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This study addresses hurricane hazard to the state of New York in past, present, and future using synthetic storms generated by the Columbia Hazard model (CHAZ) and climate inputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), in conjunction with historical observations. The projected influence of anthropogenic climate change on future hazard is quantified by the normalized differences in statistics of hurricane hazard between the recent historical period (1951–2005) and two future periods under the representative concentration pathway 8.5 warming scenario: the near future (2006–40) and the late-twenty-first century (2070–99). Changes in return periods of storms affecting the state at given intensities are computed, as are wind hazards for individual counties. Other storm characteristics examined include hurricane intensity, forward speed, heading, and rate of change of the heading. The 10th, 25th, 50th, 75th, and 90th percentiles of these characteristics mostly change by less than 3% from the historical to the near future period. In the late-twenty-first century, CHAZ projects a clear upward trend in New York hurricane intensity as a consequence of increasing potential intensity and decreasing vertical wind shear in the vicinity. CHAZ also projects a decrease in translation speed and an increasing probability of approach from the east. Changes in hurricane wind hazard, however, are epistemically uncertain because of a fundamental uncertainty in CHAZ projections of New York State hurricane frequency in which frequency either increases or decreases depending on which humidity variable is used in the environmental index that controls genesis in the model. Thus, projected changes in the wind hazards are reported separately under storylines of increasing or decreasing frequency.

The authors describe a tropical cyclone risk model for the Philippines, using methods that are open-source and can be straightforwardly generalized to other countries. Wind fields derived from historical observations, as well as those from an environmentally-forced tropical cyclone hazard model are combined with data representing exposed value and vulnerability to determine asset losses. Exposed value is represented by the LitPop dataset, which assumes total asset value is distributed across a country following population density and nightlights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel (2011), with free parameters chosen by a sensitivity analysis in which simulated and historical reported damages are compared for different parameter values, and further constrained by information from household surveys about regional building characteristics. Use of different vulnerability parameters for the region around Manila yields much better agreement between simulated and actually reported losses than does a single set of parameters for the entire country. Despite the improvements from regionally refined vulnerability, the model predicts no losses for a substantial number of destructive historical storms, a difference the authors hypothesize is due to the use of wind speed as the sole metric of tropical cyclone hazard, omitting explicit representation of storm surge and/or rainfall. Bearing these limitations in mind, this model can be used to estimate return levels for tropical cyclone-caused wind hazards and asset losses for regions across the Philippines, relevant to some disaster risk reduction and management tasks; this model also provides a platform for further development of open-source tropical cyclone risk modeling.

The Madden–Julian oscillation (MJO) and the boreal summer intraseasonal oscillation (BSISO) are fundamental modes of variability in the tropical atmosphere on the intraseasonal time scale. A linear model, using a moist shallow water equation set on an equatorial beta plane, is developed to provide a unified treatment of the two modes and to understand their growth and propagation over the Indian Ocean. Moisture is assumed to increase linearly with longitude and to decrease quadratically with latitude. Solutions are obtained through linear stability analysis, considering the gravest (n = 1) meridional mode with nonzero meridional velocity. Anomalies in zonal moisture advection and surface fluxes are both proportional to those in zonal wind, but of opposite sign. With observation-based estimates for both effects, the zonal advection dominates, and drives the planetary-scale instability. With a sufficiently small meridional moisture gradient, the horizontal structure exhibits oscillations with latitude and a northwest–southeast horizontal tilt in the Northern Hemisphere, qualitatively resembling the observed BSISO. As the meridional moisture gradient increases, the horizontal tilt decreases and the spatial pattern transforms toward the “swallowtail” structure associated with the MJO, with cyclonic gyres in both hemispheres straddling the equatorial precipitation maximum. These results suggest that the magnitude of the meridional moisture gradient shapes the horizontal structures, leading to the transformation from the BSISO-like tilted horizontal structure to the MJO-like neutral wave structure as the meridional moisture gradient changes with the seasons. The existence and behavior of these intraseasonal modes can be understood as a consequence of phase speed matching between the equatorial mode with zero meridional velocity (analogous to the dry Kelvin wave) and a local off-equatorial component that is characterized by considering an otherwise similar system on an f plane.

