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The Madden–Julian oscillation (MJO) is the dominant intraseasonal wave phenomenon influencing extreme weather and climate worldwide. Realistic simulations and accurate predictions of MJO genesis are the cornerstones for successfully monitoring, forecasting, and managing meteorological disasters 3–4 weeks in advance. Nevertheless, the genesis processes and emerging precursor signals of an eastward-propagating MJO event remain largely uncertain. Here, we find that the MJO genesis processes observed in the past four decades exhibit remarkable diversity with different seasonality and can be classified objectively into four types, namely, a novel downstream origin from the westward-propagating intraseasonal oscillation (WPISO; 20.4%), localized breeding from the Indian Ocean suppressed convection (IOSC; 15.4%), an upstream succession of the preceding weakly dispersive (WD; 25.9%), and strongly dispersive (SD; 38.3%) MJO. These four types are associated with different oceanic background states, characterized by central Pacific cooling, southern Maritime Continent warming, eastern Pacific cooling, and central Pacific warming for the WPISO, IOSC, WD, and SD types, respectively. The SD type is also favored during the easterly phase of the stratospheric quasi-biennial oscillation. Diverse convective initiations possibly imply various kinds of propagations of MJO. The subseasonal reforecasts indicate robustly distinct prediction skills for the diverse MJO genesis. A window of opportunity for skillful week 3–4 prediction probably opens with the aid of the WPISO-type MJO precursor, which has increased the predictability of primary MJO onset by 1 week. These findings suggest that the diversified MJO genesis can be skillfully foreseen by monitoring unique precursor signals and can also serve as benchmarks for evaluating contemporary models’ modeling and predicting capabilities.

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.

Anthropogenic aerosols are effective radiative forcing agents that perturb the Earth’s climate. Major emission sources shifted from the western to eastern hemisphere around the 1980s. An ensemble of single-forcing simulations with an Earth System Model reveals two stages of aerosol-induced climate change in response to the global aerosol increase for 1940–1980 and the zonal shift of aerosol forcing for 1980–2020, respectively. Here, using idealized experiments with hierarchical models, we show that the aerosol increase and shift modes of aerosol-forced climate change are dynamically distinct, governed by the inter-hemispheric energy transport and basin-wide ocean–atmosphere interactions, respectively. The aerosol increase mode dominates in the motionless slab ocean model but is damped by ocean dynamics. Free of zonal-mean energy perturbation, characterized by an anomalous North Atlantic warming and North Pacific cooling, the zonal shift mode is amplified by interactive ocean dynamics through Bjerknes feedback. Both modes contribute to a La Nina-like pattern over the equatorial Pacific. We suggest that a global perspective that accommodates the evolving geographical distribution of aerosol emissions is vital for understanding the aerosol-forced historical climate change.

Major disruptions of the winter season, high-latitude, stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, subseasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, and fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.

Geoscientific Model Development, 15 (13), ISSN:1991-9603, ISSN:1991-959X

Significance Little had been known about whether the ongoing climate changes had already affected observed global tropical cyclones (TCs). This study revealed that a climate change in global TC activity over 1980 to 2018 has been more evident in the spatial pattern of TC occurrence, rather than the number of global TCs. The total effect of anthropogenic greenhouse gases, aerosols, and volcanic eruptions on global TC distribution is spatially inhomogeneous: Increases and decreases in TC occurrence depend on the region. However, our climate models project decreases in the number of global TCs toward the end of the 21st century due to the dominant effect of greenhouse gases on decreasing TC occurrence in most of the tropics, consistent with many previous studies., Owing to the limited length of observed tropical cyclone data and the effects of multidecadal internal variability, it has been a challenge to detect trends in tropical cyclone activity on a global scale. However, there is a distinct spatial pattern of the trends in tropical cyclone frequency of occurrence on a global scale since 1980, with substantial decreases in the southern Indian Ocean and western North Pacific and increases in the North Atlantic and central Pacific. Here, using a suite of high-resolution dynamical model experiments, we show that the observed spatial pattern of trends is very unlikely to be explained entirely by underlying multidecadal internal variability; rather, external forcing such as greenhouse gases, aerosols, and volcanic eruptions likely played an important role. This study demonstrates that a climatic change in terms of the global spatial distribution of tropical cyclones has already emerged in observations and may in part be attributable to the increase in greenhouse gas emissions.

