114 results on '"Mahmood, Rashed"'
Search Results
2. Prepositional Errors Among Undergraduate ESL Learners in Pakistan
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Shafique, Haroon, Mahmood, Rashed, Coombe, Christine, Series Editor, and Ali Raza, Naziha, editor
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- 2022
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3. Improving the forecast quality of near-term climate projections by constraining internal variability based on decadal predictions and observations
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Donat, Markus, primary, Mahmood, Rashed, additional, Cos, Josep, additional, Ortega, Pablo, additional, and Doblas-Reyes, Francisco J, additional
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- 2024
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4. Overview paper: New insights into aerosol and climate in the Arctic
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Abbatt, Jonathan PD, Leaitch, W Richard, Aliabadi, Amir A, Bertram, Allan K, Blanchet, Jean-Pierre, Boivin-Rioux, Aude, Bozem, Heiko, Burkart, Julia, Chang, Rachel YW, Charette, Joannie, Chaubey, Jai P, Christensen, Robert J, Cirisan, Ana, Collins, Douglas B, Croft, Betty, Dionne, Joelle, Evans, Greg J, Fletcher, Christopher G, Galí, Martí, Ghahremaninezhad, Roghayeh, Girard, Eric, Gong, Wanmin, Gosselin, Michel, Gourdal, Margaux, Hanna, Sarah J, Hayashida, Hakase, Herber, Andreas B, Hesaraki, Sareh, Hoor, Peter, Huang, Lin, Hussherr, Rachel, Irish, Victoria E, Keita, Setigui A, Kodros, John K, Köllner, Franziska, Kolonjari, Felicia, Kunkel, Daniel, Ladino, Luis A, Law, Kathy, Levasseur, Maurice, Libois, Quentin, Liggio, John, Lizotte, Martine, Macdonald, Katrina M, Mahmood, Rashed, Martin, Randall V, Mason, Ryan H, Miller, Lisa A, Moravek, Alexander, Mortenson, Eric, Mungall, Emma L, Murphy, Jennifer G, Namazi, Maryam, Norman, Ann-Lise, O'Neill, Norman T, Pierce, Jeffrey R, Russell, Lynn M, Schneider, Johannes, Schulz, Hannes, Sharma, Sangeeta, Si, Meng, Staebler, Ralf M, Steiner, Nadja S, Thomas, Jennie L, von Salzen, Knut, Wentzell, Jeremy JB, Willis, Megan D, Wentworth, Gregory R, Xu, Jun-Wei, and Yakobi-Hancock, Jacqueline D
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Climate Action ,Astronomical and Space Sciences ,Atmospheric Sciences ,Meteorology & Atmospheric Sciences - Abstract
Motivated by the need to predict how the Arctic atmosphere will change in a warming world, this article summarizes recent advances made by the research consortium NETCARE (Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments) that contribute to our fundamental understanding of Arctic aerosol particles as they relate to climate forcing. The overall goal of NETCARE research has been to use an interdisciplinary approach encompassing extensive field observations and a range of chemical transport, earth system, and biogeochemical models. Several major findings and advances have emerged from NETCARE since its formation in 2013. (1) Unexpectedly high summertime dimethyl sulfide (DMS) levels were identified in ocean water (up to 75 nM) and the overlying atmosphere (up to 1 ppbv) in the Canadian Arctic Archipelago (CAA). Furthermore, melt ponds, which are widely prevalent, were identified as an important DMS source (with DMS concentrations of up to 6 nM and a potential contribution to atmospheric DMS of 20 % in the study area). (2) Evidence of widespread particle nucleation and growth in the marine boundary layer was found in the CAA in the summertime, with these events observed on 41 % of days in a 2016 cruise. As well, at Alert, Nunavut, particles that are newly formed and grown under conditions of minimal anthropogenic influence during the months of July and August are estimated to contribute 20 % to 80 % of the 30-50 nm particle number density. DMS-oxidation-driven nucleation is facilitated by the presence of atmospheric ammonia arising from seabird-colony emissions, and potentially also from coastal regions, tundra, and biomass burning. Via accumulation of secondary organic aerosol (SOA), a significant fraction of the new particles grow to sizes that are active in cloud droplet formation. Although the gaseous precursors to Arctic marine SOA remain poorly defined, the measured levels of common continental SOA precursors (isoprene and monoterpenes) were low, whereas elevated mixing ratios of oxygenated volatile organic compounds (OVOCs) were inferred to arise via processes involving the sea surface microlayer. (3) The variability in the vertical distribution of black carbon (BC) under both springtime Arctic haze and more pristine summertime aerosol conditions was observed. Measured particle size distributions and mixing states were used to constrain, for the first time, calculations of aerosol-climate interactions under Arctic conditions. Aircraft- and ground-based measurements were used to better establish the BC source regions that supply the Arctic via long-range transport mechanisms, with evidence for a dominant springtime contribution from eastern and southern Asia to the middle troposphere, and a major contribution from northern Asia to the surface. (4) Measurements of ice nucleating particles (INPs) in the Arctic indicate that a major source of these particles is mineral dust, likely derived from local sources in the summer and long-range transport in the spring. In addition, INPs are abundant in the sea surface microlayer in the Arctic, and possibly play a role in ice nucleation in the atmosphere when mineral dust concentrations are low. (5) Amongst multiple aerosol components, BC was observed to have the smallest effective deposition velocities to high Arctic snow (0.03 cm s1).
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- 2019
5. Clean air policies are key for successfully mitigating Arctic warming
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von Salzen, Knut, Whaley, Cynthia H., Anenberg, Susan C., Van Dingenen, Rita, Klimont, Zbigniew, Flanner, Mark G., Mahmood, Rashed, Arnold, Stephen R., Beagley, Stephen, Chien, Rong-You, Christensen, Jesper H., Eckhardt, Sabine, Ekman, Annica M. L., Evangeliou, Nikolaos, Faluvegi, Greg, Fu, Joshua S., Gauss, Michael, Gong, Wanmin, Hjorth, Jens L., Im, Ulas, Krishnan, Srinath, Kupiainen, Kaarle, Kühn, Thomas, Langner, Joakim, Law, Kathy S., Marelle, Louis, Olivié, Dirk, Onishi, Tatsuo, Oshima, Naga, Paunu, Ville-Veikko, Peng, Yiran, Plummer, David, Pozzoli, Luca, Rao, Shilpa, Raut, Jean-Christophe, Sand, Maria, Schmale, Julia, Sigmond, Michael, Thomas, Manu A., Tsigaridis, Kostas, Tsyro, Svetlana, Turnock, Steven T., Wang, Minqi, and Winter, Barbara
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- 2022
- Full Text
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6. New insights into aerosol and climate in the Arctic
