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      Multidimensional risk in a nonstationary climate: Joint probability of increasingly severe warm and dry conditions

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          Abstract

          New framework reveals global warming’s impact on risk that multiple regions experience hot and dry conditions simultaneously.

          Abstract

          We present a framework for quantifying the spatial and temporal co-occurrence of climate stresses in a nonstationary climate. We find that, globally, anthropogenic climate forcing has doubled the joint probability of years that are both warm and dry in the same location (relative to the 1961–1990 baseline). In addition, the joint probability that key crop and pasture regions simultaneously experience severely warm conditions in conjunction with dry years has also increased, including high statistical confidence that human influence has increased the probability of previously unprecedented co-occurring combinations. Further, we find that ambitious emissions mitigation, such as that in the United Nations Paris Agreement, substantially curbs increases in the probability that extremely hot years co-occur with low precipitation simultaneously in multiple regions. Our methodology can be applied to other climate variables, providing critical insight for a number of sectors that are accustomed to deploying resources based on historical probabilities.

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          An Overview of CMIP5 and the Experiment Design

          The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
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            Global Carbon Budget 2016

            Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry ( E FF ) are based on energy statistics and cement production data, respectively, while emissions from land-use change ( E LUC ), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth ( G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink ( S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink ( S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1 σ , reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), E FF was 9.3 ± 0.5 GtC yr −1 , E LUC 1.0 ± 0.5 GtC yr −1 , G ATM 4.5 ± 0.1 GtC yr −1 , S OCEAN 2.6 ± 0.5 GtC yr −1 , and S LAND 3.1 ± 0.9 GtC yr −1 . For year 2015 alone, the growth in E FF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr −1 , showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr −1 that took place during 2006–2015. Also, for 2015, E LUC was 1.3 ± 0.5 GtC yr −1 , G ATM was 6.3 ± 0.2 GtC yr −1 , S OCEAN was 3.0 ± 0.5 GtC yr −1 , and S LAND was 1.9 ± 0.9 GtC yr −1 . G ATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller S LAND for that year. The global atmospheric CO 2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in E FF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of E FF in 2016, the growth rate in atmospheric CO 2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink ( S LAND ) in response to El Niño conditions of 2015–2016. From this projection of E FF and assumed constant E LUC for 2016, cumulative emissions of CO 2 will reach 565 ± 55 GtC (2075 ± 205 GtCO 2 ) for 1870–2016, about 75 % from E FF and 25 % from E LUC . This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_2016 ).
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              Pair-copula constructions of multiple dependence

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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                November 2018
                28 November 2018
                : 4
                : 11
                : eaau3487
                Affiliations
                [1 ]Department of Earth System Science, Stanford University, Stanford, CA, USA.
                [2 ]Woods Institute for the Environment, Stanford University, Stanford, CA, USA.
                [3 ]Department of Statistics, Universidad Carlos III de Madrid, Madrid, Spain.
                [4 ]UC3M-BS Institute of Financial Big Data, Universidad Carlos III de Madrid, Madrid, Spain.
                Author notes
                [* ]Corresponding author. Email: asarhadi@ 123456stanford.edu
                Author information
                http://orcid.org/0000-0001-9038-9619
                http://orcid.org/0000-0003-0904-6542
                http://orcid.org/0000-0002-6316-397X
                http://orcid.org/0000-0003-1992-9904
                http://orcid.org/0000-0002-8856-4964
                Article
                aau3487
                10.1126/sciadv.aau3487
                6261656
                30498780
                2e6d4d38-e9da-4495-9aee-9202a48919de
                Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 31 May 2018
                : 30 October 2018
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000015, U.S. Department of Energy;
                Funded by: doi http://dx.doi.org/10.13039/100005492, Stanford University;
                Funded by: SPANISH GOVERNMENT - MINISTERIO DE ECONOMIA, INDUSTRIA Y COMPETITIVIDAD;
                Award ID: MINECO2015- 66593-P
                Funded by: UC3M-BS Institute of Financial Big Data;
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Climatology
                Climatology
                Custom metadata
                Nielsen Marquez

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