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This study shows that the frequency of North American summertime (June–August) heat extremes is skillfully predicted several months in advance in the newly developed Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system. Using a statistical optimization method, the average predictability time, we identify three large-scale components of the frequency of North American summer heat extremes that are predictable with significant correlation skill. One component, which is related to a secular warming trend, shows a continent-wide increase in the frequency of summer heat extremes and is highly predictable at least 9 months in advance. This trend component is likely a response to external radiative forcing. The second component is largely driven by the sea surface temperatures in the North Pacific and North Atlantic and is significantly correlated with the central U.S. soil moisture. The second component shows largest loadings over the central United States and is significantly predictable 9 months in advance. The third component, which is related to the central Pacific El Niño, displays a dipole structure over North America and is predictable up to 4 months in advance. Potential implications for advancing seasonal predictions of North American summertime heat extremes are discussed.

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.

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 Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in the northern midlatitudes. We explore the representation and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and Earth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: 1) restoring the coupled model’s SST and atmospheric variables toward existing reanalyses, or 2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation. Both systems use an ocean model with 1° resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be essential to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence and divergence, which forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022.

The Kuroshio-Oyashio Extension (KOE) is the North Pacific oceanic frontal zone where air-sea heat and moisture exchanges allow strong communication between the ocean and atmosphere. Using satellite observations and reanalysis datasets, we show that the KOE surface heat flux variability constitutes an essential component of the seasonal and decadal Pacific ocean/atmosphere variability. We first show a strong covariability between the winter air-sea heat exchange and decadal fluctuations of the Kuroshio Extension (KE) sea surface height (SSH; the SSH reflects upper-ocean heat content anomalies). Interannual to decadal variations of ocean subsurface heat content become strongly connected to the surface during early winter (i.e., November-December-January, NDJ), where they influence the strong ocean-to-atmosphere heat transfer over the KOE. During the early winter (NDJ), the enhanced Aleutian-Low-like atmospheric circulation associated with KE SSH helps to induce a substantial sea-air temperature difference through northwesterly winds over the warm ocean surface. The analysis over an extended time period (i.e., 1959-2022) exhibits that the KOE upward latent and sensible heat flux anomalies have been significantly enhanced since the late 1980s mainly due to increasing variance of the oceanic variability (e.g., KOE sea surface temperature) rather than atmospheric forcing changes (e.g., Aleutian Low). Our findings suggest that winter KOE heat flux variations can be useful climate proxies (e.g., KE SSH) as a physical indicator that links the subsurface ocean and atmosphere.

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.

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 and 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 timescales.

The Atlantic meridional overturning circulation (AMOC) is of great importance in Earth’s climate system, and reconstructing its structure and variability by combining observations with a coupled model is a key step in understanding historical and future states of AMOC. However, models always have systematic errors called bias owing to imperfect numerical representation of the real world. Model bias and the sparse nature of ocean observations, particularly in deep oceans, make it difficult to generate a complete historical picture of AMOC structure and variability. Here, two coupled models that are biased with respect to each other are used to design “twin” experiments to systematically study the influence of model bias on AMOC reconstruction. One model is used to produce the “observations” that sample the “true” solution of the AMOC to be reconstructed, while the other model is used to incorporate the “observations” to reconstruct the “truth” through coupled data assimilation (CDA). The degree to which the “truth” is recovered by a CDA scheme assesses the critical role of coherent (both upper- and deep-ocean incorporate enough observations to mitigate stratification instability) ocean stratification on AMOC reconstruction. Results show that balancing restoration of climatology and assimilation of observations is vital to better reconstruct AMOC structure and variability, given that most ocean observations are only available in the upper 2000 m. The gained results serve as a guideline in ocean-state estimation with a balance of deep restoring and upper data constraint for climate prediction initialization, especially for decadal predictions.

