Bibliography - Mitchell Bushuk

1. Blanchard-Wrigglesworth, E, and Mitchell Bushuk, May 2019: Robustness of Arctic sea-ice predictability in GCMs. Climate Dynamics, 52(9-10), DOI:10.1007/s00382-018-4461-3.
   Abstract

   General circulation models have been amply used to quantify Arctic sea-ice predictability. While models share some common aspects of predictability loss with increasing forecast lead time, there is significant model spread in the magnitude and timing of predictability loss. Here we show that inter-model differences in predictability are linked to inter-model differences in the persistence timescales of sea-ice anomalies that are unique to each model, with models that exhibit longer persistence having higher potential predictability. Given this result and previous work showing that in a single model control simulation the magnitude of persistence fluctuates between multi-annual periods of high and low persistence, we assess whether initial-value predictability is dependent on the persistence state of the initial conditions. We find that predictability is not clearly impacted by the persistence state of the initial conditions, suggesting that predictability may be robust within a constant climate mean state.

2. Bonan, D B., Mitchell Bushuk, and Michael Winton, in press: A spring barrier for regional predictions of summer Arctic sea ice. Geophysical Research Letters. DOI:10.1029/2019GL082947. May 2019.
   Abstract

   Seasonal forecast systems can skillfully predict summer Arctic sea‐ice up to four months in advance. For some regions, however, there is a springtime predictability barrier that causes forecasts initialized prior to May to be less skillful. Since this barrier has only been documented in a few general circulation models (GCMs), we evaluate GCMs participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). We first show sea‐ice volume skillfully predicts summer sea ice‐area (SIA) and has similar skill to a perfect model experiment. Given this result, we assess regional SIA predictability across CMIP5 and find a universal predictability barrier in late spring. For SIA at each summer target month in the marginal seas of the Arctic basin, a notable drop in prediction skill occurs from June to May in each GCM. This suggests summer sea‐ice forecasts initialized after June 1 will have better prediction skill than forecasts initialized before.

3. Bushuk, Mitchell, Rym Msadek, Michael Winton, Gabriel A Vecchi, Xiaosong Yang, Anthony Rosati, and Richard G Gudgel, March 2019: Regional Arctic sea–ice prediction: potential versus operational seasonal forecast skill. Climate Dynamics, 52(5-6), DOI:10.1007/s00382-018-4288-y.
   Abstract

   Seasonal predictions of Arctic sea ice on regional spatial scales are a pressing need for a broad group of stakeholders, however, most assessments of predictability and forecast skill to date have focused on pan-Arctic sea–ice extent (SIE). In this work, we present the first direct comparison of perfect model (PM) and operational (OP) seasonal prediction skill for regional Arctic SIE within a common dynamical prediction system. This assessment is based on two complementary suites of seasonal prediction ensemble experiments performed with a global coupled climate model. First, we present a suite of PM predictability experiments with start dates spanning the calendar year, which are used to quantify the potential regional SIE prediction skill of this system. Second, we assess the system’s OP prediction skill for detrended regional SIE using a suite of retrospective initialized seasonal forecasts spanning 1981–2016. In nearly all Arctic regions and for all target months, we find a substantial skill gap between PM and OP predictions of regional SIE. The PM experiments reveal that regional winter SIE is potentially predictable at lead times beyond 12 months, substantially longer than the skill of their OP counterparts. Both the OP and PM predictions display a spring prediction skill barrier for regional summer SIE forecasts, indicating a fundamental predictability limit for summer regional predictions. We find that a similar barrier exists for pan-Arctic sea–ice volume predictions, but is not present for predictions of pan-Arctic SIE. The skill gap identified in this work indicates a promising potential for future improvements in regional SIE predictions.

