Changes in Arctic clouds during intervals of rapid sea ice loss
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We investigate the behavior of clouds during rapid sea ice loss events (RILEs) in the Arctic, as simulated by multiple ensemble projections of the 21st century in the Community Climate System Model (CCSM3). Trends in cloud properties and sea ice coverage during RILEs are compared with their secular trends between 2000 and 2049 during summer, autumn, and winter. The results suggest that clouds promote abrupt Arctic climate change during RILEs through increased (decreased) cloudiness in autumn (summer) relative to the changes over the first half of the 21st century. The trends in cloud characteristics (cloud amount, water content, and radiative forcing) during RILEs are most strongly and consistently an amplifying effect during autumn, the season in which RILEs account for the majority of the secular trends. The total cloud trends in every season are primarily due to low clouds, which show a more robust response than middle and high clouds across RILEs. Lead-lag correlations of monthly sea ice concentration and cloud cover during autumn reveal that the relationship between less ice and more clouds is enhanced during RILEs, but there is no evidence that either variable is leading the other. Given that Arctic cloud projections in CCSM3 are similar to those from other state-of-the-art GCMs and that observations show increased autumn cloudiness associated with the extreme 2007 and 2008 sea ice minima, this study suggests that the rapidly declining Arctic sea ice will be accentuated by changes in polar clouds.
KeywordsArctic Clouds Abrupt change Rapid change Sea ice CCSM
Climate change in the Arctic is proceeding at a pace consistent with or even exceeding climate model projections (Serreze et al. 2007, Stroeve et al. 2007, Wang and Overland 2009). Although an amplified polar response to greenhouse forcing has long been simulated by GCMs (Manabe and Stouffer 1980; Mitchell et al. 1990, Holland and Bitz 2003), recent evidence suggests that the transition to a much warmer and less icy state may be punctuated by intervals of rapid climate change. Model simulations of the 21st century have produced these sorts of rapid ice loss events (RILEs) for expected future conditions (Holland et al. 2006; Winton 2006), and some have proposed that the record-setting minimum sea ice coverage during 2007 and 2008 indicates that the system may already be undergoing a “tipping point” of abrupt change (Lindsay and Zhang 2005, Lenton et al. 2008). This recent empirical and theoretical evidence of rapid polar climate shifts is consistent with the paleoclimate record, which shows numerous instances of extremely rapid high-latitude climate variations (e.g., Jansen 1987, Alley et al. 1993, Brook et al. 1996).
The mechanisms for triggering abrupt Arctic climate change have been investigated in many studies and include contributions from the ocean via the meridional overturning circulation (e.g., Manabe and Stouffer 1988), the cryosphere through positive snow- and sea-ice feedbacks (e.g., Li et al. 2005), the land from methane release in permafrost (Brook et al. 2008), and the atmosphere via shifts in the jet stream (Eisenman et al. 2009). Analyzing RILEs in a set of seven projections of 21st century climate, Holland et al. (2006) identified rapidly increasing poleward ocean heat transport as an important factor in driving episodes of abrupt ice retreat. Likewise, Winton (2006) highlighted surface albedo feedbacks as a key driver in the simulated abrupt elimination of future Arctic sea ice under transient greenhouse forcing.
In contrast, studies of rapid polar climate change have given little consideration to the role that clouds may play, whether as a driver of or responder to RILEs or even as a possible braking mechanism (negative feedback). Given that clouds strongly influence the Arctic radiation budget, have been undergoing discernible trends in recent decades (Schweiger 2004, Wang and Key 2005, Liu et al. 2007), and are expected to change significantly as polar climate evolves in the future (Vavrus et al. 2009a), we feel that an investigation of the relationship between clouds and rapid ice loss is especially timely.
