Development and Evaluation of an Earth-System Model – Hadgem2
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Western Europe Is Warming Much Faster Than Expected
Clim. Past, 5, 1–12, 2009 www.clim-past.net/5/1/2009/ Climate © Author(s) 2009. This work is distributed under of the Past the Creative Commons Attribution 3.0 License. Western Europe is warming much faster than expected G. J. van Oldenborgh1, S. Drijfhout1, A. van Ulden1, R. Haarsma1, A. Sterl1, C. Severijns1, W. Hazeleger1, and H. Dijkstra2 1KNMI (Koninklijk Nederlands Meteorologisch Instituut), De Bilt, The Netherlands 2Institute for Marine and Atmospheric Research, Utrecht University, The Netherlands Received: 28 July 2008 – Published in Clim. Past Discuss.: 29 July 2008 Revised: 22 December 2008 – Accepted: 22 December 2008 – Published: 21 January 2009 Abstract. The warming trend of the last decades is now so by Regional Climate models (RCMs) do not deviate much strong that it is discernible in local temperature observations. from GCMs, as the prescribed SST and boundary condition This opens the possibility to compare the trend to the warm- determine the temperature to a large extent (Lenderink et al., ing predicted by comprehensive climate models (GCMs), 2007). which up to now could not be verified directly to observations By now, global warming can be detected even on the grid on a local scale, because the signal-to-noise ratio was too point scale. In this paper we investigate the high tempera- low. The observed temperature trend in western Europe over ture trends observed in western Europe over the last decades. the last decades appears much stronger than simulated by First we compare these with the trends expected on the basis state-of-the-art GCMs. The difference is very unlikely due of climate model experiments. -
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. -
Improving Climate Model Accuracy by Exploring Parameter Space with an O(105) Member
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-198 Manuscript under review for journal Geosci. Model Dev. Discussion started: 23 October 2018 c Author(s) 2018. CC BY 4.0 License. 1 Improving climate model accuracy by exploring parameter space with an O(105) member 2 ensemble and emulator 3 Sihan Li1,2, David E. Rupp3, Linnia Hawkins3,6, Philip W. Mote3,6, Doug McNeall4, Sarah 4 N. Sparrow2, David C. H. Wallom2, Richard A. Betts4,5, Justin J. Wettstein6,7,8 5 1Environmental Change Institute, School of Geography and the Environment, University 6 of Oxford, Oxford, United Kingdom 7 2Oxford e-Research Centre, University of Oxford, Oxford, United Kingdom 8 3Oregon Climate Change Research Institute, College of Earth, Ocean, and Atmospheric 9 Science, Oregon State University, Corvallis, Oregon 10 4Met Office Hadley Centre, FitzRoy Road, Exeter, United Kingdom 11 5College of Life and Environmental Sciences, University of Exeter, Exeter, UK 12 6College of Earth, Ocean, and Atmospheric Science, Oregon State University, Corvallis, 13 Oregon 14 7Geophysical Institute, University of Bergen, Bergen, Norway 15 8Bjerknes Centre for Climate Change Research, Bergen, Norway 16 Correspondence to: Sihan Li ([email protected]) 17 18 19 20 21 22 23 1 Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-198 Manuscript under review for journal Geosci. Model Dev. Discussion started: 23 October 2018 c Author(s) 2018. CC BY 4.0 License. 24 Abstract 25 Understanding the unfolding challenges of climate change relies on climate models, many 26 of which have large summer warm and dry biases over Northern Hemisphere continental 27 mid-latitudes. -
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 ............................................................................................................. -
Prep Publi Catio on Cop Py
Attribution of Extreme Weather Events in the Context of Climate Change PREPUBLICATION COPY Committee on Extreme Weather Events and Climate Change Attribution Board on Atmospheric Sciencees and Climate Division on Earth and Life Studies This prepublication version of Attribution of Extreme Weather Events in the Context of Climate Change has been provided to the public to facilitate timely access to the report. Although the substance of the report is final, editorial changes may be made throughout the text and citations will be checked prior to publication. The final report will be available through the National Academies Press in spring 2016. Copyright © National Academy of Sciences. All rights reserved. Attribution of Extreme Weather Events in the Context of Climate Change THE NATIONAL ACADEMIES PRESS 500 Fifth Street, NW Washington, DC 20001 This study was supported by the David and Lucile Packard Foundation under contract number 2015- 63077, the Heising-Simons Foundation under contract number 2015-095, the Litterman Family Foundation, the National Aeronautics and Space Administration under contract number NNX15AW55G, the National Oceanic and Atmospheric Administration under contract number EE- 133E-15-SE-1748, and the U.S. Department of Energy under contract number DE-SC0014256, with additional support from the National Academy of Sciences’ Arthur L. Day Fund. Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project. International Standard Book Number-13: International Standard Book Number-10: Digital Object Identifier: 10.17226/21852 Additional copies of this report are available for sale from the National Academies Press, 500 Fifth Street, NW, Keck 360, Washington, DC 20001; (800) 624-6242 or (202) 334-3313; http://www.nap.edu. -
