Assessment of Snow, Sea Ice, and Related Climate Processes in Canada's Earth-System Model and Climate Prediction System

Abstract

Abstract. This study assesses the ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the Canadian Earth-system Model 2 (CanESM2) to predict and simulate snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth-System Models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive, and initial condition ensembles of CanESM2 and other 25 models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as coldregion temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow cover over the Canadian land mass, reflecting a broader Northern Hemisphere positive bias. It also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational 30 uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea-ice trends there. The strengths and weaknesses of the modeling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations.Improvements in climate prediction systems like CanSIPS rely not just on simulation quality but also on using novel 35The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-157 Manuscript under review for journal The Cryosphere Discussion started: 5 September 2017 c Author(s) 2017. CC BY 4.0 License. 2 observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea-ice thickness initialization using statistical predictors available in real time.

Canadian contributions to environmetrics

“…Essentially these simulation experiments produce a sample of independent realizations of the evolution of climate that is consistent with a specified combination of external forcing factors. An early example of such an ensemble, which consists of 50 simulations of the evolution of climate from 1950 to 2100, was produced with the CanESM2 global climate model of the Canadian Centre for Climate Modelling and Analysis (see Kushner et al, 2018, and https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c; accessed 1 June 2022). Each of these simulations was subsequently “downscaled” to a finer resolution over North America using the CanRCM4 regional climate model as described in Scinocca et al (2016); see also https://open.canada.ca/data/en/dataset/83aa1b18-6616-405e-9bce-af7ef8c2031c accessed 1 June 2022).…”

Improving permafrost physics in the coupled Canadian Land Surface Scheme (v. 3.6.2) and Canadian Terrestrial Ecosystem Model (v. 2.1) (CLASS-CTEM)

“…The 'Snow cover: Brown03' experiment shows similar spring albedo, and latent and sensible heat fluxes changes to 'Snow cover: Yang97' but also has a general reduction in winter SWE and a reduction in runoff for the south central Siberian region compared to 'Snow cover: Yang97' (not shown). Comparing the simulated SWE from both 'Snow cover: Yang97' and 'Snow cover: Brown03' to Blended-5 (see Section 2.3; Mudryk et al, 2015;Kushner et al, 2018) 5…”