skip to main content

Title: An Internal Atmospheric Process Determining Summertime Arctic Sea Ice Melting in the Next Three Decades: Lessons Learned from Five Large Ensembles and Multiple CMIP5 Climate Simulations
Abstract Arctic sea ice melting processes in summer due to internal atmospheric variability have recently received considerable attention. A regional barotropic atmospheric process over Greenland and the Arctic Ocean in summer (June–August), featuring either a year-to-year change or a low-frequency trend toward geopotential height rise, has been identified as an essential contributor to September sea ice loss, in both observations and the CESM1 Large Ensemble (CESM-LE) of simulations. This local melting is further found to be sensitive to remote sea surface temperature (SST) variability in the east-central tropical Pacific Ocean. Here, we utilize five available large “initial condition” Earth system model ensembles and 31 CMIP5 models’ preindustrial control simulations to show that the same atmospheric process, resembling the observed one and the one found in the CESM-LE, also dominates internal sea ice variability in summer on interannual to interdecadal time scales in preindustrial, historical, and future scenarios, regardless of the modeling environment. However, all models exhibit limitations in replicating the magnitude of the observed local atmosphere–sea ice coupling and its sensitivity to remote tropical SST variability in the past four decades. These biases call for caution in the interpretation of existing models’ simulations and fresh thinking about models’ credibility in more » simulating interactions of sea ice variability with the Arctic and global climate systems. Further efforts toward identifying the causes of these model limitations may provide implications for alleviating the biases and improving interannual- and decadal-time-scale sea ice prediction and future sea ice projection. « less
Authors:
; ; ; ; ; ; ;
Award ID(s):
1744598
Publication Date:
NSF-PAR ID:
10232638
Journal Name:
Journal of Climate
Volume:
33
Issue:
17
Page Range or eLocation-ID:
7431 to 7454
ISSN:
0894-8755
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Over the past 40 years, the Arctic sea ice minimum in September has declined. The period between 2007 and 2012 showed accelerated melt contributed to the record minima of 2007 and 2012. Here, observational and model evidence shows that the changes in summer sea ice since the 2000s reflect a continuous anthropogenically forced melting masked by interdecadal variability of Arctic atmospheric circulation. This variation is partially driven by teleconnections originating from sea surface temperature (SST) changes in the east-central tropical Pacific via a Rossby wave train propagating into the Arctic [herein referred to as the Pacific–Arctic teleconnection (PARC)], which represents the leading internal mode connecting the pole to lower latitudes. This mode has contributed to accelerated warming and Arctic sea ice loss from 2007 to 2012, followed by slower declines in recent years, resulting in the appearance of a slowdown over the past 11 years. A pacemaker model simulation, in which we specify observed SST in the tropical eastern Pacific, demonstrates a physically plausible mechanism for the PARC mode. However, the model-based PARC mechanism is considerably weaker and only partially accounts for the observed acceleration of sea ice loss from 2007 to 2012. We also explore features of large-scalemore »circulation patterns associated with extreme melting periods in a long (1800 yr) CESM preindustrial simulation. These results further support that remote SST forcing originating from the tropical Pacific can excite significant warm episodes in the Arctic. However, further research is needed to identify the reasons for model limitations in reproducing the observed PARC mode featuring a cold Pacific–warm Arctic connection.

