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7 oktober 2009 Challenge the future Delft University of Technology ‘Simulation and Modeling Hierarchies of our Climate System’ A. Pier Siebesma KNMI &

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Presentation on theme: "7 oktober 2009 Challenge the future Delft University of Technology ‘Simulation and Modeling Hierarchies of our Climate System’ A. Pier Siebesma KNMI &"— Presentation transcript:

1 7 oktober 2009 Challenge the future Delft University of Technology ‘Simulation and Modeling Hierarchies of our Climate System’ A. Pier Siebesma KNMI & TU Delft The Netherlands

2 2 Simulation and modeling hierarchies Uncertainties in Future Climate model Predictions °C IPCC 2007 PastFuture Present 1900

3 3 Verstoorde wolken in een opwarmend klimaat Earth’s Global Energy Balance 342 W/m W/m W/m 2 Temperature Incoming solar radiation Reflected solar radiation Outgoing long wave radiation

4 4 Verstoorde wolken in een opwarmend klimaat Increase of Greenhouse Gases……. 342 W/m W/m 2 ….increase of temperature Incoming solar radiation Reflected solar radiation …..decrease in outgoing long wave radiation

5 5 Verstoorde wolken in een opwarmend klimaat ……restored new Equilibrium 342 W/m W/m W/m 2 Higher equilibrium temperature Incoming solar radiation Reflected solar radiation Outgoing long wave radiation

6 6 Verstoorde wolken in een opwarmend klimaat If climate would be static………… Top of the atmosphere :Planetary Albedo R: net radiation at the TOA External Perturbation Zero feedback gain

7 7 Verstoorde wolken in een opwarmend klimaat If climate would be static………… Top of the atmosphere :Planetary Albedo R: net radiation at the TOA External Perturbation Zero feedback gain Planck Parameter Forcing for 2XCO2 Direct Warming

8 8 Verstoorde wolken in een opwarmend klimaat Dynamical Climate Model Feedbacks perturbation zero feedback gain feedback processes

9 9 Verstoorde wolken in een opwarmend klimaat Dufresne & Bony, Journal of Climate 2008 Radiative effects only Water vapor feedback Surface albedo feedback Cloud feedback Cloud effects “remain the largest source of uncertainty” in model based estimates of climate sensitivity IPCC XCO 2 Scenario for 12 Climate Models

10 10 Verstoorde wolken in een opwarmend klimaat Primarily due to marine low clouds “Marine boundary layer clouds are at the heart of tropical cloud feedback uncertainties in climate models” (duFresne&Bony 2005 GRL) Stratocumulus Shallow cumulus

11 11 Verstoorde wolken in een opwarmend klimaat 1. How did I get here? ~1    m - 1m ~10 7 m~10 5 m ~10 3 m The planetary scale Cloud cluster scale Cloud scale Cloud microphysical scale

12 10 m100 m1 km10 km100 km1000 km10000 km turbulence  Cumulus clouds Cumulonimbus clouds Mesoscale Convective systems Extratropical Cyclones Planetary waves Large Eddy Simulation (LES) Model Cloud System Resolving Model (CSRM) Numerical Weather Prediction (NWP) Model Global Climate Model No single model can encompass all relevant processes DNS mm Cloud microphysics

13 Resolved Scales turbulence ~ 100 km convection clouds radiation Small scalesLarge scales Schematic View of how scales are connected in traditional GCM’s Depiction of the interaction between resolved and parameterized unresolved cloud-related processes (convection, turbulence, clouds and radiation) in present-day climate models. (from Siebesma et al, Perturbed Clouds in our climate system MIT) Which are the problems, errors and uncertainties that we have to face with this approach?

14 1. Inherent lack of understanding of certain physical processes < 100  m [100,600  m] >800  m Source : Andrew Heymsfield Uncertainties in ice and mixed phase microphysics: Supersaturation Liquid vs ice Habits Size distribution Sedimentation Interaction with radiation No fundamental equations available describing these properties and processes.

15 With: q l : cloud liquid water q l : critical threshold H : Heaviside function A : Autoconversion rate : Kessler Autoconversion Rate (Kessler 1969) Example 1: Autoconversion of cloud water to precipitation in warm clouds Autoconversion rate is a convex function: Larson et al. JAS 2001 q l =1 g/kg q l =0 GCM grid box Subgrid variability of liquid water needs to be known in order to estimate the autoconversion………. 2. Non-linear character of many cloud related processes

16 Example 2: Cloud fraction and Cloud liquid water q sat qtqt T q sat(T) qtqt. (T,q t ) Cloud fraction: Cloud liquid water: Subgrid variability of temperature and humidity needs to be known in order to estimate the grid box cloud fraction a c and liquid water content q l

17 Plane parallel cloud Scu Cloud albedo Liquid water path (LWP) x x   (LWP) <  (LWP) Neglecting Cloud inhomogeneity causes a positive bias in the cloud albedo. Example 3: Cloud Albedo Bias

18 These biased errors slowly go away when increasing model resolution: Typically allowed if x < 100m So for all models operating at a coarser resolution additional information about the underlying Probability Density Function (pdf) is required of temperature, humidity (and vertical velocity). For Example: So if only….. we would know the pdf.

