applied hydrology climate change and hydrology (i) - gcms and climate change scenarios professor...

Applied HydrologyClimate Change and Hydrology

(I) - GCMs and Climate Change Scenarios

Professor Ke-Sheng Cheng

Department of Bioenvironmental Systems Engineering

National Taiwan University

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, …

04/19/23 Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

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Neelin, 2011. Neelin, 2011. Climate Change and Climate ModelingClimate Change and Climate Modeling

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. Neelin, 2011. Climate Change and Climate Modeling, Climate Change and Climate Modeling, Cambridge Cambridge UPUP

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.

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.


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


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).


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.

08/22/2013 2013 AsiaFlux, HESSS, GCEER, KSAFM Joint Conference 8

• Climate extremes and weather extremes


• Climate extremes


• Weather extremes


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

Climate models - a brief overviewClimate models - a brief overview


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


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 detailed

processes, 3-D response.


Global mean surface temperatures estimated since preindustrial times

•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.

From the University of East Anglia CRU (data following Brohan et al. 2006; Rayner et al. 2006)


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.


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.

04/19/23 18Lab for Remote Sensing Hydrology and Spatial Modeling Dept of Bioenvironmental Systems Eng, NTU

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.


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• 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.


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• 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.


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• 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


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GCMs


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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.

The parameterization problem


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 finer grids. But there are always smaller scales.Scale interaction is one of the main effects that makes climate modeling challenging.


Typical atmospheric GCM grid

Constructing a Climate ModelConstructing a Climate Model

Figure 5.1

•For each grid cell, single value of each variable (temp., vel.,…)Finite number of equations•Vertical coordinate follows topography, grid spacing varies•Transports (fluxes) of mass, energy, moisture into grid cell Budget 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•100yr=4million 15min stepsNeelin, 2011. Neelin, 2011. Climate Change and Climate Modeling, Climate Change and Climate Modeling, Cambridge Cambridge

UPUP

Vertical column showing parameterized physics so smallscale processes within a single column in a GCM

Treatment of sub-grid scale processes Treatment of sub-grid scale processes


Topography of western North America at 0.3 and 3.0resolutions

Resolution and computational cost Resolution and computational cost


Topography of North America at 0.5 and 5.0 resolutions


• 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 half time step twice as many time steps to simulate same number of years• Doubling resolution in x, y & z (# grid cells) (# of time steps) cost increases by factor of 24 =16• Increase horizontal resolution, 5 to 0.5 degrees factor of 10 in each

horizontal direction. So even if kept vertical grid same, (# grid cells)(# of t steps)= 103

• Suppose also double vertical res. 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. 10000 times res. in horizontal, if same in

vertical and time 1016 times the computational time … and will still have to parameterize raindrop, ice crystal coalescence etc.

Resolution and computational cost Resolution and computational cost


• 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)


Numerical representation of atmos. and oceanic eqns.Numerical representation of atmos. and oceanic eqns.

Finite differencing of a pressure fieldFinite difference versus spectral modelsFinite difference versus spectral models


Spectral representation of a pressure field


Climate drift

Climate simulations and climate driftClimate 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.


Radiative forcing as a function of time for various climate forcing scenarios

Commonly used scenariosCommonly used scenarios

SRES:• A1FI (fossil intensive), • A1T (green technology), • A1B (balance of these), • A2, B2 (regional economics) • B1 “greenest”• IS92a scenario used in manystudies before 2005

Top of the atmosphere radiative imbalance warming due to the net

effects of GHG and other forcings

from the Special Report on Emissions Scenarios


Adapted from Meehl et al., 2007 in Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report

SRES emissions scenarios, cont’d

A1 scenario family: assumes low population growth, rapid economic growth, reduction in regional income differences

A1FI : Fossil fuel IntensiveA1B: 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.


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


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.


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] Figure 7.5

2010-2039

2040-2069

2070-2099

Spatial patterns of the response to Spatial patterns of the response to time-dependent warming time-dependent warming scenariosscenarios


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


January and July surface temperature

from HadCM3 averaged 2040-2069 (SRES A2 scenario)

January

July


January and July surface temperature

from NCAR_CCSM3 averaged 2040-2069 (SRES A2 scenario)

January

July


30yr. avg annual surface air temperature response

for 3 climate models centered on 2055 relative

to the average during 1961-1990

Figure 7.7

GFDL-CM2.0

NCAR-CCSM3

MPI-ECHAM5


Comparing projections of Comparing projections of different climate modelsdifferent climate models

Comparing projections of different climate modelsComparing 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


NCAR-CCSM3

(mm/day)

Precipitation from 3 models for Jun.-Aug.

2070-2099 average minus 1961-90 avg (SRES A2 scenario)


MPI-ECHAM5

GFDL-CM2.0


Precipitation from 3 models for Dec.-Feb.

2070-2099 average minus 1961-90 avg (SRES A2 scenario)

NCAR-CCSM3

(mm/day)

MPI-ECHAM5

GFDL-CM2.0



Precipitation from HadCM3 for Dec.-Feb. 2070-2099 avg. (SRES A2)


Precipitation from HadCM3 for Jun.-Aug. 2070-2099 avg. (SRES A2)


• 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


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applied hydrology climate change and hydrology (i) - gcms and climate change scenarios professor...

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