Recent satellite measurements reveal that the extremely low coverage of late-season boreal sea ice during 2007 and 2008 coincided with highly positive cloud anomalies overlying the unusually extensive regions of open water in the Arctic Ocean (Levinson and Lawrimore 2008, Kay and Gettelman 2009). This association agrees with the relationship between changes in sea ice and clouds suggested by the response in transient greenhouse forcing experiments in the Coupled Model Intercomparison Project (CMIP3) archive. Vavrus et al. (2009a) found that the majority of CMIP3 models simulated increasing amounts of clouds in all seasons during the 21st century and that the cloud gains were closely linked to evaporation increases, which appear to provide most of the moisture source for the added cloudiness.
In this paper we explore the behavior of Arctic clouds during intervals of rapid sea ice loss, as simulated by an ensemble of climate model integrations in the Community Climate System Model (CCSM3) for the early-middle 21st century. The major questions we address are the following: (1) What kinds of cloud changes occur during RILEs, compared with the simulated secular trends in clouds?, (2) Do cloud changes amplify or dampen the warming and sea ice loss during RILEs?, (3) How do cloud changes during RILEs vary by season?, and (4) Are clouds acting as a driver of or a responder to the rapid sea ice decreases?
2 Model description and experimental design
The CCSM3 is a fully coupled global climate model of the atmosphere, ocean, sea ice, and land systems (Collins et al. 2006a). The atmospheric component is the Community Atmosphere Model, version 3 (CAM3) (Collins et al. 2006b), which employs T85 horizontal resolution (~1.4°) and 26 levels in a hybrid-sigma pressure coordinate system. The ocean model is POP version 1.4.3 (Smith and Gent 2004), which includes an isopycnal transport parameterization (Gent and McWilliams 1990) and uses a nominal horizontal resolution of 1°. The dynamic-thermodynamic sea ice model—run on the same grid as the ocean component—is the Community Sea Ice Model (CSIM) (Briegleb et al. 2004), whose features include an elastic-viscous-plastic rheology (Hunke and Dukowicz 1997), a sub-gridscale ice thickness distribution (Thorndike et al. 1975) and the thermodynamics of Bitz and Lipscomb (1999). The land component is the Community Land Model (CLM3) (Bonan et al. 2002), which contains ten sub-surface soil layers and computes exchanges of energy, mass, and momentum with the atmosphere. The model uses a sub-grid mosaic of observed plant functional types on the same spatial grid as the atmosphere.
A full description of CAM3’s treatment of clouds is given in Collins et al. (2006b) and Boville et al. (2006). Clouds are categorized as either convective or stratiform and are calculated separately at three levels (low, middle, and high). Condensate varies between ice and liquid as a quadratic function of temperature, using threshold temperatures of 243 and 263 K, with different settling velocities for liquid and ice-phase as functions of particle size characterized by the effective radius. The model uses the prognostic cloud-water parameterization of Rasch and Kristjánsson (1998) that was updated by Zhang et al. (2003). CAM3 also includes the radiative effects of aerosols in the calculation of shortwave fluxes and heating rates, based on an aerosol assimilation for the period 1995–2000. The model employs a standard maximum-random cloud overlap scheme (Collins 2001) and separate parameterizations for shallow (Hack 1994) and deep (Zhang and McFarlane 1995) convection. Cloud fraction is determined diagnostically for convective and stratiform clouds, using separate calculations for deep and shallow convection. Stratiform clouds are a function of the grid-box mean relative humidity at each level that varies quadratically from a threshold humidity of 80% over land and 90% elsewhere.
The Arctic cloud simulation of CCSM3 was evaluated by Vavrus and Waliser (2008), who showed that the model produces accurate cloud amounts during summer but overestimates low cloudiness during winter, similar to many GCMs. The monthly total Arctic cloud cover ranges from 70% (December) to 79% (August) averaged over 70–90°N. Walsh et al. (2008) rank CCSM3 as the third best GCM in its simulation of cloud fraction at Barrow, AK, based on a collection of 18 climate models in the CMIP3 collection. CCSM3’s liquid cloud condensate and thus cloud optical depth in the Arctic is known to be too high (Gorodetskaya et al. 2008, Miao and Wang 2008), but its surface cloud radiative forcing compares very favorably to measurements from the AVHRR Polar Pathfinder, outperforming all other GCMs evaluated over ice-covered regions (Karlsson and Svensson 2009).
