Diatoms make good proxies for palaeoenvironmental reconstructions: their exquisite silica cell walls can be identified to species level (mostly); they preserve well in sediments (usually) and they are sensitive to multiple environmental variables.
Being sensitive to multiple environmental variables is both a blessing and a curse for a proxy. The advantage is that the proxy can potentially be used to reconstruct different environmental variables, perhaps even from the same site. The disadvantage is that changes in environmental variables other than the one of interest might cause spurious changes in our reconstruction.
Transfer functions used to reconstruct past environmental conditions from fossil biotic assemblages using the modern relationship between species and the environment make a number of assumptions. One of them is that environmental variables other than the one of interest have negligible influence on the biotic assemblages used in the reconstruction (or that the joint distribution of environmental variables remains the same). If this assumption is violated, entirely spurious reconstructions can be generated – Steve Juggins’ sick science.
This assumption should make us cautious of generating and interpreting multiple reconstructions from a single proxy record. Conceptually, it is not impossible to generate multiple reliable records, but it is difficult enough to test if one reconstruction is reliable, testing two will be so much harder. If there are multiple variables that could justifiably be reconstructed, there is a problem whether or not all the variables are reconstructed. Assumptions need to be checked.
There is an obvious direct physical relationship between summer sea-ice and summer SST – ice melts in warm water. This relationship should be stable through time, although as the summer sea-ice and SST reconstructions are essentially non-linear transformations of each other, there is little extra information in having both reconstructions.
The relationship between summer SST and winter sea-ice is strong but less direct, depending on the thermal inertia of the ocean to stop ice forming where summers are warm. The relationship between SST and winter sea-ice could change, for example, if seasonal insolation changes, resulting in non-analogue condition.
Some parts of the ice-SST phase space shown in figure 1 are clearly unlikely to exist under any plausible climate. High winter ice concentrations and high summer SST cannot coexist because of the thermal inertia of the deep Southern Ocean (in shallow brackish sites like the Baltic, this combination is possible).
Other parts of the ice-SST phase space might exist under other climates. For example, during the early Holocene, summer insolation was higher and winter insolation lower. Did this change the relationship between summer SST and winter sea-ice? The optimistic would argue that transfer functions can extrapolate into areas of the phase space not sampled by the modern environment, but the ability of transfer functions to extrapolate is limited.
We can explore how the sea ice-SST relationship might have changed in the past with the output of CMIP5 climate models and use this analysis to evaluate whether sea-ice and SST reconstructions are likely to be affected by non-analogue conditions.
I’ve used the CCSM4 model runs for the pre-industrial (PI), mid-Holocene (MH) and last glacial maximum (LGM). Very conventionally, monthly climatologies of all the variables are available from the CMIP5 archive. I’ve inspected the data on its native grid rather than converting to an equal area grid, and used all grid points south of 40°S.
The relationship between sea-ice and SST is a little different in the model PI and the instrumental record. In the latter, there is little winter sea-ice at locations with summer SST above 2°C and below this temperature, sea-ice concentrations rise rapidly. I’m not greatly surprised by the discrepancy between modelled and observed sea-ice, it is a difficult environmental variable to model. More important for my current purpose is the difference between the different time-slices.
The cold left side of the ice-SST relationship is fairly constant in the three time-slices. The warm right side varies though, with the limit of sea-ice associated with 0.5°C warmer temperatures at the LGM. The very warm grid-points at the LGM with some sea-ice are situated in shallow water between Argentina and the Falkland Islands. The LGM grid-points tend to be more concentrated towards the left side of the relationship than the other two time periods. I’m not sure what the physics behind these changes are but might be due to the changing latitude of the sea-ice edge.
Except for the grid points near the Falkland Islands, I don’t think the differences between the phase space in the different time periods are large enough to have a severe impact on the transfer functions. There are a few more models with data available, these need to be examined to check that the CCSM4 results are representative.
The CMIP5 models can be used to check for non-analogue climates relevant to other transfer functions. This test is probably most useful where the variables are highly correlated in the modern environment.
Even if the CCSM4 output does not indicate non-analogue problems in SST-sea ice phase space, I have other concerns about sea-ice transfer functions, both for diatoms and other proxies like dinoflagellate cysts, as I discussed at the sea-ice proxy workshop in Bremerhaven.
- As sea ice-SST relationship is so strong, there is little or no extra information in sea-ice reconstructions above that in an SST reconstruction. As SST is not a bounded variable, it is easier to deal with.
- The ecological link between sea-ice and the biota needs to be demonstrated. For diatoms this has been done, with some taxa, for example those producing IP25 in the Northern Hemisphere, known to be associated with sea-ice. With dinocysts, the link is less clear.
- The season that is important is not clear. Winter sea-ice might not be important because of the low productivity under snow-covered ice with little or no sunlight. Spring or summer sea-ice is more likely to be directly linked to the biota.
- It is very difficult to collect observations evenly along the sea-ice concentration gradient because this gradient is very steep. Most sea-ice calibration sets I know have many observations at low/no and high sea-ice, but few at intermediate sea-ice. This will bias performance estimates.
- The ever present spatial autocorrelation has largely been ignored by papers developing sea-ice transfer functions.
- The role of other environmental variables, such as nutrient concentrations, which have strong relationships with SST in the Southern Ocean, in driving assemblage composition has been little studied.
Perhaps someone will write a critical review of the methods used to reconstruct sea-ice.