Using GFDL's new coupled model SPEAR, we have developed a decadal coupled reanalysis/initialization system (DCIS) that does not use subsurface ocean observations. In DCIS, the winds and temperature in the atmosphere, along with sea surface temperature (SST), are restored to observations. Under this approach the ocean component of the coupled model experiences a sequence of surface heat and momentum fluxes that are similar to observations. DCIS offers two initialization approaches, called A1 and A2, which differ only in the atmospheric forcing from observations. In A1, the atmospheric winds/temperature are restored toward the JRA reanalysis; in A2, surface pressure observations are assimilated in the model. Two sets of coupled reanalyses have been completed during 1961–2019 using A1 and A2, and they show very similar multi‐decadal variations of the Atlantic Meridional Overturning Circulation (AMOC). Two sets of retrospective decadal forecasts were then conducted using initial conditions from the A1 and A2 reanalyses. In comparison with previous prediction system CM2.1, SPEAR‐A1/A2 shows comparable skill of predicting the North Atlantic subpolar gyre SST, which is highly correlated with initial values of AMOC at all lead years. SPEAR‐A1 significantly outperforms CM2.1 in predicting multi‐decadal SST trends in the Southern Ocean (SO). Both A1 and A2 have skillful prediction of Sahel precipitation and the associated ITCZ shift. The prediction skill of SST is generally lower in A2 than A1 especially over SO presumably due to the sparse surface pressure observations.

We describe the Geophysical Fluid Dynamics Laboratory'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 preindustrial 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 quasiperiodicity 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.

Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.

Climate variations have a profound impact on marine ecosystems and the communities that depend upon them. Anticipating ecosystem shifts using global Earth system models (ESMs) could enable communities to adapt to climate fluctuations and contribute to long-term ecosystem resilience. We show that newly developed ESM-based marine biogeochemical predictions can skillfully predict satellite-derived seasonal to multiannual chlorophyll fluctuations in many regions. Prediction skill arises primarily from successfully simulating the chlorophyll response to the El Niño-Southern Oscillation and capturing the winter reemergence of subsurface nutrient anomalies in the extratropics, which subsequently affect spring and summer chlorophyll concentrations. Further investigations suggest that interannual fish-catch variations in selected large marine ecosystems can be anticipated from predicted chlorophyll and sea surface temperature anomalies. This result, together with high predictability for other marine-resource-relevant biogeochemical properties (e.g., oxygen, primary production), suggests a role for ESM-based marine biogeochemical predictions in dynamic marine resource management efforts.

This study shows that the frequency of North American summer hot days are skillfully predictable months in advance in the newly-developed GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless System for Prediction and EArth System Research) seasonal forecast system. We also demonstrate that climate change, the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO) and atmosphere-land feedback all contribute to the seasonal predictive skill of the frequency of North American summer hot days. Using a statistical optimization method (Average Predictability Time) we identify two large-scale components of the frequency of North American summer hot days that are predictable with significant correlation skill. One component shows an increase in the frequency of summer hot days everywhere over North America and is highly predictable at least 9 months in advance. This component is related to a secular warming trend. Another predictable component shows largest loadings over the central U.S., and is significantly predictable 6 months ahead. This second component is related to the PDO and the AMO, and is significantly correlated with the central U.S. soil water. These findings have potential implications for predictions of North American summer hot days on seasonal time scales.

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).

This paper presents an experimental study of high cyclic synchronous vibration observed on the top pinion of an integrally geared centrifugal compressor. This cyclic synchronous vibration was different from the previously reported Morton effect. In a typical cycle, the vibration began with a long quiet period, then took off and followed by settle-down. Sometimes, vibration peaked above a shutdown limit, which subsequently tripped the compressor and then the air separation plant. Frequency spectra showed this new cyclic vibration was dominated by synchronous vibration. To obtain reliable and meaningful phase information for the diagnosis, a new signal processing technique was developed to analyze the historic vibration data captured without a key phasor. An experimental study of this new rotordynamic phenomenon was conducted on the machine in operation. Test data showed the high cyclic synchronous vibration was closely related to Morton effect though it does not have a significant phase shift. An effective remedy measure was therefore taken, and the cyclic synchronous vibration was eliminated. Since then, this compressor has been running smoothly for 17 months. A possible mechanism of the cyclic vibration is discussed in this paper.

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 1.0° (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1° to 0.25°. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM4 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 ESM4 models.

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.

