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Applied Hydrology Climate Change and Hydrology (I) - GCMs and Climate Change Scenarios

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Title: Applied Hydrology Climate Change and Hydrology (I) - GCMs and Climate Change Scenarios


1
Applied HydrologyClimate Change and
Hydrology(I) - GCMs and Climate Change Scenarios
  • Professor Ke-Sheng Cheng
  • Department of Bioenvironmental Systems
    Engineering
  • National Taiwan University

2
Climate dynamics, climate change and climate
prediction
  • Climate average condition of the atmosphere,
    ocean, land surfaces and the ecosystems in them.
  • e.g., "Baja California has a desert climate
  • Weather state of atmosphere and ocean at given
    moment.
  • Climate includes average measures of
    weather-related variability.
  • e.g., probability of a major rainfall event
    occurring in July in Baja, variations of
    temperature that typically occur during January
    in Chicago,

Neelin, 2011. Climate Change and Climate Modeling
3
  • Climate quantities defined by averaging over the
    weather
  • Average taken over January of many different
    years to obtain a climatological value for
    January, many Februaries to obtain February
    climatology, etc.

Climatology of sea surface temperature for
January (15 year average)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
4
  • Climate change
  • occurring on many time scales, including those
    that affect human activities.
  • time period used in the average will affect the
    climate that one defines.
  • e.g., 1950-1970 will differ from the average from
    1980-2000.
  • Climate variability
  • essentially all the variability that is not just
    weather.
  • e.g., ice ages, warm climate at the time of
    dinosaurs, drought in African Sahel region, and
    El Niño.

Climate change usually refers to changes in
statistical properties of climate variables. A
stationary climate process can and usually do
exhibit climate variability.
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
5
  • Anthropogenic climate change due to human
    activities.
  • e.g., ozone hole, acid rain, and global warming.

Data from the Program for Model Diagnosis and
Intercomparison (PCMDI) archive.
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
6
  • Global warming predicted warming, associated
    changes in the climate system in response to
    increases in "greenhouse gases" emitted into
    atmosphere by human activities.
  • Greenhouse gases e.g., carbon dioxide, methane
    and chlorofluorocarbons trace gases that absorb
    infrared radiation, affect the Earth's energy
    budget.
  • warming tendency, known as the greenhouse effect
  • Global change human-induced changes more
    generally (including ozone hole).
  • Environmental change even more general
    (including air, water pollution, deforestation,
    ecosystems change, )
  • Climate prediction endeavor to predict not only
    human-induced changes but the natural variations.
    e.g., El Niño

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
7
  • Climate Dynamics or Climate Science studies
    climate and climate change processes (older term,
    climatology).
  • Climatology now used for average variables, e.g.,
    the January precipitation climatology.
  • Climate models
  • Mathematical representations
  • of the climate system
  • typically equations for temperature,
  • winds, ocean currents and other climate
  • variables solved numerically on computers.
  • Climate System or Earth System global,
  • interlocking system atmosphere, ocean, land
    surfaces, sea and land ice, and biosphere (plant
    and animal component).

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
8
Changes in climate/weather
  • Climate extremes or weather extremes?
  • Extreme rainfalls are results of severe weather
    events.
  • Changes in climate can affect occurrences and
    frequencies of extreme weather events.
  • Studies which evaluate the impact of climate
    change on rainfall extremes by comparing to
    rainfall climatology may be misleading.

9
  • Climate extremes and weather extremes

10
  • Climate extremes

11
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12
  • Weather extremes

13
Climate models - a brief overview
  • Motions, temperature, etc. governed by basic laws
    of physics solved numerically
  • e.g., divide the atmosphere and ocean into
    discrete grid boxes
  • equation for balance of forces, energy inputs
    etc. for each box.
  • obtain the acceleration of the fluid in the box,
    its rate of change of temperature, etc.
  • from this compute the new velocity, temperature,
    etc. one time step later (e.g., twenty minutes
    for the atmosphere, hour for ocean).
  • equations for each box depend on the values in
    neighboring boxes.
  • computation is done for a million or so grid
    boxes over the globe.
  • repeated for the next time step, and so on until
    the desired length of simulation is obtained.
  • common to simulate decades or centuries in
    climate runs
  • computational cost a factor