Positive precipitation biases over western North America have remained a pervasive problem in the current generation of coupled global climate models. These biases are substantially reduced, however, in a version of the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution (FLOR) coupled climate model with systematic sea surface temperature (SST) biases artificially corrected through flux adjustment. This study examines how the SST biases in the Atlantic and Pacific Oceans contribute to the North American precipitation biases. Experiments with the FLOR model in which SST biases are removed in the Atlantic and Pacific are carried out to determine the contribution of SST errors in each basin to precipitation statistics over North America. Tropical and North Pacific SST biases have a strong impact on northern North American precipitation, while tropical Atlantic SST biases have a dominant impact on precipitation biases in southern North America, including the western United States. Most notably, negative SST biases in the tropical Atlantic in boreal winter induce an anomalously strong Aleutian low and a southward bias in the North Pacific storm track. In boreal summer, the negative SST biases induce a strengthened North Atlantic subtropical high and Great Plains low-level jet. Each of these impacts contributes to positive annual mean precipitation biases over western North America. Both North Pacific and North Atlantic SST biases induce SST biases in remote basins through dynamical pathways, so a complete attribution of the effects of SST biases on precipitation must account for both the local and remote impacts.

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.

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. The ocean component of OM4 uses version 6 of the Modular Ocean Model and the sea ice component uses version 2 of the Sea Ice Simulator, which have identical horizontal grid layouts (Arakawa C‐grid). We follow the Coordinated Ocean‐sea ice Reference Experiments 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. Modular Ocean Model version 6 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 middepth 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.

This study investigates the causes of the double intertropical convergence zone (ITCZ) bias by disentangling the individual contribution of regional sea surface temperature (SST) biases. We show that a previously suggested Southern Ocean warm bias effect in displacing the zonal-mean ITCZ southward is diminished by the southern midlatitude cold bias effect. The northern extratropical cold bias turns out to be most responsible for a southward-displaced zonal-mean precipitation, but the zonal-mean diagnostics poorly represent the spatial pattern of the tropical Pacific response. Examination of longitude-latitude structure indicates that the overall spatial pattern of tropical precipitation bias is largely shaped by the local SST bias. The southeastern tropical Pacific wet bias is driven by warm bias along the west coast of South America with negligible influence from the Southern Ocean warm bias. While our model experiments are idealized with ocean dynamics being absent, the results shed light on where preferential foci should be applied in model development to improve the certain features of tropical precipitation bias.

Ocean variability is a dominant source of remote rainfall predictability, but in many cases the physical mechanisms driving this predictability are not fully understood. This study examines how oce...

Hurricane Sandy (2012) experienced an unusual westward turning and made landfall in New Jersey after its northward movement over the Atlantic Ocean. The landfall caused severe casualties and great economic losses. The westward turning took place in the midlatitude Atlantic where the climatological mean wind is eastward. The cause of this unusual westward track is investigated through both observational analysis and model simulations. The observational analysis indicates that the hurricane steering flow was primarily controlled by atmospheric intraseasonal oscillation (ISO), which was characterized by a pair of anticyclonic and cyclonic circulation systems. The anticyclone to the north was part of a global wave train forced by convection over the tropical Indian Ocean through Rossby wave energy dispersion, and the cyclone to the south originated from the tropical Atlantic through northward propagation. Hindcast experiments using a global coupled model show that the model is able to predict the observed circulation pattern as well as the westward steering flow 6 days prior to Sandy’s landfall. Sensitivity experiments with different initial dates confirm the important role of the ISO in establishing the westward steering flow in the midlatitude Atlantic. Thus the successful numerical model experiments suggest a potential for extended-range dynamical tropical cyclone track predictions.

