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  • 1
    Online Resource
    Online Resource
    Berlin, Heidelberg :Springer Berlin / Heidelberg,
    Keywords: Geography. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (434 pages)
    Edition: 1st ed.
    ISBN: 9783540498568
    Series Statement: Springer Praxis Bks.
    DDC: 363.179909113
    Language: English
    Note: Intro -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- Figures -- Tables -- Abreviations and acronyms -- Contributors -- Authors -- Introduction -- 1 Sourees of anthropogenie pollution in the Nordie Seas and Arctic -- 1.1 RADIOACTIVE CONTAMINATION: CLASSIFICATION AND DESCRIPTION OF SOURCES -- 1.1.1 Classification of sources -- 1.1.1.1 Primary and secondary sources -- 1.1.1.2 Global and point sources -- 1.1.2 Nuclear power plants (NPPs) -- 1.1.3 Nuclear industry enterprises -- 1.1.3.1 European reprocessing plants -- 1.1.3.2 Russian nucear industry enterprises -- 1.1.4 Scientific and research reactors and laboratories -- 1.1.5 Special combines -- 1.1.6 Nuclear weapons tests and "peaceful" nuclear explosions -- 1.1.6.1 Nuclear military test explosions -- 1.1.6.2 Underground civilian ("peaceful'') nuclear explosions -- 1.1.7 Military bases, nuclear icebreakers, and submarines -- 1.1.8 Miscellaneous accidents -- 1.2 RADIOACTIVE POLLUTION: MAJOR RUSSIAN NUCLEAR INDUSTRIES -- 1.2.1 The Mayak Production Association, Chelyabinsk -- 1.2.2 The Siberian Chemical Combine, Tomsk-7 -- 1.2.3 The Mining Chemical Combine. Krasnoyarsk-26 -- 1.3 NON-RADIOACTIVE POLLUTION -- 1.3.1 Main sources of marine pollution in the Russian Arctic -- 1.3.1.1 Barents Sea -- 1.3.1.2 White Sea -- 1.3.1.3 Kara Sea -- 1.3.1.4 Laptev Sea -- 1.3.2 Distribution of pollution in the Russian Arctic Seas and coastal areas -- 1.3.2.1 Barents Sea -- 1.3.2.2 White Sea -- 1.3.2.3 Kara Sea -- 1.3.2.4 Laptev Sea -- 2 Study region and environmental datasets -- 2.1 GEOGRAPHICAL DESCRIPTION OF THE STUDY REGION -- 2.1.1 The Ob' and Yenisei River systems -- 2.1.1.1 The Ob' River -- 2.1.1.2 Yenisei River -- 2.1.2 Kara Sea region -- 2.1.2.1 General description -- 2.1.2.2 Oceanographic regime -- 2.1.3 The Nordic Seas and adjacent seas. , 2.2 DESCRIPTION OF ENVIRONMENTAL AND POLLUTION DATA -- 2.2.1 Databases and information system -- 2.2.2 Environmental data -- 2.2.3 Radioactive and non-radioactive pollution data -- 3 Generie model system (GMS) for simulation of radioaetive spread in the aquatie environment -- 3.1 RATIONALE, CONCEPT, AND STRUCTURE OF THE GMS -- 3.1.1 GMS structure and data streams -- 3.1.2 Modeling management -- 3.2 ATLANTIC AND ARCTIC OCEAN MODEL -- 3.2.1 General model description -- Primitive equations -- Momentum equation -- Continuity equation -- Hydrostatic equation -- Heat and salt conservation equations -- 3.2.2 Radionuclide tracer module -- 3.2.3 Model validation results -- 3.2.4 Extension and validation of the Arctic/North Atlantic model . -- Description of the new version of MICOM -- Validating the new model using simulated and observed 129 I from Sellafield and La Hague -- Summary -- 3.3 KARA SEA SHELF SEA MODEL -- 3.3.1 General model description -- Ocean circulation model -- Ice cover model -- Atmospheric impact -- Initial and boundary comditions -- 3.3.2 Model validation results -- 3.4 THE OB' AND YENISEI RIVER AND ESTUARY MODELS -- 3.4.1 One-dimensional model to simulate the transport of radionuclides in a river system-RIVTOX -- Sub-model of river hydraulics -- Sediment transport sub-model -- Sub-model of radionuclide transport -- Boundary and initial conditions in river network -- Boudary and initial conditions -- Numerical solution -- 3.4.2 Numerical model for three-dimensional dispersion simu­lation of radionuclides in stratified water bodies- THREETOX -- Hydrodynamics -- Ice thermohydrodynamics -- Sediment transport -- Radionuclide transport -- Numerical setup -- 3.4.3 River model validation results -- 4 Studies of potential radioaetive