This manuscript discusses the challenges in detecting and attributing recently observed trends in the Atlantic hurricanes and the epistemic uncertainty we face in assessing future hurricane risk. Data used here include synthetic storms downscaled from five CMIP5 models by the Columbia HAZard model (CHAZ), and directly simulated storms from high-resolution climate models. We examine three aspects of recent hurricane activity: the upward trend and multi-decadal oscillation of the annual frequency, the increase in storm wind intensity, and the downward trend in the forward speed. Some datasets suggest that these trends and oscillation are forced while others suggest that they can be explained by natural variability. Future projections under warming climate scenarios also show a wide range of possibilities, especially for the annual frequencies, which increase or decrease depending on the choice of moisture variable used in the CHAZ model and on the choice of climate model. The uncertainties in the annual frequency lead to epistemic uncertainties in the future hurricane risk assessment. Here, we investigate the reduction of epistemic uncertainties on annual frequency through a statistical practice – likelihood analysis. We find that historical observations are more consistent with the simulations with increasing frequency but we are not able to rule out other possibilities. We argue that the most rational way to treat epistemic uncertainty is to consider all outcomes contained in the results. In the context of hurricane risk assessment, since the results contain possible outcomes in which hurricane risk is increasing, this view implies that the risk is increasing.

Within the North Indian Ocean basin, tropical cyclone (TC) activity over the Bay of Bengal (BoB) is substantially greater than over the Arabian Sea (AS). The authors attempt to quantify the roles of specific environmental factors in order to understand the reasons for this difference between the two basins. Environmental variables are considered in the basin as a whole and in the immediate times and places at which cyclognesis and storm intensification occur. The results for the two sub-basins are compared to determine which environmental variables differed significantly between the sub-basins. A tropical cyclone genesis index (TCGI) is also examined to determine whether the AS-BoB difference can be explained in terms of the environment using this index. Though the overall level of activity in both is under-predicted by the index by about a factor of two, the index shows a relative diffrence between the basins that is approximately consistent with that in observations. Based on that partial success, climatologies of the individual factors that comprise the index are examined to determine which ones are most important in the AS-BoB diffrence. Column relative humidity and vertical wind shear emerge as the most likely candidates. A closer examination suggests that column relative humidity is the more important factor and thus that the AS has fewer cyclones than the BoB primarily because it has a drier lower troposphere.

The stratospheric quasi-biennial oscillation (QBO) and the tropospheric Madden–Julian oscillation (MJO) are strongly linked in boreal winter. In this Review, we synthesize observational and modelling evidence for this QBO–MJO connection and discuss its effects on MJO teleconnections and subseasonal-to-seasonal predictions. After 1980, observations indicate that, during winters when lower-stratospheric QBO winds are easterly, the MJO is ~40% stronger and persists roughly 10 days longer compared with when QBO winds are westerly. Global subseasonal forecast models, in turn, show a 1-week improvement (or 25% enhancement) in MJO prediction skill in QBO easterly versus QBO westerly phases. Despite the robustness of the observed QBO–MJO link and its global impacts via atmospheric teleconnections, the mechanisms that drive the connection are uncertain. Theories largely centre on QBO-related temperature stratification effects and subsequent impacts on deep convection, although other hypotheses propose that cloud radiative effects or QBO impacts on wave propagation might be important. Most numerical models, however, are unable to reproduce the observed QBO–MJO relationship, suggesting biases, deficiencies or omission of key physical processes in the models. While future work must strive to better understand all aspects of the QBO–MJO link, focus is needed on establishing a working mechanism and capturing the connection in models. The quasi-biennial oscillation and Madden–Julian oscillation are strongly connected during boreal winter, typified by a more active and persistent Madden–Julian oscillation during easterly quasi-biennial oscillation phases. This Review outlines the characteristics and potential mechanisms of this coupling, as well as the implications for seasonal-to-seasonal prediction.

The Propagation of Intraseasonal Tropical Oscillations (PISTON) experiment conducted a field campaign in August–October 2018. The R/V Thomas G. Thompson made two cruises in the western North Pacific region north of Palau and east of the Philippines. Using select field observations and global observational and reanalysis datasets, this study describes the large-scale state and evolution of the atmosphere and ocean during these cruises. Intraseasonal variability was weak during the field program, except for a period of suppressed convection in October. Tropical cyclone activity, on the other hand, was strong. Variability at the ship location was characterized by periods of low-level easterly atmospheric flow with embedded westward propagating synoptic-scale atmospheric disturbances, punctuated by periods of strong low-level westerly winds that were both connected to the Asian monsoon westerlies and associated with tropical cyclones. In the most dramatic case, westerlies persisted for days during and after tropical cyclone Jebi had passed to the north of the ship. In these periods, the sea surface temperature was reduced by a couple of degrees by both wind mixing and net surface heat fluxes that were strongly (~200 W m−2) out of the ocean, due to both large latent heat flux and cloud shading associated with widespread deep convection. Underway conductivity–temperature transects showed dramatic cooling and deepening of the ocean mixed layer and erosion of the barrier layer after the passage of Typhoon Mangkhut due to entrainment of cooler water from below. Strong zonal currents observed over at least the upper 400 m were likely related to the generation and propagation of near-inertial currents.