The East Asian summer monsoon (EASM) shows notable change during the summer after El Nino peak. This “delayed” response, however, is variable and difficult to predict. Here, we revisit this issue by separating El Nino decays into early transition and late transition. In the summer after an early transition, the central-to-eastern Pacific evolves into a La Nina condition, with positive rainfall anomaly occurring over most parts of eastern China. In contrast, in the summer after a late transition, the central-to-eastern Pacific sea surface temperature (SST) anomaly remains neutral or slightly above normal; correspondingly, the East Asian rainfall anomaly shows a tripolar structure with positive anomaly over the Yangtze-Huaihe River valley and negative anomalies over northern and southern China. These different rainfall responses are mainly related to different locations of the anomalous anticyclone (AAC) over the western North Pacific (WNP): it is centered at (165°E, 25°N) for late-transition El Ninos, but at (135°E, 16°N) for early-transition El Ninos. During the late transition, the AAC–SST feedback, identified by the dipole SST mode consisting of WNP cooling and northern Indian Ocean (NIO) warming, mainly works to support the WNP AAC. During the early transition, the AAC–SST feedback is weak and mainly attributed to NIO warming. The strong easterly anomaly over the western equatorial Pacific, which is tied to the central-to-eastern equatorial Pacific cooling and dipole precipitation pattern from western equatorial Pacific to the Maritime Continent, occurs to support the AAC and pulls it equatorward. These distinct responses exist in the last century, and the CMIP5 models can reproduce these distinct responses well except that the models underestimate the AAC–SST feedback for late-transition El Ninos. The findings in this study help predict the EASM rainfall in post-El Nino years, but the key is the accurate prediction of the timing of decay.

One of the emerging topics in climate prediction is the issue of the so-called “signal-to-noise paradox”, characterized by too small signal-to-noise ratio in current model predictions that cannot reproduce the realistic signal. Recent studies have suggested that seasonal-to-decadal climate can be more predictable than ever expected due to the paradox. But no studies, to the best of our knowledge, have been focused on whether the signal-to-noise paradox exists in subseasonal predictions. The present study seeks to address the existence of the paradox in subseasonal predictions based on (i) coupled model simulations participating in phase 5 and phase 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively), and (ii) subseasonal hindcast outputs from the Subseasonal Experiment (SubX) and the Subseasonal-to-Seasonal Prediction (S2S) projects. Of particular interest is the possible existence of the paradox in the new generation of GFDL SPEAR model, through the diagnosis of which may help identify potential issues in the new forecast system to guide future model development and initialization. Here we investigate the paradox issue using two methods: the ratio of predictable component defined as the ratio of predictable component in the real world to the signal-to-noise ratio in models and the persistence/dispersion characteristics estimated from a Markov model framework. The preliminary results suggest a potentially widespread occurrence of the signal-to-noise paradox in subseasonal predictions, further implying some room for improvement in future ensemble-based subseasonal predictions.

We present the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), an atmosphere model developed by the Geophysical Fluid Dynamics Laboratory (GFDL) coupling the nonhydrostatic FV3 Dynamical Core to a physics suite originally taken from the Global Forecast System. SHiELD is designed to demonstrate new capabilities within its components, explore new model applications, and to answer scientific questions through these new functionalities. A variety of configurations are presented, including short‐to‐medium‐range and subseasonal‐to‐seasonal prediction, global‐to‐regional convective‐scale hurricane and contiguous U.S. precipitation forecasts, and global cloud‐resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System or FV3‐based models, including operational implementations of the Unified Forecast System. Continued development of SHiELD has shown improvement upon existing models. The flagship 13‐km SHiELD demonstrates steadily improved large‐scale prediction skill and precipitation prediction skill. SHiELD and the coarser‐resolution S‐SHiELD demonstrate a superior diurnal cycle compared to existing climate models; the latter also demonstrates 28 days of useful prediction skill for the Madden‐Julian Oscillation. The global‐to‐regional nested configurations T‐SHiELD (tropical Atlantic) and C‐SHiELD (contiguous United States) show significant improvement in hurricane structure from a new tracer advection scheme and promise for medium‐range prediction of convective storms.

