SlideShare une entreprise Scribd logo
1  sur  25
Télécharger pour lire hors ligne
Advancing Climate Prediction Science –
          Decadal Prediction
                                   Mojib Latif
        Leibniz Institute of Marine Sciences, Kiel University, Germany




WCC-3, Geneva, 31 Aug-4 Sep 2009
Outline
    • Why decadal prediction
    • Mechanisms of decadal
      variability
    • What is the decadal
      predictability potential
    • Challenges
WCC-3, Geneva, 31 Aug-4 Sep 2009
“Climate surprises”



                                   cooling




WCC-3, Geneva, 31 Aug-4 Sep 2009
Decadal variations in Sahel rainfall




                                   ?




WCC-3, Geneva, 31 Aug-4 Sep 2009
Decadal variations in Atlantic
               hurricane activity




WCC-3, Geneva, 31 Aug-4 Sep 2009
Decadal variability in sea level
                             Linear trend 1993-2003




                                   Kwajalein (8°44’N, 167°44’E)




WCC-3, Geneva, 31 Aug-4 Sep 2009
Global change prediction is a joint
    initial/boundary value problem




IPCC 2007

              Projections were not initialized in IPCC-AR4


WCC-3, Geneva, 31 Aug-4 Sep 2009
The uncertainty in climate
        projections for the 21st century

                      internal variability



                                          scenario
                       unpredictable external influences



                               model bias



                                                  Hawkins and Sutton 2009
WCC-3, Geneva, 31 Aug-4 Sep 2009
Outline
    • Why decadal prediction
    • Mechanisms of decadal
      variability
    • What is the decadal
      predictability potential
    • Challenges
WCC-3, Geneva, 31 Aug-4 Sep 2009
Internal vs. external influences




        How much did internal decadal variability contribute
        to the warming during the recent decades?
WCC-3, Geneva, 31 Aug-4 Sep 2009
Decadal variations in the
                North Atlantic Oscillation




     How much of the decadal NAO variability is forced by
     changes in the boundary conditions?
WCC-3, Geneva, 31 Aug-4 Sep 2009
Decadal North Atlantic sea surface
        temperature variations
                                    North Atlantic SST
                                       o            o
                                    (70 W - 0, 0 - 60 N)




      Changes in hurricane activity and Sahel rain, for
      instance, can be traced back to variations in Atlantic
      sea surface temperature (SST)
WCC-3, Geneva, 31 Aug-4 Sep 2009
Outline
    • Why decadal prediction
    • Mechanisms of decadal
      variability
    • What is the decadal
      predictability potential
    • Challenges
WCC-3, Geneva, 31 Aug-4 Sep 2009
Potential predictability of
           surface air temperature (SAT)
Derived from control integrations with climate models
                                                                    North Atlantic SST




                                                        Boer 2004

                                                                                    Knight et al. 2005




          The North Atlantic Sector appears to be one of the
          promising regions
WCC-3, Geneva, 31 Aug-4 Sep 2009
Predictability of the Meridional
        Overturning Circulation (MOC)




                                                  Hurrell et al. 2009




     The MOC is predicable at a lead of one to two decades
     in perfect model studies
WCC-3, Geneva, 31 Aug-4 Sep 2009
Unpredictable external influences
             Solar constant 1976-2008




                                        ?


  Strong volcanic eruptions, for instance, can cause global
  cooling of about 0,2°C for a few years and persist even
  longer in the ocean heat content. If they happen, we can
  exploit their long-lasting climatic effects.

WCC-3, Geneva, 31 Aug-4 Sep 2009
Large spread for the next decade

                                   observations




                             3 climate model hind/forecasts




                               „MOC“




                                                    Hurrell et al. 2009




WCC-3, Geneva, 31 Aug-4 Sep 2009
Outline
    • Why decadal prediction
    • Mechanisms of decadal
      variability
    • What is the decadal
      predictability potential
    • Challenges
WCC-3, Geneva, 31 Aug-4 Sep 2009
Climate observing system
                      Example: ocean observing system
                                   Multi-national Argo fleet




                                   We need climate observations to
                                   initialize the models to forecast
                                   variations up to decadal time
                                   scales

WCC-3, Geneva, 31 Aug-4 Sep 2009
Model biases are large
           Typical bias in surface air temperature (SAT)
           ensemble mean
           error




          IPCC 2007


               Errors of several degrees C in some regions
WCC-3, Geneva, 31 Aug-4 Sep 2009
Gulfstream SST front




                  Represention of small-scale processes
WCC-3, Geneva, 31 Aug-4 Sep 2009
Resolution matters




