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 phenologically- relevant 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 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 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