skip to main content


Title: Increasing confidence in projecting the Arctic ice-free year with emergent constraints
Abstract An ice-free Arctic summer is a landmark of global change and has the far-reaching climate, environmental, and economic impacts. However, the Coupled Model Intercomparison Project Phase 6 models’ projected occurrence remains notoriously uncertain. Finding emergent constraints to reduce the projection uncertainties has been a foremost challenge. To establish a physical basis for the constraints, we first demonstrate, with numerical experiments, that the observed trend of Arctic ice loss is primarily driven by the Arctic near-surface air temperature. Thus, two constraints are proposed: the Arctic sea ice sensitivity that measures Arctic sea ice response to the local warming, and the Arctic amplification sensitivity that assesses how well the model responds to anthropogenic forcing and allocates heat to the Arctic region. The two constraints are complementary and nearly scenario-independent. The model-projected first Arctic ice-free year significantly depends on the model’s two climate sensitivities. Thus, the first Arctic ice-free year can be predicted by the linear combination of the two Arctic sensitivity measures. Based on model-simulated sensitivity skills, 20 CMIP models are divided into two equal number groups. The ten realistic-sensitivity models project, with a likelihood of 80%, the ice-free Arctic will occur by additional 0.8 °C global warming from 2019 level or before 2040 under the SSP2-4.5 (medium emission) scenario. The ten realistic-sensitivity models’ spread is reduced by about 70% compared to the ten underestimate-sensitivity models’ large spread. The strategy for creating physics-based emergent constraints through numerical experiments may be instrumental for broad application to other fields for advancing robust projection and understanding uncertainty sources.  more » « less
Award ID(s):
1744598
NSF-PAR ID:
10357916
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Environmental Research Letters
Volume:
16
Issue:
9
ISSN:
1748-9326
Page Range / eLocation ID:
094016
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    The recent Arctic sea ice loss is a key driver of the amplified surface warming in the northern high latitudes, and simultaneously a major source of uncertainty in model projections of Arctic climate change. Previous work has shown that the spread in model predictions of future Arctic amplification (AA) can be traced back to the inter-model spread in simulated long-term sea ice loss. We demonstrate that the strength of future AA is further linked to the current climate’s, observable sea ice state across the multi-model ensemble of the 6th Coupled Model Intercomparison Project (CMIP6). The implication is that the sea-ice climatology sets the stage for long-term changes through the 21st century, which mediate the degree by which Arctic warming is amplified with respect to global warming. We determine that a lower base-climate sea ice extent and sea ice concentration (SIC) in CMIP6 models enable stronger ice melt in both future climate and during the seasonal cycle. In particular, models with lower Arctic-mean SIC project stronger future ice loss and a more intense seasonal cycle in ice melt and growth. Both processes systemically link to a larger future AA across climate models. These results are manifested by the role of climate feedbacks that have been widely identified as major drivers of AA. We show in particular that models with low base-climate SIC predict a systematically stronger warming contribution through both sea-ice albedo feedback and temperature feedbacks in the future, as compared to models with high SIC. From our derived linear regressions in conjunction with observations, we estimate a 21st-century AA over sea ice of 2.47–3.34 with respect to global warming. Lastly, from the tight relationship between base-climate SIC and the projected timing of an ice-free September, we predict a seasonally ice-free Arctic by mid-century under a high-emission scenario.

     
    more » « less
  2. Abstract

    The rapid decline of Arctic sea ice, including sea ice area (SIA) retreat and sea ice thinning, is a striking manifestation of global climate change. Analysis of 40 CMIP6 models reveals a very large spread in both model simulations of the September SIA and thickness and the timing of a summer ice-free Arctic Ocean. The existing SIA-based evaluation metrics are deficient due to observational uncertainty, prominent internal variability, and indirect Arctic response to global forcing. Given the critical roles of sea ice thickness (SIT) in determining Arctic ice variation throughout the seasonal cycle and the April SIT bridging the winter freezing and summer melting processes, we propose two SIT-based metrics, the April mean SIT and summer SIA response to April SIT, to assess climate models’ capability to reproduce the historical change of the Arctic sea ice area. The selected 11 good models reduce the uncertainty in the projected first ice-free Arctic by 70% relative to 11 poor models. The chosen models’ ensemble mean projects the first ice-free year in 2049 (2043) under the shared socio-economic pathways (SSP)2-4.5 (SSP5-8.5) scenario with one standard deviation of the inter-model spread of 12.0 (8.9) years.

     
    more » « less
  3. Abstract

    State‐of‐the‐art climate models simulate a large spread in the projected decline of Arctic sea‐ice area (SIA) over the 21st century. Here we diagnose causes of this intermodel spread using a simple model that approximates future SIA based on present SIA and the sensitivity of SIA to Arctic temperatures. This model accounts for 70%–95% of the intermodel variance, with the majority of the spread arising from present‐day biases. The remaining spread arises from intermodel differences in Arctic warming, with some contribution from differences in the local sea‐ice sensitivity. Using observations to constrain the projections moves the probability of an ice‐free Arctic forward by 10–35 years when compared to unconstrained projections. Under a high‐emissions scenario, an ice‐free Arctic will likely (66% probability) occur between 2036 and 2056 in September and between 2050 and 2068 from July to October. Under a medium‐emissions scenario, the “likely” date occurs between 2040 and 2062 in September and much later in the 21st century from July to October.

     
    more » « less
  4. null (Ed.)
    Abstract Winter Arctic sea ice loss has been simulated with varying degrees of abruptness across global climate models (GCMs) run in phase 5 of the Coupled Model Intercomparison Project (CMIP5) under the high-emissions extended RCP8.5 scenario. Previous studies have proposed various mechanisms to explain modeled abrupt winter sea ice loss, such as the existence of a wintertime convective cloud feedback or the role of the freezing point as a natural threshold, but none have sought to explain the variability of the abruptness of winter sea ice loss across GCMs. Here we propose a year-to-year local positive feedback cycle in which warm, open oceans at the start of winter allow for the moistening and warming of the lower atmosphere, which in turn increases the downward clear-sky longwave radiation at the surface and suppresses ocean freezing. This situation leads to delayed and diminished winter sea ice growth and allows for increased shortwave absorption from lowered surface albedo during springtime. Last, the ocean stores this additional heat throughout the summer and autumn seasons, setting up even warmer ocean conditions that lead to further sea ice reduction. We show that the strength of this feedback, as measured by the partial temperature contributions of the different surface heat fluxes, correlates strongly with the abruptness of winter sea ice loss across models. Thus, we suggest that this feedback mechanism may explain intermodel spread in the abruptness of winter sea ice loss. In models in which the feedback mechanism is strong, this may indicate the possibility of hysteresis and thus irreversibility of sea ice loss. 
    more » « less
  5. null (Ed.)
    The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near- surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice that of the global/Northern Hemisphere mean. Model spread is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the twenty-first century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015 to 2095. It is found that the largest model uncertainties are consistent cold bias in the oceanic regions in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that large intermodel spread and uncertainties exist in the CMIP6 models’ simulation and projection of the Arctic near- surface temperature and that there are different responses over the ocean and land in the Arctic to greenhouse gas forcing. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread. 
    more » « less