An observing system simulation experiment (OSSE) using an ensemble coupled data assimilation system was designed to investigate the impact of deep ocean Argo profile assimilation in a biased numerical climate system. Based on the modern Argo observational array and an artificial extension to full depth, “observations” drawn from one coupled general circulation model (CM2.0) were assimilated into another model (CM2.1). Our results showed that coupled data assimilation with simultaneous atmospheric and oceanic constraints plays a significant role in preventing deep ocean drift. However, the extension of the Argo array to full depth did not significantly improve the quality of the oceanic climate estimation within the bias magnitude in the twin experiment. Even in the “identical” twin experiment for the deep Argo array from the same model (CM2.1) with the assimilation model, no significant changes were shown in the deep ocean, such as in the Atlantic meridional overturning circulation and the Antarctic bottom water cell. The small ensemble spread and corresponding weak constraints by the deep Argo profiles with medium spatial and temporal resolution may explain why the deep Argo profiles did not improve the deep ocean features in the assimilation system. Additional studies using different assimilation methods with improved spatial and temporal resolution of the deep Argo array are necessary in order to more thoroughly understand the impact of the deep Argo array on the assimilation system.

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.

Because of its persistence on seasonal time scales, 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-yr control integration and a suite of initialized ensemble forecasts from a fully coupled global climate model. This 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.

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 are 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 time scales.

Uncertainty in cumulus convection parameterization is one of the most important causes of model climate drift through interactions between large-scale background and local convection that use empirically set parameters. Without addressing the large-scale feedback, the calibrated parameter values within a convection scheme are usually not optimal for a climate model. This study first designs a multiple-column atmospheric model that includes large-scale feedbacks for cumulus convection and then explores the role of large-scale feedbacks in cumulus convection parameter estimation using an ensemble filter. The performance of convection parameter estimation with or without the presence of large-scale feedback is examined. It is found that including large-scale feedbacks in cumulus convection parameter estimation can significantly improve the estimation quality. This is because large-scale feedbacks help transform local convection uncertainties into global climate sensitivities, and including these feedbacks enhances the statistical representation of the relationship between parameters and state variables. The results of this study provide insights for further understanding of climate drift induced from imperfect cumulus convection parameterization, which may help improve climate modeling.

The seasonal variability of the mean kinetic energy (MKE) and eddy kinetic energy (EKE) of the Gulf Stream (GS) is examined using high-resolution regional ocean model simulations. A set of three numerical experiments with different surface wind and buoyancy forcing is analyzed to investigate the mechanisms governing the seasonal cycle of upper ocean energetics. In the GS along-coast region, MKE has a significant seasonal cycle that peaks in summer, while EKE has two comparable peaks in May and September near the surface; the May peak decays rapidly with depth. In the off-coast region, MKE has a weak seasonal cycle that peaks in summer, while EKE has a dominant peak in May and a secondary peak in September near the surface. The May peak also decays with depth leaving the September peak as the only seasonal signal below 100 m. An analysis of the three numerical experiments suggests that the seasonal variability in the local wind forcing significantly impacts the September peak of the along-coast EKE through a local-flow barotropic instability process. Alternatively, the seasonal buoyancy forcing primarily impacts the flow baroclinic instability and is consequently related to the May peak of the upper ocean EKE in both regions. The analysis results indicate that the seasonal cycle of the along-coast MKE is influenced by both local energy generation by wind and the advection of energy from upstream regions. Finally, the MKE cycle and the September peak of EKE in the off-coast region are mainly affected by advection of energy from remote regions, giving rise to correlations with the seasonal cycle of remote winds.

