ABSTRACT

Lake surface water temperature (LSWT) responds rapidly to changes in climatic variables. This response is heterogeneous in space and its spatial distribution is primarily influenced by lake bathymetry and latitude. Such heterogeneity is not captured by one-dimensional water temperature models, which can accurately predict only the average LSWT. We performed a spatially distributed application of the hybrid physically based/data-driven model air2water to predict the LSWT variability in the 5 Laurentian Great Lakes and to deepen our understanding of the role of local depth and latitude in shaping this heterogeneous response. Daily remotely sensed LSWT data were used to calibrate and validate the model during 1995–2018, and additional simulations considering a synthetic warmer climate scenario in which air temperature was increased by 2 °C were run to assess the inter- and intra-lake differences in LSWT warming rates. The model reproduces the observed spatial distribution of LSWT with an average root mean squared error of 1.2 °C and suggests that, under the warmer scenario, the LSWT of the 5 lakes could increase heterogeneously, with the deepest zones showing the maximum warming rates. Summer stratification lengthening is expected to increase with higher local depth; this behaviour attenuates with increasing latitude, whereas the LSWT warming is essentially dependent on the local depth, irrespective of latitude. We highlight the importance of accounting for LSWT spatial heterogeneity to adequately assess the thermal response of the Great Lakes to a warming climate.

摘要

湖面水温 (LSWT) 对气候变量的变化反应迅速。这种响应在空间上是异质的，其空间分布主要受湖泊水深和纬度的影响。一维水温模型无法捕捉到这种异质性，它只能准确预测平均 LSWT。我们执行了基于物理/数据驱动的混合模型air2water的空间分布应用预测 5 Laurentian Great Lakes 的 LSWT 变异性，并加深我们对局部深度和纬度在塑造这种异质响应中的作用的理解。1995 年至 2018 年期间，每日遥感 LSWT 数据用于校准和验证模型，并在考虑到气温升高 2°C 的合成温暖气候情景下进行额外模拟，以评估湖内和湖内的差异。 LSWT 升温率。该模型再现了观测到的 LSWT 空间分布，平均均方根误差为 1.2 °C，表明在较温暖的情况下，5 个湖泊的 LSWT 可能不均匀地增加，最深的区域显示出最大的升温速率。夏季地层延长预计会随着局部深度的增加而增加；这种行为随着纬度的增加而减弱，而 LSWT 变暖基本上取决于当地的深度，而与纬度无关。我们强调了考虑 LSWT 空间异质性以充分评估五大湖对气候变暖的热响应的重要性。