Development of a Chironomid-based Air Temperature Inference Model for the Central Canadian Arctic

Subfossil midge remains were identified in surface sediment recovered from 88 lakes in the central Canadian Arctic. These lakes spanned five vegetation zones, with the southern-most lakes located in boreal forest and the northern-most lakes located in mid-Arctic tundra. The lakes in the calibration...

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Bibliographic Details
Published in:Journal of Paleolimnology
Main Authors: Porinchu, David, Rolland, Nicolas, Moser, Katrina
Format: Article in Journal/Newspaper
Language:unknown
Published: Scholarship@Western 2009
Subjects:
Paleolimnology
Chironomids
Inference model
Air temperature
Transfer function
Arctic
Paleoclimate
Midges
Climate change
Geography
Tundra
Online Access:https://ir.lib.uwo.ca/geographypub/121
https://doi.org/10.1007/s10933-008-9233-3
Description
Summary:Subfossil midge remains were identified in surface sediment recovered from 88 lakes in the central Canadian Arctic. These lakes spanned five vegetation zones, with the southern-most lakes located in boreal forest and the northern-most lakes located in mid-Arctic tundra. The lakes in the calibration are characterized by ranges in depth, summer surface-water temperature (SSWT), average July air temperature (AJAT) and pH of 15.5 m, 10.60°C, 8.40°C and 3.69, respectively. Redundancy analysis (RDA) indicated that maximum depth, pH, AJAT, total nitrogen-unfiltered (TN-UF), Cl and Al capture a large and statistically significant fraction of the overall variance in the midge data. Inference models relating midge abundances and AJAT were developed using different approaches including: weighted averaging (WA), weighted averaging-partial least squares (WA-PLS) and partial least squares (PLS). A chironomid-based inference model, based on a two-component WA-PLS approach, provided robust performance statistics with a high coefficient of determination (r 2 = 0.77) and low root mean square error of prediction (RMSEP = 1.03°C) and low maximum bias. The use of a high-resolution gridded climate data set facilitated the development of the midge-based inference model for AJAT in a region with a paucity of meteorological stations and where previously only the development of a SSWT inference model was possible.

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