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WP4.1: Feedbacks and climate surprises ( IPSL, HC, LGGE, CNRM, UCL, NERSC)

WP4.1: Feedbacks and climate surprises ( IPSL, HC, LGGE, CNRM, UCL, NERSC). WP4.1 has two main objectives (a) to quantify the role of different feedbacks in the Earth system on the climate predictions uncertainty, (b) to investigate the risk of abrupt climate changes associated to THC.

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WP4.1: Feedbacks and climate surprises ( IPSL, HC, LGGE, CNRM, UCL, NERSC)

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  1. WP4.1: Feedbacks and climate surprises(IPSL, HC, LGGE, CNRM, UCL, NERSC) • WP4.1 has two main objectives • (a) to quantify the role of different feedbacks in the Earth system on the climate predictions uncertainty, • (b) to investigate the risk of abrupt climate changes associated to THC.

  2. Tasks • Task 4.1.a: Analysis and evaluation of the physical processes involved in the water vapour and cloud feedbacks in the Tropics • Task 4.1.b: Quantification of the climate-carbon cycle feedback, with a specific focus on terrestrial carbon cycle sensitivity to climate change • Task 4.1.c: Explore the effects of non-linear feedbacks in the atmosphere-land-ocean-cryosphere system and the risks of abrupt climate change/climate surprises

  3. Feedbacks CFMIP (HC and IPSL) C4MIP (HC and IPSL) Climate surprises CNRM IPSL LGGE NERSC UCL Partners involved

  4. Deliverables • D4.1.1: Characterisation of the water vapour and cloud feedbacks in response to anthropogenic forcing. (Month 18) Hadley Centre and IPSL • D4.1.2: Analysis of the results from the first phase of the Coupled Climate Carbon Cycle Intercomparison project (C4MIP). (Month 18) Hadley Centre and IPSL

  5. Milestones and expected result • M4.1.1: Development of methodologies to explore climate feedbacks, tested initially on existing simulations, for use with the ENSEMBLES multi-model system (Month 12) Hadley Centre and IPSL • M4.1.2: Assessment of feedbacks in existing simulations to provide benchmark against which the new ENSEMBLES multi-model system can be judged (Month 18) Hadley Centre and IPSL

  6. Analysis of cloud feedbacks: IPSL work (1) The range of response in the CMIP runs is still very large

  7. Analysis of cloud feedbacks: IPSL work (2) Cloud radiative properties are mainly driven by: - large-scale circulation • local thermodynamic structure (e.g. Pierrehumbert 1995, Miller 1997, Larson et al. 1999, Lindzen et al. 2001) Analysis of cloud properties as a function of circulation regimes allows to separate the cloud variations du to circulation changes from the variations du to thermodynamic or other changes. We will characterise, for the ENSEMLES models, as a function of circulation regimes: • mean cloud properties in current climate • cloud properties sensitivity in recent climate (inter-annual variability) • cloud properties sensitivity with anthropogenic forcings

  8. CO2: + 1% / year X : Clouds, CRF … XCO2 -XCTL « current climate » Relationship between X and Temperature : Analysis of cloud feedbacks: IPSL work (3) Goal: to relate the cloud sensitivity at inter-annual time scale to climate change response XCTL(t)-XCTL

  9. Analysis of cloud feedbacks: Hadley work (C. Senior) CFMIP Climate Sensitivity dT2CO2 • The range in CFMIP slab model responses has not reduced since the TAR. • Cloud feedback is still the biggest uncertainty, but clear-sky feedbacks still make a significant contribution. • The magnitude of the positive SW cloud feedback is the biggest feedback uncertainty. Further analysis shows this is mainly driven by changes in lower level cloud (2.4-6.1K) CFMIP CRF responseLWSWNet (Wm-2K-1) CFMIP Clear responseLWSWNet (Wm-2K-1)

  10. Analysis of feedbacks in a multi-model ensemblePossible future Hadley Centre Work (C. Senior) • We will continue to analyse the CFMIP models focussing initially on; • Developing cleaner feedback separation methods (e.g. approximate partial radiative perturbation (PRP) method equivalent to Wetherald & Manabe 1988) • Use of a range of diagnostic techniques aimed at demonstrating a relationship of cloud response to climate change with cloud response to present day variability, e.g. cloud clustering. We aim to identify the cloud types primarily responsible for the different cloud feedback between models.

  11. Climate-carbon feedbackC4MIP • Existing simulations from IPSL and HC • New simulations with common protocol • Feedback analysis • Climate sensitivity • Carbon cycle sensitivity to climate • Carbon cycle sensitivity to CO2 • Gain of the C-C feedback

  12. Climate-carbon feedbackC4MIP Hadley IPSL Methodology: feedback analysis (Friedlingstein et al., 2003) g =  ( L + O ) / ( 1 + L + O )

  13. Hadley Centre: Strong, positive carbon cycle feedbacks • Our coupled Climate-Carbon cycle model simulates strong, positive feedbacks over 21st Century • When climate-carbon cycle feedbacks are included (red line) we see much higher rates of CO2 increase and climate change. • Extra C comes from terrestrial biosphere • increased soil respiration (T) greater than increased growth (CO2) • Soil respiration is a key uncertainty in the size of the carbon feedback • Perform off-line simulations with different soil carbon model • “RothC” – 4-pool soil carbon model • Forced with climate data from coupled model simulations • Compare with original (single pool) soil carbon model also run off-line

  14. Modelled change in soil C (kg C m-2) between 1860 and 2100 Assumes constant land use. HadCM3 climate model with land C feedbacks included IS92a “Business as usual scenario” HadCM3LC for soil RothC for soil Jones et al. GCB (2004)

  15. Changes in global soil carbon amount (GtC) predicted by RothC and HADCM3LC RothC HadCM3

  16. Climate surprises • CNRM • IPSL • LGGE • NERSC • UCL

  17. The role of salinity in climate response to GHG forcing Eric Guilyardi, Pascale Braconnot, Didier Swingedouw (LSCE/IPSL) Paul Williams (CGAM) • Questions: • How can salinity modify the ocean response to GHG forcing ? • Why does the water cycle in coupled GCMs display such a variety of reponses (i.e. THC in IPCC AR3) ? • Method: • Understand present-day salinity structure in CGCMs (mechanisms, feedback loops, time and space scales) • Identify processes likely to be perturbed by GHGF • Analyse scenario runs to assess relative importance of previously identified processes.

  18. Impact of a globally modified fresh water flux in the IPSL coupled GCM R0 THC index EPR0 CTL EP0 100 years Swingedouw et al., in preparation

  19. CNRM: Feedbacks and climate surprises • Region of interest: the Arctic, data: IPCC • From the simulations (CNRM): • Few « ice surprises »… except a large positive sea ice anomaly for ~15 years in the preindustrial experiment • Increasing river discharge into the Arctic basin during the 21st century • Questions: • Triggering / Mechanism of the large ice anomaly ? Relation with THC ? Atmospheric circulation ? • Role of increasing Arctic river discharge on surface ocean and sea ice in transient climate change experiments ? • Experiments: • Try to reproduce a large sea ice anomaly after identifying the underlying mechanism (validation of suggested mech.) • Sensitivity experiments involving modified river runoff (to be defined)

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