Oliver Andrews

@article{9dd55625930643029cfe9fcfda4ba418,

title = {Updated projections of UK heat-related mortality using policy-relevant global warming levels and socio-economic scenarios},

author = {Katie Jenkins and Alan Kennedy-Asser and Oliver Andrews and Y. T. Eunice Lo},

doi = {10.1088/1748-9326/ac9cf3},

issn = {1748-9326},

year  = {2022},

date = {2022-11-04},

journal = {Environmental Research Letters},

volume = {17},

number = {11},

publisher = {IOP Publishing Ltd},

abstract = {High temperatures and heatwaves are associated with significant impacts on human health. With continued global temperature increases, extreme thresholds relevant to health will be exceeded more frequently. This study provides an updated spatial analysis of heat-related mortality for the UK, using the UK Climate Projections (UKCP18) at 1.5 to 4°C global warming levels, and embedding population and demographic data from the recently released UK Shared Socioeconomic Pathways (UK-SSPs). Climate change will lead to an increase in heat-related mortality in the future, exacerbated by increased exposure due to increasing population. We find an increase from ~1,400 average annual deaths in the near-past (1990-2019) (95% CI: 1299 to 1486), to ~2,500 (2304 to 2794), ~3,700 (3280 to 4214), ~8,200 (7376 to 9072) and >18,000 (16,690 to 20,394) average annual deaths at 1.5, 2, 3 and 4°C respectively (assuming no adaptation). This is considered a high-end estimate due to the assumption of high population growth (UK-SSP5). Older populations are shown to be most vulnerable. A large proportion of heat-related deaths (76% (74 to 79%) with 1.5°C global warming) are attributed to more moderate (1-5°C) increases above regional temperature thresholds as opposed to extremes. Our results provide a timely update that can serve as a first step to supporting future UK climate policy and risk assessments. Future research considering nonlinearity in the health response to heat exposure is vital.},

note = {Acknowledgments: K J, A K-A and O A acknowledge support from UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme project OpenCLIM (Open CLimate Impacts Modelling framework, NE/T013931/1). Data availability statement: The data that underpin this study are cited in the references and supplementary information. This data is freely available online: UKCP18 Regional Projections on a 12 km grid over the UK for 1980-2080: https://catalogue.ceda.acuk/uuid/589211abeb844070a95d061c8cc7f604; HadUK-Grid gridded and regional average climate observations for the UK: http://catalogue.ceda.acuk/uuid/4dc8450d889a491ebb20e724debe2dfb; UK-SSPs: www.ukclimateresilience.org/products-of-the-uk-ssps-project/; UK gridded population based on Census 2011 and Land Cover Map 2007: https://doi.org/10.5285/61f10c74-8c2c-4637-a274-5fa9b2e5ce44; UK Population, 2011 census: www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/2011censuspopulationandhouseholdestimatesfortheunitedkingdom; Mortality statistics (England and Wales): www.ons.gov.uk/peoplepopulationandcomms.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsregisteredbyareaofusualresidenceenglandandwales; Mortality statistics (Scotland): www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/deaths/deaths-time-series-data; Mortality statistics (Norther Ireland): www.nisra.gov.uk/publications/death-statistics and www.nisra.gov.uk/publications/death-statistics. The data that support the findings of this study are openly available at the following URL/DOI: https://osf.io/eyf3b/?view_only=32057e9182654b63b05f1a58fc5fbf6b.},