This study aims to assess whether, and the extent to which, an increase in atmospheric resolution of the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-Oriented Low Ocean Resolution version of CM2.5 (FLOR) with 50-km resolution and the High-Resolution FLOR (HiFLOR) with 25-km resolution improves the simulation of the El Niño–Southern Oscillation (ENSO)–tropical cyclone (TC) connections in the western North Pacific (WNP). HiFLOR simulates better ENSO–TC connections in the WNP including TC track density, genesis, and landfall than FLOR in both long-term control experiments and sea surface temperature (SST)- and sea surface salinity (SSS)-restoring historical runs (1971–2012). Restoring experiments are performed with SSS and SST restored to observational estimates of climatological SSS and interannually varying monthly SST. In the control experiments of HiFLOR, an improved simulation of the Walker circulation arising from more realistic SST and precipitation is largely responsible for its better performance in simulating ENSO–TC connections in the WNP. In the SST-restoring experiments of HiFLOR, more realistic Walker circulation and steering flow during El Niño and La Niña are responsible for the improved simulation of ENSO–TC connections in the WNP. The improved simulation of ENSO–TC connections with HiFLOR arises from a better representation of SST and better responses of environmental large-scale circulation to SST anomalies associated with El Niño or La Niña. A better representation of ENSO–TC connections in HiFLOR can benefit the seasonal forecasting of TC genesis, track, and landfall; improve understanding of the interannual variation of TC activity; and provide better projection of TC activity under climate change.

The Intergovernmental Panel on Climate Change (IPCC) fifth assessment of projected global and regional ocean temperature change is based on global climate models that have coarse (∼100 km) ocean and atmosphere resolutions. In the Northwest Atlantic, the ensemble of global climate models has a warm bias in sea surface temperature due to a misrepresentation of the Gulf Stream position; thus, existing climate change projections are based on unrealistic regional ocean circulation. Here we compare simulations and an atmospheric CO2 doubling response from four global climate models of varying ocean and atmosphere resolution. We find that the highest resolution climate model (∼10 km ocean, ∼50 km atmosphere) resolves Northwest Atlantic circulation and water mass distribution most accurately. The CO2 doubling response from this model shows that upper-ocean (0–300 m) temperature in the Northwest Atlantic Shelf warms at a rate nearly twice as fast as the coarser models and nearly three times faster than the global average. This enhanced warming is accompanied by an increase in salinity due to a change in water mass distribution that is related to a retreat of the Labrador Current and a northerly shift of the Gulf Stream. Both observations and the climate model demonstrate a robust relationship between a weakening Atlantic Meridional Overturning Circulation (AMOC) and an increase in the proportion of Warm-Temperate Slope Water entering the Northwest Atlantic Shelf. Therefore, prior climate change projections for the Northwest Atlantic may be far too conservative. These results point to the need to improve simulations of basin and regional-scale ocean circulation.

The TAO/TRITON array is the cornerstone of the tropical Pacific and ENSO observing system. Motivated by the recent rapid decline of the TAO/TRITON array, the potential utility of TAO/TRITON was assessed for ENSO monitoring and prediction. The analysis focused on the period when observations from Argo floats were also available. We coordinated observing system experiments (OSEs) using the global ocean data assimilation system (GODAS) from the National Centers for Environmental Prediction and the ensemble coupled data assimilation (ECDA) from the Geophysical Fluid Dynamics Laboratory for the period 2004–2011. Four OSE simulations were conducted with inclusion of different subsets of in situ profiles: all profiles (XBT, moorings, Argo), all except the moorings, all except the Argo and no profiles. For evaluation of the OSE simulations, we examined the mean bias, standard deviation difference, root-mean-square difference (RMSD) and anomaly correlation against observations and objective analyses. Without assimilation of in situ observations, both GODAS and ECDA had large mean biases and RMSD in all variables. Assimilation of all in situ data significantly reduced mean biases and RMSD in all variables except zonal current at the equator. For GODAS, the mooring data is critical in constraining temperature in the eastern and northwestern tropical Pacific, while for ECDA both the mooring and Argo data is needed in constraining temperature in the western tropical Pacific. The Argo data is critical in constraining temperature in off-equatorial regions for both GODAS and ECDA. For constraining salinity, sea surface height and surface current analysis, the influence of Argo data was more pronounced. In addition, the salinity data from the TRITON buoys played an important role in constraining salinity in the western Pacific. GODAS was more sensitive to withholding Argo data in off-equatorial regions than ECDA because it relied on local observations to correct model biases and there were few XBT profiles in those regions. The results suggest that multiple ocean data assimilation systems should be used to assess sensitivity of ocean analyses to changes in the distribution of ocean observations to get more robust results that can guide the design of future tropical Pacific observing systems.

