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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?