Polar surface temperatures are expected to warm 2–3 times faster than the global-mean surface temperature: a phenomenon referred to as polar warming amplification. Therefore, understanding the individual process contributions to the polar warming is critical to understanding global climate sensitivity. The Coupled Feedback Response Analysis Method (CFRAM) is applied to decompose the annual- and zonal-mean vertical temperature response within a transient 1% yr−1 CO2 increase simulation of the NCAR Community Climate System Model, version 4 (CCSM4), into individual radiative and nonradiative climate feedback process contributions. The total transient annual-mean polar warming amplification (amplification factor) at the time of CO2 doubling is +2.12 (2.3) and +0.94 K (1.6) in the Northern and Southern Hemisphere, respectively. Surface albedo feedback is the largest contributor to the annual-mean polar warming amplification accounting for +1.82 and +1.04 K in the Northern and Southern Hemisphere, respectively. Net cloud feedback is found to be the second largest contributor to polar warming amplification (about +0.38 K in both hemispheres) and is driven by the enhanced downward longwave radiation to the surface resulting from increases in low polar water cloud. The external forcing and atmospheric dynamic transport also contribute positively to polar warming amplification: +0.29 and +0.32 K, respectively. Water vapor feedback contributes negatively to polar warming amplification because its induced surface warming is stronger in low latitudes. Ocean heat transport storage and surface turbulent flux feedbacks also contribute negatively to polar warming amplification. Ocean heat transport and storage terms play an important role in reducing the warming over the Southern Ocean and Northern Atlantic Ocean.

Using CALIPSO‐CloudSat‐Clouds and the Earth's Radiant Energy System‐Moderate Resolution Imaging Spectrometer data set, this study documents the seasonal variation of sea ice, cloud, and atmospheric properties in the Arctic (70°N–82°N) for 2007–2010. A surface‐type stratification—consisting Permanent Ocean, Land, Permanent Ice, and Transient Sea Ice—is used to investigate the influence of surface type on low‐level Arctic cloud liquid water path (LWP) seasonality. The results show significant variations in the Arctic low‐level cloud LWP by surface type linked to differences in thermodynamic state. Subdividing the Transient Ice region (seasonal sea ice zone) by melt/freeze season onset dates reveals a complex influence of sea ice variations on low cloud LWP seasonality. We find that lower tropospheric stability is the primary factor affecting the seasonality of cloud LWP. Our results suggest that variations in sea ice melt/freeze onset have a significant influence on the seasonality of low‐level cloud LWP by modulating the lower tropospheric thermal structure and not by modifying the surface evaporation rate in late spring and midsummer. We find no significant dependence of the May low‐level cloud LWP peak on the melt/freeze onset dates, whereas and September/October low‐level cloud LWP maximum shifts later in the season for earlier melt/later freeze onset regions. The Arctic low cloud LWP seasonality is controlled by several surface‐atmosphere interaction processes; the importance of each varies seasonally due to the thermodynamic properties of sea ice. Our results demonstrate that when analyzing Arctic cloud‐sea ice interactions, a seasonal perspective is critical.

In the present work, we simulate the Pliocene climate with the EC-Earth climate model as an equilibrium state for the current warming climate induced by rising CO₂ in the atmosphere. The simulated Pliocene climate shows a strong Arctic amplification featuring pronounced warming sea surface temperature (SST) over the North Atlantic, in particular over the Greenland Sea and Baffin Bay, which is comparable to geological SST reconstructions from the Pliocene Research, Interpretation and Synoptic Mapping group (PRISM; Dowsett et al., 2016). To understand the underlying physical processes, the air–sea heat flux variation in response to Arctic sea ice change is quantitatively assessed by a climate feedback and response analysis method (CFRAM) and an approach similar to equilibrium feedback assessment. Given the fact that the maximum SST warming occurs in summer while the maximum surface air temperature warming happens during winter, our analyses show that a dominant ice-albedo effect is the main reason for summer SST warming, and a 1 % loss in sea ice concentration could lead to an approximate 1.8 W m⁻² increase in shortwave solar radiation into open sea surface. During the winter months, the insulation effect induces enhanced turbulent heat flux out of the sea surface due to sea ice melting in previous summer months. This leads to more heat released from the ocean to the atmosphere, thus explaining why surface air temperature warming amplification is stronger in winter than in summer.

Model warming projections, forced by increasing greenhouse gases, have a large inter-model spread in both their geographical warming patterns and global mean values. The inter-model warming pattern spread (WPS) limits our ability to foresee the severity of regional impacts on nature and society. This paper focuses on uncovering the feedbacks responsible for the WPS. Here, we identify two dominant WPS modes whose global mean values also explain 98.7% of the global warming spread (GWS). We show that the ice-albedo feedback spread explains uncertainties in polar regions while the water vapor feedback spread explains uncertainties elsewhere. Other processes, including the cloud feedback, contribute less to the WPS as their spreads tend to cancel each other out in a model-dependent manner. Our findings suggest that the WPS and GWS could be significantly reduced by narrowing the inter-model spreads of ice-albedo and water vapor feedbacks, and better understanding the spatial coupling between feedbacks.

Extreme weather events such as cold-air outbreaks (CAOs) pose great threats to human life and the socioeconomic well-being of modern society. In the past, our capability to predict their occurrences has been constrained by the 2-week predictability limit for weather. We demonstrate here for the first time that a rapid increase of air mass transported into the polar stratosphere, referred to as the pulse of the stratosphere (PULSE), can often be predicted with a useful degree of skill 4–6 weeks in advance by operational forecast models. We further show that the probability of the occurrence of continental-scale CAOs in midlatitudes increases substantially above normal conditions within a short time period from 1 week before to 1–2 weeks after the peak day of a PULSE event. In particular, we reveal that the three massive CAOs over North America in January and February of 2014 were preceded by three episodes of extreme mass transport into the polar stratosphere with peak intensities reaching a trilli... AbstractExtreme weather events such as cold-air outbreaks (CAOs) pose great threats to human life and the socioeconomic well-being of modern society. In the past, our capability to predict their occurrences has been constrained by the 2-week predictability limit for weather. We demonstrate here for the first time that a rapid increase of air mass transported into the polar stratosphere, referred to as the pulse of the stratosphere (PULSE), can often be predicted with a useful degree of skill 4–6 weeks in advance by operational forecast models. We further show that the probability of the occurrence of continental-scale CAOs in midlatitudes increases substantially above normal conditions within a short time period from 1 week before to 1–2 weeks after the peak day of a PULSE event. In particular, we reveal that the three massive CAOs over North America in January and February of 2014 were preceded by three episodes of extreme mass transport into the polar stratosphere with peak intensities reaching a trilli...