Observational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

The stratospheric quasi-biennial oscillation (QBO) induces temperature anomalies in the lower stratosphere and tropical tropopause layer (TTL) that are cold when lower-stratospheric winds are easterly and warm when winds are westerly. Recent literature has indicated that these QBO temperature anomalies are potentially important in influencing the tropical troposphere, and particularly in explaining the relationship between the QBO and the Madden–Julian oscillation (MJO). The authors examine the variability of QBO temperature anomalies across several time scales using reanalysis and observational datasets. The authors find that, in boreal winter relative to other seasons, QBO temperature anomalies are significantly stronger (i.e., colder in the easterly phase of the QBO and warmer in the westerly phase of the QBO) on the equator, but weaker off the equator. The equatorial and subtropical changes compensate such that meridional temperature gradients and thus (by thermal wind balance) equatorial zonal wind anomalies do not vary in amplitude as the temperature anomalies do. The same pattern of stronger on-equatorial and weaker off-equatorial QBO temperature anomalies is found on decadal time scales: stronger anomalies are seen for 1999–2019 compared to 1979–99. The causes of these changes to QBO temperature anomalies, as well as their possible relevance to the MJO–QBO relationship, are not known.

Here we explore the relationship between the global climatological characteristics of tropical cyclones (TCs) in climate models and the modeled large-scale environment across a large number of models. We consider the climatology of TCs in 30 climate models with a wide range of horizontal resolutions. We examine if there is a systematic relationship between the climatological diagnostics for the TC activity [number of tropical cyclones (NTC) and accumulated cyclone energy (ACE)] by hemisphere in the models and the environmental fields usually associated with TC activity, when examined across a large number of models. For low-resolution models, there is no association between a conducive environment and TC activity, when integrated over space (tropical hemisphere) and time (all years of the simulation). As the model resolution increases, for a couple of variables, in particular vertical wind shear, there is a statistically significant relationship in between the models’ TC characteristics and the environmental characteristics, but in most cases the relationship is either nonexistent or the opposite of what is expected based on observations. It is important to stress that these results do not imply that there is no relationship between individual models’ environmental fields and their TC activity by basin with respect to intraseasonal or interannual variability or due to climate change. However, it is clear that when examined across many models, the models’ mean state does not have a consistent relationship with the models’ mean TC activity. Therefore, other processes associated with the model physics, dynamical core, and resolution determine the climatological TC activity in climate models.

Tropical cyclone (TC) activity is examined using the Columbia Hazard model (CHAZ), a statistical–dynamical downscaling system, with environmental conditions taken from simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) for both the historical period and a future scenario under the representative concentration pathway 8.5. Projections of individual global and basin TC frequency depend sensitively on the choice of moisture variable used in the tropical genesis cyclone index (TCGI) component of CHAZ. Simulations using column relative humidity show an increasing trend in the future, while those using saturation deficit show a decreasing trend, although both give similar results in the historical period. While the projected annual TC frequency is also sensitive to the choice of model used to provide the environmental conditions, the choice of humidity variable in the TCGI is more important. Changes in TC frequency directly affect the projected TCs’ tracks and the frequencies of strong storms on both basin and regional scales. This leads to large uncertainty in assessing regional and local storm hazards. The uncertainty here is fundamental and epistemic in nature. Increases in the fraction of major TCs, rapid intensification rate, and decreases in forward speed are insensitive to TC frequency, however. The present results are also consistent with prior studies in indicating that those TC events that do occur will, on average, be more destructive in the future because of the robustly projected increases in intensity.

Climate change is a global problem, yet it is experienced at the local scale, in ways that are both place-specific and specific to the accidents of weather history. This article takes the dichotomy between the global and the local as a starting point to develop a critique of the normative approach within climate science, which is global in various ways and thereby fails to bring meaning to the local. The article discusses the ethical choices implicit in the current paradigm of climate prediction, how irreducible uncertainty at the local scale can be managed by suitable reframing of the scientific questions, and some particular epistemic considerations that apply to climate change in the global South. The article argues for an elevation of the narrative and for a demotion of the probabilistic from its place of privilege in the construction and communication of our understanding of global warming and its local consequences.

