Challenges in Climate Modelling
23rd PhD Workshop on International Climate Policy
Noel Aquilina
Valletta - Malta, 20-21 Oct 2011
Contents
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Current Climate Science Challenges
WCRP
IPCC
Contribution from the Department of Physics
The way forward?
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The reliable detection and attribution of changes in climate is fundamental
to understanding the scientific basis of climate change and?
Detection of change is defined as the process of demonstrating that climate
has changed in some defined statistical sense. An identified change is
detected in observations if its likelihood of occurrence by chance due to
internal variability alone is determined to be small (<10%).
Attribution is defined as the process of evaluating the relative contributions
of multiple causal factors to a change or event. The process of attribution
requires the detection of a change in the observed variable or closely
associated variables.
To ensure a robust and consistent assessment of attribution results there is
a need to clarify the different approaches to attribution of observed
changes to specified causes.
The use of ‘uncertainty terminology’ and the assessment of confidence
levels.
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Attribution seeks to determine whether a specified set of external forcings
and/or drivers are the cause of an observed change in a specific system.
For example, increased greenhouse gas concentrations may be a forcing for an
observed change in the climate system. In turn, changed climate may be an
external driver on crop yields or glacier mass.
i.
ii.
iii.
iv.
Single-Step Attribution to External Forcings
Multi-Step Attribution
Associative Pattern Attribution
Attribution to a Change in Climatic Conditions (Climate Change)
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Single-Step Attribution to External Forcings
Example: Application of a detection analysis to an area burnt by forest fire.
The use of calculated regression coefficient of interannual variations in area
burnt against regional fire season temperature allowed a relationship to
estimate anthropogenically forced variations in a period. [Gillett et al, 2004]

Multi-Step Attribution
Example: Link between rising atmospheric CO2 and the reduced calcifying
abilities of reef building of tropical corals. Decling pH and carbonate ion
concentrations are linked to increasing atmospheric CO2. Second step
verified experimentally. [De’ath et al, 2009]
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Associative Pattern Attribution
Example: Assessment of 20 years of data of significant changes in physical
and biological systems. Spatial pattern of observed impacts is compared
with observed climate trends using statistical pattern-comparison measures.
Changes consistent with known responses in the region, not influenced by
other driving forces. [Rosenzweig et al, 2008]

Attribution to a Change in Climatic Conditions (Climate Change)
It addresses the link between impacts and the climate as driver based on
the understanding of processes.
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The World Climate Research Programme’s (WCRP) mission?
... is to facilitate analysis and prediction of Earth system variability
and change for use in an increasing range of practical applications of
direct relevance, benefit and value to society.
The two overarching objectives of the WCRP are:
i) to determine the predictability of climate;
ii) to determine the effect of human activities on climate
The WCRP is sponsored by the World Meteorological Organization
(WMO), the International Council for Science (ICSU) and the
Intergovernmental Oceanographic Commission (IOC) of UNESCO.
http://www.wcrp-climate.org/
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The main foci of the WCRP research are:
 Observing changes in the components of the Earth system.
[atmosphere, ocean, land and cryosphere]
 Improving our knowledge and understanding of global and
regional climate variability and change, and of the mechanisms.
 Assessing and attributing significant trends in global and regional
climates.
 Developing and improving numerical models that are capable of
simulating and assessing the climate system for a wide range of
space and time scales.
 Investigating the sensitivity of the climate system to natural and
human-induced forcing and estimating the changes.
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 Numerical models range from models of a particular process,
through complex global and regional climate or earth system models.
 Applications? including processes studies, data assimilation and
analysis, attribution, historical and paleo-climate simulation,
seasonal to interannual climate prediction, future climate
projections, and regional downscaling.
 Climate services and related information used for societal and
policy purposes are largely based on the output of such models.
 Basic model development has recently been identified to be in
decline. WCRP through its many activities is in a unique position to
advance this important issue
i) by formulating and implementing a new strategy for model
development and
ii) by promoting the importance of model development to its
stakeholders and funding agencies. It is imperative that any new
WCRP structure takes into account this challenge in contributing
to solutions.
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 Modelling is central to almost all WCRP activities and there are a
number of well established working groups that deal with several
aspects:
Working Group on Coupled Modelling (WGCM)
Working Group on Numerical Experimentation (WGNE)
Working Group on Seasonal to interannual Prediction (WGSIP)
Working Group on Ocean Models Development (WGOMD)
GEWEX Modelling and Prediction Panel (GMPP)
WCRP – CORE PROJECTS
SPARC – STRATOSPHERIC PROCESSES AND THEIR ROLE IN CLIMATE
http://www.sparc-climate.org/
http://www.sparc-climate.org/homeatmosphericchemistryandaerosols/
GEWEX – GLOBAL ENERGY & WATER CYCLE EXPERIMENT
http://www.gewex.org/
CLIVAR – CLIMATE VARIABILITY
http://www.clivar.org/
CLIC - CLIMATE & CRYOSPHERE
http://www.climate-cryosphere.org/en/
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What does the IPCC suggest for the upcoming AR5?
