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Arctic Climate Feedback Response to Local Sea-Ice Concentration and2

Remote Sea Surface Temperature Changes in PAMIP Simulations

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Matthew T. Jenkins* 1, Aiguo Dai 1, Clara Deser 2

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University at Albany, State University of New York, Albany, NY, USA
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National Center for Atmospheric Research, Boulder, CO, USA8
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Submitted to: Climate Dynamics

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Date: December 22, 2023

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1st Revision: May 24, 2024

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*Corresponding Author: Matthew T. Jenkins (mtjenkins@albany.edu); ORCiD: 0000-0001-5279-1893
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Abstract25

Local and remote processes have been suggested to drive Arctic amplification (AA) – the enhanced

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warming of the Arctic region relative to other areas under increased greenhouse gases. We use

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Polar Amplification Model Intercomparison Project (PAMIP) simulations with changes in Arctic28

sea-ice with fixed global sea surface temperature (SST), or changes in global SST with fixed Arctic29

sea-ice to untangle the climate response to Arctic sea-ice loss or SST-induced warming,

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respectively. In response to Arctic sea-ice loss, the surface albedo feedback activates in summer

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mainly to increase oceanic heat uptake, leading to weak summertime warming. During winter,

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Arctic sea-ice loss greatly enhances oceanic heat release, which produces Arctic bottom-heavy

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warming and triggers positive lapse rate and cloud feedbacks, leading to large AA. In contrast,

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enhanced atmospheric energy convergence into the Arctic becomes the dominant contributor to35

relatively small AA under global SST-induced warming. Water vapor feedback contributes to36

Arctic warming but opposes AA due to larger tropical than Arctic moistening under SST-induced

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warming with fixed Arctic sea-ice. We also find top-heavy to uniform (bottom-heavy) Arctic

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warming and moistening in the Arctic mid-upper (lower) troposphere in the SST (Arctic sea-ice)

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perturbation runs, producing a negative-neutral (positive) Arctic lapse rate feedback, respectively.

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Lastly, we show that the responses to global SST or polar SIC perturbations are linearly separable.

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Our results suggest that large AA is caused primarily by sea-ice loss and resultant local changes

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in surface fluxes, while increased poleward energy transport can only produce weak AA under43

fixed sea ice.

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Keywords: Arctic amplification; sea-ice loss; climate feedback; global warming; Arctic warming;

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ocean heat release; atmospheric energy transport

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1. Introduction

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The Arctic region warms faster than the rest of the world in response to increased

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greenhouse gas (GHG) concentrations – a phenomenon known as Arctic amplification (AA)55

(Serreze and Barry 2011; Walsh 2014; England et al. 2021; Taylor et al. 2022). Many mechanisms

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have been proposed to explain AA such as surface albedo feedback (Hall 2004; Winton 2006),

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increased surface downwelling longwave (LW) radiation from enhanced poleward energy

58transport (Cai 2005; Henry et al. 2021), increased water vapor, or clouds (Ghatak and Miller 2013;

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Burt et al. 2016; Gong et al. 2017; Monroe et al. 2021), Arctic positive lapse rate feedback (Pithan

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and Mauritsen 2014; Goosse et al. 2018), and increased upward oceanic energy fluxes due to sea-61

ice loss (Deser et al. 2010; Kumar et al. 2010; Screen and Simmonds 2010a, b; Boeke and Taylor

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2018; Dai et al. 2019; Sejas and Taylor 2023). The local and remote mechanisms suggested to63

contribute to AA are tightly coupled (Feldl et al. 2017b; Henry et al. 2021; Dai and Jenkins 2023),

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making the exact causes of AA unclear in a fully coupled system. For instance, sea-ice loss largely

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shapes the spatial patterns of Arctic surface warming and positive lapse rate feedback (Feldl et al.

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2020; Boeke et al 2021) by increasing upward surface energy fluxes in autumn and winter that in-

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turn influences Arctic atmospheric energy convergence and LW cloud feedbacks in non-summer

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months (Jenkins and Dai 2021). Further, warming in low-mid latitude regions influences Arctic

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mid-upper tropospheric warming through changes in atmospheric energy convergence into the70

Arctic, affecting the structure of Arctic warming profiles and lapse rate feedback (Perlwitz et al.

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2015; Feldl et al. 2020; Hay et al. 2022). Additionally, Liang et al. (2022) showed that AA weakens

72in the future for greater CO2 concentrations due to weaker Arctic and global warming differences.

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Thus, more work is needed to understand how local and remote processes influence Arctic

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warming and AA.

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Arctic sea-ice loss plays an essential role in local Arctic warming (Dai et al. 2019; Linke76

et al. 2023b) and may contribute to warmer winters in northern hemisphere mid-latitude areas (Sun77

et al. 2016). As sea-ice retreats, increased energy transfer from warm, open water surfaces to the78

frigid overlying atmosphere during polar night contributes to large AA (Kumar et al. 2010; Deser79

et al. 2010; Screen and Simmonds 2010a, b; Boeke and Taylor 2018; Taylor et al. 2018; Dai et al.

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2019; Dai and Jenkins 2023). Exclusion of sea-ice loss effects from models greatly weakens AA.

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Specifically, Dai et al. (2019) showed that AA weakens in model experiments with 1%/year CO2
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increases and fixed SIC for surface flux calculations, and that negligible additional AA will occur

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after sea-ice completely melts away. Davy and Griewank (2023) confirmed this finding by

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showing that as the rate of sea-ice loss decreases in the future, concurrent AA weakens. Lastly,85

previous studies suggest that increased surface heat capacity associated with sea-ice loss affects

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AA seasonality because more energy input (release) is required to raise (cool) the temperature of

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open water than sea ice (Dwyer et al. 2012; Hahn et al. 2022; Hu et al. 2022; Sejas et al. 2023).

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However, the heat capacity of the ocean surface changes little with sea-ice loss as water mass is

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conserved after ice melts. Thus, the reduced ice-insulation effect associated with sea-ice loss is the90

main driver of large cold season AA (Dai and Jenkins 2023).

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Another process underlying AA is the lapse rate feedback that depends on local vertical92

warming structures (Pithan and Mauritsen 2014; Linke et al. 2023a; Zhou et al. 2023). Under a93bottom-heavy warming profile, outgoing LW radiation at the top of the atmosphere (TOA) is

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reduced relative to vertically uniform warming, thereby enhancing surface warming (Boeke et al.

952021; Dai and Jenkins 2023). In contrast, a top-heavy warming profile, as seen in the tropics,

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suppresses surface warming by increasing outgoing LW radiation (Colman and Soden 2021). The

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lapse rate feedback has been considered as a major contributor to AA due to its large Arctic versus

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tropical warming effect (Pithan and Mauritsen 2014; Goosse et al. 2018; Hahn et al. 2021).

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Previous studies have attributed Arctic bottom-heavy warming and the resultant positive lapse rate

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feedback to high lower-tropospheric stability, which effectively traps warming at the surface

101(Bintanja et al. 2011; Pithan and Mauritsen 2014). However, recent studies suggest that Arctic

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lapse rate feedback is strongly correlated with surface warming patterns and sea-ice loss (Feldl et103

al. 2020; Boeke et al. 2021; Jenkins and Dai 2021) rather than stability strength (Jenkins and Dai

1042022; Dai and Jenkins 2023). Remote processes, such as enhanced moist static energy convergence105

into the Arctic, may also influence Arctic lapse rate feedback by favoring warming in the mid-106

upper troposphere (Feldl et al. 2020), leading to negative lapse rate feedback.

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During summer, surface albedo and water vapor feedbacks activate in the Arctic in

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response to greenhouse gas (GHG) forcing. The surface albedo feedback makes a large positive

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contribution to Arctic energy imbalance in summer (Hall 2004; Winton 2006; Pithan and110

Mauritsen 2014; Goosse et al. 2018; Hahn et al. 2021); however, most of the enhanced shortwave

111(SW) absorption preferably warms the ocean mixed layer rather than near-surface air (Dai 2021;
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Dai and Jenkins 2023). Additionally, water vapor feedback has been suggested to contribute to113

Arctic warming (Ghatak and Miller 2013; Gong et al. 2017) but oppose Arctic amplification due

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to larger moistening in tropical regions than polar areas under increased GHGs (Pithan and115

Mauritsen 2014; Hahn et al. 2021). Jenkins and Dai (2022) showed that water vapor feedback and116

sea-ice loss spatial patterns are weakly correlated in ERA5 reanalysis data, but they did not117

quantify the underlying local and remote drivers of Arctic water vapor feedback. An improved

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understanding of Arctic water vapor feedback is needed as it enhances Arctic surface warming and119

melts sea ice, indirectly contributing to AA through the sea-ice feedback (Dai et al. 2019; Dai and120

Jenkins 2023). Moreover, water vapor feedback may interact with other processes by changing

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patterns of atmospheric latent energy transport (Chung and Feldl 2023) or amplifying other climate

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feedbacks (Beer and Eisenman 2022).

