Sheila Trampush and Liz Hajek

Slides:



Advertisements
Similar presentations
New estimates of Earth system sensitivity from the Cenozoic Introduction: Earth system sensitivity Case study 1: The mid Pliocene Case study 2: The PETM.
Advertisements

A probabilistic approach to exploring global dynamics Nicky Grigg, Fabio Boschetti, Markus Brede, John Finnigan CSIRO, Australia AIMES Open Science Conference,
The Normal Distribution
Mmax and the Maximum Catalog Magnitude Martin Chapman Department of Geosciences Virginia Tech Blacksburg, VA Mmax Workshop Golden, Colorado.
The importance of Pliocene time slices for environmental synthesis and climate modelling Alan M. Haywood Co-authors: Caroline Prescott, Aisling Dolan,
Sensitivity Analysis of a Spatially Explicit Fish Population Model Applied to Everglades Restoration Ren é A. Salinas and Louis J. Gross The Institute.
The Deuterium Excess record from the Siple Dome ice core Annalisa Schilla, James White, Eric Steig, Ed Brook.
29 April 2011Viereck: Space Weather Workshop 2011 The Recent Solar Minimum: How Low Was It? What Were The Consequences? Rodney Viereck NOAA Space Weather.
El Niño Effects on Goleta Coast Wave Climate
Recap of WY ENSO is typically very stable from Oct-Jan.
Using observations to reduce uncertainties in climate model predictions Maryland Climate Change Workshop Prof. Daniel Kirk-Davidoff.
458 Population Projections (policy analysis) Fish 458; Lecture 21.
Holocene and Pleistocene Sedimentation on the Antarctic Shelf Why study this topic? 1)Holocene: period of dramatic S. Ocean changes.
Scaling Up Marine Sediment Transport Patricia Wiberg University of Virginia The challenge: How to go from local, event-scale marine sediment transport.
Uncertainty Analysis of Climate Change Effects on Runoff for the Pacific Northwest Greg Karlovits and Jennifer Adam Department of Civil and Environmental.
Impacts of Climate Change on Physical Systems PPT
Inherent Uncertainties in Nearshore Fisheries: The Biocomplexity of Flow, Fish and Fishing Dave Siegel 1, Satoshi Mitarai 1, Crow White 1, Heather Berkley.
Presented by: Akindele Balogun.
Hui-Hua Lee 1, Kevin R. Piner 1, Mark N. Maunder 2 Evaluation of traditional versus conditional fitting of von Bertalanffy growth functions 1 NOAA Fisheries,
Composite of Sea Level – for last 600 k years. Note that SL was not always extremely low during glacial periods. From Rabineau et al, EPSL, 2006.
Hydraulic Geometry Brian Bledsoe Department of Civil Engineering Colorado State University.
Long-Term Salinity Prediction with Uncertainty Analysis: Application for Colorado River Above Glenwood Springs, CO James Prairie Water Resources Division,
The Drainage Basin “your friend, and all of its secrets”
Paleoclimatology Why is it important? Angela Colbert Climate Modeling Group October 24, 2011.
A Multidecadal Midge-Based Temperature Reconstruction From the Great Basin, United States Provides Evidence of Warmer Conditions During The Medieval Climatic.
Evaluating Scour Potential in Stormwater Catchbasin Sumps Using a Full-Scale Physical Model and CFD Modeling Humberto Avila Universidad del Norte, Department.
Think Big and Long Scale - Global System Time - Global systems don’t change instantly.
The Sea Floor as a Sediment Trap: Contributions to JGOFS from Benthic Flux Studies Richard A. Jahnke Skidaway Institute of Oceanography Milestones and.
Erin L. McClymont Department of Geography, Durham University Aurora Elmore (Durham University), Benjamin Petrick (Newcastle University), Sev Kender (British.
Changes in Floods and Droughts in an Elevated CO 2 Climate Anthony M. DeAngelis Dr. Anthony J. Broccoli.
Estimating scales of environmental signals and stratigraphic preservation Liz Hajek – Penn State University.
 [Climate History from Deep Sea Sediments]. How can we use deep sea sediment samples to determine the effects of climate change, and how can we use that.