4. Ding, Q, A Schweiger, M L L'Heureux, E J Steig, D S Battisti, Nathaniel C Johnson, E Blanchard-Wrigglesworth, S Po-Chedley, Q Zhang, K J Harnos, and Mitchell Bushuk, et al., January 2019: Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations. Nature Geoscience, 12(1), DOI:10.1038/s41561-018-0256-8.
   Abstract

   The relative contribution and physical drivers of internal variability in recent Arctic sea ice loss remain open questions, leaving up for debate whether global climate models used for climate projection lack sufficient sensitivity in the Arctic to climate forcing. Here, through analysis of large ensembles of fully coupled climate model simulations with historical radiative forcing, we present an important internal mechanism arising from low-frequency Arctic atmospheric variability in models that can cause substantial summer sea ice melting in addition to that due to anthropogenic forcing. This simulated internal variability shows a strong similarity to the observed Arctic atmospheric change in the past 37 years. Through a fingerprint pattern matching method, we estimate that this internal variability contributes to about 40–50% of observed multi-decadal decline in Arctic sea ice. Our study also suggests that global climate models may not actually underestimate sea ice sensitivities in the Arctic, but have trouble fully replicating an observed linkage between the Arctic and lower latitudes in recent decades. Further improvements in simulating the observed Arctic–global linkage are thus necessary before the Arctic’s sensitivity to global warming in models can be quantified with confidence.

5. Bushuk, Mitchell, Rym Msadek, Michael Winton, Gabriel A Vecchi, Richard G Gudgel, Anthony Rosati, and Xiaosong Yang, April 2017: Summer enhancement of Arctic sea-ice volume anomalies in the September-ice zone. Journal of Climate, 30(7), DOI:10.1175/JCLI-D-16-0470.1.
   Abstract

   Due to its persistence on seasonal timescales, Arctic sea-ice thickness (SIT) is a potential source of predictability for summer sea-ice extent (SIE). New satellite observations of SIT represent an opportunity to harness this potential predictability via improved thickness initialization in seasonal forecast systems. In this work, the evolution of Arctic sea-ice volume anomalies is studied using a 700-year control integration and a suite of initialized ensemble forecasts from a fully-coupled global climate model. Our analysis is focused on the September sea-ice zone, as this is the region where thickness anomalies have the potential to impact the SIE minimum. The primary finding of this paper is that, in addition to a general decay with time, sea-ice volume anomalies display a summer enhancement, in which anomalies tend to grow between the months of May and July. This summer enhancement is relatively symmetric for positive and negative volume anomalies and peaks in July regardless of the initial month. Analysis of the surface energy budget reveals that the summer volume anomaly enhancement is driven by a positive feedback between the SIT state and the surface albedo. The SIT state affects surface albedo through changes in the sea-ice concentration field, melt-onset date, snow coverage, and ice-thickness distribution, yielding an anomaly in the total absorbed shortwave radiation between May and August, which enhances the existing SIT anomaly. This phenomenon highlights the crucial importance of accurate SIT initialization and representation of ice-albedo feedback processes in seasonal forecast systems.

6. Bushuk, Mitchell, and D Giannakis, June 2017: The Seasonality and Interannual Variability of Arctic Sea Ice Reemergence. Journal of Climate, 30(12), DOI:10.1175/JCLI-D-16-0549.1.
   Abstract

   There is a significant gap between the potential predictability of Arctic sea-ice area and the current forecast skill of operational prediction systems. One route to closing this gap is improving understanding of the physical mechanisms, such as sea-ice reemergence, that underlie this inherent predictability. Sea-ice reemergence refers to the tendency of melt season sea-ice area anomalies to recur the following growth season, and growth season anomalies to recur the following melt season. This study builds on earlier work, providing a mode-based analysis of the seasonality and interannual variability of three distinct reemergence mechanisms. These mechanisms are studied using a common set of coupled modes of variability obtained via coupled nonlinear Laplacian spectral analysis, a data analysis technique for high-dimensional multivariate datasets. The coupled modes capture the co-variability of sea-ice concentration (SIC), sea-surface temperature (SST), sea-level pressure (SLP), and sea-ice thickness (SIT) in a control integration of a global climate model. Using a parsimonious reemergence mode family, the spatial characteristics of growth-to-melt season reemergence are studied, and an SIT–SIC reemergence mechanism is examined. A set of reemergence metrics to quantify the amplitude and phase of growth-to-melt reemergence are introduced. Metrics quantifying SST–SIC and SLP–SIC mechanisms for melt-to-growth reemergence are also computed. A simultaneous comparison of the three reemergence mechanisms, with focus on their seasonality and interannual variability, is performed. Finally, the conclusions are tested in a model hierarchy, consisting of models that share the same sea-ice component but differ in their atmospheric and oceanic formulation.

