The current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next-generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice–ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SSTs) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (~10%) from up to 3 times larger than this (~30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190 km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from every 5 days to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.
The decline of Arctic sea‐ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea‐ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically‐driven SIM export and negative feedbacks from sea‐ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice‐thickness observations collected after melt onset are particularly critical for summer Arctic sea‐ice predictions.
We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL ‐ the AM4 atmosphere model, MOM6 ocean code, LM4 land model and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0o (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1o to 0.25o. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM 4 models, but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time‐mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM 4 models.
The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of NOAA. SPEAR is an effort to develop a seamless system for prediction and research across timescales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called Ocean Tendency Adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ocean data assimilation as 3‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially SST forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño‐Southern Oscillation (ENSO).
We examine CMIP6 simulations of Arctic sea‐ice area and volume. We find that CMIP6 models produce a wide spread of mean Arctic sea‐ice area, capturing the observational estimate within the multi‐model ensemble spread. The CMIP6 multi‐model ensemble mean provides a more realistic estimate of the sensitivity of September Arctic sea‐ice area to a given amount of anthropogenic CO2 emissions and to a given amount of global warming, compared with earlier CMIP experiments. Still, most CMIP6 models fail to simulate at the same time a plausible evolution of sea‐ice area and of global mean surface temperature. In the vast majority of the available CMIP6 simulations, the Arctic Ocean becomes practically sea‐ice free (sea‐ice area < 1 million km2) in September for the first time before the year 2050 in each of the four emission scenarios SSP1‐1.9, SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 examined here.
We document the configuration and emergent simulation features from the Geophysical Fluid Dynamics Laboratory (GFDL) OM4.0 ocean/sea‐ice model. OM4 serves as the ocean/sea‐ice component for the GFDL climate and Earth system models. It is also used for climate science research and is contributing to the Coupled Model Intercomparison Project version 6 Ocean Model Intercomparison Project (CMIP6/OMIP). The ocean component of OM4 uses version 6 of the Modular Ocean Model (MOM6) and the sea‐ice component uses version 2 of the Sea Ice Simulator (SIS2), which have identical horizontal grid layouts (Arakawa C‐grid). We follow the Coordinated Ocean‐sea ice Reference Experiments (CORE) protocol to assess simulation quality across a broad suite of climate relevant features. We present results from two versions differing by horizontal grid spacing and physical parameterizations: OM4p5 has nominal 0.5° spacing and includes mesoscale eddy parameterizations and OM4p25 has nominal 0.25° spacing with no mesoscale eddy parameterization.
MOM6 makes use of a vertical Lagrangian‐remap algorithm that enables general vertical coordinates. We show that use of a hybrid depth‐isopycnal coordinate reduces the mid‐depth ocean warming drift commonly found in pure z* vertical coordinate ocean models. To test the need for the mesoscale eddy parameterization used in OM4p5, we examine the results from a simulation that removes the eddy parameterization. The water mass structure and model drift are physically degraded relative to OM4p5, thus supporting the key role for a mesoscale closure at this resolution.
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.
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.
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.
Bushuk, Mitchell, D M Holland, T P Stanton, and Alon A Stern, August 2019: Ice scallops: a laboratory investigation of the ice–water interface. Journal of Fluid Mechanics, 873, DOI:10.1017/jfm.2019.398. Abstract
Ice scallops are a small-scale (5–20 cm) quasi-periodic ripple pattern that occurs at the ice–water interface. Previous work has suggested that scallops form due to a self-reinforcing interaction between an evolving ice-surface geometry, an adjacent turbulent flow field and the resulting differential melt rates that occur along the interface. In this study, we perform a series of laboratory experiments in a refrigerated flume to quantitatively investigate the mechanisms of scallop formation and evolution in high resolution. Using particle image velocimetry, we probe an evolving ice–water boundary layer at sub-millimetre scales and 15 Hz frequency. Our data reveal three distinct regimes of ice–water interface evolution: a transition from flat to scalloped ice; an equilibrium scallop geometry; and an adjusting scallop interface. We find that scalloped-ice geometry produces a clear modification to the ice–water boundary layer, characterized by a time-mean recirculating eddy feature that forms in the scallop trough. Our primary finding is that scallops form due to a self-reinforcing feedback between the ice-interface geometry and shear production of turbulent kinetic energy in the flow interior. The length of this shear production zone is therefore hypothesized to set the scallop wavelength.
Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of Observing System Experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea-ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea-surface temperature (SST) satellite observations and subsurface conductivity-temperature-depth (CTD) measurements. The SST data is found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal timescales.
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.
We describe GFDL's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the pre‐industrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top‐of‐atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño‐Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasi‐periodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.
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.
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.
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.
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.
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.
