Aerosol, Interhemispheric Gradient, and Climate Sensitivity Ching-Yee Chang Department of Geography University of California Berkeley Lawrence Livermore.

Slides:



Advertisements
Similar presentations
North Pacific and North Atlantic multidecadal variability: Origin, Predictability, and Implications for Model Development Thanks to: J. Ba, N. Keenlyside,
Advertisements

1 Trend and Year-to-year Variability of Land-Surface Air Temperature and Land-only Precipitation Simulated by the JMA AGCM By Shoji KUSUNOKI, Keiichi MATSUMARU,
Modeling the MOC Ronald J Stouffer Geophysical Fluid Dynamics Laboratory NOAA The views described here are solely those of the presenter and not of GFDL/NOAA/DOC.
Scaling Laws, Scale Invariance, and Climate Prediction
Double ITCZ Phenomena in GCM’s Marcus D. Williams.
1 Geophysical Fluid Dynamics Laboratory Review June 30 - July 2, 2009.
A unifying view of climate change in the Sahel linking intra-seasonal, inter-annual and longer time scales Alessandra Giannini, Seyni Salack, Tiganadaba.
Projections of Future Atlantic Hurricane Activity Hurricane Katrina, Aug GFDL model simulation of Atlantic hurricane activity Tom Knutson NOAA /
Consistency of recently observed trends over the Baltic Sea basin with climate change projections 7th Study Conference on BALTEX June 2013, Sweden.
Semyon A. Grodsky and James A. Carton, University of Maryland, College Park, MD The PIRATA (PIlot Research Array moored in the Tropical Atlantic) project.
Earth Systems Science Chapter 6 I. Modeling the Atmosphere-Ocean System 1.Statistical vs physical models; analytical vs numerical models; equilibrium vs.
The Role of Internally Generated Megadroughts and External Solar Forcing in Long Term Pacific Climate Fluctuations Gerald A. Meehl NCAR.
Detection of Human Influence on Extreme Precipitation 11 th IMSC, Edinburgh, July 2010 Seung-Ki Min 1, Xuebin Zhang 1, Francis Zwiers 1 & Gabi Hegerl.
Climate Change in the Sahel Michela Biasutti in collaboration with : Alessandra Giannini, Adam Sobel, Isaac Held.
MET 112 Global Climate Change - Lecture 11 Future Predictions Craig Clements San Jose State University.
Extreme Precipitation
Protecting our Health from Climate Change: a Training Course for Public Health Professionals Chapter 2: Weather, Climate, Climate Variability, and Climate.
1 Regional Climate Change Summary of TAR Findings How well do the Models Work at Regional Scales? Some Preliminary Simulation Results Understanding Climate.
Jae-Heung Park, Soon-Il An. 1.Introduction 2.Data 3.Result 4. Discussion 5. Summary.
Statistical Analyses of Historical Monthly Precipitation Anomalies Beginning 1900 Phil Arkin, Cooperative Institute for Climate and Satellites Earth System.
Sub-Saharan rainfall variability as simulated by the ARPEGE AGCM, associated teleconnection mechanisms and future changes. Global Change and Climate modelling.
Assessing trends in observed and modelled climate extremes over Australia in relation to future projections Extremes in a changing climate, KNMI, The Netherlands,
Atlantic Multidecadal Variability and Its Climate Impacts in CMIP3 Models and Observations Mingfang Ting With Yochanan Kushnir, Richard Seager, Cuihua.
Natural and Anthropogenic Drivers of Arctic Climate Change Gavin Schmidt NASA GISS and Columbia University Jim Hansen, Drew Shindell, David Rind, Ron Miller.
Changes of Seasonal Predictability Associated with Climate Change Kyung Jin and In-Sik Kang Climate Environment System Research Center Seoul National University.
Causes of Climate Change Over the Past 1000 Years Thomas J. Crowley Presentation by Jessica L. Cruz April 26, 2001.
Climate Change and Global Warming Michael E. Mann Department of Environmental Sciences University of Virginia Symposium on Energy for the 21 st Century.
1 Hadley Centre The Atlantic Multidecadal Oscillation: A signature of persistent natural thermohaline circulation cycles in observed climate Jeff Knight,
Projecting changes in climate and sea level Thomas Stocker Climate and Environmental Physics, Physics Institute, University of Bern Jonathan Gregory Walker.
The Influence of Tropical-Extratropical Interactions on ENSO Variability Michael Alexander NOAA/Earth System Research Lab.
Impacts of Aerosols on Climate Extremes in the USA Nora Mascioli.
C20C Workshop ICTP Trieste 2004 The Influence of the Ocean on the North Atlantic Climate Variability in C20C simulations with CSRIO AGCM Hodson.