Walker circulation response to an extratropical energy perturbation depends on the degree of atmosphere-ocean coupling., Walker circulation variability and associated zonal shifts in the heating of the tropical atmosphere have far-reaching global impacts well into high latitudes. Yet the reversed high latitude–to–Walker circulation teleconnection is not fully understood. Here, we reveal the dynamical pathways of this teleconnection across different components of the climate system using a hierarchy of climate model simulations. In the fully coupled system with ocean circulation adjustments, the Walker circulation strengthens in response to extratropical radiative cooling of either hemisphere, associated with the upwelling of colder subsurface water in the eastern equatorial Pacific. By contrast, in the absence of ocean circulation adjustments, the Walker circulation response is sensitive to the forcing hemisphere, due to the blocking effect of the northward-displaced climatological intertropical convergence zone and shortwave cloud radiative effects. Our study implies that energy biases in the extratropics can cause pronounced changes of tropical climate patterns.

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.

Motivated by increasing demand in the community for intraseasonal predictions of weather extremes, predictive skill of tropical cyclogenesis is investigated in this study based on a global coupled model system. Limited intraseasonal cyclogenesis prediction skill with a high false alarm rate is found when averaged over about 600 tropical cyclones (TCs) over global oceans from 2003 to 2013, particularly over the North Atlantic (NA). Relatively skillful genesis predictions with more than 1-week lead time are only evident for about 10% of the total TCs. Further analyses suggest that TCs with relatively higher genesis skill are closely associated with the Madden–Julian oscillation (MJO) and tropical synoptic waves, with their geneses strongly phase-locked to the convectively active region of the MJO and low-level cyclonic vorticity associated with synoptic-scale waves. Moreover, higher cyclogenesis prediction skill is found for TCs that formed during the enhanced periods of strong MJO episodes than those during weak or suppressed MJO periods. All these results confirm the critical role of the MJO and tropical synoptic waves for intraseasonal prediction of TC activity. Tropical cyclogenesis prediction skill in this coupled model is found to be closely associated with model predictability of several large-scale dynamical and thermodynamical fields. Particularly over the NA, higher predictability of low-level relative vorticity, midlevel humidity, and vertical zonal wind shear is evident along a tropical belt from the West Africa coast to the Caribbean Sea, in accord with more predictable cyclogenesis over this region. Over the extratropical NA, large-scale variables exhibit less predictability due to influences of extratropical systems, leading to poor cyclogenesis predictive skill.

This study describes the performance of two Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric general circulation models (AGCMs) in simulating the climatologies of planetary boundary layer (PBL) parameters, with a particular focus on the diurnal cycles. The two models differ solely in the PBL parameterization: one uses a prescribed K-profile parameterization (KPP) scheme with an entrainment parameterization, and the other employs a turbulence kinetic energy (TKE) scheme. The models are evaluated through comparison with the reanalysis ensemble, which is generated from European Centre for Medium-Range Weather Forecasts (ECMWF) twentieth-century reanalysis (ERA-20C), ERA-Interim, NCEP CFSR, and NASA MERRA, and the following systematic biases are identified. The models exhibit widespread cold biases in the high latitudes, and the biases are smaller when the KPP scheme is used. The diurnal cycle amplitudes are underestimated in most dry regions, and the model with the TKE scheme simulates larger amplitudes. For the near-surface winds, the models underestimate both the daily means and the diurnal amplitudes; the differences between the models are relatively small compared to the biases. The role of the PBL schemes in simulating the PBL parameters is investigated through the analysis of vertical profiles. The Sahara, which is suitable for focusing on the role of vertical mixing in dry PBLs, is selected for a detailed analysis. It reveals that compared to the KPP scheme, the heat transport is weaker with the TKE scheme in both convective and stable PBLs as a result of weaker vertical mixing, resulting in larger diurnal amplitudes. Lack of nonlocal momentum transport from the nocturnal low-level jets to the surfaces appears to explain the underestimation of the near-surface winds in the models.