This paper introduces a logistic regression model for the extratropical transition (ET) of tropical cyclones in the North Atlantic and the western North Pacific, using elastic net regularization to select predictors and estimate coefficients. Predictors are chosen from the 1979–2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to 2 days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic and 92% of those in the western North Pacific. In total, 90% of transition time errors are less than 24 h. Select examples of the model’s performance on individual storms illustrate its strengths and weaknesses. Two versions of the model are presented: an “operational model” that may provide baseline guidance for operational forecasts and a “hazard model” that can be integrated into statistical TC risk models. As instantaneous diagnostics for tropical/extratropical status, both models’ zero lead time predictions perform about as well as the widely used cyclone phase space (CPS) in the western North Pacific and better than the CPS in the North Atlantic, and predict the timings of the transitions better than CPS in both basins.

The frequency with which tropical cyclones (TCs) occur controls all other aspects of tropical cyclone risk since a storm that does not occur can do no harm. Yet this frequency is poorly understood. There is no accepted theory that explains the average number of TCs that occur each year on the Earth, nor how that number will change with global warming. Arguments based on global budgets of heat or moisture do not yet appear helpful, nor does a detailed understanding of the physical processes of TC genesis. Empirical indices that predict TC frequency as a function of large‐scale environmental variables can explain some of its relative variations in space and time, but not its absolute value. Global numerical models with horizontal grid spacings on the order of 25–50 km have allowed much improved simulations of TC activity, however. Many such models project a decrease in frequency with warming, but some project an increase. Idealized simulations, including those at higher resolutions, offer promise by allowing a systematic, deductive investigation of the roles of individual environmental factors. In addition to the larger‐scale environmental modulation of genesis likelihood, precursor disturbances, or “seeds”, may exert an independent influence on TC frequency.

Large-scale structural change is essential if we are to decarbonize the economy rapidly enough to avert the worst of global warming. Individual actions by the well-meaning won’t be enough. Yet the ...

Here we discuss recent progress in understanding tropical cyclone (TC) subseasonal variability and its prediction. There has been a concerted effort to understand the sources of predictability at subseasonal time-scales, and this effort has continued to make progress in recent years. Besides the Madden-Julian Oscillation (MJO), other modes of variability affect TCs at these time-scales, in particular various equatorial waves. Additionally, TC activity is also modulated by extratropical processes via Rossby wave breaking.There has also been progress in the ability of models to simulate the MJO and its modulation of TC activity. Community efforts have created multi-model ensemble datasets, which have made it possible to evaluate the forecast skill of the MJO and TCs on subseasonal time-scales in multiple forecasting systems. While there is positive skill in some cases, there is strong dependence on the ensemble system considered, the basin examined, and whether the storms have extratropical influences or not. Furthermore, the definition of skill differs among studies. Forecasting centers are currently issuing subseasonal TC forecasts using various techniques (statistical, statistical-dynamical and dynamical). There is also a strong interest in the private sector for forecasts with 3-4 weeks lead time. Keywords: tropical cyclones, subseasonal, forecasts, hurricanes, MJO

This study examines the relationship between the Madden–Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO) in a limited-area cloud-resolving model with parameterized large-scale dynamics. The model is used to simulate two consecutive MJO events that occurred during the late fall and early winter of 2011. To test the influence of the QBO on the simulated MJO events, various QBO states are imposed via the addition of characteristic wind and temperature anomalies. In experiments with only QBO temperature anomalies imposed (without corresponding zonal wind anomalies) the strength of convection during MJO active phases is amplified for the QBO easterly phase [an anomalously cold tropical tropopause layer (TTL)] compared to the westerly QBO phase (a warm TTL), as measured by outgoing longwave radiation, cloud fraction, and large-scale ascent. This response is qualitatively consistent with the observed MJO–QBO relationship. The response of precipitation is weaker, and is less consistent across variations in the simulation configuration. Experiments with only imposed QBO wind anomalies (without corresponding temperature anomalies) show much weaker effects altogether than those with imposed temperature anomalies, suggesting that TTL temperature anomalies are a key pathway through which the QBO can modulate the MJO. Sensitivity tests indicate that the QBO influence on MJO convection depends on both the amplitude and the height of the QBO temperature anomaly: lower-altitude and larger-amplitude temperature anomalies have more pronounced effects on MJO convection.

