Search

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 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.

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.

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.

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 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.

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.

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.

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 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.

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.

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.

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.

The prediction of precipitation by two ocean–atmosphere ensemble systems is compared with observations and each other over a broad range of time scales. The systems are the 2015 version of the Australian Bureau of Meteorology's POAMA with a T47 atmosphere, and the 2011 version of the European Centre for Medium Range Weather Forecasts’ (ECMWF) monthly system with a variable atmospheric resolution of T639 to T319. To facilitate the comparison across a seamless range of time scales, verification against observations is performed using data averaged over time windows equal in length to the forecast lead time, from 1 day to 4 weeks. In addition to this ‘actual’ skill, potential skill is computed by taking one ensemble member as truth and computing how well the other members forecast that member. Overall, ECMWF shows higher actual skill than POAMA across all time scales and in both the tropics and extratropics, as expected given its greater sophistication. ECMWF is particularly more skilful than POAMA in the tropics for the shorter leads. Consistent between the two systems, however, is that as lead time and averaging window are simultaneously increased the near-equatorial skill remains approximately constant, whereas it drops in all other latitude bands. As a result, both systems show much higher skill in the tropics than extratropics for the one week time scale and beyond, with that skill concentrated over the equatorial Pacific. Although potential skill in both systems is almost everywhere higher than their actual skill, there remains a strong similarity in the spatial patterns of potential and actual skill for the longer time scales. Within-model comparisons of potential and actual skill show largest differences for POAMA in the tropics at short lead times, and largest differences for ECMWF in the southern hemisphere high latitudes (50-70°S). The implications of these findings are discussed.

The authors perform 3D cloud-resolving simulations of radiative–convective equilibrium (RCE) in a rotating framework, with interactive radiation and surface fluxes and fixed sea surface temperature. A tropical cyclone is allowed to develop spontaneously from a homogeneous environment, rather than initializing the circulation with a weak vortex or moist bubble (as is often done in numerical simulations of tropical cyclones). The resulting tropical cyclogenesis is compared to the self-aggregation of convection that occurs in nonrotating RCE simulations. The feedbacks leading to cyclogenesis are quantified using a variance budget equation for the column-integrated frozen moist static energy. In the initial development of a broad circulation, feedbacks involving longwave radiation and surface enthalpy fluxes dominate, which is similar to the initial phase of nonrotating self-aggregation. Mechanism denial experiments are also performed to determine the extent to which the radiative feedbacks that are essential to nonrotating self-aggregation are important for tropical cyclogenesis. Results show that radiative feedbacks aid cyclogenesis but are not strictly necessary.

During the second half of 2014, the tropical Pacific was in a state marginally consistent with El Nino. While oceanic indicators were indicative of a weak El Nino event, a number of atmospheric indicators were not, and a number of forecast centers did not declare an El Nino. Nonetheless, the most active tropical cyclone basins of the northern hemisphere—those of the North Atlantic and Pacific—showed tropical cyclone statistics that in some respects were consistent with El Nino. In particular, the numbers of relatively intense storms in the four basins considered—major hurricanes in the Eastern North Pacific and North Atlantic, super typhoons in the Western North Pacific, and hurricanes in the Central North Pacific—formed a pattern strongly consistent with El Nino.

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.

The authors present a global climatology of tropical cyclones (TCs) that undergo extratropical transition (ET). ET is objectively defined based on a TC’s trajectory through the cyclone phase space (CPS), which is calculated using storm tracks from 1979–2017 best track data and geopotential height fields from reanalysis datasets. Two reanalyses are used and compared for this purpose, the Japanese 55-yr Reanalysis and the ECMWF interim reanalysis. The results are used to study the seasonal and geographical distributions of storms undergoing ET and interbasin differences in the statistics of ET occurrence. About 50% of all TCs in the North Atlantic and the western North Pacific undergo ET. In the Southern Hemisphere, ET fractions range from about 20% in the south Indian Ocean and the Australian region to 45% in the South Pacific. In the majority of ETs, TCs become thermally asymmetric before forming a cold core. However, a substantial fraction of TCs take the reverse pathway, developing a cold core before becoming thermally asymmetric. This pathway is most common in the eastern North Pacific and the North Atlantic. Different ET pathways can be linked to different geographical trajectories and environmental settings. In ETs over warmer sea surface temperatures, TCs tend to lose their thermal symmetry while still maintaining a warm core. Landfalls by TCs undergoing ET occur 3–4 times per year in the North Atlantic and 7–10 times per year in the western North Pacific, while coastal regions in the Australian region are affected once every 1–2 years.

