Transcript NC-CSC pilot: Delivering climate projections on regional scales to support adaptation planning NC-CSC Science Workshop Andrea J.

NC-CSC pilot:
Delivering climate
projections on
regional scales to
support adaptation
planning
NC-CSC Science Workshop
2013
Andrea J. Ray, Ph.D., NOAA Earth System Research Lab
Thanks to: Amy Symstad, Dominique Bachelet, P. Shafroth, L Perry,
Max Post van der Burg, R. Sojda, Brant Liebmann, Joe Barsugli, Bob Means,
Jeffrey T. Morisette, Dennis Ojima
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Joint Pilot with
North Central Climate Science Center
Overarching goal
• To explore together the “best available climate information” to support key
land management questions and how to provide that information.
• To develop a deliberate, ongoing interaction to prototype how NCPP will work
with CSCs to develop and deliver needed climate information products.
• Build capacity in the NC CSC by providing NCPP’s translational information for
climate data used as input to USGS‐based ecological modeling
• 4 projects funded summer 2012. Will discuss two:
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Riparian Corridors
Grasslands & Forests
Plains & Prairie Potholes
Sage Grouse
• Wyoming Basin REA
• Discussions with other LCC, CSCs, NOAA Fisheries, etc
APPROACH: How are they currently using climate projections and what choices have they
made? what evaluation and comparisons are the ecologists interested in, what do we
think they should be interested in?
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Indices from the Ecology literature
BioClim Indices
• Annual Mean Temperature/Annual Precipitation
• Mean Diurnal Range (Mean of monthly (max temp - min temp))
• Isothermality (BIO2/BIO7) (* 100)
• Temperature Seasonality (standard deviation *100)
• Max Temperature of Warmest Month
• Min Temperature of Coldest Month
• Temperature Annual Range (BIO5-BIO6)
• Mean Temperature of Wettest Quarter/ Driest Quarter
• Mean Temperature of Warmest Quarter/Coldest Quarter
• Precipitation of Wettest Month/Driest Month
• Precipitation Seasonality (Coefficient of Variation)
• Precipitation of Wettest Quarter/Precipitation of Driest Quarter
• Precipitation of Warmest Quarter/Coldest Quarter
Others:
• Humidity
• Growing degree days, Chilling & forcing units
• Annual dryness index: (DD5)0.5/Mean Annual Ppt
• Palmer Drought Index, Std Precip Index (SPI)
• Ratio of growing season ppt to mean annual ppt
• Stream temperature e.g. July avg temp for trout
Regional Climate Futures
Challenge #1 – RCM historical climate may be too wet to use as is
RegCM3 – Northern R.M. tile
Interpolation of met. data
From A. Symstad, USGS/Wind Cave, & D.
Bachelet
Regional Climate Futures
Challenge #3 – boundary conditions cause edge effects
From A. Symstad, USGS/Wind Cave, & D.
Bachelet
Regional Climate Futures
Challenge #2 – not all RCM tiles are equal
PACIFIC
SOUTHWEST
TILE
SOUTHERN
ROCKIES
TILE
The five RegCM3 model domains: Pacific Northwest (PNW), Pacific
Southwest (PSW), Northern Rocky Mountains (NRM), Southern Rocky
Mountains (SRM), and Eastern North America
(ENA).
From A. Symstad, USGS/Wind Cave & D. Bachelet
Precipitation
Precipitation
Temperature
Temperature
PACIFIC SOUTHWEST TILE
SOUTHERN ROCKIES TILE
From A. Symstad, USGS/Wind Cave & D. Bachelet
Concerns with few model runs dynamically
Projected annual temperature and
downscaled
precipitation change
Colorado Plateau REA region
112 downscaled runs from Maurer et al.