The author argues that in the present historical moment, the only climate science that is truly usable is that which is oriented towards adaptation, because current policies and politics are so far from what would be needed to avert dangerous climate change that scientific uncertainty is not a limiting factor on mitigation. The author considers what implications this might have for climate science and climate scientists.

Responses of extreme precipitation to global warming are of great importance to society and ecosystems. Although observations and climate projections indicate a general intensification of extreme precipitation with warming on global scale, there are significant variations on the regional scale, mainly due to changes in the vertical motion associated with extreme precipitation. Here, we apply quasigeostrophic diagnostics on climate-model simulations to understand the changes in vertical motion, quantifying the roles of dry (large-scale adiabatic flow) and moist (small-scale convection) dynamics in shaping the regional patterns of extreme precipitation sensitivity (EPS). The dry component weakens in the subtropics but strengthens in the middle and high latitudes; the moist component accounts for the positive centers of EPS in the low latitudes and also contributes to the negative centers in the subtropics. A theoretical model depicts a nonlinear relationship between the diabatic heating feedback ( α ) and precipitable water, indicating high sensitivity of α (thus, EPS) over climatological moist regions. The model also captures the change of α due to competing effects of increases in precipitable water and dry static stability under global warming. Thus, the dry/moist decomposition provides a quantitive and intuitive explanation of the main regional features of EPS.

The authors analyze the global statistics of tropical cyclones undergoing extratropical transition (ET) in the Forecast‐oriented Low Ocean Resolution version of CM2.5 with Flux Adjustment (FLOR‐FA). The cyclone phase space (CPS) is used to diagnose ET. A simulation of the recent historical climate is analyzed and compared with data from the Japanese 55‐year Reanalysis (JRA‐55), and then a simulation of late 21st century climate under Representative Concentration Pathway 4.5 is compared to the historical simulation. When CPS is applied to the FLOR‐FA output in the historical simulation, the results diverge from those obtained from JRA‐55 by having an unrealistic number of ET cases at low latitudes, due to the presence of strong local maxima in the upper‐level geopotential. These features mislead CPS into detecting a cold core where one is not present. The misdiagnosis is largely corrected by replacing the maxima required by CPS with the 95th percentile values, smoothing the CPS trajectories in time, or both. Other climate models may contain grid‐scale structures akin to those in FLOR‐FA and, when used for CPS analysis, require solutions such as those discussed here. Comparisons of ET in the projected future climate with the historical climate show a number of changes that are robust to the details of the ET diagnosis, though few are statistically significant according to standard tests. Among them are an increase in the ET fraction and a reduction in the mean latitude at which ET occurs in the western North Pacific.

We will first examine the skill of probabilistic tropical cyclone (TC) occurrence and intensity (ACE - accumulated cyclone energy) predictions in the Subseasonal to Seasonal (S2S) dataset. We show that some of the models in the S2S dataset have skill in predicting TC occurrence 4 weeks in advance. In contrast, only one of the models (ECMWF) has skill in predicting the anomaly of TC occurrence from the seasonal climatology beyond week 1. For models with significant mean biases, calibrating the forecast can improve the models’ prediction skill. In contrast, for models with small mean biases, calibration does not guarantee an improvement in model skill as measured by the Brier Skill Score. We then focus only on the ECMWF model and using cluster analysis examine the sensitivity of the North Atlantic TC tracks biases to various factors, such as model resolution, lead time, and tracking. We also explore how well the ECMWF North Atlantic TC model tracks in each cluster simulate the known response to climate modes, such as ENSO and MJO. By applying simple bias corrections to each cluster of Atlantic TC tracks, we examine if we can improve the model skill in landfall prediction in the US and Caribbean.

Here I will describe recent results on the influence of climate change on tropical cyclones (TC) using the Columbia Hazard (CHAZ) model. Using environmental conditions from reanalysis and climate models and a statistical-dynamical downscaling methodology (Lee et al. 2018), CHAZ generates synthetic TCs that can be used to analyze TC risk. I will first discuss the current knowledge and uncertainties in TC frequency projections. Then I will present our recent projections on TC frequency using CHAZ. Focusing on the North Atlantic, I will finish by discussing how we can use a combination of observations, high-resolution models and CHAZ synthetic TCs in the historical period to inform the reliability of the models' TC frequency projections. Reference:Lee, C.-Y., M.K. Tippett, A.H. Sobel, and S.J. Camargo, 2018. An environmentally forced tropical cyclone hazard model. J. Adv. Model. Earth Sys., 10, doi: 10.1002/2017MS001186.