Climate change and sediment budgets? Jaak Monbaliu Albin Ullmann.
Ergodicity in Natural Fault Systems K.F. Tiampo, University of Colorado J.B. Rundle, University of Colorado W. Klein, Boston University J. Sá Martins,
Long-Term Changes in Global Sea Level Craig S. Fulthorpe University of Texas Institute for Geophysics John A. and Katherine G. Jackson School of Geosciences.
Questions? Discussion? Age Model Exercise I Note: FA = FO and LA = LO throughout investigation!
Landform Geography Fluvial Landforms.
Some Properties of “aftershocks” Some properties of Aftershocks Dave Jackson UCLA Oct 25, 2011 UC BERKELEY.
A new calibrated deglacial drainage history for North America and evidence for an Arctic trigger for the Younger Dryas Lev Tarasov and W. R. Peltier University.
Extracting valuable information from a multimodel ensemble Similarly, number of RCMs are used to generate fine scales features from GCM coarse resolution.
Probability. Hydrologic data series 1.Complete series Use all of the data. DateDepth (cm) 4/28/ /20/ /30/ /11/ /5/ /22/050.3.
Sea level Workshop – Paris 2006 Assessing the impact of long term trends in extreme sea levels on offshore and coastal installations Ralph Rayner Marine.
Local Predictability of the Performance of an Ensemble Forecast System Liz Satterfield and Istvan Szunyogh Texas A&M University, College Station, TX Third.
Collaborative Interaction in Virtual Environments Trevor J. Dodds Roy A. Ruddle Visualization and Virtual Reality Research Group School of Computing University.
Chapter 15: Present and Future Climate Part 2—Projections for the future.
References Conclusions Objectives DYNAMICAL DOWNSCALING OF WIND RESOURCES IN COMPLEX TERRAIN OF CROATIA Stjepan Ivatek-Šahdan, Kristian Horvath and Alica.
CLIMATE Part I: Factors that affect climate. What is Weather? Weather = all natural phenomena within the atmosphere at a given time (hours to days)
TRACE METAL AND DIOXIN DEPOSITION HISTORY IN HURRICANE KATRINA IMPACTED MARSH SEDIMENT Gopal Bera* and Alan Shiller (The University of Southern Mississippi,
The 8.2Kyr event Julia Tindall Freshwater hosing experiments Ron Kahana.
AIM AIM point-scale plot-scale hillslope-scale
Sheila Trampush and Liz Hajek
Proxy data Our knowledge of past climate is based on analysis of “proxy” records, or natural histories that are preserved & are sensitive to climate Ice.
Study Evaluation of Random Set Method on Results from Reliability analysis of Finite Element in Deep Excavation Article Code: 443 Presenter Mehdi Poormousavian.
Modelling Ancient Earth Climates Manchester Geologist Association
Sea-level influence on sedimentation
Exponential – Geometric - Density Independent Population Growth
A Marginal marine particulate organic carbon flux and δ13C responds to global warming at the Paleocene-Eocene boundary B Our work focuses on elucidating.
Michael E. Mann, Raymond S. Bradley and Malcolm K
Warm saline deep water production in
Philip D. Gingerich, University of Michigan
Ned Nikolov, Ph. D. and Karl Zeller, Ph. D
Investigating Dansgaard-Oeschger events via a 2-D ocean model
Range.
GENESYS Current Functionality
Tipping Points or Critical Transitions
Slides for GGR 314, Global Warming Chapter 4: Climate Models and Projected Climatic Change Course taught by Danny Harvey Department of Geography University.
Figure 3.3. Change in average annual runoff by the 2050s under the SRES A2 emissions scenario and different climate models (Arnell,
Future Inundation Frequency of Coastal Critical Facilities
Alan F. Hamlet, Andrew W. Wood, Dennis P. Lettenmaier,
Marginal marine particulate organic carbon flux and δ13C responds to global warming at the Paleocene-Eocene boundary A) Accumulation of particulate organic.
Presentation transcript:

Sheila Trampush and Liz Hajek Evaluating the Effect of Autogenic Sedimentation on the Preservation of Climate Proxy Records: Modeling and Examples from the Paleocene Eocene Thermal Maximum Sheila Trampush and Liz Hajek

