On June 2nd Aquanty will have a couple representatives at the 56th Congress of Canadian Meteorological and Oceanographic Society (CMOS).
Join session #14030 “Monitoring and Modelling Cryospheric Change” for a presentation by Aquanty’s climate intern and PhD candidate Tyler Herrington, and session #4040 “Atmosphere, Ocean, and Climate Dynamics” for a presentation by UoT post-doctoral researcher and Canada 1 Water collaborator Mani Mahdinia.
Click here to register and attend these presentations.
The Impact of Major North American Lakes in WRF for Regional Climate Applications
Session: 4042 Atmosphere, Ocean, and Climate Dynamics
Date and time: 02/06/2022 12:55pm CST
Authors: Mani Mahdinia, Andre Erler, Richard Peltier
Presented by / Présenté par: Mani Mahdinia
Contact: mani.mahdinia@utoronto.ca
Abstract: In this study, we focus upon the influence of major lakes on the North American climate, and how well different lake models do in achieving realistic simulations. We also investigate the ability of these models to properly represent specific lake effects such as ice-in and -out dates and lake effect precipitation. The study design and domain setup follows CORDEX guidelines for historical regional climate modelling and we employ the recent ERA5 reanalysis product. The lakes of concern to us include the Laurentian Great Lakes, which straddle the US-Canada border; the Great Slave and Great Bear Lakes of the Northwest Territories; and the Lakes Winnipeg and Winnipegosis. These are the largest lake clusters on the Canadian land mass and are characterized by a range of depths and environmental conditions. We employ the WRF model, a widely-used state-of-the-art regional climate model, at 0.11 degree resolution. There have been several previous attempts to implement lake models in WRF (e.g. FLake), but most of these have focused solely upon the Great Lakes. However, when considering the climate across all of Canada, other large lake regions such as Great Slave and Great Bear Lakes or Lakes Winnipeg and Winnipegosis are also of critical importance. Our goals in the current work are, first, to produce a direct comparison of different column lake models (e.g., FLAKE or enhanced versions of CLM/WRF lake model) when applied to the Great Lakes, second, to provide a general assessment of lake effects and perform an assessment of column lake models at high latitudes (for Great Slave and Great Bear Lakes), and third, trend analysis and comparison of lake surface temperatures, ice, and lake-effect precipitation for all the major lakes. The simulations will encompass an extensive historic period during which reanalysis data is available.
Investigating the Impact of Snow Cover on Permafrost Soil Temperatures in Modern Reanalysis and Data Assimilation Systems
Session: 14030 Monitoring and Modelling Cryospheric Change
Date and time: 02/06/2022 03:25pm CST
Authors: Tyler Herrington , Christopher Fletcher, Andre Erler
Presented by / Présenté par: Tyler Herrington
Contact: therring@uwaterloo.ca
Abstract: Soil temperatures are required as input for hydrological models, and for numerical weather prediction. At high latitudes, accurate permafrost representation is important as soil respiration from melting permafrost may act as a positive feedback on warming. Validation of Arctic soil temperatures in reanalysis and Land Data Assimilation System (LDAS) products (hereafter reanalysis products) has been historically limited because widespread in-situ reference observations have generally been unavailable. Here we validate pan-Arctic soil temperatures from eight reanalysis and Land Data Assimilation System (LDAS) products, at 1-degree spatial resolution, using in situ soil temperature data from diverse measurement networks across Eurasia and North America. We find that most products are biased cold by 2–7 K across the Arctic. Near-surface soil temperature biases and Root Mean Square Error (RMSE) were generally largest in the cold season, and many products overestimate the observed variability in soil temperatures over the cold season. In addition, preliminary results show that the cold season RMSE in many products was more than 1.5 times as large when snow was present in satellite-based snow cover datasets, though there is substantial variability between products. We attempt to explain the large spread in cold season Arctic soil temperatures by reconciling differences in snow cover between reanalysis products and satellite-based snow cover, and variability in snow cover between reanalysis products. Our hypothesis is that RMSE and bias in soil temperature will be largest when reanalysis products and satellite snow-cover show substantial disagreement; likely when monthly mean air temperatures are close to the freezing point. We also examine a subset of reanalysis products at a higher resolution (0.05 degrees ) to test the impact of spatial resolution on soil temperature performance, and preliminary results suggest that small improvements in pan-Arctic soil temperature biases may be achieved by using higher resolution soil temperature data.