Characteristics of tropical cyclones (TCs) in global climate models (GCMs) are known to be influenced by details of the model configurations, including horizontal resolution and parameterization schemes. Understanding model-to-model differences in TC characteristics is a prerequisite for reducing uncertainty in future TC activity projections by GCMs. This study performs a process-level examination of TC structures in eight GCM simulations that span a range of horizontal resolutions from 1° to 0.25°. A recently developed set of process-oriented diagnostics is used to examine the azimuthally averaged wind and thermodynamic structures of the GCM-simulated TCs. Results indicate that the inner-core wind structures of simulated TCs are more strongly constrained by the horizontal resolutions of the models than are the thermodynamic structures of those TCs. As expected, the structures of TC circulations become more realistic with smaller horizontal grid spacing, such that the radii of maximum wind (RMW) become smaller, and the maximum vertical velocities occur off the center. However, the RMWs are still too large, especially at higher intensities, and there are rising motions occurring at the storm centers, inconsistently with observations. The distributions of precipitation, moisture, and radiative and surface turbulent heat fluxes around TCs are diverse, even across models with similar horizontal resolutions. At the same horizontal resolution, models that produce greater rainfall in the inner-core regions tend to simulate stronger TCs. When TCs are weak, the radial gradient of net column radiative flux convergence is comparable to that of surface turbulent heat fluxes, emphasizing the importance of cloud–radiative feedbacks during the early developmental phases of TCs.

Prediction and predictability of tropical intraseasonal convection in the WMO subseasonal to seasonal (S2S) forecast database is assessed using the real-time OLR based MJO (ROMI) index. ROMI prediction skill in the S2S models, as measured by the maximum lead time at which the bivariate correlation coefficient between forecasts and observations exceeds 0.6, ranges from ~ 15 to ~ 36 days in boreal winter, which is 5–10 days higher than the MJO circulation prediction skill based on the MJO RMM index. ROMI prediction skill is systematically lower by 5–10 days in summer than in winter. Predictability measures show similar seasonal contrast in the two seasons. These results indicate that intraseasonal convection is inherently less predictable in summer than in winter. Further evaluation of correlation skill assuming either perfect amplitude or perfect phase forecasts indicates that phase bias is the main contributor to skill degradation at longer forecast lead times. Nearly all the S2S models have lesser skill for target dates in which the MJO convection is centered over the Maritime Continent (MC) in boreal winter, and phase bias contributes to this MC prediction barrier. This issue is less prevalent in boreal summer. Many S2S models significantly underestimate ROMI amplitudes at longer forecast leads. Probabilistic evaluation of the S2S model skills in forecasting ROMI amplitude is further assessed using the ranked probability skill score (RPSS). RPSS varies significantly across models, from no skill to more than 30 days, which is partly due to model configuration and partly due to amplitude bias. Accounting for the systematic underestimates of the amplitude improves RPSS.

Climate model projections of extreme precipitation intensity depend heavily on the region: some regions will experience exceptionally strong increases in extreme precipitation intensity, while other regions will experience decreases in extreme precipitation intensity. These regional variations are closely related to regional changes in large scale ascent during extreme precipitation events—i.e. “extreme ascent”—but the drivers of extreme ascent changes remain poorly understood. Using output from a large ensemble of the Canadian Earth System Model version 2 (CanESM2), we show that subtropical changes in extreme ascent likely result from changes in the horizontal scale of ascending anomalies, which are in turn associated with changes in vertical stability. Near the equator, changes in the seasonal mean circulation may be an important factor influencing extreme ascent, but this finding is model-dependent.

This study proposes a set of process-oriented diagnostics with the aim of understanding how model physics and numerics control the representation of tropical cyclones (TCs), especially their intensity distribution, in GCMs. Three simulations are made using two 50-km GCMs developed at NOAA’s Geophysical Fluid Dynamics Laboratory. The two models are forced with the observed sea surface temperature [Atmospheric Model version 2.5 (AM2.5) and High Resolution Atmospheric Model (HiRAM)], and in the third simulation, the AM2.5 model is coupled to an ocean GCM [Forecast-Oriented Low Ocean Resolution (FLOR)]. The frequency distributions of maximum near-surface wind near TC centers show that HiRAM tends to develop stronger TCs than the other models do. Large-scale environmental parameters, such as potential intensity, do not explain the differences between HiRAM and the other models. It is found that HiRAM produces a greater amount of precipitation near the TC center, suggesting that associated greater diabatic heating enables TCs to become stronger in HiRAM. HiRAM also shows a greater contrast in relative humidity and surface latent heat flux between the inner and outer regions of TCs. Various fields are composited on precipitation percentiles to reveal the essential character of the interaction among convection, moisture, and surface heat flux. Results show that the moisture sensitivity of convection is higher in HiRAM than in the other model simulations. HiRAM also exhibits a stronger feedback from surface latent heat flux to convection via near-surface wind speed in heavy rain-rate regimes. The results emphasize that the moisture–convection coupling and the surface heat flux feedback are critical processes that affect the intensity of TCs in GCMs.

