissions mitigation and impact analysis.

Setting the scenes Climatologists use model-based 'scenarios' to provide plausible descriptions of how the future might unfold when evaluating uncertainty about the effects of human actions on climate. The traditional method of establishing these scenarios was a time-consuming sequential process, each discipline taking turns to add data and complexity. As Richard Moss and colleagues explain in a Perspectives review, climate change researchers have now established a new coordinated parallel process that integrates the tasks of developing scenarios, making projections and evaluating their impact. These 'next generation' scenarios should make for faster, more rigorous assessment of proposals for climate mitigation and adaptation. Advances in the science and observation of climate change are providing a clearer understanding of the inherent variability of Earth’s climate system and its likely response to human and natural influences. The implications of climate change for the environment and society will depend not only on the response of the Earth system to changes in radiative forcings, but also on how humankind responds through changes in technology, economies, lifestyle and policy. Extensive uncertainties exist in future forcings of and responses to climate change, necessitating the use of scenarios of the future to explore the potential consequences of different response options. To date, such scenarios have not adequately examined crucial possibilities, such as climate change mitigation and adaptation, and have relied on research processes that slowed the exchange of information among physical, biological and social scientists. Here we describe a new process for creating plausible scenarios to investigate some of the most challenging and important questions about climate change confronting the global community.

Understanding the history of how humans have interacted with the rest of nature can help clarify the options for managing our increasingly interconnected global system. Simple, deterministic relationships between environmental stress and social change are inadequate. Extreme drought, for instance, triggered both social collapse and ingenious management of water through irrigation. Human responses to change, in turn, feed into climate and ecological systems, producing a complex web of multidirectional connections in time and space. Integrated records of the co-evolving human-environment system over millennia are needed to provide a basis for a deeper understanding of the present and for forecasting the future. This requires the major task of assembling and integrating regional and global historical, archaeological, and paleoenvironmental records. Humans cannot predict the future. But, if we can adequately understand the past, we can use that understanding to influence our decisions and to create a better, more sustainable and desirable future.

Net primary production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approximately 5600 global data points with observed mean annual NPP, land cover class, precipitation, and temperature were compiled. Precipitation was better correlated with NPP than temperature, and it explained much more of the variability in mean annual NPP for grass- or shrub-dominated systems (r2 = 0.68) than for tree-dominated systems (r2 = 0.39). For a given precipitation level, tree-dominated systems had significantly higher NPP (100-150 g C·m-2·yr-1) than non-tree-dominated systems. Consequently, previous empirical models developed to predict NPP based on precipitation and temperature (e.g., the Miami model) tended to overestimate NPP for non-tree-dominated systems. Our new model developed at the National Center for Ecological Analysis and Synthesis (the NCEAS model) predicts NPP for tree-dominated systems based on precipitation and temperature; but for non-tree-dominated systems NPP is solely a function of precipitation because including a temperature function increased model error for these systems. Lower NPP in non-tree-dominated systems is likely related to decreased water and nutrient use efficiency and higher nutrient loss rates from more frequent fire disturbances. Late 20th century aboveground and total NPP for global potential native vegetation using the NCEAS model are estimated to be 28 Pg and 46 Pg C/yr, respectively. The NCEAS model estimated an 13% increase in global total NPP for potential vegetation from 1901 to 2000 based on changing precipitation and temperature patterns.

In this paper, we explore the regional nature of global environmental challenges. We take a broad approach by examining the scientific foundation that is needed to support policy and decision making and identifying some of the most important barriers to progress that are truly scale-dependent. In so doing, we hope to show that understanding global environmental changes requires understanding a number of intrinsically regional phenomena, and that successful decision making likewise requires an integrated approach that accounts for a variety of regional Earth system processes—which we define to include both human activities and environmental systems that operate or interact primarily at sub-continental scales. Understanding regional processes and phenomena, including regional decision-making processes and information needs, should thus be an integral part of the global change research agenda. To address some of the key issues and challenges, we propose an integrated regional modeling approach that accounts for the dynamic interactions among physical, ecological, biogeochemical, and human processes and provides relevant information to regional decision makers and stakeholders.

The northern coasts of the Gulf of Mexico (GoM) are highly vulnerable to the direct threats of climate change, such as hurricane-induced storm surge, and such risks are exacerbated by land subsidence and global sea-level rise. This paper presents an application of a coastal storm surge model to study the coastal inundation process induced by tide and storm surge, and its response to the effects of land subsidence and sea-level rise in the northern Gulf coast. The unstructured-grid finite-volume coastal ocean model was used to simulate tides and hurricane-induced storm surges in the GoM. Simulated distributions of co-amplitude and co-phase lines for semi-diurnal and diurnal tides are in good agreement with previous modeling studies. The storm surges induced by four historical hurricanes (Rita, Katrina, Ivan, and Dolly) were simulated and compared to observed water levels at National Oceanic and Atmospheric Administration tide stations. Effects of coastal subsidence and future global sea-level rise on coastal inundation in the Louisiana coast were evaluated using a “change of inundation depth” parameter through sensitivity simulations that were based on a projected future subsidence scenario and 1-m global sea-level rise by the end of the century. Model results suggested that hurricane-induced storm surge height and coastal inundation could be exacerbated by future global sea-level rise and subsidence, and that responses of storm surge and coastal inundation to the effects of sea-level rise and subsidence are highly nonlinear and vary on temporal and spatial scales.

Many contemporary societal challenges manifest themselves in the domain of human–environment interactions. There is a growing recognition that responses to these challenges formulated within current disciplinary boundaries, in isolation from their wider contexts, cannot adequately address them. Here, we outline the need for an integrated, transdisciplinary synthesis that allows for a holistic approach, and, above all, a much longer time perspective. We outline both the need for and the fundamental characteristics of what we call “integrated history.” This approach promises to yield new understandings of the relationship between the past, present, and possible futures of our integrated human–environment system. We recommend a unique new focus of our historical efforts on the future, rather than the past, concentrated on learning about future possibilities from history. A growing worldwide community of transdisciplinary scholars is forming around building this Integrated History and future of People on Earth (IHOPE). Building integrated models of past human societies and their interactions with their environments yields new insights into those interactions and can help to create a more sustainable and desirable future. The activity has become a major focus within the global change community.

[...] many of the detailed modeling needs identified in the workshop were addressed at the World Modeling Summit for Climate Prediction (6-9 May 2008), organized to develop a strategy to revolutionize prediction of the climate through the twenty-first century and in particular to help address the threat of global climate change at the regional level. [...] emissions scenarios for the Fifth Assessment Report (AR5) are now being developed in a coordinated fashion among the climate and Impacts, Adaptation, and Mitigation (IAM) communities, with \"new scenarios\" that will allow a previously missing consistency across the analyses of the three IPCC Working Groups.

The Platform for Regional Integrated Modeling and Analysis (PRIMA) is an innovative modeling system developed at Pacific Northwest National Laboratory (PNNL) to simulate interactions among natural and human systems at scales relevant to regional decision making. PRIMA brings together state-of-the-art models of regional climate, hydrology, agriculture and land use, socioeconomics, and energy systems using a flexible coupling approach. Stakeholder decision support needs underpin the application of the platform to regional issues, and an uncertainty characterization process is used to identify robust decisions. The platform can be customized to inform a variety of complex questions, such as how a policy in one sector might affect the ability to meet climate mitigation targets or adaptation goals in another sector. Current numerical experiments focus on the eastern United States, but the framework is designed to be regionally flexible. This paper provides a high-level overview of PRIMA’s functional capabilities and describes some key challenges and opportunities associated with integrated regional modeling.

Although there is a great deal of information concerning responses to increases in atmospheric CO₂ at the tissue and plant levels, there are substantially fewer studies that have investigated ecosystem-level responses in the context of integrated carbon, water, and nutrient cycles. Because our understanding of ecosystem responses to elevated CO₂ is incomplete, modeling is a tool that can be used to investigate the role of plant and soil interactions in the response of terrestrial ecosystems to elevated CO₂. In this study, we analyze the responses of net primary production (NPP) to doubled CO₂ from 355 to 710 ppmv among three biogeochemistry models in the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP): BIOME-BGC (BioGeochemical Cycles), Century, and the Terrestrial Ecosystem Model (TEM). For the conterminous United States, doubled atmospheric CO₂ causes NPP to increase by 5% in Century, 8% in TEM, and 11% in BIOME-BGC. Multiple regression analyses between the NPP response to doubled CO₂ and the mean annual temperature and annual precipitation of biomes or grid cells indicate that there are negative relationships between precipitation and the response of NPP to doubled CO₂ for all three models. In contrast, there are different relationships between temperature and the response of NPP to doubled CO₂ for the three models: there is a negative relationship in the responses of BIOME-BGC, no relationship in the responses of Century, and a positive relationship in the responses of TEM. In BIOME-BGC, the NPP response to doubled CO₂ is controlled by the change in transpiration associated with reduced leaf conductance to water vapor. This change affects soil water, then leaf area development and, finally, NPP. In Century, the response of NPP to doubled CO₂ is controlled by changes in decomposition rates associated with increased soil moisture that results from reduced evapotranspiration. This change affects nitrogen availability for plants, which influences NPP. In TEM, the NPP response to doubled CO₂ is controlled by increased carboxylation which is modified by canopy conductance and the degree to which nitrogen constraints cause down-regulation of photosynthesis. The implementation of these different mechanisms has consequences for the spatial pattern of NPP responses, and represents, in part, conceptual uncertainty about controls over NPP responses. Progress in reducing these uncertainties requires research focused at the ecosystem level to understand how interactions between the carbon, nitrogen, and water cycles influence the response of NPP to elevated atmospheric CO₂.