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Cloud feedback impacts TOA and surface energy fluxes (Wetherald and Manabe 1988),

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but their response to local and remote processes is not fully understood. Previous studies have125

found an increase in local Arctic low cloud amounts and cloud water content in response to local

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sea-ice loss due to strong cold season ocean-atmosphere coupling (Schweiger et al. 2008; Kay and127

Gettelman 2009; Eastman and Warren 2010; Liu et al. 2012; Taylor et al. 2015; Kay et al. 2016;

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Morrison et al. 2018, 2019; Jenkins and Dai 2022; Jenkins et al. 2023; Taylor and Monroe 2023).

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Increased surface downwelling LW radiation from local Arctic cloud increases slows sea ice

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growth during Arctic autumn and winter, lengthening exposure of open water surfaces to heat the131

overlying air during the cold season (Monroe et al. 2021). Nonlocal cloud feedbacks may also

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contribute to Arctic warming and AA by affecting remote surface warming patterns and thus

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atmospheric energy transport into the Arctic (Vavrus et al. 2004; Middlemas et al. 2020).

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Increased energy transport from midlatitudes into the Arctic has been suggested to135

influence AA (Cai 2005; Roe et al. 2015; Feldl et al. 2017b; Soldatenko 2021). Without sea-ice

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loss and associated surface heating, enhanced poleward atmospheric energy transport produces

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only weak AA in model simulations (Alexeev et al. 2005; Merlis and Henry 2018; Henry et al.

1382021). On the other hand, inclusion of sea-ice loss effects in model simulations reduces

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atmospheric energy transport into the Arctic due to decreased temperature gradients between

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middle and high latitudes (Hwang et al. 2011; Jenkins and Dai 2021; Audette et al. 2021; Hahn et141

al. 2023). However, Cardinale and Rose (2023) showed that an increase in the fraction of the Arctic
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142energy convergence used to heat the surface may overcome the total decrease in Arctic energy

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convergence, contributing to winter Arctic warming. Inhomogeneous spatial patterns of radiative

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forcing also influence atmospheric poleward energy transport (Stuecker et al. 2018; Virgin and145

Smith 2019). When radiative forcing is negative in the Arctic, atmospheric poleward energy146transport increases to offset the energy imbalance, inducing small AA (Virgin and Smith 2019).

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Additionally, Stuecker et al. (2018) found that atmospheric energy transport became an important

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contributor to AA in response to radiative forcing applied only in midlatitudes in fully coupled149

simulations, but they did not examine the effects of sea-ice loss in shaping the Arctic warming in

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response to such forcing.

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The relative importance of sea-ice loss, positive climate feedbacks, and atmospheric energy

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transport in shaping AA is still debated and merits further investigation. Arctic climate feedbacks

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have been estimated in coupled model simulations (Pithan and Mauritsen 2014; Sejas et al. 2014;

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Goosse et al. 2018; Stuecker et al. 2018; Previdi et al. 2020; Hahn et al. 2021); however, the155

influence of local sea-ice loss or remote SST warming on climate feedbacks cannot be explicitly

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quantified in a fully coupled system. To address these points, we use atmosphere-only simulations

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from the Polar Amplification Model Intercomparison Project (PAMIP; Smith et al. 2019) to158

answer the following questions:159

1. What are the impacts of local Arctic SIC changes through enhanced oceanic heating of the160

atmosphere or global SST changes and background warming in atmosphere-only model

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simulations on Arctic surface warming, AA, radiative climate feedbacks, and atmospheric

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energy transport?

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2. Do the individual responses to SST warming or Arctic SIC loss sum to the total response164

to the combined influences of SST warming and Arctic SIC loss occurring simultaneously?

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The PAMIP experiments allow us to separate the climate response to perturbations in local sea ice

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or remote SST changes in model simulations under fixed GHG concentrations. The SST

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perturbation runs represent the climatic effects of background global warming without large AA,

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while the Arctic SIC change simulations show the impact from Arctic sea-ice loss without169

background global warming.

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2. Methods

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2.1 PAMIP experiments

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We investigate how changes in global SST and/or local SIC impact Arctic surface

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warming, AA, climate feedbacks, and atmospheric energy transport using PAMIP atmosphere-

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only time slice experiments (Table 1; Smith et al. 2019). PAMIP experiment 1.1 (pdSST-pdSIC)

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serves as the control run where global SST and polar (i.e., Arctic and Antarctic) SIC fields are176

fixed at their present-day (pd) (i.e., year 2000) values. To isolate the response to global SST

177changes, we compare the pdSST-pdSIC run to PAMIP experiments 1.3 (piSST-pdSIC) and 1.4

178(futSST-pdSIC) where polar (i.e., Arctic and Antarctic) SIC remains fixed at present-day

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conditions and SSTs over open water surfaces are set to preindustrial (pi) and future (fut) states180(defined below), respectively. Likewise, we difference the pdSST-pdSIC run with PAMIP

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experiments 1.5 (pdSST-piArcSIC) and 1.6 (pdSST-futArcSIC) where SSTs outside the Arctic

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region are fixed at their present-day values and Arctic SIC is changed to preindustrial and future

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states to separate the impacts of sea-ice loss from other forcings. For the pdSST-piArcSIC and184

pdSST-futArcSIC simulations, SSTs are specified at their preindustrial or future values in regions

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where preindustrial or future SIC deviates by more than 10% of the present-day state, respectively

186(Smith et al. 2019).

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Figure 1 shows the maps of prescribed SST and SIC changes for the preindustrial (Fig. 1a,188b) or future (Fig. 1c, d) cases. To facilitate comparison with the future changes, which are relative189

to present-day, the historical changes are computed as present-day minus preindustrial in Fig. 1

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and all other figures. We also compute the difference between pdSST-pdSIC and experiment 1.2

191(piSST-piSIC; referred to as TOTAL) where global SSTs and polar SIC are changed

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simultaneously to their preindustrial states. We compare the results from TOTAL to the difference

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between pdSST-pdSIC and the sum of piSST-pdSIC, pdSST-piArcSIC and pdSST-piAntSIC

194(referred to as SUM) to assess the linearity of the total climate response to both polar SIC and195

global SST changes. The preindustrial, present-day, and future time periods correspond to196

estimated Arctic SIC and/or global SST conditions under global-mean surface temperatures of

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13.67°C, 14.24°C, and 15.67°C, respectively (Smith et al. 2019), which correspond to a historical198

warming of 0.57oC and a future warming of 1.43oC relative to present-day. Their corresponding

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SIC changes are also much larger for the future case than the historical case (Fig. 1). Present-day

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SST and SIC fields are based on the 1979-2008 climatology from the Hadley Center Sea Ice and7

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Sea Surface Temperature dataset (HadISST; Rayner et al. 2003). Preindustrial and future SST and

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SIC fields are derived from the CMIP5 historical and RCP8.5 experiments for 31 models,203

respectively (Smith et al. 2019). See Appendix A of Smith et al. (2019) for more details.

