Balwada, Dhruv, Ryan Abernathey, Shantanu Acharya, Alistair Adcroft, Judith Brener, V Balaji, Mohamed Aziz Bhouri, Joan Bruna, Mitchell Bushuk, Will Chapman, Alex Connolly, Julie Deshayes, Carlos Fernandez-Granda, Pierre Gentine, Anastasiia Gorbunova, William Gregory, Arthur Guillaumin, Shubham Gupta, Marika M Holland, J Emmanuel Johnsson, Julien Le Sommer, Ziwei Li, Nora Loose, Feiyu Lu, Paul A O'Gorman, Pavel Perezhogin, Brandon G Reichl, Andrew C Ross, Aakash Sane, Sara Shamekh, Tarun Verma, Janni Yuval, Lorenzo Zampieri, Cheng Zhang, and Laure Zanna, December 2024: Learning machine learning with Lorenz-96. Journal of Open Source Education, 7(82), DOI:10.21105/jose.00241. Abstract
Machine learning (ML) is a rapidly growing field that is starting to touch all aspects of our lives, and science is not immune to this. In fact, recent
work in the field of scientific ML, i.e. combining ML and with conventional scientific problems, is leading to new breakthroughs in notoriously hard problems, which might have seemed too distant till a few years ago. One such age-old problem is that of turbulence closures in fluid flows. This closure or parameterization problem is particularly relevant for environmental fluids, which span a large range of scales from the size of the planet down to millimeters, and remains a big challenge in the way of improving forecasts of weather and projections of climate.
Bushuk, Mitchell, Sahara Ali, David A Bailey, Qing Bao, Lauriane Batté, Uma S Bhatt, Edward Blanchard-Wrigglesworth, Ed Blockley, Gavin Cawley, Junhwa Chi, François Counillon, Philippe Goulet Coulombe, Richard I Cullather, Francis X Diebold, Arlan Dirkson, Eleftheria Exarchou, Maximilian Göbel, William Gregory, Virginie Guemas, Lawrence C Hamilton, Bian He, Sean Horvath, Monica Ionita, Jennifer E Kay, Eliot Kim, Noriaki Kimura, Dmitri Kondrashov, Zachary M Labe, Woo-Sung Lee, Younjoo J Lee, Cuihua Li, Xuewei Li, Yongcheng Lin, Yanyun Liu, Wieslaw Maslowski, François Massonnet, Walter N Meier, William J Merryfield, Hannah Myint, Juan C Acosta Navarro, Alek Petty, Fangli Qiao, David Schröder, Axel Schweiger, Qi Shu, Michael Sigmond, Michael Steele, Julienne Stroeve, Nico Sun, Steffen Tietsche, Michel Tsamados, Keguang Wang, Jianwu Wang, Wanqui Wang, Yiguo Wang, Yun Wang, James Williams, Qinghua Yang, Xiaojun Yuan, Jinlun Zhang, and Yongfei Zhang, July 2024: Predicting September Arctic sea ice: A multi-model seasonal skill comparison. Bulletin of the American Meteorological Society, 105(7), DOI:10.1175/BAMS-D-23-0163.1. Abstract
This study quantifies the state-of-the-art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multi-model dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–2020 for predictions of Pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on June 1, July 1, August 1, and September 1. This diverse set of statistical and dynamical models can individually predict linearly detrended Pan-Arctic SIE anomalies with skill, and a multi-model median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to Pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and Central Arctic sectors. The skill of dynamical and statistical models is generally comparable for Pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least three months in advance.
Fogarty, Joseph, Elie Bou-Zeid, Mitchell Bushuk, and Linette N Boisvert, September 2024: How many parameters are needed to represent polar sea ice surface patterns and heterogeneity?The Cryosphere, 18(9), DOI:10.5194/tc-18-4335-2024. Abstract
Sea ice surface patterns encode more information than can be represented solely by the ice fraction. The aim of this paper is thus to establish the importance of using a broader set of surface characterization metrics and to identify a minimal set of such metrics that may be useful for representing sea ice in Earth system models. Large-eddy simulations of the atmospheric boundary layer over various idealized sea ice patterns, with equivalent ice fractions and average floe areas, demonstrate that the spatial organization of ice and water can play a crucial role in determining boundary layer structures. Thus, various methods used to quantify heterogeneity in categorical lattice-based spatial data, such as those used in landscape ecology and Geographic Information System (GIS) studies, are employed here on a set of recently declassified high-resolution sea ice surface images. It is found that, in conjunction with ice fraction, patch density (representing the fragmentation of the surface), the splitting index (representing variability in patch size), and the perimeter–area fractal dimension (representing the tortuosity of the interface) are all required to describe the two-dimensional pattern exhibited by a sea ice surface. For surfaces with anisotropic patterns, the orientation of the surface relative to the mean wind is also needed. Finally, scaling laws are derived for these relevant landscape metrics, allowing for their estimation using aggregated spatial sea ice surface data at any resolution. The methods used in and the results gained from this study represent a first step toward developing further methods for quantifying variability in polar sea ice surfaces and for parameterizing mixed ice–water surfaces in coarse geophysical models.