Predictions of changes of the land monsoon rainfall (LMR) in the coming decades are of vital importance for successful sustainable economic development. Current dynamic models, though, have shown little skill in the decadal prediction of the Northern Hemisphere (NH) LMR (NHLMR). The physical basis and predictability for such predictions remain largely unexplored. Decadal change of the NHLMR reflects changes in the total NH continental precipitation, tropical general circulation, and regional land monsoon rainfall over northern Africa, India, East Asia, and North America. Using observations from 1901 to 2014 and numerical experiments, it is shown that the decadal variability of the NHLMR is rooted primarily in (i) the north–south hemispheric thermal contrast in the Atlantic–Indian Ocean sector measured by the North Atlantic–south Indian Ocean dipole (NAID) sea surface temperature (SST) index and (ii) an east–west thermal contrast in the Pacific measured by an extended El Niño–Southern Oscillation (XEN) index. Results from a 500-yr preindustrial control experiment demonstrate that the leading mode of decadal NHLMR and the associated NAID and XEN SST anomalies may be largely an internal mode of Earth’s climate system, although possibly modified by natural and anthropogenic external forcing. A 51-yr, independent forward-rolling decadal hindcast was made with a hybrid dynamic conceptual model and using the NAID index predicted by a multiclimate model ensemble. The results demonstrate that the decadal changes in the NHLMR can be predicted approximately a decade in advance with significant skills, opening a promising way forward for decadal predictions of regional land monsoon rainfall worldwide.

In Part II of this two-part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part I. Part II provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.

The severity of the double Intertropical Convergence Zone (DI) problem in climate models can be measured by a tropical precipitation asymmetry index (PAI), indicating whether tropical precipitation favors the Northern Hemisphere or the Southern Hemisphere. Examination of 19 Coupled Model Intercomparison Project phase 5 models reveals that the PAI is tightly linked to the tropical sea surface temperature (SST) bias. As one of the factors determining the SST bias, the asymmetry of tropical net surface heat flux in Atmospheric Model Intercomparison Project (AMIP) simulations is identified as a skillful predictor of the PAI change from an AMIP to a coupled simulation, with an intermodel correlation of 0.90. Using tropical top-of-atmosphere (TOA) fluxes, the correlations are lower but still strong. However, the extratropical asymmetries of surface and TOA fluxes in AMIP simulations cannot serve as useful predictors of the PAI change. This study suggests that the largest source of the DI bias is from the tropics and from atmospheric models.

Most current climate models suffer from pronounced cloud and radiation biases in the Southern Ocean (SO) and in the tropics. Using one GFDL climate model, this study investigates the migration of the intertropical convergence zone (ITCZ) with prescribed top-of-the-atmosphere (TOA) shortwave radiative heating in the SO (50°–80°S) versus the southern tropics (ST; 0°–20°S). Results demonstrate that the ITCZ position response to the ST forcing is twice as strong as the SO forcing, which is primarily driven by the contrasting sea surface temperature (SST) gradient over the tropics; however, the mechanism for the formation of the SST pattern remains elusive. Energy budget analysis reveals that the conventional energetic constraint framework is inadequate in explaining the ITCZ shift in these two perturbed experiments. For both cases, the anomalous Hadley circulation does not contribute to transport the imposed energy from the Southern Hemisphere to the Northern Hemisphere, given a positive mean gross moist stability in the equatorial region. Changes in the cross-equatorial atmospheric energy are primarily transported by atmospheric transient eddies when the anomalous ITCZ shift is most pronounced during December–May. The partitioning of energy transport between the atmosphere and ocean shows latitudinal dependence: the atmosphere and ocean play an overall equivalent role in transporting the imposed energy for the extratropical SO forcing, while for the ST forcing, the imposed energy is nearly completely transported by the atmosphere. This contrast originates from the different ocean heat uptake and also the different meridional scale of the anomalous ocean circulation.

Upon publication, it came to our attention that certain revisions were necessary to honor the agreement between University of Hawaii and APEC Climate Center for the project, “Development of APCC Seamless Prediction System”. There are three sections that are to be corrected to accurately reflect the agreement of project.

Based on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden–Julian oscillation (MJO) prediction skill in boreal wintertime (November–April) is evaluated by analyzing 11 years (2003–13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model’s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.