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Table 1. Summary of PAMIP experiments used in the analysis (from Smith et al. 2019).
Model Simulation
1.1 pdSST-pdSIC

Full Name
Present day sea surface temperature
Present-day sea-ice concentration
Preindustrial sea surface temperature
Preindustrial sea-ice concentration

Description
Year 2000 global SST andpolar SIC; control run.
1.2 piSST-piSIC
Historical global SST and polar
SIC; assesses total climate
response to SST and SICchanges.
1.3 piSST-pdSIC
Preindustrial sea surface temperature Historical (1.3) and future (1.4)
Present-day sea-ice concentration
global SST with polar SIC
fixed at year 2000 conditions;
1.4 futSST-pdSIC
Future sea surface temperatureassesses role of background
Present-day sea-ice concentration
warming
without
sea-icefeedback.
1.5 pdSST-piArcSIC Present-day sea surface temperature Historical (1.5) and future (1.6)
Preindustrial sea-ice concentration
Arctic SIC with global SST
1.6 pdSST-futArcSIC Present-day sea surface temperature fixed at year 2000 conditions;assesses role of Arctic sea-ice
Future sea ice concentration
feedback without backgroundwarming.
1.7 pdSST-piAntSIC Present-day SSTHistorical Antarctic SIC with
Preindustrial Antarctic SIC
global SST fixed at year 2000
conditions; assesses role ofAntarctic sea-ice feedback
without background warming.
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Fig. 1. (a, c) Annual mean changes in SST (K; shading) and Arctic SIC (%; contours; interval 5%)
for the (a) historical (present-day minus preindustrial) and (c) future warming (future minus
present-day) cases. Changes in SIC for the (b) historical and (d) future cases are shown as shadingin (b) and (d) for clarity.

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We use monthly-mean output from five models (i.e., AWI-CM1-1-MR, CESM2, CNRM-

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CM6-1, CanESM5, IPSL-CM6A-LR) that provided the necessary fields for our analysis. AWI-

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CM1-1-MR and CNRM-CM6-1 did not output the necessary variables for some calculations in

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piSST-piSIC (i.e., TOTAL) and is excluded in our comparison of piSST-piSIC to the sum of215

piSST-pdSIC, pdSST-piArcSIC, and pdSST-piAntSIC (i.e., SUM). Each model and experiment216

are initialized on 1 April 2000 and are run for 14-months, discarding the first two months as spin-217

up (Smith et al. 2019). To improve robustness of the results, we analyze the ensemble mean of the218

100 ensemble runs with varied initial conditions for each model and experiment as atmospheric

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internal variability can mask the climatic response to SIC or SST changes (Screen et al. 2014). We

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define the Arctic region as the area poleward of 67°N following previous work (e.g., Dai et al.

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2019; Jenkins and Dai 2022) because most Arctic sea-ice exists poleward of this latitude and the9222

Arctic is mostly ocean surface in this region. We exclude land surfaces in our Arctic regional

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averages because surface warming is strongest over oceanic areas (Boeke and Taylor 2018; Dai et224

al. 2019) but inclusion of land areas does not qualitatively affect our results. Globally averaged

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fields include both land and ocean surfaces. For this study, we calculate AA as the difference226

between Arctic (excluding land) and global surface air temperature (ΔTas) changes (AA =227

ΔTas,ARCTIC - ΔTas,GLOBAL) rather than as the ratio of Arctic to global warming to avoid dividing by

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near-zero values for global-mean surface air temperature changes.

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2.2 Energy budgets

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The vertically integrated energy budget equation (Eq. 1) for an atmospheric column

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↓
↓
accounts for the net TOA radiative flux (𝑅!"#; positive downward), net surface energy flux (𝑅%&';232

positive downward), change in local energy storage in the atmospheric column ( (* ), and horizontal

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convergence of energy (−∇ ∙ 𝑭# ) (Trenberth 1997; Fasullo and Trenberth 2008):

()

()

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(*

↓
↓
= 𝑅!"#− 𝑅%&'− ∇ ∙ 𝑭#

,

(1)

where
+

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𝐸 = , ∫- ! (𝑐- 𝑇 + 𝐿𝑞 + 𝑔𝑧) 𝑑𝑝.
"#$

(2)

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In Eq. (2), 𝐸 is the vertically integrated moist static energy, where cpT, Lq, and gz denote

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atmospheric internal energy, latent energy, and potential energy, respectively. Atmospheric kinetic

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energy storage is small and is not included in Eq. (2), following previous studies (Oort and Vonder

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↓
↓
Haar 1976; Trenberth and Solomon 1994). For the flux terms, we calculate 𝑅!"#and 𝑅%&'
as:

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↓
𝑅!"#
= 𝐴𝑆𝑅↓ − 𝑂𝐿𝑅↑

(3)

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↓
↓
↑
𝑅%&'
= 𝑆𝑊/)!,%&'− 𝐿𝑊/)!,%&'− 𝑆𝐻↑ − 𝐿𝐻↑

(4)

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↓
↑
where 𝐴𝑆𝑅↓ , 𝑂𝐿𝑅↑ , 𝑆𝑊/)!,%&', 𝐿𝑊/)!,%&', 𝑆𝐻↑ , and 𝐿𝐻↑ are the TOA absorbed SW radiation244(positive downward), TOA outgoing LW radiation (positive upward), net surface SW radiation

245(positive downward), net surface LW radiation (positive upward), surface sensible and latent heat

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flux (positive upward), respectively. Note that the latent heat term does not account for the latent

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heat consumed in snow melt in Eq. (4). To estimate oceanic heat uptake (OHU), we calculate the
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net surface energy flux (Eq. 4) over ocean surfaces only. For the simulations with perturbed global

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SST and fixed Arctic SIC, changes in OHU implicitly include changes in oceanic energy

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convergence in addition to oceanic heat storage changes as historical and future SST values are251

obtained using a coupled atmosphere-ocean. In contrast, the OHU term in the simulations with252

perturbed Arctic SIC and fixed global SST is dominated by seasonal oceanic heat storage changes

253(Dai 2021; Hu et al. 2022) as oceanic heat transport changes little with fixed SST values.

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We compute the horizontal atmospheric energy convergence (−∇ ∙ 𝑭# ) by rearranging theterms in Eq. (1) to obtain:()↓↓−∇ ∙ 𝑭𝑨 = 𝑅%&'
− 𝑅!"#+ (* .

(5)257

Eq. (5) shows that the net convergence of the horizontal energy flux (in W m-2) into a column is258

linked to the difference between the energy absorbed at the surface and net TOA radiation, and259

changes in local energy storage. Note that the local energy storage term is calculated using a260

month-to-month time derivative of Eq. (2) and is necessary for calculating monthly energy

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convergence but sums to zero in the annual mean. We also calculate the atmospheric energy262transport (AET; in PW) into the region north of a given latitude (f) by taking the area integral of263

the net energy convergence over the region following previous studies (Hwang and Frierson 2010;

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Feldl et al. 2017a):

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3/2

𝐴𝐸𝑇(𝜙) = ∫6

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()

↓
↓
− 𝑅!"#+ (* < 𝑎2 𝑐𝑜𝑠 𝜙 𝑑𝛾𝑑𝜙.
∫4 ;𝑅%&'(6)

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In Eq. (6), 𝑎 is the radius of Earth (~6.371 × 106 m), 𝛾 is the longitude, and 𝜙 is the latitude.

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AET(f) represents the total energy crosses the latitude circle at f (positive northward). For our

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Arctic region, f=67oN.

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2.3 Climate feedback calculations

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The response of the atmospheric energy budget to a climate perturbation, assuming
negligible changes in atmospheric energy storage, is:
↓
↓
Δ𝑅!"#
− Δ𝑅%&'− Δ(∇ ∙ 𝑭𝑨 ) = 0

(7)

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↓
↓
where Δ𝑅!"#, Δ𝑅%&', and Δ(∇ ∙ 𝑭𝑨 ) are changes in the net TOA radiative flux, net surface energy

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flux, and atmospheric horizontal energy convergence at each grid point, respectively (Stuecker et
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al. 2018; Hahn et al. 2021; Zhou et al. 2023). We use the Pendergrass et al. (2018) CESM1-CAM5

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radiative kernels to decompose changes in the TOA net radiative flux into individual contributions

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from changes in surface albedo (∆Rα), water vapor (∆Rq), air temperature (∆RT), and clouds (∆RC):

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↓
Δ𝑅!"#
= Δ𝑅7↓ + Δ𝑅8↓ + Δ𝑅!↓ + Δ𝑅'↓ .

(8)

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GHG concentrations remain fixed at year 2000 levels in the PAMIP simulations, so we exclude an280

effective radiative forcing term from our TOA flux change decomposition. The annual-mean

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residual TOA radiative flux changes (i.e., the difference between the actual TOA radiation change282

and sum of radiative feedback contributions in Eq. (8)) are 0.29 W m-2 and 0.27 W m-2 for historical283

and future global SST changes with fixed SIC, and 1.15 W m-2 and 1.79 W m-2 for historical and284

future Arctic SIC with fixed global SST. These residual TOA fluxes account for errors related to285

the radiative kernels and physical processes not included in our feedback decomposition. We also286

normalize the TOA flux changes in Eq. (8) by the annual-mean local surface air temperature

287

change (∆Tas) to calculate the climate feedback parameter (λi) for each variable using:288∑9 𝜆9 = 𝜆7 + 𝜆8 + 𝜆 ! + 𝜆' =↓ <:; ↓ <:; ↓ <:; ↓
:;%
'
"
(

:!)!