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}

@article{e65f2b44bd1e45c2a1c3fb666dca6848,

title = {Quantifying risks avoided by limiting global warming to 1.5 or 2°C above pre-industrial levels},

author = {Rachel Warren and Oliver Andrews and Sally Brown and Felipe J. Colón-González and Nicole Forstenhaeusler and David E. H. J. Gernaat and Philip Goodwin and Ian Harris and Helen He and Chris Hope and Desmond Manful and Timothy J. Osborn and Jeff Price and Detlef Vuuren and Rebecca Mary Wright},

doi = {10.1007/s10584-021-03277-9},

issn = {0165-0009},

year  = {2022},

date = {2022-06-29},

urldate = {2022-06-29},

journal = {Climatic Change},

volume = {172},

number = {3-4},

publisher = {Springer},

abstract = {The Paris Agreement aims to constrain global warming to ‘well below 2 °C’ and to ‘pursue efforts’ to limit it to 1.5 °C above pre-industrial levels. We quantify global and regional risk-related metrics associated with these levels of warming that capture climate change–related changes in exposure to water scarcity and heat stress, vector-borne disease, coastal and fluvial flooding and projected impacts on agriculture and the economy, allowing for uncertainties in regional climate projection. Risk-related metrics associated with 2 °C warming, depending on sector, are reduced by 10–44% globally if warming is further reduced to 1.5 °C. Comparing with a baseline in which warming of 3.66 °C occurs by 2100, constraining warming to 1.5 °C reduces these risk indicators globally by 32–85%, and constraining warming to 2 °C reduces them by 26–74%. In percentage terms, avoided risk is highest for fluvial flooding, drought, and heat stress, but in absolute terms risk reduction is greatest for drought. Although water stress decreases in some regions, it is often accompanied by additional exposure to flooding. The magnitude of the percentage of damage avoided is similar to that calculated for avoided global economic risk associated with these same climate change scenarios. We also identify West Africa, India and North America as hotspots of climate change risk in the future.},

note = {Funding: This research leading to these results received funding from the UK Government, Department for Business, Energy and Industrial Strategy, as part of the Implications of global warming of 1.5 °C and 2 °C project. OA, YH, JP and RW were also funded by joint UK NERC and UK Government Department of BEIS grant NE/P01495X/1.},

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pubstate = {published},

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}

@article{d1e67180761d4ac4bcc783eff79d8cd0,

title = {Projected risks associated with heat stress in the UK Climate Projections (UKCP18)},

author = {Alan Kennedy-Asser and Gwilym Owen and Gareth J. Griffith and Oliver Andrews and Y. T. Eunice Lo and Dann M. Mitchell and Katie Jenkins and Rachel F. Warren},

doi = {10.1088/1748-9326/ac541a},

issn = {1748-9326},

year  = {2022},

date = {2022-03-01},

journal = {Environmental Research Letters},

volume = {17},

number = {3},

publisher = {IOP Publishing Ltd},

abstract = {Summer heat extremes in the UK pose a risk to health (amongst other sectors) and this is exacerbated by localised socio-economic factors that contribute to vulnerability. Here, regional climate model simulations from the UK Climate Projections are used to assess how different elements of extreme heat will vary across the UK in the future under global mean surface temperature warming levels of +1.5 °C, +2.0 °C and +3.0 °C above pre-industrial. Heat stress metrics incorporating daily maximum and minimum temperature, temperature variability and vapour pressure are included. These show qualitatively similar spatial patterns for the recent past, with the most pronounced heat hazards found in south-eastern regions of the UK. Projected heat hazard changes across the UK are not homogeneous, with southern regions (e.g. Greater London, South East) showing greater increases in maximum temperatures and northern regions (e.g. Scotland and Northern Ireland) showing greater increases in humidity. With +3.0 °C warming, the relative change in combined heat hazards is found to be greatest in the south-western UK, however, in absolute terms, south-eastern regions will still experience the greatest hazards. When combined with socio-economic factors, hotspots of high heat stress risk emerge in parts of London, the Midlands and eastern England along with southern and eastern coastal regions. Weighting of different heat risk factors is subjective and to this end we have developed and made available an interactive app which allows users to assess sensitivities and uncertainties in the projected UK heat risk.},

note = {Funding Information: A K A, O A and R W acknowledge support from the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme (NE/S017267/1 and NE/T013931/1). The programme is co-delivered by Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, ESRC. G G is funded by an ESRC Postdoctoral Fellowship (ES/T009101/1). YTEL was funded under the NERC project, HAPPI-Health (NE/R009554/1). D M M acknowledges a NERC fellowship (NE/N014057/1) and Turing Institute fellowship. Development of this Shiny App was supported by funds from Policy Bristol.},

