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Slide1 : Theme 4: Scales and Sustainability Ecological Effects of Climate Driven Processes Through Time Kelly T. Redmond Western Regional Climate Center Desert Research Institute Reno Nevada Mojave Desert Science Symposium University of Redlands Redlands California November 16-18, 2004


Slide2 : Some general points about climate Climate and fluctuation Fluctuation and variability: an inherent property of climate Energy and mass Reservoirs Flows Typically driven by spatial gradients Thresholds (including phase changes of key constituents) Temporal scales Microseconds to eons - approx 16 orders of magnitude Boundary conditions on one scale are initial conditions at others High and low pressure areas for the next five minutes Sea surface temperature for tomorrow’s forecast Continental positions for the next millennium Spatial scales Microns to planetary - approx 10 orders of magnitude Climate entails constant fluctuation, at all scales Fluctuation at one scale is stasis at another scale Snail vs turtle perspective


Slide3 : Change and fluctuation in desert environments Deserts are defined by aridity, but water is the major driver of change Highly skewed precipitation statistics Yuma: 17 days per year with precipitation (every 21 days) 51 hours of precipitation annually (0.6 % of the time) 12 percent of the annual average falls during the wettest hour Annual average falls in one day about once per hundred years Very unlikely that these are stationary statistics What is a sufficient averaging interval for calculating event rates? Deserts spend most of their time in “waiting” mode – military analogy Wind somewhat less troublesome, statistically, but still not easy Temperature more tractable. Temp, Wind, Rel Hum always present. Recharge highly nonlinear, mountains and heavy thunderstorms Climate behavior in driest environments is tied to wettest areas


Slide4 : 14 Nov 2004 Water Vapor 2100 GMT


Slide5 : CA Stovepipe Wells 1 SW, Death Valley National Park (Climate Reference Network) 36.6 N 117.1 W 80’ May 6, 2004


Slide6 : Reanalysis Resolution: Global Regional (slightly smaller; pixel resolution)


Slide8 : Annual Precipitation PRISM - OSU


Slide9 : Mean July Max Temperature PRISM - OSU


Slide10 : Oct-Mar Apr-May-June Fraction of Annual Total Precipitation, by Season July-Aug


Slide11 : Jan Feb Mar Apr Percent of Annual Precipitation


Slide12 : May Jun Jul Aug Percent of Annual Precipitation


Slide13 : Sep Oct Nov Dec Percent of Annual Precipitation


Slide14 : Chaos and nonlinear dynamics: Does this subject have a role? On the face of it, immediate practical applications seem elusive. However, there seems much to offer in terms of appreciating limitations of knowledge of behavior of system components. Fundamental and theoretical limitations, what is simply not possible. Practical limitations, so we don’t waste our time on the wrong things. The subject seems to have a great deal of value in informing us how strongly to hold on to certain beliefs about our ability to direct events in preferred directions. It also brings up the subject of predictability: Under what circumstances is prediction even possible? What situations might be more predictable than others? (“more predictable” = greater likelihood of correct outcome) When should we refrain from prediction, when should we try? This is a major area of inquiry in climate and atmospheric science. Combined physical and biological systems are incredibly complicated. How predictable is the evolution of ecological systems?


Slide15 : “Life must be lived forward, but it can only be understood backward.” - Soren Kierkegaard


Slide16 : Sat 2004 Nov 14 00 GMT - 000 hr Sat 2004 Nov 14 0000 GMT - 000 hr Ensemble Forecasting - 23 members NOAA Climate Diagnostics Center


Slide17 : 000 Hr Forecast NOAA Climate Diagnostics Center


Slide18 : Sat 2004 Nov 14 0000 GMT - 024 hr Sat 2004 Nov 14 0000 GMT - 048 hr NOAA Climate Diagnostics Center


Slide19 : Sat 2004 Nov 14 0000 GMT - 072 hr Sat 2004 Nov 14 0000 GMT - 120 hr NOAA Climate Diagnostics Center


Slide20 : 120 Hr Forecast NOAA Climate Diagnostics Center Wed Eve Nov 24 2004 00 GMT 5 PM PST


Slide21 : Sat 2004 Nov 14 0000 GMT - 168 hr Sat 2004 Nov 14 0000 GMT - 240 hr NOAA Climate Diagnostics Center


Slide22 : Sat 2004 Nov 14 0000 GMT - 360 hr Sat 2004 Nov 14 0000 GMT - 360 hr NOAA Climate Diagnostics Center


Slide23 : 360 Hr Forecast NOAA Climate Diagnostics Center


Slide24 : NOAA Climate Diagnostics Center Sea Surface Temperature Departure from Average Week of 2004 Oct 31 – Nov 06


Slide26 : Seasonal precipitation outlook Nov-Dec-Jan 2004-05 EC means Equal Chances (no forecast !)


Slide27 : Six models, 12 opinions, for Northern California. 1900-2100 Precipitation Temperature Thanks to Mike Dettinger, Scripps / USGS


Slide28 : Trends 1966+ Feb-Mar-Apr Trends 1966+ Annual, Full Year. Source: Climate Prediction Center


Slide29 : 1 oC


Slide30 : 75 mm


Slide31 : 1 oC


Slide32 : 1 oC


Slide33 : 1 oC


Slide34 : 10 mm


Slide35 : Southern Nevada Climate Division Nov-April Precipitation 1895-2004


Slide36 : Southern Nevada Climate Division Nov-April Temperature 1895-2004


Slide37 : For sustainability: The climate backdrop may be changing.


Slide38 : Desert Drought: How do you recognize it ???


