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

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

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?

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?

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

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

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

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

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

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

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

Aggrading delta: accumulation rate >> compensation scale 30 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

Prograding delta: accumulation rate << compensation scale 6 -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

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

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