(9)

289

For clarity, we use the term feedback to refer to the unnormalized TOA radiative flux changes

290(units: W m-2) in Eq. (8) and feedback parameter to refer to the normalized TOA radiative fluxes

291(units: W m-2 K-1) in Eq. (9).

292

Radiative kernels are computed by perturbing one climate variable in a radiative transfer

293model and keeping all other variables fixed to produce a TOA radiative flux response, which is294

divided by the amount of the perturbed variable change to derive the TOA flux change per unit

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variable change (Soden et al. 2008). To calculate the surface albedo feedback, we compute the296

product of the surface albedo kernel (Kα) and changes in surface albedo (∆α): Δ𝑅7 = 𝐾7 ∗ Δ𝛼. For

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water vapor (Eq. 10) and temperature (Eq. 11) feedbacks, we vertically integrate the product of298

the kernel and change in each respective variable from the surface (ps) to the tropopause (pTOA):-299

Δ𝑅8 = ∫- !

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Δ𝑅! = 𝐾!= ∗ Δ𝑇>= + ∫- !

"#$𝐾8 ∗ Δln(𝑞) 𝑑𝑝
-

"#$

(10)

𝐾!) ∗ Δ𝑇> 𝑑𝑝

12

(11)

301

where q and Ta represent specific humidity and air temperature, respectively. Radiative emissions

302

from water vapor scale with the natural logarithm of specific humidity, so we use ∆ln(q) in Eq.

303(10) as done previously (Shell et al. 2008). Also, note that the temperature feedback accounts for

304

changes in surface temperature, which is computed by taking the product of the surface

305

temperature kernel (KTs) and change in surface air temperature (∆Tas) (Block and Mauritsen 2013;306

Jenkins and Dai 2021). Further, we assume that the tropopause pressure increases with latitude

307

from 100 hPa at the equator to 300 hPa at the poles following Pithan and Mauritsen (2014) to mask308

out the stratosphere. To calculate Planck and lapse rate feedbacks, we separate the temperature309

feedback (∆RT) into a component associated with vertically uniform warming equal to that of the310

surface (Planck feedback; ∆RPL) and deviations from the vertically uniform warming profile (lapse

311

rate feedback; ∆RLR):
Δ𝑅! = Δ𝑅?@ + Δ𝑅@;312-

= 𝐾!= ∗ Δ𝑇>= + ∫- !

313

"#$

-

𝐾!) ∗ Δ𝑇>= 𝑑𝑝 + ∫- !

"#$𝐾!) ∗ (𝛥𝑇> − 𝛥𝑇>= ) 𝑑𝑝

(12)

314

More details on Planck and lapse rate feedback calculations are provided in Jenkins and Dai (2021)

315

and Dai and Jenkins (2023).

316

The change in cloud radiative forcing (∆CRF) – the difference between all-sky and clear-317

sky radiative fluxes – provides a simple estimate of the energetic effects of clouds but does not318

represent cloud feedback as other processes also affect this difference (Soden et al. 2008; Block319

and Mauritsen 2013). To compute cloud feedback (∆RC), we subtract a cloud masking (CM) term

320

from the ∆CRF to account for the effects of changes in surface albedo, temperature, and water321

vapor on ∆CRF (Soden et al. 2008):

322
323

Δ𝑅' = Δ𝐶𝑅𝐹 − 𝐶𝑀

(13)

where
-

-

"#$

"#$

324

𝐶𝑀 = (𝐾7 − 𝐾7' ) ∗ Δ𝛼 + ∫- ! N𝐾!) − 𝐾!') O ∗ 𝛥𝑇> 𝑑𝑝 + ∫- ! N𝐾8 − 𝐾8' O ∗ 𝛥𝑙 𝑛(𝑞) 𝑑𝑝. (14)325

In Eq. (14) 𝐾9 and 𝐾9' are the all-sky and clear-sky kernels for surface albedo (α), air temperature

326(Ta), and water vapor (q). GHG concentrations are fixed in the PAMIP runs so we exclude a GHG

327

masking term in Eq. (14).
13

328

2.4 Potential warming contribution estimates329

To facilitate comparison, we quantify climate feedbacks, oceanic heat uptake, and330

horizontal atmospheric energy convergence in terms of their potential warming contributions

331

following previous studies (e.g., Pithan and Mauritsen 2014; Goosse et al. 2021; Stuecker et al.

332

2018; Hahn et al. 2021). The potential warming contribution from the ith climate feedback (DTi = ∆Ri

333/𝜆!" , in K) represents a hypothetic warming amount needed to rebalance the TOA energy flux

334

change (∆Ri = λi∆Tas) through the negative Planck feedback at a new equilibrium state. Similarly,335

we can scale the other flux changes to estimate their potential warming contributions, and the total

336

potential warming amount (∆T) is estimated as (Goosse et al. 2018; Hahn et al. 2021):337∑ $ %&"#

Δ𝑇 = − ! !

$$%$& %&"#

− $%

$$%

−

%((∇∙𝑭𝑨 )
$$%

−

%-./
$$%

(15)

338

where 𝜆!" (in W m-2 K-1) is the global-mean Planck feedback parameter and 𝜆A?@ is the deviation of339

the local (λPL) Planck feedback parameter from its global mean: 𝜆A?@ = 𝜆?@ − 𝜆̅?@ . As noted by340

Dai and Jenkins (2023), this estimated warming amount often does not represent a real warming

341

contribution as the TOA flux change (∆Ri) may not be used to directly raise surface air temperature342

or the temperature response may be delayed. We average the terms in Eq. (15) over the Arctic343(67°-90°N) and the tropics (23.5°S-23.5°N) to estimate the potential warming contribution of each344

process to surface warming and AA as done previously (Pithan and Mauritsen 2014; Goosse et al.

345

2018; Stuecker et al. 2018; Hahn et al. 2021). We define the tropical region as 23.5°S-23.5°N as

346

these are the latitude bands between the Tropic of Capricorn and Tropic of Cancer; however,347

averaging over other latitude ranges for the tropics (e.g., 30°S-30°N) does not impact the results.

348

3. Results349

3.1 Surface warming response to changes in global SST or Arctic SIC

350

We first examine the annual-mean surface air temperature response to historical and future351

global SST (Fig. 2a, b) or Arctic SIC (Fig. 2c, d) changes shown in Figure 1. The globe experiences352

relatively uniform warming in pdSST-pdSIC relative to piSST-pdSIC (Fig. 2a, referred to as353

historical warming) and in futSST-pdSIC relative to pdSST-pdSIC (Fig. 2b, referred to as future354

warming), with slightly greater magnitude in the future SST case than the historical case. Thus,

355

the SST perturbation runs show background global warming without noticeable AA. In contrast,
14

356

reduced Arctic sea-ice leads to large warming over Arctic oceanic areas with little temperature

357

change south of ~60oN and over northern high latitude land surfaces in both the historical and358

future perturbed SIC runs (Fig. 2c, d). Note that the local Arctic warming is larger for the future359

case than the historical case as the future sea-ice loss is larger (Fig. 1c, d) and that the largest

360

historical warming (Fig. 2c) occurs over the Barents-Kara Seas region where there is large sea-ice

361

loss (Fig. 1b).

362

The seasonal cycle of the surface air temperature changes averaged over the Arctic (Fig.

363

3a) and globe (Fig. 3b) shows different responses to global SST or Arctic SIC perturbations. Global

364

SST perturbations produce small Arctic warming during historical (~0.5-1.0 K) and future (~1.0-

365

2.0 K) periods for October-March and summer warming in the future global SST perturbation

366

simulation is larger than the future Arctic SIC experiment (Fig. 3a). The global-mean surface

367

temperature warms by ~0.8 K for the historical and ~1.2 K for the future SST cases, with little368

seasonal variation (Fig. 3b). Thus, there is small AA during October-March while the summer

369

Arctic warming is weaker than the global-mean warming in the SST perturbation experiments

370(Fig. 3c). In contrast, Arctic sea-ice loss produces large Arctic warming from October-January for371

the historical and future cases, with weak warming in summer (Fig. 3a). Note that the peak

372

warming shifts from October in the historical case to November in the future case. The global-

373

mean warming response to the SIC changes is weak throughout most of the year except during late374

autumn and early winter (Fig. 3b), which is due to the large warming in the Arctic (Fig. 2c-d). As

375

a result, AA is strong from October-January for the two perturbed SIC cases, especially for the376

future SIC case (up to 7 K), while the AA is weak during the summer months (Fig. 3c).