In this study, we perform online sea ice bias correction within a Geophysical Fluid Dynamics Laboratory global ice-ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023, https://doi.org/10.1029/2023ms003757) for the purpose of predicting sea ice concentration (SIC) data assimilation (DA) increments. An initial implementation of the CNN shows systematic improvements in SIC biases relative to the free-running model, however large summertime errors remain. We show that these residual errors can be significantly improved with a novel sea ice data augmentation approach. This approach applies sequential CNN and DA corrections to a new simulation over the training period, which then provides a new training data set to refine the weights of the initial network. We propose that this machine-learned correction scheme could be utilized for generating improved initial conditions, and also for real-time sea ice bias correction within seasonal-to-subseasonal sea ice forecasts.
Trotechaud, Sandrine, Bruno Tremblay, James Williams, Joy Romanski, Anastasia Romanou, Mitchell Bushuk, William J Merryfield, and Rym Msadek, April 2024: Predictability of the minimum sea ice extent from winter Fram Strait Ice Area Export: Model versus observations. Journal of Climate, 37(7), DOI:10.1175/JCLI-D-22-0931.12361-2377. Abstract
Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous offshore ice drift along the Eurasian coastline, leading to local ice thickness anomalies at the onset of the melt season—a signal then amplified by the ice–albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE; a proxy for Eurasian coastal divergence) is present in global climate model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counterintuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large, or late in the simulation when the sea ice is thin, and/or when internal variability increases. More important, periods with weak correlation between winter FSIAE and the following Sept SIE dominate, suggesting that summer melt processes generally dominate over late-winter preconditioning and May SIT anomalies. In general, we find that the coupling between the winter FSIAE and ice thickness anomalies along the Eurasian coastline at the onset of the melt season is a ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.
Blanchard-Wrigglesworth, Edward, Mitchell Bushuk, François Massonnet, Lawrence C Hamilton, Cecilia M Bitz, Walter N Meier, and Uma S Bhatt, March 2023: Forecast skill of the Arctic Sea Ice Outlook 2008–2022. Geophysical Research Letters, 50(6), DOI:10.1029/2022GL102531. Abstract
We assess the skill of forecasts of Arctic September sea ice in the Sea Ice Outlook over 2008–2022. The multi-model median June initialized forecast of September sea ice extent (SIE) is slightly more skilled (RMSE = 0.48 million km2) than a damped anomaly forecast, but July and August initialized forecasts (RMSE = 0.52 and 0.36 million km2 respectively) do not beat this benchmark. The skill of individual dynamical and statistical SIE forecasts is lower than the multi-model median forecast skill. Overall skill is lower than expected from retrospective forecasts. Several forecasts initialized in early September 2021 and 2022 imply physically improbable values. Spatial forecasts of sea ice concentration show multi-model forecast skill and an improvement in individual forecast skill in recent years. Initial conditions show large spread in sea ice volume and a positive correlation between initialized sea ice volume and September SIE forecast. Summer weather has an impact on forecast error.
Buchovecky, Benjamin, Graeme A MacGilchrist, Mitchell Bushuk, F Alexander Haumann, Thomas L Frölicher, Natacha Le Grix, and John P Dunne, October 2023: Potential predictability of the spring bloom in the Southern Ocean sea ice zone. Geophysical Research Letters, 50(20), DOI:10.1029/2023GL105139. Abstract
Every austral spring when Antarctic sea ice melts, favorable growing conditions lead to an intense phytoplankton bloom, which supports much of the local marine ecosystem. Recent studies have found that Antarctic sea ice is predictable several years in advance, suggesting that the spring bloom might exhibit similar predictability. Using a suite of perfect model predictability experiments, we find that November net primary production (NPP) is potentially predictable 7 to 10 years in advance in many Southern Ocean regions. Sea ice extent predictability peaks in late winter, followed by absorbed shortwave radiation and NPP with a 2 to 3 months lag. This seasonal progression of predictability supports our hypothesis that sea ice and light limitation control the inherent predictability of the spring bloom. Our results suggest skillful interannual predictions of NPP may be achievable, with implications for managing fisheries and the marine ecosystem, and guiding conservation policy in the Southern Ocean.