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}

@article{3170358b45ac4a7483cd1a157a64088f,

title = {The impacts of ocean acidification on marine trace gases and the implications for atmospheric chemistry and climate},

author = {Frances E. Hopkins and Parvadha Suntharalingam and Marion Gehlen and Oliver Andrews and Stephen D. Archer and Laurent Bopp and Erik Buitenhuis and Isabelle Dadou and Robert Duce and Nadine Goris and Tim Jickells and Martin Johnson and Fiona Keng and Cliff S. Law and Kitack Lee and Peter S. Liss and Martine Lizotte and Gillian Malin and J. Colin Murrell and Hema Naik and Andrew P. Rees and Jörg Schwinger and Phillip Williamson},

doi = {10.1098/rspa.2019.0769},

issn = {1364-5021},

year  = {2020},

date = {2020-05-27},

journal = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},

volume = {476},

number = {2237},

publisher = {The Royal Society},

abstract = {Surface ocean biogeochemistry and photochemistry regulate ocean–atmosphere fluxes of trace gases critical for Earth’s atmospheric chemistry and climate. The oceanic processes governing these fluxes are often sensitive to the changes in ocean pH (or pCO2) accompanying ocean acidification (OA), with potential for future climate feedbacks. Here, we review current understanding (from observational, experimental and model studies) on the impact of OA on marine sources of key climate-active trace gases, including dimethyl sulfide (DMS), nitrous oxide (N2O), ammonia and halocarbons. We focus on DMS, for which available information is considerably greater than for other trace gases. We highlight OA-sensitive regions such as polar oceans and upwelling systems, and discuss the combined effect of multiple climate stressors (ocean warming and deoxygenation) on trace gas fluxes. To unravel the biological mechanisms responsible for trace gas production, and to detect adaptation, we propose combining process rate measurements of trace gases with longer term experiments using both model organisms in the laboratory and natural planktonic communities in the field. Future ocean observations of trace gases should be routinely accompanied by measurements of two components of the carbonate system to improve our understanding of how in situ carbonate chemistry influences trace gas production. Together, this will lead to improvements in current process model capabilities and more reliable predictions of future global marine trace gas fluxes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{54f437bc877b44ef93bc982803232c76,

title = {Decadal trends in the ocean carbon sink},

author = {Tim DeVries and Corinne Le Quéré and Oliver Andrews and Sarah Berthet and Judith Hauck and Tatiana Ilyina and Peter Landschützer and Andrew Lenton and Ivan D Lima and Michael Nowicki and Jörg Schwinger and Roland Séférian},

doi = {10.1073/pnas.1900371116},

issn = {0027-8424},

year  = {2019},

date = {2019-06-11},

journal = {Proceedings of the National Academy of Sciences of the United States of America},

volume = {116},

number = {24},

pages = {11646–11651},

publisher = {National Academy of Sciences},

abstract = {Measurements show large decadal variability in the rate of [Formula: see text] accumulation in the atmosphere that is not driven by [Formula: see text] emissions. The decade of the 1990s experienced enhanced carbon accumulation in the atmosphere relative to emissions, while in the 2000s, the atmospheric growth rate slowed, even though emissions grew rapidly. These variations are driven by natural sources and sinks of [Formula: see text] due to the ocean and the terrestrial biosphere. In this study, we compare three independent methods for estimating oceanic [Formula: see text] uptake and find that the ocean carbon sink could be responsible for up to 40% of the observed decadal variability in atmospheric [Formula: see text] accumulation. Data-based estimates of the ocean carbon sink from [Formula: see text] mapping methods and decadal ocean inverse models generally agree on the magnitude and sign of decadal variability in the ocean [Formula: see text] sink at both global and regional scales. Simulations with ocean biogeochemical models confirm that climate variability drove the observed decadal trends in ocean [Formula: see text] uptake, but also demonstrate that the sensitivity of ocean [Formula: see text] uptake to climate variability may be too weak in models. Furthermore, all estimates point toward coherent decadal variability in the oceanic and terrestrial [Formula: see text] sinks, and this variability is not well-matched by current global vegetation models. Reconciling these differences will help to constrain the sensitivity of oceanic and terrestrial [Formula: see text] uptake to climate variability and lead to improved climate projections and decadal climate predictions.},