spread in the Nordie Seas and Aretie using the generie model system (GMS). , 4.1 SIMULATION OF PAST CONTAMINATION OF THE NORDIC SEAS AND ARCTIC FROM ANTHROPOGENIC RELEASES -- 4.1.1 River and estuary transport and dilution of radioactive pollutants from rivers to the Kara Sea -- Reconstruction of past contamination of the Ob' River by the Mayak PA nuclear plant -- Reconstruction of past contamination of the Yenisei River by the MCC nuclear plant -- 4.1.2 Transport and dilution of radioactive waste and dissolved pollutants in the Kara Sea -- Transport of Strontium-90 from the Ob' River from 1949 to 1965 (Historical Run 1) -- Transport of strontium-90 and cesium-137 from Yenisei River from 1958 to 1993 (Historical Run 2) -- 4.1.3 Transport and dilution of radioactive waste and dissolved pollutants from all sources -- Modeling the transport and dilution of radioactive waste and dessolved pollutants -- Setup of the radionuclide simulation (hindcast) -- Results -- 4.2 SCENARIOS FOR POTENTIAL FUTURE RELEASES OF RADIOACTIVITY -- 4.2.1 The Mayak PA scenario -- 4.2.2 "Krasnoyarsk" scenario -- 4.2.3 "Tomsk" scenario -- 4.2.4 "C02-doubling" scenario -- 4.2.5 "Submarine" seenarios -- 4.3 ASSESSMENTS OF POTENTIAL ACCIDENTAL RELEASES FOR THE 21ST CENTURY -- 4.3.1 Potential radioactive contamination from rivers to the Kara Sea -- River runoff in the 20th and 21st centuries -- Simulation of flux of radionuclides to the Kara Sea from a hypothetical accidental release from nuclear plants -- 4.3.2 Potential radioactive contamination in the Kara Sea -- Transport of 90 Sr from the Ob' to the Kara in 2084-2086 (Scenario I) -- Transport of 137 Cs from the Ob' in 2084-2089 (Scenario II) -- Transport of 90 Sr from Ob' to the Kara Sea during 2084-2086 -- 137 Cs transport from the Yenisei during 2089-2094 (Scenario IV) -- 90 SR transport from the Yenisei for the 2084-2086 (Scenario V). , 4.4 TRANSOPORT OF RADIOACTIVITY IN THE ARCTIC AND POSSIBLE IMPACT OF CLIMATE CHANGE -- 4.4.1 Accident scenario of 90Sr from the Ob' and Yenisei Rivers -- 4.4.2 Spread of accidentally released 90Sr under present and 2 * CO2 warming seenarios -- 4.5 POTENTIAL TRANSPORT OF RADIOACTIVITY FROM SUBMARINE ACCIDENTS -- 4.5.1 Local model simulations -- 4.5.2 Large-scale model simulations -- 5 Studies of the spread of non-radioactive pollutants in the Arctic using the generic model system (GMS) -- 5.1 APPROACH TO SIMULATION OF POLLUTANTS IN THE AQUATIC ENVIRONMENT -- 5.1.1 Persistent organic pollutants -- 5.1.2 Basic processes and equations for modeling -- 5.1.3 Modeling POP transport in the environment -- Equilibrium sorption -- Volatilization -- Biodegradation -- Exchange of PCBs between and bottom sediments -- 5.2 MODELLING PCB SPREAD IN ARCTIC RIVERS AND COASTAL WATERS USING THE GMS -- 5.2.1 Modification of the models for simulation of PCBs -- Sorption -- Volatilization -- Biodegradation (dechlorination) -- Direct exchange of PCBs between water and bottom sediments -- 5.2.2 GMS application to simulate the transport and fate of PCBs released in the Yenisei River and estuary -- 5.3 MODELING PETROLEUM HYDROCARBON SPREAD USING THE GMS -- 5.3.1 Processes of oil spread in the marine environment -- 5.3.2 Modeling oil spread in the marine environment -- 6 Assessment and input to risk management -- 6.1 INTRODUCTION -- 6.1.1 Purpose, endpoints, and philosophy -- 6.1.2 Source term characteristics -- 6.1.3 Environmental characteristics -- 6.1.4 Time frames and societal assumptions -- 6.2 SCENARIOS -- 6.2.1 Source term seenarios -- 6.2.2 Climate seenarios -- 6.3 FORMULATION AND IMPLEMENTATION OF DOSE MODELS -- 6.4 RESULTS -- 6.5 CONCLUSIONS. , APPENDICES A Time series of annual average concentrations of radionuclides in water and sediments by accident scenario and Iocation used for dose caIcuIations -- APPENDICES B Doses to individuals in critical groups from all accident seenarios given by radionuclide and exposure pathway -- Afterword -- References -- Index.
