Transcript Regional_Modelling_Dynamical_Downscaling_DHeinx

Regional Climate Modelling and Dynamical
Downscaling
Climate Data for Agricultural Modelling Workshop,
Kasetsart University, 26th February -1st March 2013
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Objectives of the session
• To introduce the PRECIS regional climate
modelling system
• To review the method for obtaining fine-scale
climate information from global climate models
(GCMs) from regional climate models (RCMs)
such as PRECIS.
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What is PRECIS?
• Providing REgional Climates for Impact Studies
• Regional climate modelling (RCM) system that can be
applied to any area of the globe
• Used to generate detailed projections of future climate
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Why was PRECIS developed?
• UNFCCC requirement to assess national vulnerability and
plans for adaptation
• National Communications
• Both need estimates of impacts
• Impacts need detailed scenarios of future climate
• PRECIS can provide these detailed scenarios of future
climate
• UNFCCC requirement on the UK to assist capacity building
and technology transfer
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Who is PRECIS for?
• Anyone interested in understanding climate change and
its potential impacts
• Highly relevant for scientists involved in vulnerability and
adaptation studies (particularly for National
Communications documents)
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Predicting impacts
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What is a Regional Climate Model?
• Mathematical model of the atmosphere and land surface (and
sometimes the ocean)
• ‘High’ resolution: Produces data in
grid cells < 50km in size
• Spans a limited area (region) of the globe
• Contains representations of many of the important physical
processes within the climate system
• Cloud
• Radiation
• Rainfall
• Atmospheric aerosols
• Soil hydrology
• etc.
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The components of PRECIS
• The RCM
• User interface to design and configure RCM experiments
• Display and data processing software
• Lateral boundary conditions (the input data)
• Training course and materials
• Technical and Scientific Support (by internet forum and email)
• Website (http://www.metoffice.gov.uk/precis)
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The PRECIS user interface
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PRECIS user interface: Main functionality
• Region specification
• Choice of domain
• Land surface configuration
• RCM and Emissions scenario
• Period of the simulation
• Output data
• Run
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PRECIS user interface
Example of
graphical
runtime
monitoring
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Minimum hardware requirements
Computer: PC running under the Linux operating system
Memory : 512MB minimum; 1+ GB recommended
Minimum 250GB disk space + offline storage for archiving data
Simulation speed proportional to CPU speed
How fast does it go?
30 year integration, 100x100, 50km grid points
• 1 core:
~ 2.5 months
• 4 cores:
~ 2.75 weeks
• 8 cores:
~ 12.5 days
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Support and follow-up
• Support
• E-mail to the Hadley Centre ([email protected])
• Online discussion forum hosted by
http://climateprediction.net
• Web site
• http://www.metoffice.gov.uk/precis
• news
• updates
• resources
• Collaboration/workshops
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What PRECIS can deliver
• PRECIS can provide:
• climate scenarios for any region
• an estimate of uncertainty due to different emissions
• an estimate of uncertainty due to climate variability
• Data available from PRECIS
• Comprehensive and consistent meteorological and physical data for
the atmosphere and land-surface
• Hourly and daily data as well as longer timescale averages
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PRECIS: Summary
• PRECIS: The Hadley Centre’s regional climate modelling system
• Resolution of 50km (25km for small areas)
• Runs on a Linux based PC; supplied on a DVD with
sample driving data
• Can be set up by the user over any area of the globe
• Useful for vulnerability and adaptation studies and climate
research
• Provides the capacity to locally produce scenarios of
climate change
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Why downscaling?
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Why downscaling?
Main reason: GCM lack regional details due to coarse
resolution for many climate studies -> needs fine scale
information to be derived from GCM output.
• Smaller scale climate results from an interaction between
global climate and local physiographic details
• There is an increasing need to better understand the
processes that determine regional climate
• Impact assessors need regional detail to assess
vulnerability and possible adaptation strategies
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From global to local climate …
… from a GCM grid to the point of interest.
How to downscale?
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Downscaling techniques
Coarse atmospheric data (T, Q, winds, pressure etc)
100-300km
• Statistical: based on statistical relationship
between large- and local- scale
fine scale value = F (large-scale variables)
• Dynamical: Numerical models at high
resolution over region of interest
• high resolution AGCM, limited area model
(regional climate model)
• Statistical/Dynamical
50km-1km:
region, city,
Fields etc
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Local atmospheric data (T, Q, winds, pressure etc)
Statistical Downscaling
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Categories of statistical techniques
• Weather generators
• Markov chain, spell length
• Transfer functions
• linear regression, piecewise interpolation, artificial neural
networks
• Weather typing
• Analogue methods, classification and tree analysis
Assumptions made for statistical
downscaling
Relies on large-scale predictors for which Climate System Models are
most skilful:
• Several grid lengths
• Tropospheric variables (away from the surface)
• Dynamic variables (geopotential, wind, temperature)
The transfer function must remain valid in different climate conditions:
• Hard to demonstrate
• Can be evaluated by comparison with other approaches
The predictors must encompass the entire climate change signal:
• Importance of testing several predictors
• Uncertainties related to the choice of predictors
Regional climate models
(Dynamical downscaling)
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What is a Regional Climate Model?