Bushuk, Mitchell, Lorenzo M Polvani, and Mark R England, September 2023: Comparing the impacts of ozone-depleting substances and carbon dioxide on Arctic sea ice loss. Environmental Research Climate, 2(4), DOI:10.1088/2752-5295/aced61. Abstract
The rapid decline of Arctic sea ice is widely believed to be a consequence of increasing atmospheric concentrations of greenhouse gases (GHGs). While carbon dioxide (CO2) is the dominant GHG contributor, recent work has highlighted a substantial role for ozone-depleting substances (ODS) in Arctic sea ice loss. However, a careful analysis of the mechanisms and relative impacts of CO2 versus ODS on Arctic sea ice loss has yet to be performed. This study performs this comparison over the period 1955–2005 when concentrations of ODS increased rapidly, by analyzing a suite of all-but-one-forcing ensembles of climate model integrations, designed to isolate the forced response to individual forcing agents in the context of internal climate variability. We show that ODS have played a significant role in year-round Arctic sea ice extent and volume trends over that period, accounting for 64% and 32% of extent and volume trends, respectively. These impacts represent 50% and 38% of the impact from CO2 forcing, respectively. We find that ODS act via similar physical processes to CO2, causing sea ice loss via increased summer melt, and not sea ice dynamics changes. These findings imply that the future trajectory of ODS emissions will play an important role in future Arctic sea ice evolution.
Feng, Xiaofang, Qinghua Ding, Liguang Wu, Charles Jones, Huijun Wang, Mitchell Bushuk, and Dániel Topál, September 2023: Comprehensive representation of tropical–extratropical teleconnections obstructed by tropical Pacific convection biases in CMIP6. Journal of Climate, 36(20), DOI:10.1175/JCLI-D-22-0523.17041-7059. Abstract
The central role of tropical sea surface temperature (SST) variability in modulating Northern Hemisphere (NH) extratropical climate has long been known. However, the prevailing pathways of teleconnections in observations and the ability of climate models to replicate these observed linkages remain elusive. Here, we apply maximum covariance analysis between atmospheric circulation and tropical SST to reveal two coexisting tropical–extratropical teleconnections albeit with distinctive spatiotemporal characteristics. The first mode, resembling the Pacific–North American (PNA) pattern, favors a tropical–Arctic in-phase (warm Pacific–warm Arctic) teleconnection in boreal spring and winter. However, the second mode, with a slight seasonal preference of summer, is manifested as an elongated Rossby wave train emanating from the tropical eastern Pacific that features an out-of-phase relationship (cold Pacific–warm Arctic) between tropical central Pacific SSTs and temperature variability over the Arctic (referred to as the PARC mode). While climate models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) appear to successfully simulate the PNA mode and its temporal characteristics, the majority of models’ skill in reproducing the PARC mode is obstructed to some extent by biases in simulating low-frequency SST and rainfall variability over the tropical eastern Pacific and the climatological mean flow over the North Pacific during boreal summer. Considering the contribution of the PARC mode in shaping low-frequency climate variations over the past 42 years from the tropics to the Arctic, improving models’ capability to capture the PARC mode is essential to reduce uncertainties associated with decadal prediction and climate change projection over the NH.
Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, can closely mirror the systematic bias patterns of the dynamical model. In this study, we use convolutional neural networks (CNNs) to learn a mapping from model state variables to analysis increments, in order to showcase the feasibility of a data-driven model parameterization which can predict state-dependent model errors. We undertake this problem using an ice-ocean data assimilation system within the Seamless system for Prediction and EArth system Research (SPEAR) model, developed at the Geophysical Fluid Dynamics Laboratory, which assimilates satellite observations of sea ice concentration every 5 days between 1982 and 2017. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are states and tendencies of sea ice concentration, sea-surface temperature, ice velocities, ice thickness, net shortwave radiation, ice-surface skin temperature, sea-surface salinity, as well as a land-sea mask. We find the CNN is able to make skillful predictions of the increments in both the Arctic and Antarctic and across all seasons, with skill that consistently exceeds that of a climatological increment prediction. This suggests that the CNN could be used to reduce sea ice biases in free-running SPEAR simulations, either as a sea ice parameterization or an online bias correction tool for numerical sea ice forecasts.
Intrieri, Janet, Amy Solomon, Christopher Cox, Ayumi Fujisaki-Manome, Mitchell Bushuk, Jia Wang, and Jennifer Hutchings, November 2023: Workshop on advancing NOAA’s modeling for improved sea ice forecasts: Defining priorities and key collaborations. Bulletin of the American Meteorological Society, 104(11), DOI:10.1175/BAMS-D-23-0253.1.
