research needs for decadal to centennial climate prediction: from observations to modelling julia...
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![Page 1: Research Needs for Decadal to Centennial Climate Prediction: From observations to modelling Julia Slingo, Met Office, Exeter, UK & V. Ramaswamy. GFDL,](https://reader036.vdocuments.mx/reader036/viewer/2022070402/56649f225503460f94c3ad01/html5/thumbnails/1.jpg)
Research Needs for Decadal to Centennial Climate Prediction: From observations to modelling
Julia Slingo, Met Office, Exeter, UK
&
V. Ramaswamy. GFDL, Princeton, USA
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Climate Change Projections and Uncertainties
IPCC AR4
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931
2535
Natural Variability Carbon Cycle
Downscaling Model Uncertainty
15
20
43
22
2020’s 2080’sWinter rainfall in south east
EnglandImproved model physics e.g.
clouds
Increased understanding of earth
system processes – more uncertainty?
Benefits of initialisation for near-
term projections
Quantifying uncertainties – setting research priorities
Higher resolution global
models
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Challenges for Centennial Projections:
Earth System Modelling
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Land physicsand hydrologyOcean circulation
Atmospheric circulation and radiation
Land physicsand hydrology
Ocean ecology andbiogeochemistry
Atmospheric circulation and radiationAllows interactive CO2
Ocean circulation
Plant ecology andland use
Climate Model
Earth System Model
Sea Ice
Sea Ice
Moving from Climate to Earth System Models: Balancing the carbon cycle
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Carbon-climate feedback and centennial climate change
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More Earth System Modelling challenges
• How can we reduce the uncertainties in current estimates of the carbon-climate feedback?
• How do missing or poorly represented processes such as the nitrogen cycle, plant adaptation to climate change, vegetation dynamics, and plankton dynamics affect current model results?
• What other biogeochemical feedbacks involving methane, ozone and aerosols play a significant role on the centennial timescale?
• How can Earth System Modelling inform decision-making when climate change is one of many drivers for environmental change (e.g. food security, water resources and quality, biodiversity, air quality)?
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Earth System Modelling:Combining the needs of adaptation and mitigation
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931
2535
Natural Variability Carbon Cycle
Downscaling Model Uncertainty
15
20
43
22
2020’s 2080’sWinter rainfall in south east
EnglandImproved model physics e.g.
clouds
Increased understanding of earth
system processes – more uncertainty?
Benefits of initialisation for near-
term projections
Quantifying uncertainties – setting research priorities
Higher resolution global
models
![Page 10: Research Needs for Decadal to Centennial Climate Prediction: From observations to modelling Julia Slingo, Met Office, Exeter, UK & V. Ramaswamy. GFDL,](https://reader036.vdocuments.mx/reader036/viewer/2022070402/56649f225503460f94c3ad01/html5/thumbnails/10.jpg)
Challenges for Decadal Prediction:
Initialisation and Evaluation
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Decadal predictions of global annual mean surface temperature
Observations
Forecast/hindcast
Forecast from 2008
Forecast from 2009
Smith et al., 2007
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Impact of initialisation on hindcast skill5 year mean (JJASON) surface temp
15x15 degrees
DePreSys-NoAssim correlationDePreSys anomaly correlation
• HadCM3
• 9 member perturbed physics ensemble
• Starting every Nov from 1960 to 2005
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Improved predictions of multi-year Atlantic hurricane frequencies
Nor
mal
ised
ano
mal
y
DePreSys NoAssim
5-year mean JJASON number of model storms (coloured) and observed hurricanes (black)
Skill comes from SSTs in tropical Pacific and N. Atlantic sub-polar gyres, and from wind shear in hurricane development
regions
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Sub-surface ocean observations: A limiting factor in estimating skill and predictability
19801960 2007
• Need historical tests to assess likely skill of forecasts
• Far fewer sub-surface ocean observations in the past
Doug Smith, Met Office Hadley Centre
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Temperature at 300m : June 2007 from 1960 observational base
Analysis using all obs Analysis using sub-sampled (1960) obs
June 2007 obs June 1960 obs
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Variability versus Anthropogenic Forcing of
the Physical Climate System
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20 centuries of NINO3 SSTsannual means & 20yr low-pass
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Major uncertainty in Chemistry-Climate
Interactions
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LandOceanSea Ice
Mixed-Layer
Deep Ocean
SST
Surface Flux
Clear Sky Cloudy Sky
Aerosols DropletsActivation
SW Radiation
LW RadiationEvaporation Precipitation
Atmosphere
Coupled Chemistry-Aerosol-Climate model
Aerosols and Climate
Global Air Quality and Climate
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Aerosol-Cloud Interactions in GFDL’s Newest Physical Climate Model (CM3)
20
CM3 CM2.1
Direct effects – Sulfate and organic carbon
~0
(assuming internal mixing of sulfate and black carbon)
-1.3
(external mixing)
Direct effects - Black carbon
0.5
(external mixing)
Indirect effects -1.3 Not included
Radiative Flux Perturbation w/m2
Comparison of Simulated Aerosol Properties with Observations
Observations (AERONET)
Observations (AERONET)
MODEL
MODEL
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Capturing High-Resolution Phenomena
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Atlantic Hurricanes in a Warming World
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Most recent GFDL downscaling study (Bender et al, Science, 2010) see https://www.gfdl.noaa.gov/21st-century-projections-of-intense-hurricanes
Uses two downscaling steps: Global CMIP3 models => regional model of Atlantic hurricane season regional model => operational GFDL hurricane prediction system
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Conclusion: Best estimate is for doubling of cat 4-5 storms in Atlantic by end of century, despite decrease in total number of tropical cyclonesMuch of the uncertainty arises from global model input
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Conclusions I
• Emerging need for centennial and decadal projections. They pose similar and differing challenges.
• Earth system processes potentially increase uncertainty in centennial projections, especially in the upper range of warming.
• Initialising decadal projections can reduce uncertainty at least for a few years ahead.
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Conclusions II
• Observations of the sub-surface ocean and the full earth system may limit our ability to provide more confident projections.
• Natural variability in the context of forced change is challenging.
• High resolution modelling is opening up new avenues for more detailed projections of regional climate change and high impact phenomena.