15

377
378
379

Fig. 2. Multi-model ensemble mean changes in annual-mean surface air temperature (∆Tas) in
response to (a, c) historical and (b, d) future (a, b) SST and (c, d) SIC changes shown in Fig. 1.

380
16

381
382
383
384
385

Fig. 3. Multi-model ensemble mean seasonal cycle of surface air temperature changes (∆Tas; in K)
in response to historical (black lines) and future (red lines) SST (dashed lines) and SIC (solid lines)
perturbations shown in Fig. 1 averaged over the (a) Arctic (67°-90°N) and (b) globe, and (c) Arctic
minus global-mean difference (i.e., Arctic amplification). Land surfaces are excluded in (a).

386

3.2 Surface energy budget response to Global SST or local Arctic SIC changes

387

Increased upward surface energy fluxes over sea-ice retreat areas have been shown to drive388

large Arctic warming and AA in winter (Deser et al. 2010; Boeke and Taylor 2018; Taylor et al.

389

2018; Dai et al. 2019). In response to SST warming with fixed SIC, we find little change in the net390

surface energy flux, net surface SW, SH, and LH fluxes over the Arctic Ocean throughout the year

391

(Fig. 4). The upward net surface LW flux decreases by ~1 W m-2 for both the historical and future392

SST warming cases with fixed SIC (Fig. 4c). This represents a small increase in the downward

393

LW radiation, likely due to increased water vapor and enhanced atmospheric energy convergence394

into the Arctic, rather than changes to surface conditions, as shown below. The suppressed Arctic395

surface warming and weak oceanic energy flux response to SST warming without SIC changes is396

consistent with Dai et al. (2019), who found similar results in model simulations with increasing397

CO2 concentrations and fixed Arctic sea-ice in flux calculations.

398

Arctic sea-ice loss greatly influences the magnitude and seasonal cycle of the Arctic399

oceanic heat flux. From May-August, oceanic absorption of energy increases by ~6-12 W m-2 in400

response to historical and future SIC loss (Fig. 4a) while during October-March oceanic release401of energy increases by ~12-18 W m-2 (Fig. 4a). Most of the increased oceanic energy absorption402

from May-August is due to increased absorption of SW radiation (Fig. 4b), with negligible changes403

in net surface LW, SH, and LH fluxes (Fig. 4c-e) during summer. In contrast, net surface LW, SH,

404

and LH fluxes are the main contributors to the enhanced cold-season oceanic energy release in405

response to Arctic sea-ice loss (Fig. 4d, e). Further, the ocean surface emits more LW radiation to406

the atmosphere from October-March for historical and future Arctic sea-ice loss runs (Fig. 4c).

407

The large increases in upward surface energy fluxes in response to sea-ice loss play an important408

role in enhancing warming of the surface air and AA during winter (Fig. 3a).

409

17

410
411
412
413
414
415
416
417
418

Fig. 4. Arctic (67°-90°N) multi-model ensemble mean seasonal cycle of changes in (a) OHU(positive downward), (b) net surface SW flux (positive downward), (c) net surface LW flux
(positive upward), (d) SH flux (positive upward), and (e) LH flux (positive upward) in response
to historical (black lines) and future (red lines) SST (dashed lines) and SIC (solid lines)
perturbations shown in Fig. 1. All values are in W m-2 and land surfaces are excluded fromaverages. (f) The seasonal cycle of the historical (black lines) and future (red lines) global SST
changes (left y-axis; dashed lines) and Arctic SIC loss (right y-axis; solid lines) specified in the
SIC and SST perturbation experiments.

419
420

3.3 Feedback seasonal cycles and warming contributions

421

The contrasting surface warming responses to global SST changes or local sea-ice loss

422

greatly influence Arctic climate feedbacks and atmospheric energy convergence changes. Figure423

5 shows the seasonal cycle of TOA radiative contributions of the climate feedbacks and424

atmospheric energy convergence; however, their corresponding climate feedback parameters (i.e.,425

the TOA radiative flux changes normalized the annual-mean Arctic surface warming) show similar426

seasonal cycles (not shown). Under global SST warming with fixed SIC, Arctic atmospheric427

energy convergence (Fig. 5f) and water vapor feedback (Fig. 5b) become important contributors
18

428

to the Arctic TOA flux change. Specifically, atmospheric energy convergence into the Arctic

429

responds similarly to historical and future SST warming, with increases of ~4-7 W m-2 during

430

October-March and ~2-4 W m-2 from April-September (Fig. 5f). This suggests that without

431

changes in sea ice, increased atmospheric energy transport becomes an important contributor to432

small cold season Arctic warming and AA (Fig. 3). Further, the magnitude and seasonal cycle of433

the water vapor feedback is similar between the historical and future SST cases, with maximum

434

water vapor feedback from May-August and minimum water vapor feedback during October-

435

March (Fig. 5b). This is expected as the warm-season Arctic would see larger water vapor

436

increases due to its warmer mean air temperatures. Arctic surface albedo (Fig. 5a), lapse rate (Fig.

4375c), and Planck (Fig. 5d) feedbacks weakly respond to SST increases without sea-ice loss. Lastly,438

we note that the net cloud feedback produces slight cooling in response to SST increases for June-

439

August (Fig. 5e).

440

In response to sea-ice loss, Arctic surface albedo feedback increases by ~7-8 W m-2 and

441~12-18 W m-2 for historical and future cases during the sunlit months (i.e., April-September) due

442

to increased exposure of dark water surfaces (Fig. 5a). The ocean, rather than the atmosphere,

443

absorbs much of the extra SW radiation (Fig. 4a), resulting in weak summer surface warming (Fig.

4443a). Cloud feedback is negative in response to sea-ice loss during April-August, and the cooling is445

larger in the future SIC case (-1.5~-4.5 W m-2) than the preindustrial SIC run (-1.0~-1.5 W m-2).

446

Lapse rate (Fig. 5c) and Planck (Fig. 5d) feedbacks weakly respond to historical or future Arctic447

SIC changes in summer due to small surface warming (Fig. 3a) during the sunlit season. We also448

find negligible water vapor feedback in response to Arctic sea-ice loss throughout the year, which

449

differs from the noticeable water vapor feedback in response to SST warming (Fig. 5b).

450

The large cold-season surface warming in response to historical and future Arctic sea-ice

451

loss enhances Arctic lapse rate (Fig. 5c) and Planck (Fig. 5d) feedbacks. When Arctic surface

452

warming (Fig. 3a) and AA (Fig. 3c) peak from October-December, the lapse rate feedback453

increases the incoming TOA radiative flux by ~4-6 W m-2 (~8-11 W m-2) and the Planck feedback454

opposes warming by -6~-8 W m-2 (-16~-20 W m-2) due to historical (future) sea-ice loss. Note that455

the month of maximum (minimum) lapse rate (Planck) feedback in the historical and future SIC

456

cases (Fig. 5c) corresponds to the month of peak Arctic surface warming (Fig. 3a), which in turn457

is related to peak oceanic heating (Fig. 4a) induced by sea-ice loss (Fig. 4f) in these simulations.
19

458

The cloud feedback in response to future Arctic sea-ice loss also enhances the net incoming TOA

459

radiative flux from October-January by ~2.5-3.0 W m-2, but the cloud feedback is weak (<1.0 W

460m-2) during winter in response to historical sea-ice loss (Fig. 5e). In contrast to the SST change

461

simulations, Arctic atmospheric energy convergence weakens by ~4 W m-2 and ~7 W m-2 in462

response to historical and future sea-ice loss from November-December, respectively (Fig. 5f).

463

Enhanced Arctic warming in response to sea-ice loss in the non-summer months (Fig. 3a) weakens

464

the temperature gradient between the midlatitudes and polar regions, thus reducing atmospheric465

energy convergence into the Arctic region.