Global climate models (GCMs) struggle to simulate polar clouds, especially low-level clouds that contain supercooled liquid and closely interact with both the underlying surface and large-scale atmosphere. Here we focus on GFDL's latest coupled GCM–CM4–and find that polar low-level clouds are biased high compared to observations. The CM4 bias is largely due to moisture fluxes that occur within partially ice-covered grid cells, which enhance low cloud formation in non-summer seasons. In simulations where these fluxes are suppressed, it is found that open water with an areal fraction less than 5% dominates the formation of low-level clouds and contributes to more than 50% of the total low-level cloud response to open water within sea ice. These findings emphasize the importance of accurately modeling open water processes (e.g., sea ice lead-atmosphere interactions) in the polar regions in GCMs.
Luo, Rui, Qinghua Ding, Ian Baxter, Xianyao Chen, Zhiwei Wu, Mitchell Bushuk, and Hailong Wang, April 2023: Uncertain role of clouds in shaping summertime atmosphere-sea ice connections in reanalyses and CMIP6 models. Climate Dynamics, DOI:10.1007/s00382-023-06785-9. Abstract
Downwelling longwave radiation (DLR) driven by the atmospheric and cloud conditions in the troposphere is suggested to be a dominant factor to determine the summertime net surface energy budget over the Arctic Ocean and thus plays a key role to shape the September sea ice. We use reanalyses and the self-organizing map (SOM) method to distinguish CMIP6 model performance in replicating the observed strong atmosphere-DLR connection. We find all models can reasonably simulate the linkage between key atmosphere variables and the clear sky DLR but behave differently in replicating the atmosphere-DLR connection due to cloud forcing. In ERA5 and strongly coupled models, tropospheric high pressure is associated with decreased clouds in the mid- and high-levels and increased clouds near the surface. This out-of-phase structure indicates that DLR cloud forcing is nearly neutral, making the clear sky DLR more important to bridge JJA circulation to late-summer sea ice. In MERRA-2 and weakly coupled models, tropospheric clouds display a vertically homogeneous reduction; the cloud DLR is thus strongly reduced due to the cooling effect, which partially cancels out the clear sky DLR and makes the total DLR less efficient to translate circulation forcing to sea ice. The differences of cloud vertical distribution in CMIP6 appear to be differentiated by circulation related relative humidity. Therefore, a better understanding of the discrepancy of different reanalyses and remote sensing products is critical to comprehensively evaluate simulated interactions among circulation, clouds, sea ice and energy budget at the surface in summer.
Massonnet, François, Sandra Barreira, Antoine Barthélemy, Roberto Bilbao, Edward Blanchard-Wrigglesworth, Ed Blockley, David H Bromwich, Mitchell Bushuk, Xiaoran Dong, Helge F Goessling, Will Hobbs, Doroteaciro Iovino, Woo-Sung Lee, Cuihua Li, Walter N Meier, William J Merryfield, Eduardo Moreno-Chamarro, and Yushi Morioka, et al., May 2023: SIPN South: Six years of coordinated seasonal Antarctic sea ice predictions. Frontiers in Marine Science, 10, DOI:10.3389/fmars.2023.1148899. Abstract
Antarctic sea ice prediction has garnered increasing attention in recent years, particularly in the context of the recent record lows of February 2022 and 2023. As Antarctica becomes a climate change hotspot, as polar tourism booms, and as scientific expeditions continue to explore this remote continent, the capacity to anticipate sea ice conditions weeks to months in advance is in increasing demand. Spurred by recent studies that uncovered physical mechanisms of Antarctic sea ice predictability and by the intriguing large variations of the observed sea ice extent in recent years, the Sea Ice Prediction Network South (SIPN South) project was initiated in 2017, building upon the Arctic Sea Ice Prediction Network. The SIPN South project annually coordinates spring-to-summer predictions of Antarctic sea ice conditions, to allow robust evaluation and intercomparison, and to guide future development in polar prediction systems. In this paper, we present and discuss the initial SIPN South results collected over six summer seasons (December-February 2017-2018 to 2022-2023). We use data from 22 unique contributors spanning five continents that have together delivered more than 3000 individual forecasts of sea ice area and concentration. The SIPN South median forecast of the circumpolar sea ice area captures the sign of the recent negative anomalies, and the verifying observations are systematically included in the 10-90% range of the forecast distribution. These statements also hold at the regional level except in the Ross Sea where the systematic biases and the ensemble spread are the largest. A notable finding is that the group forecast, constructed by aggregating the data provided by each contributor, outperforms most of the individual forecasts, both at the circumpolar and regional levels. This indicates the value of combining predictions to average out model-specific errors. Finally, we find that dynamical model predictions (i.e., based on process-based general circulation models) generally perform worse than statistical model predictions (i.e., data-driven empirical models including machine learning) in representing the regional variability of sea ice concentration in summer. SIPN South is a collaborative community project that is hosted on a shared public repository. The forecast and verification data used in SIPN South are publicly available in near-real time for further use by the polar research community, and eventually, policymakers.
