© Crown copyright Met Office Regional climate models: formulation, design and evaluation RCM Data...

© Crown copyright Met Office

Regional climate models: formulation, design and evaluationRCM Data Analysis workshop, Malaysian Met. Dept., Nov 2012


Goals of the session

• To provide an overview of the scientific formulation of the regional climate model

• To identify and understand the issues which need to be considered when setting up a regional climate model experiment (or suite of experiments).

• To provide information on the importance and methodology for evaluation and validation of regional climate model experiments


Model formulation

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Models: General Description

• Global/Regional climate models are models of the climate system, including the atmosphere, oceans, land-surface and more.

• The advective (relating to motion) and thermodynamic (relating to heat) evolution of atmospheric pressure, winds, temperature and moisture (prognostic variables) are simulated, while including the effects of many other climate processes. physical processes.

• Other useful meteorological quantities (diagnostic variables) are derived consistently within the model from the prognostic variables, such as precipitation, evaporation, soil moisture, cloud cover and many more.


Main components of globalclimate models

• Atmospheric dynamics

• Model grid

• Physical parameterizations

• Initial and boundary conditions of the model


Atmospheric dynamics

• The evolution of pressure, winds, temperature and moisture are governed by the laws of energy and conservation.

• Conservation of momentum

• Conservation of mass

• Conservation of energy


Horizontal equation of motion(the conservation of momentum)

• Newton’s 2nd law: Force = Mass Acceleration

• The change in velocity of an air parcel is dependent on

• the Coriolis force

• the pressure gradient force

• gravity

• friction

12 r

Dp

Dt

UU g F


The hydrostatic assumption(taking vertical motions out of the model)

• At synoptic scales, vertical accelerations due to the conservation of momentum are small and may be ignored.

• Newton’s equation in the vertical then simplifies to

• Relates vertical pressure variations with gravity and atmospheric density

• I.e. there are only two explicit forces acting in the vertical (pressure gradient force and gravity) and these act to cancel each other out

p

z

dg

d


Vertical equation of motion(the conservation of mass)

• The continuity equation:

Relates changes in density with divergence in the wind velocity field.

• Vertical motions are inferred from areas of convergence and divergence in the horizontal wind field (and assuming incompressibility of the atmosphere).

• Areas of convergence and divergence in the horizontal diagnose a vertical transfer of mass between vertical layers.

1 DU 0

Dt


Atmospheric heat and moisture(the conservation of energy)

• First law of thermodynamics:

• heat added = change in internal energy + work done

• Equation of state for a gas :

• Temperature and water vapour are also advected

• Temperature/moisture at a point in the atmosphere can change either due to cooler or warmer/drier or moister air being blown to that point (advection), or from local effects such as evaporation or condensation arising from the equation of state and the first law of thermodynamics

pv RT


Discretizing the model equations

• All model equations are solved numerically on a discrete 3-dimensional grid spanning the area of the model domain and the depth of the atmosphere and ocean

• The model simulates values at discrete, evenly spaced points in time

• The period between each point in time is called the model’s timestep

• Spatially, data is an average over a grid box

• Temporally, it should be the same

time

o


Vertical exchange between layersof momentum, heat and moisture

Horizontal exchangebetween columnsof momentum, heat and moisture

Vertical exchangebetween layersof momentum, heat and saltsby diffusion, convectionand upwelling

Orography, vegetation and surface characteristics included at each grid box surface

Vertical exchange between layersby diffusion and advection

The three dimensional model grid

15° W60° N 3.75°

2.5°

11.25° E47.5° N



• Atmosphere and ocean dynamics

• Model grid




The model grid

• Hybrid vertical coordinate

• Combination of terrain following and atmospherics pressure

• 19 vertical levels (lowest at 50m, highest at 5Pa)

• Regular lat-lon grid in the horizontal

• ‘Arakawa B’ grid layout

• P = pressure, temperature and moisture related variables

• W = wind related variables

W

P

PP

P

W


The rotated pole

• The coordinate pole of the RCM grid is usually rotated

• The RCM’s north pole is not in the usual position

• This ensures numerical stability without the need for non-physical filtering

• Avoids high latitudes where filtering is necessary

• RCM grid boxes are quasi-regular in area

• All grid boxes are near the equator




• Model grid




Physical processes


Parameterization of physical processes

• Important processes occur in the atmosphere on scales smaller than those which are resolved by the grid of the dynamical part of the model.