                                                     Minobe et al. 2008


  The AGCM has T239 horizontal resolution (~50 km) and 48 levels

  Compared to the smoothed SST run, rain-bearing low
  pressure systems tend to develop along the Gulf Stream
  front in the control simulation

WCC-3, Geneva, 31 Aug-4 Sep 2009
Where are we today?
   • A decadal predictability potential for a number
     of societal relevant quantities is well
     established.
   • We need a better understanding of the
     mechanisms of decadal variability
   • We need a suitable climate observing system
     (ocean, land surface, sea ice...)
   • We need „good“ models! We know from NWP
     that reduction of systematic bias helps. Biases
     in climate models are still large

WCC-3, Geneva, 31 Aug-4 Sep 2009
To realize the full decadal
     predictability potential we need
     a coordinated scientific
     programme under the auspices
     of the World Climate Research
     Programme (WCRP)

    Thank you for your attention
WCC-3, Geneva, 31 Aug-4 Sep 2009
Decadal variability in sea level
                            Topex/Poseidon 1993-2005




WCC-3, Geneva, 31 Aug-4 Sep 2009

Contenu connexe

Tendances

Recent trends of minimum and maximum surface temperatures over eastern africa
Recent trends of minimum and maximum surface temperatures over eastern africaRecent trends of minimum and maximum surface temperatures over eastern africa
Recent trends of minimum and maximum surface temperatures over eastern africa
cenafrica
 
Hydrological Modelling of Slope Stability
Hydrological Modelling of Slope StabilityHydrological Modelling of Slope Stability
Hydrological Modelling of Slope Stability
Grigoris Anagnostopoulos
 
1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt
grssieee
 

Tendances (20)

Soil and Water Engineering 04
Soil and Water Engineering 04Soil and Water Engineering 04
Soil and Water Engineering 04
 
DSD-INT 2017 Coupling 3D models and earth observation to develop algae foreca...
DSD-INT 2017 Coupling 3D models and earth observation to develop algae foreca...DSD-INT 2017 Coupling 3D models and earth observation to develop algae foreca...
DSD-INT 2017 Coupling 3D models and earth observation to develop algae foreca...
 
F43012934
F43012934F43012934
F43012934
 
Recent trends of minimum and maximum surface temperatures over eastern africa
Recent trends of minimum and maximum surface temperatures over eastern africaRecent trends of minimum and maximum surface temperatures over eastern africa
Recent trends of minimum and maximum surface temperatures over eastern africa
 
DSD-INT 2018 Characterizing the drivers of coral reef hydrodynamics at the Ro...
DSD-INT 2018 Characterizing the drivers of coral reef hydrodynamics at the Ro...DSD-INT 2018 Characterizing the drivers of coral reef hydrodynamics at the Ro...
DSD-INT 2018 Characterizing the drivers of coral reef hydrodynamics at the Ro...
 
Lisbon talk for SteepStreams
Lisbon talk  for SteepStreamsLisbon talk  for SteepStreams
Lisbon talk for SteepStreams
 
DSD-INT 2017 Delft3D FM - validation of hydrodynamics (1D,2D,3D) - van Dam
DSD-INT 2017 Delft3D FM - validation of hydrodynamics (1D,2D,3D) - van DamDSD-INT 2017 Delft3D FM - validation of hydrodynamics (1D,2D,3D) - van Dam
DSD-INT 2017 Delft3D FM - validation of hydrodynamics (1D,2D,3D) - van Dam
 
Ccpca mineart
Ccpca mineartCcpca mineart
Ccpca mineart
 
DSD-INT 2018 An Engineering Approach to construction of a Storm Surge Model f...
DSD-INT 2018 An Engineering Approach to construction of a Storm Surge Model f...DSD-INT 2018 An Engineering Approach to construction of a Storm Surge Model f...
DSD-INT 2018 An Engineering Approach to construction of a Storm Surge Model f...
 
DSD-INT 2017 Coastal morphology change predictions during Hurricane Ike in Ga...
DSD-INT 2017 Coastal morphology change predictions during Hurricane Ike in Ga...DSD-INT 2017 Coastal morphology change predictions during Hurricane Ike in Ga...
DSD-INT 2017 Coastal morphology change predictions during Hurricane Ike in Ga...
 
Simone Fatichi
Simone FatichiSimone Fatichi
Simone Fatichi
 
DSD-INT 2017 Beware: Bayesian Estimation Of Wave Attack In Reef Environments ...
DSD-INT 2017 Beware: Bayesian Estimation Of Wave Attack In Reef Environments ...DSD-INT 2017 Beware: Bayesian Estimation Of Wave Attack In Reef Environments ...
DSD-INT 2017 Beware: Bayesian Estimation Of Wave Attack In Reef Environments ...
 