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Abbatt, Jonathan PD, Leaitch, W Richard, Aliabadi, Amir A, Bertram, Alan K, Blanchet, Jean-Pierre, Boivin-Rioux, Aude, Bozem, Heiko, Burkart, Julia, Chang, Rachel YW, Charette, Joannie, Chaubey, Jai P, Christensen, Robert J, Cirisan, Ana, Collins, Douglas B, Croft, Betty, Dionne, Joelle, Evans, Greg J, Fletcher, Christopher G, Ghahremaninezhad, Roghayeh, Girard, Eric, Gong, Wanmin, Gosselin, Michel, Gourdal, Margaux, Hanna, Sarah J, Hayashida, Hakase, Herber, Andreas B, Hesaraki, Sareh, Hoor, Peter, Huang, Lin, Hussherr, Rachel, Irish, Victoria E, Keita, Setigui A, Kodros, John K, Köllner, Franziska, Kolonjari, Felicia, Kunkel, Daniel, Ladino, Luis A, Law, Kathy, Levasseur, Maurice, Libois, Quentin, Liggio, John, Lizotte, Martine, Macdonald, Katrina M, Mahmood, Rashed, Martin, Randall V, Mason, Ryan H, Miller, Lisa A, Moravek, Alexander, Mortenson, Eric, Mungall, Emma L, Murphy, Jennifer G, Namazi, Maryam, Norman, Ann-Lise, O'Neill, Norman T, Pierce, Jeffrey R, Russell, Lynn M, Schneider, Johannes, Schulz, Hannes, Sharma, Sangeeta, Si, Meng, Staebler, Ralf M, Steiner, Nadja S, Galí, Martí, Thomas, Jennie L, von Salzen, Knut, Wentzell, Jeremy JB, Willis, Megan D, Wentworth, Gregory R, Xu, Jun-Wei, and Yakobi-Hancock, Jacqueline D
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Earth Sciences ,Atmospheric Sciences ,Climate Action ,Life Below Water ,Astronomical and Space Sciences ,Meteorology & Atmospheric Sciences ,Atmospheric sciences ,Climate change science - Abstract
Abstract. Motivated by the need to predict how the Arctic atmosphere will change in a warming world, this article summarizes recent advances made by the research consortium NETCARE (Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments) that contribute to our fundamental understanding of Arctic aerosol particles as they relate to climate forcing. The overall goal of NETCARE research has been to use an interdisciplinary approach encompassing extensive field observations and a range of chemical transport, earth system, and biogeochemical models. Several major findings and advances have emerged from NETCARE since its formation in 2013 . (1) Unexpectedly high summertime dimethyl sulfide (DMS) levels were identified in ocean water and the overlying atmosphere in the Canadian Arctic Archipelago (CAA). Furthermore, melt ponds, which are widely prevalent, were identified as an important DMS source. (2) Evidence was found of widespread particle nucleation and growth in the marine boundary layer in the CAA in the summertime. DMS-oxidation-driven nucleation is facilitated by the presence of atmospheric ammonia arising from sea bird colony emissions, and potentially also from coastal regions, tundra, and biomass burning. Via accumulation of secondary organic material (SOA), a significant fraction of the new particles grow to sizes that are active in cloud droplet formation. Although the gaseous precursors to Arctic marine SOA remain poorly defined, the measured levels of common continental SOA precursors (isoprene and monoterpenes) were low, whereas elevated mixing ratios of oxygenated volatile organic compounds were inferred to arise via processes involving the sea surface microlayer. (3) The variability in the vertical distribution of black carbon (BC) under both springtime Arctic haze and more pristine summertime aerosol conditions was observed. Measured particle size distributions and mixing states were used to constrain, for the first time, calculations of aerosol–climate interactions under Arctic conditions. Aircraft- and ground-based measurements were used to better establish the BC source regions that supply the Arctic via long-range transport mechanisms. (4) Measurements of ice nucleating particles (INPs) in the Arctic indicate that a major source of these particles is mineral dust, likely derived from local sources in the summer and long-range transport in the spring. In addition, INPs are abundant in the sea surface microlayer in the Arctic, and possibly play a role in ice nucleation in the atmosphere when mineral dust concentrations are low. (5) Amongst multiple aerosol components, BC was observed to have the smallest effective deposition velocities to high Arctic snow.
- Published
- 2018
7. How Credibly Do CMIP6 Simulations Capture Historical Mean and Extreme Precipitation Changes?
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Donat, Markus G., primary, Delgado‐Torres, Carlos, additional, De Luca, Paolo, additional, Mahmood, Rashed, additional, Ortega, Pablo, additional, and Doblas‐Reyes, Francisco J., additional
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- 2023
- Full Text
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8. Comparison of various drought indices to monitor drought status in Pakistan
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Adnan, Shahzada, Ullah, Kalim, Shuanglin, Li, Gao, Shouting, Khan, Azmat Hayat, and Mahmood, Rashed
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- 2018
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9. Constraining decadal variability regionally improves near-term projections of hot, cold and dry extremes
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Barcelona Supercomputing Center, De Luca, Paolo, Delgado Torres, Carlos, Mahmood, Rashed, Samsó, Margarida, Donat, Markus, Barcelona Supercomputing Center, De Luca, Paolo, Delgado Torres, Carlos, Mahmood, Rashed, Samsó, Margarida, and Donat, Markus
- Abstract
Hot, cold and dry meteorological extremes are often linked with severe impacts on the public health, agricultural, energy and environmental sectors. Skillful predictions of such extremes could therefore enable stakeholders to better plan and adapt to future impacts of these events. The intensity, duration and frequency of such extremes are affected by anthropogenic climate change and modulated by different modes of climate variability. Here we use a large multi-model ensemble from the Coupled Model Intercomparison Project Phase 6 and constrain these simulations by sub-selecting those members whose global SST anomaly patterns are most similar to observations at a given point in time, thereby phasing in the decadal climate variability with observations. Hot and cold extremes are skillfully predicted over most of the globe, with also a widespread added value from using the constrained ensemble compared to the unconstrained full CMIP6 ensemble. On the other hand, dry extremes show skill only in some regions with results sensitive to the index used. Still, we find skillful predictions and added skill for dry extremes in some regions such as western north America, southern central and eastern Europe, southeastern Australia, southern Africa and the Arabian peninsula. We also find that the added skill in the constrained ensemble is due to a combination of improved multi-decadal variations in phase with observed climate extremes and improved representation of long-term changes. Our results demonstrate that constraining decadal variability in climate projections can provide improved estimates of temperature extremes and drought in the next twenty years, which can inform targeted adaptation strategies to near-term climate change., This research has been partly supported by the Horizon2020 LANDMARC project (grant agreement No. 869367) and the Horizon Europe ASPECT project (grant number 101081460). PDL has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No 101059659. CDT acknowledges financial support from the Spanish Ministry for Science and Innovation (FPI PRE2019-509 08864 financed by MCIN/AEI/10.13039/501100011033 and by FSE invierte en tu futuro). MGD is grateful for support by the AXA Research Fund. The authors are further grateful for the support by the Department of Research and Universities of the Government of Catalonia to the Climate Variability and Change Research Group (Code: 2021 SGR 00786)., Peer Reviewed, Postprint (author's final draft)