Aerosol cooling reduces tropical cyclone (TC) potential intensity (PI) more strongly, by about a factor of 2 per degree of sea surface temperature change, than greenhouse gas warming increases it. This study analyzes single-forcing and historical experiments from phase 5 of the Coupled Model Intercomparison Project, aiming to understand the physical mechanisms behind this difference. Calculations are done for the tropical oceans of each hemisphere during the relevant TC seasons, emphasizing multimodel means. PI theory is used to interpret the difference in the PI response to aerosol and greenhouse gas forcings in terms of three factors. The net surface turbulent heat flux (sum of the latent and sensible heat fluxes) explains half of the difference, thermodynamic efficiency explains at most a small fraction, and surface wind speed does not explain the remainder, perhaps because of the use of monthly mean data. Changes in turbulent surface heat fluxes are interpreted as responses to surface radiative flux changes in the context of the energy balance of the ocean mixed layer. Radiative kernels are used to estimate what fractions of the surface radiative flux changes are feedbacks due to temperature and water vapor changes. The greater effect of aerosol forcing occurs because shortwave forcing has a greater direct, temperature-independent component at the surface than does longwave forcing, for a forcing amplitude that provokes the same SST change. This conclusion recalls prior work on the response of precipitation to radiative forcing, and the similarities and differences between precipitation and potential intensity in this regard are discussed.

The hazard to the city of Mumbai, India, from a possible severe tropical cyclone under the recent historical climate is considered. The authors first determine, based on a review of primary sources, that the Bombay Cyclone of 1882, documented in a number of print and Internet sources and claimed to have caused 100 000 or more deaths, did not occur. Two different tropical cyclone hazard models, both of which generate large numbers of synthetic cyclones using environmental data—here taken from reanalyses in the satellite era—as input, are then used to quantify the hazard, in conjunction with historical observations. Both models indicate that a severe cyclone landfall at or near Mumbai is possible, though unlikely in any given year. Return periods for wind speeds exceeding 100 kt (1 kt = 0.5144 m s−1) (the threshold for category 3 in the Saffir–Simpson hurricane wind scale) at Mumbai itself are estimated to be in the range of thousands to greater than 10 000 years, while the return period for a storm with maximum wind speed of 100 kt or greater passing within 150 km of Mumbai (possibly close enough to generate a substantial storm surge at the city) is estimated to be around 500 years. Return periods for winds exceeding 65 kt (hurricane intensity on the Saffir–Simpson hurricane wind scale) are estimated to be around 200 years at Mumbai itself, and 50–90 years within 150 km. Climate change is not explicitly considered in this study, but the hazard to the city is likely to be increasing because of sea level rise as well as changes in storm climatology.

As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations. In the face of deep uncertainties, the known limitations of this approach are becoming increasingly apparent. An alternative is thus emerging which may be called a ‘storyline’ approach. We define a storyline as a physically self-consistent unfolding of past events, or of plausible future events or pathways. No a priori probability of the storyline is assessed; emphasis is placed instead on understanding the driving factors involved, and the plausibility of those factors. We introduce a typology of four reasons for using storylines to represent uncertainty in physical aspects of climate change: (i) improving risk awareness by framing risk in an event-oriented rather than a probabilistic manner, which corresponds more directly to how people perceive and respond to risk; (ii) strengthening decision-making by allowing one to work backward from a particular vulnerability or decision point, combining climate change information with other relevant factors to address compound risk and develop appropriate stress tests; (iii) providing a physical basis for partitioning uncertainty, thereby allowing the use of more credible regional models in a conditioned manner and (iv) exploring the boundaries of plausibility, thereby guarding against false precision and surprise. Storylines also offer a powerful way of linking physical with human aspects of climate change.