7. Bushuk, Mitchell, Rym Msadek, Michael Winton, Gabriel A Vecchi, Richard G Gudgel, Anthony Rosati, and Xiaosong Yang, May 2017: Skillful regional prediction of Arctic sea ice on seasonal timescales. Geophysical Research Letters, 44(10), DOI:10.1002/2017GL073155.
   Abstract

   Recent Arctic sea ice seasonal prediction efforts and forecast skill assessments have primarily focused on pan-Arctic sea-ice extent (SIE). In this work, we move towards stakeholder-relevant spatial scales, investigating the regional forecast skill of Arctic sea ice in a Geophysical Fluid Dynamics Laboratory (GFDL) seasonal prediction system. Using a suite of retrospective initialized forecasts spanning 1981–2015 made with a coupled atmosphere-ocean-sea ice-land model, we show that predictions of detrended regional SIE are skillful at lead times up to 11 months. Regional prediction skill is highly region and target month dependent, and generically exceeds the skill of an anomaly persistence forecast. We show for the first time that initializing the ocean subsurface in a seasonal prediction system can yield significant regional skill for winter SIE. Similarly, as suggested by previous work, we find that sea-ice thickness initial conditions provide a crucial source of skill for regional summer SIE.

8. Shean, D E., K Christianson, K M Larson, S R M Ligtenberg, I R Joughin, B E Smith, C Max Stevens, Mitchell Bushuk, and D M Holland, November 2017: GPS-derived estimates of surface mass balance and ocean-induced basal melt for Pine Island Glacier ice shelf, Antarctica. The Cryosphere, 11(6), DOI:10.5194/tc-11-2655-2017.
   Abstract

   In the last 2 decades, Pine Island Glacier (PIG) experienced marked speedup, thinning, and grounding-line retreat, likely due to marine ice-sheet instability and ice-shelf basal melt. To better understand these processes, we combined 2008–2010 and 2012–2014 GPS records with dynamic firn model output to constrain local surface and basal mass balance for PIG. We used GPS interferometric reflectometry to precisely measure absolute surface elevation (zsurf) and Lagrangian surface elevation change (Dzsurf∕ Dt). Observed surface elevation relative to a firn layer tracer for the initial surface (zsurf − zsurf0′) is consistent with model estimates of surface mass balance (SMB, primarily snow accumulation). A relatively abrupt  ∼  0.2–0.3 m surface elevation decrease, likely due to surface melt and increased compaction rates, is observed during a period of warm atmospheric temperatures from December 2012 to January 2013. Observed Dzsurf∕ Dt trends (−1 to −4 m yr−1) for the PIG shelf sites are all highly linear. Corresponding basal melt rate estimates range from  ∼  10 to 40 m yr−1, in good agreement with those derived from ice-bottom acoustic ranging, phase-sensitive ice-penetrating radar, and high-resolution stereo digital elevation model (DEM) records. The GPS and DEM records document higher melt rates within and near features associated with longitudinal extension (i.e., transverse surface depressions, rifts). Basal melt rates for the 2012–2014 period show limited temporal variability despite large changes in ocean temperature recorded by moorings in Pine Island Bay. Our results demonstrate the value of long-term GPS records for ice-shelf mass balance studies, with implications for the sensitivity of ice–ocean interaction at PIG.

9. Christianson, K, and Mitchell Bushuk, et al., October 2016: Sensitivity of Pine Island Glacier to observed ocean forcing. Geophysical Research Letters, 43(20), DOI:10.1002/2016GL070500.
   Abstract

   We present subannual observations (2009–2014) of a major West Antarctic glacier (Pine Island Glacier) and the neighboring ocean. Ongoing glacier retreat and accelerated ice flow were likely triggered a few decades ago by increased ocean-induced thinning, which may have initiated marine ice sheet instability. Following a subsequent 60% drop in ocean heat content from early 2012 to late 2013, ice flow slowed, but by < 4%, with flow recovering as the ocean warmed to prior temperatures. During this cold-ocean period, the evolving glacier-bed/ice shelf system was also in a geometry favorable to stabilization. However, despite a minor, temporary decrease in ice discharge, the basin-wide thinning signal did not change. Thus, as predicted by theory, once marine ice sheet instability is underway, a single transient high-amplitude ocean cooling has only a relatively minor effect on ice flow. The long-term effects of ocean temperature variability on ice flow, however, are not yet known.

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