Slide40 : Standardized Precipitation Index Percentile 01 – Month thru Oct 2004


Slide41 : Standardized Precipitation Index Percentile 12 – Month thru Oct 2004


Slide42 : Standardized Precipitation Index Percentile 72 – Month thru Oct 2004


Slide43 : Standardized Precipitation Index Percentiles Southern Nevada All Time Scales 01 – 72 months


Slide49 : 14 Nov 2004 Water Vapor 2100 GMT


Slide50 : Courtesy of Nate Mantua, U Washington


Slide51 : Positive Negative Mantua et al.


Slide52 : Time scales of atmospheric variability Turbulent scale variability Hourly scale variability Weekly scale variability Monthly scale variability Seasonal scale variability Annual scale variability ENSO scale variability Decadal scale variability Century scale variability Millennium scale variability Orbital scale variability Geological scale variability


Slide53 : Time scales of atmospheric variability Turbulent scale variability Hourly scale variability Weekly scale variability Monthly scale variability Seasonal scale variability Annual scale variability ENSO scale variability Decadal scale variability Career scale variability Century scale variability Millennium scale variability Orbital scale variability Geological scale variability


Slide54 : NOAA Climate Diagnostics Center Sea Surface Temperature Departure from Average Week of 2004 Oct 31 – Nov 06


Slide55 : Courtesy Klaus Wolter & Mike Timlin, Climate Diagnostics Center Thru Oct 2004


Slide56 : Washington Arizona Central Sierra Redmond & Koch, 1991, updated. ENSO


Slide58 : Data: Van West & Altschul, 1997


Slide59 : Figure 10 from: K.T. Redmond, Y.Enzel, P.K. House, and F. Biondi, 2002. Climate variability and flood frequency at decadal to millennial time scales. pp 21-45, in Ancient Floods, Modern Hazards: Principles and Applications of Paleoflood Hydrology, editors: P.K. House, R.H. Webb, V.R. Baker, and D.R. Levish. American Geophysical Union, 385 p.


Slide60 : Climate Stationarity “The history of climate is a non-stationary time series.” * There are no true climatic “normals” (states to which the climate must return). Climate never repeats itself exactly. Climate is always fluctuating, on all scales. There are always surprises remaining. We can thus never stop observing or monitoring. * “The Paradigm of Climatology: An Essay” Reid A. Bryson, Bulletin of the American Meteorological Society, 1997, 78(3), 449-455


Slide61 : Sustainability What is it that we want to sustain? The process or the outcome? What do we value? How something looks, or acts, or is? or How it got that way? What enabled a system to get to the state it is in? These processes usually involve climate and other aspects of the physical environment. The climate processes at work span scales from turbulence to tectonics, from seconds to millenia. Is the same mix of causative forces still at work? A la Thomas Wolfe: “We can’t go home again.” … can we? The key question – What do we value?


Slide62 : A few thoughts on management We cannot really manage natural systems themselves. We have limited understanding of how these systems work. We have limited knowledge of the full status of all relevant pieces. We have limited control over certain inputs and boundary conditions. We have limited ability to predict the consequences of actions. Ecological forecasting much more difficult than weather forecasting. We have limited ability to evaluate consequences. We have limited ability to correctly ascribe consequences to causes. All we can manage is our interaction with those systems. This is all we really have control over. All of our understanding points to Nature as fundamentally probabilistic. We should learn to work and think in this mode as much as we can. This is the current mode for the prediction of climate. Decision-making under uncertainty: We do have piecewise understanding of internal workings, status, boundary conditions, and predictability, and of some of the probability distributions. Despite uncertainty, we have to make decisions anyway.


Slide63 : Climate Monitoring as a Priority Need long, continuous, homogeneous time series Keep present monitoring going For automated equipment, basic scale typically hourly Maintenance is crucial, and neglected far too often So is documentation Siting and exposure need attention, documentation, constancy Hypothesis-driven monitoring? Hypothesis: Huge demand for climate data. Conclusion: Yes. P < 0.00000000001 Need context for short term field programs and process studies. Many key relationships only discovered in retrospect, after the fact Access to climate information vital Ecological scales – small long-term clusters much needed CEMP, NTS, TREX


Slide64 : Design Considerations for Weather and Climate Monitoring in Channel Islands National Park Kelly Redmond and Greg McCurdy November 2004  Western Regional Climate Center Desert Research Institute 2215 Raggio Parkway Reno Nevada 89512-1095


Slide67 : Representativeness of measurements In space In time Consistency versus accuracy


Slide72 : www.calclim.dri.edu


Slide73 : Southern California


Slide74 : TREX – Terrain Induced Rotors Experiment Independence CA Owens Valley www.trex.dri.edu 6 mi 10 km


Slide75 : 1 mile 1 km TREX – Terrain Induced Rotors Experiment Independence CA Owens Valley


Slide76 : Community Environmental Monitoring Program


Slide77 : Yucca Mountain Network Nevada Test Site Network


Slide78 : ACIS – Applied Climate Information System Available daily from HPRCC


Slide79 : Southern Nevada Climate Division October Precipitation 1895-2004


Slide80 : CA Southeast Desert Climate Division October Precipitation 1895-2004


Slide81 : CA South Coast Climate Division October Precipitation 1895-2004


Slide82 : ACIS – Applied Climate Information System Available daily from HPRCC


Slide83 : Experimental OSU / WRCC Prism 1 km Monthly Climate Products


Slide84 : Experimental OSU / WRCC Prism 1 km Monthly Climate Products


Slide85 : Experimental OSU / WRCC Prism 1 km Monthly Climate Products


Slide86 : Experimental OSU / WRCC Prism 1 km Monthly Climate Products


Slide87 : “You can observe a lot, just by watching.” - Yogi Berra