A single-column modeling approach is proposed to study the interaction between convection and large-scale dynamics using the quasigeostrophic (QG) framework. This approach extends the notion of “parameterization of large-scale dynamics,” previously applied in the tropics via the weak temperature gradient approximation and other comparable methods, to the extratropics, where balanced adiabatic dynamics plays a larger role in inducing large-scale vertical motion. The diabatic heating in an air column is resolved numerically by a single-column model or a cloud-resolving model. The large-scale vertical velocity, which controls vertical advection of temperature and moisture, is computed through the QG omega equation including the dry adiabatic terms and the diabatic heating term. The component due to diabatic heating can be thought of as geostrophic adjustment to that heating and couples the convection to the large-scale vertical motion. The approach is demonstrated using two representations of convection: a single-column model and linear response functions derived by Z. Kuang from a large set of cloud-resolving simulations. The results are qualitatively similar in both cases. The behavior of convection that is strongly coupled to large-scale dynamics is significantly different from that in the uncoupled case. The positive feedback of the diabatic heating on the large-scale vertical motion reduces the stability of the system, extends the decay time scale after initial perturbations, and increases the amplitude of convective responses to transient large-scale perturbations or imposed forcings. The diabatic feedback of convection on vertical motion is strongest for horizontal wavelengths on the order of the Rossby deformation radius.

The authors investigate the effects of cloud–radiation interaction and vertical wind shear on convective ensembles interacting with large-scale dynamics in cloud-resolving model simulations, with the large-scale circulation parameterized using the weak temperature gradient approximation. Numerical experiments with interactive radiation are conducted with imposed surface heat fluxes constant in space and time, an idealized lower boundary condition that prevents wind–evaporation feedback. Each simulation with interactive radiation is compared to a simulation in which the radiative heating profile is held constant in the horizontal and in time and is equal to the horizontal-mean profile from the interactive-radiation simulation with the same vertical shear profile and surface fluxes. Interactive radiation is found to reduce mean precipitation in all cases. The magnitude of the reduction is nearly independent of the vertical wind shear but increases with surface fluxes. Deep shear also reduces precipitation, though by approximately the same amount with or without interactive radiation. The reductions in precipitation due to either interactive radiation or deep shear are associated with strong large-scale ascent in the upper troposphere, which more strongly exports moist static energy and is quantified by a larger normalized gross moist stability.

The effects of turbulent surface fluxes and radiative heating on tropical deep convection are compared in a series of idealized cloud-system-resolving simulations with parameterized large-scale dynamics. Two methods of parameterizing the large-scale dynamics are used: the weak temperature gradient (WTG) approximation and the damped gravity wave (DGW) method. Both surface fluxes and radiative heating are specified, with radiative heating taken as constant in the vertical in the troposphere. All simulations are run to statistical equilibrium. In the precipitating equilibria, which result from sufficiently moist initial conditions, an increment in surface fluxes produces more precipitation than an equal increment of column-integrated radiative heating. This is straightforwardly understood in terms of the column-integrated moist static energy budget with constant normalized gross moist stability. Under both large-scale parameterizations, the gross moist stability does in fact remain close to constant over a wide range of forcings, and the small variations that occur are similar for equal increments of surface flux and radiative heating. With completely dry initial conditions, the WTG simulations exhibit hysteresis, maintaining a dry state with no precipitation for a wide range of net energy inputs to the atmospheric column. The same boundary conditions and forcings admit a rainy state also (for moist initial conditions), and thus multiple equilibria exist under WTG. When the net forcing (surface fluxes minus radiative heating) is increased enough that simulations that begin dry eventually develop precipitation, the dry state persists longer after initialization when the surface fluxes are increased than when radiative heating is increased. The DGW method, however, shows no multiple equilibria in any of the simulations.

