Search

Global models are frequently used for tropical cyclone (TC) prediction and climate projections, but have biases in their representation of TCs that are not fully understood. The objective of thiswork is to assess howwell and howrobustly physical processes that are important for TC development are represented in modern reanalysis products, and to consider whether reanalyses can serve as an observationally-constrained reference against which model representation of these physical processes can be evaluated. Differences in the representation of large-scale environmental variables relevant to TC development do not readily explain the spread in TC climatologies across climate models, as found in prior work, or across reanalysis datasets, as shown here. This motivates the use of process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled and can be used to identify areas to target for model improvement. Using the column-integrated moist static energy (MSE) variance budget, we analyze radiative and surface flux feedbacks across five different reanalyses. We construct an intensity bin composite of the MSE variance budget to compare storms of similar intensity. Our results point to some fundamental differences across reanalyses in how they represent MSE variance and surface flux and radiative feedbacks in TCs, which could contribute to differences across reanalyses in how they represent TCs, but other factors also likely contribute. Any future work that evaluates these diagnostics in GCMs against reanalyses should do so cautiously, and efforts should be undertaken to provide a true observational estimate of these processes.

This study evaluates tropical cyclone (TC) rainfall structures in the CMIP6 HighResMIP global climate model (GCM) simulations against satellite rainfall retrievals. We specifically focus on TCs within the deep tropics (25°S–25°N). Analysis of TC rain rate composites indicates that in comparison to the satellite observations at the same intensity, many HighResMIP simulations tend to overproduce rain rates around TCs, in terms of both maximum rain rate magnitude and area-averaged rain rates. In addition, as model horizontal resolution increases, the magnitude of the peak rain rate appears to increase. However, the area-averaged rain rates decrease with increasing horizontal resolution, partly due to the TC eyewall being located closer to the TC center, thus occupying a smaller area and contributing less to the area-averaged rain rates. The effect of ocean coupling is to lower the TC rain rates, bringing them closer to the satellite observations, due to reduced horizontal moisture flux convergence and surface latent heat flux beneath TCs. Examination of horizontal rain rate distributions indicates that vertical wind shear–induced rainfall asymmetries in HighResMIP-simulated TCs are qualitatively consistent with the observations. In addition, a positive relationship is observed between the area-averaged inner-core rainfall and TC intensification likelihoods across the HighResMIP simulations, as GCM simulations producing stronger TCs more frequently have the greater rainfall close to the center, in agreement with previous theoretical and GCM simulation results.

In 2021, people of Hispanic and Latinx origin made up 6% of the atmospheric and Earth sciences workforce of the United States, yet they represent 20% of the population. Motivated by this disparity in Hispanic and Latinx representation in the atmospheric and Earth science workforce, this manuscript documents the lack of representation through existing limited demographic data. The analysis presents a clear gap in participation by Hispanic and Latinx people in academic settings, with a widening gap through each education and career stage. Several factors and challenges impacting the representation disparity include the lack of funding for and collaboration with Hispanic Serving Institutions, limited opportunities due to immigration status, and limited support for international research collaborations. We highlight the need for actionable steps to address the lack of representation and provide targeted recommendations to federal funding agencies, educational institutions, faculty, and potential employers. While we wait for system cultural change from our scientific institutions, grassroots initiatives like those proudly led by the AMS Committee for Hispanic and Latinx Advancement will emerge to address the needs of the Hispanic and Latinx scientific and broader community. We briefly highlight some of those achievements. Lasting cultural change can only happen if our leaders are active allies in the creation of a more diverse, equitable, and inclusive future. Alongside our active allies we will continue to champion for change in our weather, water, and climate enterprise.

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.