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- 2023
10. How Credibly Do CMIP6 Simulations Capture Historical Mean and Extreme Precipitation Changes?
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Barcelona Supercomputing Center, Donat, Markus, Delgado Torres, Carlos, Luca, Paolo de, Mahmood, Rashed, Ortega Montilla, Pablo, Doblas-Reyes, Francisco, Barcelona Supercomputing Center, Donat, Markus, Delgado Torres, Carlos, Luca, Paolo de, Mahmood, Rashed, Ortega Montilla, Pablo, and Doblas-Reyes, Francisco
- Abstract
Future precipitation changes are typically estimated from climate model simulations, while the credibility of such projections needs to be assessed by their ability to capture observed precipitation changes. Here we evaluate how skillfully historical climate simulations contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6) capture observed changes in mean and extreme precipitation. We find that CMIP6 historical simulations skillfully represent observed precipitation changes over large parts of Europe, Asia, northeastern North America, parts of South America and western Australia, whereas a lack of skill is apparent in western North America and parts of Africa. In particular in regions with moderate skill the availability of very large ensembles can be beneficial to improve the simulation accuracy. CMIP6 simulations are regionally skillful where they capture observed (positive or negative) trends, whereas a lack of skill is found in regions characterized by negative observed precipitation trends where CMIP6 simulates increases., We are grateful for support by the Departament de Recerca i Universitats de la Generalitat de Catalunya for the Climate Variability and Change (CVC) Research Group (Reference: 2021 SGR 00786), and research funding by the Horizon 2020 LANDMARC project (grant agreement no. 869367), the Horizon Europe ASPECT project (Grant 101081460), and the AXA Research Fund. CDT acknowledges financial support from the Spanish Ministry for Science and Innovation (FPI PRE2019–509 08864 financed by MCIN/AEI/http://doi.org/10.13039/501100011033). PDL received funding from the Horizon Europe Research and Innovation Programme, Grant 101059659. We thank the climate modeling groups contributing to CMIP6 for producing and making available their model output. We are grateful to Margarida Samsó and Pierre–Antoine Bretonnière for downloading, formatting and managing the large data sets of climate simulations and observations used in this study., Peer Reviewed, Postprint (published version)
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- 2023
11. Constraining variability in large ensembles of climate model simulations to provide skillful predictions and attribute predictability on multi-decadal timescales
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Mahmood, Rashed, Donat, Markus, Ortega, Pablo, and Doblas-Reyes, Francisco
- Abstract
Projections of near-term climate are strongly affected by the uncertainties related to internal climate variability. Here we present a novel approach to constrain variability in large ensembles of climate model simulations by phasing in variability between the model simulations and observed climate. Different approaches can be considered to implement the constraint; that focuses primarily on the phasing of climate variability or include also signatures related to the forced climate response. The constraint selects members primarily based on phasing of variability or those with (additionally) ‘more correct’ signature of a sea surface temperature (SST) warming trend (or at least more similar to observations). The constrained ensembles show significant added value over the unconstrained ensemble in predicting surface air temperature over a 20 year period after initialization. The overall skill of the constrained ensemble over the first ten forecast years is qualitatively similar to that of the initialized decadal predictions; however, the constrained ensemble provides skillful predictions over larger regions of the globe compared to the decadal predictions. In addition, the forecast times for the constrained ensemble can be as long as the projection simulations are available.We also demonstrate the applicability of the constraint in attributing the predictability of regional and global climate variations to SST variability in different ocean regions. Furthermore, based on different approaches, as mentioned above, the constrained ensemble can be used to quantify (and attribute) the portion of predictability related to global patterns of variability alone, and variability together with the warming trend., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)
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- 2023
12. Projecting South Asian summer precipitation in CMIP3 models: A comparison of the simulations with and without black carbon
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Li, Shuanglin and Mahmood, Rashed
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- 2017
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13. Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal timescales – a poor man's initialized prediction system
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Mahmood, Rashed, primary, Donat, Markus G., additional, Ortega, Pablo, additional, Doblas-Reyes, Francisco J., additional, Delgado-Torres, Carlos, additional, Samsó, Margarida, additional, and Bretonnière, Pierre-Antoine, additional
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- 2022
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14. Reply on RC2
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Mahmood, Rashed, primary
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- 2022
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15. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study
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Whaley, Cynthia H., primary, Mahmood, Rashed, additional, von Salzen, Knut, additional, Winter, Barbara, additional, Eckhardt, Sabine, additional, Arnold, Stephen, additional, Beagley, Stephen, additional, Becagli, Silvia, additional, Chien, Rong-You, additional, Christensen, Jesper, additional, Damani, Sujay Manish, additional, Dong, Xinyi, additional, Eleftheriadis, Konstantinos, additional, Evangeliou, Nikolaos, additional, Faluvegi, Gregory, additional, Flanner, Mark, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Giardi, Fabio, additional, Gong, Wanmin, additional, Hjorth, Jens Liengaard, additional, Huang, Lin, additional, Im, Ulas, additional, Kanaya, Yugo, additional, Krishnan, Srinath, additional, Klimont, Zbigniew, additional, Kühn, Thomas, additional, Langner, Joakim, additional, Law, Kathy S., additional, Marelle, Louis, additional, Massling, Andreas, additional, Olivié, Dirk, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Peng, Yiran, additional, Plummer, David A., additional, Popovicheva, Olga, additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Sand, Maria, additional, Saunders, Laura N., additional, Schmale, Julia, additional, Sharma, Sangeeta, additional, Skeie, Ragnhild Bieltvedt, additional, Skov, Henrik, additional, Taketani, Fumikazu, additional, Thomas, Manu A., additional, Traversi, Rita, additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Turnock, Steven, additional, Vitale, Vito, additional, Walker, Kaley A., additional, Wang, Minqi, additional, Watson-Parris, Duncan, additional, and Weiss-Gibbons, Tahya, additional
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- 2022
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16. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme : a multi-species, multi-model study
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Whaley, Cynthia H., Mahmood, Rashed, von Salzen, Knut, Winter, Barbara, Eckhardt, Sabine, Arnold, Stephen, Beagley, Stephen, Becagli, Silvia, Chien, Rong-You, Christensen, Jesper, Damani, Sujay Manish, Dong, Xinyi, Eleftheriadis, Konstantinos, Evangeliou, Nikolaos, Faluvegi, Gregory, Flanner, Mark, Fu, Joshua S., Gauss, Michael, Giardi, Fabio, Gong, Wanmin, Hjorth, Jens Liengaard, Huang, Lin, Im, Ulas, Kanaya, Yugo, Krishnan, Srinath, Klimont, Zbigniew, Kuhn, Thomas, Langner, Joakim, Law, Kathy S., Marelle, Louis, Massling, Andreas, Olivie, Dirk, Onishi, Tatsuo, Oshima, Naga, Peng, Yiran, Plummer, David A., Popovicheva, Olga, Pozzoli, Luca, Raut, Jean-Christophe, Sand, Maria, Saunders, Laura N., Schmale, Julia, Sharma, Sangeeta, Skeie, Ragnhild Bieltvedt, Skov, Henrik, Taketani, Fumikazu, Thomas, Manu A., Traversi, Rita, Tsigaridis, Kostas, Tsyro, Svetlana, Turnock, Steven, Vitale, Vito, Walker, Kaley A., Wang, Minqi, Watson-Parris, Duncan, Weiss-Gibbons, Tahya, Whaley, Cynthia H., Mahmood, Rashed, von Salzen, Knut, Winter, Barbara, Eckhardt, Sabine, Arnold, Stephen, Beagley, Stephen, Becagli, Silvia, Chien, Rong-You, Christensen, Jesper, Damani, Sujay Manish, Dong, Xinyi, Eleftheriadis, Konstantinos, Evangeliou, Nikolaos, Faluvegi, Gregory, Flanner, Mark, Fu, Joshua S., Gauss, Michael, Giardi, Fabio, Gong, Wanmin, Hjorth, Jens Liengaard, Huang, Lin, Im, Ulas, Kanaya, Yugo, Krishnan, Srinath, Klimont, Zbigniew, Kuhn, Thomas, Langner, Joakim, Law, Kathy S., Marelle, Louis, Massling, Andreas, Olivie, Dirk, Onishi, Tatsuo, Oshima, Naga, Peng, Yiran, Plummer, David A., Popovicheva, Olga, Pozzoli, Luca, Raut, Jean-Christophe, Sand, Maria, Saunders, Laura N., Schmale, Julia, Sharma, Sangeeta, Skeie, Ragnhild Bieltvedt, Skov, Henrik, Taketani, Fumikazu, Thomas, Manu A., Traversi, Rita, Tsigaridis, Kostas, Tsyro, Svetlana, Turnock, Steven, Vitale, Vito, Walker, Kaley A., Wang, Minqi, Watson-Parris, Duncan, and Weiss-Gibbons, Tahya
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- 2022
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17. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study
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Barcelona Supercomputing Center, Whaley, Cynthia H., Mahmood, Rashed, von Salzen, Knut, Winter, Barbara, Eckhardt, Sabine, Barcelona Supercomputing Center, Whaley, Cynthia H., Mahmood, Rashed, von Salzen, Knut, Winter, Barbara, and Eckhardt, Sabine
- Abstract