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  • 2
    Publication Date: 2020-01-20
    Description: ©2019. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
    Description: A coordinated set of large ensemble atmosphere‐only simulations is used to investigatethe impacts of observed Arctic sea ice‐driven variability (SIDV) on the atmospheric circulation during1979–2014. The experimental protocol permits separating Arctic SIDV from internal variability andvariability driven by other forcings including sea surface temperature and greenhouse gases. The geographicpattern of SIDV is consistent across seven participating models, but its magnitude strongly depends onensemble size. Based on 130 members, winter SIDV is ~0.18 hPa2for Arctic‐averaged sea level pressure(~1.5% of the total variance), and ~0.35 K2for surface air temperature (~21%) at interannual and longertimescales. The results suggest that more than 100 (40) members are needed to separate Arctic SIDV fromother components for dynamical (thermodynamical) variables, and insufficient ensemble size always leadsto overestimation of SIDV. Nevertheless, SIDV is 0.75–1.5 times as large as the variability driven by otherforcings over northern Eurasia and Arctic.
    Description: Published
    Description: e2019GL085397
    Description: 5A. Ricerche polari e paleoclima
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
    Publication Date: 2022-03-01
    Description: To examine the atmospheric responses to Arctic sea ice variability in the Northern Hemisphere cold season (from October to the following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily varying sea ice, sea surface temperature, and radiative forcings prescribed during the 1979–2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multimodel ensemble mean (MMEM) shows decreasing sea level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drive a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual covariability between sea ice extent in the Barents–Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the covariability in MMEMs. The interannual sea ice decline followed by a negative North Atlantic Oscillation–like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.
    Description: Published
    Description: 8419–8443
    Description: 2A. Fisica dell'alta atmosfera
    Description: JCR Journal
    Keywords: Arctic ; Sea ice ; Atmospheric circulation ; Climate models ; 01.01. Atmosphere
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 4
    Publication Date: 2022-05-26
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Liang, Y., Kwon, Y., Frankignoul, C., Danabasoglu, G., Yeager, S., Cherchi, A., Gao, Y., Gastineau, G., Ghosh, R., Matei, D., Mecking, J., V., Peano, D., Suo, L., & Tian, T. Quantification of the arctic sea ice-driven atmospheric circulation variability in coordinated large ensemble simulations. Geophysical Research Letters, 47(1), (2020): e2019GL085397, doi:10.1029/2019GL085397.
    Description: A coordinated set of large ensemble atmosphere‐only simulations is used to investigate the impacts of observed Arctic sea ice‐driven variability (SIDV) on the atmospheric circulation during 1979–2014. The experimental protocol permits separating Arctic SIDV from internal variability and variability driven by other forcings including sea surface temperature and greenhouse gases. The geographic pattern of SIDV is consistent across seven participating models, but its magnitude strongly depends on ensemble size. Based on 130 members, winter SIDV is ~0.18 hPa2 for Arctic‐averaged sea level pressure (~1.5% of the total variance), and ~0.35 K2 for surface air temperature (~21%) at interannual and longer timescales. The results suggest that more than 100 (40) members are needed to separate Arctic SIDV from other components for dynamical (thermodynamical) variables, and insufficient ensemble size always leads to overestimation of SIDV. Nevertheless, SIDV is 0.75–1.5 times as large as the variability driven by other forcings over northern Eurasia and Arctic.