keywords = {},

pubstate = {published},

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}

@article{a10b652fecd745e39ae3a3b2edda74ce,

title = {Implications for workability and survivability in populations exposed to extreme heat under climate change: A modelling study},

author = {Oliver Andrews and Corinne Le Quéré and Tord Kjellstrom and Bruno Lemke and Andy Haines},

doi = {10.1016/S2542-5196(18)30240-7},

issn = {2542-5196},

year  = {2018},

date = {2018-12-01},

journal = {The Lancet Planetary Health},

volume = {2},

number = {12},

pages = {e540–e547},

publisher = {Elsevier},

abstract = {Background: Changes in temperature and humidity due to climate change affect living and working conditions. An understanding of the effects of different global temperature changes on population health is needed to inform the continued implementation of the Paris Climate Agreement and to increase global ambitions for greater cuts in emissions. By use of historical and projected climate conditions, we aimed to investigate the effects of climate change on workability (ie, the ability to work) and survivability (the ability to survive). Methods: In this modelling study, we estimated the changes in populations exposed to excessive heat stress between the recent past (ie, 1986–2005) and 2100. We used climate data from four models to calculate the wet-bulb globe temperature, an established heat exposure index that can be used to assess the effects of temperature, humidity, and other environmental factors on humans. We defined and applied thresholds for risks to workability (where the monthly mean of daily maximum wet-bulb globe temperature exceeds 34°C) and survivability (where the maximum daily wet-bulb globe temperature exceeds 40°C for 3 consecutive days), and we used population projections to quantify changes in risk associated with different changes to the global temperature. Findings: The risks to workability increase substantially with global mean surface temperature in all four climate models, with approximately 1 billion people affected globally after an increase in the global temperature of about 2·5°C above pre-industrial levels. There is greater variability between climate models for exposures above the threshold for risks to survivability than for risks to workability. The number of people who are likely to be exposed to heat stress exceeding the survivability threshold increases with global temperature change, to reach around 20 million people globally after an increase of about 2·5°C, estimated from the median of the models, but with a large model uncertainty. More people are likely to be exposed to heat stress in urban than in rural areas. Population exposure can fluctuate over time and change substantially within one decade. Interpretation: Exposure to excessive heat stress is projected to be widespread in tropical or subtropical low-income and middle-income countries, highlighting the need to build on the Paris Agreement regarding global temperature targets, to protect populations who have contributed little to greenhouse gas emissions. The non-linear dependency of heat exposure risk on temperature highlights the importance of understanding thresholds in coupled human-climate systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{9f2e116c3f8d4e8d895e559dc2159bb2,

title = {Global peak in atmospheric radiocarbon provides a potential definition for the onset of the Anthropocene epoch in 1965},

author = {Chris S. M. Turney and Jonathan Palmer and Mark A. Maslin and Alan Hogg and Christopher J. Fogwill and John Southon and Pavla Fenwick and Gerhard Helle and Janet M. Wilmshurst and Matt McGlone and Christopher Bronk Ramsey and Zoë Thomas and Mathew Lipson and Brent Beaven and Richard T. Jones and Oliver Andrews and Quan Hua},

