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Boreal summer intraseasonal oscillation (BSISO) is a primary source of predictability for summertime weather and climate on the subseasonal-to-seasonal (S2S) time scale. Using the GFDL SPEAR S2S prediction system, we evaluate the BSISO prediction skills based on 20-yr (2000–19) hindcast experiments with initializations from May to October. It is revealed that the overall BSISO prediction skill using all hindcasts reaches out to 22 days as measured by BSISO indices before the bivariate anomalous correlation coefficient (ACC) drops below 0.5. Results also show that the northeastward-propagating canonical BSISO (CB) event has a higher prediction skill than the northward dipole BSISO (DB) event (28 vs 23 days). This is attributed to CB’s more periodic nature, resulting in its longer persistence, while DB events are more episodic accompanied by a rapid demise after reaching maximum enhanced convection over the equatorial Indian Ocean. From a forecaster’s perspective, a precursory strong Kelvin wave component in the equatorial western Pacific signifies the subsequent development of a CB event, which is likely more predictable. Investigation of individual CB events shows a large interevent spread in terms of their prediction skills. For CB, the events with weaker and fluctuating amplitude during their lifetime have relatively lower prediction skills likely linked to their weaker convection–circulation coupling. Interestingly, the prediction skills of individual CB events tend to be relatively higher and less scattered during late summer (August–October) than those in early summer (May–July), suggestive of the seasonal modulation on the evolution and predictability of BSISO.

The advance of subseasonal-to-seasonal (S2S) prediction largely depends on dynamical models’ ability to predict some major intrinsic modes in the climate system, including the boreal summer intraseasonal oscillation (BSISO). Using a newly developed S2S prediction system, we thoroughly evaluated its performance in predicting BSISO, and revealed the skill dependence on the BSISO propagation diversity. Here we provide physical explanations of what influences the BSISO predictions and identify different precursory signals for two types of BSISO, which have important implications for operational forecasts.

 

Abstract Landfalling tropical cyclones (LTCs) are the most devastating disaster to affect the U.S., while the demonstration of skillful subseasonal (between 10 days and one season) prediction of LTCs is less promising. Understanding the mechanisms governing the subseasonal variation of TC activity is fundamental to improving its forecast, which is of critical interest to decision-makers and the insurance industry. This work reveals three localized atmospheric circulation modes with significant 10–30 days subseasonal variations: Piedmont Oscillation (PO), Great America Dipole (GAD), and the Subtropical High ridge (SHR) modes. These modes strongly modulate precipitation, TC genesis, intensity, track, and landfall near the U.S. coast. Compared to their strong negative phases, the U.S. East Coast has 19 times more LTCs during the strong positive phases of PO, and the Gulf Coast experiences 4–12 times more frequent LTCs during the positive phases of GAD and SHR. Results from the GFDL SPEAR model show a skillful prediction of 13, 9, and 22 days for these three modes, respectively. Our findings are expected to benefit the prediction of LTCs on weather timescale and also suggest opportunities exist for subseasonal predictions of LTCs and their associated heavy rainfalls. 

The role of ocean forcing on Atlantic multidecadal variability (AMV) is assessed from the (downward) heat flux–SST relation in the framework of a new stochastic climate theory forced by red noise ocean forcing. Previous studies suggested that atmospheric forcing drives SST variability from monthly to interannual time scales, with a positive heat flux–SST correlation, while heat flux induced by ocean processes can drive SST variability at decadal and longer time scales, with a negative heat flux–SST correlation. Here, first, we develop a theory to show how the sign of heat flux–SST correlation is affected by atmospheric and oceanic forcing with time scale. In particular, a red noise ocean forcing is necessary for the sign reversal of heat flux–SST correlation. Furthermore, this sign reversal can be detected equivalently in three approaches: the low-pass correlation at lag zero, the unfiltered correlation at long (heat flux) lead, and the real part of the heat flux–SST coherence. Second, we develop a new scheme in combination with the theory to assess the magnitude and time scale of the red noise ocean forcing for AMV in the GFDL SPEAR model (Seamless System for Prediction and Earth System Research) and observations. In both the model and observations, the ocean forcing on AMV is in general comparable with the atmospheric forcing, with a 90% probability greater than the atmospheric forcing in observations. In contrast to the white noise atmospheric forcing, the ocean forcing has a persistence time comparable or longer than a year, much longer than the SST persistence of ∼3 months. This slow ocean forcing is associated implicitly with slow subsurface ocean dynamics.

A new theoretical framework is developed to estimate the ocean forcing on Atlantic multidecadal variability form heat flux–SST relations in climate models and observation. Our estimation shows the ocean forcing is comparable with the atmospheric forcing and, in particular, has a slow time scale of years.

 

Abstract A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables. 

Abstract 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 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.