Observational studies suggest that the stratospheric quasi-biennial oscillation (QBO) can modulate tropical deep convection. The authors use a cloud-resolving model with a limited domain, representing a convective column in the tropics, to study the mechanisms of this modulation. The large-scale circulation is parameterized using the weak temperature gradient (WTG) approximation, under which the parameterized large-scale vertical motion acts to relax the horizontal-mean temperature toward a specified reference profile. Temperature variations typically seen in easterly and westerly phases are imposed in the upper troposphere and lower stratosphere of this reference profile. The responses of convection are studied over different sea surface temperatures, holding the reference temperature profile fixed. This can be thought of as studying the response of convection to the QBO over different “relative SSTs” and also corresponds to different equilibrium precipitation rates in the control simulation. The equilibrium precipitation rate shows slight increases in response to a QBO easterly phase temperature perturbation over small SST anomalies and strong decreases over large SST anomalies, and vice versa for the QBO westerly phase perturbation. A column moist static energy budget analysis reveals that the QBO modulates the convective precipitation through two pathways: it changes the high-cloud properties and thus the column radiative cooling, and it alters the shape of the large-scale vertical motion and thus the efficiency of energy transport by the large-scale flow. The nonmonotonicity of the precipitation response with respect to relative SST results from the competition of these two effects.

This study investigates projected changes in the length of the tropical cyclone season due to greenhouse gas increases. Two sets of simulations are analyzed, both of which capture the relevant features of the observed annual cycle of tropical cyclones in the recent historical record. Both sets use output from the general circulation models (GCMs) of either phase 3 or phase 5 of the CMIP suite (CMIP3 and CMIP5, respectively). In one set, downscaling is performed by randomly seeding incipient vortices into the large-scale atmospheric conditions simulated by each GCM and simulating the vortices’ evolution in an axisymmetric dynamical tropical cyclone model; in the other set, the GCMs’ sea surface temperature (SST) is used as the boundary condition for a high-resolution global atmospheric model (HiRAM). The downscaling model projects a longer season (in the late twenty-first century compared to the twentieth century) in most basins when using CMIP5 data but a slightly shorter season using CMIP3. HiRAM with either CMIP3 or CMIP5 SST anomalies projects a shorter tropical cyclone season in most basins. Season length is measured by the number of consecutive days that the mean cyclone count is greater than a fixed threshold, but other metrics give consistent results. The projected season length changes are also consistent with the large-scale changes, as measured by a genesis index of tropical cyclones. The season length changes are mostly explained by an idealized year-round multiplicative change in tropical cyclone frequency, but additional changes in the transition months also contribute.

Realistic simulations of the Madden–Julian oscillation (MJO) by global climate models (GCMs) remain a great challenge. To evaluate GCM simulations of the MJO, the U.S. CLIVAR MJO Working Group developed a standardized set of diagnostics, providing a comprehensive assessment of statistical properties of the MJO. Here, a suite of complementary diagnostics has been developed that provides discrimination and assessment of MJO simulations based on the perception that the MJO propagation has characteristic dynamic and thermodynamic structures. The new dynamics-oriented diagnostics help to evaluate whether a model produces eastward-propagating MJOs for the right reasons. The diagnostics include 1) the horizontal structure of boundary layer moisture convergence (BLMC) that moistens the lower troposphere to the east of a convection center, 2) the preluding eastward propagation of BLMC that leads the propagation of MJO precipitation by about 5 days, 3) the horizontal structure of 850-hPa zonal wind and its ...

Subseasonal probabilistic prediction of tropical cyclone (TC) genesis is investigated here using models from the Seasonal to Subseasonal (S2S) Prediction dataset. Forecasts are produced for basin-wide TC occurrence at weekly temporal resolution. Forecast skill is measured using the Brier skill score relative to a seasonal climatology that varies monthly through the TC season. Skill depends on models’ characteristics, lead time, and ensemble prediction design. Most models show skill for week 1 (days 1–7), the period when initialization is important. Among the six S2S models examined here, the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the best performance, with skill in the Atlantic, western North Pacific, eastern North Pacific, and South Pacific at week 2. Similarly, the Australian Bureau of Meteorology (BoM) model is skillful in the western North Pacific, South Pacific, and across northern Australia at week 2. The Madden–Julian oscillation (MJO) modulates observed TC genesis, and there is a relationship, across models and lead times, between models’ skill scores and their ability to accurately represent the MJO and the MJO–TC relation. Additionally, a model’s TC climatology also influences its performance in subseasonal prediction. The dependence of the skill score on the simulated climatology, MJO, and MJO–TC relationship, however, varies from one basin to another. Skill scores increase with the ensemble size, as found in previous weather and seasonal prediction studies.