This paper provides a summary of the Workshop on Sub-Seasonal to Seasonal (S2S) Predictability of Extreme Weather and Climate, held at Columbia University, December 6–7, 2016. The 2-day workshop was attended by over 100 people and took stock of recent developments in Sub-seasonal to Seasonal predictability, S2S extreme weather phenomena, and real world predictions and use of forecasts. Workshop motivations, new findings, and outstanding questions discussed are described.

The processes that lead to changes in the propagation and maintenance of the Madden‐Julian Oscillation (MJO) as a response to increasing CO2 are examined by analyzing moist static energy budget of the MJO in a series of NASA GISS model simulations. It is found changes in MJO propagation is dominated by several key processes. Horizontal moisture advection, a key process for MJO propagation, is found to enhance predominantly due to an increase in the mean horizontal moisture gradients. The terms that determine the strength of the advecting wind anomalies, the MJO horizontal scale and the dry static stability, are found to exhibit opposing trends that largely cancel out. Furthermore, reduced sensitivity of precipitation to changes in column moisture, i.e., a lengthening in the convective moisture adjustment time scale, also opposes enhanced propagation. The dispersion relationship of Adames and Kim, which accounts for all these processes, predicts an acceleration of the MJO at a rate of ∼3.5% K−1, which is consistent with the actual phase speed changes in the simulation. For the processes that contribute to MJO maintenance, it is found that damping by vertical MSE advection is reduced due to the increasing vertical moisture gradient. This weaker damping is nearly canceled by weaker maintenance by cloud‐radiative feedbacks, yielding the growth rate from the linear moisture mode theory nearly unchanged with the warming. Furthermore, the estimated growth rates are found to be a small, negative values, suggesting that the MJO in the simulation is a weakly damped mode., Key Points The moisture mode framework of Adames and Kim is used to understand the MJO's response to increasing CO2 in the GISS GCMThe moisture mode framework successfully predicts the rate of MJO's phase speed increase with the warmingThe acceleration of the MJO in a warmer climate is due to the changes in the mean state, moisture‐convection coupling, and the MJO's scale

The spatial distribution of return intervals for U.S. hail size is explored within the framework of extreme value theory using observations from the period 1979–2013. The center of the continent has experienced hail in excess of 5 in. (127 mm) during the past 30 yr, whereas hail in excess of 1 in. (25 mm) is more common in other regions, including the West Coast. Observed hail sizes show heavy quantization toward fixed-diameter reference objects and are influenced by spatial and temporal biases similar to those noted for hail occurrence. Recorded hail diameters have been growing in recent decades because of improved reporting. These data limitations motivate exploration of extreme value distributions to represent the return periods for various hail diameters. The parameters of a Gumbel distribution are fit to dithered observed annual maxima on a national 1° × 1° grid at locations with sufficient records. Gridded and kernel-smoothed return sizes and quantiles up to the 200-yr return period are determined for the fitted Gumbel distribution. These results are used to illustrate return levels for hail greater than a given size for at least one location within each 1° × 1° grid box for the United States.

A set of idealized cloud-permitting simulations is performed to explore the influence of small islands on precipitating convection as a function of large-scale wind speed. The islands are situated in a long narrow ocean domain that is in radiative–convective equilibrium (RCE) as a whole, constraining the domain-average precipitation. The island occupies a small part of the domain, so that significant precipitation variations over the island can occur, compensated by smaller variations over the larger surrounding oceanic area. While the prevailing wind speeds vary over flat islands, three distinct flow regimes occur. Rainfall is greatly enhanced, and a local symmetric circulation is formed in the time mean around the island, when the prevailing large-scale wind speed is small. The rainfall enhancement over the island is much reduced when the wind speed is increased to a moderate value. This difference is characterized by a change in the mechanisms by which convection is forced. A thermally forced sea breeze due to surface heating dominates when the large-scale wind is weak. Mechanically forced convection, on the other hand, is favored when the large-scale wind is moderately strong, and horizontal advection of temperature reduces the land–sea thermal contrast that drives the sea breeze. Further increases of the prevailing wind speed lead to strong asymmetry between the windward and leeward sides of the island, owing to gravity waves that result from the land–sea contrast in surface roughness as well as upward deflection of the horizontal flow by elevated diurnal heating. Small-amplitude topography (up to 800-m elevation is considered) has a quantitative impact but does not qualitatively alter the flow regimes or their dependence on wind speed.

