Direct and Indirect Shortwave Radiative Effects of Sea Salt Aerosols
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Aerosol Effective Radiative Forcing in the Online Aerosol Coupled CAS
atmosphere Article Aerosol Effective Radiative Forcing in the Online Aerosol Coupled CAS-FGOALS-f3-L Climate Model Hao Wang 1,2,3, Tie Dai 1,2,* , Min Zhao 1,2,3, Daisuke Goto 4, Qing Bao 1, Toshihiko Takemura 5 , Teruyuki Nakajima 4 and Guangyu Shi 1,2,3 1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; [email protected] (H.W.); [email protected] (M.Z.); [email protected] (Q.B.); [email protected] (G.S.) 2 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China 3 College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100029, China 4 National Institute for Environmental Studies, Tsukuba 305-8506, Japan; [email protected] (D.G.); [email protected] (T.N.) 5 Research Institute for Applied Mechanics, Kyushu University, Fukuoka 819-0395, Japan; [email protected] * Correspondence: [email protected]; Tel.: +86-10-8299-5452 Received: 21 September 2020; Accepted: 14 October 2020; Published: 17 October 2020 Abstract: The effective radiative forcing (ERF) of anthropogenic aerosol can be more representative of the eventual climate response than other radiative forcing. We incorporate aerosol–cloud interaction into the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System (CAS-FGOALS-f3-L) by coupling an existing aerosol module named the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) and quantified the ERF and its primary components (i.e., effective radiative forcing of aerosol-radiation interactions (ERFari) and aerosol-cloud interactions (ERFaci)) based on the protocol of current Coupled Model Intercomparison Project phase 6 (CMIP6). -
Climate Models and Their Evaluation
8 Climate Models and Their Evaluation Coordinating Lead Authors: David A. Randall (USA), Richard A. Wood (UK) Lead Authors: Sandrine Bony (France), Robert Colman (Australia), Thierry Fichefet (Belgium), John Fyfe (Canada), Vladimir Kattsov (Russian Federation), Andrew Pitman (Australia), Jagadish Shukla (USA), Jayaraman Srinivasan (India), Ronald J. Stouffer (USA), Akimasa Sumi (Japan), Karl E. Taylor (USA) Contributing Authors: K. AchutaRao (USA), R. Allan (UK), A. Berger (Belgium), H. Blatter (Switzerland), C. Bonfi ls (USA, France), A. Boone (France, USA), C. Bretherton (USA), A. Broccoli (USA), V. Brovkin (Germany, Russian Federation), W. Cai (Australia), M. Claussen (Germany), P. Dirmeyer (USA), C. Doutriaux (USA, France), H. Drange (Norway), J.-L. Dufresne (France), S. Emori (Japan), P. Forster (UK), A. Frei (USA), A. Ganopolski (Germany), P. Gent (USA), P. Gleckler (USA), H. Goosse (Belgium), R. Graham (UK), J.M. Gregory (UK), R. Gudgel (USA), A. Hall (USA), S. Hallegatte (USA, France), H. Hasumi (Japan), A. Henderson-Sellers (Switzerland), H. Hendon (Australia), K. Hodges (UK), M. Holland (USA), A.A.M. Holtslag (Netherlands), E. Hunke (USA), P. Huybrechts (Belgium), W. Ingram (UK), F. Joos (Switzerland), B. Kirtman (USA), S. Klein (USA), R. Koster (USA), P. Kushner (Canada), J. Lanzante (USA), M. Latif (Germany), N.-C. Lau (USA), M. Meinshausen (Germany), A. Monahan (Canada), J.M. Murphy (UK), T. Osborn (UK), T. Pavlova (Russian Federationi), V. Petoukhov (Germany), T. Phillips (USA), S. Power (Australia), S. Rahmstorf (Germany), S.C.B. Raper (UK), H. Renssen (Netherlands), D. Rind (USA), M. Roberts (UK), A. Rosati (USA), C. Schär (Switzerland), A. Schmittner (USA, Germany), J. Scinocca (Canada), D. Seidov (USA), A.G. -
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15 NOVEMBER 2006 A R O R A A N D B O E R 5875 The Temporal Variability of Soil Moisture and Surface Hydrological Quantities in a Climate Model VIVEK K. ARORA AND GEORGE J. BOER Canadian Centre for Climate Modelling and Analysis, Meteorological Service of Canada, University of Victoria, Victoria, British Columbia, Canada (Manuscript received 4 October 2005, in final form 8 February 2006) ABSTRACT The variance budget of land surface hydrological quantities is analyzed in the second Atmospheric Model Intercomparison Project (AMIP2) simulation made with the Canadian Centre for Climate Modelling and Analysis (CCCma) third-generation general circulation model (AGCM3). The land surface parameteriza- tion in this model is the comparatively sophisticated Canadian Land Surface Scheme (CLASS). Second- order statistics, namely variances and covariances, are evaluated, and simulated variances are compared with observationally based estimates. The soil moisture variance is related to second-order statistics of surface hydrological quantities. The persistence time scale of soil moisture anomalies is also evaluated. Model values of precipitation and evapotranspiration variability compare reasonably well with observa- tionally based and reanalysis estimates. Soil moisture variability is compared with that simulated by the Variable Infiltration Capacity-2 Layer (VIC-2L) hydrological model driven with observed meteorological data. An equation is developed linking the variances and covariances of precipitation, evapotranspiration, and runoff to soil moisture variance via a transfer function. The transfer function is connected to soil moisture persistence in terms of lagged autocorrelation. Soil moisture persistence time scales are shorter in the Tropics and longer at high latitudes as is consistent with the relationship between soil moisture persis- tence and the latitudinal structure of potential evaporation found in earlier studies. -
Documentation and Software User’S Manual, Version 4.1
The Canadian Seasonal to Interannual Prediction System version 2 (CanSIPSv2) Canadian Meteorological Centre Technical Note H. Lin1, W. J. Merryfield2, R. Muncaster1, G. Smith1, M. Markovic3, A. Erfani3, S. Kharin2, W.-S. Lee2, M. Charron1 1-Meteorological Research Division 2-Canadian Centre for Climate Modelling and Analysis (CCCma) 3-Canadian Meteorological Centre (CMC) 7 May 2019 i Revisions Version Date Authors Remarks 1.0 2019/04/22 Hai Lin First draft 1.1 2019/04/26 Hai Lin Corrected the bias figures. Comments from Ryan Muncaster, Bill Merryfield 1.2 2019/05/01 Hai Lin Figures of CanSIPSv2 uses CanCM4i plus GEM-NEMO 1.3 2019/05/03 Bill Merrifield Added CanCM4i information, sea ice Hai Lin verification, 6.6 and 9 1.4 2019/05/06 Hai Lin All figures of CanSIPSv2 with CanCM4i and GEM-NEMO, made available by Slava Kharin ii © Environment and Climate Change Canada, 2019 Table of Contents 1 Introduction ............................................................................................................................. 4 2 Modifications to models .......................................................................................................... 6 2.1 CanCM4i .......................................................................................................................... 6 2.2 GEM-NEMO .................................................................................................................... 6 3 Forecast initialization ............................................................................................................. -
Aerosols, Their Direct and Indirect Effects
5 Aerosols, their Direct and Indirect Effects Co-ordinating Lead Author J.E. Penner Lead Authors M. Andreae, H. Annegarn, L. Barrie, J. Feichter, D. Hegg, A. Jayaraman, R. Leaitch, D. Murphy, J. Nganga, G. Pitari Contributing Authors A. Ackerman, P. Adams, P. Austin, R. Boers, O. Boucher, M. Chin, C. Chuang, B. Collins, W. Cooke, P. DeMott, Y. Feng, H. Fischer, I. Fung, S. Ghan, P. Ginoux, S.-L. Gong, A. Guenther, M. Herzog, A. Higurashi, Y. Kaufman, A. Kettle, J. Kiehl, D. Koch, G. Lammel, C. Land, U. Lohmann, S. Madronich, E. Mancini, M. Mishchenko, T. Nakajima, P. Quinn, P. Rasch, D.L. Roberts, D. Savoie, S. Schwartz, J. Seinfeld, B. Soden, D. Tanré, K. Taylor, I. Tegen, X. Tie, G. Vali, R. Van Dingenen, M. van Weele, Y. Zhang Review Editors B. Nyenzi, J. Prospero Contents Executive Summary 291 5.4.1 Summary of Current Model Capabilities 313 5.4.1.1 Comparison of large-scale sulphate 5.1 Introduction 293 models (COSAM) 313 5.1.1 Advances since the Second Assessment 5.4.1.2 The IPCC model comparison Report 293 workshop: sulphate, organic carbon, 5.1.2 Aerosol Properties Relevant to Radiative black carbon, dust, and sea salt 314 Forcing 293 5.4.1.3 Comparison of modelled and observed aerosol concentrations 314 5.2 Sources and Production Mechanisms of 5.4.1.4 Comparison of modelled and satellite- Atmospheric Aerosols 295 derived aerosol optical depth 318 5.2.1 Introduction 295 5.4.2 Overall Uncertainty in Direct Forcing 5.2.2 Primary and Secondary Sources of Aerosols 296 Estimates 322 5.2.2.1 Soil dust 296 5.4.3 Modelling the Indirect -
Large-Scale Tropospheric Transport in the Chemistry–Climate Model Initiative (CCMI) Simulations
Atmos. Chem. Phys., 18, 7217–7235, 2018 https://doi.org/10.5194/acp-18-7217-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Large-scale tropospheric transport in the Chemistry–Climate Model Initiative (CCMI) simulations Clara Orbe1,2,3,a, Huang Yang3, Darryn W. Waugh3, Guang Zeng4, Olaf Morgenstern 4, Douglas E. Kinnison5, Jean-Francois Lamarque5, Simone Tilmes5, David A. Plummer6, John F. Scinocca7, Beatrice Josse8, Virginie Marecal8, Patrick Jöckel9, Luke D. Oman10, Susan E. Strahan10,11, Makoto Deushi12, Taichu Y. Tanaka12, Kohei Yoshida12, Hideharu Akiyoshi13, Yousuke Yamashita13,14, Andreas Stenke15, Laura Revell15,16, Timofei Sukhodolov15,17, Eugene Rozanov15,17, Giovanni Pitari18, Daniele Visioni18, Kane A. Stone19,20,b, Robyn Schofield19,20, and Antara Banerjee21 1Goddard Earth Sciences Technology and Research (GESTAR), Columbia, MD, USA 2Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA 3Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA 4National Institute of Water and Atmospheric Research, Wellington, New Zealand 5National Center for Atmospheric Research (NCAR), Atmospheric Chemistry Observations and Modeling (ACOM) Laboratory, Boulder, USA 6Climate Research Branch, Environment and Climate Change Canada, Montreal, QC, Canada 7Climate Research Branch, Environment and Climate Change Canada, Victoria, BC, Canada 8Centre National de Recherches Météorologiques UMR 3589, Météo-France/CNRS, -
I.1 a Brief History of AOGCM Tuning Methods Over the Past 30 Years Or So Ronald J Stouffer GFDL/NOAA
I.1 A brief history of AOGCM tuning methods over the past 30 years or so Ronald J Stouffer GFDL/NOAA Thirty years ago, when the first global AOGCMs were being developed, the atmospheric component when run with observed SST and sea ice distributions typically had globally av- eraged radiative imbalances of more than 10 w/m**2 at the top of the model atmosphere. Many of these models also had large internal sources/sinks of heat and/or water. Modelers quickly discovered that these atmospheric models, when coupled, experienced large cli- mate drifts due to these imbalances. Modelers started to tune their cloud schemes, chang- ing the cloud distribution and cloud radiative properties, to achieve a better radiation bal- ance. Several modeling groups also started to use flux adjustment schemes to account for the remaining radiation imbalances. As the AOGCMs have improved over the years, the need for flux adjustments has dimin- ished. Higher resolution models are able to have realistic AMOCs (and associated realistic meridional heat transports). Also modelers have addressed many of the heat and water sinks/sources present in the early models. One area of continuing challenge is clouds. As the cloud schemes have become more complex, tuning the model radiatively has become more difficult. There are many more observations of the relating to the detailed processes in modern cloud schemes. Often, it is difficult to tune these cloud schemes to obtain a bet- ter radiation balance and at the same time, have the cloud processes be realistic. This can create a tension between the process scientists and those building the AOGCM. -
Shortwave Radiative Forcing
International Journal of Environmental Science and Development, Vol. 4, No. 2, April 2013 Sea Salt Aerosols: Shortwave Radiative Forcing Winai Meesang, Surat Bualert, and Pantipa Wonglakorn (AOD) (Haywood et al. 1999).Sea salt aerosols play a dual Abstract—Effect of sea salt aerosol on short wave spectrum role in affecting the atmospheric radiative balance. Directly, energy is the study of solar reduction and the reduction sea salt particles interact with the incoming solar radiation percentage due to sea salt aerosol. The research measured short and the outgoing terrestrial radiation. Unlike the more wave radiation from the sun by using spectroradiometer, model hydrophobic soil dust aerosol, sea salt particles uptake water MS700. The spectroraiometers were installed at two levels: the first level was set at 10 meters height from ground that called readily and, hence, are highly scattered at shortwave (SW; “control unit” representing the rays of the sun directly (direct solar) wavelengths with virtually no absorption (e.g., Incoming) and the second level was set at one meter height that Takemura et al. 2002). Sea salt aerosol is not absorptive of called “blank unit” measuring radiation from the sun passed solar radiation; it causes similar direct radiative perturbations through the blank chamber (Representing a decrease in solar at the surface and at the top of the atmosphere (TOA). In radiation on Chamber / blank) and “laboratory unit” was a addition, sea salt aerosol can influence the formation and chamber with dry sea salt aerosol (Representing a decrease of the radiation from the sun and dried sea salt), then the study lifetime of clouds by acting as cloud condensation nuclei would find the percent reduction of solar radiation. -
Climate Modelling Primer
A Climate Modelling Primer A Climate Modelling Primer, Third Edition. K. McGuffie and A. Henderson-Sellers. © 2005 John Wiley & Sons, Ltd ISBN: 0-470-85750-1 (HB); 0-470-85751-X (PB) A Climate Modelling Primer THIRD EDITION Kendal McGuffie University of Technology, Sydney, Australia and Ann Henderson-Sellers ANSTO Environment, Australia Copyright © 2005 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 Email (for orders and customer service enquiries): [email protected] Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to [email protected], or faxed to (+44) 1243 770620. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The Publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. -
Constraining Climate Sensitivity from the Seasonal Cycle in Surface Temperature
4224 JOURNAL OF CLIMATE VOLUME 19 Constraining Climate Sensitivity from the Seasonal Cycle in Surface Temperature RETO KNUTTI AND GERALD A. MEEHL National Center for Atmospheric Research,* Boulder, Colorado MYLES R. ALLEN AND DAVID A. STAINFORTH Atmospheric and Oceanic Physics, Oxford University, Oxford, United Kingdom (Manuscript received 16 June 2005, in final form 29 November 2005) ABSTRACT The estimated range of climate sensitivity has remained unchanged for decades, resulting in large un- certainties in long-term projections of future climate under increased greenhouse gas concentrations. Here the multi-thousand-member ensemble of climate model simulations from the climateprediction.net project and a neural network are used to establish a relation between climate sensitivity and the amplitude of the seasonal cycle in regional temperature. Most models with high sensitivities are found to overestimate the seasonal cycle compared to observations. A probability density function for climate sensitivity is then calculated from the present-day seasonal cycle in reanalysis and instrumental datasets. Subject to a number of assumptions on the models and datasets used, it is found that climate sensitivity is very unlikely (5% probability) to be either below 1.5–2 K or above about 5–6.5 K, with the best agreement found for sensitivities between 3 and 3.5 K. This range is narrower than most probabilistic estimates derived from the observed twentieth-century warming. The current generation of general circulation models are within that range but do not sample the highest values. 1. Introduction spheric CO2 concentration, equivalent to a radiative forcing of about 3.7 W mϪ2 (Myhre et al. -
Cccma CMIP6 Model Updates
CCCma CMIP6 Model Updates CanESM2! CanESM5! CMIP5 CMIP6 AGCM4.0! AGCM5! CTEM NEW COUPLER CTEM5 CMOC LIM2 CanOE OGCM4.0! Model Improvements NEMO3.4! Atmosphere Ocean − model levels increased from 35 to 49 − new ocean model based on NEMO3.4 (ORCA1) st nd − aerosol updates (1 and 2 indirect effects) − LIM2 sea-ice component − improved treatment of volcanic aerosol − new in-house coupler developed − improved aerosol radiative effects for black and organic carbon Ocean Biogeochemistry − subgrid scale lakes added (FLAKE) − new parameterization, the Canadian Ocean Ecosystem model, CanOE Land Surface − double the number of biogeochemical tracers − land-surface scheme updated CLASS2.7→CLASS3.6 − increase number of classes of phytoplankton, − improved treatment of snow and snow albedo zooplankton and detritus from one to two − land biogeochemistry → wetlands added with − prognostic iron cycle methane emissions CanESM Functionality − new mineral dust parameterization − new “relaxed CO2” option for specified CO2 concentration simulations Other issues: 1. We are currently in the process of migrating to a new supercomputing system – being installed now and should be running on it over the next few months. 2. Global climate model development is integrated with development of operational seasonal prediction system, decadal prediction system, and regional climate downscaling system. 3. We are also increasingly involved in aspects of ‘climate services’ – providing multi-model climate scenario information to impact and adaptation users, decision-makers, -
The Outlook of Ethiopian Long Rain Season from the Global Circulation Model Solomon Addisu Legesse*
Legesse Environ Syst Res (2016) 5:16 DOI 10.1186/s40068-016-0066-1 RESEARCH Open Access The outlook of Ethiopian long rain season from the global circulation model Solomon Addisu Legesse* Abstract Background: The primary reason to study summer monsoon (long rain season) all over Ethiopia was due to the atmospheric circulation displays a spectacular annual cycle of rainfall in which more than 80 % of the annual rain comes during the summer season comprised of the months June–September. Any minor change in rainfall intensity from the normal conditions imposes a severe challenge on the rural people since its main livelihood is agriculture which mostly relies on summer monsoon. This research work, entitled, ‘The outlook of Ethiopian long rain season from the global circulation model’ has been conducted to fill such knowledge gaps of the target population. The objectives of the research were to examine the global circulation model output data and its outlooks over Ethiopian summer. To attain this specific objective, global circulation model output data were used. These data were analyzed by using Xcon, Matlab and grid analysis and display system computer software programs. Results: The results revealed that Ethiopian summer rainfall (long rain season) has been declined by 70.51 mm in the past four decades (1971–2010); while the best performed models having similar trends to the historical observed rainfall data analysis predicted that the future summer mean rainfall amount will decline by about 60.07 mm (model cccma) and 89.45 mm (model bccr). Conclusions: To conclude, the legislative bodies and development planners should design strategies and plans by taking into account impacts of declining summer rainfall on rural livelihoods.