• The effects of these unresolved (sub-grid scale) processes are deduced from the large scale state variables predicted by the model (wind, pressure, temperature, moisture).

• This procedure is called parameterization.


Physical parameterizations• Some examples:

• Clouds and precipitation

• Radiation

• Atmospheric aerosols

• Boundary layer

• Land surface

• Gravity wave drag

The strategy or method in which a sub-grid scale process is parameterized by the model is referred to as a scheme. Many different schemes for dealing with various physical processes have been developed by climate scientists. Each climate model (including PRECIS) will use a different set of schemes as part of its unique formulation.


Large scale clouds and precipitation

• Results from the large scale movement of air masses affecting grid box mean moisture levels

• Due to dynamical assent (and radiative cooling and turbulent mixing)

• Cloud water and cloud ice are simulated

• Conversion of cloud water to precipitation depends on

• the amount of cloud water present

• precipitation falling into the grid box from above (seeder-feeder enhancement)

• Precipitation can evaporate and melt


Convection and convective precipitation

• Cloud formation is calculated from the simulated profiles of

• temperature

• pressure

• humidity

• aerosol particle concentration

• Entrainment and detrainment

• Anvils of convective plumes are represented


Radiation

• The daily, seasonal and annual cycles of incoming heat from the sun (shortwave insolation) are simulated

• Short-wave and long-wave energy fluxes modelled separately

• SW fluxes depend on

• the solar zenith angle, absorptivity (the fraction of the incident radiation absorbed or absorbable), albedo (reflected radiation/incident radiation) and scattering (deflection) ability

• LW fluxes depend on

• the amount an emitting medium that is present, temperature and emissivity (radiation emitted/radiation emitted by a black body of the same temperature)


Surface processes: MOSES

• Exchange of heat and moisture between the earth’s surface, vegetation and atmosphere

• Surface fluxes of heat and moisture

• Precipitation stored in the vegetation canopy

• Released to soil or atmosphere

• Depends on vegetation type

• Heat and moisture exchanges between the (soil) surface and the atmosphere pass through the canopy

• Sub-surface fluxes of heat and moisture in the soil

• 4 layer soil model

• Root action (evapotranspiration)

• Water phase changes

• Permeability depending on soil type

• Run-off of surface and sub-surface water to the oceans

• Moses II: tiled representation of sub-grid heterogeneity

• Changed vegetation types with respect to Moses I

q, T

q, T

q, T

q, T

q, T

q, T




• Model grid




Initial conditions

• All climate models require information about the initial state of the atmosphere at the beginning of the climate model experiment. These are the initial conditions of the model experiment.

• At the beginning of an experiment, the RCM needs values for all of the prognostic variables throughout the atmosphere and deep soil.

• Derived from the same source as the driving GCM or reanalysis experiment


Lateral Boundary Conditions

• LBCs = Meteorological boundary conditions at the lateral (side) boundaries of the RCM domain

• They constrain the prognostic variables of the RCM throughout the simulation

• ‘Driving data’ comes from a GCM or analyses

• Lateral Boundary condition variables:

• Wind

• Temperature

• Water vapour

• Surface pressure

S tat e va ria b les

State variables

State variables

Stat

e va

riab

les


Other boundary conditions • Information required by the model for the duration of a simulation

• Constant data applied at the surface

• Land-sea mask

• Orographic fields (e.g. surface heights above sea level, StDev of altitude)

• Vegetation and soil characteristics (e.g. surface albedo, height of canopy)

• Time varying data applied at the surface

• SST and SICE fractions

• Anthropogenic SO2 emissions

• Dimethyl sulphide (DMS) emissions

• Time varying data applied throughout the atmosphere

• Atmospheric ozone (O3)

• Constant data applied throughout the atmosphere

• Natural SO2 emissions volcanos

• Annual cycle data applied throughout the atmosphere

• Chemical oxidants (OH, HO2, H2O2, O3)

These are referred toIn PRECIS as“ancillaries” andare found in the $ANCIL_MASTER directory


Summary

• Models utilise a finite three-dimensional resolution and timestep

• Models solve (integrate) the governing differential equations of mass, momentum and energy

• Sub-grid scale processes are parameterized

• Prognostic variables take information from timestep to timestep

• Other quantities diagnosed – diagnostic variables

• Initial conditions and boundary conditions are necessary components of the model.


