Climate Modeling Elissa Lynn SCRO August 27, 28/ 2013
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Why are climate models important for DWR?
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What is a model?
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Mathematical representation on a computer
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http://www.windows2universe.org/earth/climate/climate_modeling.html
Building a Global Climate Model National Earth Science Teachers
Association
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NASA Climate EssentialsMany good videos
http://svs.gsfc.nasa.gov/ClimateEssentials/ NASA Earth Observatory
introduction to global climate models
http://www.climatewatch.noaa.gov/video/2012/the-pushy-pacific-variability-and-change-in-global-temperature
Global temperature change 1850-2100 National Center for Atmospheric
Research http://www.youtube.com/watch?v=U9YAzrmeOGI Global
temperature change projection (older) National Geophysical Fluid
Dynamics Laboratory
http://www.gfdl.noaa.gov/patterns-of-greenhouse-warming-ar4#movies
Videos for office/home viewing Selected videos are on the class
website as well
Processes in Climate Models Image from National Center for
Atmospheric Research
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Future Uncertainty Data collection Computer model variability
Feedback mechanisms Spatial Resolution Why so hard to do? Trivia
Time: How long does it take a modern global climate model to
simulate 150 years?
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Climate models are used to explore possible future changes in
climate Climate models dont predict future changes in climate
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Weather Modeling vs Climate Modeling Weather Forecasting What
will the weather be on a specific day in a specific place Short
term (7-10 days) Future Climate Simulations What will the climate
trends be in a region in the future Long term (100 years)
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Climate models are used to explore possible future changes in
climate Climate models dont predict future changes in climate
Scientists & planners use information from many models to make
decisions
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Mercury Venus Earth Mars Which planet is the warmest?
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www.ecy.wa.gov/climatechange/whatis.htm How will these
greenhouse gases change in the future?
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Green House Gases in Climate Models Actual greenhouse gas
emissions were higher than future scenarios used in 2007 IPCC
studies Colored lines greenhouse gas emissions scenarios Black dots
actual greenhouse gas emissions Future greenhouse gas
concentrations cover a higher range in 2013 IPCC studies -Called
RCP or Representative Concentration Pathway -Number at end
indicates radiative forcing value at the end of the century 2.6,
4.5, 6.0, and 8.5 W/m 2 in 2100 Concentration CO 2 equivalent from
all greenhouse gases RCP8.5 RCP6 RCP4.5 RCP2.6 Fossil Fuel CO 2
Emissions IPCC = Intergovernmental Panel on Climate Change, RCP =
Representative Concentration Pathway
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Green House Gases in Climate Models IPCC = Intergovernmental
Panel on Climate Change RCP = Representative Concentration Pathway
for future greenhouse gas concentrations RCP overview:
http://link.springer.com/article/10.1007/s10584-011-0148-z RCP4.5
Incoming solar radiation Atmosphere with changing greenhouse gas
concentrations traps and re-emits radiation Radiation emitted to
space Radiative forcing is the change in the radiation balance due
to changes in greenhouse gas concentrations Increased radiation
leads to warming
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Green House Gases in Climate Models IPCC = Intergovernmental
Panel on Climate Change RCP = Representative Concentration Pathway
for future greenhouse gas concentrations RCP overview:
http://link.springer.com/article/10.1007/s10584-011-0148-z IPCC
2013 is using greenhouse gas concentrations called Representative
Concentration Pathways Scenarios reflect a range of future
radiative forcings change in radiation balance at the surface of
the troposphere due to changes in greenhouse gas concentrations
Names refer to forcing values in the year 2100 2.6, 4.5, 6.0, and
8.5 W/m 2, respectively RCP8.5 RCP6 RCP4.5 RCP2.6 Concentration CO
2 equivalent from all greenhouse gases Incoming solar radiation
Atmosphere with changing greenhouse gas concentrations traps and
re-emits radiation Radiation emitted to space Radiative forcing is
the change in the radiation balance due to changes in greenhouse
gas concentrations Increased radiation leads to warming
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Climate models are used to explore possible future changes in
