USA-NPN: Climate/Hydrology Breakout
Juio Betancourt, Ben Cook, Jonathan Hanes, Greg McCabe, Tim Owen, Mark Schwartz, Alison
Steiner, Adam Terando
Tuesday, October 06, 2009: Scoping a Plan for a National Phenological Assessment:
1. What is the full inventory of relevant climatic and hydrological variables and indices that
can be mined from a comprehensive review and registry of broadly applicable
phenological models?
2. What are the spatial (regional) and temporal patterns of past variation in phenologicallyrelevant climatic and hydrological variables and indices?
3. How faithfully do GCM’s and downscaled climate models simulate these spatial and
temporal patterns?
4. What is the association between large-scale modes of climate variability (e.g., global
SST’s, ENSO, NAO, PDO, NAM, AMO, etc.) and these spatial and temporal patterns of
phenological variation?
5. In a detection and attribution modeling framework, how much of the variance in
continental-scale phenological variation can be attributed to natural vs. anthropogenic
forcing?
6. What is the potential for long-lead (seasonal) forecasting of phenologically-relevant
climatic and hydrologic variables, and its application?
7. What is the relationship between phenologically-relevant climatic/hydrological variables
and both synoptic and site-specific phenological observations?
Climate Data:
 Model Calibration/Validation/Prediction
o Reanalysis versus observed gridded input
o Spatial and temporal granularity
 Regional versus continental scale
 Biosphere domain (ocean/atmosphere/surface)
 Models have limitations (e.g., monthly snow cover)
o Ensemble versus. stand-alone
o Uncertainty characterization
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o Statistical versus mechanistic/deterministic approaches
 Downscaling; work in anomaly space
 Signal-to-noise issue (annual versus longer-term)
o Simulate growing degree days or other phenological indicator as a function of
climate modes and secular change: Detection attribution through statistical
simulation of relevant phenology forcing variables.
o Models must be able to handle desired spatial and temporal modes at the
continental scale (do they get the variance right).
Data Observations
o In-situ versus remotely-sensed data
o Instrumental versus proxy sources
o Hydrological measures (e.g., streamflow, water temperature, snow pack)
o Solar forcing measures (e.g., latent and sensible heat fluxes)
o Pre-requisite data needs
 Daily precipitation
 Daily max/min temperature
 Others (snowfall/depth,ET, soil moisture)
o Correlation to phenological measures
Phenological Data:
 Detection (Schwartz spring indices – Correlation of first leaf and and grassland; first
bloom and forest land)
o Species selection: native, domesticated, invasive
 Traditional measures (e.g., birds, lilacs, wheat)
o Seasonal pulses and baseline growth characteristics
o Force/Feedback Paradigm (Identifying direct and coincidental impacts)
 Detectability: Sharpness of response (e.g., bud break versus time of full bloom); step
changes vs. gradual trends.
 Attribution (Habitat change example: climate change – jet stream shift – biennial
oscillation stops - cone production in boreal conifers drops – bird population impact)
 Spatial range of phenological measures (Local [egg-laying insects and plants] vs.
migratory [birds])
 Data Rescue
o Digitization: Scanning and keying of data
Product Development:
 Relevance (Utility of output for managers and decision makers)
 Accessibility/Usability (Thresholds, bounds, indices)
o Biogeography (distribution) model
o Non-stationary vs. equilibrium models
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o Species range models
o Phenological indicator inputs into statistical models
Familiarity (Plant hardiness mapping, migratory mapping)
Client/Stakeholder Delineation
Registry of Data Sources
Slide: GCMs and Downscaling – what are the gridded products available (pros and cons)?
Katherine Hayhoe approach to downscaling. Work in anomaly space. (Adam, Ben, Allison)
Potential Contributors to NPA-Climate/Hydrology:
Noah Diffenbaugh, Stanford U.
Claudia Tebaldi, UBC, Vancouver