While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, O3, BC, and SO), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases wer, Assessments from the Russian ship-based campaign were performed with the support of RFBR project no. 20-55-12001 and according to the development program of the Interdisciplinary Scientific and Educational School of M.V. Lomonosov Moscow State University “Future Planet and Global Environmental Change”. Development of the methodology for aethalometric data treatment was supported by RSF project no. 19-77-30004. The BC observations on R/V Mirai were supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan (Arctic Challenge for Sustainability (ArCS) project). Contributions by SMHI were funded by the Swedish Environmental Protection Agency under contract NV-03174-20 and the Swedish Climate and Clean Air Research program (SCAC) as well as partly by the Swedish National Space Board (NORD-SLCP, grant agreement ID: 94/16) and the EU Horizon 2020 project Integrated Arctic Observing System (INTAROS, grant agreement ID: 727890). Work on ACE-FTS analysis was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). Julia Schmale received funding from the Swiss National Science Foundation (project no. 200021_188478). Duncan Watson-Parris received funding from NERC projects NE/P013406/1 (A-CURE) and NE/S005390/1 (ACRUISE) as well as funding from the European Union's Horizon 2020 research and innovation program iMIRACLI under Marie Skłodowska-Curie grant agreement no. 860100. LATMOS has been supported by the EU iCUPE (Integrating and Comprehensive Understanding on Polar Environments) project (grant agreement no. 689443) under the European Network for Observing our Changing Planet (ERA-Planet), as well as access to IDRIS HPC resources (GENCI allocation A009017141) and the IPSL mesoscale computing center (CICLAD: Calcul Intensif pour le CLimat, l’Atmosphère et la Dynamique) for model simulations. Naga Oshima was supported by the Japan Society for the Promotion of Science KAKENHI (grant nos. JP18H03363, JP18H05292, and, Peer Reviewed, "Article signat per més de 50 autors/es: Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons ", Postprint (published version)
- Published
- 2022
18. Supplementary material to "Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal time scales – a ‘poor-man’ initialized prediction system"
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Mahmood, Rashed, primary, Donat, Markus G., additional, Ortega, Pablo, additional, Doblas-Reyes, Francisco J., additional, Delgado-Torres, Carlos, additional, Samsó, Margarida, additional, and Bretonnière, Pierre-Antoine, additional
- Published
- 2022
- Full Text
- View/download PDF
19. Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal time scales – a ‘poor-man’ initialized prediction system
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Mahmood, Rashed, primary, Donat, Markus G., additional, Ortega, Pablo, additional, Doblas-Reyes, Francisco J., additional, Delgado-Torres, Carlos, additional, Samsó, Margarida, additional, and Bretonnière, Pierre-Antoine, additional
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- 2022
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- View/download PDF
20. Constraining Decadal Variability Yields Skillful Projections of Near‐Term Climate Change
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Mahmood, Rashed, primary, Donat, Markus G., additional, Ortega, Pablo, additional, Doblas‐Reyes, Francisco J., additional, and Ruprich‐Robert, Yohan, additional
- Published
- 2021
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- View/download PDF
21. Supplementary material to "Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study"
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Whaley, Cynthia H., primary, Mahmood, Rashed, additional, von Salzen, Knut, additional, Winter, Barbara, additional, Eckhardt, Sabine, additional, Arnold, Stephen, additional, Beagley, Stephen, additional, Becagli, Silvia, additional, Chien, Rong-You, additional, Christensen, Jesper, additional, Damani, Sujay M., additional, Eleftheriadis, Kostas, additional, Evangeliou, Nikolaos, additional, Faluvegi, Gregory S., additional, Flanner, Mark, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Giardi, Fabio, additional, Gong, Wanmin, additional, Hjorth, Jens Liengaard, additional, Huang, Lin, additional, Im, Ulas, additional, Kanaya, Yugo, additional, Krishnan, Srinath, additional, Klimont, Zbigniew, additional, Kühn, Thomas, additional, Langner, Joakim, additional, Law, Kathy S., additional, Marelle, Louis, additional, Massling, Andreas, additional, Olivié, Dirk, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Peng, Yiran, additional, Plummer, David A., additional, Popovicheva, Olga, additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Sand, Maria, additional, Saunders, Laura N., additional, Schmale, Julia, additional, Sharma, Sangeeta, additional, Skov, Henrik, additional, Taketani, Fumikazu, additional, Thomas, Manu A., additional, Traversi, Rita, additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Turnock, Steven, additional, Vitale, Vito, additional, Walker, Kaley A., additional, Wang, Minqi, additional, Watson-Parris, Duncan, additional, and Weiss-Gibbons, Tahya, additional
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- 2021
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22. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study
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Whaley, Cynthia H., primary, Mahmood, Rashed, additional, von Salzen, Knut, additional, Winter, Barbara, additional, Eckhardt, Sabine, additional, Arnold, Stephen, additional, Beagley, Stephen, additional, Becagli, Silvia, additional, Chien, Rong-You, additional, Christensen, Jesper, additional, Damani, Sujay M., additional, Eleftheriadis, Kostas, additional, Evangeliou, Nikolaos, additional, Faluvegi, Gregory S., additional, Flanner, Mark, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Giardi, Fabio, additional, Gong, Wanmin, additional, Hjorth, Jens Liengaard, additional, Huang, Lin, additional, Im, Ulas, additional, Kanaya, Yugo, additional, Krishnan, Srinath, additional, Klimont, Zbigniew, additional, Kühn, Thomas, additional, Langner, Joakim, additional, Law, Kathy S., additional, Marelle, Louis, additional, Massling, Andreas, additional, Olivié, Dirk, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Peng, Yiran, additional, Plummer, David A., additional, Popovicheva, Olga, additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Sand, Maria, additional, Saunders, Laura N., additional, Schmale, Julia, additional, Sharma, Sangeeta, additional, Skov, Henrik, additional, Taketani, Fumikazu, additional, Thomas, Manu A., additional, Traversi, Rita, additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Turnock, Steven, additional, Vitale, Vito, additional, Walker, Kaley A., additional, Wang, Minqi, additional, Watson-Parris, Duncan, additional, and Weiss-Gibbons, Tahya, additional
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- 2021
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23. Delay in the onset of South Asian summer monsoon induced by local black carbon in an AGCM
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Mahmood, Rashed and Li, Shuanglin
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- 2013
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24. Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model
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Im, Ulas, primary, Tsigaridis, Kostas, additional, Faluvegi, Gregory, additional, Langen, Peter L., additional, French, Joshua P., additional, Mahmood, Rashed, additional, Thomas, Manu A., additional, von Salzen, Knut, additional, Thomas, Daniel C., additional, Whaley, Cynthia H., additional, Klimont, Zbigniew, additional, Skov, Henrik, additional, and Brandt, Jørgen, additional
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- 2021
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25. A Novel Initialization Technique for Decadal Climate Predictions
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Volpi, Danila, primary, Meccia, Virna L., additional, Guemas, Virginie, additional, Ortega, Pablo, additional, Bilbao, Roberto, additional, Doblas-Reyes, Francisco J., additional, Amaral, Arthur, additional, Echevarria, Pablo, additional, Mahmood, Rashed, additional, and Corti, Susanna, additional
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- 2021
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26. Toward Consistent Observational Constraints in Climate Predictions and Projections
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Hegerl, Gabriele C., primary, Ballinger, Andrew P., additional, Booth, Ben B. B., additional, Borchert, Leonard F., additional, Brunner, Lukas, additional, Donat, Markus G., additional, Doblas-Reyes, Francisco J., additional, Harris, Glen R., additional, Lowe, Jason, additional, Mahmood, Rashed, additional, Mignot, Juliette, additional, Murphy, James M., additional, Swingedouw, Didier, additional, and Weisheimer, Antje, additional
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- 2021
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27. A novel initialization technique for decadal climate predictions
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Barcelona Supercomputing Center, Volpi, Danila, Meccia, Virna L., Guemas, Virginie, Ortega Montilla, Pablo, Bilbao, Roberto, Doblas-Reyes, Francisco, Amaral, Arthur, Echevarria, Pablo, Mahmood, Rashed, Corti, Susanna, Barcelona Supercomputing Center, Volpi, Danila, Meccia, Virna L., Guemas, Virginie, Ortega Montilla, Pablo, Bilbao, Roberto, Doblas-Reyes, Francisco, Amaral, Arthur, Echevarria, Pablo, Mahmood, Rashed, and Corti, Susanna
- Abstract
Model initialization is a matter of transferring the observed information available at the start of a forecast to the model. An optimal initialization is generally recognized to be able to improve climate predictions up to a few years ahead. However, systematic errors in models make the initialization process challenging. When the observed information is transferred to the model at the initialization time, the discrepancy between the observed and model mean climate causes the drift of the prediction toward the model-biased attractor. Although such drifts can be generally accounted for with a posteriori bias correction techniques, the bias evolving along the prediction might affect the variability that we aim at predicting, and disentangling the small magnitude of the climate signal from the initial drift to be removed represents a challenge. In this study, we present an innovative initialization technique that aims at reducing the initial drift by performing a quantile matching between the observed state at the initialization time and the model state distribution. The adjusted initial state belongs to the model attractor and the observed variability amplitude is scaled toward the model one. Multi-annual climate predictions integrated for 5 years and run with the EC-Earth3 Global Coupled Model have been initialized with this novel methodology, and their prediction skill has been compared with the non-initialized historical simulations from CMIP6 and with the same decadal prediction system but based on full-field initialization. We perform a skill assessment of the surface temperature, the heat content in the ocean upper layers, the sea level pressure, and the barotropic ocean circulation. The added value of the quantile matching initialization is shown in the North Atlantic subpolar region and over the North Pacific surface temperature as well as for the ocean heat content up to 5 years. Improvements are also found in the predictive skill of the Atlantic Meridional O, This study was supported by the project LISTEN funded by the European Commission Horizon 2020 Marie Skłodowska-Curie Actions - IF (GA 799930). The authors thankfully acknowledge the computer resources from the ECMWF special project INCIPIT (spitvolp) and the technical assistance provided by ECMWF and BSC. The climate simulations have been performed using Autosubmit workflow manager (Manubens-Gil et al., 2016). The authors thank Paolo Davini for providing the restart files of the historical simulation used to implement the quantile matching. We acknowledge Saskia Loosveldt and the earthdiags and ESMValTool suite developers, as well as the startR and s2dverification (Manubens et al., 2018) software packages developers, as these tools were used to postprocess, analyze, and visualize the results presented in this work. PO acknowledges support by the Spanish Ministry of Economy, Industry and Competitiveness through the Ramon y Cajal grant (RYC-2017-22772)., Peer Reviewed, Postprint (published version)