doi = {10.1038/s41598-018-20970-5},

issn = {2045-2322},

year  = {2018},

date = {2018-02-19},

journal = {Scientific Reports},

volume = {8},

publisher = {Nature Publishing Group},

abstract = {Anthropogenic activity is now recognised as having profoundly and permanently altered the Earth system, suggesting we have entered a human-dominated geological epoch, the ‘Anthropocene’. To formally define the onset of the Anthropocene, a synchronous global signature within geological-forming materials is required. Here we report a series of precisely-dated tree-ring records from Campbell Island (Southern Ocean) that capture peak atmospheric radiocarbon (14C) resulting from Northern Hemisphere-dominated thermonuclear bomb tests during the 1950s and 1960s. The only alien tree on the island, a Sitka spruce (Picea sitchensis), allows us to seasonally-resolve Southern Hemisphere atmospheric 14C, demonstrating the ‘bomb peak’ in this remote and pristine location occurred in the last-quarter of 1965 (October-December), coincident with the broader changes associated with the post-World War II ‘Great Acceleration’ in industrial capacity and consumption. Our findings provide a precisely-resolved potential Global Stratotype Section and Point (GSSP) or ‘golden spike’, marking the onset of the Anthropocene Epoch.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2059,

title = {Global Peak in Atmospheric Radiocarbon Provides a Potential Definition for the Onset of the Anthropocene Epoch in 1965},

author = {C Turney and J Palmer and M Maslin and A Hogg and C Fogwill and J Southon and P Fenwick and G Helle and J Wilmshurst and M McGlone and C Ramsay and Z Thomas and M Lipson and B Beaven and R Jones and O Andrews and Q Hua},

url = {https://www.nature.com/articles/s41598-018-20970-5},

doi = {10.1038/s41598-018-20970-5},

year  = {2018},

date = {2018-01-01},

journal = {Scientific Reports},

volume = {8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2078,

title = {Populations exposed to extreme heat under climate change: implications for workability and survivability},

author = {O Andrews and C Le Quéré and T Kjellstrom and B Lemke and A Haines},

year  = {2018},

date = {2018-01-01},

journal = {The Lancet},

volume = {Submitted},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{2011,

title = {Risks associated with global warming of 1.5 or 2C},

author = {R Warren and O Andrews and S Brown and N Forstenhaeusler and D Gernaat and P Goodwin and I Harris and Y He and C Home and FJ Colón-González and R Nicholls and T Osborn and J Price and D Van Vuuren and R Wright},

year  = {2018},

date = {2018-01-01},

publisher = {Tyndall Centre Briefing Note},

abstract = {
This Briefing Note summarises research examining the risks associated with global warming of 1.5textdegreeC and 2textdegreeC funded by the UK department for Business, Energy and Industrial Strategy (BEIS) undertaken by researchers at the Tyndall Centre.
},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{96cac32365294374b9b8db85f202529f,

title = {Biogeochemical modelling of dissolved oxygen in a changing ocean},

author = {Oliver Andrews and Erik Buitenhuis and Corinne Le Quéré and Parvadha Suntharalingam},

doi = {10.1098/rsta.2016.0328},

issn = {1364-503X},

year  = {2017},

date = {2017-09-13},

journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},

volume = {375},

number = {2102},

publisher = {The Royal Society},

abstract = {Secular decreases in dissolved oxygen concentration have been observed within the tropical oxygen minimum zones (OMZs) and at mid- to high latitudes over the last approximately 50 years. Earth system model projections indicate that a reduction in the oxygen inventory of the global ocean, termed ocean deoxygenation, is a likely consequence of on-going anthropogenic warming. Current models are, however, unable to consistently reproduce the observed trends and variability of recent decades, particularly within the established tropical OMZs. Here, we conduct a series of targeted hindcast model simulations using a state-of-the-art global ocean biogeochemistry model in order to explore and review biases in model distributions of oceanic oxygen. We show that the largest magnitude of uncertainty is entrained into ocean oxygen response patterns due to model parametrization of pCO2-sensitive C : N ratios in carbon fixation and imposed atmospheric forcing data. Inclusion of a pCO2-sensitive C : N ratio drives historical oxygen depletion within the ocean interior due to increased organic carbon export and subsequent remineralization. Atmospheric forcing is shown to influence simulated interannual variability in ocean oxygen, particularly due to differences in imposed variability of wind stress and heat fluxes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{1960,