© Crown copyright Met Office Decadal predictions of the Atlantic ocean and hurricane numbers Doug Smith, Nick Dunstone, Rosie Eade, David Fereday, James.
© Crown copyright Met Office Regional Temperature and Precipitation changes under high- end global warming Michael Sanderson, Deborah Hemming, Richard.
IPCC WG1 AR5: Key Findings Relevant to Future Air Quality Fiona M. O’Connor, Atmospheric Composition & Climate Team, Met Office Hadley Centre.
Evaluation of climate models, Attribution of climate change IPCC Chpts 7,8 and 12. John F B Mitchell Hadley Centre How well do models simulate present.
Past and Future Changes in Southern Hemisphere Tropospheric Circulation and the Impact of Stratospheric Chemistry-Climate Coupling Collaborators: Steven.
Environment Canada Environnement Canada Effects of elevated CO 2 on modelled ENSO variability Bill Merryfield Canadian Centre for Climate Modelling and.
Research Needs for Decadal to Centennial Climate Prediction: From observations to modelling Julia Slingo, Met Office, Exeter, UK & V. Ramaswamy. GFDL,
Sahel Climate Change in the IPCC AR4 models Michela Biasutti in collaboration with : Alessandra Giannini, Adam Sobel, Isaac.
Mechanisms of drought in present and future climate Gerald A. Meehl and Aixue Hu.
2010/ 11/ 16 Speaker/ Pei-Ning Kirsten Feng Advisor/ Yu-Heng Tseng
Oaverview of IPCC reports Kyoto, Copenhagen, Russia’s & America’s Role, IPCC Reports etc. June 2, 2014 Return to Home Page.
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.
Beyond CMIP5 Decadal Predictions and the role of aerosols in the warming slowdown Doug Smith, Martin Andrews, Ben Booth, Nick Dunstone, Rosie Eade, Leon.
PAPER REVIEW R Kirsten Feng. Impact of global warming on the East Asian winter monsoon revealed by nine coupled atmosphere-ocean GCMs Masatake.
Exploring the Possibility to Forecast Annual Mean Temperature with IPCC and AMIP Runs Peitao Peng Arun Kumar CPC/NCEP/NWS/NOAA Acknowledgements: Bhaskar.
Climatic implications of changes in O 3 Loretta J. Mickley, Daniel J. Jacob Harvard University David Rind Goddard Institute for Space Studies How well.
Future Projections of Precipitation Characteristics in Asia.
Chapter 6 Future climate changes Climate system dynamics and modelling Hugues Goosse.
1 MET 112 Global Climate Change MET 112 Global Climate Change - Lecture 12 Future Predictions Eugene Cordero San Jose State University Outline  Scenarios.
Indo-Pacific Sea Surface Temperature Influences on Failed Consecutive Rainy Seasons over Eastern Africa** Andy Hoell 1 and Chris Funk 1,2 Contact:
Climate Change Information Seminar Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) – the relevance to FAO’s activities Claudia.
Multidecadal simulations of the Indian monsoon in SPEEDY- AGCM and in a coupled model Annalisa Bracco, Fred Kucharski and Franco Molteni The Abdus Salam.
What drives the observed variability and decadal trends in North African dust export? David A. Ridley, Colette L. Heald Dept. Civil & Environmental Engineering,
Cooperative Research Programs (CoRP) Satellite Climate Studies Branch (SCSB) 1 1 Reconstruction of Near-Global Precipitation Variations Based on Gauges.
Consistency of recent climate change and expectation as depicted by scenarios over the Baltic Sea Catchment and the Mediterranean region Hans von Storch,
Key ingredients in global hydrological response to external forcing Response to warming => Increased horizontal moisture fluxes => Poleward expansion of.
June Haiyan Teng NCAR/CGD
Improved Historical Reconstructions of SST and Marine Precipitation Variations Thomas M. Smith1 Richard W. Reynolds2 Phillip A. Arkin3 Viva Banzon2 1.
Climate Change Climate change scenarios of the
Can recently observed precipitation trends over the Mediterranean area be explained by climate change projections? Armineh Barkhordarian1, Hans von Storch1,2.
Slow down of the THC and increasing hurricane activity
The absorption of solar radiation in the climate system
Impact of the vertical resolution on Climate Simulation using CESM
Lamont-Doherty Earth Observatory
20th Century Sahel Rainfall Variability in IPCC Model Simulations and Future Projection Mingfang Ting With Yochanan Kushnir, Richard Seager, Cuihua Li,
Decadal prediction in the Pacific
Korea Ocean Research & Development Institute, Ansan, Republic of Korea
Comparing the Greenhouse Sensitivities of CCM3 and ECHAM4.5
Presentation transcript:

Aerosol, Interhemispheric Gradient, and Climate Sensitivity Ching-Yee Chang Department of Geography University of California Berkeley Lawrence Livermore National Lab Seminar April 27, 2011 Collaborators: John Chiang (UC Berkeley) Michael Wehner (Lawrence Berkeley Lab)

Sulfate Aerosols and Climate Difference in SAT caused by sulfate aerosol indirect effect (Rotstayn & Lohmann 2002) Direct forcing from anthropogenic sulfate forcing (Kiehl & Briegleb 1993) IPCC AR4

Large uncertainty in the aerosol forcing Kiehl 2007 IPCC AR4

Outline Sulfate aerosol control of Tropical Atlantic climate over the 20 th century (Chang et al. 2011, in press for Journal of Climate) Projection of the Interhemispheric Gradient in 21 st century Interhemispheric Gradient and Transient Climate Response

Sulfate aerosol control of Tropical Atlantic climate over the 20 th century

Atlantic interhemispheric SST gradient over the 20 th century Interhemispheric SST index = South – North box 21-yr running mean on annual mean data Hadley SST, ERSST, Kaplan SST HADISST ERSST KAPLAN SST

Ensemble Empirical Mode Decomposition (EEMD) Modes from Ensemble Empirical Mode Decomposition (EEMD) analysis: Multidecadal (mode 4) and trend (mode 5) HADISST ERSST KAPLAN SST

The interhemispheric SST gradient and the meridional position of ITCZ Mode 1 of an MCA of SSTA and 10m winds, and regression of the SSTA mode 1 time series on precipitation (Chiang and Vimont, 2004)

Equatorial meridional winds ICOADS Winds Smith et al. 2010: Reconstructed global precip CRU TS 2.1 land precip. south-north June-July-August precipitation

CMIP3 models simulation of Atlantic Interhemispheric Gradient in 20 th century climate experiment

Variance explained by this EOF: 49% Most of the projection coefficient are positive (Most models have a upward trend in the SSTA gradient indices) Projection of EOF1 onto each run 1 st EOF of AITG indices from 71 model runs South Hemisphere warming more than North Hemisphere This trend mitigates after 1980

T-test value=4.41 p-value = (assuming 71 d.o.f. for the 20 th century runs, and 44 for the preindustrial) Mean of trend in SSTA gradient significantly different from preindustrial run Unit: 0.1K/100yr

Regression of SSTA onto model-index EOF1 Hadley SSTA Stronger warming in the South Atlantic Model ensemble averaged SSTA

Southward shift of ITCZ Regression of Precip. Anomaly onto model-index EOF1 Model ensemble averaged Precip. anomaly CRU Precip. anomaly

Attribution the cause of the trend  Trend behavior appears in model ensemble mean  Most likely to be externally forced  Single forcing runs

Results from single forcing runs CCSM3 (2 members) PCM1 (4 members) GISS modelE (1 member)s ) SST gradient index ITCZ index most resembles the 1st EOF of the indices of the 20C expt.

CCSM3 sulfate aerosol emission forcing data CCSM3 simulated sulfate aerosol optical depth

EOF1 from different subsets of models  EOF1 from AIE models capture the turn of the trend better  AIE models simulate the AITG trend closer to observation AIE models: Models with both Aerosol Direct and Indirect Effect No-AIE models: Models with only aerosol direct, but no Indirect Effect X-axis unit: 0.1K/100yr

Modeled SSTA and Precip.A regression on model-index EOF1 Warming asymmetry and ITCZ southward shift stronger in AIE models Models with Aerosol Indirect Effect Models without Aerosol Indirect Effect

Summary I  Interhemispheric gradient of Atlantic SST found to have an upward trend before 1980, indicating stronger warming in the South Atlantic and southward shift of ITCZ  A similar positive trend is detected in the IPCC models.  This trend is likely due the north-south disparity in anthropogenic sulfate aerosol emissions

3 different scenarios are examined A1B, A2, B1 Both Atlantic and Pacific sectors Projection of the Interhemispheric Gradient in 21 st century Indices are defined as south box minus north box