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
14
  • Also close relationship to weather forecasting
    models
  • Major differences
  • complexity of the climate system.
  • range of phenomena at different time scales.
  • messier clouds, aerosols, vegetation, ...
  • More attention to processes that affect the long
    term

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
15
  • The most complex climate models, known as General
    Circulation Models or GCMs.
  • Once a phenomena has been simulated in a GCM, it
    is not necessarily easy to understand.
  • Intermediate complexity climate models are also
    used.
  • construct a model based on same physical
    principles as a GCM but only aspects important to
    the target phenomenon are retained.
  • e.g., first used to simulate, understand and
    predict El Niño.
  • Simple climate models
  • e.g., globally averaged energy-balance model, to
    understand essential aspects of the greenhouse
    effect.
  • Global warming simulations with GCMs Þ detailed
    processes, 3-D response.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
16
Global mean surface temperatures estimated since
preindustrial times
From the University of East Anglia CRU (data
following Brohan et al. 2006 Rayner et al. 2006)
  • Anomalies relative to 1961-1990 mean
  • Annual average values of combined near-surface
    air temperature over continents and sea surface
    temperature over ocean.
  • Curve smoothing similar to a decadal running
    average.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
17
  • Anomaly departure from normal climatological
    conditions.
  • calculated by difference between value of a
    variable at a given time, e.g., pressure or
    temperature for a particular month, and
    subtracting the climatology of that variable.
  • Climatology includes the normal seasonal cycle.
  • e.g., anomaly of summer rainfall for June, July
    and August 1997, average of rainfall over that
    period minus averages of all June, July and
    August values over a much longer period, such as
    1950-1998.
  • To be precise, the averaging time period for the
    anomaly and the averaging time period for the
    climatology should be specified.
  • e.g., monthly averaged SST anomalies relative to
    1950-2000 mean.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
18
Global Circulation Models (GCMs)
  • Computer models that
  • are capable of producing a realistic
    representation of the climate, and
  • can respond to the most obvious quantifiable
    perturbations.
  • Derived based on weather forecasting models.

19
Weather forecasting models
  • The physical state of the atmosphere is updated
    continually drawing on observations from around
    the world using surface land stations, ships,
    buoys, and in the upper atmosphere using
    instruments on aircraft, balloons and satellites.
  • The model atmosphere is divided into 70 layers
    and each level is divided up into a network of
    points about 40 km apart.

20
  • Standard weather forecasts do not predict sudden
    switches between stable circulation patterns
    well. At best they get some warning by using
    statistical methods to check whether or not the
    atmosphere is in an unpredictable mood. This is
    done by running the models with slightly
    different starting conditions and seeing whether
    the forecasts stick together or diverge rapidly.

21
  • This ensemble approach provides a useful
    indication of what modelers are up against when
    they seek to analyses the response of the global
    climate to various perturbations and to predict
    the course it will following in the future.
  • The GCMs cannot represent the global climate in
    the same details as the numerical weather
    predictions because they must be run for decades
    and even centuries ahead in order to consider
    possible changes.

22
  • Typically, most GCMs now have a horizontal
    resolution of between 125 and 400 km, but retain
    much of the detailed vertical resolution, having
    around 20 levels in the atmosphere.
  • Challenges for potential GCMs improvement
  • Modeling clouds formation and distribution
  • Tropical storms (typhoons and hurricanes)
  • Land-surface processes
  • Winds, waves and currents
  • Other greenhouse gases

23
GCMs
24
The parameterization problem
  • For each grid box in a climate model, only the
    average across the grid box of wind, temperature,
    etc. is represented.
  • In the observations, many fine variations occur
    inside,
  • e.g., squall lines, cumulonimbus clouds, etc.
  • The average of these small scale effects has
    important impacts on large-scale climate.
  • e.g., clouds primarily occur at small scales, yet
    the average amount of sunlight reflected by
    clouds affects the average solar heating of a
    whole grid box.
  • Average effects of the small scales on the grid
    scale must be included in the climate model.
  • These averages change with the parameters of
    large-scale fields that affect the clouds, such
    as moisture and temp.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
25
  • Method of representing average effects of clouds
    (or other small scale effects) over a grid box
    interactively with the other variables known as
    parameterization.
  • Successes and difficulties of parameterization
    important to accuracy of climate models.
  • finer grid implies greater computational costs
    (or shorter simulation)
  • As computers become faster Þ finer grids.
  • But there are always smaller scales.
  • Scale interaction is one of the main effects that
    makes climate modeling challenging.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
26
Typical atmospheric GCM grid
Constructing a Climate Model
  • For each grid cell, single value of each variable
    (temp., vel.,)
  • ÞFinite number of equations
  • Vertical coordinate follows topography, grid
    spacing varies
  • Transports (fluxes) of mass, energy, moisture
    into grid cell ÞBudget involving immediate
    neighbors (in balance of forces, PGF involves
    neighbors)
  • Effects passed from neighbor to neighbor until
    global
  • Budget gives change of temperature, velocity,
    etc., one time step (e.g. 15 min) later
  • 100yr4million 15min steps