Experimental Design


Outline

• Why a good experimental design is important

• Factors to consider for a well-designed RCM experiment


Why a good experimental design is important


Experimental design

• Inappropriately designed experiments may not address the relevant scientific issues or provide sufficient data which is needed (e.g. analysis or inputs to impacts models)

• Well-designed experiments enable a researcher to plan and manage the computing resources actually needed


PRECIS Graphical User Interface

Choice of model domain and resolution

RCM, GCM/ Reanalysis and scenario

Experiment start date and run length (with spin-up)

Output data configurations

Run

Monitor

Stop

Map of Region

Fine scale configurations to region


Factors to consider


Simulation Length

• Fluctuations in climate occur over seasonal, annual, decadal timescales and beyond

• A reliable estimate of climate must include an element of decadal variability

• At present, at least 31 years of model integration time is possible for both current and future climates


Strength of the North Atlantic Winter Westerlies 1867-2000

Substantial variations over the last century


Choice of Regional Model

• Currently there are two regional climate models (RCMs) within PRECIS

• HadRM3P: (the original PRECIS RCM) which is derived from the high resolution atmospheric model HadAM3P, which is itself derived from HadCM3

• The upcoming releases of PRECIS version 3 will utilise the HadGEM3-RA RCM derived from the HadGEM3 GCM


Future emission scenarios

• All of the SRES scenarios are considered equally plausible simulating climates for a range of scenarios is desirable to assess uncertainty

• Currently available to PRECIS: A2 (higher emissions), A1B (medium emissions) and B2 (lower emissions)

• PRECIS version 2.0 will provide the ability to downscale CMIP5 GCMs which utilise Representative Concentration Pathways (RCPs)


CO2 in SRES emissions scenarios


RCP Concentrations –CMIP5(Representative Concentration Pathways (RCPs))


Ensemble Simulations

• An ensemble is a collection of different (but equally plausible) model representations of a particular climatic state.

• Each ensemble member has the same external forcing (e.g. CO2 concentrations) but unique initial conditions

• These different starting states give a different evolution between each ensemble member but do not alter the climate

• Combining results from each member of an ensemble of the same RCM gives a larger sample of data, which allows for a more robust analysis and allows one source of uncertainty (natural variability) to be assessed


Spin-up time

• The model system (GCM+RCM) takes some time for all of its components to reach full equilibrium

• The full simulation length should take this into account. Output data from the spin-up period should not be used in any analysis

• 1 year should added to the overall simulation length to account for the spin-up period

Soil variables at equilibrium

Atmospheric variables at equilibrium

Up to 1 week 1 year

START


Choice of model domain: Location of lateral boundary conditions

Factor to consider Why?

Boundaries not over complex

terrain

Avoid noise due to topography/

LBC data mismatch

Area of interest far from

boundaries

Prevent noise from contaminating

the RCM results

Include/Don’t include forcings and

circulation directly affecting

the area of interest

Allow for development of fine-

scale detail without degrading

large scale behaviour


Choice of model domain: Size

• The domain should be large enough to allow full development of internal mesoscale circulations

• The domain should be small enough so that the RCM’s climate remains constrained, or close to, that of the driving model.

• It should be small enough so that a simulation can be completed in a reasonable amount of time

- Time taken is proportional to number of grid points


Choice of horizontal resolution

• Two resolution are available:

• 0.440.44 and 0.220.22

• ~50km and ~25km, respectively

• At 0.22, improvements will be seen due to better resolved mountains and coastlines

• For a given area, simulation time at 0.22 degrees is 6 times that of 0.44

• Use, perhaps, 0.44 for long integrations over full sized regions and ensembles

• Consider, perhaps, 0.22 for sensitivity studies over shorter timescales or smaller areas encompassing fine-scale features (e.g. very small islands)

~50km resolution

~25km resolution


Choice of Output Data: Variables

• All of the model data (diagnostic output) which will be needed must be produced from the simulation and archived.

