Early LIG Spatial Comparison

principle, age models are built based upon linear interpolation operates under the as-sumption that the sedimentation rate remains constant. The resulting synchronized records include errors based upon Monte-Carlo analysis to propagate the errors asso-ciated with both the uncertainty linked to the SST reconstruction as well as the age uncertainties of the AICC2012 chronology. The Monte-Carlo analysis utilizes 1000 age model simulations, adding random noise to the SST reconstruction values within the space of the method’s error, and randomly perturbing the age of each tie-point within the age uncertainty. This method resulted in a combined uncertainty for both reconstruction method and age. The data compilation claims to reconstruct boreal summer sea surface temperature, as is given by the respective authors of each of the individual records

Model Runs Employed for Comparison

These proxy compilations are compared to simulations of the early and mid LIG, LIG-130 and LIG-125, as already described in Chapter 3. Additionally, as the records are available as time series as well as time composites, some of the records are compared against a transient comparison of the LIG. This simulation was created using the greenhouse gas (GHG) concentrations described in the Paleoclimate Model Intercomparison Project (PMIP) project as well as orbital parameters calculated using the Berger and Loutre [1991] routine. Both of these forcings were accelerated by a factor of 10, following a method initially documented by Lorenz and Lohmann [2004]. This simulation is hereafter referred to as LIG-T.

4.1. COMPARISON AGAINST NORTH ATLANTIC MULTI-PROXY SUMMER SEA SURFACE TEMPERATURE RECONSTRUCTIONS

A

Anomalous Sea Surface Temperature

Capron et al., 2014

B

Calcite δ

O (Abs)

N. Pachyderma Dextre N. Pachyderma Senetre G. Bulloides

C

Precipitation δ

O

-2.0 -1.0 -0.75 -0.5 -0.1 0.1 0.5 0.75 1.0 2.0

‰

-2.0 -1.5 -1.0 -0.5 -0.25-0.1 0.10.25 0.5 1.0 1.5 2.0

Δ°C

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0

‰ LIG 130 ky BP

Figure 4.1: A) Spatial comparison of SSST changes relative to PI. B) Comparison of availableδ¹⁸O_C cores. C) Comparison of the NEEM ice core. Sea surface temperatures are constructed with the average of the upper 10 meters of water.

realized by COSMOS-WISOmatches the distribution of this cooling signature quite well, capturing the cooling area itself as well as the fronts and locations of warming slightly further north. Solely the southern extent of the cooling is not well represented. However, this may be a difficulty caused by the course model resolution.

While the dataset’s SSST reconstruction is a combination of multiple proxy types;

some of the cores also offer calcite measurements of plankticδ¹⁸O_C. These measurements can be used as an independent test of the proxy/model comparison, thereby providing a separate benchmark. Unfortunately, only a preliminary analysis of these cores is possible, as Monte Carlo simulations have not yet been performed. Thus, an estimation of both measurement as well as age error is not yet available (Emilie Capron,personal communication).

While direct surface values were used to compare SSSTs in order maintain compa-rability with other modeling studies, theδ¹⁸O_C values are treated slightly differently.

As calcifying foraminifera live in the surface layers of the ocean, and not strictly at the direct surface, an average of the upper 150 m is generated from the simulated values in order to compare against theδ¹⁸O_Cof the sediment cores.

The comparison between the availableδ¹⁸O_C in the cores and in the model is shown in Figure 4.1-B. The model calcite was calculated from simulatedδ¹⁸O_swandT_swbased upon the Shackleton [1974] paleothermometry equation, shown below:

T=16.9−4.38·(δ¹⁸O_C−δ¹⁸O_sw)+0.1·(δ¹⁸O_C−δ¹⁸O_sw)² (4.1) where bothδ¹⁸O_C andδ¹⁸O_sware measured on the Pee Dee Belemnite (PDB) stan-dard. Following a conversion by Hut [1987], this can be expressed relative to the SMOW standard via:

δ¹⁸O_sw(PDB)=δ¹⁸O_{sw(V SMOW)}−0.27 (4.2)

4.1. COMPARISON AGAINST NORTH ATLANTIC MULTI-PROXY SUMMER SEA SURFACE TEMPERATURE RECONSTRUCTIONS

As several different species are used when measuringδ¹⁸O_C, each is presented as a separate symbol in Figure 4.1-B; however, the model only reconstructs a general calcite value. The match between the model δ¹⁸O_C and data is also fairly close, solely the southern extent is once again not captured. As before, this may be due to the coarse model resolution.

−6 −4 −2 0 2 4 6

Modelδ¹⁸Oc, Model SSST

−6

−4

−2 0 2 4 6

Proxyδ18Oc,ProxySSST

−36 −34 −32 −30 −28

Modelδ¹⁸OIce

−36

−34

−32

−30

−28

NEEMδ18OIce

LIG-130 Model/Data Comparison Differences

Figure 4.2: Errors of the model/data comparison for LIG-130. Orange diamonds show anomalous temperature reconstructions, red, green, and blue symbols show calcite values, and the purple hexagon shows the NEEM ice core. The Y axis represents the measured SSST,δ¹⁸O_C andδ¹⁸O_ice values, X shows the simulated values.

Figure 4.2 shows the deviations of the simulation from the data. The SSST changes, shown with the orange diamonds, are underestimated by the simulation, and most of the points fall below the 1-1 fit line, which would indicate the position of a perfect model/data match. While this might at first appear as if the simulation does not reproduce the

measured SSSTs very well, the uncertainties in the data would suggest that the fit could be improved if the error range is utilized; a point which will be expanded upon in the discussion. Furthermore, it should be noted that the reconstructions rely heavily on faunal assemblages, which in turn must make the assumption that transfer functions or modern analogs are useful for interpretations of the past environment. Other methods, such as Mg/Ca or Sr/Ca reconstructions, would instead rely on geochemical principles which can be extensively tested via laboratory studies, and as such might be considered to be more reliable.

When examining the difference in calcite reconstructions, it can be seen that the simulation and the data have a much smaller disagreement, and as theδ¹⁸O_C signal is a combination of both sea temperature as well asδ¹⁸O_sw, the match between simulation and data could be used to validate two different variables in the model. The largest differences inδ¹⁸O_C occur solely in the southern extent of the available data, where the model and data have opposite signs. Here, as with the spatial distribution of temperatures shown in Figure 4.1, it could be argued that the model resolution is insufficient to adequately show boundaries and fronts of ocean features as they may occur in the real world.