This study examines two sets of high-resolution coupled model forecasts starting from no-tropical cyclone (TC) and correct-TC-statistics initial conditions to understand the role of TC events on climate prediction. While the model with no-TC initial conditions can quickly spin-up TCs within a week, the initial conditions with a corrected TC distribution can produce more accurate forecast of sea surface temperature up to 1.5 months and maintain larger ocean heat content up to 6 months due to enhanced mixing from continuous interactions between initialized and forecasted TCs and the evolving ocean states. The TC-enhanced tropical ocean mixing strengthens the meridional heat transport in the Southern Hemisphere driven primarily by Southern Ocean surface Ekman fluxes but weakens the Northern Hemisphere poleward transport in this model. This study suggests a future plausible initialization procedure for seamless weather-climate prediction when individual convection-permitting cyclone initialization is incorporated into this TC-statistics-permitting framework.

Portions of western North America have experienced prolonged drought over the last decade. This drought has occurred at the same time as the global warming hiatus—a decadal period with little increase in global mean surface temperature. Climate models and observational analyses are used to clarify the dual role of recent tropical Pacific changes in driving both the global warming hiatus and North American drought. When observed tropical Pacific wind stress anomalies are inserted into coupled models, the simulations produce persistent negative sea surface temperature anomalies in the eastern tropical Pacific, a hiatus in global warming, and drought over North America driven by SST-induced atmospheric circulation anomalies. In the simulations herein the tropical wind anomalies account for 92% of the simulated North American drought during the recent decade, with 8% from anthropogenic radiative forcing changes. This suggests that anthropogenic radiative forcing is not the dominant driver of the current drought, unless the wind changes themselves are driven by anthropogenic radiative forcing. The anomalous tropical winds could also originate from coupled interactions in the tropical Pacific or from forcing outside the tropical Pacific. The model experiments suggest that if the tropical winds were to return to climatological conditions, then the recent tendency toward North American drought would diminish. Alternatively, if the anomalous tropical winds were to persist, then the impact on North American drought would continue; however, the impact of the enhanced Pacific easterlies on global temperature diminishes after a decade or two due to a surface reemergence of warmer water that was initially subducted into the ocean interior.

The seasonal predictability of extratropical storm tracks in the Geophysical Fluid Dynamics Laboratory’s (GFDL)’s high-resolution climate model has been investigated using an average predictability time analysis. The leading predictable components of extratropical storm tracks are the ENSO-related spatial patterns for both boreal winter and summer, and the second predictable components are mostly due to changes in external radiative forcing and multidecadal oceanic variability. These two predictable components for both seasons show significant correlation skill for all leads from 0 to 9 months, while the skill of predicting the boreal winter storm track is consistently higher than that of the austral winter. The predictable components of extratropical storm tracks are dynamically consistent with the predictable components of the upper troposphere jet flow for both seasons. Over the region with strong storm-track signals in North America, the model is able to predict the changes in statistics of extremes connected to storm-track changes (e.g., extreme low and high sea level pressure and extreme 2-m air temperature) in response to different ENSO phases. These results point toward the possibility of providing skillful seasonal predictions of the statistics of extratropical extremes over land using high-resolution coupled models.

This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

The authors characterize impacts on heat in the ocean climate system from transient ocean mesoscale eddies. Their tool is a suite of centennial-scale 1990 radiatively forced numerical climate simulations from three GFDL coupled models comprising the Climate Model, version 2.0–Ocean (CM2-O), model suite. CM2-O models differ in their ocean resolution: CM2.6 uses a 0.1° ocean grid, CM2.5 uses an intermediate grid with 0.25° spacing, and CM2-1deg uses a nominal 1.0° grid. Analysis of the ocean heat budget reveals that mesoscale eddies act to transport heat upward in a manner that partially compensates (or offsets) for the downward heat transport from the time-mean currents. Stronger vertical eddy heat transport in CM2.6 relative to CM2.5 accounts for the significantly smaller temperature drift in CM2.6. The mesoscale eddy parameterization used in CM2-1deg also imparts an upward heat transport, yet it differs systematically from that found in CM2.6. This analysis points to the fundamental role that ocean mesoscale features play in transient ocean heat uptake. In general, the more accurate simulation found in CM2.6 provides an argument for either including a rich representation of the ocean mesoscale in model simulations of the mean and transient climate or for employing parameterizations that faithfully reflect the role of eddies in both lateral and vertical heat transport.

