1 / 18

P.L. Vidale*, D. Lawrence, J. Slingo, M. Roberts, J. Donners, M.E. Demory

Land-atmosphere coupling strength in a GCM: the role of resolution and of boundary layer processes. P.L. Vidale*, D. Lawrence, J. Slingo, M. Roberts, J. Donners, M.E. Demory * NCAS Centre for Global Atmospheric Modeling (CGAM), Univ. of Reading, UK.

malo
Télécharger la présentation

P.L. Vidale*, D. Lawrence, J. Slingo, M. Roberts, J. Donners, M.E. Demory

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Land-atmosphere coupling strength in a GCM: the role of resolution and of boundary layer processes. P.L. Vidale*, D. Lawrence, J. Slingo, M. Roberts, J. Donners, M.E. Demory *NCAS Centre for Global Atmospheric Modeling (CGAM), Univ. of Reading, UK

  2. “HadGEM/HiGEM-ESM”– The Future:A fully interactive Earth System Model Online External Forcing plus other B.C.s CLIMATE Offline Macro and micro physics; Direct & Indirect Effects Greenhouse Effect Human Emissions AEROSOLS GHG’s CH4, O3, N2O, CFC Fires: soot Mineral dust Oxidants: OH, H2O2 HO2,O3 Human Emissions CO2 N deposition 03, UV radiation CHEMISTRY ECOSYSTEMS Biogenic Emissions: CH4,DMS,VOC’s Dry deposition: stomatal conductance Land-use changes Emissions (After Peter Cox) Source: Tim Johns, Hadley Centre

  3. GCM configurations for centennial time scales State-of-the-art simulations contributed by the UK to IPCC (AR4) are limited to N96 (~135 km), as compared with N48 (~270 km) during the IPCC TAR; We (CGAM) believe that we need to resolve weather, in order to study the climate system If we want to resolve weather over such time scales, we need to radically increase resolution 135 km ATM 270 km ATM 135 km ATM 270 km ATM 2.5ox1.25o O 1o-1/3o O 1/3o O 1/3o O 90 km ATM 60 km ATM 90 km ATM 60 km ATM

  4. Increasing complexity and resolution at the same time is extremely expensive. Climate Modelling and Prediction requires huge supercomputers

  5. HadGEM 1 First results from ESC • 5-10 years of coupled simulations, using a chain of HC models with increasing complexity and resolution; • Impacts on well-known Pacific cold bias and precipitation in the warm pool; • HiGEM can reproduce ENSO, which is virtually absent in HadGEM1; • Some work still ahead with sea-ice and land surface biases; • Preliminary work of this kind is needed to confirm portability/reproducibility and to identify the best model for ESM building. HadGEM 1a HiGEM 1

  6. Land-atmosphere coupling strength diagnostic W(rite) - 16-member ensemble forced with June 1 initial conditions from each year of a 16-year climatological SST control run. Soil moisture from W1 experiment recorded. R(ead) - 16-member ensemble where, at every timestep, simulated soil moisture is discarded and replaced with values from W1 experiment. Ω = measure of time series similarity between ensemble members W ― SST R ― SST and Soil moisture ΩP(W) = 0.07 ΩP(R)= 0.85

  7. Global Land-Atmosphere Coupling Experiment ( ) Impact of all land surface prognostic variables

  8. Land-atmosphere coupling strength (HadAM3-MOSES2)

  9. HadAM3 also has a problem with precipitation frequency

  10. HC GCMs also have considerable summer biases…

  11. Summary of comparative studies for HadAM3-HadGEM1-MOSES2 land surface coupling strength • For precipitation, extremely low level of surface-atmosphere feedback, -as compared to other GCMs-, also recently confirmed by comparison to observations in Dirmeyer et al.; • Koster et al. have shown that soil moisture variability can control surface temperature variability in HadAM3; • ET shows a moderate level of coupling (and [ΩE(S)-ΩE(W)]*σE(W) is in the high range, so soil moisture can affect evaporation); • However, in Lawrence and Slingo (2005) process study: • excluded soil moisture limitations in P coupling, pointed to low SM variability (also in Vidale et al., Climatic Change, 2005); • pointed to the wrong phase of ML diurnal cycle and its unrealistic development; • and too frequent/too weak precipitation too early in the day; • Are the mechanisms near the surface, in the BL or above ? • Are model biases coincident with regions where coupling strength should be larger ?

  12. SL diurnal evolution at FIFE site (JA): HadAM3 Criteria for separation of dry and wet days as in observational work of BB95 From Lawrence and Slingo 2005: Model does not seem to be soil moisture limited; Model seems to abort ML growth soon after local noon; MSE buildup interrupted, with consequences for buildup of convection. Question: can a model with more resolution -especially in the vertical-, and more realistic vertical mixing, do a better job ?

  13. SL diurnal evolution at FIFE site (JA): HadGEM1 • 2x resolution in horizontal and vertical (even more in BL); • HadGEM1 uses a non-local turbulent scheme + BL profile classes; also allows interplay of convective mass flux parameterization and BL scheme; • However, diurnal cycle very similar to that seen in HadAM3 by Lawrence+Slingo: • Early ML development; • MSE buildup ceases soon after local noon • Question: could vegetation be chronically heat-stressed, leading to a positive (dry+hot) feedback and periods of drought ? Dry days Wet days

  14. Uncertainty in the definition of optimal environmental temperature for physiology and biophysics Sellers et al. 1997 • Models exhibit huge range (about 18K), both linked to vegetation classes and to model formulation; • Shape of curves is also very variable; • Previous experiments with CHRM (PL Vidale, Europe) and with HadAM3 (C. Taylor, Sahel) indicated strong sensitivity; • Is positive feedback really so easy to trigger at these GCM scales? MOSES 2 C4 plants C3 plants

  15. Betts and Ball 1995 Latent heat fluxes and BL heights in two HiGEM experiments: mean JA diurnal cycles 6 12 BL height Latent heat flux • CTL experiment (top row) has often too warm conditions for vegetation activity during the peak of solar heating; • VEG experiment can develop the moister and shallower BL needed to the indirect SM-precipitation feedback Local noon

  16. SL moisture and heat fluxes in the two HiGEM experiments: mean JA diurnal cycles 2m specific humidity Sensible heat flux • CTL experiment (top) dries up SL during early morning; SH fluxes always high; • VEG experiment can develop the moister and lower Bowen ratios seen in obs. Betts and Ball 1995 6 12 Local noon

  17. SL diurnal evolution at FIFE site (JA): HiGEM Dry days Experiment EACRA (right) has heat-resilient vegetation, so that LH flux does not shut down so easily at noon. Dry days Wet days Wet days

  18. Summary • Investigation of BL processes related to land surface-atmosphere feeback in HadGEM1-HiGEM showed a model behavior very similar to that observed within HadAM3; • Despite new BL resolution+formulation HadGEM1/HiGEM produce a mean diurnal cycle similar to that in HadAM3; • Modeled vegetation is not soil moisture limited over FIFE, but it could be heat stressed: • Tests with heat-resilient vegetation indicate that variability of summer BL development/growth could be improved; • Conditions for direct and indirect (shallower+moister ML) soil moisture-precipitation feedbacks could be met if positive dry+hot feedback is avoided (stomatal suicide); • However, it may well be that convective mass flux is activated too early and “steals” BL air, interrupting MSE buildup and creating shallow convection; • Need to repeat “coupling strength” ensemble experiments and sensitivity studies with new AGCM, so as to have better sample, and to expand analysis to other sites.

More Related