Early Warning of Simulated Amazon Dieback

Early Warning of Simulated Amazon Dieback

Chris BoultonC.A.Boulton@ex.ac.uk

HadCM3-ESE

Consists of 57 members with perturbed parameters under 3 emissions scenarios.

Defined an Amazon region to test the methods on and pulled out time series for the forest (BL fraction etc) and drivers (temperature, CO2, ...).

There are a range of behaviours to test methods and see how they work.

Slight worry that models which don’t show dieback by 2100 (transient change) do eventually dieback (committed change) and indicators should pick this up too.

Testing Variance - aknaa

Testing Variance - aknab

Dry-Season Resilience

Dry-Season Resilience

Dry-Season Resilience

(Moving) Cross Mapping

Method adapted from CCM but uses a sliding window length to test ‘causality of a driver over time’.

Shadow Manifolds – Sugihara et al. 2012

Create shadow manifolds as seen in diagram (M_x etc).

Attempt to map the alternative time series onto this manifold (i.e. Map y onto M_x

Correlation between Y and Y|M_x gives a measure of causality.

Simple Exampley = 1/4*x^4 - 1/2*x^2 – m*x m increased from 0 to 2*sqrt(3)/9

Using MCM in the Amazon models

Model shows dieback by 2100 and looks similar to tipping model.

This one shows no dieback but has same pattern. Suggests a tipping post 2100?