Other 109 GCM
ECHAM in pink
% Change in annual precipitation
25
20
15
10
5
0
-5
-10
-15
-20
-25
0.0
% Change in annual precipitation
30
20
10
0
-10
-20
-30
0.0
1.0
1.0
• A strength of dynamical
downscaling is in the regional
processes represented
runs
ECHAM5 A2
• A “con” is that its
Runs 2 & 3
computationally expensive, so
few models and runs are
downscaled
• Hostetler (USGS) downscaled
one run each from ECHAM and
GFDL
• Solution? At least look at the
Change in annual temperature, deg C
model runs in the context of
2.0
3.0
4.0
5.0
many models
Other 109 GCM • Know – how does the GCM run
runs
selected comparewarmer/cooler/drier
2.0
3.0
4.0
Change in annual temperature, deg C
5.0
Difference between
1968-99 climatology and
2015-30 (top) and 20452060 (lower)
Concerns with few model runs downscaled
• From Daniels, FAQs,
“Figure 5—Scatterplot of
change in annual average
temperature (°C) and
precipitation (%) projected
by different GCMs for the
2040s (2030-2059) in the
Upper Missouri River
Basin. Triangles represent
GCM simulations of the B1
emissions scenario; circles
= A1B; and squares = A2.
Large bold symbols
represent multi-model
averages for each
emissions scenario
(source: Littell and other
2011).”
“Time series”
• Ecologists say that the
sequence of years is
important for many
systems & interest in
runs of wet/dry years
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Basic & Evaluating; WWA/RISA
Neither ensemble means or
simply selecting one or a few
runs is a good choice
Poor choice to use just
one model run from one
GCM – GCM’s aren’t
intended as
“predictions” of a
sequence, but intended
to generate a new
“climatology”
• false sense of “reality”
from only one or a few
model runs
• Solution???? We’re
working on it….
The model chosen matters… often arbitrary
• Regional studies may
select GCMs based on
some evaluation of how
the represent
• However…often
“arbitrary” choices of
models*** e.g. b/c a
colleague has used it, or
another
• Issue: ***
• Solution???? They’re
not wrong…different
representations of
future; KNOW which
you’re picking – could
intentionally pick
different climates.
Some observations
APPROACH: How are they currently using climate projections and what choices
have they made? what evaluation and comparisons are the ecologists
interested in, what do we think they should be interested in?
• Downscale to very fine resolution, 1km – tempting b/c a lot of ecological
variation/observations are at this scale -- but is the fine scale actually adding
value? Meaningful?
• Bioclim indices – widely used, but do the 30 year averages “wash out” extremes
and variability that’s an important feature driving the ecology??
• Are there better indices?
• Potential pitfalls for comparing studies based on one set of GCMs downscaled
to studies based on another set
 Many different downscaling projects use different GCMs, often not easily
comparted
 Hostetler, Rehfeldt, Mauerer, MACA/Abatzoglu/JohnA, Stamm etc and with
models downscaled –
 Initial solution: look at the data, see how they compare to a common
downscaling or each other (As with observations)
Dynamic processes
storm tracks, high pressure/blocking, great plains low level jet, etc
My list: climate science guidance & translational info
1) “Downscaling,” how low can you go? What meaningful information at different
resolutions down, what’s meaningful at 1km?
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Scales appropriate to regions and phenomena – and how to communicate the limits of
downscaling
Temperature broader scale, so likely represented better at 4-12-15km– climatology/statistics
PPT varies more spatially, but statistics might be OK at those scales, and no better represented
at small downscaling
How do different downscaling products compare??? Maurer-Hostetler-NARCCAP
2) “Time series,” sequencing of years -- many ecological questions sensitive to sequence
3) “Decadal” outlook: what’s more/less likely over in 10-20-30 years, given natural
variability and trend (vs just trend)
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How to take advantage of new CMIP5 decadal runs, statistical methods (LIM), etc
Triangulate among methods?
Are we passing thresholds?
4) ET/PET & drought indicators – and projections of these
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Improving PET/ET in MCI ecological model
Need soil moisture/dryness for vegetation type/structure/succession, fire, transition, invasives
• Multi-year problem Katherine discussed
5) Seasonal-interannual outlooks – for management actions in the next seasons & year or
two – and how might these change in the future. Many applications, likely builds
capacity for adaptation, but has been a bit “left behind” in CC interest
Challenge: Comparing results from different GCMs &
their downscaling
• Many ecological and hydrological studies published – the basis for land & ecosystem
management plans, REAs
• Need to know
• Each have made choices, sometimes arbitrary -- but how to compare results
• WY REA, for example wants to use Rehfeldt, but he downscaled different GCMs from
Hostetler, which they’re required to consider
• Solution?
