Tropical and Midlatitude Impact on Seasonal Polar Predictability in the Community Earth System Model

Abstract

The impact on seasonal polar predictability from improved tropical and midlatitude forecasts is explored using a perfect-model experiment and applying a nudging approach in a GCM. We run three sets of 7-month long forecasts: a standard free-running forecast and two nudged forecasts in which atmospheric winds, temperature, and specific humidity (U, V, T, Q) are nudged toward one of the forecast runs from the free ensemble. The two nudged forecasts apply the nudging over different domains: the tropics (30°S–30°N) and the tropics and midlatitudes (55°S–55°N). We find that the tropics have modest impact on forecast skill in the Arctic or Antarctica both for sea ice and the atmosphere that is mainly confined to the North Pacific and Bellingshausen–Amundsen–Ross Seas, whereas the midlatitudes greatly improve Arctic winter and Antarctic year-round forecast skill. Arctic summer forecast skill from May initialization is not strongly improved in the nudged forecasts relative to the free forecast and is thus mostly a “local” problem. In the atmosphere, forecast skill improvement from midlatitude nudging tends to be largest in the polar stratospheres and decreases toward the surface.

Nonstationary Teleconnection Between the Pacific Ocean and Arctic Sea Ice

“…The inability of GCMs to simulate the observed PARC teleconnection suggests that either model bias is impacting the relationship between the Pacific Ocean and Arctic sea ice or that there is significant internal variability in the evolution of this teleconnection and observations sample an extreme realization. For instance, Blanchard‐Wrigglesworth and Ding () note that although the ensemble mean of 40 large ensemble members in CESM1(CAM5) fails to simulate the Pacific Ocean teleconnection to Arctic sea ice, individual ensemble members are able to simulate the correct relationship, which suggests a role for internal variability.…”

Process drivers, inter-model spread, and the path forward: A review of amplified Arctic warming

“…Understanding the partitioning between local and remotely-induced warming is crucial for reducing uncertainty in the impacts of non-well mixed climate forcings (e.g., aerosols and the effects of emission reductions; Chung and Räisänen, 2011). Further, simulated Arctic warming and variability may depend on the models' representation of tropical Pacific variability (e.g., Ding et al, 2019;Baxter et al 2019) and improving Arctic projections may require improved modeling of teleconnections.…”

“…Through dynamic heating and increased moisture transport into the Arctic, the wave dynamics increase the DLW radiation and lead to warming. The role of tropical Pacific Rossby wave-driven teleconnections to the Arctic has been highlighted for observed warming over northeastern Canada and Greenland (Ding et al, 2014) and Arctic sea ice trends and variability (Ding et al 2017;Ding et al 2019;Baxter et al 2019, Topal et al 2020. Planetary waves dominate the transport of heat and moisture into the Arctic and can drive temperature increases (Graversen and Burtu 2016;Baggett and Lee 2017).…”

Quantifying the Contribution of Internal Atmospheric Drivers to Near-Term Projection Uncertainty in September Arctic Sea Ice

“…Previous study has successfully identified a linkage between changes in the atmospheric circulation in summer and the following decrease in September sea ice extent (SSIE). A stronger barotropic anticyclonic circulation trend over the Arctic in the troposphere favors a warmer and moister lower troposphere through circulation-driven adiabatic descent, resulting in the thermal melting of sea ice via the emission of more downward longwave radiation (Ding et al 2017(Ding et al , 2019Baxter et al 2019;Topál et al 2020;Luo et al 2021). This mechanism is thought to work over temporal scales from interannual to interdecadal time scales.…”

“…This mechanism is thought to work over temporal scales from interannual to interdecadal time scales. Model experiments and a fingerprint pattern marching method have suggested that this summer circulation trend is primarily internally driven and that this internal variability may have contributed about 30%-50% to the melting of September sea ice during the last few decades (Ding et al 2017(Ding et al , 2019. Baxter et al (2019) suggested that this anomalous summer high pressure is not only a local process, but also a response to a remote driver of sea surface temperatures (SSTs) manifested as a Rossby wave train originating from the tropics to the Arctic triggered by a cold SST anomaly in the eastern tropical Pacific.…”