Subseasonal forecasts of California precipitation during the unusual winters of 2015-16 and 2016-17 are examined in this study. It is shown that two different ensemble forecast systems were able to predict monthly precipitation anomalies in California during these periods with some skill in forecasts initialized near or at the start of the month. The unexpected anomalies in February 2016, as well as in January and February 2017, were associated with shifts in the position of the jet stream over the northeast Pacific in a manner broadly consistent with associations found in larger ensembles of forecasts. These results support the broader notion that what is unpredictable atmospheric noise at the seasonal time scale can become predictable signal at the subseasonal time scale, despite that the lead times and verification averaging times associated with these forecasts are outside the predictability horizons of canonical mid-range weather forecasting.

Western North Pacific tropical cyclone (TC) model tracks are analyzed in two large multimodel ensembles, spanning a large variety of models and multiple future climate scenarios. Two methodologies are used to synthesize the properties of TC tracks in this large data set: cluster analysis and mass moment ellipses. First, the models' TC tracks are compared to observed TC tracks' characteristics, and a subset of the models is chosen for analysis, based on the tracks' similarity to observations and sample size. Potential changes in track types in a warming climate are identified by comparing the kernel smoothed probability distributions of various track variables in historical and future scenarios using a Kolmogorov-Smirnov significance test. Two track changes are identified. The first is a statistically significant increase in the north-south expansion, which can also be viewed as a poleward shift, as TC tracks are prevented from expanding equatorward due to the weak Coriolis force near the equator. The second change is an eastward shift in the storm tracks that occur near the central Pacific in one of the multimodel ensembles, indicating a possible increase in the occurrence of storms near Hawaii in a warming climate. The dependence of the results on which model and future scenario are considered emphasizes the necessity of including multiple models and scenarios when considering future changes in TC characteristics.

The effect of coupling a slab ocean mixed layer to atmospheric convection is examined in cloud-resolving model (CRM) simulations in vertically sheared and unsheared environments without Coriolis force, with the large-scale circulation parameterized using the Weak Temperature Gradient (WTG) approximation. Surface fluxes of heat and moisture as well as radiative fluxes are fully interactive, and the vertical profile of domain-averaged horizontal wind is strongly relaxed toward specified profiles with vertical shear that varies from one simulation to the next. Vertical wind shear is found to play a critical role in the simulated behavior. There exists a threshold value of the shear strength above which the coupled system develops regular oscillations between deep convection and dry nonprecipitating states, similar to those found earlier in a much more idealized model which did not consider wind shear. The threshold value of the vertical shear found here varies with the depth of the ocean mixed layer. The time scale of the spontaneously generated oscillations also varies with mixed layer depth, from 10 days with a 1 m deep mixed layer to 50 days with a 10 m deep mixed layer. The results suggest the importance of the interplay between convection organized by vertical wind shear, radiative feedbacks, large-scale dynamics, and ocean mixed layer heat storage in real intraseasonal oscillations.

Tropical cyclone intensification processes are explored in six high-resolution climate models. The analysis framework employs process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled. These diagnostics include budgets of column moist static energy and the spatial variance of column moist static energy, where the column integral is performed between fixed pressure levels. The latter allows for the quantification of the different feedback processes responsible for the amplification of moist static energy anomalies associated with the organization of convection and cyclone spinup, including surface flux feedbacks and cloud-radiative feedbacks. Tropical cyclones (TCs) are tracked in the climate model simulations and the analysis is applied along the individual tracks and composited over many TCs. Two methods of compositing are employed: a composite over all TC snapshots in a given intensity range, and a composite over all TC snapshots at the same stage in the TC life cycle (same time relative to the time of lifetime maximum intensity for each storm). The radiative feedback contributes to TC development in all models, especially in storms of weaker intensity or earlier stages of development. Notably, the surface flux feedback is stronger in models that simulate more intense TCs. This indicates that the representation of the interaction between spatially varying surface fluxes and the developing TC is responsible for at least part of the intermodel spread in TC simulation.

Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers’ model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis.