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Adaptation requires science that analyzes decisions, identifies vulnerabilities, improves foresight, and develops options. Informing the extensive preparations needed to manage climate risks, avoid damages, and realize emerging opportunities is a grand challenge for climate change science. U.S. President Obama underscored the need for this research when he made climate preparedness a pillar of his climate policy. Adaptation improves preparedness and is one of two broad and increasingly important strategies (along with mitigation) for climate risk management. Adaptation is required in virtually all sectors of the economy and regions of the globe, for both built and natural systems ( 1 ).

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 atmospheric composition, temperature and sea level implications out to 2300 of new reference and cost-optimized stabilization emissions scenarios produced using three different Integrated Assessment (IA) models are described and assessed. Stabilization is defined in terms of radiative forcing targets for the sum of gases potentially controlled under the Kyoto Protocol. For the most stringent stabilization case (“Level 1” with CO₂ concentration stabilizing at about 450 ppm), peak CO₂ emissions occur close to today, implying (in the absence of a substantial CO₂ concentration overshoot) a need for immediate CO₂ emissions abatement if we wish to stabilize at this level. In the extended reference case, CO₂ stabilizes at about 1,000 ppm in 2200—but even to achieve this target requires large and rapid CO₂ emissions reductions over the twenty-second century. Future temperature changes for the Level 1 stabilization case differ noticeably between the IA models even when a common set of climate model parameters is used (largely a result of different assumptions for non-Kyoto gases). For the Level 1 stabilization case, there is a probability of approximately 50% that warming from pre-industrial times will be less than (or more than) 2°C. For one of the IA models, warming in the Level 1 case is actually greater out to 2040 than in the reference case due to the effect of decreasing SO₂ emissions that occur as a side effect of the policy-driven reduction in CO₂ emissions. This effect is less noticeable for the other stabilization cases, but still leads to policies having virtually no effect on global-mean temperatures out to around 2060. Sea level rise uncertainties are very large. For example, for the Level 1 stabilization case, increases range from 8 to 120 cm for changes over 2000 to 2300.

THE ultimate goal of the UN Framework Convention on Climate Change is to achieve \"stabilization of greenhouse-gas concentrations at a level that would prevent dangerous anthropogenic interference with the climate system\". With the concentration targets yet to be determined, Working Group I of the Intergovernmental Panel on Climate Change developed a set of illustrative pathways for stabilizing the atmospheric CO 2 concentration at 350, 450, 550, 650 and 750 p.p.m.v. over the next few hundred years 1,2 . But no attempt was made to determine whether the implied emissions might constitute a realistic transition away from the current heavy dependence on fossil fuels. Here we devise new stabilization profiles that explicitly (albeit qualitatively) incorporate considerations of the global economic system, estimate the corresponding anthropogenic emissions requirements, and assess the significance of the profiles in terms of global-mean temperature and sea level changes. Our findings raise a number of important issues for those engaged in climate-change policy making, particularly with regard to the optimal timing of mitigation measures.

An Introductory Editorial for a special issue of Climatic Change: Climate Change Impacts for the Conterminous USA

Undirected st-connectivity is an important problem in computing. There are algorithms for this problem that use O(n) time and ones that use O(log n) space. The main result of this paper is that, in a very natural structured model, these upper bounds are not simultaneously achievable. Any probabilistic jumping automaton for graphs (JAG) requires either space $\\Omega( \\log^2 n / \\log log n )$ or time $n^{(1 + \\Omega ( 1 / \\log \\log n )) }$ to solve undirected st-connectivity.

Directed st-connectivity is the problem of detecting whether there is a path from a distinguished vertex s to a distinguished vertex t in a directed graph. We prove time--space lower bounds of $ST = \\Omega({n^{2} \\log n \\over \\log (n \\log n/S)})$ and $S^{1 \\over 2}T = \\Omega(m (n \\log n)^{1 \\over 2})$ for directed st-connectivity on Cook and Rackoff's jumping automaton for graphs (JAG) model [SIAM J. Comput., 9(1980), pp. 636--652], where n is the number of vertices and m the number of edges in the input graph, S is the space, and T the time used by the JAG. These lower bounds are simple and elegant, they approach the known upper bound of T = O(m) when S approaches $\\Theta(n \\log n)$, and they are the first time--space tradeoffs for JAGs with an unrestricted number of jumping pebbles.