Liu, J. et al., PNAS, 2012 World Weather Open Science Conference, Montreal, Canada, August 17, 2014 Jiping Liu University at Albany, State University of.

Slides:



Advertisements
Similar presentations
Climate Change: Science, Impacts, Risks and Response Scientific Basis for Human Induced Climate Change Jagadish Shukla Department of Atmospheric, Oceanic.
Advertisements

CMIP5: Overview of the Coupled Model Intercomparison Project Phase 5
Climate Change & Global Warming: State of the Science overview December 2009 Nathan Magee.
Climate change in centuries in observational and model data Evgeny Volodin, Institute of Numerical Mathematics RAS, Moscow, Russia.
Scaling Laws, Scale Invariance, and Climate Prediction
Observed Tropical Expansion: Impact on the Hydrological and Energy Cycles New Investigator Research Summary Joel Norris (Lead PI, UC San Diego) Robert.
Projections of Future Atlantic Hurricane Activity Hurricane Katrina, Aug GFDL model simulation of Atlantic hurricane activity Tom Knutson NOAA /
Climate Change and Malaysia
Climate modeling Current state of climate knowledge – What does the historical data (temperature, CO 2, etc) tell us – What are trends in the current observational.
Outline Further Reading: Detailed Notes Posted on Class Web Sites Natural Environments: The Atmosphere GG 101 – Spring 2005 Boston University Myneni L31:
Climate Forecasting Unit Multi-annual forecasts of Atlantic tropical cyclones in a climate service context Louis-Philippe Caron WWSOC, Montreal, August.
Protecting our Health from Climate Change: a Training Course for Public Health Professionals Chapter 2: Weather, Climate, Climate Variability, and Climate.
Overview of Coupled Model Intercomparison Project (CMIP) and IPCC AR5 Activities Ronald J Stouffer Karl Taylor, Jerry Meehl and many others June 2009.
Alaskan Arctic Economic Access : Faster than Expected James Overland NOAA Pacific Marine Environmental Laboratory Seattle.
5. Future climate predictions Global average temperature and sea-level are projected to rise under all IPCC scenarios Temperature: +1.8°C (B1) to +4.0°C.
Turn Down the Heat: State of the Climate (and Australia) February 2014 Damien Lockie.
The Future of Arctic Sea Ice Authors: Wieslaw Maslowski, Jaclyn Clement Kinney, Matthew Higgins, and Andrew Roberts Brian Rosa – Atmospheric Sciences.
CLIMARES, NERSC, October 2009 Arctic climate and future scenarios Ola M. Johannessen and Mats Bentsen Nansen Environmental and Remote Sensing Center.
Climate Variability & Change - Past & Future Decades Brian Hoskins Director, Grantham Institute for Climate Change, Imperial College London Professor of.
Barcelona, 2015 Ocean prediction activites at BSC-IC3 Virginie Guemas and the Climate Forecasting Unit 9 February 2015.
Apresentação de Resultados do IPCC AR4 WG1 Jose A. Marengo CPTEC/INPE.
Outline Further Reading: Detailed Notes Posted on Class Web Sites Natural Environments: The Atmosphere GE 101 – Spring 2007 Boston University Myneni L30:
The 21 st century changes in the Arctic sea ice cover as a function of its present state: what can we learn from CMIP5 models ? T. Fichefet, F. Massonnet,
Temperature trends in the upper troposphere/ lower stratosphere as revealed by CCMs and AOGCMs Eugene Cordero, Sium Tesfai Department of Meteorology San.
Modern Climate Change Climate change in the past Climate predictability Climate forcing Climate models Emission “scenarios” & climate of the 21 st century.
Numerical modelling of possible catastrophic climate changes E.V. Volodin, N. A. Diansky, V.Ya. Galin, V.P. Dymnikov, V.N. Lykossov Institute of Numerical.
Climate Modeling Jamie Anderson May Monitoring tells us how the current climate has/is changing Climate Monitoring vs Climate Modeling Modeling.
Human Induced Climate Change: The IPCC Fourth Assessment AKE-Programme Annual Conference the German Physical Society (DPG) Regensberg, March
Projecting changes in climate and sea level Thomas Stocker Climate and Environmental Physics, Physics Institute, University of Bern Jonathan Gregory Walker.
Using Global Ocean Models to Project Sea Level Rise Robert Hallberg NOAA / GFDL.
Climate change and sediment budgets? Jaak Monbaliu Albin Ullmann.
World Climate Research Programme Climate Information for Decision Making Ghassem R. Asrar Director, WCRP.
Status of the Sea Ice Model Testing of CICE4.0 in the coupled model context is underway Includes numerous SE improvements, improved ridging formulation,
IPCC WG1 AR5: Key Findings Relevant to Future Air Quality Fiona M. O’Connor, Atmospheric Composition & Climate Team, Met Office Hadley Centre.
Research Needs for Decadal to Centennial Climate Prediction: From observations to modelling Julia Slingo, Met Office, Exeter, UK & V. Ramaswamy. GFDL,
CLIVAR/OCB Working Group: Oceanic Carbon Uptake in CMIP5 Models Ocean CO 2 uptake (Gt/yr) (climate change - control) Friedlingstein et al. [2006]
Sahel Climate Change in the IPCC AR4 models Michela Biasutti in collaboration with : Alessandra Giannini, Adam Sobel, Isaac.
Methane in the atmosphere; direct and indirect climate effects Gunnar Myhre Cicero.
Office of Research and Development National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division Using Dynamical Downscaling to Project.
 CLIMATE MODEL PRECIPITATION TREND ANALYSIS IN THE 20 TH CENTURY Karen Rivas Key Acknowledgements: Xubin Zeng & Koichi Sakaguchi UA/NASA Space Grant Symposium.
The evolution of climate modeling Kevin Hennessy on behalf of CSIRO & the Bureau of Meteorology Tuesday 30 th September 2003 Canberra Short course & Climate.
CLIMATE2030: A Japanese Project for Decadal Climate Prediction Masahide Kimoto Center for Climate System Research University of Tokyo and SPAM Team SPAM.
OCO 10/27/10 GFDL Activities in Decadal Intialization and Prediction A. Rosati, S. Zhang, T. Delworth, Y. Chang, R. Gudgel Presented by G. Vecchi 1. Coupled.
Arctic Minimum 2007 A Climate Model Perspective What makes these two special? Do models ever have 1 year decline as great as observed from September 2006.
Contribution of MPI to CLIMARES Erich Roeckner, Dirk Notz Max Planck Institute for Meteorology, Hamburg.
Exploring the Possibility to Forecast Annual Mean Temperature with IPCC and AMIP Runs Peitao Peng Arun Kumar CPC/NCEP/NWS/NOAA Acknowledgements: Bhaskar.
Chapter 6 Future climate changes Climate system dynamics and modelling Hugues Goosse.
WCC-3, Geneva, 31 Aug-4 Sep 2009 Advancing Climate Prediction Science – Decadal Prediction Mojib Latif Leibniz Institute of Marine Sciences, Kiel University,
Modern and projected meteorological data for climate impact studies Vasily Kokorev.
ENSC 425/625 Chapter 2UNBC1 Chapter 2 Systems approach Objectives: Couplings & Feedback loops Equilibrium states Perturbations & Forcings CO 2 -temp.-photosyn.
Coordinated climate change experiments to be assessed as part of the IPCC AR5 Gerald A. Meehl National Center for Atmospheric Research Boulder, Colorado.
Great Lakes Ice Database, 1973-present 1/15 Great Lakes Ice & Climate Research, Modeling, and Applications Jia Wang Integrated Physical & Ecological Modeling.
ENVIRONMENTAL SCIENCE TEACHERS’ CONFERENCE ENVIRONMENTAL SCIENCE TEACHERS’ CONFERENCE, Borki Molo, Poland, 7-10 February 2007 The warming trend for the.
IPCC First Assessment Report 1990 IPCC Second Assessment Report: Climate Change 1995 IPCC Third Assessment Report: Climate Change 2001 IPCC Fourth Assessment.
RAL, 2012, May 11 Research behaviour Martin Juckes, 11 May, 2012.
Future climate changes
Global Impacts and Consequences of Climate Change
Climate Change Climate change scenarios of the
Mid Term II Review.
Ronald J Stouffer Karl Taylor, Jerry Meehl and many others
Tore Furevik Geophysical Institute, University of Bergen
An Approach to Enhance Credibility of Decadal-Century Scale Arctic
National Center for Atmospheric Research
Globale Mitteltemperatur
Guiding AMBITION in mitigation and adaptation
Globale Mitteltemperatur
Decadal prediction in the Pacific
Time for action – climate change, risks and challenges
Process oriented evaluation of coupled climate-carbon cycle models
Globale Mitteltemperatur
Presentation transcript:

Liu, J. et al., PNAS, 2012 World Weather Open Science Conference, Montreal, Canada, August 17, 2014 Jiping Liu University at Albany, State University of New York Contributors: Mirong Song, Radley Horton, Yongyun Hu, and Chaoyuan Yang Reducing Spread in Climate Model Projections of a September Ice-Free Arctic

IPCC AR4 “… late-summer sea ice is projected to disappear almost completely towards the end of the 21st century under the SRES A2 scenario in some models” (2007) Arctic might be ice-free in Arctic Marine Shipping Assessment report (2009) Wadhams, Duarte, Maslowski, et al. Large uncertainty in the projected timing of the ice-free Arctic in a warming environment!

Land physics and hydrology Ocean ecology, biogeochemistry Atmospheric circulation and radiation Atmospheric chemistry, aerosols Ocean circulation Plant ecology, land use Climate/Earth System Models Sea Ice CMIP5 vs. CMIP3 A more diverse set of model types A number of improvements in physics, numerical algorithms, and configurations A new set of scenarios called Representative Concentration Pathways (RCPs) Interactive CO 2 CMIP5 - Coupled Model Intercomparison Project Phase 5 A coordinated project by climate modeling community An important resource for IPCC AR5, and beyond

~650 ppm CO 2 ~1370 ppm CO 2 RCP4.5: medium-mitigation emission scenario RCP8.5: high-emission scenario

Time-series of simulated September sea ice extent from 1979 to 2100 for 30 CMIP5 models (thick black: observation; thick red: multi-model ensemble mean)

Projected timing of September ice-free Arctic (defined as less than 1 million km2) for 30 CMIP5 models under RCP4.5 (blue bars) and RCP8.5 (red bars) RCP8.5: !

Two different approaches: Model selection (based on the ability to reproduce the observed sea ice climatology and variability since 1979) Constrained estimation (based on the strong and persistent relationship between present and future sea ice conditions)

(a) Climatology and (b) linear trend of September sea ice extent for observations (black bar on left) and each CMIP5 model (gray bars) during % of observed climatology 30% of observed trend

ACCESS1.0, ACCESS1.3, BNU-ESM, CESM1-BGC, CESM1-CAM5, HADGEM2-CC, MIROC5, MIRO-ESM.CHEM, MPI- ESM.MR Time-series of simulated (color lines) September sea ice extent from 1979 to 2100 for 9 selected models Timing of ice-free: 2054

ensemble mean (9 selected models) Obs. when September ice extent is below 1.7 million km 2 (ensemble mean) Sea ice concentration Sea ice thickness

Two different approaches: Model selection (based on the ability to reproduce the observed sea ice climatology and variability since 1979) Constrained estimation (based on strong and persistent relationship between present and future sea ice conditions)

Scatter plot of September sea ice extent in present mean state ( ) versus September sea ice extent in the projected future state ( ) simulated by 30 CMIP5 models (small dots) (big dot: observed present mean state) r=0.93 r=0.95 present state projected state present state projected state

centered on centered on 2097 Evolution of correlation (blue lines) and constrained estimation of September sea ice extent (red lines) based on the relationship between simulated September ice extent averaged for and projected September ice extent averaged for 5-year sliding windows Timing of ice-free: (centered on 2058)

Projected timing for the September ice-free Arctic in CMIP5 models (RCP8.5) Large spread: (original) Reduced spread: ! Increased maritime activities in the Arctic Ocean and substantial climate impacts have been emerging in the Arctic in advance of the ice-free state!

CMIP5: decadal prediction experiments 10-year simulations initialized every 5-year yrs

September sea ice extent (black: observations; thick red: ensemble mean of decadal hindcasts) NCEP CFSv2NASA GEOS-5

Temporal correlation of September sea ice extent anomaly between observation and decadal hindcasts (lead time of 1-year) NCEP CFSv2NASA GEOS-5

NCEP CFSv2NASA GEOS-5 Temporal correlation of September sea ice extent anomaly between observation and decadal hindcasts (lead time of 2-5 year average)

Thanks for your attention!