466
467
468
469
470
471
472
473

Fig. 5. Arctic (67°-90°N) multi-model ensemble mean seasonal cycle of the (a) surface albedo,(b) water vapor, (c) lapse rate, (d) Planck, and (e) cloud feedbacks, and (f) changes in atmosphericenergy convergence into the Arctic in response to historical (black lines) and future (red lines) SST(dashed lines) and SIC (solid lines) changes shown in Fig. 1. All values are in W m-2 and land
surfaces are excluded in averages except for the case shown in (f).

474model for the historical (Fig. 6a) and future (Fig. 6b) SST warming cases, with slightly larger

Warmer SSTs enhance poleward atmospheric energy transport at all latitudes for each

20

475

increases in the northern hemisphere than southern hemisphere from October-March. All models,

476

except CESM2, show enhanced cold season northward energy transport with peak increases of477

around ~45°-50°N for the SST warming cases. In CESM2, atmospheric energy transport shows478

maximum increases around 30°N for October-March. Thus, without large Arctic warming related479

to sea-ice loss, the atmosphere displaces energy surpluses poleward. For the SIC perturbation

480

experiments, there is a net decrease in poleward atmospheric energy transport around 30°-90°N

481

with a maximum decrease around 60°N but little change south of 30°N for both historical (Fig.

4826c) and future (Fig. 6d) sea-ice loss, consistent with previous studies (Deser et al. 2015; Audette483

et al. 2021). Again, CESM2 is an outlier compared to the rest of the models for the future ∆SIC

484

run as northward atmospheric energy transport increases from 30°-60°N (Fig. 6d) for this model.

485

Therefore, SST-induced background warming enhances atmospheric poleward energy transport

486

into the polar regions, while large Arctic warming in response to sea-ice loss weakens atmospheric487

poleward energy transport over the northern mid-high latitudes.

488
489
490

Fig. 6. Changes in October-March mean northward energy transport in response to (a, c) historicaland (b, d) future (a, b) SST and (c, d) SIC changes shown in Fig. 1.
21

491
492

Figure 7 shows the potential warming contributions of the climate feedbacks over the493

Arctic and the tropics for October-March as AA is largest in autumn and winter. We recognize

494that warm season feedbacks in fully-coupled climate models indirectly affect Arctic surface

495

warming in winter by increasing summer oceanic energy storage that is later released to the496

atmosphere in the cold season (Dai 2021). However, the delayed winter oceanic heat release due497

to increased summer oceanic heat storage is absent in atmosphere-only simulations with specified

498

SST and SIC. Atmospheric energy convergence is the largest contributor for October-March (Fig.

499

7a, b) Arctic warming under historical (Fig. 7a) and future (Fig. 7b) global SST changes, as it

500

redistributes the energy from the lower latitude oceans, where SSTs increase, to the Arctic region.

501

In contrast, oceanic heat release opposes AA in response to global SST warming for October-502

March (Fig. 7a, b) because the warmer SSTs produce a greater ocean-to-atmosphere energy flux

503

outside the Arctic, thus causing more warming in the tropics than in the Arctic. We note that the504

warming contribution of -∆OHU in the tropics and cooling effect of -∆OHU in the Arctic may be

505

related to reduced poleward oceanic heat transport that is implicitly included in the historical and506

future SST fields. However, analyses of simulations with a coupled atmosphere-ocean are needed507

to confirm the role of oceanic heat transport on Arctic and tropical warming. Water vapor feedback508

makes a small contribution to Arctic warming due to low October-March mean temperatures but

509

contributes to ~1 K of warming in the tropics in response to global SST warming (Fig. 7b),510

opposing AA. Without sea-ice loss, lapse rate feedback contributes little to Arctic warming but

511

produces weak tropical cooling in response to historical (Fig. 7a) and future (Fig. 7b) SST

512

increases for the cold season. The local Planck feedback (relative to the global-mean Planck513

feedback) slightly contributes to AA in the SST warming runs because the cooling effects from514

Planck feedback are slightly less in the Arctic region than over the rest of the world (Fig. 7).

515

Surface albedo feedback contributes to negligible Arctic warming or AA from October-March in

516

response to global SST increases and fixed Arctic SIC during for historical (Fig. 7a) and future

517(Fig. 7b) cases.

518

In response to Arctic sea-ice loss with fixed global SSTs, oceanic heat release is the largest

519

contributor to AA from October-March in historical (Fig. 7c) and future (Fig. 7d) SIC cases,

520

followed by the positive lapse rate feedback. This supports previous studies that showed that sea22

521

ice loss and oceanic energy release during Arctic winter are necessary to trigger large surface

522

warming and thus strong positive lapse rate feedback in the Arctic (Feldl et al. 2020; Jenkins and523

Dai 2021; Dai and Jenkins 2023). The local Planck feedback (relative to the global-mean Planck524

feedback) also contributes to Arctic warming and AA in response to historical (Fig. 7c) and future525(Fig. 7d) Arctic SIC changes by cooling the Arctic region less than the tropics. Additionally,

526

positive cloud feedback makes a slight contribution to cold-season Arctic warming and AA in

527

response to future Arctic SIC loss (Fig. 7d), but the contribution is negligible in the historical SIC

528

loss run (Fig. 7c). Water vapor feedback is suppressed over the Arctic and globe in the historical529(Fig. 7c) and future (Fig. 7d) SIC runs, suggesting that local sea-ice loss and water vapor feedback530

are decoupled, as found previously (Jenkins and Dai 2021). In contrast to the perturbed SST runs,

531

the atmosphere displaces energy away from the Arctic in response to cold season sea-ice loss (Fig.

532

7c,d), thus opposing AA.

533

Note that warming contributions from changes in oceanic heat release (-∆OHU) and

534

changes in Arctic atmospheric energy convergence in response to historical (Fig. 7a) and future535(Fig. 7b) SST warming in CESM2 differ from the other models during October-March.

536

Specifically, CESM2 oceanic heat release slightly contributes to Arctic warming whereas in the537

other models, oceanic heat release contributes to Arctic cooling. Due to the warming effect of the

538-∆OHU term in response to SST changes in CESM2, Arctic atmospheric energy convergence539

weakens and does not need to compensate the cooling effect of -∆OHU as in the other models

540(Fig. 7a, b). Further, CESM2 -∆OHU makes a weaker positive contribution to AA during October-

541

March in response to historical (Fig. 7c) and future (Fig. 7d) Arctic SIC. Atmospheric energy542

convergence thus opposes AA less in CESM2 than the other models as -∆OHU produces less543

Arctic cooling in CESM2 than the other models. We realize that more work is needed to validate544

these statements.

23

545
546
547
548
549
550

Fig. 7. Inter-model spread in ensemble mean, October-March potential warming contributions (inK) of Arctic (67°-90°N) vs. tropical (23.5°S-23.5°N) surface albedo (α), water vapor (q), Planck(PL’), lapse rate (LR), and cloud (C) feedbacks, and changes in oceanic heat release (-ΔOHU;
positive upwards), and atmospheric energy convergence (Δ(−∇ ∙ 𝑭𝑨 )) in response to (a, c)
historical and (b, d) future (a, b) SST and (c, d) SIC perturbations shown in Fig. 1.

551
552

3.4 Physical processes underlying climate feedbacks

553

Water vapor feedback is complicated in high latitudes due to local temperature inversions

554

and low amounts of water vapor (Curry et al. 1995; Sejas et al. 2018). Global maps reveal that555

SST warming (Fig. 8a, b) has a larger effect than local sea-ice loss (Fig. 8c, d) on water vapor

556

feedback in both the Arctic and the rest of the globe. Specifically, water vapor feedback is largest
24

557

near the equator at ~2-5 W m-2 in response to historical (Fig. 8a) and future (Fig. 8b) SST warming

558

and decreases poleward to ~0.5-1.0 W m-2 in the Arctic region (Fig. 8a, b). The cold-season water559

vapor feedback is weak in response to Arctic sea-ice loss (Fig. 8c, d), including over the Arctic

560

where low-level specific humidity increases (Fig. 9c, d). This is due to low or negative values of561

the October-March LW and net (i.e., LW+SW) water vapor kernel in the Arctic lower troposphere562(Fig. 10a, c). Because the water vapor feedback is most sensitive to upper tropospheric water vapor

563

content (Shell et al. 2008; Soden et al. 2008; Pendergrass et al. 2018), the low-level water vapor

564

increases in response to Arctic sea-ice loss do not lead to large TOA flux changes.