CCSM3 simulates Arctic sea ice reasonably well compared with late-20th century observations in terms of its spatial distribution and mass budget terms (e.g., Holland et al. 2006, 2008; Gerdes and Koberle 2007). Furthermore, CCSM3 is one of only two CMIP3 models with trends over the latter part of the 20th century that are consistent with the observed satellite era ice loss (Stroeve et al. 2007).
The simulations analyzed here consist of the same seven ensemble members of 21st century simulations used by Holland et al. (2006) to document the characteristics of abrupt reductions in Arctic sea ice in CCSM3. These integrations began from ensembles at the end of 1870–1999 simulations that were driven with observed variations in greenhouse gas concentration, volcanic eruptions, sulfates, ozone, and solar forcing. All seven ensemble members were then forced for the 21st century with the SRES A1B forcing scenario (Nakicenovic et al. 2000), a “middle of the road” case that projects atmospheric CO2 concentrations to rise to 720 ppm by 2100 and aerosol emissions to rise until the 2020s, then decline through 2050 and beyond. Details on the treatment of aerosols in CCSM3 can be found in Collins et al. (2006b).
3.1 Time-mean response to greenhouse forcing
The simulated Arctic cloud response to the projected increases in greenhouse gases by the late 21st century compared with late 20th century conditions is described in detail for the CMIP3 models by Vavrus et al. (2009a). The CCSM3 sea ice response is discussed by Holland et al. (2006). Virtually all boreal sea ice melts off during summer by the end of this century in CCSM3 simulations, although a considerable wintertime ice pack persists (9.9E6 km2 maximum monthly area compared with 13.4E6 km2 in the late 20th century). The typical GCM cloud response during future warming is greater cloudiness in all seasons with a spatial pattern of cloud gains that generally aligns with regions of large sea ice reductions. In the CMIP3 models the greatest cloud increases occur during autumn, correlating with large increases in surface cloud radiative forcing (CRF) that appear to act as a positive feedback on the warming. Increased surface evaporation within the Arctic is the variable most closely associated with the enhanced polar cloudiness. Total cloud increases are mostly attributable to changes at low and high tropospheric levels, but the spatial pattern of the greater vertically integrated cloud amount matches much more closely with the low cloud increase. Winter-time total cloud increases are almost as large as those during autumn, while the smallest and most spatially uniform cloud gain is projected for summer.
3.2 Changes during RILEs
The behavior of the Arctic climate system during intervals of abrupt sea ice retreat resembles the time-mean response described above. The similarities are particularly strong during autumn, which is also the most responsive season during RILEs. Because all ten of the RILEs that occur among the seven experiments take place within the first half of the 21st century, the focus of this section will be confined to the years 2000–2049. We also only consider the time-averaged response over three seasons—summer (June–August), autumn (September–November), and winter (December–February)—because the springtime response is much weaker. To more effectively diagnose the impact of cloud changes during summer, we adopt the modified CRF formula of Vavrus (2006), which utilizes downwelling (instead of net) solar radiation to mitigate the competing influence of large surface albedo changes on CRF.
The RILEs analyzed here include all of those identified by Holland et al. (2006), who defined an abrupt event as one in which the derivative of the five-year running mean smoothed September ice extent timeseries exceeds a loss of 0.5 million km2year−1. In this study we add an additional first event in experiment 2, which nearly meets the above criterion and has been analyzed in detail by Vavrus et al. (2009b). The duration of RILEs ranges from 3 to 9 years (average length 4.8 years), as defined by the time around the transition for which the derivative of the smoothed September ice extent timeseries exceeds a loss of 0.15 million km2 year−1 (Holland et al. 2006).
Due to the pronounced secular trends in most variables, we cannot effectively diagnose the behavior of the system by simply comparing averages during RILEs with the multi-decadal averages. Instead, we compare the trends in variables during RILEs versus their 50-year trends to establish the relative response of the system during periods of abrupt change. The ratio of these two trends provides a measure of amplification, such that ratios greater than 1 indicate an enhanced change during RILEs, ratios between 0 and 1 a muted change, and negative ratios a counteracting response.