title = {Global Carbon Budget 2017},

author = {C Le Quéré and R M Andrew and P Friedlingstein and S Sitch and J Pongratz and AC Manning and J I Korsbakken and GP Peters and J Canadell and RB Jackson and TA Boden and P Tans and OD Andrews and VK Arora and D Bakker and L Barbero and M Becker and R Betts and L Bopp and F Chevallier and L P Chini and P Ciais and CE Cosca and J Cross and K Currie and T Gasser and I Harris and J Hauck and V Haverd and RA Houghton and CW Hunt and G Hurtt and T Ilyina and A K Jain and E Kato and M Kautz and RF Keeling and K Klein Goldewijk and A Körtzinger and P Landschutzer and N Lef{`e}vre and A Lenton and S Lienert and I Lima and D Lombardozzi and N Metzl and F Millero and P M S Monteiro and D R Munro and J E M S. Nabel and S Nakaoka and Y Nojiri and XA. Pad{'i}n and A Peregon and B Pfeil and D Pierrot and B Poulter and G Rehder and J Reimer and C Rödenbeck and J Schwinger and R Seferian and I Skjelvan and B D Stocker and H Tian and B Tilbrook and I T Van Der Laan-Luijkx and GR Van der Werf and S Van Heuven and N Viovy and N Vuichard and A P Walker and AJ Watson and A Wiltshire and S Zaehle and D Zhu},

url = {https://www.earth-syst-sci-data-discuss.net/essd-2017-123/},

doi = {https://doi.org/10.5194/essd-2017-123},

year  = {2017},

date = {2017-01-01},

journal = {Earth System Science Data},

abstract = {
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 the five major components of the global carbon budget and their uncertainties. 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 land-cover change data and bookkeeping 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 ocean CO_{2} sink (S_{OCEAN}) and terrestrial CO_{2} sink (S_{LAND}) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B_{IM}), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of our imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as textpm1σ. For the last decade available (2007–2016), E_{FF} was 9.4 textpm 0.5 GtC yr^{-1}, E_{LUC}1.3 textpm 0.7 GtC yr^{-1}, G_{ATM} 4.7 textpm 0.1 GtC yr^{-1}, S_{OCEAN} 2.4 textpm 0.5 GtC yr^{-1}, and S_{LAND} 3.0 textpm 0.8 GtC yr^{-1}, with a budget imbalance B_{IM} of 0.6 GtC yr^{-1} indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in E_{FF} was approximately zero and emissions remained at 9.9 textpm 0.5 GtC yr^{-1}. Also for 2016, E_{LUC} was 1.3 textpm 0.7 GtC yr^{-1}, G_{ATM} was 6.1 textpm 0.2 GtC yr^{-1}, S_{OCEAN} was 2.6 textpm 0.5 GtC yr^{-1} and S_{LAND} was 2.7 textpm 1.0 GtC yr^{-1}, with a small B_{IM} of -0.3 GtC. G_{ATM} continued to be higher in 2016 compared to the past decade (2007–2016), reflecting in part the higher fossil emissions and smaller S_{LAND} for that year consistent with El Ni~no conditions. The global atmospheric CO_{2} concentration reached 402.8 textpm 0.1 ppm averaged over 2016. For 2017, preliminary data indicate a renewed growth in E_{FF} of +2.0 % (range of 0.8 % to 3.0 %) based on national emissions projections for China, USA, and India, and projections of Gross Domestic Product corrected for recent changes in the carbon intensity of the economy for the rest of the world. For 2017, initial data indicate an increase in atmospheric CO_{2} concentration of around 5.3 GtC (2.5 ppm), attributed to a combination of increasing emissions and receding El Ni~no conditions. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quéré et al., 2016; 2015b; 2015a; 2014; 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017.
},