19 Resolved Scales turbulence ~ 250 km convection clouds radiation Small scalesLarge scales 3. Interactions between the various subgrid processes Subgrid processes strongly interact with each other while in (most) GCM’s they only interact indirectly through the mean state leading to inconsistencies and biases.

20 4. Statistical versus Stochastic Convection ~500km Traditionally (convection) parameterizations are deterministic: Instantaneous grid-scale flow and mean state is taken as input and convective response is deterministic One to one correspondency between sub-grid state and resolved state assumed. Conceptually assumes that spatial average is a good proxy for the ensemble mean. e.g. subgrid cloud fraction :

21 4. Statistical versus Stochastic Convection resolution 100m 1km 1000km 100km Convection Explicitly resolved Statistical ensemble mean Deterministic convection parameterization Stochastic Convection That takes into account fluctuations so that the ensemble mean is not satisfied each timestep but more in a canonical sense Microcanonical limit

22 New Pathways

23 Resolved Scales 3.5 km turbulence convection clouds radiation Pathway 1: Global Cloud Resolving Modelling (Brute Force) NICAM simulation: MJO DEC2006 Experiment MTSAT-1R NICAM 3.5km Miura et al. (2007, Science) 3.5km run: 7 days from 25 Dec 2006 Short timeslices Testbed for interactions: deep convection and the large scale Boundary clouds, turbulence, radiation still unresolved

24 Pathway 2: Superparameterization 2D CRM turbulence (b) convection clouds radiation 5 km 250 km Resolved Scales

25 Pathway 2: Superparameterization What do we get? Explicit deep convection Explicit fractional cloudiness Explicit cloud overlap and possible 3d cloud effects Convectively generated gravity waves But….. A GCM using a super-parameterization is 2 to 3 orders of magnitude more expensive than a GCM that uses conventional parameterizations. On the other hand super-parameterizations provide a way to utilize more processors for a given GCM resolution Boundary Layer Clouds, Microphysics and Turbulence still needs to be parameterized

26 Remarks: Resolved Scales turbulence convection clouds radiation ~100 km Large scales Unresolved scales Resolved Scales Pathway 3: Consistent based parameterizations Increase consistency between the parameterizations! How? Topic of the coming week.

27 Single Column Models Climate Models NWP Models Direct Numerical Simulation Large Eddy Simulation Use the full range of observations and simulation hierarchy.... GEWEX Cloud Systems Study (GCSS) Strategy. Field Campaigns Instrumented Sites Global Observational Data sets

28 Scale Hierarchy High Low Direct Numerical Simulati on Large Eddy Simulations Global Climate Simulations Laboratory experiments Atmospheric Profiling stations Field campaigns Satellite data Observations resolution

29 ll qtqt zizi w e  l,i w e q t,i Vq t,0 V l,0 Minimal Mixed layer Model Plus closure: Example: Bulk Model (“parameterization”) of Scu topped PBL

30 Scale Hierarchy High Low low high Direct Numerical Simulati on Level of “understanding ” or conceptualisation Large Eddy Simulations Global Climate Simulations Laboratory experiments Atmospheric Profiling stations Field campaigns Satellite data Observations Models/Parameterizations resolution Mixed layer models microphysical models

31 Scale Hierarchy High Low low high Direct Numerical Simulati on Conceptual models Statistical Mechanics Self-Organised Criticality Level of “understanding ” or conceptualisation Large Eddy Simulations Global Climate Simulations Laboratory experiments Atmospheric Profiling stations Field campaigns Satellite data Observations Models/Parameterizations resolution Mixed layer models Interface & microphysical models

32 Complexity P.W. Anderson “More is Different”, Science 1977 “the emerge of collective properties with a large number of interacting elements” Interacting elements: Atoms in Physics Macromolecules in Biology People in economics Convective cloud elements in atmospheric Science? Dielectric Breakdown Galaxy distribution Internet structure

33 Phase Transitions in Statistical Mechanics Toy Example: Percolation p=probability of having a conductive bond 1-p = probability of having a insulating bond p=0.3 p c =0.5 p=0.8 insulating conducting Transition point At the transistion point: scaling laws, fractal geometry, renormalization group theory But……This exotic behaviour is only achieved if the order parameter (p) is exactly set to the critical point.

34 Self Organized Criticality In many non-equilibrium systems self-similarity and fractal behaviour emerges spontaneously (including atmosperic turbulence, convection and clouds) Toy model that displays this features: Sand Pile Model. Add randomly grains of sand: Redistribute grains if a local threshold slope is reached: Causing avalanches of any scale (power law).

35 Scale Hierarchy High Low low high Direct Numerical Simulati on Conceptual models Statistical Mechanics Self-Organised Criticality Level of “understanding ” or conceptualisation Large Eddy Simulations Global Climate Simulations Laboratory experiments Atmospheric Profiling stations Field campaigns Satellite data Observations Models/Parameterizations resolution Mixed layer models Interface & microphysical models


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