• We’re comparing Hostetler & Rehfeldt downscaling for particular variables,
comparing both to Maurer
• If the variables themselves have differences, e.g. warmer/cooler/drier, we’ll have
documented the source
General observations across the projects
 Climate projections & gridded observational data are widely available from
multiple sites -- need overall guidance, vs from each data provider
 However, little consistent information on evaluations, guidance on use, or
“translational information”
 Many different downscaling projects use different GCMs, often not easily comparted
 With Hostetler, Rehfeldt, Mauerer, MACA/Abatzoglu/JohnA, Stamm etc and with
models downscaled – see TABLE
 How to handle tis?
 Downscale to very fine resolution, 1km – but is the fine scale actually
adding value? Meaningful?
 Bioclim indices – widely used, but do the 30 year averages “wash out” extremes
and variability that’s an important feature in the
 How to put the climate info in context relevant for the managers?
 Give them the same critical eye as ecological data you use – PLOT & explore
the data just as you would biological data {GRAPHIC from daymet and from
Laura
 Climate scientists need to do a better job of explaining what different
products should and shouldn’t be used for.
 Results of objective & quantitative evaluation
 Narratives, which may include qualitative and quantitative aspects of data use;
e.g., expert guidance on the suitability of the data for an application; also
narratives that provide summary information of how the climate has changed or
how it will change
 Guidance on appropriate uses & interpretation
Next steps??
• Many ecological and hydrological studies published – the basis for work like REAs
• Each have made choices, sometimes arbitrary -- but how to compare results
• WY REA, for example wants to use Rehfeldt, but he downscaled different GCMs from
Hostetler, which they’re required to consider
• We’re comparing Hostetler & Rehfeldt downscaling for particular variables
• If the variables themselves have differences, e.g. warmer/cooler/drier, we’ll have
documented the source
• Many sources of climate data, but most don’t provide guidance or translational info
General questions/needs across the projects
 Climate projections & gridded observational data are widely available from
multiple sites
 However, little consistent information on evaluations, guidance on use, or “translational information”
 Give them the same critical eye as ecological data you use – PLOT & explore the data just as
you would biological data {GRAPHIC from daymet and from Laura}
 Climate scientists need to do a better job of explaining what different products should and
shouldn’t be used for.
 Results of objective & quantitative evaluation
 Narratives, which may include qualitative and quantitative aspects of data use; e.g., expert guidance
on the suitability of the data for an application; also narratives that provide summary information of how
the climate has changed or how it will change
 Guidance on appropriate uses & interpretation
 Characterize & interpret uncertainty
 “Time series” data into the future, using the projections as “predictions”
 The sequence of events matters for a lot of ecological studies -- Neither ensemble means or simply
selecting one or a few runs is a good choice
 GRAPHIC
Hostetler/USGS Dynamical Downscaling – being used a lot in ecological studies
 how do the 3 GCM selected runs compare {GRAPHIC}
 Consider this for any GCMS your project is using
 Comparing results among available analysis projects
Challenge: Comparing results from different GCMs &
their downscaling
• Many ecological and hydrological studies published – the basis for work like REAs
• Each have made choices, sometimes arbitrary -- but how to compare climate results to
inform ecological questions?
• WY REA, for example wants to use Rehfeldt, but he downscaled different GCMs from
Hostetler, which they’re required to consider
• Solution?