We investigate the influence of ocean component resolution on simulation of climate sensitivity using variants of the GFDL CM2.5 climate model incorporating eddy-resolving (1/10°) and eddy-parameterizing (1°) ocean resolutions. Two parameterization configurations of the coarse-resolution model are used yielding a three-model suite with significant variation in the transient climate response (TCR). The variation of TCR in this suite and in an enhanced group of 10 GFDL models is found to be strongly associated with the control climate Atlantic meridional overturning circulation (AMOC) magnitude and its decline under forcing. We find that it is the AMOC behavior rather than resolution per se that accounts for most of the TCR differences. A smaller difference in TCR stems from the eddy-resolving model having more Southern Ocean surface warming than the coarse models.

Quantifying the effect of the seawater density changes on sea level variability is of crucial importance for climate change studies, as the sea level cumulative rise can be regarded as both an important climate change indicator and a possible danger for human activities in coastal areas. In this work, as part of the Ocean Reanalysis Intercomparison Project, the global and regional steric sea level changes are estimated and compared from an ensemble of 16 ocean reanalyses and 4 objective analyses. These estimates are initially compared with a satellite-derived (altimetry minus gravimetry) dataset for a short period (2003-2010). The ensemble mean exhibits a significant high correlation at both global and regional scale, and the ensemble of ocean reanalyses outperforms that of objective analyses, in particular in the Southern Ocean. The reanalysis ensemble mean thus represents a valuable tool for further analyses, although large uncertainties remain for the inter-annual trends. Within the extended intercomparison period that spans the altimetry era (1993-2010), we find that the ensemble of reanalyses and objective analyses are in good agreement, and both detect a trend of the global steric sea level of 1.0 and 1.1 +/- 0.05 mm/year, respectively. However, the spread among the products of the halosteric component trend exceeds the mean trend itself, questioning the reliability of its estimate. This is related to the scarcity of salinity observations before the Argo era. Furthermore, the impact of deep ocean layers is non-negligible on the steric sea level variability (22 and 12 % for the layers below 700 and 1500 m of depth, respectively), although the small deep ocean trends are not significant with respect to the products spread.

Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux adjustment.” A suite of 12-month duration retrospective forecasts is performed over the 1981–2012 period, after initializing the climate model to observationally constrained conditions at the start of each forecast period, using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basinwide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally aggregated regional TC activity months in advance are feasible.

Global tropical cyclone (TC) activity is simulated by the Geophysical Fluid Dynamics Laboratory (GFDL) Climate Model, version 2.5 (CM2.5), which is a fully coupled global climate model with a horizontal resolution of about 50 km for the atmosphere and 25 km for the ocean. The present climate simulation shows a fairly realistic global TC frequency, seasonal cycle, and geographical distribution. The model has some notable biases in regional TC activity, including simulating too few TCs in the North Atlantic. The regional biases in TC activity are associated with simulation biases in the large-scale environment such as sea surface temperature, vertical wind shear, and vertical velocity. Despite these biases, the model simulates the large-scale variations of TC activity induced by El Niño–Southern Oscillation fairly realistically. The response of TC activity in the model to global warming is investigated by comparing the present climate with a CO2 doubling experiment. Globally, TC frequency decreases (−19%) while the intensity increases (+2.7%) in response to CO2 doubling, consistent with previous studies. The average TC lifetime decreases by −4.6%, while the TC size and rainfall increase by about 3% and 12%, respectively. These changes are generally reproduced across the different basins in terms of the sign of the change, although the percent changes vary from basin to basin and within individual basins. For the Atlantic basin, although there is an overall reduction in frequency from CO2 doubling, the warmed climate exhibits increased interannual hurricane frequency variability so that the simulated Atlantic TC activity is enhanced more during unusually warm years in the CO2-warmed climate relative to that in unusually warm years in the control climate.