DSD-INT 2017 Breaching of coastal defences under extreme storm surges - El S...
DSD-INT 2017 Breaching of coastal defences under extreme storm surges  - El S...DSD-INT 2017 Breaching of coastal defences under extreme storm surges  - El S...
DSD-INT 2017 Breaching of coastal defences under extreme storm surges - El S...
 
Hydrological Modelling of Slope Stability
Hydrological Modelling of Slope StabilityHydrological Modelling of Slope Stability
Hydrological Modelling of Slope Stability
 
DSD-INT 2017 Infragravity Period Oscillations In A Channel Harbor Near A Rive...
DSD-INT 2017 Infragravity Period Oscillations In A Channel Harbor Near A Rive...DSD-INT 2017 Infragravity Period Oscillations In A Channel Harbor Near A Rive...
DSD-INT 2017 Infragravity Period Oscillations In A Channel Harbor Near A Rive...
 
Incoming solar rediation imbalance
Incoming solar rediation imbalanceIncoming solar rediation imbalance
Incoming solar rediation imbalance
 
DSD-INT 2017 Moth plant dispersion Modelling based on synoptic weather patter...
DSD-INT 2017 Moth plant dispersion Modelling based on synoptic weather patter...DSD-INT 2017 Moth plant dispersion Modelling based on synoptic weather patter...
DSD-INT 2017 Moth plant dispersion Modelling based on synoptic weather patter...
 
DSD-INT 2017 A Metamodel To Estimate Run-Up Along Coral Reef-Lined Shorelines...
DSD-INT 2017 A Metamodel To Estimate Run-Up Along Coral Reef-Lined Shorelines...DSD-INT 2017 A Metamodel To Estimate Run-Up Along Coral Reef-Lined Shorelines...
DSD-INT 2017 A Metamodel To Estimate Run-Up Along Coral Reef-Lined Shorelines...
 
1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt
 
DSD-INT 2017 Keynote: Modelling Morphodynamic Impacts of Storm Clusters - Kar...
DSD-INT 2017 Keynote: Modelling Morphodynamic Impacts of Storm Clusters - Kar...DSD-INT 2017 Keynote: Modelling Morphodynamic Impacts of Storm Clusters - Kar...
DSD-INT 2017 Keynote: Modelling Morphodynamic Impacts of Storm Clusters - Kar...
 

En vedette

Nural network ER. Abhishek k. upadhyay
Nural network ER. Abhishek  k. upadhyayNural network ER. Abhishek  k. upadhyay
Nural network ER. Abhishek k. upadhyay
abhishek upadhyay
 
Neural networks for the prediction and forecasting of water resources variables
Neural networks for the prediction and forecasting of water resources variablesNeural networks for the prediction and forecasting of water resources variables
Neural networks for the prediction and forecasting of water resources variables
Jonathan D'Cruz
 
Artificial neural network
Artificial neural networkArtificial neural network
Artificial neural network
DEEPASHRI HK
 

En vedette (15)

Artificial Neural Networks for Storm Surge Prediction in North Carolina
Artificial Neural Networks for Storm Surge Prediction in North CarolinaArtificial Neural Networks for Storm Surge Prediction in North Carolina
Artificial Neural Networks for Storm Surge Prediction in North Carolina
 
Nural network ER. Abhishek k. upadhyay
Nural network ER. Abhishek  k. upadhyayNural network ER. Abhishek  k. upadhyay
Nural network ER. Abhishek k. upadhyay
 
Presentation: Wind Speed Prediction using Radial Basis Function Neural Network
Presentation: Wind Speed Prediction using Radial Basis Function Neural NetworkPresentation: Wind Speed Prediction using Radial Basis Function Neural Network
Presentation: Wind Speed Prediction using Radial Basis Function Neural Network
 
Stock market analysis using ga and neural network
Stock market analysis using ga and neural networkStock market analysis using ga and neural network
Stock market analysis using ga and neural network
 
Application of cgpann in solar irradiance
Application of cgpann in solar irradianceApplication of cgpann in solar irradiance
Application of cgpann in solar irradiance
 
Goswami Climate Change And Indian Monsoon Cse Workshop
Goswami  Climate Change And Indian Monsoon Cse WorkshopGoswami  Climate Change And Indian Monsoon Cse Workshop
Goswami Climate Change And Indian Monsoon Cse Workshop
 
DisEMBL - Artificial neural network prediction of protein disorder
DisEMBL - Artificial neural network prediction of protein disorderDisEMBL - Artificial neural network prediction of protein disorder
DisEMBL - Artificial neural network prediction of protein disorder
 