Radiative–convective equilibrium (RCE) describes an idealized state of the atmosphere in which the vertical temperature profile is determined by a balance between radiative and convective fluxes. While RCE has been applied extensively over oceans, its application over the land surface has been limited. The present study explores the properties of RCE over land using an atmospheric single-column model (SCM) from the Laboratoire de Météorologie Dynamique–Zoom, version 5B (LMDZ5B) general circulation model coupled in temperature and moisture to a land surface model using a simplified bucket model with finite moisture capacity. Given the presence of a large-amplitude diurnal heat flux cycle, the resultant RCE exhibits multiple equilibria when conditions are neither strictly water nor energy limited. By varying top-of-atmosphere insolation (through changes in latitude), total system water content, and initial temperature conditions the sensitivity of the land RCE states is assessed, with particular emphasis on the role of clouds. Based on this analysis, it appears that a necessary condition for the model to exhibit multiple equilibria is the presence of low-level clouds coupled to the diurnal cycle of radiation. In addition the simulated surface precipitation rate varies nonmonotonically with latitude as a result of a tradeoff between in-cloud rain rate and subcloud rain reevaporation, thus underscoring the importance of subcloud layer processes and unsaturated downdrafts. It is shown that clouds, especially at low levels, are key elements of the internal variability of the coupled land–atmosphere system through their feedback on radiation.

It is well known that vertical wind shear can organize deep convective systems and greatly extend their lifetimes. Much less is known about the influence of shear on the bulk properties of tropical convection in statistical equilibrium. To address the latter question, the authors present a series of cloud-resolving simulations on a doubly periodic domain with parameterized large-scale dynamics based on the weak temperature gradient (WTG) approximation. The horizontal-mean horizontal wind is relaxed strongly in these simulations toward a simple unidirectional linear vertical shear profile in the troposphere. The strength and depth of the shear layer are varied as control parameters. Surface enthalpy fluxes are prescribed. The results fall in two distinct regimes. For weak wind shear, time-averaged rainfall decreases with shear and convection remains disorganized. For larger wind shear, rainfall increases with shear, as convection becomes organized into linear mesoscale systems. This nonmonotonic dependence of rainfall on shear is observed when the imposed surface fluxes are moderate. For larger surface fluxes, convection in the unsheared basic state is already strongly organized, but increasing wind shear still leads to increasing rainfall. In addition to surface rainfall, the impacts of shear on the parameterized large-scale vertical velocity, convective mass fluxes, cloud fraction, and momentum transport are also discussed.

Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) project changes to the seasonality of both tropical sea surface temperature (SST) and precipitation when forced by an increase in greenhouse gases. Nearly all models project an amplification and a phase delay of the annual cycle for both quantities, indicating a greater annual range and extrema reached later in the year. The authors investigate the nature of the seasonal precipitation changes in AGCM experiments forced by SST perturbations, which represent idealizations of the changes in annual mean, amplitude, and phase as simulated by CMIP5 models. A uniform SST warming is sufficient to force both amplification and a delay of the annual cycle of precipitation. The amplification is due to an increase in the annual mean vertical water vapor gradient, while the delay is affected by changes in the seasonality of the circulation. A budget analysis of this simulation reveals a large degree of similarity with the CMIP5 results. In the second experiment, only the seasonal characteristics of SST are changed. In response to an amplified annual cycle of SST, the annual cycle of precipitation is amplified, while for a delayed SST, the annual cycle of precipitation is delayed. Assuming that SST changes can entirely explain the seasonal precipitation changes, the AGCM simulations herein suggest that the annual mean warming explains most of the amplitude increase and much of the phase delay in the CMIP5 models. However, imperfect agreement between the changes in the SST-forced AGCM simulations and the CMIP5 coupled simulations suggests that coupled effects may play a significant role.

