1. Sheila Trampush and Liz Hajek
The impact of variable sedimentation on reconstructing climate signals in fluviodeltaic and shallow marine deposits
When we try to predict whether the stratigraphic record will record a climatic event, we consider both the size of the event and how “noisy“ the depositional system is.
So, in the case of the PETM we have a clear lithologic expression, here in the presence of the Claret conglomerate, in the Bighorn basin in this photo, it’s marked by the boundary sandstone. So, the PETM was high enough magnitude or a simple enough sedimentary system that it leaves a signature. However, we don’t identify the PETM with the sediments alone, we use carbon isotopes. The purpose of this study is to understand ifthe appearance of the isotopic record has been altered.
Sheila Trampush and Liz Hajek

2. Proxy record variability in the PETM
Manners et al. (2013)
Can landscape dynamics produce this level of variability?
Trampush & Hajek, in review

3. Does high sed. or low variability make a better record?
Hajek & Straub, Annual Review of Earth and Planetary Sciences, in press
Bighorn Basin, Wyoming, USA
High subsidence +
High sediment supply
= Good record?
Mid-Atlantic margin, Maryland, USA
Low subsidence +
Low sediment supply
= Bad record?

4. Does high sed. or low variability make a better record?
Bighorn Basin, Wyoming, USA
Highly variable environment (autogenic + environmental)
= bad record?
Mid-Atlantic margin, Maryland, USA
Limited variability environment
(mostly environmental)
= good record?

5. Stochastic approximation of variability in sedimentation
Event frequencies in modern systems
(e.g. Discharge probability in streams)
Sediment transport events in experiments (e.g. deposition on an experimental delta)
Molnar et al., 2006
Ganti et al., 2011
Double Pareto is a reasonable model for environmental and autogenic variability

6. Sedimentation Variability
Building synthetic proxy records
Trampush & Hajek, in review
Model 1
Model 2
500 synthetic records per model
Count how many preserve an event
Count how many preserve the original signal total duration, magnitude, and rate of onset
Sedimentation Rate
Model 3
Model 4
Sedimentation Variability

7. All models produce “good” and “bad” records
41% of 500 synthetic records are within 50% error of input signal
13% of 500 synthetic records are within 50% of input signal
(Bighorn basin)
Say 4 in 10 records are “good” best case; worst case 1 in 10 may be “good”
15% of 500 synthetic records are within 50% of input signal
8% of 500 synthetic records are within 50% of input signal
(Mid-Atlantic margin)
Trampush & Hajek, in review

8. Ensemble records improve chance of capturing true signal
95% within 50% error
45% within 50% error
Simple correlation (linear sedimentation rates and height of onset)
Averaging over records yields the correct signal for synthetic records
Simple model reproduces variability observed in PETM records
PETM ensemble in line with what is observed in the deep marine
Add PETM record
Trampush & Hajek, in review

9. Need to scale autogenic variability to the sed. rate
Spatiotemporal variability in sed. transport networks
DeltaRCM from Liang et al. [2016]
Need to scale autogenic variability to the sed. rate

10. Compensation scale approximates max. autogenic scale
Compensation scale: the amount of time it takes to build and fill topographic lows created by the transport system
Describe 2 figures to emphasize which is low and high variability
Hajek & Straub, Annual Review of Earth and Planetary Sciences, in press

11. End-member comparison of sed. rate and variability
Aggrading delta
Prograding delta
100
100 35 6
200
200
Deposit 7X thicker than compensation scale
Deposit is as thick as the compensation scale

12. Aggrading delta: accumulation rate >> compensation scale30 25 20 15 10 5 20 40 60 80
100
120
140
160
180
200 35 -6 1
δ13C [‰] 35 -6 1
δ13C [‰] 35 -6 1
δ13C [‰] 35 -6
δ13C [‰] 1

13. Prograding delta: accumulation rate << compensation scale6 -6 20 40 60 80
100
120
140
160
180
200 -6 1
δ13C [‰] 6 6 -6 1
δ13C [‰] 6 -6 1
δ13C [‰] 6 -6 1
δ13C [‰] -6 1
δ13C [‰] 6

14. More records may be needed in high variability environments
Low variability relative to sed. rate
High variability relative to sed. rate
Where we are now is what sort of sampling is required both number and spatial distribution in systems of different sizes and with different autogenic size and signal size

15. Variability vs. long-term sed. rate is measurable in the ancient