Tropical cyclone precipitation (TCP), accounting for some of the most extreme rainfall events, can lead to severe flooding and landslides, which often occur together as compound natural hazards during a tropical cyclone landfall. The impact due to TCP is largely associated with its intensity and spatial extent as the storm approaches landfall. Yet it is not entirely clear how TCP intensity and spatial extent vary from one tropical cyclone to another. In this study, we employ an advanced geostatistical framework to determine the TCP intensity and spatial extent along cyclone tracks for different cyclone categories, defined using the wind speed and tropical cyclone lifetime maximum intensity (LMI) at each track point (“point intensity-LMI”). The results show that when a tropical cyclone with an LMI of a supertyphoon makes landfall and has weakened to tropical storm strength it usually produces the most intense rainfall and covers the largest spatial extent. The total TCP amount estimated using the varying spatial extent helps to determine more accurately the amount of seasonal rainfall that is from tropical cyclones in China. We also determined the rainfall trend from 1951 to 2019 for TCP and found that when compared with the inland stations the historical TCP rainfall trend in those stations near the coastline of China is significantly increasing. Significance Statement Heavy rainfall caused by tropical cyclones has caused huge direct or indirect economic losses in the coastal areas of China. This impact is particularly significant when the rainfall intensity is high and the area of heavy rainfall is extensive. Here we investigate the rainfall intensity and spatial extent by classifying and comparing the different types of tropical cyclones impacting China with varying intensities. To do this, we group the tropical cyclone tracks of the western North Pacific Ocean during the last seven decades according to the strength of wind speed across the cyclone tracks. We found that the largest areas and heaviest intensities of rainfall occur when a supertyphoon has weakened to a tropical storm at landfall. When considering all tropical cyclones and their rainfall contribution to rainfall over land stations, we found that tropical cyclone rainfall has become heavier in most coastal areas of China.

This paper assesses the skill of the Saudi-King Abdulaziz University coupled ocean–atmosphere Global Climate Model, namely Saudi-KAU CGCM, in forecasting the El Niño-Southern Oscillation (ENSO)-related sea surface temperature. The model performance is evaluated based on a reforecast of 38 years from 1982 to 2019, with 20 ensemble members of 12-month integrations. The analysis is executed on ensemble mean data separately for boreal winter (December to February: DJF), spring (March to May: MAM), summer (June to August: JJA), and autumn (September to November: SON) seasons. It is found that the Saudi-KAU model mimics the observed climatological pattern and variability of the SST in the tropical Pacific region. A cold bias of about 0.5–1.0 °C is noted in the ENSO region during all seasons at 1-month lead times. A statistically significant positive correlation coefficient is observed for the predicted SST anomalies in the tropical Pacific Ocean that lasts out to 6 months. Across varying times of the year and lead times, the model shows higher skill for autumn and winter target seasons than for spring or summer ones. The skill of the Saudi-KAU model in predicting Niño 3.4 index is comparable to that of state-of-the-art models available in the Copernicus Climate Change Service (C3S) and North American Multi-Model Ensemble (NMME) projects. The ENSO skill demonstrated in this study is potentially useful for regional climate services providing early warning for precipitation and temperature variations on sub-seasonal to seasonal time scales.

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.

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.

In this paper we analyze Atlantic Ocean hurricane activity in the European Centre for Medium-Range Weather Forecasts (ECMWF) monthly hindcasts for the period 1998–2017. The main climatological characteristics of Atlantic tropical cyclone (TC) activity are considered at different lead times and across the entire ECMWF ensemble using three diagnostic variables: the number of tropical cyclones, the number of hurricanes, and the accumulated cyclone energy. The impacts of changing horizontal resolution and stochastic parameterization are clear in these diagnostic variables. The model skill scores for the number of tropical cyclones and accumulated cyclone energy by lead time are also computed. Using cluster analysis, we compare the characteristics of the forecast TC tracks with observations. Although four of the ECMWF clusters have similar characteristics to observed ones, one of the ECMWF clusters does not have a corresponding one in observations. We consider the predictability of each of these clusters, as well the modulation of their frequency by climate modes, such as the El Niño–Southern Oscillation and the Madden–Julian oscillation, taking advantage of the very large sample size of TC datasets in these hindcasts.

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.

Assessing the role of anthropogenic warming from temporally inhomogeneous historical data in the presence of large natural variability is difficult and has caused conflicting conclusions on detection and attribution of tropical cyclone (TC) trends. Here, using a reconstructed long-term proxy of annual TC numbers together with high-resolution climate model experiments, we show robust declining trends in the annual number of TCs at global and regional scales during the twentieth century. The Twentieth Century Reanalysis (20CR) dataset is used for reconstruction because, compared with other reanalyses, it assimilates only sea-level pressure fields rather than utilize all available observations in the troposphere, making it less sensitive to temporal inhomogeneities in the observations. It can also capture TC signatures from the pre-satellite era reasonably well. The declining trends found are consistent with the twentieth century weakening of the Hadley and Walker circulations, which make conditions for TC formation less favourable.

