Download Uncertainty in the Water Cycle - Alaska Center for Climate

Uncertainty in the Water Cycle
Jessie Cherry,
Research Assistant Professor,
International Arctic Research Center and
Institute of Northern Engineering
University of Alaska Fairbanks
http://denali.iarc.uaf.edu/~jcherry/
The Concept of
Uncertainty
• Things whose truth or falsity is not
known to you
• Future, past, and present
• Can’t escape; but can learn to manage
and even quantify
• The better we understand uncertainty,
the less risk we have to assume
Why Do We Care about the
Uncertainty in the Water Cycle?
• Need information about precipitation, snow,
lake or reservoir level, runoff, evaporation for
regular management decisions
• There is a big question about whether the
modes of variability of the water cycle will
change if we go into a new climate regime
• In many communities clean, reliable water
sources are increasingly vulnerable
ACIA
“It is probable that there
was an increase in arctic
precipitation over the past
century”
Arctic Climate Impacts
Assessment, 2005
Outline
• Types of uncertainty related to the water
cycle, particularly in the high North
• What is our best estimate of how
uncertainty could change in the future?
• As advisors and decision makers, how
best can we manage uncertainty
Types of uncertainty related to
the water cycle:
• Spatially sparse observation network and
biased locations for measurement mean that
estimates of spatial variability are poor
• Very few instrumental measurements far into
the past with consistent sensors; changing
network over time creates inhomogeneities
• Quantities are difficult to measure
• Temporal and spatial variability in the water
cycle is inherently very high
Types of uncertainty related to
the water cycle:
• Spatially sparse observation network and
biased locations for measurement mean that
estimates of spatial variability are poor
• Very few instrumental measurements far into
the past with consistent sensors; changing
network over time creates inhomogeneities
• Quantities are difficult to measure
• Temporal and spatial variability in the water
cycle is inherently very high
Data Used in Estimating Climatologies
J.J. Simpson et al., 2005
Comparative Climatologies for 1971-1990:
Temperature
Temp Climatology
J.J. Simpson et al., 2005
Comparative Climatologies for 1971-1990:
Precipitation
Precip Climatology
J.J. Simpson et al., 2005
Types of uncertainty related to
the water cycle:
• Spatially sparse observation network and
biased locations for measurement mean that
estimates of spatial variability are poor
• Very few instrumental measurements far into
the past with consistent sensors; changing
network over time creates inhomogeneities
• Quantities are difficult to measure
• Temporal and spatial variability in the water
cycle is inherently very high
Rawlins network change
Counterintuitive
precipitation trends,
relative to discharge trend,
partially explained by gage
density change
Rawlins et al., 2006, GRL
Types of uncertainty related to
the water cycle:
• Spatially sparse observation network and
biased locations for measurement mean that
estimates of spatial variability are poor
• Very few instrumental measurements far into
the past with consistent sensors; changing
network over time creates inhomogeneities
• Quantities are difficult to measure
• Temporal and spatial variability in the water
cycle is inherently very high
Physics of a precip gauge
WMO SPIR, 1998
The Observer Effect (relates to the
Heisenberg Uncertainty Principle):
Observer effect refers to changes that the act of
observing will make on the phenomenon being
observed. For example, for us to "see" an electron, a
photon must first interact with it, and this interaction
will change the path of that electron.
In quantum physics, the Heisenberg uncertainty
principle states that locating a particle in a small region
of space makes the momentum of the particle
uncertain; and conversely, that measuring the
momentum of a particle precisely makes the position
uncertain.
Disorganized Flow
Cherry, Feb 2007
Prudhoe Bay, Feb 2007
Cherry, Feb 2007
Mechanical failure of unmanned
precip gauge
WMO SPIR, 1998
Bear Mauling
Cherry, June 2006
Statistical correction through gauge
intercomparison
Statistical Correction Factor
January
Yang et
al., 2005
Statistical Correction Factor
July
Yang et
al., 2005
Sources of uncertainty in
statistical gauge correction
• Functionality/reliability of input data
(wind, temperature measurements)
• Reliance on goodness of fit of correction
equation
• Mean and variance of present/future
events is similar to that used during test
sampling period
Pan-Arctic Snowfall Reconstruction (PASR)
Daily for 1936-2003
Snow depth
stations
Test sites
Arctic Catchment
Cherry et al., WRR, 2005; JHM, 2007
Sources of uncertainty in modelreconstructed precipitation
• Errors in data used for forcing and
restoring
• Errors in model physics
• Representativeness of station data used
to estimate the above errors
Types of uncertainty related to
the water cycle:
• Spatially sparse observation network and
biased locations for measurement mean that
estimates of spatial variability are poor
• Very few instrumental measurements far into
the past with consistent sensors; changing
network over time creates inhomogeneities
• Quantities are difficult to measure
• Temporal and spatial variability in the water
cycle is inherently very high
North Slope
Snotel Gauge
Network
Variability of snow depth
Cherry/Berezovskaya
gamma
NRCS
Modes of Variability in Time
and Space
• Alphabet soup: ENSO, PDO, NAO, AO, MJO,
etc.
• These are patterns of variance than emerge
from statistical analyses of data and may or
may not have underlying physical dynamics
that are well-understood
• There are still significant deviations from
‘modal means’ when the climate is firmly set
in one regime; there are also inter-modal
interactions
JJA La Nina Anomalies
Inter-modal interaction
ElNino/AOminus
LaNina/AO-
Difference
Plots:
precipitation
ElNino/AO+
minus
LaNina/AO+
Bond and Harrison, 2006
How does uncertainty about
the future compare to that of
the past or present?
How do current models predict the future of the
hydrologic cycle?
What about future observation networks ?
Future observation technologies ?
Future modeling breakthroughs ?
Future climate modes or regimes ?
IPCC projected temperature,
precipitation, and pressure changes
Meehl et al., 2007
IPCC projected water cycle changes
Meehl et al., 2007
Sources of Uncertainties in
the Models
• Assumptions about human socio-economic
development and human decision-making
• Model physics, limits to our understanding
• Representation of variability in modes and
otherwise (including abrupt change, use of
ensembles)
• Incorporation of observations into models
(and all of the uncertainty inherent in past or
present observations)
Emissions
trajectories:
components of
the whole
How does uncertainty about
the future compare to that of
the past or present?
How do current models predict the future of the
hydrologic cycle?
What about future observation networks ?
Future observation technologies ?
Future modeling breakthroughs ?
Future climate modes or regimes ?
What is to be done?
Lots!
•Need better observation networks (ground-based and
remote sensing) and can use numerical methods to
determine ideal network density, etc
•Need better sensor technologies for cold regions
•Need to be more sophisticated in our representation
of variability in climate models
•Need to educate the general public more about the
use of statistical information in decision-making with
regard to water and climate
Thanks for
Your
The
endAttention!
Questions?