• And preferably backed up to a hard medium

• PRECIS diagnostics available as standard are a prescribed list of:

• Hourly means (optional) (~14 Gb/year)

• Daily means, maxima, minima (optional) (~2 Gb/year)

• Monthly, seasonal, annual, decadal means, maxima and minima (~1 Gb/year)


Choice of Output Data: Format

• Diagnostic data file format

• Binary files

• Header (data descriptor) & data, organized sequentially

• PP (the Met Office’s own file format) is the output format

• PP vs. netCDF/GRIB

• netCDF/GRIB are used widely used and are recognised by many data processing and graphics packages

• PP to netCDF/GRIB conversion is easy PP is good for sharing


Evaluation


Contents

What is a model evaluationWhy evaluating model output is importantEvaluation componentsEvaluation techniquesExamples


What is a model evaluation?Why is it important?

• What:

• An assessment of how closely the model is able to simulate the present-day, observed climate

• Why:

• It enables you to gain familiarity with the model characteristics

• It indicates which aspects of the model simulation are most credible …

• … and therefore indicates how to make the best, most credible, use of the data to answer the relevant questions


Assessing how well the RCM represents the current climate (1)

There is the potential for four separate validationsGCM vs. ObservationsRCM driven by GCM vs. GCMRCM driven by GCM vs. ObservationsRCM driven by observations vs. observations

RCM GCM

Observations

consistency

realism realism



Model system = GCM + RCM

Q1. Are there discrepancies in the model system?Between the parts of the systemBetween a part of the system and ‘reality’

Q2. If so, why? Systematic model bias (error in the model’s physical formulation)Spatial sampling issues (differences in resolution of model and

observations)Observational error (gridding issues, instrument dependent errors)



RCM errors (biases) have up to three sourcesPhysical errors in the GCM affecting the LBCsRCM/GCM consistency errorsPhysical errors in the RCM

RCM GCM

Observations

consistency

realism realism


Techniques for answering these questions (1)

Assess as many meteorological variables as possibleAt least: T1.5, precipitation, upper air winds

Compare physically related variables within the modelE.g. In cool and wet conditions we may expect high soil moisture. Is this

so?



Use both spatial and temporal information

Spatial:Full fieldsSmaller areasVertical profilesArea averages

Temporal:TimeseriesMeansHigher order statistics (variability, extremes)Different seasons, different regimes

These are just some examples



Compare like with like

Data only have skill at spatial scales resolved by their grids.

In general, aggregate or interpolate datasets to the coarsest grid before comparing.

Cannot, in general, compare individual model years with their corresponding observed years

Can not guarantee that the modelled atmospheric response to external forcings (e.g. SST, CO2) will match that of the real atmosphere.

However when using observed LBCs, this comparison can be worth while, as the LBCs provide an additional constraint to reality.


Example 1: Daily circulation variability

Variability of daily 500hPa geopotential heights: winter

ERA15 RCM


Example 2a: Seasonal mean precipitation


Example 2b: Frequency of wet days


Example 3: Wet day frequency for 3 RCMs over the Alps in summer compared to observations

OBS


Example 4: Extreme rainfall event in a river catchment

Trends in maximum temperature over Bangladesh

Area average precipitation in the Jhelum river basin (Pakistan) for September 1992, showing RCM simulations at 50 and 25 km and observations


Observed data

• Use as many as possible observed datasets for validating the regional model

• Gridded datasets

• E.g. CRU (land surface), ERA40 (atmosphere)

• Station data

• Need to be aware of differences in spatial scales with model outputs


Summary

• Model validation is ESSENTIAL:

• A simulation may be over a new areas where the model performance is untested

• It enables you to gain familiarity with the model characteristics

• It is an indicator of how much credibility the RCM results have, and how they may be best used


Questions

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