Transcript ********* 1 - Indico

The importance of GRID computing in
the investigation of climate
and its change
Eleni Katragkou
Department of Meteorology and Climatology
Aristotle University of Thessaloniki
GREECE
CLIMATE AND ITS CHANGE
Climate encompasses the statistics of key meteorological
variables in a given region over long periods.
Climate change is an inherent characteristic of climate.
What is now considered as normal, may turn out to be
abnormal when a longer time perspective is employed.
Why we need to study climate change?
It is important to assess impacts of climate change
already underway and address adaptation strategies to
reduce vulnerability and risks of climate change.
Risk management of extreme events and disasters in a
wide range of areas is required to be ‘climate change
proofed’.
Intergovernmental Panel of Climate Change (ipcc)
The importance of climate and climate change
2007 Nobel Peace Prize
3
Components of the climate system
Atmosphere:atmos+sphere (ατμός=vapor)
Biosphere: bio+sphere (βίο=life)
Lithosphere:litho+sphere (λίθος=rock)
Cryosphere : cryo+sphere (κρύο=cold)
Hydrosphere:hydro+sphere (ύδωρ = water)
Human activity
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What produces climate-change?
Natural causes
External causes
Internal causes
Input of solar radiation and how it
is seasonally and geographically
distributed.
Component parts of the climate
system function to store, distribute
and release energy.
a.
Direct solar radiation input (solar
cycle)
b.
Indirect solar inputs
(orbital variations)
a.
Volcanic activity
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What produces climate-change?
Human-induced causes
Greenhouse gases
Greenhouse gases (GHGs) effectively absorb outgoing
long-wave radiation. This is trapped and radiated back
downwards, resulting in the surface temperature for the
earth becoming warmer.

Rapidly increasing concentrations of GHGs as a result of
human activities are implicated in current warming trends.

The figure shows changes in greenhouse gases carbon
dioxide (CO2), methane (CH4), nitrous oxide (N2O) indicated
from ice core and modern data. X axes indicates years before
2005.

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What produces climate-change?
Human-induced causes
Aerosols (particles)
Human induced activities (factories, autos, trucks, aircrafts, ships, power plants, fireplaces)
result in emissions of either primary aerosols or gaseous aerosol precursors (gases convert to
particles) which lead to formation of secondary aerosols.

Aerosols affect incoming sunlight either by reflecting it (e.g. sulfate) and tend to cause a
net cooling of the surface air, or by steadily absorbing sunlight (e.g. soot) (aerosol direct effect)

Moreover particles have the potential to alter the physical characteristics of clouds (e.g.
changing the number and the size of cloud droplets) affecting, thus, cloud reflectivity,
precipitation efficiency and the lifetime of cloud (aerosol indirect effect)

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Direct aerosol effect
Indirect aerosol effect
What produces climate-change?
Human-induced causes
Changing landscape
Modifications of the earth’s surface could potentially influence the
immediate climate of certain regions.

Clearing large areas of tropical rain forests to create land for farms and
cattle ranges will most likely cause a decrease in evaporative cooling, leading
in turn to a warming.

The reflectivity of deforested area changes, causing an increase in desert
conditions (desertification)

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Evidence of human-induced
climate change

Observational evidence comes from long-term
observational records of changing atmospheric
composition (CO2), surface temperature etc.

Some sceptics argue that is still a quantum leap to
attribute cause (increasing greenhouse gases) and
effect (warming).

Climate model data have attributed climate change
to increasing green house gases (IPCC 1995; 2001;
2007)

The figure shows the simulated changes in global
surface air temperature from 1860 to 2000 made by
different climate models using different forcing
agents versus measured temperature.
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MODELLING THE CLIMATE SYSTEM
Introduction to climate modelling

Climate models that simulate the
physical processes of the
atmosphere are called General
Circulation Models (GCMs).

GCMs use mathematics and the
laws of physics to describe the
general behavior of the
atmosphere.

The primary earth system
components that are simulated by a
GCM include the atmosphere,
oceans, land surface and the
cryosphere.
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Tools to assess climate: Climate models

GCMs divide the atmosphere, oceans, and land into a 3-dimensional grid system.

Differential equations are used to relate fundamental physical quantities
(Temperature, Pressure, Winds, Specific Humidity) to each other.

Each equation is solved at discrete grid points on the earth’s surface, at a fixed time
interval (time-step) and several vertical layers, defined by the regular grid.