Wang, Yunhe, Xiaojun Yuan, Yibin Ren, Mitchell Bushuk, Qi Shu, Cuihua Li, and Xiaofeng Li, September 2023: Subseasonal prediction of regional Antarctic sea ice by a deep learning model. Geophysical Research Letters, 50(17), DOI:10.1029/2023GL104347. Abstract
Antarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice-related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium-Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice.
Yang, Qinghua, Yongwu Xiu, Hao Luo, Jinfei Wang, Jack C Landy, Mitchell Bushuk, Yiguo Wang, Jiping Liu, and Dake Chen, June 2023: Better synoptic and subseasonal sea ice thickness predictions are urgently required: a lesson learned from the YOPP data validation. Environmental Research Letters, 18(7), DOI:10.1088/1748-9326/acdcaa.
Zeng, Jingwen, Qinghua Yang, Xuewei Li, Xiaojun Yuan, Mitchell Bushuk, and Dake Chen, April 2023: Reducing the spring barrier in predicting summer Arctic sea ice concentration. Geophysical Research Letters, 50(8), DOI:10.1029/2022GL102115. Abstract
The predictive skill of summer sea ice concentration (SIC) in the Arctic presents a steep decline when initialized before June, which is the so-called spring predictability barrier for Arctic sea ice. This study explores the potential influence of surface heat flux, cloud and water vapor anomalies on monthly to seasonal predictions of Arctic SIC anomalies. The results show an enhancement in skill predicting Arctic September SIC in the models that use surface fluxes, clouds, or water vapor in combination with SIC and surface sea temperature as predictors when initialized in boreal spring. This result shows the potential to reduce the spring barrier for Arctic SIC predictions by including the surface heat budget. The enhanced predictive skill can be very likely linked to the improved representation of the thermodynamics associated with water vapor and cloudiness anomalies in spring.
Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt-season sea ice thickness (SIT) observations. The first year-round SIT observations, retrieved from CryoSat-2 from 2011 to 2020, are assimilated into the GFDL ocean–sea ice model. The model's SIT anomaly field is brought into significantly better agreement with the observations, particularly in the Central Arctic. Although the short observational period makes forecast assessment challenging, we find that the addition of May–August SIT assimilation improves September local sea ice concentration (SIC) and extent forecasts similarly to SIC-only assimilation. Although most regional forecasts are improved by SIT assimilation, the Chukchi Sea forecasts are degraded. This degradation is likely due to the introduction of negative correlations between September SIC and earlier SIT introduced by SIT assimilation, contrary to the increased correlations found in other regions.
Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict pan-Arctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems.
Landy, Jack C., Geoffrey J Dawson, Michel Tsamados, and Mitchell Bushuk, et al., 2022: A year-round satellite sea-ice thickness record from CryoSat-2. Nature, 609, DOI:10.1038/s41586-022-05058-5517-522. Abstract
Arctic sea ice is diminishing with climate warming1 at a rate unmatched for at least 1,000 years2. As the receding ice pack raises commercial interest in the Arctic3, it has become more variable and mobile4, which increases safety risks to maritime users5. Satellite observations of sea-ice thickness are currently unavailable during the crucial melt period from May to September, when they would be most valuable for applications such as seasonal forecasting6, owing to major challenges in the processing of altimetry data7. Here we use deep learning and numerical simulations of the CryoSat-2 radar altimeter response to overcome these challenges and generate a pan-Arctic sea-ice thickness dataset for the Arctic melt period. CryoSat-2 observations capture the spatial and the temporal patterns of ice melting rates recorded by independent sensors and match the time series of sea-ice volume modelled by the Pan-Arctic Ice Ocean Modelling and Assimilation System reanalysis8. Between 2011 and 2020, Arctic sea-ice thickness was 1.87 ± 0.10 m at the start of the melting season in May and 0.82 ± 0.11 m by the end of the melting season in August. Our year-round sea-ice thickness record unlocks opportunities for understanding Arctic climate feedbacks on different timescales. For instance, sea-ice volume observations from the early summer may extend the lead time of skillful August–October sea-ice forecasts by several months, at the peak of the Arctic shipping season.