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- 2021
28. Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model
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Im, Ulas, Tsigaridis, Kostas, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Thomas, Manu, von Salzen, Knut, Thomas, Daniel C., Whaley, Cynthia H., Klimont, Zbigniew, Skov, Henrik, Brandt, Jorgen, Im, Ulas, Tsigaridis, Kostas, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Thomas, Manu, von Salzen, Knut, Thomas, Daniel C., Whaley, Cynthia H., Klimont, Zbigniew, Skov, Henrik, and Brandt, Jorgen
- Abstract
The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990-2014) and future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (> 60 degrees N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Inter-comparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO(4)(2-)burdens decrease significantly in all simulations by 10 %-60% following the reductions of 7 %-78% in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030-2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol-radiation interactions (RFARI) of -0.39 +/- 0.01Wm(-2), which is -0.08Wm(-2) larger than the 1990-2010 mean forcing (-0.32Wm(-2)), of which -0.24 +/- 0.01Wm(-2) was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of --0.35 to -0.40Wm(-2) for the same period, which is -0.01 to -0.06Wm(-2) larger than the 199
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- 2021
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29. Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model
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Barcelona Supercomputing Center, Im, Ulas, Tsigaridis, Kostas, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Thomas, Manu A., Salzen, Knut von, Thomas, Daniel C., Whaley, Cynthia H., Klimont, Zbigniew, Skov, Henrik, Brandt, Jørgen, Barcelona Supercomputing Center, Im, Ulas, Tsigaridis, Kostas, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Thomas, Manu A., Salzen, Knut von, Thomas, Daniel C., Whaley, Cynthia H., Klimont, Zbigniew, Skov, Henrik, and Brandt, Jørgen
- Abstract
The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990–2014) and future (2015–2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60∘ N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO2−4), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO2−4 burdens decrease significantly in all simulations by 10 %–60 % following the reductions of 7 %–78 % in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030–2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol–radiation interactions (RFARI) of −0.39±0.01 W m−2, which is −0.08 W m−2 larger than the 1990–2010 mean forcing (−0.32 W m−2), of which −0.24±0.01 W m−2 was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of −0.35 to −0.40 W m−2 for the same period, which is −0.01 to −0.06 W m−2 larger than the 1990–2010 mean forcing of, This research has been supported by the Aarhus University Interdisciplinary Centre for Climate Change (iClimate) OH fund (no. 2020-0162731), the FREYA project funded by the Nordic Council of Ministers (grant agreement nos. MST-227-00036 and MFVM-2019-13476), and the EVAM-SLCF funded by the Danish Environmental Agency (grant agreement no. MST-112-00298). Kostas Tsigaridis and Gregory Faluvegi thank the NASA Modeling, Analysis and Prediction program (MAP) for support. Zbigniew Klimont was financially supported by the EU-funded Action on Black Carbon in the Arctic (EUA-BCA) under the EU Partnership Instrument. Joshua P. French was partially supported by NSF award 1915277., Peer Reviewed, Postprint (published version)
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- 2021
30. Toward consistent observational constraints in climate predictions and projections
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Barcelona Supercomputing Center, Hegerl, Gabriele C., Ballinger, Andrew P., Booth, Ben B. B., Borchert, Leonard F., Brunner, Lukas, Donat, Markus, Doblas-Reyes, Francisco, Mahmood, Rashed, Barcelona Supercomputing Center, Hegerl, Gabriele C., Ballinger, Andrew P., Booth, Ben B. B., Borchert, Leonard F., Brunner, Lukas, Donat, Markus, Doblas-Reyes, Francisco, and Mahmood, Rashed
- Abstract
Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulati, All authors were supported by the EUCP project funded by the European Commission's Horizon 2020 programme, Grant Agreement number 776613. JM was also supported by the french ANR MOPGA project ARCHANGE and by the EU-H2020 Blue Action (GA 727852) and 4C projects (GA 821003). MGD also received funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964., Peer Reviewed, "Article signat per 14 autors/es: Gabriele C. Hegerl, Andrew P. Ballinger, Ben B. B. Booth, Leonard F. Borchert, Lukas Brunner, Markus G. Donat, Francisco J. Doblas-Reyes, Glen R. Harris, Jason Lowe, Rashed Mahmood, Juliette Mignot, James M. Murphy, Didier Swingedouw and Antje Weisheimer", Postprint (published version)
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- 2021
31. Sediments deposition due to soil erosion in the watershed region of Mangla Dam
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Butt, Mohsin Jamil, Mahmood, Rashed, and Waqas, Ahmad
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- 2011
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32. The Combined Effect of Vegetation and Soil Erosion in the Water Resource Management
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Butt, Mohsin Jamil, Waqas, Ahmad, Mahmood, Rashed, and Climate, Snow and Hydrology Research Group (CSHRG)
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- 2010
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33. Aerosols and their impacts on future Arctic climate change under different emission projections in the GISS-E2.1 Earth system model
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Im, Ulas, primary, Tsigaridis, Kostas, additional, Faluvegi, Gregory S., additional, Langen, Peter L., additional, French, Joshua P., additional, Mahmood, Rashed, additional, Manu, Thomas, additional, von Salzen, Knut, additional, Thomas, Daniel C., additional, Whaley, Cynthia H., additional, Klimont, Zbigniew, additional, Skov, Henrik, additional, and Brandt, Jørgen, additional
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- 2021
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34. Modelling the relationship between liquid water content and cloud droplet number concentration observed in low clouds in the summer Arctic and its radiative effects
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Barcelona Supercomputing Center, Dionne, Joelle, Salzen, Knut von, Cole, Jason, Mahmood, Rashed, Leaitch, W. Richard, Lesins, Glen, Folkins, Ian, Chang, Rachel, Barcelona Supercomputing Center, Dionne, Joelle, Salzen, Knut von, Cole, Jason, Mahmood, Rashed, Leaitch, W. Richard, Lesins, Glen, Folkins, Ian, and Chang, Rachel
- Abstract
Low clouds persist in the summer Arctic with important consequences for the radiation budget. In this study, we simulate the linear relationship between liquid water content (LWC) and cloud droplet number concentration (CDNC) observed during an aircraft campaign based out of Resolute Bay, Canada, conducted as part of the Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments study in July 2014. Using a single-column model, we find that autoconversion can explain the observed linear relationship between LWC and CDNC. Of the three autoconversion schemes we examined, the scheme using continuous drizzle (Khairoutdinov and Kogan, 2000) appears to best reproduce the observed linearity in the tenuous cloud regime (Mauritsen et al., 2011), while a scheme with a threshold for rain (Liu and Daum, 2004) best reproduces the linearity at higher CDNC. An offline version of the radiative transfer model used in the Canadian Atmospheric Model version 4.3 is used to compare the radiative effects of the modelled and observed clouds. We find that there is no significant difference in the upward longwave cloud radiative effect at the top of the atmosphere from the three autoconversion schemes (p=0.05) but that all three schemes differ at p=0.05 from the calculations based on observations. In contrast, the downward longwave and shortwave cloud radiative effect at the surface for the Wood (2005b) and Khairoutdinov and Kogan (2000) schemes do not differ significantly (p=0.05) from the observation-based radiative calculations, while the Liu and Daum (2004) scheme differs significantly from the observation-based calculation for the downward shortwave but not the downward longwave fluxes., This research has been supported by the Natural Sciences and Engineering Research Council of Canada (Discovery Grants RGPIN-2014-05173 and RGPIN 155649) and the Marine Environmental Observation, Prediction and Response Network (MEOPAR), which is a federally funded Networks of Centres of Excellence (NCE) (EC1-RC-DAL)., Peer Reviewed, Postprint (published version)
- Published
- 2020
35. Supplementary material to "Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model"
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Im, Ulas, primary, Tsigaridis, Kostas, additional, Faluvegi, Gregory, additional, Langen, Peter L., additional, French, Joshua P., additional, Mahmood, Rashed, additional, Manu, Thomas, additional, von Salzen, Knut, additional, Thomas, Daniel C., additional, Whaley, Cynthia H., additional, Klimont, Zbigniew, additional, Skov, Henrik, additional, and Brandt, Jørgen, additional
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- 2021
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36. Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal time scales - a 'poor-man' initialized prediction system.
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Mahmood, Rashed, Donat, Markus G., Ortega, Pablo, Doblas-Reyes, Francisco J., Delgado-Torres, Carlos, Samsó, Margarida, and Bretonnière, Pierre-Antoine
- Subjects
- *
GLOBAL temperature changes , *GLOBAL warming , *OCEAN temperature , *CLIMATE change , *VARIANCES , *FORECASTING - Abstract
Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure and precipitation on decadal to multi-decadal time scales. We find that the constrained projections show significant skill in predicting the climate of the following ten to twenty years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first two decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades. [ABSTRACT FROM AUTHOR]
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- 2022
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37. Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal time scales - a 'poorman' initialized prediction system.
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Mahmood, Rashed, Donat, Markus G., Ortega, Pablo, Doblas-Reyes, Francisco J., Delgado-Torres, Carlos, Samsó, Margarida, and Bretonnière, Pierre-Antoine
- Subjects
CLIMATE change ,SURFACE temperature ,SEA level ,OCEAN temperature ,ACCURACY - Abstract
Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure and precipitation on decadal to multi-decadal time scales. We find that the constrained projections show significant skill in predicting the climate of the following ten to twenty years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first two decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades. [ABSTRACT FROM AUTHOR]
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- 2022
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38. Modelling the relationship between liquid water content and cloud droplet number concentration observed in low clouds in the summer Arctic and its radiative effects
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Dionne, Joelle, primary, von Salzen, Knut, additional, Cole, Jason, additional, Mahmood, Rashed, additional, Leaitch, W. Richard, additional, Lesins, Glen, additional, Folkins, Ian, additional, and Chang, Rachel Y.-W., additional
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- 2020
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39. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study.
- Author
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Whaley, Cynthia H., Mahmood, Rashed, Salzen, Knut von, Winter, Barbara, Eckhardt, Sabine, Arnold, Stephen, Beagley, Stephen, Becagli, Silvia, Chien, Rong-You, Christensen, Jesper, Damani, Sujay Manish, Eleftheriadis, Kostas, Evangeliou, Nikolaos, Faluvegi, Greg, Flanner, Mark, Fu, Joshua S., Gauss, Michael, Giardi, Fabio, Gong, Wanmin, and Hjorth, Jens Liengaard
- Abstract
The Arctic atmosphere is warming rapidly and its relatively pristine environment is sensitive to the long-range transport of atmospheric pollutants. While carbon dioxide is the main cause for global warming, short-lived climate forcers (SLCFs) such as methane, ozone, and particles also play a role in Arctic climate on near-term time scales. Atmospheric modelling is critical for understanding the abundance and distribution of SLCFs throughout the Arctic atmosphere, and is used as a tool towards determining SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models, assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over four years (2008-2009 and 2014-2015) conducted for the 2021 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship and aircraft-based observations. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean was able to represent the general features of SLCFs in the Arctic, though vertical mixing, long-range transport, deposition, and wildfire emissions remain highly uncertain processes. These need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. [ABSTRACT FROM AUTHOR]
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- 2021
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40. Overview paper: New insights into aerosol and climate in the Arctic
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Abbatt, Jonathan P. D., Leaitch, W. Richard, Aliabadi, Amir A., Bertram, Allan K., Blanchet, Jean-Pierre, Boivin-Rioux, Aude, Bozem, Heiko, Burkart, Julia, Chang, Rachel Y. W., Charette, Joannie, Chaubey, Jai P., Christensen, Robert J., Cirisan, Ana, Collins, Douglas B., Croft, Betty, Dionne, Joelle, Evans, Greg J., Fletcher, Christopher G., Galí, Martí, Ghahremaninezhad, Roghayeh, Girard, Eric, Gong, Wanmin, Gosselin, Michel, Gourdal, Margaux, Hanna, Sarah J., Hayashida, Hakase, Herber, Andreas B., Hesaraki, Sareh, Hoor, Peter, Huang, Lin, Hussherr, Rachel, Irish, Victoria E., Keita, Setigui A., Kodros, John K., Köllner, Franziska, Kolonjari, Felicia, Kunkel, Daniel, Ladino, Luis A., Law, Kathy, Levasseur, Maurice, Libois, Quentin, Liggio, John, Lizotte, Martine, Macdonald, Katrina M., Mahmood, Rashed, Martin, Randall V., Mason, Ryan H., Miller, Lisa A., Moravek, Alexander, Mortenson, Eric, Mungall, Emma L., Murphy, Jennifer G., Namazi, Maryam, Norman, Ann-Lise, O'Neill, Norman T., Pierce, Jeffrey R., Russell, Lynn M., Schneider, Johannes, Schulz, Hannes, Sharma, Sangeeta, Si, Meng, Staebler, Ralf M., Steiner, Nadja S., Thomas, Jennie L., von Salzen, Knut, Wentzell, Jeremy J. B., Willis, Megan D., Wentworth, Gregory R., Xu, Jun-Wei, Yakobi-Hancock, Jacqueline D., Abbatt, Jonathan P. D., Leaitch, W. Richard, Aliabadi, Amir A., Bertram, Allan K., Blanchet, Jean-Pierre, Boivin-Rioux, Aude, Bozem, Heiko, Burkart, Julia, Chang, Rachel Y. W., Charette, Joannie, Chaubey, Jai P., Christensen, Robert J., Cirisan, Ana, Collins, Douglas B., Croft, Betty, Dionne, Joelle, Evans, Greg J., Fletcher, Christopher G., Galí, Martí, Ghahremaninezhad, Roghayeh, Girard, Eric, Gong, Wanmin, Gosselin, Michel, Gourdal, Margaux, Hanna, Sarah J., Hayashida, Hakase, Herber, Andreas B., Hesaraki, Sareh, Hoor, Peter, Huang, Lin, Hussherr, Rachel, Irish, Victoria E., Keita, Setigui A., Kodros, John K., Köllner, Franziska, Kolonjari, Felicia, Kunkel, Daniel, Ladino, Luis A., Law, Kathy, Levasseur, Maurice, Libois, Quentin, Liggio, John, Lizotte, Martine, Macdonald, Katrina M., Mahmood, Rashed, Martin, Randall V., Mason, Ryan H., Miller, Lisa A., Moravek, Alexander, Mortenson, Eric, Mungall, Emma L., Murphy, Jennifer G., Namazi, Maryam, Norman, Ann-Lise, O'Neill, Norman T., Pierce, Jeffrey R., Russell, Lynn M., Schneider, Johannes, Schulz, Hannes, Sharma, Sangeeta, Si, Meng, Staebler, Ralf M., Steiner, Nadja S., Thomas, Jennie L., von Salzen, Knut, Wentzell, Jeremy J. B., Willis, Megan D., Wentworth, Gregory R., Xu, Jun-Wei, and Yakobi-Hancock, Jacqueline D.
- Abstract
Motivated by the need to predict how the Arctic atmosphere will change in a warming world, this article summarizes recent advances made by the research consortium NETCARE (Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments) that contribute to our fundamental understanding of Arctic aerosol particles as they relate to climate forcing. The overall goal of NETCARE research has been to use an interdisciplinary approach encompassing extensive field observations and a range of chemical transport, earth system, and biogeochemical models. Several major findings and advances have emerged from NETCARE since its formation in 2013. (1) Unexpectedly high summertime dimethyl sulfide (DMS) levels were identified in ocean water (up to 75 nM) and the overlying atmosphere (up to 1 ppbv) in the Canadian Arctic Archipelago (CAA). Furthermore, melt ponds, which are widely prevalent, were identified as an important DMS source (with DMS concentrations of up to 6 nM and a potential contribution to atmospheric DMS of 20 % in the study area). (2) Evidence of widespread particle nucleation and growth in the marine boundary layer was found in the CAA in the summertime, with these events observed on 41 % of days in a 2016 cruise. As well, at Alert, Nunavut, particles that are newly formed and grown under conditions of minimal anthropogenic influence during the months of July and August are estimated to contribute 20 % to 80 % of the 30–50 nm particle number density. DMS-oxidation-driven nucleation is facilitated by the presence of atmospheric ammonia arising from seabird-colony emissions, and potentially also from coastal regions, tundra, and biomass burning. Via accumulation of secondary organic aerosol (SOA), a significant fraction of the new particles grow to sizes that are active in cloud droplet formation. Although the gaseous precursors to Arctic marine SOA remain poorly defined, the measured levels of common continental SOA precursors (isoprene
- Published
- 2019
41. Seasonality of global and Arctic black carbon processes in the Arctic Monitoring and Assessment Programme models
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Mahmood, Rashed, von Salzen, Knut, Flanner, Mark, Sand, Maria, Langner, Joakim, Wang, Hailong, and Huang, Lin
- Subjects
Global Climate Models ,Aerosols ,wet scavenging ,black carbon budgets ,Atmospheric Composition and Structure ,Aerosols and Particles ,Aerosol and Clouds ,Convective Processes ,Oceanography: Biological and Chemical ,Pollution: Urban and Regional ,Paleoceanography ,Arctic pollution ,Atmospheric Processes ,transport ,Cloud Physics and Chemistry ,Global Change ,Clouds and Aerosols ,Research Articles ,Research Article - Abstract
This study quantifies black carbon (BC) processes in three global climate models and one chemistry transport model, with focus on the seasonality of BC transport, emissions, wet and dry deposition in the Arctic. In the models, transport of BC to the Arctic from lower latitudes is the major BC source for this region. Arctic emissions are very small. All models simulated a similar annual cycle of BC transport from lower latitudes to the Arctic, with maximum transport occurring in July. Substantial differences were found in simulated BC burdens and vertical distributions, with Canadian Atmospheric Global Climate Model (CanAM) (Norwegian Earth System Model, NorESM) producing the strongest (weakest) seasonal cycle. CanAM also has the shortest annual mean residence time for BC in the Arctic followed by Swedish Meteorological and Hydrological Institute Multiscale Atmospheric Transport and Chemistry model, Community Earth System Model, and NorESM. Overall, considerable differences in wet deposition efficiencies in the models exist and are a leading cause of differences in simulated BC burdens. Results from model sensitivity experiments indicate that convective scavenging outside the Arctic reduces the mean altitude of BC residing in the Arctic, making it more susceptible to scavenging by stratiform (layer) clouds in the Arctic. Consequently, scavenging of BC in convective clouds outside the Arctic acts to substantially increase the overall efficiency of BC wet deposition in the Arctic, which leads to low BC burdens and a more pronounced seasonal cycle compared to simulations without convective BC scavenging. In contrast, the simulated seasonality of BC concentrations in the upper troposphere is only weakly influenced by wet deposition in stratiform clouds, whereas lower tropospheric concentrations are highly sensitive., Key Points Seasonal variations of black carbon (BC) mass budgets in the Arctic are simulatedGood agreement in simulated annual mean transport of BC to the Arctic in modelsConvective wet removal is important for differences in modeled BC concentration
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- 2016
42. New insights into aerosol and climate in the Arctic
- Author
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Abbatt, Jonathan P. D., Leaitch, W. Richard, Aliabadi, Amir A., Bertram, Alan K., Blanchet, Jean-Pierre, Boivin-Rioux, Aude, Bozem, Heiko, Burkart, Julia, Chang, Rachel Y. W., Charette, Joannie, Chaubey, Jai P., Christensen, Robert J., Cirisan, Ana, Collins, Douglas B., Croft, Betty, Dionne, Joelle, Evans, Greg J., Fletcher, Christopher G., Ghahremaninezhad, Roghayeh, Girard, Eric, Gong, Wanmin, Gosselin, Michel, Gourdal, Margaux, Hanna, Sarah J., Hayashida, Hakase, Herber, Andreas B., Hesaraki, Sareh, Hoor, Peter, Huang, Lin, Hussherr, Rachel, Irish, Victoria E., Keita, Setigui A., Kodros, John K., Köllner, Franziska, Kolonjari, Felicia, Kunkel, Daniel, Ladino, Luis A., Law, Kathy S., Levasseur, Maurice, Libois, Quentin, Liggio, John, Lizotte, Martine, Macdonald, Katrina M., Mahmood, Rashed, Martin, Randall V., Mason, Ryan H., Miller, Lisa A., Moravek, Alexander, Mortenson, Eric, Mungall, Emma L., Murphy, Jennifer G., Namazi, Maryam, Norman, Ann-Lise, O'Neill, Norman T., Pierce, Jeffrey R., Russell, Lynn M., Schneider, Johannes, Schulz, Hannes, Sharma, Sangeeta, Si, Meng, Staebler, Ralf M., Steiner, Nadja S., Gali, Marti, Thomas, Jennie L., von Salzen, Knut, Wentzell, Jeremy J. B., Willis, Megan D., Wentworth, Gregory R., Xu, Jun-Wei, Yakobi-Hancock, Jacqueline D., Department of Chemistry [University of Toronto], University of Toronto, Environment and Climate Change Canada, School of Engineering [Guelph], University of Guelph, Department of Chemistry [Vancouver] (UBC Chemistry), University of British Columbia (UBC), Département des sciences de la terre et de l'atmosphère [Montréal] (SCTA), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Institut des Sciences de la MER de Rimouski (ISMER), Université du Québec à Rimouski (UQAR), Institute for Atmospheric Physics [Mainz] (IPA), Johannes Gutenberg - Universität Mainz (JGU), Aerosol Physics and Environmental Physics [Vienna], University of Vienna [Vienna], Department of Physics and Atmospheric Science [Halifax], Dalhousie University [Halifax], Department of Chemistry [Lewisburg], Bucknell University, Department of Chemical Engineering and Applied Chemistry (CHEM ENG), Department of Geography and Environmental Management [Waterloo], University of Waterloo [Waterloo], Departement de Biologie [Québec], Université Laval [Québec] (ULaval), School of Earth and Ocean Sciences [Victoria] (SEOS), University of Victoria [Canada] (UVIC), Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Centre d'Applications et de Recherches en TELédétection (CARTEL), Université de Sherbrooke [Sherbrooke], Department of Atmospheric Science [Fort Collins], Colorado State University [Fort Collins] (CSU), Particle Chemistry Department [Mainz], Max Planck Institute for Chemistry (MPIC), Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Centro de Ciencias de la Atmosfera [Mexico], Universidad Nacional Autónoma de México (UNAM), TROPO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), Department of Biology [Québec], Air Quality Processes Research Section, Canadian Centre for Climate Modelling and Analysis (CCCma), Institute of Ocean Sciences [Sidney] (IOS), Fisheries and Oceans Canada (DFO), Department of Mathematics [Isfahan], University of Isfahan, Department of Physics and Astronomy [Calgary], University of Calgary, Scripps Institution of Oceanography (SIO), University of California [San Diego] (UC San Diego), University of California-University of California, Lawrence Berkeley National Laboratory [Berkeley] (LBNL), and National Research Council of Canada (NRC)
- Subjects
[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph] ,[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology - Abstract
International audience; Motivated by the need to predict how the Arctic atmosphere will change in a warming world, this article summarizes recent advances made by the research consortium NETCARE (Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments) that contribute to our fundamental understanding of Arctic aerosol particles as they relate to climate forcing. The overall goal of NETCARE research has been to use an interdisciplinary approach encompassing extensive field observations and a range of chemical transport, earth system, and biogeochemical models. Several major findings and advances have emerged from NETCARE since its formation in 2013 . (1) Unexpectedly high summertime dimethyl sulfide (DMS) levels were identified in ocean water and the overlying atmosphere in the Canadian Arctic Archipelago (CAA). Furthermore, melt ponds, which are widely prevalent, were identified as an important DMS source. (2) Evidence was found of widespread particle nucleation and growth in the marine boundary layer in the CAA in the summertime. DMS-oxidation-driven nucleation is facilitated by the presence of atmospheric ammonia arising from sea bird colony emissions, and potentially also from coastal regions, tundra, and biomass burning. Via accumulation of secondary organic material (SOA), a significant fraction of the new particles grow to sizes that are active in cloud droplet formation. Although the gaseous precursors to Arctic marine SOA remain poorly defined, the measured levels of common continental SOA precursors (isoprene and monoterpenes) were low, whereas elevated mixing ratios of oxygenated volatile organic compounds were inferred to arise via processes involving the sea surface microlayer. (3) The variability in the vertical distribution of black carbon (BC) under both springtime Arctic haze and more pristine summertime aerosol conditions was observed. Measured particle size distributions and mixing states were used to constrain, for the first time, calculations of aerosol–climate interactions under Arctic conditions. Aircraft- and ground-based measurements were used to better establish the BC source regions that supply the Arctic via long-range transport mechanisms. (4) Measurements of ice nucleating particles (INPs) in the Arctic indicate that a major source of these particles is mineral dust, likely derived from local sources in the summer and long-range transport in the spring. In addition, INPs are abundant in the sea surface microlayer in the Arctic, and possibly play a role in ice nucleation in the atmosphere when mineral dust concentrations are low. (5) Amongst multiple aerosol components, BC was observed to have the smallest effective deposition velocities to high Arctic snow.