Global climate vs. local sedimentation Fluvial Coastal Shelf Colorado Wyoming New Jersey Maryland Foreman et al., 2012 Baczynski et al., 2013 Stassen et al, 2015 Self-Trail et al, 2012

Can landscape dynamics be responsible? Constant sedimentation = best preserved? High sedimentation = best preserved? Manners et al. (2013)

Landscape Dynamics High Sedimentation, High variability High Sedimentation, Low variability Fluvial Lacustrine Low Sedimentation, High variability Low Sedimentation, Low variability Coastal Shelf Deep Marine

(Double Pareto distribution) Stochastic Sedimentation Model Sedimentation Bed elevation Proxy signal Record 50 350 350 400 Mean sedimentation High: 30 cm/Kyr Low: 10 cm/Kyr 40 30 Density [%] Total Duration: 200 Kyr 300 250 250 20 10 Time [Kyr] Time [Kyr] Elevation [m] 200 Recovery: 180 Kyr -2 2 Annual event size [m] 150 150 (Double Pareto distribution) 100 year event: +/- 16 cm or 5 cm 1,000 year event: +/- 170 cm or 40 cm 10,000 year event: +/- 360 cm or 140 cm 100 Onset: 20 Kyr 50 50 Total model time: 350 ky 200 400 -6 -4 -2 2 -6 -4 -2 2 Elevation [m] δ13C [‰] δ13C [‰]

Example Synthetic Records Variable Preservation in All Models Proxy Example Synthetic Records 120 80 350 400 150 100 60 300 250 80 100 Time [Kyr] Elevation [m] 200 60 40 150 40 50 100 20 20 50 -6 -4 -2 2 -6 -4 -2 2 -6 -4 -2 2 -6 -4 -2 2 -6 -4 -2 2 δ13C [‰] δ13C [‰] δ13C [‰] δ13C [‰] δ13C [‰]

Variable Preservation in All Models Trampush & Hajek, in prep

Model Summary High Sedimentation, High variability High Sedimentation, Low variability Record Preserved: 88% (53%) Magnitude: -4.6‰ (-5.2 to -2.2‰) Onset: 15 Kyr (2 to 57 Kyr) Recovery: 131 Kyr (51 to 215 Kyr) High probability of modified record Record Preserved: 100% (89%) Magnitude: -5.0‰ (-5.2 to -4.5‰) Onset: 19 Kyr (3 to 46 Kyr) Recovery: 171 Kyr (116 to 215 Kyr) Highest probability of a good record Fluvial Lacustrine Low Sedimentation, High variability Low Sedimentation, Low variability Record Preserved: 69% (44%) Magnitude: -4.1‰ (-5.1 to -1.5‰) Onset: 10 Kyr (2 to 58 Kyr) Recovery: 117 Kyr (40 to 210 Kyr) Lowest probability of a good record Record Preserved: 87% (58%) Magnitude: -4.7‰ (-5.2 to -2.6‰) Onset: 16 Kyr (2 to 53 Kyr) Recovery: 138 Kyr (51 to 224 Kyr) High probability of a modified record Coastal Shelf Deep Marine

Ensemble records are more reliable 300 200 Time [Kyr] 100 -6 -4 -2 2 -6 -4 -2 2 -6 -4 -2 2 -6 -4 -2 2 -6 -4 -2 2 δ13C [‰] δ13C [‰] δ13C [‰] δ13C [‰] δ13C [‰]

Can landscape dynamics be responsible? Fluvial Coastal Shelf Colorado Wyoming New Jersey Maryland Foreman et al., 2012 Baczynski et al., 2013 Stassen et al, 2015 Self-Trail et al, 2012

Wide variety of records in fluvial and shelf environments Trampush & Hajek, in prep

Conclusions Preservation of proxy systems is sensitive to the sedimentation rate and variability Individual records can be very different from actual event, but averages of large numbers of records can recover the true event rates and durations The “body” of the PETM could be entirely an artifact of variable sedimentation (but it’s possible that it’s not)

Ensemble records are more reliable