NS2 3.5 Weather Forecasting
NS2 3.5 Weather ForecastingNS2 3.5 Weather Forecasting
NS2 3.5 Weather Forecasting
 
Neural networks for the prediction and forecasting of water resources variables
Neural networks for the prediction and forecasting of water resources variablesNeural networks for the prediction and forecasting of water resources variables
Neural networks for the prediction and forecasting of water resources variables
 
Neural
NeuralNeural
Neural
 
Neural Networks
Neural NetworksNeural Networks
Neural Networks
 
Artificial intelligence NEURAL NETWORKS
Artificial intelligence NEURAL NETWORKSArtificial intelligence NEURAL NETWORKS
Artificial intelligence NEURAL NETWORKS
 
Neural network & its applications
Neural network & its applications Neural network & its applications
Neural network & its applications
 
Artificial neural network
Artificial neural networkArtificial neural network
Artificial neural network
 
Data Science - Part VIII - Artifical Neural Network
Data Science - Part VIII -  Artifical Neural NetworkData Science - Part VIII -  Artifical Neural Network
Data Science - Part VIII - Artifical Neural Network
 

Similaire à Advancing Climate Prediction Science – Decadal Prediction

1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt
grssieee
 
Climate change and internal variability in Europe
Climate change and internal variability in EuropeClimate change and internal variability in Europe
Climate change and internal variability in Europe
ipcc-media
 
Mercator Ocean newsletter 43
Mercator Ocean newsletter 43Mercator Ocean newsletter 43
Mercator Ocean newsletter 43
Mercator Ocean International
 
World new climático
World new climáticoWorld new climático
World new climático
Jaume Satorra
 

Similaire à Advancing Climate Prediction Science – Decadal Prediction (20)

Global observation session
Global observation sessionGlobal observation session
Global observation session
 
ITU_JTF_Madrid 2013_INTRO_ C BARNES_V2.ppt
ITU_JTF_Madrid 2013_INTRO_ C BARNES_V2.pptITU_JTF_Madrid 2013_INTRO_ C BARNES_V2.ppt
ITU_JTF_Madrid 2013_INTRO_ C BARNES_V2.ppt
 
1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt
 
Goos09talk
Goos09talkGoos09talk
Goos09talk
 
Climate change and internal variability in Europe
Climate change and internal variability in EuropeClimate change and internal variability in Europe
Climate change and internal variability in Europe
 
Geophysical Challenge in Oil and Gas Project
Geophysical Challenge in Oil and Gas ProjectGeophysical Challenge in Oil and Gas Project
Geophysical Challenge in Oil and Gas Project
 
Klimaatsverandering oor Suid-Afrika van die volgende paar dekades tot die vol...
Klimaatsverandering oor Suid-Afrika van die volgende paar dekades tot die vol...Klimaatsverandering oor Suid-Afrika van die volgende paar dekades tot die vol...
Klimaatsverandering oor Suid-Afrika van die volgende paar dekades tot die vol...
 
Research Plan
Research PlanResearch Plan
Research Plan
 
General circulation model
General circulation modelGeneral circulation model
General circulation model
 
From IPCC WGI AR5 to AR6
From IPCC WGI AR5 to AR6From IPCC WGI AR5 to AR6
From IPCC WGI AR5 to AR6
 
Climate change prediction
Climate change predictionClimate change prediction
Climate change prediction
 
How do we study recent climate change? Development of global temperature data...
How do we study recent climate change? Development of global temperature data...How do we study recent climate change? Development of global temperature data...
How do we study recent climate change? Development of global temperature data...
 
Mercator Ocean newsletter 43
Mercator Ocean newsletter 43Mercator Ocean newsletter 43
Mercator Ocean newsletter 43
 
CLIMATE CHANGE AND GLOBAL WATER RESOURCES
CLIMATE CHANGE AND GLOBAL WATER RESOURCESCLIMATE CHANGE AND GLOBAL WATER RESOURCES
CLIMATE CHANGE AND GLOBAL WATER RESOURCES
 
Burntwood 2013 - Why climate models are the greatest feat of modern science, ...
Burntwood 2013 - Why climate models are the greatest feat of modern science, ...Burntwood 2013 - Why climate models are the greatest feat of modern science, ...
Burntwood 2013 - Why climate models are the greatest feat of modern science, ...
 