Arctic-averaged trends per year (70–90°N) during the first half of the 21st century and during RILEs
Mean trend (2000–2049)
Mean trend during RILEs
Ratio of trends
Interquartile range of ratios
Spatial pattern correlation (60–90°N)
Summer ice cover
Summer total cloud
Summer low cloud
Summer mid cloud
Summer high cloud
Autumn ice cover
Autumn total cloud
Autumn low cloud
Autumn middle cloud
Autumn high cloud
Winter ice cover
Winter total cloud
Winter low cloud
Winter middle cloud
Winter high cloud
Part of the reason for the decreasing winter clouds during RILEs may be the pronounced anticyclonic pressure trend, which encompasses almost the entire region poleward of 60°N and maximizes at 1.5 hPa year−1 over the Laptev Sea (Fig. 5h). This relationship is consistent with observations showing reduced Arctic winter cloud amount during anticyclonic flow anomalies (Liu et al. 2007). Although somewhat surprising in light of the decreasing pressure trend over the Arctic Ocean during RILEs in summer and autumn, the strongly increasing SLP in winter is consistent with some observational evidence. Francis et al. (2009) showed that atmospheric pressure over the Arctic tends to be significantly higher in the winter following summers with anomalously low sea ice concentration, presumably because enhanced heating of the lower troposphere from expanded open-water coverage increases geopotential heights locally.
Given the decreasing winter cloudiness during RILEs, an unexpected finding is that the CRF trend is positive—and relatively robust among experiments—over most of the polar regions, including the Arctic Ocean (Fig. 5c), at a rate slightly higher than the secular trend (Table 1). A key factor is probably the trend in cloud liquid water path, which agrees fairly well with the changing CRF and whose (similarly robust) Arctic-averaged amplification factor during RILEs (1.12) is nearly the same as that of CRF (1.05). Apparently, the increasing moisture availability due to expanding open water during RILEs counteracts the radiative effects from the decline in cloud amount, causing the remaining clouds to be more effective heat-trapping agents during winter.
3.3 Role of clouds: driver or responder?
Ensemble-mean, detrended lead-lag correlations between the average monthly Arctic sea ice concentration and total cloud amount (70–90°N) during autumn between 2000 and 2049
Ensemble-mean, detrended lead-lag correlations between the average monthly Arctic sea ice concentration and total cloud amount (70–90°N) during autumn among all RILEs
The corresponding correlations averaged over the ten RILEs demonstrate that a much stronger relationship emerges when the ice coverage is rapidly declining (Table 3). The magnitude of all the coefficients is at least 0.3 and exceeds 0.5 in several cases. Correlations averaged across all time lags are again largest for November cloud amount (−0.57), and they are higher in every month during RILEs than the corresponding values across the entire pre-2050 time frame. Again comparing the average of the correlations showing no time lag, sea ice leading clouds, and clouds leading sea ice (Fig. 6), we find that these relationships are almost twice as strong (r = −0.48) during RILEs than during the whole 50-year record, but curiously these three different lead-lag correlations during RILEs are identical. This match indicates that at least on the monthly timescales considered here, there is no evidence to resolve the question of whether the pronounced autumn sea ice and cloudiness anomalies during RILEs are triggered by the ice or the clouds. This result suggests that the very rapid response of surface radiative fluxes to changes in cloudiness in the Arctic (Intrieri et al. 2002) necessitates that lead-lag correlations be calculated at higher temporal resolution, but unfortunately daily output from these simulations was not available. We also acknowledge that other sea ice variables such as freeze-up date and growth rates might be more sensitive to the warming influence of increased autumn cloudiness, but we retain ice concentration as our comparative metric due to its close physical linkage with surface evaporation and therefore cloud formation.