• Comprehensive physical high
resolution climate model that covers a
limited area of the globe
• Includes the atmosphere and land
surface components of the climate
system (at least)
• Contains representations of the
important processes within the climate
system
• e.g. clouds, radiation, precipitation
One way nesting methodology
• A RCM is a limited area model (LAM),
similar to those used in numerical weather
prediction (NWP), i.e. short term weather
forecasting
• LAMs are driven at the boundaries by GCM
or observed data
• Lateral (side) and bottom (sea surface)
• LAMs are highly dependent on their
boundary conditions and can not exist
without them
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Boundary conditions
Limited area regional models require meteorological
information at their edges (lateral boundaries)
This data provides the interface between the regional
model’s domain and the rest of the world.
The climate of a region is always strongly
influenced by the global situation
These data are necessarily provided by global general
circulation models (GCMs)
or from observed datasets with global coverage (reanalysis experiments)
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Lateral boundary conditions (I)
• LBCs = Meteorological boundary conditions at the lateral (side)
boundaries of the RCM domain
• They constrain the RCM throughout its simulation
• Provide the information the RCM needs from outside its domain
• Data come from a GCM or observations
• Lateral boundary condition variables
• Temperature
• Water
• Pressure
• Aerosols
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LBC variables
• Wind
LBC variables
LBC variables
Lateral boundary conditions (II)
• Relaxation method (PRECIS)
State variables
• Large scale forcing of low wave number
components
• Important issues
• Spatial resolution of driving data
• Updating frequency of driving data
RCM
interior
State variables
S. v.
• Spectral nesting
S. v.
• Large scale forcing merged with internal
solution over a lateral buffer zone
Sea surface boundary conditions
• Two methods of supplying SST and sea ice:
• Using outputs from a coupled AOGCM
• Need good quality simulation of SST and sea ice in model
• Necessary for future simulations
• Using observed values
• Useful for the present-day simulation.
• For future climate need add changes in SST and ice from a
coupled GCM to the observed values – complicated
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Sources of errors in RCMs
• The RCM adds fine detail to the large-scale and
shouldn’t deviate from it.
• Two sources of error:
• Large scale driving fields (external)
• Model physical formulation (internal).
Simulation length
• Minimum period
• 10 years to reasonably study the mean climate
• Preferably
• 30 years to study higher order statistics, climate
variability, extremes, etc
Added value of RCMs
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RCMs simulate
current climate
more realistically
Patterns of present-day
winter precipitation
over Great Britain
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Represent smaller islands
Projected changes in summer surface air temperature between
present day and the end of the 21st century.
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Predict climate change with more detail
Projected changes in winter precipitation between now and 2080s.
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Simulate and predict changes in
extremes more realistically
Frequency of winter days over the Alps with different daily rainfall
thresholds.
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Simulate cyclones and hurricanes
A tropical cyclone is evident in the RCM (right) but not in the GCM
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RCM data can be used to drive other
models
A cyclone in the Bay of Bengal simulated by an RCM and the
resulting high water levels in the Bay simulated by a coastal shelf
model.
Crop impacts using high resolution
projections from PRECIS
* Clear messages on future temperatures from the
application of a regional climate model
* Precipitation projections are less certain, giving climate
change information with different levels of confidence
* Application of this information to assess climate change
impacts on crops still provides clear messages for the
need for adaptation
Temperature changes over Caribbean
land areas in two climate projections
• High resolution modelling delivers consistent message on large
warming over land even with different sea temperature changes
• Temperature changes >3K by 2080s under the B2 scenario
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The message on precipitation
change is less clear
• Precipitation changes of up to +/- 20% or greater by 2080s
under the B2 scenario
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Clear impact on Caribbean crops in
2050s with +2ºC and+/-20% precip.
Crop
Temperature
Change
(oC)
% Change in
Precipitation
Yield
(kg/ha)
Change in
Yield
0
+2
+2
0
+20
-20
3356
3014
2888
-10%
-14%
Beans
0
+2
+2
0
+20
-20
1354
1164
1093
-14%
-19%
Maize
0
+2
+2
0
+20
-20
4511
3737
3759
-22%
-17%
Rice
Table: Simulated crop yields under current climate and with a 2 ºC temperature increase accompanied by either a 20% increase or decrease in rainfall.
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Suitability of regionalisation
techniques
Method
Strengths
Weaknesses
Statistical
 High resolution
 Computationally
cheap
 Dependent on empirical
relationships derived for presentday climate
 Few variables available
 Not easily relocatable
 Dependent on surface boundary
conditions from couple model
 Computationally expensive
High- res
AGCMs
Regional
models
 High (very high)
resolution
 Can represent
extremes
 Physically based
 Many variables
 RCM: easily
relocatable
 Dependent on driving model &
surface boundary conditions
 Possible lack of two-way nesting
 Computationally expensive
 ( Have to parameterise across
scales )
Summary
• Downscaling techniques are used to add fine
scale details to a GCM projection
• Several methods are available with their own
strengths and weaknesses
• PRECIS is a physically-based and
computationally accessible regional climate
model for downscaling GCM projections
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Questions
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