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}

@article{0269483cb241496b82515904c245e2d5,

title = {ESMValTool (v1.0) - a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP},

author = {Veronika Eyring and Mattia Righi and Axel Lauer and Martin Evaldsson and Sabrina Wenzel and Colin Jones and Alessandro Anav and Oliver Andrews and Irene Cionni and Edouard L. Davin and Clara Deser and Carsten Ehbrecht and Pierre Friedlingstein and Peter Gleckler and Klaus-Dirk Gottschaldt and Stefan Hagemann and Martin Juckes and Stephan Kindermann and John Krasting and Dominik Kunert and Richard Levine and Alexander Loew and Jarmo Mäkelä and Gill Martin and Erik Mason and Adam S. Phillips and Simon Read and Catherine Rio and Romain Roehrig and Daniel Senftleben and Andreas Sterl and Lambertus H. Ulft and Jeremy Walton and Shiyu Wang and Keith D. Williams},

doi = {10.5194/gmd-9-1747-2016},

issn = {1991-9603},

year  = {2016},

date = {2016-05-10},

journal = {Geoscientific Model Development},

volume = {9},

number = {5},

pages = {1747–1802},

publisher = {Copernicus Gesellschaft mbH},

abstract = {A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected essential climate variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases, and soil hydrology–climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the Coupled Model Intercomparison Project (CMIP) ensemble. This will facilitate and improve ESM evaluation beyond the state-of-the-art and aims at supporting such activities within CMIP and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user.},

note = {© Author(s) 2016. This work is distributed under the Creative Commons Attribution 3.0 License.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{1549,

title = {Global Carbon Budget 2016},

author = {C Le Quéré and R M Andrew and J Canadell and S Sitch and J Ivar Korsbakken and GP Peters and AC Manning and TA Boden and P Tans and RA Houghton and RF Keeling and S Alin and OD Andrews and P Anthoni and L Barbero and L Bopp and F Chevallier and L P Chini and P Ciais and K Currie and C Delire and SC Doney and P Friedlingstein and T Gkritzalis and I Harris and J Hauck and V Haverd and M Hoppema and K Klein Goldewijk and A K Jain and E Kato and A Körtzinger and P Landschutzer and N Lef{`e}vre and A Lenton and S Lienert and D Lombardozzi and J R Melton and N Metzl and F Millero and P M S Monteiro and D R Munro and J E M S. Nabel and S Nakaoka and K O{textquoteright}Brien and A Olsen and A Omar and T Ono and D Pierrot and B Poulter and C Rödenbeck and J E Salisbury and R Seferian and I Skjelvan and B D Stocker and A J Sutton and T Takahashi and H Tian and B Tilbrook and I T Van Der Laan-Luijkx and GR Van der Werf and N Viovy and A P Walker and A Wiltshire and S Zaehle},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995791034&doi=10.5194%2fessd-8-605-2016&partnerID=40&md5=523714d0db7a7fb33e7e667a6694f581},

doi = {10.5194/essd-8-605-2016},

year  = {2016},

date = {2016-01-01},

journal = {Earth System Science Data},

volume = {8},

pages = {605-649},

chapter = {605},

abstract = {
Accurate assessment of anthropogenic carbon dioxide (CO2) 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. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) 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 textpm reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006-2015), EFF was 9.3textpm0.5 GtC yr-1, ELUC 1.0textpm0.5 GtC yr-1, GATM 4.5textpm0.1 GtC yr-1, SOCEAN 2.6textpm0.5 GtC yr-1, and SLAND 3.1textpm0.9 GtC yr-1. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9textpm0.5 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, ELUC was 1.3textpm0.5 GtC yr-1, GATM was 6.3textpm0.2 GtC yr-1, SOCEAN was 3.0textpm0.5 GtC yr-1, and SLAND was 1.9textpm0.9 GtC yr-1. GATM was higher in 2015 compared to the past decade (2006-2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4textpm0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with C0.2% (range of-1.0 to C1.8 %) 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 EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Nin~o conditions of 2015-2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565textpm55 GtC (2075textpm205 GtCO2) for 1870-2016, about 75% from EFF and 25% from ELUC. 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. textcopyright 2016 Author(s).
},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{1738,