In previous work the authors demonstrated an empirical relation, in the form of an index, between U.S. monthly tornado activity and monthly averaged environmental parameters. Here a detailed comparison is made between the index and reported tornado activity. The index is a function of two environmental parameters taken from the North American Regional Reanalysis: convective precipitation (cPrcp) and storm relative helicity (SRH). Additional environmental parameters are considered for inclusion in the index, among them convective available potential energy, but their inclusion does not significantly improve the overall climatological performance of the index. The aggregate climatological dependence of reported monthly U.S. tornado numbers on cPrcp and SRH is well described by the index, although it fails to capture nonsupercell and cool season tornadoes. The contributions of the two environmental parameters to the index annual cycle and spatial distribution are examined with the seasonality of cPrcp (maximum during summer) relative to SRH (maximum in winter) accounting for the index peak value in May. The spatial distribution of SRH establishes the central U.S. “tornado alley” of the index, while the spatial distribution of cPrcp enhances index values in the South and Southeast and suppresses them west of the Rockies and over elevation. At the scale of the NOAA climate regions, the largest deficiency of the index climatology occurs over the central region where the index peak in spring is too low and where the late summer drop-off in the reported number of tornadoes is poorly captured. This index deficiency is related to its sensitivity to SRH, and increasing the index sensitivity to SRH improves the representation of the annual cycle in this region. The ability of the index to represent the interannual variability of the monthly number of U.S. tornadoes can be ascribed during most times of the year to interannual variations of cPrcp rather than of SRH. However, both factors are important during the peak spring period. The index shows some skill in representing the interannual variability of monthly tornado numbers at the scale of NOAA climate regions.

Author(s): Serr, YL; Haase, JS; Adams, DK; Fu, Q; Ackerman, TP; Alexander, MJ; Arellano, A; Back, L; Emanuel, K; Kuang, Z; Mapes, B; Neelin, D; Raymond, D; Sobel, AH; Staten, PW; Subramanian, A; Thompson, DWJ; Vecchi, G; Wood, R; Zuidema, P

The skill with which a coupled ocean–atmosphere model is able to predict precipitation over a range of time scales (days to months) is analyzed. For a fair comparison across the seamless range of scales, the verification is performed using data averaged over time windows equal in length to the lead time. At a lead time of 1 day, skill is greatest in the extratropics around 40°–60° latitude and lowest around 20°, and has a secondary local maximum close to the equator. The extratropical skill at this short range is highest in the winter hemisphere, presumably due to the higher predictability of winter baroclinic systems. The local equatorial maximum comes mostly from the Pacific Ocean, and thus appears to be mostly from El Niño–Southern Oscillation (ENSO). As both the lead time and averaging window are simultaneously increased, the extratropical skill drops rapidly with lead time, while the equatorial maximum remains approximately constant, causing the equatorial skill to exceed the extratropical at leads of greater than 4 days in austral summer and 1 week in boreal summer. At leads longer than 2 weeks, the extratropical skill flattens out or increases, but remains below the equatorial values. Comparisons with persistence confirm that the model beats persistence for most leads and latitudes, including for the equatorial Pacific where persistence is high. The results are consistent with the view that extratropical predictability is mostly derived from synoptic-scale atmospheric dynamics, while tropical predictability is primarily derived from the response of moist convection to slowly varying forcing such as from ENSO.

Basinwide convective anomalies over the Indian Ocean (IO) associated with the Madden–Julian oscillation (MJO) sometimes propagate eastward and reach the west Pacific (WP), but sometimes do not. Long-term observations and reanalysis products are used to investigate the difference between the propagating and nonpropagating MJO events. IO convection onset events associated with the MJO are grouped into three categories based on the strengths of the simultaneous dry anomalies over the eastern Maritime Continent and WP. The IO convection anomaly preferentially makes eastward propagation and reaches the WP when the dry anomaly is stronger. Analysis of the column-integrated moist static energy (MSE) budget shows that horizontal advection moistens the atmosphere to the east of the positive MSE anomaly associated with the active convection over the IO and is of sufficient magnitude to explain the eastward propagation of the positive MSE anomaly. Interpretation is complicated, however, by lack of closure in the MSE budget. A residual term, of smaller but comparable magnitude to the horizontal advection, also moistens the column to the east of the positive MSE anomaly. Nonetheless, the authors decompose the horizontal advection term into contributions from different scales and find that a dominant contribution is from free-tropospheric meridional advection by the intraseasonal time scale wind anomalies. The positive meridional advection in between the convective and dry anomalies is induced by the anomalous poleward flow, which is interpreted as part of the Rossby wave response to the dry anomaly. The poleward flow advects the climatological MSE, which peaks at the equator, and moistens to the east of IO convective anomaly.