Title: Applied Hydrology Climate Change and Hydrology (I) - GCMs and Climate Change Scenarios
1Applied HydrologyClimate Change and
Hydrology(I) - GCMs and Climate Change Scenarios
- Professor Ke-Sheng Cheng
- Department of Bioenvironmental Systems
Engineering - National Taiwan University
2Climate 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
8Changes 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 11(No Transcript)
12 13Climate 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
16Global 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
18Global 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.
19Weather 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
23GCMs
24The 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
26Typical 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
27Vertical 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
28Topography of western North America at 0.3 and
3.0 resolutions
Resolution and computational cost
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
29Topography of North America at 0.5 and 5.0
resolutions
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
30Resolution 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
32Finite 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
33Spectral representation of a pressure field
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
34Climate 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
35Radiative 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
36SRES 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
37Model 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
38Global 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
39Response 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
40Response 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
41January and July surface temperature from HadCM3
averaged 2040-2069 (SRES A2 scenario)
January
July
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
42January and July surface temperature from
NCAR_CCSM3 averaged 2040-2069 (SRES A2 scenario)
January
July
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
4330yr. 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
44Comparing 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
45Comparing 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
46Precipitation 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
4730yr. 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
48Precipitation 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
49Precipitation from HadCM3 for Dec.-Feb. 2070-2099
avg. (SRES A2)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
50Precipitation 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(No Transcript)