Wang, Yunhe, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang, April 2022: Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model. The Cryosphere, 16(3), DOI:10.5194/tc-16-1141-20221141-1156. Abstract
In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Pacific-Arctic sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the sea ice concentration (SIC) anomaly correlation coefficient (ACC) between predictions and observations, increased by 32 % in the Bering Sea and 18 % in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. SIC trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. We find that subsurface ocean heat content (OHC) provides a crucial source of prediction skill in all seasons, especially in the cold season, and adding sea ice thickness (SIT) to the regional Markov model has a substantial contribution to the prediction skill in the warm season but a negative contribution in the cold season. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model.
The continuing decline of the summertime sea ice cover has reduced the sea ice path that must be traversed to Arctic destinations and through the Arctic between the Atlantic and Pacific Oceans, stimulating interest in trans–Arctic Ocean routes. Seasonal prediction of the sea ice cover along these routes could support the increasing summertime ship traffic taking advantage of recent low ice conditions. We introduce the minimum Arctic sea ice path (MIP) between Atlantic and Pacific Oceans as a shipping-relevant metric that is amenable to multidecadal hindcast evaluation. We show, using 1992–2017 retrospective predictions, that bias correction is necessary for the GFDL Seamless System for Prediction and Earth System Research (SPEAR) forecast system to improve upon damped persistence seasonal forecasts of summertime daily MIP between the Atlantic and Pacific Oceans both east and west of Greenland, corresponding roughly to the Northeast and Northwest Passages. Without bias correction, only the Northwest Passage MIP forecasts have lower error than a damped persistence forecast. Using the forecast ensemble spread to estimate a lower bound on forecast error, we find large opportunities for forecast error reduction, especially at lead times of less than 2 months. Most of the potential improvement remains after linear removal of climatological and trend biases, suggesting that significant error reduction might come from improved initialization and simulation of subannual variability. Using a different passive microwave sea ice dataset for calculating error than was used for data assimilation increases the raw forecast errors but not the trend anomaly forecast errors.
One of the most puzzling observed features of recent climate has been a multidecadal surface cooling trend over the subpolar Southern Ocean (SO). In this study we use large ensembles of simulations with multiple climate models to study the role of the SO meridional overturning circulation (MOC) in these sea surface temperature (SST) trends. We find that multiple competing processes play prominent roles, consistent with multiple mechanisms proposed in the literature for the observed cooling. Early in the simulations (twentieth century and early twenty-first century) internal variability of the MOC can have a large impact, in part due to substantial simulated multidecadal variability of the MOC. Ensemble members with initially strong convection (and related surface warming due to convective mixing of subsurface warmth to the surface) tend to subsequently cool at the surface as convection associated with internal variability weakens. A second process occurs in the late-twentieth and twenty-first centuries, as weakening of oceanic convection associated with global warming and high-latitude freshening can contribute to the surface cooling trend by suppressing convection and associated vertical mixing of subsurface heat. As the simulations progress, the multidecadal SO variability is suppressed due to forced changes in the mean state and increased oceanic stratification. As a third process, the shallower mixed layers can then rapidly warm due to increasing forcing from greenhouse gas warming. Also, during this period the ensemble spread of SO SST trend partly arises from the spread of the wind-driven Deacon cell strength. Thus, different processes could conceivably have led to the observed cooling trend, consistent with the range of possibilities presented in the literature. To better understand the causes of the observed trend, it is important to better understand the characteristics of internal low-frequency variability in the SO and the response of that variability to global warming.
The current GFDL seasonal prediction system, the Seamless System for Prediction and Earth System Research (SPEAR), has shown skillful prediction of Arctic sea ice extent with atmosphere and ocean constrained by observations. In this study we present improvements in subseasonal and seasonal predictions of Arctic sea ice by directly assimilating sea ice observations. The sea ice initial conditions from a data assimilation (DA) system that assimilates satellite sea ice concentration (SIC) observations are used to produce a set of reforecast experiments (IceDA) starting from the first day of each month from 1992 to 2017. Our evaluation of daily sea ice extent prediction skill concludes that the SPEAR system generally outperforms the anomaly persistence forecast at lead times beyond 1 month. We primarily focus our analysis on daily gridcell-level sea ice fields. SIC DA improves prediction skill of SIC forecasts prominently in the June-, July-, August-, and September-initialized reforecasts. We evaluate two additional user-oriented metrics: the ice-free probability (IFP) and ice-free date (IFD). IFP is the probability of a grid cell experiencing ice-free conditions in a given year, and IFD is the first date on which a grid cell is ice free. A combined analysis of IFP and IFD demonstrates that the SPEAR model can make skillful predictions of local ice melt as early as May, with modest improvements from SIC DA.