Figure 5.1
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
27
Vertical column showing parameterized physics so
smallscale processes within a single column in a
GCM
Treatment of sub-grid scale processes
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
28
Topography of western North America at 0.3 and
3.0 resolutions
Resolution and computational cost
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
29
Topography of North America at 0.5 and 5.0
resolutions
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
30
Resolution and computational cost
  • Computational time (computer time per
    operation)
  • (operations per equation)(No. equations
    per grid-box)
  • (number of grid boxes)(number of time
    steps per simulation)
  • Increasing resolution grid boxes increases
    time step decreases
  • Half horizontal grid size Þ half time step
  • Þ twice as many time steps to simulate
    same number of years
  • Doubling resolution in x, y z Þ 222( grid
    cells)

  • 2( of time steps)
  • Þ cost increases by factor of 24 16
  • Increase horizontal resolution, 5 to 0.5 degrees
    Þ factor of 10 in each horizontal direction. So
    even if kept vertical grid same, 1010( grid
    cells)10( of t steps) 103
  • Suppose also double vertical res. Þ 2000 times
    the computational time
  • i.e. costs same to run low-res. model for
    40 years as high res. for 1 week
  • To model clouds, say 50m res. Þ 10000 times res.
    in horizontal, if same in vertical and time Þ
    1016 times the computational time and will
    still have to parameterize raindrop, ice crystal
    coalescence etc.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
31
  • Why time step must decrease when grid size
    decreases
  • Time step must be small enough to accurately
    capture time evolution and for smaller grid size,
    smaller time scales enter.
  • A key time scale time it takes wind or wave
    speed to cross a grid box.
  • e.g., if fastest wind 50 m/s, crosses 200 km
    grid box in 1 hour
  • If time step longer, more than 1 grid box will be
    crossed can yield amplifying small scale noise
    until model blows up
  • (for accuracy, time step should be
    significantly shorter)

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
32
Finite differencing of a pressure field
Numerical representation of atmos. and oceanic
eqns.
Finite difference versus spectral models
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
33
Spectral representation of a pressure field
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
34
Climate drift
Climate simulations and climate drift
Examples of model integrations (or runs,
simulations or experiments), starting from
idealized or observed initial conditions. Spin-up
to equilibrated model climatology is required
(centuries for deep ocean). Model climate differs
slightly from observed (model error aka climate
drift) climate change experiments relative to
model climatology.
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
35
Radiative forcing as a function of time for
various climate forcing scenarios
Commonly used scenarios
Top of the atmosphere radiative imbalance Þ
warming due to the net effects of GHG and other
forcings
from the Special Report on Emissions Scenarios
  • SRES
  • A1FI (fossil intensive),
  • A1T (green technology),
  • A1B (balance of these),
  • A2, B2 (regional economics)
  • B1 greenest
  • IS92a scenario used in many
  • studies before 2005