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, -
Global Climate Models and Their Limitations Anthony Lupo (USA) William Kininmonth (Australia) Contributing: J
1 Global Climate Models and Their Limitations Anthony Lupo (USA) William Kininmonth (Australia) Contributing: J. Scott Armstrong (USA), Kesten Green (Australia) 1. Global Climate Models and Their Limitations Key Findings Introduction 1.1 Model Simulation and Forecasting 1.2 Modeling Techniques 1.3 Elements of Climate 1.4 Large Scale Phenomena and Teleconnections Key Findings Confidence in a model is further based on the The IPCC places great confidence in the ability of careful evaluation of its performance, in which model general circulation models (GCMs) to simulate future output is compared against actual observations. A climate and attribute observed climate change to large portion of this chapter, therefore, is devoted to anthropogenic emissions of greenhouse gases. They the evaluation of climate models against real-world claim the “development of climate models has climate and other biospheric data. That evaluation, resulted in more realism in the representation of many summarized in the findings of numerous peer- quantities and aspects of the climate system,” adding, reviewed scientific papers described in the different “it is extremely likely that human activities have subsections of this chapter, reveals the IPCC is caused more than half of the observed increase in overestimating the ability of current state-of-the-art global average surface temperature since the 1950s” GCMs to accurately simulate both past and future (p. 9 and 10 of the Summary for Policy Makers, climate. The IPCC’s stated confidence in the models, Second Order Draft of AR5, dated October 5, 2012). as presented at the beginning of this chapter, is likely This chapter begins with a brief review of the exaggerated. -
Atmospheric Dispersion of Radioactive Material in Radiological Risk Assessment and Emergency Response
Progress in NUCLEAR SCIENCE and TECHNOLOGY, Vol. 1, p.7-13 (2011) REVIEW Atmospheric Dispersion of Radioactive Material in Radiological Risk Assessment and Emergency Response YAO Rentai * China Institute for Radiation Protection, P.O.Box 120, Taiyuan, Shanxi 030006, China The purpose of a consequence assessment system is to assess the consequences of specific hazards on people and the environment. In this paper, the studies on technique and method of atmospheric dispersion modeling of radioactive material in radiological risk assessment and emergency response are reviewed in brief. Some current statuses of nuclear accident consequences assessment in China were introduced. In the future, extending the dispersion modeling scales such as urban building scale, establishing high quality experiment dataset and method of model evaluation, improved methods of real-time modeling using limited inputs, and so on, should be promoted with high priority of doing much more work. KEY WORDS: atmospheric model, risk assessment, emergency response, nuclear accident 11) I. Introduction from U.S. NOAA, and SPEEDI/WSPEEDI from The studies and developments of techniques and methods Japan/JAERI. However, the needs of emergency of atmospheric dispersion modeling of radioactive material management may not be well satisfied by existing models in radiological risk assessment and emergency response which are not well designed and confronted with difficulty have evolved over the past 50-60 years. The three marked in detailed constructions of local wind and turbulence -
Lecture 29. Introduction to Atmospheric Chemical Transport Models
Lecture 29. Introduction to atmospheric chemical transport models. Part 1. Objectives: 1. Model types. 2. Box models. 3. One-dimensional models. 4. Two-dimensional models. 5. Three-dimensional models. Readings: Graedel T. and P.Crutzen. “Atmospheric change: an earth system perspective”. Chapter 15.”Bulding environmental chemical models”, 1992. 1. Model types. Mathematical models provide the necessary framework for integration of our understanding of individual atmospheric processes and study of their interactions. Note, that atmosphere is a complex reactive system in which numerous physical and chemical processes occur simultaneously. • Atmospheric chemical transport models are defined according to their spatial scale: Model Typical domain scale Typical resolution Microscale 200x200x100 m 5 m Mesoscale(urban) 100x100x5 km 2 km Regional 1000x1000x10 km 20 km Synoptic(continental) 3000x3000x20 km 80 km Global 65000x65000x20km 50x50 1 Figure 29.1 Components of a chemical transport model (Seinfeld and Pandis, 1998). 2 • Domain of the atmospheric model is the area that is simulated. The computation domain consists of an array of computational cells, each having uniform chemical composition. The size of cells determines the spatial resolution of the model. • Atmospheric chemical transport models are also characterized by their dimensionality: zero-dimensional (box) model; one-dimensional (column) model; two-dimensional model; and three-dimensional model. • Model time scale depends on a specific application varying from hours (e.g., air quality model) to hundreds of years (e.g., climate models) 3 Two principal approaches to simulate changes in the chemical composition of a given air parcel: 1) Lagrangian approach: air parcel moves with the local wind so that there is no mass exchange that is allowed to enter the air parcel and its surroundings (except of species emissions). -