A further way of measuring the sea ice-cloud coupling and the possible role of clouds in that relationship is to consider the reverse correlation: how does sea ice vary during intervals of extremely rapid cloud increases (during autumn)? To address this question, we identified the 10 intervals of most rapid autumn cloud increases, based on the average cloud fraction trends over a 3- to 9- year window to match the range of the 10 RILEs. We find that the average interannual trend in autumn ice fraction among these 10 “rapid cloud gain events” (−0.015 year−1) is nearly three times larger than the corresponding ice-fraction trend over the entire 50-year simulation (−0.0052 year−1). Furthermore, the rate of ice loss during autumn in all 10 of these cases exceeds the 50-year average ice-fraction trend, and the majority of these cases (6 out of 10) coincide with the 10 RILEs. Moreover, the magnitude of the ice-loss trends during seven of these ten events is among the maximum 10% of all the trends simulated across the ensemble members. These results demonstrate that the strong relationship we identified between extremely rapid sea ice reductions and large cloud increases also operates in reverse—i.e., extremely rapid (autumn) cloud increases coincide with large reductions in sea ice. This finding underscores the close sea ice-cloud association and suggests that increasing autumn cloudiness may be more than a passive response to abrupt declines in Arctic sea ice.
4 Synthesis and discussion
An important question is whether the cloud changes during RILEs act as a feedback mechanism to enhance or mitigate the transient shift of the Arctic toward a warmer, less icy state. Although we forego a formal feedback analysis in this study, our results suggest that clouds generally reinforce the declining trends of sea ice during RILEs. This conclusion is strongest for autumn, when the cloud changes are largest and most consistent and the CRF is comprised almost entirely of the (warming) longwave radiation term. CCSM3 simulates its largest positive CRF during autumn with a maximum of over 40 W m−2 in October, in agreement with satellite observations (Karlsson and Svensson 2009). The amplified cloud increases in autumn during RILEs should therefore boost the rapid ice reductions by more effectively trapping the outgoing surface longwave energy and re-radiating some of it back to the surface. The particularly pronounced increases in autumn CRF during RILEs (eight times as large as the secular trend) underscores this mechanism.
In other seasons the evidence for a cloud feedback is less obvious, but during summer the rate of increase in total cloudiness during RILEs is only 15% as large as the secular trend in this season when clouds cool the surface (Schweiger and Key 1994), and the increasing rate of cloud liquid water during RILEs is also smaller than the multi-decadal rate. Furthermore, although summertime low clouds increase over the first half of the 21st century, they decrease on average Arctic-wide during RILEs. Because Arctic low-level clouds are the predominant and most radiatively significant cloud type (Uttal et al. 2002), their decline means that more solar energy can reach the surface during summer and thus enhance warming (we note, however, that in our simulations most of this extra heating would have to be transmitted indirectly to the ice surface because the reduced summertime total cloudiness during RILEs occurs primarily over land) (Sect. 3.2). This interpretation of the summer feedback role of clouds in RILEs is complicated by the amplified negative trend in CRF during this season (Table 1), but changes in CRF when sunlight is present are known to be strongly influenced by changes in surface albedo and thus provide no simple explanation for the influence of the cloud response itself (Sect. 3.2). In winter, clouds decrease during RILEs at every level in conjunction with rising atmospheric pressure, seemingly favoring sea ice growth over this season in which clouds strongly warm the Arctic surface. We remind the reader, however, that in winter the areally averaged trend in CRF is slightly more positive during RILEs than over the early 21st century (Sect. 3.2), presumably due to the similar sized amplification of the cloud liquid water trend. These magnified positive trends in CRF and liquid condensate are also relatively robust (Fig. 7) and suggest that cloud changes in this season may still serve as a positive feedback, despite the overall decreasing trend in winter cloud amount.