Adapted from Meehl et al., 2007 in in IPCC Fourth
Assessment Report
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
36
SRES emissions scenarios, contd
  • A1 scenario family assumes low population
    growth, rapid economic growth, reduction in
    regional income differences
  • A1FI Fossil fuel Intensive
  • A1B energy mix, incl. non-fossil fuel
  • A2 uneven regional economic growth, high income
    toward non-fossil, population 15 billion in 2100
  • B1 like A1 but switch to information and service
    economy, introduction of resource-efficient
    technology. Emphasis on global solutions to
    economic, social, and environmental
    sustainability, including improved equity.
  • No explicit consideration of treaties
  • Natural forcings e.g., volcanoes set to avg. from
    20th C.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
37
Model names (a sample)
  • CCMA_CGCM3.1, Canadian Community Climate Model
  • CNRM_CM3, Meteo-France, Centre National de
    Recherches Meteorologiques
  • CSIRO_MK3.0, CSIRO Atmospheric Research,
    Australia
  • GFDL_CM2.0, NOAA Geophysical Fluid Dynamics
    Laboratory
  • GFDL_CM2.1, NOAA Geophysical Fluid Dynamics
    Laboratory
  • GISS_ER, NASA Goddard Institute for Space
    Studies, ModelE20/Russell
  • MIROC3.2_medres, CCSR/NIES/FRCGC, medium
    resolution
  • MPI_ECHAM5, Max Planck Institute for Meteorology,
    Germany
  • MRI_CGCM2.3.2a, Meteorological Research
    Institute, Japan
  • NCAR_CCSM3.0, NCAR Community Climate System
    Model
  • NCAR_PCM1, NCAR Parallel Climate Model (Version
    1)
  • UKMO_HADCM3, Hadley Centre for Climate
    Prediction, Met Office, UK

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
38
Global average warming simulations in 11 climate
models
  • Global avg. sfc. air temp. change
  • (ann. means rel. to 1901-1960 base period)
  • Est. observed greenhouse gas aerosol forcing,
    followed by
  • SRES A2 scenario (inset) in 21st century
  • (includes both GHG and aerosol forcing)

Data from the Program for Model Diagnosis and
Intercomparison (PCMDI) archive.
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
39
Response to the SRES A2 scenario GHG and sulfate
aerosol forcing in surface air temperature
relative to the average during 1961-90 from the
Hadley Centre climate model (HadCM3)choosing
one model simulation through the 21st century as
an example later compare models or average
results from several models
Spatial patterns of the response to
time-dependent warming scenarios
2010-2039
2040-2069
2070-2099
Figure 7.5
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
40
Response to the SRES A2 scenario GHG and sulfate
aerosol forcing in surface air temperature
relative to the average during 1961-90 from the
National Center for Atmospheric Research
Community Climate Simulation Model (NCAR_CCSM3)
2010-2039
2040-2069
2070-2099
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
41
January and July surface temperature from HadCM3
averaged 2040-2069 (SRES A2 scenario)
January
July
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
42
January and July surface temperature from
NCAR_CCSM3 averaged 2040-2069 (SRES A2 scenario)

January
July
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
43
30yr. avg annual surface air temperature response
for 3 climate models centered on 2055 relative to
the average during 1961-1990
Comparing projections of different climate models
GFDL- CM2.0
NCAR- CCSM3
MPI- ECHAM5
Figure 7.7
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
44
Comparing projections of different climate models
  • Provides estimate of uncertainty
  • Differences often occur with physical processes
    e.g., shift of jet stream, reduction of soil
    moisture,
  • At regional scales (size of country or state)
    more disagreement
  • Precip challenging at regional scales

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
45
Comparing projections of different climate models
GFDL- CM2.0
Precipitation from 3 models for Jun.-Aug.
2070-2099 average minus 1961-90 avg (SRES A2
scenario)
NCAR- CCSM3
MPI- ECHAM5
(mm/day)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
46
Precipitation from 3 models for Jun.-Aug.
2070-2099 average minus 1961-90 avg (SRES A2
scenario)
Comparing projections of different climate models
GFDL- CM2.0
HadCM3
MPI- ECHAM5
(mm/day)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
47
30yr. avg annual surface air temperature response
for 3 climate models centered on 2055 relative to
the average during 1961-90
Comparing projections of different climate models
GFDL- CM2.0
HadCM3
MPI- ECHAM5
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
48
Precipitation from 3 models for Dec.-Feb.
2070-2099 average minus 1961-90 avg (SRES A2
scenario)
Comparing projections of different climate models
GFDL- CM2.0
NCAR- CCSM3
MPI- ECHAM5
(mm/day)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
49
Precipitation from HadCM3 for Dec.-Feb. 2070-2099
avg. (SRES A2)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
50
Precipitation from HadCM3 for Jun.-Aug. 2070-2099
avg. (SRES A2)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
51
  • From GCMs to hydrological process modeling
  • Study of hydrological processes requires spatial
    and temporal resolutions which are much smaller
    than GCMs can offer.
  • Downscaling techniques have been developed to
    downscale GCM outputs to desired scales.
  • Dynamic downscaling
  • Statistical downscaling

52
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