climate. Global Climate ModelEstimate of Future Greenhouse
Gases
Available Climate Change Scenarios From 2007 IPCC [CMIP3] 112
available scenarios (GCM x Greenhouse Gas) Subset of 12 scenarios
recommended by Californias Climate Action Team From 2013 IPCC
[CMIP5] More available scenarios More variables available Higher
spatial and temporal resolution Models include improved
representations of processes Regional impacts in California are
similar to CMIP3 IPCC=Intergovernmental Panel on Climate Change,
CMIP=Coupled Model Intercomparison Project
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Downscaling Converting Global model output into Regional or
Local information into Regional or Local information More specific,
Higher resolution, More useful Images from Mike Dettinger,
Scripps/USGS Demonstration: Climate Modeling and Legos
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http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/ Website
for Downscaled Climate Change Scenarios
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July Temperature Change 14 GCMs X 3 RCP emissions Scenarios
IPCC 5 th Assessment (CMIP5) models Warming is substantial in
climate projections Cayan et al. Scripps/USGS 2013
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Climate Change Temperatures OBSERVED Summer warming higher than
winter Interior warming greater than coastal/marine Nighttime
warming has exceeded daytime in last few decades PROJECTED Heat
wave incidence: more frequent, intense, durable Precipitation
change uncertain Probably will remain extremely variable Consensus
of newest models shows little change in Northern Cal Continued
potential for extremely heavy events CLIMATE CHANGE
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Climate scientists and modelers explore possible future climate
conditions DWR staff assess potential impacts and risks that those
climate change conditions could have on Californias water resources
http://www.water.ca.gov/climatechange/articles.cfm
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Decision-Makers: Keep it simple. Climate Information Providers:
Heres the info use it wisely. Modelers and Data Analyzers: Keep it
manageable. Adapted from Levi Brekke USBR Climate Change
Information Who is involved? What do they want? Decision
Requirements Emission Scenarios Climate Simulations Spatial
Downscaling Modeling to support Decision Making Land Cover Changes
Societal Changes
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Building Coalitions SCRIPPS Institute of Oceanography U.S.
Geological Survey California Energy Commission UC Berkeley Lawrence
Livermore Lab Santa Clara University Lawrence Berkeley Lab UC
Davis
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Building Coalitions SCRIPPS Institute of Oceanography U.S.
Geological Survey California Energy Commission UC Berkeley Lawrence
Livermore Lab Santa Clara University Lawrence Berkeley Lab UC
Davis
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What Climate Change Scenarios/Models Should We Use? Bookends
scenarios that represent high/middle/low changes Ensemble - Use
information from a group of models/scenarios - One way to address
variability and uncertainty Other?
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UNCERTAINTY Climate Projections Converting global data to
local/regional scales Changes in societal values $$$$ Ecosystem
response Water cycle changes Future water demands Sea level rise
Adaptation Strategies Population Land use changes
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Take Home Message Global climate models explore possible future
climate conditions, they dont predict future climate conditions DWR
uses results from global climate models to evaluate possible
impacts of climate change on Californias water resources
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Extra Slides
Slide 33
What model best represents Californias historical climate?
Figure 19. Relative error of the ensemble mean of each metric for
each CMIP5 GCM. Models are ordered from least (left) to most
(right) total relative error, where total relative error is the sum
of relative errors from all metrics, excluding the diurnal
temperature range (DTR) metrics. For 7 GCMs, the diurnal
temperature range (DTR) metrics were not calculated (white
squares). Ranking Global Climate Models by Rupp and Mote using a
multi-factor historical climate evaluation scheme for the
Evaluation Parameter Climate Models Relative Error High relative
error in SW United States No clear best model, depends on criteria
Models that didnt perform well in SW US may be excluded in future
analysis Low relative error in SW United States Least total
relative error Most total relative error Adapted from Cayan 2013
Southwestern United States