Projections of Future Tropical Cyclone Activity
Projections of Future Tropical Cyclone ActivityProjections of Future Tropical Cyclone Activity
Projections of Future Tropical Cyclone Activity
 
Climate science part 3 - climate models and predicted climate change
Climate science part 3 - climate models and predicted climate changeClimate science part 3 - climate models and predicted climate change
Climate science part 3 - climate models and predicted climate change
 
Hurricanes and Global Warming- Dr. Kerry Emanuel
Hurricanes and Global Warming- Dr. Kerry EmanuelHurricanes and Global Warming- Dr. Kerry Emanuel
Hurricanes and Global Warming- Dr. Kerry Emanuel
 
Tim Palmer, University of Oxford - OECD Workshop on “Climate change, Assumpti...
Tim Palmer, University of Oxford - OECD Workshop on “Climate change, Assumpti...Tim Palmer, University of Oxford - OECD Workshop on “Climate change, Assumpti...
Tim Palmer, University of Oxford - OECD Workshop on “Climate change, Assumpti...
 
World new climático
World new climáticoWorld new climático
World new climático
 

Dernier

Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
 

Dernier (20)

[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..Understanding the FAA Part 107 License ..
Understanding the FAA Part 107 License ..
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 

Advancing Climate Prediction Science – Decadal Prediction

  • 1. Advancing Climate Prediction Science – Decadal Prediction Mojib Latif Leibniz Institute of Marine Sciences, Kiel University, Germany WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 2. Outline • Why decadal prediction • Mechanisms of decadal variability • What is the decadal predictability potential • Challenges WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 3. “Climate surprises” cooling WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 4. Decadal variations in Sahel rainfall ? WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 5. Decadal variations in Atlantic hurricane activity WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 6. Decadal variability in sea level Linear trend 1993-2003 Kwajalein (8°44’N, 167°44’E) WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 7. Global change prediction is a joint initial/boundary value problem IPCC 2007 Projections were not initialized in IPCC-AR4 WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 8. The uncertainty in climate projections for the 21st century internal variability scenario unpredictable external influences model bias Hawkins and Sutton 2009 WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 9. Outline • Why decadal prediction • Mechanisms of decadal variability • What is the decadal predictability potential • Challenges WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 10. Internal vs. external influences How much did internal decadal variability contribute to the warming during the recent decades? WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 11. Decadal variations in the North Atlantic Oscillation How much of the decadal NAO variability is forced by changes in the boundary conditions? WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 12. Decadal North Atlantic sea surface temperature variations North Atlantic SST o o (70 W - 0, 0 - 60 N) Changes in hurricane activity and Sahel rain, for instance, can be traced back to variations in Atlantic sea surface temperature (SST) WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 13. Outline • Why decadal prediction • Mechanisms of decadal variability • What is the decadal predictability potential • Challenges WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 14. Potential predictability of surface air temperature (SAT) Derived from control integrations with climate models North Atlantic SST Boer 2004 Knight et al. 2005 The North Atlantic Sector appears to be one of the promising regions WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 15. Predictability of the Meridional Overturning Circulation (MOC) Hurrell et al. 2009 The MOC is predicable at a lead of one to two decades in perfect model studies WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 16. Unpredictable external influences Solar constant 1976-2008 ? Strong volcanic eruptions, for instance, can cause global cooling of about 0,2°C for a few years and persist even longer in the ocean heat content. If they happen, we can exploit their long-lasting climatic effects. WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 17. Large spread for the next decade observations 3 climate model hind/forecasts „MOC“ Hurrell et al. 2009 WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 18. Outline • Why decadal prediction • Mechanisms of decadal variability • What is the decadal predictability potential • Challenges WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 19. Climate observing system Example: ocean observing system Multi-national Argo fleet We need climate observations to initialize the models to forecast variations up to decadal time scales WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 20. Model biases are large Typical bias in surface air temperature (SAT) ensemble mean error IPCC 2007 Errors of several degrees C in some regions WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 21. Gulfstream SST front Represention of small-scale processes WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 22. Resolution matters Minobe et al. 2008 The AGCM has T239 horizontal resolution (~50 km) and 48 levels Compared to the smoothed SST run, rain-bearing low pressure systems tend to develop along the Gulf Stream front in the control simulation WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 23. Where are we today? • A decadal predictability potential for a number of societal relevant quantities is well established. • We need a better understanding of the mechanisms of decadal variability • We need a suitable climate observing system (ocean, land surface, sea ice...) • We need „good“ models! We know from NWP that reduction of systematic bias helps. Biases in climate models are still large WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 24. To realize the full decadal predictability potential we need a coordinated scientific programme under the auspices of the World Climate Research Programme (WCRP) Thank you for your attention WCC-3, Geneva, 31 Aug-4 Sep 2009
  • 25. Decadal variability in sea level Topex/Poseidon 1993-2005 WCC-3, Geneva, 31 Aug-4 Sep 2009