A coupled earth system model (ESM) has been developed at the Nanjing University of Information Science and Technology (NUIST) by using version 5.3 of the European Centre Hamburg Model (ECHAM), version 3.4 of the Nucleus for European Modelling of the Ocean (NEMO), and version 4.1 of the Los Alamos sea ice model (CICE). The model is referred to as NUIST ESM1 (NESM1). Comprehensive and quantitative metrics are used to assess the model’s major modes of climate variability most relevant to subseasonal-to-interannual climate prediction. The model’s assessment is placed in a multi-model framework. The model yields a realistic annual mean and annual cycle of equatorial SST, and a reasonably realistic precipitation climatology, but has difficulty in capturing the spring-fall asymmetry and monsoon precipitation domains. The ENSO mode is reproduced well with respect to its spatial structure, power spectrum, phase locking to the annual cycle, and spatial structures of the central Pacific (CP)-ENSO and eastern Pacific (EP)-ENSO; however, the equatorial SST variability, biennial component of ENSO, and the amplitude of CP-ENSO are overestimated. The model captures realistic intraseasonal variability patterns, the vertical-zonal structures of the first two leading predictable modes of Madden-Julian Oscillation (MJO), and its eastward propagation; but the simulated MJO speed is significantly slower than observed. Compared with the T42 version, the high resolution version (T159) demonstrates improved simulation with respect to the climatology, interannual variance, monsoon-ENSO lead-lag correlation, spatial structures of the leading mode of the Asian-Australian monsoon rainfall variability, and the eastward propagation of the MJO.

While tropical cyclone (TC) prediction, in particular TC genesis, remains very challenging, accurate prediction of TCs is critical for timely preparedness and mitigation. Using a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the authors studied the predictability of two destructive landfall TCs: Hurricane Sandy in 2012 and Super Typhoon Haiyan in 2013. Results demonstrate that the geneses of these two TCs are highly predictable with the maximum prediction lead time reaching 11 days. The “beyond weather time scale” predictability of tropical cyclogenesis is primarily attributed to the model’s skillful prediction of the intraseasonal Madden–Julian oscillation (MJO) and the westward propagation of easterly waves. Meanwhile, the landfall location and time can be predicted one week ahead for Sandy’s U.S landfall, and two weeks ahead for Haiyan’s landing in the Philippines. The success in predicting Sandy and Haiyan, together with low false alarms, indicates the potential of using the GFDL coupled model for extended-range predictions of TCs.

In the summer of 2012, there was a clear signal of the developing El Nino over the equatorial Pacific, and many climate models forecasted the occurrence of El Nino with a peak phase in the subsequent winter. However, the warming was aborted abruptly in late fall. Here we show that the abrupt termination of the 2012 Pacific warming was largely attributed to the anomalous sea surface temperature (SST) cooling in the northeastern and southeastern subtropical Pacific. The anomalous SST cooling induced strong easterly and low-level divergence anomalies, suppressing the development of westerly and convection anomalies over the equatorial central Pacific. Thus, the surface warming over the equatorial Pacific was decoupled from the surface wind forcing and subsurface thermocline variability, inhibiting its further development into a mature El Nino in the winter of 2012–2013. This study highlights the importance of the SST anomaly in the subtropical Pacific in El Nino prediction.

Understanding the change of equatorial Pacific trade winds is pivotal for understanding the global mean temperature change and the El Niño–Southern Oscillation (ENSO) property change. The weakening of the Walker circulation due to anthropogenic greenhouse gas (GHG) forcing was suggested as one of the most robust phenomena in current climate models by examining zonal sea level pressure gradient over the tropical Pacific. This study explores another component of the Walker circulation change focusing on equatorial Pacific trade wind change. Model sensitivity experiments demonstrate that the direct/fast response due to GHG forcing is to increase the trade winds, especially over the equatorial central-western Pacific (ECWP) (5°S–5°N, 140°E–150°W), while the indirect/slow response associated with sea surface temperature (SST) warming weakens the trade winds. Further, analysis of the results from 19 models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and the Parallel Ocean Program (POP)–Ocean Atmosphere Sea Ice Soil (OASIS)–ECHAM model (POEM) shows that the projected weakening of the trades is robust only in the equatorial eastern Pacific (EEP) ( 5°S–5°N, 150°–80°W), but highly uncertain over the ECWP with 9 out of 19 CMIP5 models producing intensified trades. The prominent and robust weakening of EEP trades is suggested to be mainly driven by a top-down mechanism: the mean vertical advection of more upper-tropospheric warming downward to generate a cyclonic circulation anomaly in the southeast tropical Pacific. In the ECWP, the large intermodel spread is primarily linked to model diversity in simulating the relative warming of the equatorial Pacific versus the tropical mean sea surface temperature. The possible root causes of the uncertainty for the trade wind change are also discussed.