Empirical modeling of summer lake surface temperatures in southwest Greenland
This work presents a method to estimate mean daily lake surface water temperatures using only air temperature, theoretical clear-sky solar radiation, and lake size. Surface water temperatures were measured at a selection of lakes in southwest Greenland during the summers of 1998–2000. The lakes are...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.521.9012 http://www.aslo.org/lo/toc/vol_49/issue_1/0271.pdf |
| Summary: | This work presents a method to estimate mean daily lake surface water temperatures using only air temperature, theoretical clear-sky solar radiation, and lake size. Surface water temperatures were measured at a selection of lakes in southwest Greenland during the summers of 1998–2000. The lakes are small (surface area,150 ha) with maximum depths ranging from 3.5 to 47 m. An empirical model requiring only local air temperature and theoretical clear-sky solar radiation is developed to predict daily mean lake surface temperatures in summer for each lake. The model approximates the slow integrated response of water temperature to meteorological forcing by applying an exponential smoothing filter to air temperature. Exponential smoothing results in a 35 % improvement in model fit compared with a model using unsmoothed air temperatures. The smoothed air temperatures and clear-sky solar radiation are linearly combined to estimate the daily mean lake surface temperatures. The smoothing parameters and the three linear coefficients of the model, obtained individually for each of 15 lakes, are found to relate to lake area and maximum depth, leading to the development of a general model. With this general model it is possible to predict the summer surface temperatures at any lake in this region where local air temperatures can be estimated. Cross-validation of the general model at each lake in turn indicated a 90 % forecast skill and average standard error of prediction of 1.08C. Examination of the daily prediction errors over time suggests a relation to strong wind |
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