Global mean of various anthropogenic forcing agents in future scenarios IPCC AR4 WG1, Fig.10.26

95-year (2004~2098) trend statistics Pacific Atlantic A1B A2 B1 Most models project downward trend of the Pacific index in the 21th century in these 3 future scenarios => North Pacific warming stronger than South Less conclusive results on the projection of the Atlantic index trend X-axis unit: 0.1K/100yr

A1B Atmos. Sulfate burden unit: 10e-6 kg/m2 Pacific Atlantic High-lat index (35~60) Tropical index (5~35) Stronger change in the interhemispheric gradient of sulfate aerosol forcing across the equator in the Pacific sector (From miub_echo model) It’s projected that most of the decrease of sulfate aerosol mainly comes from Asia More aerosol emission from Tropical Atlantic than from North Atlantic

Atmospheric Sulfate burden A1bA2 B1 All three scenarios have stronger change in sulfate aerosol forcing across the equator in the Pacific sector

1%/yr to 2xCO 2 experiment However, similar change of Pacific gradient is found in 1%/yr to double CO2 experiment, but with weaker magnitudes

Comparison: 1%/yr to 2xCO2 and A1B experiments (Yr60~Yr80) – ( Yr1~Yr20 )(2079~2098) – ( 2005~2024 )

 Most models project negative trend in the Pacific interhemispheric gradient – the rate of the warming in the north Pacific speeds up at the end of 21 st century  Possibly related to the decrease of the aerosols in the north Pacific in the future, but GHG forcing or other factors may also contribute Summary II

Interhemispheric Gradient and Climate sensitivity

Trend Statistic from different subsets of models  AIE models simulate the AITG trend closer to observation AIE models: Models with both Aerosol Direct and Indirect Effect No-AIE models: Models with only aerosol direct, but no Indirect Effect X-axis unit: 0.1K/100yr

Kiehl 2007: Total forcing inversely correlated to climate sensitivity Large uncertainty in the aerosol forcing

Equilibrium Climate sensitivity and Transient Climate Response Equilibrium Climate Sensitivity IPCC AR4 Table 8.2 Transient Climate Response 300 ppm 600 ppm CO2 concentration + Slab ocean AGCM T0 T’ CO2 concentration AGCM + OGCM 300 ppm 600 ppm T0 T’

Climate sensitivity and Transient Climate Response IPCC AR4 Table 8.2

Atlantic SSTA Grad. Trend v.s. Climate Sensitivity There seems to be a linear relationship between the gradient trend and the Transient Climate Response (TCR) among most of the models

Regional Transient Climate response Regional Transient Climate Responses (TCR) in the Tropical Atlantic regions are similar in the North and South Roughly a linear relationship between regional TCR and global TCR

A linear relationship between TCR and Interhemispheric Gradient Trend Similar regional TCRs, in the Tropical Atlantic region across the equator Roughly a linear relationship between regional TCR and global TCR Models with higher TCR are models with stronger aerosol forcing, due to the constraint of the 20C global mean SAT change Stronger aerosol forcing with larger TCR => stronger SST gradient If we also constrain the models with observed interhemispheric gradient change?

Summary III A linear relationship between the modeled Atlantic SST Interhemispheric Gradient and Transient Climate Response for most of the models This relationship can be explained by the uncertainty of the aerosol forcings among the models Further confirms that the important role of aerosol on the change of the Interhemispheric Gradient Constraint on the simulation of Interhemispheric Gradient change (or trend) may be a way to confine the uncertainty of models’ climate sensitivity

Thank you for your attention

Comparison of SAT grad. change for 20C and 1%to2xCO2

Climate sensitivity and total anthropogenic forcing In general, 20 Century temperature change = climate sensitivity × Radiative Forcing Smaller total anthropogenic forcing, larger climate sensitivity Larger total anthropogenic forcing, smaller climate sensitivity Let Consistent with Kiehl 2007

Regional TCR and interhemispheric gradient South – North Gradient change, △ G Linear relationship btw. TCR and interhemispheric gradient change

Model internal averaged Gradient change

Pacific and Atlantic Interhemispheric Gradient HADISST ERSST(NOAA) KAPLAN SST Pacific Atlantic

20C experiment PacificAtlantic 49% 51% EOF1 from all models Projection of EOF1 on each run

A1B B1 A2 Atlantic Interhemispheric SST Gradient

Pacific Interhemispheric SST Gradient A1B B1 A2