Regional Arctic sea–ice prediction: potential versus operational seasonal forecast skill

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 forecasts prior to MM combination, and a comparison is made against two benchmark MM ensembles: one uncorrected and another where individual models are adjusted for mean and trend bias. Focusing on September hindcasts over 2000–2015 initialized monthly from April–August, calibration improves forecast skill for all models tested, but greatest skill is achieved by the calibrated MM ensemble. Compared against a climatology adjusted for trends, skill is seen over most of the Arctic for all MM formulations and at all lead times, with highest skill obtained by the calibrated MM ensemble. Furthermore, despite the overall skill for each MM formulation, we find evidence of the “spring predictability skill barrier” for forecasts initialized before June.

Section: Forecast Verification Resultssupporting

confidence: 87%

Section: Forecast Verification Resultssupporting

confidence: 87%

A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice

Dirkson

Denis

Merryfield

2019

“…To circumvent this problem, we make use of two experiment suites performed using the same GCM, which allows us to assess the ability of SIV to accurately predict SIA (two quantities that can be calculated from the CMIP5 output). We use two experiments described in Bushuk et al (): a 300‐year control run and a set of PM experiments performed using the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast‐oriented Low Ocean Resolution (FLOR) climate model (Vecchi et al, ). Both the control run and PM experiments use radiative forcing and land use conditions that are representative of 1990.…”

Section: Methodsmentioning

confidence: 99%

“…The only source of forecast error in PM experiments is the chaotic growth associated with small initial condition errors; such an approach provides guidance on the upper limits of predictability by asking how well a particular model can predict itself (e.g., Collins, ). Previous PM studies have shown that the potential predictability for pan‐Arctic SIE remains statistically significant at lead times up to 1–2 years (Blanchard‐Wrigglesworth & Bushuk, ; Blanchard‐Wrigglesworth, Bitz, et al, ; Bushuk et al, ; Day et al, ; Germe et al, ; Holland et al, ; Koenigk & Mikolajewicz, ; Tietsche et al, ), which is considerably longer than that of GCM‐based initialized forecasts.…”

Section: Introductionmentioning

confidence: 98%

A Spring Barrier for Regional Predictions of Summer Arctic Sea Ice

Bonan

Bushuk

Winton

2019

Self Cite

Seasonal forecast systems can skillfully predict summer Arctic sea ice up to 4 months in advance. For some regions, however, there is a springtime predictability barrier that causes forecasts initialized prior to May to be less skillful. Since this barrier has only been documented in a few general circulation models (GCMs), we evaluate GCMs participating in phase 5 of the Coupled Model Intercomparison Project. We first show sea ice volume skillfully predicts summer sea ice area (SIA) and has similar skill to a perfect model experiment. Given this result, we assess regional SIA predictability across each GCM and find a universal predictability barrier in late spring. For SIA at each summer target month in the marginal seas of the Arctic basin, a notable drop in prediction skill occurs from June to May in each GCM. This suggests summer sea ice forecasts initialized after 1 June will have substantially better prediction skill than forecasts initialized before.

“…A quantitative picture of Arctic sea ice predictability is beginning to emerge. Studies on potential predictability in fully coupled general circulation models (GCMs; e.g., Blanchard‐Wrigglesworth et al, ; Bushuk et al, ; Day et al, ; Holland et al, ; Tietsche et al, ) and statistical and dynamical forecast systems (e.g., Blanchard‐Wrigglesworth et al, ; Bushuk et al, ; Chevallier et al, ; Guemas et al, ; Merryfield et al, ; Msadek et al, ; Petty et al, ; Sigmond et al, ; Wang et al, , ) have shown that forecasts of pan‐Arctic sea ice extent (SIE) may be skillful anywhere between 2 months and 2 years in advance. At regional scales—which is often more societally relevant—dynamical prediction systems can skillfully predict SIE on seasonal timescales (Bushuk et al, ) or even decadal timescales (Yeager et al, ).…”

Section: Introductionmentioning

confidence: 99%

Nonstationary Teleconnection Between the Pacific Ocean and Arctic Sea Ice

Bonan

Blanchard‐Wrigglesworth

2020

Over the last 40 years observations show a teleconnection between summertime Pacific Ocean sea surface temperatures and September Arctic sea ice extent. However, the short satellite observation record has made it difficult to further examine this relationship. Here, we use 30 fully coupled general circulation models (GCMs) participating in Phase 5 of the Coupled Model Intercomparison Project to assess the ability of GCMs to simulate this teleconnection and analyze its stationarity over longer timescales. GCMs can temporarily simulate the teleconnection in continuous 40‐year segments but not over longer, centennial timescales. Each GCM exhibits considerable teleconnection variability on multidecadal timescales. Further analysis shows that the teleconnection depends on an equally nonstationary atmospheric bridge from the subequatorial Pacific Ocean to the upper Arctic troposphere. These findings indicate that the modulation of Arctic sea ice loss by subequatorial Pacific Ocean variability is not fixed in time, undermining the assumption of teleconnection stationarity as defined by the satellite record.