• We’re comparing Hostetler & Rehfeldt downscaling for particular variables – and
comparing both to Maurer
• If the variables themselves have differences, e.g. warmer/cooler/drier, we’ll have
documented the source
• Another solution: Consider products that have been used/evaluated in many projects
• Statistical products:
“Maurer” – the basis for the Reclamation “SECURE Water Act” report,and extensively analyzed
– IMHO the gold standard for now – downscaled many GCMS and ensemble members/GCM;
has been run thru a hydrologic model, so available for hydroclimate variables; already being
used in DOI policy & planning. Available from several portals, with visualization tools Climate
Wizard, GeoDataPortal
Other options: Hayhoe’s downscaling to stations & other products, used in the National
Climate Assessment; WorldClim – widely used, less evaluated
• Dynamical products covering North America
NARCCAP, North American Regional Climate Change Assessment Program,
http://www.narccap.ucar.edu. IMHO the gold standard for now – 6 GCMs and multiple RCMs;
used in the 2013 National Climate Assessment
 Other options: Hostetler, aka “USGS Dynamical downscaling,”
http://pubs.usgs.gov/of/2011/1238/; http://regclim.coas.oregonstate.edu; caveat: only 3 GCMs,
Delivering climate
projections on
regional scales to
support adaptation
planning: ESRL/PSD
activities
Climate Predictions Applications Workshop
24 April 2013
Logan, UT
Andrea J. Ray, Ph.D., Jeffrey T. Morisette, Dennis Ojima
NOAA Earth System Research Lab, NC-CSC, CSU.
Thanks to: Amy Symstad, Donimique Bachelet, P. Shafroth, L Perry, Max Post Van
der Berg, Brant Liebmann, Joe Barsugli, Jeff Morisette, Dennis Ojima
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EXTRAS
“Tiles”
• Ecologists
• Edge effects
• Solution????
Joint Pilot Climate and Ecological Integrated Modeling
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Climate data based on
results of objective,
quantitative evaluation
Document challenges
and decisions made in
determining
apppropriate
information to use
Develop translational
information based on
iteration with NC-CSC
Ecological
Response
Models
• Precip run-off (PRMS)
• Phenology (UniForc)
• Seed recruitment (HEC‐EFM)
• Landscape level (MC-1)
• Habitat Niche modeling
DOI Management
Objectives/Goals
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Proposals are publicly available at:
http://www.doi.gov/csc/northcentral/NCPP-Pilot-Project.cfm
Adaptive management
Structure decision
making & value of
information
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Grasslands & forests (Symstad, Bachelet, )
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Much of the NGP is uncultivated, habitat for grassland specialists.
Woody encroachment is a serious issue on the edges of the region.
CO2 fertilization may make region more conducive to tree growth, especially in absence of
fire.
Climate projection issues
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availability and reliability of full time series
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bias correction methods
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RCM “tile” “inconsistencies”
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PET calculation
Downscaling RegCM3 output for Northern Great Plains area (Hostetler)
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Comparisons of values for tile overlap areas
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• Cottonwood and willow seed dispersal typically
occurs during or just after snowmelt peak flows
• What are the chances that seeds will land on bare,
moist surfaces along rivers - created by floods, and
exposed by flood recession
Seed dispersal during or just after the spring flood =
• exposed, bare, moist soil in the recruitment
band
• Too high: dessicate
Too high – drought stress
• Too low, scoured away
• “just right”
Receding
spring flood
Recruitment band
Too low – ice and flood scour
Base flow
Connecting the science to ecology/landscape
in a meaningful and scientifically
defensible way
• Key issues
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Observational data: how to best use in situ data?
What can we say about precipitation?
How do we account for elevation ranges
Be explicit about what we’re confident about, and
unresolved issues
• Overall objective is to develop “Reasonably
Foreseeable Climate Scenarios” (RFCS),
based on analysis and comparison among
several climate projection datasets and to
compare the RFCS to the historic period for
the region
• Goals:
– Relate to conservation elements
– Shifts vegetation classes, e.g. from semidesert shrub steppe to shrublands?
– Assess Key Ecological Attributes
WY Basin REA
Down-scaled climate data in current conditions
EPA logo
Fort Collins, Colorado
March 7, 2013
“DRAFT”
• Ecologists
• Edge effects
• Solution????
