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 phenologicallyrelevant 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
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 USANPN, 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
Bird Migrations
Bird Migrations
Bird Migrations
Bird Migrations
Bird Migrations
Bird Migrations
Cherry Blooming
Concord
Flowering
Flowering
Location
Cambridge, MA
Cayuga Lake Basin, NY
Maine
Maine
Manomet, MA
Worcester, MA
Washington, DC
Concord, MA
Concord, MA
Concord, MA
Taxa
AV
AV
AV
AV
AV
AV
WF
AV
HF, WF
HF, WF
Timespan
1980-2004
1903-1993
1899-1911
1994-2008
1970-2002
1932-1993
1921-Present
1852-Present
1963-1994
1852-1857
Flowering
Concord, MA
Flowering
Concord, MA
Gothic
Gothic, CO
Konza Prairie
Kansas
Leopold Data
Wisconsin
*Lilac Network (East) USA
*Lilac Network (West) USA
Middleborough
Middleborough, MA
Mohonk Lake
New Paltz, NY
*NA Bird Phen Prog
USA
United Kingdom
United Kingdom
*Wheat Headings (Spring) USA
*Wheat Headings (Winter) USA
Wisconsin Phen. Soc. Wisconsin HF,
HF, WF
1878, 1888-1902
HF, WF
2003-2006
HF, WF, IN, AV, MA
1973-2008
HF, WF
1981-1988, 2001-Present
HF, WF, AV, MA
1935-1945
WF
1961-Present
WF
1956-Present
HF, WF, AV, HE, IN 1970-2002
WF, HF, AV, HE 1925-Present
AV
1881-1970
WF, HF
1954-2000
HF
1931-Present
HF
1931-Present
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):
Layers
Ecological Impacts
Phenological Behavior
Potential Indicators of Phenology (PIPs)
Environmental Consequences
Climatic Drivers
Budworm Emergence
Defoliation
First Bloom Spring Index
Fire
Natural Variability and Anthropogenic Change
NW
SW
Space (i.e.,
Regions)
MW
SE
NE
Far Past Past Present Future Far Future
Time (i.e.,
Observational
Period of Record)
Research, Monitoring, and
Management Implications