The number of cells in the grid system is known as the "resolution." The more grid
cells, the higher the resolution, and the more calculations that must be computed.
Rule of thumb:
10x more CPU
for a doubling of
resolution
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1990
1995
INTERGOVERNMENTAL PANEL OF CLIMATE CHANGE (IPCC)
Development of global climate modelling
2001
2007
Different components are first developed separately and later
coupled into comprehensive climate models
Model resolution
increase in the four
IPCC Assessment 13
Reports
Linking global to regional scale
Regional Climate Models

GCMs used for climate studies and climate projections are run at coarse spatial
resolution (typically 100 Km) are thus unable to resolve important sub-grid scale
features such as clouds and topography.

Downscaling climate data is a strategy for generating regional relevant data from
GCMs.

Nesting a Regional Climate Model (RCM) into an existing GCM is one way to
downscale data.
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RCM boundary forcing

An RCM is nested within a GCM using a finger resolution grid.

Large-scale atmospheric fields (e.g. temperature, wind, surface pressure etc) at
multiple vertical layers are fed into the boundary of the RCM through a lateral buffer
zone.

The domain should be large enough to allow free development of mesoscale
atmospheric circulations but is constrained by computational factors.
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Multiple physical options in climate models

Each model has multiple physics options that
can be combined in any way.

The options typically range from simple and
efficient, to sophisticated and more
computationally costly, and from newly
developed schemes, to well-tried schemes.

These basic physics options can be combined
in several different ways producing different
model results
 Microphysics
 Long- and Shortwave radiation
 Surface Layer
 Land surface
 Urban surface
 Planetary Boundary Layer
 Cumulus parameterization
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Estimating future climate change:
1) Definition of baseline climatology

Any climate scenario must adopt a reference baseline period from which to calculate
changes in climate.

It should be representative of the present-day or recent average climate in the study
region and of a sufficient duration to encompass a range of climatic variations,
including several significant weather anomalies (e.g., severe droughts or cool
seasons) e.g. 1961-1990

Sources of baseline data include a wide variety of



observed data
reanalysis data (a combination of observed and model-simulated data)
control runs of GCM simulations
ERA-Interim is the latest global
atmospheric reanalysis
produced by the European
Centre for Medium-Range
Weather Forecasts (ECMWF)
1979 – onwards
http://www.ecmwf.int/research/era
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Estimating future climate change:
2) Preparing future emission scenarios

Scenarios are used by analysts to make projections of future GHG emissions and to
assess future vulnerability to climate change.

Producing scenarios requires estimates of future population levels, economic
activity, the structure of governance, social values, and patterns of technological
change.
Emission scenarios of TAR, IPCC, 2007
o B1 storyline and scenario family
Global solutions to economic, social and
environmental sustainability, including
improved equity, but without additional
climate initiatives.
o A2 storyline and scenario family
A very heterogeneous world.
Technological change fragmented and
slower than other storylines
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Estimating future climate change:
3a) Evaluation and Intercomparison of global climate models

Quantifying model biases is critical
to characterizing the uncertainties
associated with these climate
change projections.

Intercomparison is an essential
step in assessing the mechanisms
responsible for model differences
in poorly understood feedbacks

It is also a way of determining why
similarly forced models produce a
range of responses
Climate Model Intercomparison Project (CMIP5)
The relationship of CMIP5 to organizations established to
coordinate climate research activities internationally and to
the IPCC, the modelling centres and the climate research
community.
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Estimating future climate change:
3b) Evaluation and Intercomparison of regional climate models

The CORDEX (Coordinated Regional
Downscaling Experiment) activities are based
on the latest set of GCM climate scenarios
within CMIP5.

Aims to build a common set of Regional
Climate Model domains for dynamical
downscaling.

Provide a benchmark framework for model
evaluation and assessment.

To support and inform the climate impact
assessment and adaptation groups interested
in utilizing CORDEX material in their research.
Schematic depiction of the first phase CORDEX
experiment set-up
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Methodology of Model Ensembles

Ensembles of models represent a new resource
for studying the range of plausible climate
responses to a given forcing.
Such ensembles can be generated either
 by collecting results from a range of models from
different modelling centres (multi-model
ensembles) or


by generating multiple model versions within a
particular model structure, by varying internal
model parameters within plausible ranges
(perturbed physics ensembles).
Vautard et al., 2013, The simulation of
European heat waves from an ensemble of
regional climate models within the EUROCORDEX project, accepted in Climate
Dynamics
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Large-Scale Projections for the 21st Century
Projected surface temperature changes for the early and late 21st century relative to the
period 1980 to 1999.
The left panel shows corresponding uncertainties as the relative probabilities of estimated
global average warming from several different studies for the same periods.
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Large-Scale Projections for the 21st Century
Temperature Change
(°C at 2090-2099 relative to 1980-1999)
Best estimate
Likely range
B1 scenario
1.8
1.1-2.9
B2 scenario
2.4
1.4-3.8
A1B scenario
2.8
1.7-4.4
A2 scenario
3.4
2.0-6.4
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Drivers of climate change: IPCC reports