There have been few attempts to quantify errors in various objective analyzed (OA) fields, even though they have potential uncertainties associated with data handling and mapping methods. Here, we compare five different OA fields (EN3, GFDL, IPRC, JAMSTEC, and SIO) for 2008–2011. The variability and linear trends of the upper ocean temperature are very similar in every ocean basin, but the mean values are different from each other. This discrepancy is evident, especially around the southern ocean (± 0.07 °C in the Antarctic Ocean) where Argo observations are still sparse, which is related to different first-guess climatologies and decorrelation length scales applied to individual OA products. In the subpolar North Atlantic, detailed spatial anomalous patterns are also different. Along the boundary current areas, substantial warming (salting) anomalies with respect to WOA09 climatology are depicted by GFDL, IPRC, and SIO. By comparing with statistical bin-averaged fields and data assimilation products, we confirm that this anomalous pattern is robust, but it could be exaggerated when we calculate the anomalies with WOA09 climatology or other OA fields showing a relatively weak horizontal gradient across the boundary current regions.

Decadal prediction experiments were conducted as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) using the GFDL Climate Model, version 2.1 (CM2.1) forecast system. The abrupt warming of the North Atlantic Subpolar Gyre (SPG) that was observed in the mid-1990s is considered as a case study to evaluate forecast capabilities and better understand the reasons for the observed changes. Initializing the CM2.1 coupled system produces high skill in retrospectively predicting the mid-1990s shift, which is not captured by the uninitialized forecasts. All the hindcasts initialized in the early 1990s show a warming of the SPG; however, only the ensemble-mean hindcasts initialized in 1995 and 1996 are able to reproduce the observed abrupt warming and the associated decrease and contraction of the SPG. Examination of the physical mechanisms responsible for the successful retrospective predictions indicates that initializing the ocean is key to predicting the mid-1990s warming. The successful initialized forecasts show an increased Atlantic meridional overturning circulation and North Atlantic Current transport, which drive an increased advection of warm saline subtropical waters northward, leading to a westward shift of the subpolar front and, subsequently, a warming and spindown of the SPG. Significant seasonal climate impacts are predicted as the SPG warms, including a reduced sea ice concentration over the Arctic, an enhanced warming over the central United States during summer and fall, and a northward shift of the mean ITCZ. These climate anomalies are similar to those observed during a warm phase of the Atlantic multidecadal oscillation, which is encouraging for future predictions of North Atlantic climate.

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2...

Observations and climate simulations exhibit epochs of extreme El Niño–Southern Oscillation (ENSO) behavior that can persist for decades. Previous studies have revealed a wide range of ENSO responses to forcings from greenhouse gases, aerosols, and orbital variations, but they have also shown that interdecadal modulation of ENSO can arise even without such forcings. The present study examines the predictability of this intrinsically generated component of ENSO modulation, using a 4000-yr unforced control run from a global coupled GCM [GFDL Climate Model, version 2.1 (CM2.1)] with a fairly realistic representation of ENSO. Extreme ENSO epochs from the unforced simulation are reforecast using the same (“perfect”) model but slightly perturbed initial conditions. These 40-member reforecast ensembles display potential predictability of the ENSO trajectory, extending up to several years ahead. However, no decadal-scale predictability of ENSO behavior is found. This indicates that multidecadal epochs of extreme ENSO behavior can arise not only intrinsically but also delicately and entirely at random. Previous work had shown that CM2.1 generates strong, reasonably realistic, decadally predictable high-latitude climate signals, as well as tropical and extratropical decadal signals that interact with ENSO. However, those slow variations appear not to lend significant decadal predictability to this model’s ENSO behavior, at least in the absence of external forcings. While the potential implications of these results are sobering for decadal predictability, they also offer an expedited approach to model evaluation and development, in which large ensembles of short runs are executed in parallel, to quickly and robustly evaluate simulations of ENSO. Further implications are discussed for decadal prediction, attribution of past and future ENSO variations, and societal vulnerability.