What is sought in the new generation of climate models ? Heterogeneity
An increasing emphasis on estimates of uncertainty in the projections raise
questions about how best to evaluate and combine model results in order to
improve the reliability of projections both at the global and the local scale.
Important for future scientists using results from model intercomparison
projects
What is the potential for, and limitations of, combining multiple models for
various applications?
What are the criteria for the decision making concerning
model quality?
performance metrics?
model weighting?
and averaging?
Which are the methods used in assessing the above?
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How should climate models be evaluated?
Performance Metrics (quantitative-a statistical measure of agreement between a
simulated and an observed quantity)
Diagnostics (qualitatively-spatial maps, time series, frequency distribution)
Model Quality Metric
A measure designed to infer the skill or appropriateness of a model for a specific
purpose obtained by combining performance metrics that are important for a particular
application.
Model Quality Index
May take into account model construction, spatio-temporal resolution or inclusion of
certain components in a subjective way
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Ensemble
A group of comparable model simulations. This gives the possibility of a more accurate
estimate of a model property through a larger sample size (climatological mean of
frequency of a rare event).
Variation of results across the ensemble members gives an estimate of the uncertainty.
Multi-model ensembles include the impact of model differences.
Perturbed-physics parameter ensembles are those in which model parameters are
varied in a systematic manner to produce a more systematic estimate of a single model
uncertainty.
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Multi-model mean (unweighted)
An average of simulations in a multi-model ensemble, treating all models equal.
Several realizations of a single model (changing only the initial conditions) might be
averaged before averaging with other models.
Multi-model mean (weighted)
An average across all simulations in a multi-model ensemble that does not treat all
models equally. Weights are generally derived from some measure of the model’s ability
to simulate the observed climate (model quality metric /index).
In climate model projections the determination of weights should be a reflection of a
defined statistical framework
Projections in the IPCC 5th Assessment Report (AR5) - CMIP5 of the WCRP, in which the
research and modelling community have agreed on the type of simulations to be
performed.
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Recommendations for Ensembles
When analysing multi-model ensemble results it is important to consider:
To form an ensemble and interpreting it for a particular purpose requires an
understanding of the variations between model simulations and model set-up.
To try to identify differences between same model simulations and perturbed physics
ensembles. The latter are an added complexity. [Different models for different
configurations]
In using CMIP5 it is important to recognize differences in forcing scenarios.
A single’s model ensemble should not give an inappropriate weight to a given model in a
multi-model ensemble.
There is currently no ‘best approach’ to the combination of interdependent ensemble
members and no unique way to characterize model dependence.
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Other Initiatives – MedCLIVAR – MEDiterranean CLImate VARiability
The development of the MedCLIVAR program was endorsed by CLIVAR. The
implementation of MedCLIVAR included the establishing scientific liaison with
relevant organizations and existing programmes.
Presently MedCLIVAR includes about 70 participating scientists and 50 supporting
institutions from 16 countries.
Issues that remain challenging as a result of MedCLIVAR
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Resolution over small islands [complex topography]
Atmosphere-ocean interactions [river flows, straits, inland seas]
Depth of ocean and circulation [not particularly well parameterised]
At Regional scale, the AO models required further development [aerosols, coupled
GCM-RCM for both atmosphere and ocean]
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Investigating relationships between Teleconnections
around the Mediterranean Sea and their influence on
aerosol transport using a RCM RegCM4
[unpublished work by James Ciarlo`]
Scope: To investigate the behaviour of Teleconnections around the
Mediterranean, with respect to:
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Their influence on aerosol transport
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Interaction between individual patterns
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Reproducibility within RegCM4
Teleconnections:
Two or more distant points of atmospheric pressure that vary with a negative
relationship with respect to each other.
