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?
  1. What are the spatial (regional) and temporal patterns of past variation in phenologically-relevant climatic and hydrological variables and indices?
  1. How faithfully do GCM’s and downscaled climate models simulate these spatial and temporal patterns?
  1. 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?
  1. 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?
  1. What is the potential for long-lead (seasonal) forecasting of phenologically-relevant climatic and hydrologic variables, and its application?
  1. What is the relationship between phenologically-relevant climatic/hydrological variables and both synoptic and site-specific phenological observations?

Climate Data:

  • Model Calibration/Validation/Prediction
  • Reanalysis versus observed gridded input
  • Spatial and temporal granularity
  • Regional versus continental scale
  • Biosphere domain (ocean/atmosphere/surface)
  • Models have limitations (e.g., monthly snow cover)
  • Ensemble versus. stand-alone
  • Uncertainty characterization
  • Statistical versus mechanistic/deterministic approaches
  • Downscaling; work in anomaly space
  • Signal-to-noise issue (annual versus longer-term)
  • 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.
  • Models must be able to handle desired spatial and temporal modes at the continental scale (do they get the variance right).
  • Data Observations
  • In-situ versus remotely-sensed data
  • Instrumental versus proxy sources
  • Hydrological measures (e.g., streamflow, water temperature, snow pack)
  • Solar forcing measures (e.g., latent and sensible heat fluxes)
  • Pre-requisite data needs
  • Daily precipitation
  • Daily max/min temperature
  • Others (snowfall/depth,ET, soil moisture)
  • Correlation to phenological measures

Phenological Data:

  • Detection (Schwartz spring indices – Correlation of first leaf and and grassland; first bloom and forest land)
  • Species selection: native, domesticated, invasive
  • Traditional measures (e.g., birds, lilacs, wheat)
  • Seasonal pulses and baseline growth characteristics
  • 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
  • Digitization: Scanning and keying of data

Product Development:

  • Relevance (Utility of output for managers and decision makers)
  • Accessibility/Usability (Thresholds, bounds, indices)
  • Biogeography (distribution) model
  • Non-stationary vs. equilibrium models
  • Species range models
  • 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

Wednesday, October 07, 2009 and Thursday, October 08, 2009:

Short-Term Product Development Options:

Patterns and Drivers of Spring Onset Variations Over North America: Extend Western U.S. Product (265 Historical Climatology Stations (HCN) stations) to conterminous U.S. and Canada in lilac growing zone. Continental PCA analysis; significant PCs related to hemispheric climate indices.

Goal: First approximation of modes of variation and baseline of spring onset at continental scale. Extension of Cayan et al. 2001 BAMS work. Target Journals: Climate Dynamics, Journal of Climate, or BAMS.

Data Sources: Schwartz Spring Indices Database, NCDC GHCNv2 (homogenized daily data). Period of record: 1900-present.

Players: Mark Schwartz, Greg McCabe, Julio Betancourt, Ben Cook, Allison Steiner, Toby Alt

(Perspectives) Informing Phenological Models for the IPCC Fifth Assessment Report

(Review) Assessments, Applications, and Recommendations for Global Change Studies Using Phenological Models: Most commonly used variables and essential algorithms related to phenology.

  1. Overview of species-level models (including plants, insects, birds, crops, etc.) highlighting paucity of models and sequence dependencies (successive consecutive conditions that trigger phenological events).
  2. Discussion of regional- to continental-scale models (e.g., green-up, remotely-sensed approaches)
  3. Overview of data availability and data needs for doing regional- to continental-scale phenological work in the context of IPCC-archived variables (consider uncertainties of using climate models to force phenology models, and need for multi-model approaches). Emphasis on phenology for assessment in non-stationary climate.

Goal: Provide overview of both climate and phenological models and desired resources for future assessment. Target Journals: Global Change Biology and Science (Perspectives Section)

Data Sources: USDA Data Sources; NCDC GHCNv2 (homogenized daily data). Period of record: 1960-present.

Players (Review): Ben Cook, Allison Steiner, Adam Terando, Mark Schwartz; Perspectives: TBD

Temporal Evolution of Plant Hardiness Zones: Work collaboratively with USDA to evaluate plant hardiness zone computations and underlying dynamics.

Goal: Develop annual historic maps for the past fifty years for posting to USA-NPN, with decade-by-decade changes and projections for future decades.

Target Journals: Global Change Biology or Journal of Climate

Data Sources: USDA Data Sources; NCDC GHCNv2 (homogenized daily data). Period of record: 1960-present.

Players: Tim Owen, Adam Terando, Bill Hargrove, Geoff Henebry

Assimilation of Legacy Phenological Data Sets for North America: A Proposal to the National Center for Ecological Analysis and Synthesis (NCEAS; UC-Santa Barbara): Revisit proposal idea, focusing on measured phenological behavior relevant to climate change modes of variability.

Goal: Bring phenological sets into common format (with defined input standards) for use by National Phenology Network.

Target Proposal Submission: NCEAS (work with Lizzie Wolkovich); Deadline: January 11, 2010.

Data Sources: Phenology data sets: (Taxa: WF-woody ora, HF-herbaceous ora, AVavifauna,HE-herpifauna, IN-insects, MA-mammals).

*Datasets with wide geographic coverage:

Dataset Location Taxa Timespan

Bird Migrations Cambridge, MA AV 1980-2004

Bird Migrations Cayuga Lake Basin, NY AV 1903-1993

Bird Migrations Maine AV 1899-1911

Bird Migrations Maine AV 1994-2008

Bird Migrations Manomet, MA AV 1970-2002

Bird Migrations Worcester, MA AV 1932-1993

Cherry Blooming Washington, DC WF 1921-Present

Concord Concord, MA AV 1852-Present

Flowering Concord, MA HF, WF 1963-1994

Flowering Concord, MA HF, WF 1852-1857

Flowering Concord, MA HF, WF 1878, 1888-1902

Flowering Concord, MA HF, WF 2003-2006

Gothic Gothic, CO HF, WF, IN, AV, MA 1973-2008

Konza Prairie Kansas HF, WF 1981-1988, 2001-Present

Leopold Data Wisconsin HF, WF, AV, MA 1935-1945

*Lilac Network (East) USA WF 1961-Present

*Lilac Network (West) USA WF 1956-Present

Middleborough Middleborough, MA HF, WF, AV, HE, IN 1970-2002

Mohonk Lake New Paltz, NY WF, HF, AV, HE 1925-Present

*NA Bird Phen Prog USA AV 1881-1970

United Kingdom United Kingdom WF, HF 1954-2000

*Wheat Headings (Spring) USA HF 1931-Present

*Wheat Headings (Winter) USA HF 1931-Present

Wisconsin Phen. Soc. Wisconsin HF, WF 1962-Present

Players: Ben Cook, Julio Betancourt, Elizabeth Wolkovich

Explore complexity through linear assumptions of drivers of phenological change, considering suite of indicators (where each indicator is a hypothesis):

Comparison Matrix for Potential Indicators of Phenology (PIPs):