NOAA/PSD Climate Science in the Intermountain West
Basic & applied climate science: often at the “technical interface” between
researchers and problems faced by managers
• Evaluating climate models – how well climate features are simulated &
confidence e.g. seasonal snowpack, hydrology, storm tracks, changes in
extremes
• Climate diagnostics: provide an explanation of evolving conditions, e.g.
seasonal anomalies, heat waves, Hurricane Sandy, Texas drought, Missouri
river flooding; Assess predictability
• “Use-inspired science”: Climate & Fisheries; Arctic Impacts Assessment;
drought; Partners: NC-CSC, NMFS, FWS,NIDIS, universities, WWA/RISA, TNC
• Guidance & climate analysis: Sage brush/sage grouse, prairie potholes,
grasslands; Wyoming Basin Rapid Ecoregional Assessment. BLM, NC-CSC,
TNC
• Hydroclimate studies: climate & water management; projections of climate
change on river flows/water supply, partners: Reclamation, Army Corps,
Western Utility Climate Alliance; WWA/RISA
• Emerging topics: dust on snow; decadal predictions
Synthesis & Assessment: of existing science & often new analysis to inform specific
policy & decisionmaking processes
• 2012 National Assessments for several regions/topics & 2009 CCSP
synthesis documents (SAPs) – as authors, contributors, reviewers
• FWS Status review of the American pika, Ochotona sp, 2010
• Climate change in Colorado to support Water Management, 2008
North Central Regional Example
• What NOAA capabilities were applied?
– Evaluate &provide guidance on “best practices” on model use and
downscaling approaches
– Efforts to translate & contextualize the climate information for various DOI
& LCC decision contexts
– Builds on NOAA and NOAA-funded research at labs and universities; RISA
experience on understanding climate needs
– The USGS Geodata portal is an NCPP partner
• Address common ground with the LCC mission by
– Leverage resources and strategically target science to inform conservation
actions, in particular by supporting vulnerability assessment for climate
change
– Three projects have landscape level focus risk framework, each directed at
management goals identified by DOI and several LCC partners; 4th project
assesses the value of climate information for supporting management
decisions within the Plains and Prairie Potholes LCC
North Central Pilot Project with NOAA’s
National Climate Projection program
Woody Encroachment, Riparian Corridors
Sage Brush/Sage Grouse
Plains & Prairie Potholes
Hadley Cells
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George Hadley, 1700s
Simple Theory Explains
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N-S movement of air near equator
Trade winds blow from NE/SE
Deserts at 30 N/S Latitude
Areas of heavy rain at Equator
Location of “Subtropical Jet”
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Note: major UK Modeling Center
named after Hadley
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Hadley Cell projected to intensify
and expand northward
Joint Pilot Projects have landscape level focus, risk framework,
directed at identified management goals of DOI & LCC
partners:
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Riparian Corridors: Projecting climate change effects on
cottonwood and willow seed dispersal phenology, flood
timing, and seedling recruitment in western riparian
forests (Shafroth et al., USGS Fort Collins Science Center)
Sage Grouse & Habitats: Integrating climate and
biological data into land management decision models to
assess species and habitat vulnerability: A collaboration
for Greater Sage- ‐Grouse and their habitats (Sojda et al.,
USGS Northern Rockies Science Center)
Grasslands & Forests: Projecting Future Effects of Land
Management, Natural Disturbance, and CO2 on Woody
Encroachment in the Northern Great Plains in a Changing
Climate (Symstad et al., USGS Northern Prairie Wildlife
Research Center & Oregon State )
Assess the value of climate information for supporting
management decisions within the Plains and Prairie
Potholes LCC (Post van der Burg et al., USGS Northern
Prairie Wildlife Research Center & Fort Collins Science
Ctr.)
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Ecologist’s questions
Scale – temporal and spatial
• Downscale to very fine resolution, 1km – but is the fine scale actually adding
value? Meaningful?
• Bioclim indices – widely used, but do the 30 year averages “wash out” extremes
and variability that’s an important feature in the
• Climate often used “uncritically”
• Table with hostetler, rehfeldt, mauerer, MACA/Abatzoglu,Stamm etc and whith
models downscaled