    Description: The authors thank Editor Christina Patricola and two anonymous reviewers for their comprehensive and insightful comments, which have led to improved presentation of this manuscript. We acknowledge support by the Blue‐Action Project (European Union's Horizon 2020 research and innovation program, 727852, http://www.blue‐action.eu/index.php?id = 3498). The WHOI‐NCAR group is also supported by the US National Science Foundation (NSF) Office of Polar Programs Grants 1736738 and 1737377, and their computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory at NCAR. NCAR is a major facility sponsored by the U.S. NSF under Cooperative Agreement 1852977. The LOCEAN‐IPSL group was granted access to the HPC resources of TGCC under the Allocation A5‐017403 made by GENCI. The SST and SIC data were downloaded from the U.K. Met Office Hadley Centre Observations Datasets (http://www.metoffice.gov.uk/hadobs/hadisst).
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 5
    Publication Date: 2022-06-28
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , NonPeerReviewed
    Format: application/pdf
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  • 6
    Publication Date: 2022-06-06
    Description: Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Liang, Y.-C., Frankignoul, C., Kwon, Y.-O., Gastineau, G., Manzini, E., Danabasoglu, G., Suo, L., Yeager, S., Gao, Y., Attema, J. J., Cherchi, A., Ghosh, R., Matei, D., Mecking, J., Tian, T., & Zhang, Y. Impacts of Arctic sea ice on cold season atmospheric variability and trends estimated from observations and a multimodel large ensemble. Journal of Climate, 34(20), (2021): 8419–8443, https://doi.org/10.1175/JCLI-D-20-0578.s1.
    Description: To examine the atmospheric responses to Arctic sea ice variability in the Northern Hemisphere cold season (from October to the following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily varying sea ice, sea surface temperature, and radiative forcings prescribed during the 1979–2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multimodel ensemble mean (MMEM) shows decreasing sea level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drive a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual covariability between sea ice extent in the Barents–Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the covariability in MMEMs. The interannual sea ice decline followed by a negative North Atlantic Oscillation–like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.
    Description: We acknowledge support by the Blue-Action Project (the European Union’s Horizon 2020 research and innovation programme, #727852, http://www.blue-action.eu/index.php?id=3498). The WHOI–NCAR group was supported by the U.S. National Science Foundation (NSF) Office of Polar Programs Grants 1736738 and 1737377. Their computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory at NCAR. NCAR is a major facility sponsored by the U.S. NSF under Cooperative Agreement No. 1852977. Guillaume Gastineau was granted access to the HPC resources of TGCC under the allocations A5-017403 and A7-017403 made by GENCI. The SST and SIC data were downloaded from the U.K. Met Office Hadley Centre Observations Datasets (http://www.metoffice.gov.uk/hadobs/hadisst). The work by NLeSC was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. The simulations of IAP AGCM were supported by the National Key R&D Program of China 2017YFE0111800. The NorESM2-CAM6 simulations were performed on resources provided by UNINETT Sigma2–the National Infrastructure for High Performance Computing and Data Storage in Norway (nn2343k, NS9015K).
    Keywords: Arctic ; Sea ice ; Atmospheric circulation ; Climate models
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 7
    Publication Date: 2023-03-08
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Suo, L., Gastineau, G., Gao, Y., Liang, Y.-C., Ghosh, R., Tian, T., Zhang, Y., Kwon, Y.-O., Otterå, O., Yang, S., & Matei, D. Simulated contribution of the interdecadal Pacific oscillation to the west Eurasia cooling in 1998–2013. Environmental Research Letters, 17(9), (2022): 094021, https://doi.org/10.1088/1748-9326/ac88e5.
    Description: Large ensemble simulations with six atmospheric general circulation models involved are utilized to verify the interdecadal Pacific oscillation (IPO) impacts on the trend of Eurasian winter surface air temperatures (SAT) during 1998–2013, a period characterized by the prominent Eurasia cooling (EC). In our simulations, IPO brings a cooling trend over west-central Eurasia in 1998–2013, about a quarter of the observed EC in that area. The cooling is associated with the phase transition of the IPO to a strong negative. However, the standard deviation of the area-averaged SAT trends in the west EC region among ensembles, driven by internal variability intrinsic due to the atmosphere and land, is more than three times the isolated IPO impacts, which can shadow the modulation of the IPO on the west Eurasia winter climate.
    Description: This work is funded by the JPI Climate-Oceans ROADMAP and the Blue‐Action Project (European Union's Horizon 2020 research and innovation program, 727852). The CAM6-Nor simulations were performed on resources provided by UNINETT Sigma2—the National Infrastructure for High Performance Computing and Data Storage in Norway (nn2343k, NS9015K). The simulations of IAP4.1 were supported by National Key R&D Program of China 2017YFE0111800.