The low Antarctic sea ice extent following its dramatic decline in late 2016 has persisted over a multiyear period. However, it remains unclear to what extent this low sea ice extent can be attributed to changing ocean conditions. Here, we investigate the causes of this period of low Antarctic sea ice extent using a coupled climate model partially constrained by observations. We find that the subsurface Southern Ocean played a smaller role than the atmosphere in the extreme sea ice extent low in 2016, but was critical for the persistence of negative anomalies over 2016–2021. Prior to 2016, the subsurface Southern Ocean warmed in response to enhanced westerly winds. Decadal hindcasts show that subsurface warming has persisted and gradually destabilized the ocean from below, reducing sea ice extent over several years. The simultaneous variations in the atmosphere and ocean after 2016 have further amplified the decline in Antarctic sea ice extent.
Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.
Keen, Ann, Ed Blockley, David A Bailey, Jens Boldingh Debernard, and Mitchell Bushuk, et al., February 2021: An inter-comparison of the mass budget of the Arctic sea ice in CMIP6 models. The Cryosphere, 15(2), DOI:10.5194/tc-15-951-2021951-982. Abstract
We compare the mass budget of the Arctic sea ice for 15 models submitted to the latest Coupled Model Intercomparison Project (CMIP6), using new diagnostics that have not been available for previous model inter-comparisons. These diagnostics allow us to look beyond the standard metrics of ice cover and thickness to compare the processes of sea ice growth and loss in climate models in a more detailed way than has previously been possible.
For the 1960–1989 multi-model mean, the dominant processes causing annual ice growth are basal growth and frazil ice formation, which both occur during the winter. The main processes by which ice is lost are basal melting, top melting and advection of ice out of the Arctic. The first two processes occur in summer, while the latter process is present all year. The sea ice budgets for individual models are strikingly similar overall in terms of the major processes causing ice growth and loss and in terms of the time of year during which each process is important. However, there are also some key differences between the models, and we have found a number of relationships between model formulation and components of the ice budget that hold for all or most of the CMIP6 models considered here. The relative amounts of frazil and basal ice formation vary between the models, and the amount of frazil ice formation is strongly dependent on the value chosen for the minimum frazil ice thickness. There are also differences in the relative amounts of top and basal melting, potentially dependent on how much shortwave radiation can penetrate through the sea ice into the ocean. For models with prognostic melt ponds, the choice of scheme may affect the amount of basal growth, basal melt and top melt, and the choice of thermodynamic scheme is important in determining the amount of basal growth and top melt.
As the ice cover and mass decline during the 21st century, we see a shift in the timing of the top and basal melting in the multi-model mean, with more melt occurring earlier in the year and less melt later in the summer. The amount of basal growth reduces in the autumn, but it increases in the winter due to thinner sea ice over the course of the 21st century. Overall, extra ice loss in May–June and reduced ice growth in October–November are partially offset by reduced ice melt in August and increased ice growth in January–February. For the individual models, changes in the budget components vary considerably in terms of magnitude and timing of change. However, when the evolving budget terms are considered as a function of the changing ice state itself, behaviours common to all the models emerge, suggesting that the sea ice components of the models are fundamentally responding in a broadly consistent way to the warming climate.
It is possible that this similarity in the model budgets may represent a lack of diversity in the model physics of the CMIP6 models considered here. The development of new observational datasets for validating the budget terms would help to clarify this.
Luo, Rui, Qinghua Ding, Zhiwei Wu, Ian Baxter, Mitchell Bushuk, Yiyi Huang, and Xiquan Dong, January 2021: Summertime atmosphere–sea ice coupling in the Arctic simulated by CMIP5/6 models: Importance of large-scale circulation. Climate Dynamics, 56, DOI:10.1007/s00382-020-05543-51467-1485. Abstract
Summertime barotropic high pressure in the Arctic and its induced warmer and wetter atmosphere over sea ice are suggested to be important contributors to September sea ice loss on interannual and interdecadal time scales in the past decades. Using ERA5 and other reanalysis data, we find that atmospheric warming and moistening in the Arctic, synchronized by high latitude atmospheric circulation variability, work in tandem to melt sea ice through increasing downwelling longwave radiation at the surface. To what extent this atmosphere-longwave radiation-sea ice relationship can be captured in CMIP5 and 6 remains unknown and thus addressing this question is the objective of this study. To achieve this goal, we construct a process-oriented metric emphasizing the statistical relationship between atmospheric temperature and humidity with sea ice, which can effectively rank and differentiate the performance of 30 CMIP5 climate models in reproducing the observed connection. Based on our evaluation, we suggest that most available models in CMIP5 and 6 have a limitation in reproducing the full strength of the observed atmosphere–sea ice connection. This limitation likely stems from a weak impact of downwelling longwave radiation in linking sea ice with circulation associated with the weak sensitivity of the temperature and humidity fields to circulation variability in the Arctic. Thus, further efforts should be devoted to understanding the sources of these models’ limitations and improve skill in simulating the effects of atmospheric circulation in coupling temperature, humidity, surface radiation and sea ice together during Arctic summer.