Global average radiative forcing (RF) estimates and ranges for different forcing agents

Typical geographical extent (spatial scale) of the forcing

Assessed level of scientific understanding (LOSU)
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GRID COMPUTING IN CLIMATE STUDIES
[1] GRID enabled climate-air quality interactions
More on this EGI case study on:
http://www.egi.eu/case-studies/ozone.html
GCM forcing: ECHAM5

Regional Climate Model: RegCM3

Air quality model: CAMx 5.3

Temporal coverage:

1991-2000

2041-2050 (A1B scenario)

2091-2100 (A1B scenario)

Spatial coverage: Europe

Temporal resolution: 3 hours

Spatial resolution: 50 Km

Storage: 5 TB

CPU : 720 h * 4 cores = 2,880
GRID applications @ AUTH

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[1] GRID enabled climate-air quality interactions
Enhanced average surface
ozone concentrations at the
end of the 21st century
especially over SW Europe,
where the median of ozone
increases by 6.2 ppb
Katragkou et al., 2010, Atmospheric Chemistry and Physics, 10 (23), pp. 11805-11821
Katragkou et al., 2011, Journal of Geophysical Research D: Atmospheres, 116 (22), art. no. D22307
Zanis et al, 2011, Atmospheric Environment, 45 (36), pp. 6489-6500.
GRID applications @ AUTH
The median of summer near
surface temperature for whole
Europe is 2.7 K higher at the
end of the 21st century
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[2] GRID enabled very high resolution regional
climate simulations over SE Europe
GCM forcing: ECHAM5

Regional climate model: RegCM3

Temporal coverage: transient 1950-2100 (scenario A1B)

Spatial coverage: South Eastern Europe

Temporal resolution: 3 hours

Spatial resolution: 10 Km

Storage: 10 TB

CPU: 2700 h * 24 cores = 64,800
GRID applications @ AUTH

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[2] GRID enabled very high regional climate
simulations over SE Europe
GEOCLIMA Project:
http://www.geoclima.eu
GRID applications @ AUTH
Development of an an integrated Geographic Information System allowing the user to
visualize, manage and analyze the information which is directly or indirectly related to
climate and its future projections in Greece.
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[3] GRID enabled regional climate projections
Regional climate model: WRF 3.3.1


Spatial coverage: Europe
Temporal resolution: 3 hours
Spatial resolution: 50 Km

Hindcast simulation (Part of WRF multi-physics EURO-CORDEX)




Future projection





Reanalysis forcing: EWCMF/ERA interim
Temporal coverage: 1989-2009
CMIP5 GCM forcing: TBD
RCP future scenario: TBD
Temporal coverage: 1960-2100
Total Storage: estimated 50 TB
Total CPU: 3200 h * 64 cores = 204,800
GRID applications @ AUTH

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[3] EURO-CORDEX Hindcast evaluation
GRID applications @ AUTH
Katragkou et al, Grid enabled regional climate simulations within the EURO-CORDEX
project: ERA-interim hindcast 1990-2009, European Geosciences Union, General
Assembly 2012, 7-12 April 2013, Vienna, Austria
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Future Challenges and the role of EGI
The Earth modelling community currently faces many challenges because of
the:
 increasing complexity of Earth models
 demand on higher resolution climate relevant information needed for
impact and adaptation studies


EGI supports our work by providing:
 computational and storage resources necessary for ‘expensive’ longterm transient simulations
 the technical tools/applications necessary for optimizing and
facilitating our work
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The EGI Champion network
Welcoming new users to the grid
http://www.egi.eu/community/egi_champions/champions_network.html

EGI champions are enthusiastic scientists using grid computing for their research and keen
to go to conferences and spread the word about the benefits of working with EGI.

There is a particular interest for individuals working in research areas where grid
computing is not yet widely used.

Champions will form a network of pioneering researchers who will be able to promote
best practices and receive support with networking and communication where needed.
E. Katragkou: EGI Champion 2013
HOW TO APPLY
•Read more about the scheme in the EGI
Champions' wiki pages
https://wiki.egi.eu/wiki/Champions
•Download the application form and send it,
together with an updated CV, to
[email protected]
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Acknowledgments

European Grid Infrastructure (EGI) – EGI champions programme

Department of Meteorology and Climatology, AUTH
T. Karacostas, P. Zanis, C. Feidas, I. Tegoulias

Scientific computing centre (SCC), AUTH
P. Korosoglou and the SCC-team

Greek research and technology network (GRNET)

K. Koumantaros , HellasGRID Manager

FP6 project CECILIA (Central and Eastern Europe Climate Change Impact and
Vulnerability Assessment)

NSRF 2007-2013 project GEOKLIMA (An Integrated Geographic Information System
for visualization and management of climate information)

Research Committee, Aristotle University of Thessaloniki (AUTH)
[email protected]
34
References

Ahrens C.D., Meteorology today, An introduction to Whether, Climate and the
Environments, 2009, Brooks/Cole, Cengage Learning.