    « less
  2. Abstract The rapid decline of summer Arctic sea ice over the past few decades has been driven by a combination of increasing greenhouse gases and internal variability of the climate system. However, uncertainties remain regarding spatial and temporal characteristics of the optimal internal atmospheric mode that most favors summer sea ice melting on low-frequency time scales. To pinpoint this mode, we conduct a suite of simulations in which atmospheric circulation is constrained by nudging tropospheric Arctic (60°–90°N) winds within the Community Earth System Model, version 1 (CESM1), to those from reanalysis. Each reanalysis year is repeated for over 10 model years using fixed greenhouse gas concentrations and the same initial conditions. Composites show the strongest September sea ice losses are closely preceded by a common June–August (JJA) barotropic anticyclonic circulation in the Arctic favoring shortwave absorption at the surface. Successive years of strong wind-driven melting also enhance declines in Arctic sea ice through enhancement of the ice–albedo feedback, reaching a quasi-equilibrium response after repeated wind forcing for over 5–6 years, as the effectiveness of the wind-driven ice–albedo feedback becomes saturated. Strong melting favored by a similar wind pattern as observations is detected in a long preindustrial simulation and 400-yr paleoclimatemore »reanalysis, suggesting that a summer barotropic anticyclonic wind pattern represents the optimal internal atmospheric mode maximizing sea ice melting in both the model and natural world over a range of time scales. Considering strong contributions of this mode to changes in Arctic climate, a better understanding of its origin and maintenance is vital to improving future projections of Arctic sea ice.« less
  3. Over the last half century, the Arctic sea ice cover has declined dramatically. Current estimates suggest that, for the Arctic as a whole, nearly one-half of the observed loss of summer sea ice cover is not due to anthropogenic forcing but rather is due to internal variability. Using the 40 members of the Community Earth System Model Large Ensemble (CESM-LE), our analysis provides the first regional assessment of the role of internal variability on the observed sea ice loss. The CESM-LE is one of the best available models for such an analysis, because it performs better than other CMIP5 models for many metrics of importance. Our study reveals that the local contribution of internal variability has a large range and strongly depends on the month and region in question. We find that the pattern of internal variability is highly nonuniform over the Arctic, with internal variability accounting for less than 10% of late summer (August–September) East Siberian Sea sea ice loss but more than 60% of the Kara Sea sea ice loss. In contrast, spring (April–May) sea ice loss, notably in the Barents Sea, has so far been dominated by internal variability.

  4. Abstract. This study considersyear-to-year and decadal variations in as well as secular trendsof the sea–air CO2 flux over the 1957–2020 period,as constrained by the pCO2 measurements from the SOCATv2021 database.In a first step,we relate interannual anomalies in ocean-internal carbon sources and sinksto local interannual anomalies insea surface temperature (SST), the temporal changes in SST (dSST/dt),and squared wind speed (u2),employing a multi-linear regression.In the tropical Pacific, we find interannual variability to be dominated by dSST/dt,as arising from variations in the upwelling of colder and more carbon-rich waters into the mixed layer.In the eastern upwelling zones as well as in circumpolar bands in the high latitudes of both hemispheres,we find sensitivity to wind speed,compatible with the entrainment of carbon-rich water during wind-driven deepening of the mixed layerand wind-driven upwelling.In the Southern Ocean,the secular increase in wind speed leads to a secular increase in the carbon source into the mixed layer,with an estimated reduction in the sink trend in the range of 17 % to 42 %.In a second step,we combined the result of the multi-linear regression andan explicitly interannual pCO2-based additive correctioninto a “hybrid” estimate of the sea–air CO2 flux over the period 1957–2020.As a pCO2 mapping method,it combines (a) the ability of a regressionmore »to bridge data gaps and extrapolate intothe early decades almost void of pCO2 databased on process-related observablesand (b) the ability of an auto-regressive interpolation to follow signalseven if not represented in the chosen set of explanatory variables.The “hybrid” estimate can be applied as an ocean flux prior foratmospheric CO2 inversions covering the whole period of atmospheric CO2 data since 1957.« less
  5. Abstract Over the past decades, Arctic climate has exhibited significant changes characterized by strong Pan-Arctic warming and a large scale wind shift trending toward an anticyclonic anomaly centered over Greenland and the Arctic ocean. Recent work has suggested that this wind change is able to warm the Arctic atmosphere and melt sea ice through dynamical-driven warming, moistening and ice drift effects. However, previous examination of this linkage lacks a capability to fully consider the complex nature of the sea ice response to the wind change. In this study, we perform a more rigorous test of this idea by using a coupled high-resolution modelling framework with observed winds nudged over the Arctic that allows for a comparison of these wind-induced effects with observations and simulated effects forced by anthropogenic forcing. Our nudging simulation can well capture observed variability of atmospheric temperature, sea ice and the radiation balance during the Arctic summer and appears to simulate around 30% of Arctic warming and sea ice melting over the whole period (1979-2020) and more than 50% over the period 2000 to 2012, which is the fastest Arctic warming decade in the satellite era. In particular, in the summer of 2020, a similar wind patternmore »reemerged to induce the second-lowest sea ice extent since 1979, suggesting that large scale wind changes in the Arctic is essential in shaping Arctic climate on interannual and interdecadal time scales and may be critical to determine Arctic climate variability in the coming decades.« less