A Brief Summary of Plans for the GMAO Core Priorities and Initiatives for the Next 5 Years
A Brief Summary of Plans for the GMAO Core Priorities and Initiatives for the Next 5 years Provided as information for ROSES 2012 A.13 – MAP Developments in the GMAO are focused on the next generation systems, GEOS-6, and an Integrated Earth System Analysis and the associated modeling system that supports that analysis. GEOS-6 and IESA (1) The GEOS-6 system will be built around the next generation, non-hydrostatic atmospheric model with aerosol-cloud microphysics (advances upon the Morrison-Gettelman cloud microphysics and the Modal Aerosol Model (MAM) aerosol microphysics module for the inclusion of aerosol indirect effects) and an accompanying hybrid (ensemble-variational) 4DVar atmospheric assimilation system. (2) IESA capabilities for other parts of the earth system, including atmospheric chemical constituents and aerosols, ocean circulation, land hydrology, and carbon budget will be built upon our existing separate assimilation capabilities. The GEOS Model Our modeling strategy is driven by the need to have a comprehensive global model valid for both weather and climate and for use in both simulation and assimilation. Our main task in atmospheric modeling during the next five years will be to make the transition to GEOS-6. This direction is driven by (i) the need to improve the representation of clouds and precipitation to enable use of cloud- and precipitation-contaminated satellite radiance observations in NWP, and (ii) the research goal of understanding and predicting weather- climate connections. Development will focus on 1km to 10km resolutions that will be needed for the data assimilation system (DAS). Climate resolutions (10-100km) will not be ignored, but developments for resolutions coarser than 50 km will have lower priority. -
Assimila Blank
NERC NERC Strategy for Earth System Modelling: Technical Support Audit Report Version 1.1 December 2009 Contact Details Dr Zofia Stott Assimila Ltd 1 Earley Gate The University of Reading Reading, RG6 6AT Tel: +44 (0)118 966 0554 Mobile: +44 (0)7932 565822 email: [email protected] NERC STRATEGY FOR ESM – AUDIT REPORT VERSION1.1, DECEMBER 2009 Contents 1. BACKGROUND ....................................................................................................................... 4 1.1 Introduction .............................................................................................................. 4 1.2 Context .................................................................................................................... 4 1.3 Scope of the ESM audit ............................................................................................ 4 1.4 Methodology ............................................................................................................ 5 2. Scene setting ........................................................................................................................... 7 2.1 NERC Strategy......................................................................................................... 7 2.2 Definition of Earth system modelling ........................................................................ 8 2.3 Broad categories of activities supported by NERC ................................................. 10 2.4 Structure of the report ........................................................................................... -
Review of the Global Models Used Within Phase 1 of the Chemistry–Climate Model Initiative (CCMI)
Geosci. Model Dev., 10, 639–671, 2017 www.geosci-model-dev.net/10/639/2017/ doi:10.5194/gmd-10-639-2017 © Author(s) 2017. CC Attribution 3.0 License. Review of the global models used within phase 1 of the Chemistry–Climate Model Initiative (CCMI) Olaf Morgenstern1, Michaela I. Hegglin2, Eugene Rozanov18,5, Fiona M. O’Connor14, N. Luke Abraham17,20, Hideharu Akiyoshi8, Alexander T. Archibald17,20, Slimane Bekki21, Neal Butchart14, Martyn P. Chipperfield16, Makoto Deushi15, Sandip S. Dhomse16, Rolando R. Garcia7, Steven C. Hardiman14, Larry W. Horowitz13, Patrick Jöckel10, Beatrice Josse9, Douglas Kinnison7, Meiyun Lin13,23, Eva Mancini3, Michael E. Manyin12,22, Marion Marchand21, Virginie Marécal9, Martine Michou9, Luke D. Oman12, Giovanni Pitari3, David A. Plummer4, Laura E. Revell5,6, David Saint-Martin9, Robyn Schofield11, Andrea Stenke5, Kane Stone11,a, Kengo Sudo19, Taichu Y. Tanaka15, Simone Tilmes7, Yousuke Yamashita8,b, Kohei Yoshida15, and Guang Zeng1 1National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand 2Department of Meteorology, University of Reading, Reading, UK 3Department of Physical and Chemical Sciences, Universitá dell’Aquila, L’Aquila, Italy 4Environment and Climate Change Canada, Montréal, Canada 5Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland 6Bodeker Scientific, Christchurch, New Zealand 7National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA 8National Institute of Environmental Studies (NIES), Tsukuba, Japan 9CNRM UMR 3589, Météo-France/CNRS,