Dee et a al., The ERA-Interim reanalysis: configuration and performance of the data
assimilation system Issue, Quarterly Journal of the Royal Meteorological Society,
Volume 137, Issue 656, pages 553–597, 2011

Giorgi F., C. Jones, G.R. Asrar, Addressing climate information needs at the regional
level: the CORDEX framework, WMO Bulletin58 (3) - July 2009

IPCC, 2007: Climate Change 2007, The Physical Science Basis, by the Working Group I
contribution to Fourth Assessment Report to the IPCC, 2007.

IPCC, 2001: Climate Change 2001, The Physical Science Basis, by the Working Group I
contribution to the Third Assessment Report to the IPCC, 2001.

O’Hare G., J. Sweeney, R. Wilby, Weather, Climate and Climate Change, Human
Perspective, Pearson Education Limited 2005

Taylor K.E. , R. J. Stouffer, G. A. Meehl, An Overview of CMIP5 and the Experiment
Design, Bulletin of the American Meteorological Society 93 , (4) , 485 - 498, 2012
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BACKUP SLIDES
36
Chronology of climate modelling developments
Decade
Significant milestone
1890s
Global warming is linked to atmospheric CO2
1920s
Numerical weather forecasting is first described
1950s
First numerical weather prediction schemes
Supercomputer (IBM 701) first used specifically for weather
forecasting
1960s
Atmosphere and Ocean general circulation models (GCMs) are
first described
1970s
Supercomputer (CDC 6600) breaks the MFLOP barrier
Greenhouse modelling with GCMs
1980s
Satellites provide observations
First regional climate models (RCMs)
37
Chronology of climate modelling developments
Decade
Significant milestone
1990s
Supercomputer (Cray C90) breaks the GIGAFLOP barrier
Introduction and development of GRID computing
Transient climate model simulations
Models of atmospheric chemistry and aerosol
Development of sea-ice and land-surface schemes
Coupled Ocean-Atmosphere
2000s
Supercomputer (Cray SV1-32) breaks the TERAFLOP barrier
Coupled carbon cycle included
Multi model ensemble simulations
HellasGRID & EGEE Projects start to offer GRID Computing
resources to scientists in Europe
2010s
EGI.eu and EGI Inspire provide Production Quality Pan-European
GRID infrastructure
38
The average air temperature variations for the
past 18,000 years.
(Modified from J. T.
Houghton et al., Climate Change: The IPCC
Assessment, Cambridge University Press,
Cambridge, England, 1990.)
39
Different scale processes in climate models
Global Circulation Models

Forcings
Regional Climate models

Solar radiation input
Well mixed greenhouse gas
concentrations
 Continent-ocean distribution
 Large topographical systems
Topography
Landuse
Coastlines, Islands, Peninsulas
Inland water, Lakes, SST
distribution
 Tropospheric Aerosols and gases
(e.g. Ozone)



Effects on the general
circulation





Storm tracks
Dynamics of the ITCZ
Planetary wave patterns
Monsoons
Modes of the coupled system
(NAO, ENSO)
Forcings





Effects on regional climate





Precipitation (e.g. orographic
uplift, cyclogenesis)
Surface energy and water budgets
Mesoscale circulations (surface
winds)
Land-Atmosphere and air-sea
interactions
Extreme events
40
Key processes represented at the grid-box scale
The major processes that are typically included within a single
horizontal grid box of a GCM are:

energy transfer through the atmosphere (involving water
vapor and cloud interactions)

the direct and indirect effects of aerosols (on radiation and
precipitation)

changes in snow cover and sea ice

the storage of heat in soils and oceans

surface fluxes of heat and moisture

large scale transport of heat and water by the atmosphere and
ocean
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Climate change: feedback mechanisms
Uncertainties in predicting climate change are magnified by complex positive and negative
feedback effects which become operative as the climate system responds to change in
forcing.
Positive feedback is a process in which the effects of a small disturbance on a system include
an increase in the magnitude of the perturbation.
Negative feedback occurs when the result of a process influences the operation of the
process itself in such a way as to reduce changes. Negative feedback tends to make a system
self-regulating; it can produce stability and reduce the effect of fluctuations
Among the most significant of these feedback mechanisms is the ice-albedo feedback. It is a
positive feedback which occurs because ice/snow coverage changes enhance themselves.
Warming > Ice/snow reduction
> darker Earth surface > more
heat-absorbing surface > more
warming
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