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Level of Pattern
500 hPa
700 hPa
SLP
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Example: North Atlantic Oscillation (NAO)
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Validation: Spatial Bias and Subregional Analysis
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Validation: Sub-regional Analysis
Sea Level Pressure Annual Cycle
RegCM4.0
NCEP/NCAR
1020
1015
1005
1000
Sea Level Pressure Annual Cycle
995
RegCM4.0
NCEP/NCAR
1026
990
Icelandic Low Area
1024
985
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct1022 Nov
Pressure (hPa)
Pressure (hPa)
1010
Dec
1020
1018
1016
1014
Azores High Area
1012
1010
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
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Validation: Sub-regional Analysis
Sea Level Pressure Annual Cycle
RegCM4.0
NCEP/NCAR
1035
1030
1020
1015
Sea Level Pressure Annual Cycle
1010
RegCM4.0
W. Mediterranean
1005
NCEP/NCAR
1035
1030
1000
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct1025 Nov
Pressure (hPa)
Pressure (hPa)
1025
Dec
1020
1015
1010
1005
E. Mediterranean
1000
995
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
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Inter-pattern Relationship
NAOI
CACOI
-0.109
EAI
0.514
MOI
0.266
NCPI
0.167
SCAI
-0.165
SENAI
0.865
WeMOI
-0.110
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Influence on Transport
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Evaluation of the different versions of the Land Surface Model – MOSES in PRECIS
over Australia [unpublished work by Deandra Cutajar and Jessica Falzon]
MOSES - Met Office Surface Exchange Scheme
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Evaluation of the different versions of the Land Surface Model – MOSES in PRECIS
over Australia [unpublished work by Deandra Cutajar and Jessica Falzon]
 MOSES1 and MOSES2.2 overestimated the evaporation compared to the Reference
data.
 MOSES1’s overestimation exceeded that of MOSES 2.2 for Jan–Sep, but this changes
for Sep–Dec, leading MOSES 2.2 to be less accurate.
 Hence during the summer period (Dec–Feb), one would expect that the evaporation
rate is higher than the rest which is what both LSMs showed.
 The change between a high rate and a low rate of evaporation was not strongly
shown in the Reference data unlike the model data. Higher deviations in summer.
 Evaporation, in MOSES2.2, depends on the conductivity of the soil at the surface
which depends on the temperature. MOSES 2.2 uses a tiled representation, this
tends to result into higher temperatures than the aggregated representation used in
MOSES 1. This explains that during the summer period, the conductance at the soil
surface is high due to high temperatures which explains why MOSES 2.2 simulated
high rate of evaporation than MOSES 1.
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MOSES1-Reference
 The overestimation of MOSES 1 over Reference data is significant in the Oceanic
Islands
 Underestimation of rate of evaporation in the desert areas
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MOSES2.2 ˗ MOSES1
 The overestimation of MOSES 2.2 over MOSES 1 occurred mostly at the central part of
Australia, the desert where the formation of clouds is minimum to null.
 Desert is an area with high atmospheric pressure. In such areas cold air descends, and
warms as it gets closer to the ground, however instead of being released as
precipitation, the ground heat flux evaporates the water, increasing the rate of
evaporation.
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The CMIP5 - Coupled Model Intercomparison Project (Phase 5)
WCRP agreed to promote a new set of coordinated climate model experiments.
CMIP5 will notably provide a multi-model context for
 assessing the mechanisms responsible for model differences in poorly understood
feedbacks associated with the carbon cycle and with clouds,
 examining climate “predictability” and exploring the ability of models to predict
climate on decadal time scales, near term (out to about 2035) and long term (out to
2100 and beyond),and, more generally,
 determining why similarly forced models produce a range of responses.
New models will be HETEROGENOUS with the inclusion of interactive representations of
Biogeochemical cycles (carbon and nitrogen)
Gas-phase chemistry
Aerosols and Secondary Organic Aerosols
Ice sheets
Land use
Dynamic vegetation
Full representation of the Stratosphere
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Human decisions??
CORDEX - COordinated Regional climate Downscaling Experiment
CORDEX will produce an ensemble of multiple dynamical and statistical downscaling
models considering multiple forcing GCMs from the CMIP5 archive. Multiple common
domains covering all (or most) land areas in the World have been selected (with initial
focus on AFRICA).
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References
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http://conference2011.wcrp-climate.org/positionpapers.html
http://srren.ipcc-wg3.de/
http://www.ipcc.ch/
WCRP – Observations & Analysis:http://www.wcrp-climate.org/observations.shtml
http://www.clivar.org/organization/wgcm/wgcm.php
 IPCC, 2010: Meeting Report of the Intergovernmental Panel on Climate Change Expert
Meeting on Assessing and
 Combining Multi Model Climate Projections [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor,
and P.M. Midgley (eds.)].
 IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland, pp.
117.
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THANK YOU FOR YOUR ATTENTION
CONTACT DETAILS
NOEL AQUILINA
E: noel.aquilina@um.edu.mt
T: (+356) 2340 3036
W: um.edu.mt/science/physics/climate
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