Pauling, Andrew G., Mitchell Bushuk, and Cecilia M Bitz, May 2021: Robust inter-hemispheric asymmetry in the response to symmetric volcanic forcing in model large ensembles. Geophysical Research Letters, 48(9), DOI:10.1029/2021GL092558. Abstract
The climate response to volcanic eruptions in the twentieth century is difficult to separate from the influence of anthropogenic greenhouse gas forcing and internal variability. Here, we make use of recently available large ensembles of Earth-system model simulations to better understand the forced climate response to contemporary volcanic eruptions. While the Pinatubo eruption in June 1991 resulted in approximately symmetric forcing between the Northern and Southern Hemispheres, the ensemble-mean simulated temperature and sea ice responses it produces are asymmetric. The strongest cooling and sea ice expansion occur in the Arctic, while the responses in the Antarctic are weak. The damped high-latitude Southern Hemisphere response to volcanic forcing is analogous to the fast response to increased greenhouse gas concentrations, despite the differing physical nature of the forcing. We find that Arctic cooling in response to a Pinatubo-scale eruption may not occur due to the high internal variability in that region.
Atmospheric rivers (ARs) exert significant socioeconomic impacts in western North America, where 30% of the annual precipitation is determined by ARs that occur in less than 15% of wintertime. ARs are thus beneficial to water supply but can produce extreme precipitation hazards when making landfall. While most prevailing research has focused on the subseasonal (<5 weeks) prediction of ARs, only limited efforts have been made for AR forecasts on multiseasonal timescales (>3 months) that are crucial for water resource management and disaster preparedness. Through the analysis of reanalysis data and retrospective predictions from a new seasonal-to-decadal forecast system, this research shows the existing potential of multiseasonal AR frequency forecasts with predictive skills 9 months in advance. Additional analysis explores the dominant predictability sources and challenges for multiseasonal AR prediction.
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.
Previous studies have shown the existence of internal multidecadal variability in the Southern Ocean using multiple climate models. This variability, associated with deep ocean convection, can have significant climate impacts. In this work, we use sensitivity studies based on Geophysical Fluid Dynamics Laboratory (GFDL) models to investigate the linkage of this internal variability with the background ocean mean state. We find that mean ocean stratification in the subpolar region that is dominated by mean salinity influences whether this variability occurs, as well as its time scale. The weakening of background stratification favors the occurrence of deep convection. For background stratification states in which the low-frequency variability occurs, weaker ocean stratification corresponds to shorter periods of variability and vice versa. The amplitude of convection variability is largely determined by the amount of heat that can accumulate in the subsurface ocean during periods of the oscillation without deep convection. A larger accumulation of heat in the subsurface reservoir corresponds to a larger amplitude of variability. The subsurface heat buildup is a balance between advection that supplies heat to the reservoir and vertical mixing/convection that depletes it. Subsurface heat accumulation can be intensified both by an enhanced horizontal temperature advection by the Weddell Gyre and by an enhanced ocean stratification leading to reduced vertical mixing and surface heat loss. The paleoclimate records over Antarctica indicate that this multidecadal variability has very likely happened in past climates and that the period of this variability may shift with different climate background mean state.
Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts.
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.
Holland, Marika M., Mitchell Bushuk, Alexandra Jahn, and Andrew Roberts, 2020: Integrating Models and Observations to Better Predict a Changing Arctic Sea Ice Cover In Arctic Report Card 2020, DOI:10.25923/bx13-ja71123-129.
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 the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. 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 ODA as three‐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 sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO).
Notz, Dirk, J Dörr, David A Bailey, Ed Blockley, and Mitchell Bushuk, et al., May 2020: Arctic Sea Ice in CMIP6. Geophysical Research Letters, 47(10), DOI:10.1029/2019GL086749. Abstract
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, David 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, Qinghua, Axel Schweiger, Michelle L L'Heureux, E J Steig, David S Battisti, Nathaniel C Johnson, Edward 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 David 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.