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 8
    Publication Date: 2019-09-23
    Description: The recent increase in the rate of the Greenland ice sheet melting has raised with urgency the question of the impact of such a melting on the climate. As former model projections, based on a coarse representation of the melting, show very different sensitivity to this melting, it seems necessary to consider a multi-model ensemble to tackle this question. Here we use five coupled climate models and one ocean-only model to evaluate the impact of 0.1 Sv (1 Sv = 106 m3/s) of freshwater equally distributed around the coast of Greenland during the historical era 1965–2004. The ocean-only model helps to discriminate between oceanic and coupled responses. In this idealized framework, we find similar fingerprints in the fourth decade of hosing among the models, with a general weakening of the Atlantic Meridional Overturning Circulation (AMOC). Initially, the additional freshwater spreads along the main currents of the subpolar gyre. Part of the anomaly crosses the Atlantic eastward and enters into the Canary Current constituting a freshwater leakage tapping the subpolar gyre system. As a consequence, we show that the AMOC weakening is smaller if the leakage is larger. We argue that the magnitude of the freshwater leakage is related to the asymmetry between the subpolar-subtropical gyres in the control simulations, which may ultimately be a primary cause for the diversity of AMOC responses to the hosing in the multi-model ensemble. Another important fingerprint concerns a warming in the Nordic Seas in response to the re-emergence of Atlantic subsurface waters capped by the freshwater in the subpolar gyre. This subsurface heat anomaly reaches the Arctic where it emerges and induces a positive upper ocean salinity anomaly by introducing more Atlantic waters. We found similar climatic impacts in all the coupled ocean–atmosphere models with an atmospheric cooling of the North Atlantic except in the region around the Nordic Seas and a slight warming south of the equator in the Atlantic. This meridional gradient of temperature is associated with a southward shift of the tropical rains. The free surface models also show similar sea-level fingerprints notably with a comma-shape of high sea-level rise following the Canary Current.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 9
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    AGU (American Geophysical Union) | Wiley
    In:  Geophysical Research Letters, 41 (4). pp. 1295-1300.
    Publication Date: 2017-04-10
    Description: Atlantic multidecadal variability (AMV) is known to impact climate globally, and knowledge about the persistence of AMV is important for understanding past and future climate variability, as well as modeling and assessing climate impacts. The short observational data do not significantly resolve multidecadal variability, but recent paleoproxy reconstructions show multidecadal variability in North Atlantic temperature prior to the instrumental record. However, most of these reconstructions are land-based, not necessarily representing sea surface temperature. Proxy records are also subject to dating errors and microenvironmental effects. We extend the record of AMV 90 years past the instrumental record using principle component analysis of five marine-based proxy records to identify the leading mode of variability. The first principal component is consistent with the observed AMV, and multidecadal variability seems to persist prior to the instrumental record. Thus, we demonstrate that reconstructions of past Atlantic low-frequency variability can be improved by combining marine-based proxies.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
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  • 10
    Publication Date: 2020-04-23
    Description: This study focuses on the climatic impacts of the Atlantic Multidecadal Oscillation (AMO) as a mode of internal variability. Given the difficulties involved in excluding the effects of external forcing from internal variation, i.e., owing to the short record length of instrumental observations and historical simulations, we assess and compare the AMO and its related climatic impacts both in observations and in the “Pre-industrial” experiments of models participating in CMIP5. First, we evaluate the skill of the 25 CMIP5 models’ “Historical” simulations in simulating the observational AMO, and find there is generally a considerable range of skill among them in this regard. Six of the models with higher skill relative to the other models are selected to investigate the AMO-related climate impacts, and it is found that their “Pre-industrial” simulations capture the essential features of the AMO. A positive AMO favors warmer surface temperature around the North Atlantic, and the Atlantic ITCZ shifts northward leading to more rainfall in the Sahel and less rainfall in Brazil. Furthermore, the results confirm the existence of a teleconnection between the AMO and East Asian surface temperature, as well as the late withdrawal of the Indian summer monsoon, during positive AMO phases. These connections could be mainly caused by internal climate variability. Opposite patterns are true for the negative phase of the AMO.
    Type: Article , PeerReviewed
    Format: text
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