skip to main content


Search for: All records

Award ID contains: 1847398

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract

    We provide an assessment of the current and future states of Arctic sea ice simulated by the Community Earth System Model version 2 (CESM2). The CESM2 is the version of the CESM contributed to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). We analyze changes in Arctic sea ice cover in two CESM2 configurations with differing atmospheric components: the CESM2(CAM6) and the CESM2(WACCM6). Over the historical period, the CESM2(CAM6) winter ice thickness distribution is biased thin, which leads to lower summer ice area compared to CESM2(WACCM6) and observations. In both CESM2 configurations, the timing of first ice‐free conditions is insensitive to the choice of CMIP6 future emissions scenario. In fact, the probability of an ice‐free Arctic summer remains low only if global warming stays below 1.5°C, which none of the CMIP6 scenarios achieve. By the end of the 21st century, the CESM2 simulates less ocean heat loss during the fall months compared to its previous version, delaying sea ice formation and leading to ice‐free conditions for up to 8 months under the high emissions scenario. As a result, both CESM2 configurations exhibit an accelerated decline in winter and spring ice area, a behavior that had not been previously seen in CESM simulations. Differences in climate sensitivity and higher levels of atmospheric CO2by 2100 in the CMIP6 high emissions scenario compared to its CMIP5 analog could explain why this winter ice loss was not previously simulated by the CESM.

     
    more » « less
  2. Free, publicly-accessible full text available March 1, 2025
  3. Abstract. Climate simulation uncertainties arise from internal variability, model structure, and external forcings. Model intercomparisons (such as the Coupled Model Intercomparison Project; CMIP) and single-model large ensembles have provided insight into uncertainty sources. Under the Community Earth System Model (CESM) project, large ensembles have been performed for CESM2 (a CMIP6-era model) and CESM1 (a CMIP5-era model). We refer to these as CESM2-LE and CESM1-LE. The external forcing used in these simulations has changed to be consistent with their CMIP generation. As a result, differences between CESM2-LE and CESM1-LE ensemble means arise from changes in both model structure and forcing. Here we present new ensemble simulations which allow us to separate the influences of these model structural and forcing differences. Our new CESM2 simulations are run with CMIP5 forcings equivalent to those used in the CESM1-LE. We find a strong influence of historical forcing uncertainty due to aerosol effects on simulated climate. For the historical period, forcing drives reduced global warming and ocean heat uptake in CESM2-LE relative to CESM1-LE that is counteracted by the influence of model structure. The influence of the model structure and forcing vary across the globe, and the Arctic exhibits a distinct signal that contrasts with the global mean. For the 21st century, the importance of scenario forcing differences (SSP3–7.0 for CESM2-LE and RCP8.5 for CESM1-LE) is evident. The new simulations presented here allow us to diagnose the influence of model structure on 21st century change, despite large scenario forcing differences, revealing that differences in the meridional distribution of warming are caused by model structure. Feedback analysis reveals that clouds and their impact on shortwave radiation explain many of these structural differences between CESM2 and CESM1. In the Arctic, albedo changes control transient climate evolution differences due to structural differences between CESM2 and CESM1.

     
    more » « less
    Free, publicly-accessible full text available January 1, 2025
  4. Abstract Internal variability is the dominant cause of projection uncertainty of Arctic sea ice in the short and medium term. However, it is difficult to determine the realism of simulated internal variability in climate models, as observations only provide one possible realization while climate models can provide numerous different realizations. To enable a robust assessment of simulated internal variability of Arctic sea ice, we use a resampling technique to build synthetic ensembles for both observations and climate models, focusing on interannual variability, which is the dominant time scale of Arctic sea ice internal variability. We assess the realism of the interannual variability of Arctic sea ice cover as simulated by six models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) that provide large ensembles compared to four observational datasets. We augment the standard definition of model and observational consistency by representing the full distribution of resamplings, analogous to the distribution of variability that could have randomly occurred. We find that modeled interannual variability typically lies within observational uncertainty. The three models with the smallest mean state biases are the only ones consistent in the pan-Arctic for all months, but no model is consistent for all regions and seasons. Hence, choosing the right model for a given task as well as using internal variability as an additional metric to assess sea ice simulations is important. The fact that CMIP5 large ensembles broadly simulate interannual variability consistent within observational uncertainty gives confidence in the internal projection uncertainty for Arctic sea ice based on these models. Significance Statement The purpose of this study is to evaluate the historical simulated internal variability of Arctic sea ice in climate models. Determining model realism is important to have confidence in the projected sea ice evolution from these models, but so far only mean state and trends are commonly assessed metrics. Here we assess internal variability with a focus on the interannual variability, which is the dominant time scale for internal variability. We find that, in general, models agree well with observations, but as no model is within observational uncertainty for all months and locations, choosing the right model for a given task is crucial. Further refinement of internal variability realism assessments will require reduced observational uncertainty. 
    more » « less
  5. The sensitivity of sea ice to fire emissions highlights climate model uncertainty related to the accuracy of prescribed forcings. 
    more » « less
  6. Abstract. Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and must therefore define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator, the Arctic Ocean Observation Operator (ARC3O), to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using Community Earth System Model version 2 (CESM2) ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place. 
    more » « less
  7. null (Ed.)
    Abstract The shrinking of Arctic-wide September sea ice extent is often cited as an indicator of modern climate change; however, the timing of seasonal sea ice retreat/advance and the length of the open-water period are often more relevant to stakeholders working at regional and local scales. Here we highlight changes in regional open-water periods at multiple warming thresholds. We show that, in the latest generation of models from the Coupled Model Intercomparison Project (CMIP6), the open-water period lengthens by 63 days on average with 2 °C of global warming above the 1850-1900 average, and by over 90 days in several Arctic seas. Nearly the entire Arctic, including the Transpolar Sea Route, has at least 3 months of open water per year with 3.5 °C warming, and at least 6 months with 5 °C warming. Model bias compared to satellite data suggests that even such dramatic projections may be conservative. 
    more » « less
  8. null (Ed.)
    Abstract The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively. 
    more » « less