Although this study is focused on model simulations and thus inherently restricted, we are encouraged by the agreement between some of our major findings and the limited record of cloud-ice interactions when open water in the Arctic is unusually expansive. Following the record-setting Arctic sea ice minimum in 2007, cloudiness was observed to increase substantially during autumn over areas of ice loss (Levinson and Lawrimore 2008), similar to the simulated response during RILEs. Cloud cover was unusually sparse over the Arctic during summer 2007, possibly playing a similarly important role in generating large amounts of ice melt (Kay et al. 2008). An analogous process may be operative in our simulations, although most of the negative cloud anomalies in summer 2007 occurred over sea ice, whereas the decreasing trend in low clouds during RILEs is primarily land-based. A slightly broader observational study covering 2006–2008 conditions (Kay and Gettelman 2009) shows considerably greater amounts of low clouds over the Arctic Ocean during early autumn in the low-ice years of 2007 and 2008, compared with the much icier conditions in 2006. Similarly, satellite lidar data from 2003 to 2007 show a greater amount of low clouds during mid-autumn over open water than above sea ice (Palm et al. 2009). Given the pronounced downward trend of Arctic ice extent in recent years (Stroeve et al. 2007), we expect that more opportunities will arise in the near future to monitor the relationship between polar clouds and sea ice during times of anomalously low ice coverage.
Because the Arctic system may already be starting an abrupt transition toward a much warmer and less icy climate than at any time in the recent past, we need to understand the mechanisms that could drive this shift. Our study considers the role of clouds during such environmental changes, utilizing multiple realizations of the 21st century in CCSM3. By comparing trends in cloud properties during RILEs with their more gradual evolution over the course of the early 21st century, we identify major features that may improve understanding of the role of clouds in rapid Arctic climate change.
Clouds should increase in the Arctic as the climate warms, and the trend toward cloudier conditions will probably be most pronounced during autumn due to the maximum enhancement of evaporation during that season.
During RILEs, clouds are also expected to increase most in autumn, resulting in a potentially important positive feedback that hinders freeze-up and thus favors thinner ice.
The autumn cloud expression during RILEs is the most robust of any season, in terms of inter-ensemble spread and the consistency among the cloud amplification factors (cloud amount, water content, and radiative forcing).
During autumn, most of the changes in sea ice and cloud variables over the first half of the 21st century are realized during RILEs, including the entire secular trend in CRF.
The trends in total cloudiness during RILEs are explained almost entirely by the response of low-level clouds, rather than by middle or high clouds, and the low cloud trends show the most consistency among the simulations.
No clear lead-lag relationship is evident in autumn between changes in the coverage of sea ice and clouds during RILEs, indicating that at least on monthly timescales the two vary nearly synchronously, although the strength of the sea ice-cloud correlations is much greater when the ice coverage is rapidly declining. On seasonal timescales, however, the results suggest a possible cause-and-effect, in that decreasing low cloudiness in summer during RILEs may promote less sea ice and more clouds during autumn.
Cloud changes appear to accelerate the rapid loss of sea ice at least during autumn and possibly in winter. Both enhanced autumn cloudiness trapping more outgoing longwave radiation and increasing amounts of liquid cloud condensate during winter lead to an amplified increase in CRF. The corresponding role of clouds during summer is less certain, but the relatively smaller increase in total clouds and the decreasing trend in low clouds during RILEs suggests that a positive feedback could also be at work in that season.
A positive feedback from primarily low cloud changes amid a warming climate is supported by other GCM simulations of the Arctic’s transient and time-mean response to greenhouse forcing (e.g., Miller and Russell 2002, Vavrus 2004), in addition to a recent observational/modeling study of subtropical low-level cloud trends in recent decades (Clement et al. 2009). We know of no other previous studies, however, that have investigated the role of polar clouds during abrupt climate change.
The similarity of CCSM3’s future transient cloud response with those of other GCMs in the CMIP3 archive and the strong resemblance between our major simulated features and observations during the low-ice years of 2007–2008 suggest that clouds should be considered an important candidate among the processes hastening the retreat of Arctic sea ice. Future research could investigate whether a similar relationship exists between clouds and sea ice in the other six CMIP3 climate models exhibiting abrupt ice retreat during their 21st century simulations (Holland et al. 2006).
This study was supported by grants from the National Science Foundation (OPP-0327664, OPP-0612388, ARC-0628910, and ARC-0652838). MM Holland was supported by NASA grant NNG06GB26G. This work was also made possible by the support of UCAR through its Faculty Fellowship Program, which allowed the authors to collaborate on this project while Vavrus was a visiting scientist at NCAR.
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