Arlan Dirkson
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View article: The Effect of Ensemble Size on the Mean Squared Error and Spread–Error Relationship
The Effect of Ensemble Size on the Mean Squared Error and Spread–Error Relationship Open
Most ensemble verification diagnostics are sensitive to ensemble size, complicating the evaluation of a system’s underlying quality and the comparison of different ensemble systems. This study examines how the mean squared error (MSE) of t…
View article: Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison
Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison Open
This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting…
View article: Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence
Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence Open
There is a high demand and expectation for subseasonal to seasonal (S2S) prediction, which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S pre…
View article: Calibration of subseasonal sea‐ice forecasts using ensemble model output statistics and observational uncertainty
Calibration of subseasonal sea‐ice forecasts using ensemble model output statistics and observational uncertainty Open
In response to a growing demand for improved sea‐ice forecast guidance at shorter timescales and higher spatial resolutions, this study investigates the predictive skill of daily subseasonal sea‐ice forecasts from two state‐of‐the‐art pred…
View article: Development and Calibration of Seasonal Probabilistic Forecasts of Ice-Free Dates and Freeze-Up Dates
Development and Calibration of Seasonal Probabilistic Forecasts of Ice-Free Dates and Freeze-Up Dates Open
Dynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct s…
View article: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2)
The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2) Open
The second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ense…
View article: A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice
A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice Open
We formulate seasonal probabilistic forecasts of Arctic sea ice concentration from a multimodel (MM) ensemble constructed from six state‐of‐the‐art climate models. Trend‐adjusted quantile mapping is applied to postprocess individual model …
View article: Calibrated Probabilistic Forecasts of Arctic Sea Ice Concentration
Calibrated Probabilistic Forecasts of Arctic Sea Ice Concentration Open
Seasonal forecasts of Arctic sea ice using dynamical models are inherently uncertain and so are best communicated in terms of probabilities. Here, we describe novel statistical postprocessing methodologies intended to improve ensemble-base…
View article: Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system
Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system Open
The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow…
View article: Assessment of Snow, Sea Ice, and Related Climate Processes in Canada's Earth-System Model and Climate Prediction System
Assessment of Snow, Sea Ice, and Related Climate Processes in Canada's Earth-System Model and Climate Prediction System Open
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 …