- Published
- 2018
43. Dynamic assessment of PM2.5 exposure and health risk using remote sensing and geo-spatial big data
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Song, Yimeng, primary, Huang, Bo, additional, He, Qingqing, additional, Chen, Bin, additional, Wei, Jing, additional, and Mahmood, Rashed, additional
- Published
- 2019
- Full Text
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44. Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products
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Wei, Jing, primary, Peng, Yiran, additional, Mahmood, Rashed, additional, Sun, Lin, additional, and Guo, Jianping, additional
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- 2019
- Full Text
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45. Sensitivity of Arctic sulfate aerosol and clouds to changes in future surface seawater dimethylsulfide concentrations
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Mahmood, Rashed, primary, von Salzen, Knut, additional, Norman, Ann-Lise, additional, Galí, Martí, additional, and Levasseur, Maurice, additional
- Published
- 2019
- Full Text
- View/download PDF
46. Modelling the relationship between liquid water content and cloud droplet number concentration observed in low clouds in the summer Arctic and its radiative effects
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Dionne, Joelle, primary, von~Salzen, Knut, additional, Cole, Jason, additional, Mahmood, Rashed, additional, Leaitch, W.~Richard, additional, Lesins, Glen, additional, Folkins, Ian, additional, and Chang, Rachel~Y.-W., additional
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- 2019
- Full Text
- View/download PDF
47. Supplementary material to "Modelling the relationship between liquid water content and cloud droplet number concentration observed in low clouds in the summer Arctic and its radiative effects"
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Dionne, Joelle, primary, von~Salzen, Knut, additional, Cole, Jason, additional, Mahmood, Rashed, additional, Leaitch, W.~Richard, additional, Lesins, Glen, additional, Folkins, Ian, additional, and Chang, Rachel~Y.-W., additional
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- 2019
- Full Text
- View/download PDF
48. Inconsistency in spatial distributions and temporal trends derived from nine operational global aerosol optical depth products
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Wei, Jing, primary, Peng, Yiran, additional, Mahmood, Rashed, additional, Sun, Lin, additional, and Guo, Jianping, additional
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- 2018
- Full Text
- View/download PDF
49. Present and future aerosol impacts on Arctic climate change in 1 n the GISS-E2.1 Earth system model.
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Ulas Im, Kostas Tsigaridis, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Manu, Thomas, von Salzen, Knut, Thomas, Daniel C., Whaley, Cynthia H., Zbigniew Klimont, Skov, Henrik, and Brandt, Jørgen
- Abstract
The Arctic is warming two to three times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990-2014) and future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60 °N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Surface aerosol levels, in particular black carbon (BC) and sulfate (SO
4 2- ), have been significantly underestimated by more than 50%, with the smallest biases calculated for the nudged atmosphere-only simulations. CMIP6 simulations performed slightly better in simulating both surface concentrations of aerosols and climate parameters, compared to the Eclipse simulations. In addition, fully-coupled simulations had slightly smaller biases in aerosol levels compared to atmosphere only simulations without nudging. Arctic BC, organic carbon (OC) and SO4 2- burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39±0.01 W m-2 , of which - 0.24±0.01 W m-2 were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m-2 for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m-2 ), of which the anthropogenic aerosols accounted for -0.26 W m-2 . Finally, all simulations showed an 46 increase in the Arctic surface air temperatures both throughout the simulation period. In 2050, surface air temperatures are projected to increase by 2.4 °C to 2.6 °C in the Eclipse ensemble and 1.9 °C to 2.6 °C in the CMIP6 ensemble, compared to the 1990-2010 mean. Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss. [ABSTRACT FROM AUTHOR]- Published
- 2021
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- View/download PDF
50. Supplementary material to "New insights into aerosol and climate in the Arctic"
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Abbatt, Jonathan P. D., primary, Leaitch, W. Richard, additional, Aliabadi, Amir A., additional, Bertram, Alan K., additional, Blanchet, Jean-Pierre, additional, Boivin-Rioux, Aude, additional, Bozem, Heiko, additional, Burkart, Julia, additional, Chang, Rachel Y. W., additional, Charette, Joannie, additional, Chaubey, Jai P., additional, Christensen, Robert J., additional, Cirisan, Ana, additional, Collins, Douglas B., additional, Croft, Betty, additional, Dionne, Joelle, additional, Evans, Greg J., additional, Fletcher, Christopher G., additional, Ghahreman, Roya, additional, Girard, Eric, additional, Gong, Wanmin, additional, Gosselin, Michel, additional, Gourdal, Margaux, additional, Hanna, Sarah J., additional, Hayashida, Hakase, additional, Herber, Andreas B., additional, Hesaraki, Sareh, additional, Hoor, Peter, additional, Huang, Lin, additional, Hussherr, Rachel, additional, Irish, Victoria E., additional, Keita, Setigui A., additional, Kodros, John K., additional, Köllner, Franziska, additional, Kolonjari, Felicia, additional, Kunkel, Daniel, additional, Ladino, Luis A., additional, Law, Kathy, additional, Levasseur, Maurice, additional, Libois, Quentin, additional, Liggio, John, additional, Lizotte, Martine, additional, Macdonald, Katrina M., additional, Mahmood, Rashed, additional, Martin, Randall V., additional, Mason, Ryan H., additional, Miller, Lisa A., additional, Moravek, Alexander, additional, Mortenson, Eric, additional, Mungall, Emma L., additional, Murphy, Jennifer G., additional, Namazi, Maryam, additional, Norman, Ann-Lise, additional, O'Neill, Norman T., additional, Pierce, Jeffrey R., additional, Russell, Lynn M., additional, Schneider, Johannes, additional, Schulz, Hannes, additional, Sharma, Sangeeta, additional, Si, Meng, additional, Staebler, Ralf M., additional, Steiner, Nadja S., additional, Galí, Martí, additional, Thomas, Jennie L., additional, von Salzen, Knut, additional, Wentzell, Jeremy J. B., additional, Willis, Megan D., additional, Wentworth, Gregory R., additional, Xu, Jun-Wei, additional, and Yakobi-Hancock, Jacqueline D., additional
- Published
- 2018
- Full Text
- View/download PDF
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