565
-2

566
567
568
569

Fig. 8. Multi-model ensemble mean October-March water vapor feedback (in W m ) in responseto (a, c) historical and (b, d) future (a, b) SST and (c, d) SIC changes shown in Fig. 1.

570

loss run (~0.50-0.75 W m-2; Fig. 8c) but there are negligible water vapor feedback effects in the571

Arctic under future SIC conditions (Fig. 8d). As the October-March LW and net water vapor kernel572

is negative near the surface (Fig. 10a, c) due to temperature inversions in the Arctic (Shell et al.

573

2008; Soden et al. 2008), any increase in moisture in the lower troposphere will result in enhanced

574

radiative emission to space (i.e., a negative water vapor radiative effect). In response to future

575

Arctic SIC (Fig. 9d), there are greater increases in the natural logarithm of specific humidity

Slight positive water vapor feedback occurs over sea-ice loss areas in the historical SIC

25

576

[∆ln(q)] in the lower troposphere than in the historical case (Fig. 9c). Thus, greater future lower

577

tropospheric moistening in the Arctic region produces a more negative water vapor radiative effect578

at the TOA. We also note that there is a large spread (as shown by the standard deviation) among579

the PAMIP models and individual ensemble members in upper tropospheric moistening in the580

perturbed Arctic SIC runs, where there is little change in the mean ∆ln(q) (Fig. 9c, d). Thus, some581

ensemble members may have experienced a slight decrease in upper tropospheric ∆ln(q) in

582

response to Arctic sea-ice loss with fixed global SST, enhancing outgoing LW radiation at the

583

TOA. In contrast, the historical (Fig. 10a) and future (Fig. 10b) perturbed SST runs experienced

584

slightly greater ∆ln(q) in the upper troposphere than the lower troposphere for both warm and cold585

seasons. Due to positive values of the TOA LW and net Arctic water vapor kernel in the upper

586

troposphere (Fig. 10a, c), top-heavy moistening in response to global SST warming produces a587

positive water vapor feedback from the TOA perspective. We note that the vertical structure of588∆ln(q) is greater at each level for April-September in the perturbed SST runs than the changed

589

Arctic SIC simulations. Thus, the vertical moistening profile, in addition to the vertical structure590

of the water vapor kernel, plays a role for the Arctic summer water vapor feedback in the perturbed

591

SST experiments with fixed Arctic SIC.

26

592
593
594
595
596
597

Fig. 9. Multi-model, ensemble mean (solid lines) Arctic (67°-90°N; land surfaces excluded)changes in the natural logarithm of specific humidity (in kg kg-1; solid lines) in response to (a, c)
historical and (b, d) future (a, b) global SST and (c, d) Arctic SIC changes shown in Fig. 1. The
shading shows ±1 standard deviation from the multi-model ensemble mean profile.

598
599
600
601

Fig. 10. Profiles of the Pendergrass et al. (2018) TOA (a) LW, (b) SW, and (c) NET (LW+SW)
water vapor kernel (in W m-2 K-1 100 hPa-1) averaged over the Arctic region (67°-90°N).

27

602

Arctic low cloud amount has been suggested to increase during the cold season in response603

to sea-ice loss due to decreased lower tropospheric stability (Kay and Gettelman 2009; Jenkins et604

al. 2023), thus affecting Arctic cloud feedback (Vavrus 2004; Morrison et al. 2019; Jenkins and605

Dai 2022). We find weak October-March cloud feedback in response to perturbed SST with fixed

606

Arctic SIC for historical (Fig. 11a) and future (Fig. 11b) cases, suggesting that remote processes607

do not greatly impact Arctic cloud feedback in the cold season. On the other hand, Arctic sea-ice

608

loss produces a large positive cloud feedback response in winter, especially in regions with large609

sea-ice loss and surface warming (Fig. 11c, d). For the run with historical SIC loss, cloud feedback

610

enhances the TOA radiative flux by ~2-5 W m-2 in the Barents-Kara Seas region and by ~0.5-1.0

611

W m-2 in the Chukchi Sea, where the largest sea-ice loss and surface warming occurs. Under future612

Arctic sea-ice loss, cold-season cloud feedback is largest in the Barents-Kara Seas (~3-5 W m-2)

613

except the warming effects from clouds extend into the Central Arctic Ocean. This is likely related614

to the greater area with large sea-ice loss (Fig. 1b, d) and surface warming (Fig. 2c-d) in the future615

case than in the historical case.

28

616
617
618
619
620

Fig. 11. Multi-model ensemble mean TOA radiative flux change due to the cloud feedback(shading; in W m-2) and change in surface air temperature (cyan contours; in K) averaged over
October-March in response to (a, c) historical and (b, d) future (a, b) SST and (c, d) SIC changes.
Black contours in (c) and (d) show the change in Arctic SIC for October-March.

621
622

The lapse rate feedback experiences large seasonal and spatial variations in the Arctic in

623

response to SST warming or Arctic SIC loss. From October-March, the lapse rate feedback is

624

negative-neutral in response to the global SST warming (Fig. 12a, b) due to relatively uniform

625

vertical warming profiles (Fig. 13a, b). We note that without changes in SIC, there are negligible

626

changes in Arctic oceanic heat uptake or surface warming in the cold season, leading to suppressed

627

lapse rate feedback (Fig. 12a, b). In contrast, cold-season sea-ice loss enhances Arctic lapse rate

628

feedback for historical (Fig. 12c) and future (Fig. 12d) SIC cases when surface and lower
29

629

tropospheric warming outpaces warming in the mid-upper troposphere (Fig. 13c, d). We note that630

lapse rate feedback strengthens (~6-10 W m-2) in regions with the greatest October-March oceanic

631

heat release and surface warming in response to historical (Fig. 12c) and future (Fig. 12d) sea-ice

632

loss, consistent with previous studies (Dai et al. 2019; Feldl et al. 2020; Boeke et al. 2021; Jenkins

633

and Dai 2021, 2022; Dai and Jenkins 2023). Thus, sea-ice loss is necessary to produce bottom-

634

heavy warming and trigger Arctic positive lapse rate feedback during winter, as shown previously

635

by Dai and Jenkins (2023) using coupled model experiments.

636
637
638
639
640
641

Fig. 12. Multi-model, ensemble mean TOA radiative flux change due to the lapse rate feedback(shading; in W m-2), changes in oceanic heat uptake (black contours; in W m-2; positive
downward), and changes in surface air temperature (cyan contours; in K) averaged over OctoberMarch in response to (a, c) historical and (b, d) future (a, b) SST and (c, d) SIC changes.

30

642
643
644
645
646
647

Fig. 13. Multi-model, ensemble mean Arctic (67°-90°N; land surfaces excluded) temperaturechange profiles (in K) averaged over April-September (red lines) and October-March (black lines)
in response to the (a) historical and (b) future global SST warming, and (c) historical and (d) future
Arctic sea-ice loss.

648

3.5 Response to simultaneous SST and SIC changes

649

We compare the Arctic vs. tropical October-March potential warming contributions of

650

climate feedbacks, changes in atmospheric energy convergence and oceanic heat release in651

response to historical global SST warming and historical polar sea-ice loss together (i.e., pdSST-

652

pdSIC minus piSST-piSIC; Fig. 14a; referred to as TOTAL) and the sum of the separate responses653

to historical SST warming (i.e., pdSST-pdSIC minus piSST-pdSIC) and historical polar sea-ice

654

loss (i.e., pdSST-pdSIC minus pdSST-piArcSIC and pdSST-piAntSIC) (Fig. 14b; referred to as

655

SUM). The warming contributions of the lapse rate, water vapor, cloud, and Planck feedbacks in

656

TOTAL match SUM well, with the lapse rate feedbacks making the largest contribution to AA

657(Fig. 14). Except for CESM2 in TOTAL, the change in atmospheric energy convergence makes

658

roughly equal warming contributions to Arctic and tropical warming from October-March,659

suggesting that remote SST warming and Arctic sea-ice loss have opposing effects on the
31

660

horizontal atmospheric energy flux. The oceanic heat release changes in IPSL-CM6A-LR makes

661

a greater contribution to Arctic than tropical warming, but there are slight discrepancies between

662

CanESM5 and CESM2 oceanic heat release between TOTAL and SUM. In TOTAL, CanESM5

663

and CESM2 oceanic heat release changes contributes roughly the same amount to Arctic and664

tropical warming; however, CESM2 (CanESM5) produces slightly greater Arctic (tropical)

665

warming in SUM. The surface albedo feedback is inactive from October-March due to lack of666

sunlight and is not a major direct contributor to large cold-season AA. The differences between

667

feedbacks calculated with TOTAL and SUM are small except for oceanic heat release and668

atmospheric energy convergence changes, where there are slight differences in their Arctic vs.