title = {ESMValTool (v1.0) - a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP},

author = {Veronika Eyring and Mattia Righi and Axel Lauer and Martin Evaldsson and Sabrina Wenzel and Colin Jones and Alessandro Anav and Oliver Andrews and Irene Cionni},

doi = {DOI: 10.5194/gmd-9-1747-2016},

year  = {2016},

date = {2016-01-01},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{04b79005ba5c4302bd3c90b335f33a8f,

title = {ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth System Models in CMIP},

author = {Veronika Eyring and Mattia Righi and Martin Evaldsson and Axel Lauer and Sabrina Wenzel and Colin Jones and Alessandro Anav and Oliver Andrews and Irene Cionni and Edouard Davin and Clara Deser and Carsten Ehbrecht and Pierre Friedlingstein and Peter Gleckler and Klaus-Dirk Gottschaldt and Stefan Hagemann and Martin Juckes and Stephan Kindermann and John Krasting and Dominik Kunert and Richard Levine and Alexander Loew and Jarmo Makela and Gill Martin and Erik Mason and Adam Phillips and Simon Read and Catherine Rio and Romain Roehrig and Daniel Senftleben and Andreas Sterl and L. H. Ulft and Jeremy Walton and Shiyu Wang and Keith Williams},

doi = {10.5194/gmdd-8-7541-2015},

issn = {1991-9611},

year  = {2015},

date = {2015-09-03},

journal = {Geophysical Model Development Discussions},

volume = {8},

number = {9},

pages = {7451–7661},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{488243a8cacf4ed1b8cbb2f89ab4916f,

title = {Detecting an external influence on recent changes in oceanic oxygen using an optimal fingerprinting method},

author = {Oliver Andrews and N. L. Bindoff and P. R. Halloran and T. Ilyina and Corinne Le Quéré},

doi = {10.5194/bg-10-1799-2013},

issn = {1726-4189},

year  = {2013},

date = {2013-03-19},

journal = {Biogeosciences},

volume = {10},

pages = {1799–1813},

publisher = {European Geosciences Union},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{438,

title = {Detecting an external influence on recent changes in oceanic oxygen using an optimal fingerprinting method},

author = {OD Andrews and NL Bindoff and PR Halloran and T Ilyina and C Le Quéré},

isbn = {10.5194/bg-10-1799-2013},

year  = {2013},

date = {2013-01-01},

journal = {Biogeosciences},

volume = {10},

pages = {1799-1813},

chapter = {1799},

abstract = {
Ocean deoxygenation has been observed in all major ocean basins over the past 50 yr. Although this signal is largely consistent with oxygen changes expected from anthropogenic climate change, the contribution of external forcing to recent deoxygenation trends relative to natural internal variability is yet to be established. Here we conduct a formal optimal fingerprinting analysis to investigate if external forcing has had a detectable influence on observed dissolved oxygen concentration ([O_{2}]) changes between ∼1970 and ∼1992 using simulations from two Earth System Models (MPI-ESM-LR and HadGEM2-ES). We detect a response to external forcing at a 90% confidence level and find that observed [O_{2}] changes are inconsistent with internal variability as simulated by models. This result is robust in the global ocean for depth-averaged (1-D) zonal mean patterns of [O_{2}] change in both models. Further analysis with the MPI-ESM-LR model shows similar positive detection results for depth-resolved (2-D) zonal mean [O_{2}] changes globally and for the Pacific Ocean individually. Observed oxygen changes in the Atlantic Ocean are indistinguishable from natural internal variability. Simulations from both models consistently underestimate the amplitude of historical [O_{2}] changes in response to external forcing, suggesting that model projections for future ocean deoxygenation may also be underestimated.
},

keywords = {},

pubstate = {published},

tppubtype = {article}

}