669

tropical warming contributions (Fig. 14c).

32

670
671
672
673
674
675
676
677

Fig. 14. Inter-model spread in the ensemble mean October-March potential warming contributions(in K) for Arctic (67°-90°N) and tropical (23.5°S-23.5°N) surface albedo (α), water vapor (q),Planck (PL’), lapse rate (LR), and cloud (C) feedbacks, and changes in oceanic heat release (ΔOHU; positive upwards) and atmospheric energy convergence (Δ(−∇ ∙ 𝑭𝑨 )) in response tohistorical changes in global SST and polar SIC for (a) TOTAL (i.e., global SST and polar SIC
change together), (b) SUM (i.e., sum of the response to the SST and SIC change separately), and(c) difference between (b) and (a).
33

678
679

The northward atmospheric energy transport response to the SST and SIC perturbations is

680

similar among TOTAL (Fig. 15a) and SUM (Fig. 15b), with little difference between the two cases

681(Fig. 15c). In the tropical regions (i.e., 30°S-30°N), global SST warming enhances poleward682

atmospheric energy transport by ~0.1-0.15 PW in the southern hemisphere and ~0.1-0.35 PW in683

the northern hemisphere. Around 60°-90°N, there is little net change in atmospheric energy

684transport in response to simultaneous SST and SIC changes, suggesting that remote warming due

685

to SST changes and local Arctic warming related to sea-ice loss have opposing effects on Arctic

686

atmospheric energy transport (Fig. 6a, c). The similarity of climate feedbacks (Fig. 14) and the687

atmospheric energy transport (Fig. 15) response between TOTAL and SUM suggest that the effects

688

of SIC or SST changes can be linearly separated. In other words, the individual responses to SST

689

or SIC perturbations approximately sum to the combined influence of changes in SST and SIC.

690
34

691
692

Fig. 15. October-March northward energy transport response (in PW) in response to historicalchanges in SST and SIC for (a) TOTAL and (b) SUM, and (c) difference between (b) and (a).

693
694

4. Summary and Conclusions

695

We investigated the impacts of historical and future Arctic sea-ice loss and global SST

696

increases on Arctic climate feedbacks, atmospheric energy convergence into the Arctic, and697

oceanic heat release using PAMIP atmosphere-only simulations. The SST increase with fixed polar698

sea ice results in relatively uniform global warming with negligible AA for both historical and699

future cases. In contrast, historical and future Arctic sea-ice loss leads to large Arctic warming

700

with negligible effects south of ~60°N, although this may not be the case in fully coupled701

simulations (Deser et al. 2015). The PAMIP experiments allowed us to separate the response of702

Arctic climate feedbacks, atmospheric energy convergence, and oceanic heat release to703

background global warming without AA (as in the SST perturbation runs) or to large AA with

704

negligible warming outside the Arctic (as in the SIC change runs). We also found striking

705

similarities between the historical simulations with both SST and SIC changes together (i.e.,

706

TOTAL), and the sum of the individual responses to the historical SST and polar SIC changes (i.e.,

707SUM) in terms of Arctic climate feedbacks and atmospheric energy transport response.

708

Under warmer global SSTs without sea-ice loss, Arctic winter oceanic heat release is709

suppressed leading to weak Arctic cold season warming. Instead, enhanced poleward atmospheric710

energy convergence rather than increased oceanic heat release becomes the dominant contributor

711

to small AA in response to global SST increases with fixed Arctic sea-ice. We also found strong712

global water vapor feedback in the historical and future SST warming runs, especially in the713

tropics. Water vapor feedback and moisture intrusions into the Arctic contributes to slight Arctic

714

surface warming by enhancing downwelling LW radiation to the surface (Taylor et al. 2013; Sejas

715

et al. 2014; Song et al. 2014; Yoshimori et al. 2014; Laîné et al. 2016). However, the combined716

direct effects of enhanced atmospheric energy convergence into the Arctic and positive water717

vapor feedback produce weak Arctic warming without large sea-ice loss and enhanced oceanic

718

heat release from October-March. We also found that under global SST warming with fixed Arctic

719

SIC, the Arctic experiences vertically uniform or top-heavy warming, producing a neutral or

720

negative lapse rate feedback. Thus, the lapse rate feedback does not make a large contribution to721

Arctic warming or AA without the bottom-heavy warming effects of enhanced oceanic energy
35

722

release associated with sea-ice loss. Lastly, Arctic cloud and surface albedo feedbacks responded

723

weakly to warmer global SST with fixed Arctic SIC in the historical and future cases.

724

In contrast, retreating sea ice produces strong bottom-heavy warming and moistening in

725

autumn and winter due to enhanced oceanic energy release in regions with newly exposed water726

surfaces, as shown in previous studies (Deser et al. 2010; Screen and Simmonds 2010a, b; Boeke

727

and Taylor 2018; Dai et al. 2019; Dai and Jenkins 2023). Strong lower tropospheric warming

728

enhances Arctic positive lapse rate feedback, which greatly contributes to AA during the cold729

season (e.g., Jenkins and Dai 2021; Dai and Jenkins 2023). Additionally, bottom-heavy moistening730

in response to Arctic sea-ice loss has little impact on the TOA radiative flux due to its low

731

sensitivity to lower tropospheric water vapor (Shell et al. 2008; Soden et al. 2008; Pendergrass et732

al. 2018). Instead, enhanced moistening in the mid-upper troposphere, as in the SST warming runs,

733

increases the Arctic TOA radiative forcing by increasing water vapor’s LW absorption in the upper734

troposphere. Arctic surface albedo feedback activates during the sunlit season in response to sea-735

ice loss but does not significantly raise surface temperatures in summer. We also find reduced

736

poleward atmospheric energy transport in the northern hemisphere mid-high latitudes due to737

historical and future Arctic sea-ice loss with fixed global SST, consistent with Hahn et al. (2023).

738

We recognize that there are limitations associated with atmosphere-only model runs as the739

ocean is treated as a boundary condition. Ocean-atmosphere coupling and the oceanic component

740

of the poleward energy transport have been shown to play important roles in the atmospheric741

response to sea-ice loss (Deser et al. 2015; Tomas et al. 2016). Thus, future work may compare742

our feedback calculations to the results from models with a full-depth dynamical ocean to account743

for ocean feedbacks. Additionally, we emphasize that global SST and Arctic SIC conditions are

744

specified in PAMIP simulations and that many processes influence global SST and Arctic SIC

745

fields in fully-coupled simulations. For example, increased downwelling LW radiation from746

moisture intrusions into the Arctic or enhanced Arctic atmospheric energy convergence can shape747

the patterns of future SIC specified in PAMIP simulations (Woods and Caballero 2016; Zhang et748

al. 2023). Moreover, oceanic heat uptake/release in the simulations with changed SST and fixed749

SIC may implicitly include changes in oceanic energy convergence as the historical and future

750

SST values were estimated from models with a coupled atmosphere and ocean. Nevertheless, our

36

751

results help to untangle the influence of background global warming related to global SST changes

752

or large Arctic warming related to sea-ice loss on Arctic climate feedbacks.

753
754

Acknowledgements

755

We thank the contributors to the PAMIP project for designing and running the simulations

756

analyzed in this study.

757

Author Contributions

758

M. T. Jenkins performed the analysis for this study, made the figures, and wrote the first draft of759

the manuscript. A. Dai and C. Deser helped improve the study, the manuscript and the figures.

760

Funding761

This work is supported by the National Science Foundation (grants AGS-2015780 and OISE-

762

1743738). The National Center for Atmospheric Research is supported by the National Science

763

Foundation.

764

Data Availability Statement

765

The PAMIP model output used in this study can be downloaded from https://esgf-

766

node.llnl.gov/search/cmip6/.

767

Ethics Approval

768

Not applicable.

769

Consent for Publication

770

The authors agree to publish the paper in Climate Dynamics.

771

Competing Interests

772

The authors declare no competing interests.

773
774

37

775

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