The synoptic low-pressure systems (LPSs) formed over the downwind side of the Tibetan Plateau explain a substantial portion of summer rainfall extremes along their paths. Recent studies have found that the total extreme rainfall trend over the East Asian landmass, which features the “south flood-north drought” pattern, can be understood to a great extent by the changes in terrestrial LPSs. Yet, the energy sources fueling these storms and the environmental drivers of their long-term trends remain unclear. Utilizing a probabilistic clustering method, three clusters of terrestrial LPS tracks for the period 1979-2018 are identified. Besides the differences in trajectories that distinguish the clusters into northeastward-migrating and quasi-stationary types, prominent inter-cluster differences are found in the LPS evolution, energetics, and trends. The Lorenz energetics suggest that while condensational heating is indispensable for all three clusters, the migratory type, which has greater intensity and faster development, is more closely tied to baroclinicity. Nonetheless, the summer baroclinicity alone is not enough to sustain these LPSs as these storms dissipate quickly after propagating out of the humid monsoon region and into the drier extratropics. Over time, the occurrences of migratory LPSs decreases, and that of quasi-stationary LPSs increases. Using a Poisson model that links the LPS genesis to local environmental conditions, the decreasing occurrence of migratory LPSs is shown to result from the weakened baroclinicity, whereas the increasing occurrence of quasi-stationary LPSs is primarily driven by enhanced relative humidity and reduced steering flow in the mid-to-lower troposphere over East Asia.

Extreme weather and climate events and their impacts can occur in complex combinations, an interaction shaped by physical drivers and societal forces. In these situations, governance, markets and other decision-making structures—together with population exposure and vulnerability—create nonphysical interconnections among events by linking their impacts, to positive or negative effect. Various anthropogenic actions can also directly affect the severity of events, further complicating these feedback loops. Such relationships are rarely characterized or considered in physical-sciences-based research contexts. Here, we present a multidisciplinary argument for the concept of connected extreme events, and we suggest vantage points and approaches for producing climate information useful in guiding decisions about them. The impacts of extreme weather and climate can be amplified by physical interactions among events and across a complex set of societal factors. This Perspective discusses the concept and challenge of connected extreme events, exploring research approaches and decision-making strategies.

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.

Model projections of tropical cyclone (TC) activity response to anthropogenic warming in climate models are assessed. Observations, theory, and models, with increasing robustness, indicate rising global TC risk for some metrics that are projected to impact multiple regions. A 2°C anthropogenic global warming is projected to impact TC activity as follows. 1) The most confident TC-related projection is that sea level rise accompanying the warming will lead to higher storm inundation levels, assuming all other factors are unchanged. 2) For TC precipitation rates, there is at least medium-to-high confidence in an increase globally, with a median projected increase of 14%, or close to the rate of tropical water vapor increase with warming, at constant relative humidity. 3) For TC intensity, 10 of 11 authors had at least medium-to-high confidence that the global average will increase. The median projected increase in lifetime maximum surface wind speeds is about 5% (range: 1%–10%) in available higher-resolution studies. 4) For the global proportion (as opposed to frequency) of TCs that reach very intense (category 4–5) levels, there is at least medium-to-high confidence in an increase, with a median projected change of +13%. Author opinion was more mixed and confidence levels lower for the following projections: 5) a further poleward expansion of the latitude of maximum TC intensity in the western North Pacific; 6) a decrease of global TC frequency, as projected in most studies; 7) an increase in global very intense TC frequency (category 4–5), seen most prominently in higher-resolution models; and 8) a slowdown in TC translation speed.

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.

The authors investigate the statistical relationships between interannual variations in economic losses from Tropical Cyclones (TCs), basin-wide metrics of TC activity, and indicators of large-scale climate variability in the North Atlantic basin. The results show substantial and statistically significant correlations between the economic losses and some seasonal TC activity metrics, namely the accumulated cyclone energy and power dissipation index. These results suggest that accurate seasonal forecasts of these metrics can be taken as relevant to potential TC losses, even though the metrics in question do not take into account whether storms make landfall, let alone where they do so. The authors suggest that the degree of correlation obtained might be considered a baseline against which to measure some aspects of the performance of catastrophe models.

The ocean and atmosphere in the North Atlantic are coupled through a feedback mechanism that excites a dipole pattern in vertical wind shear (VWS), a metric that strongly controls Atlantic hurricanes. In particular, when tropical VWS is under the weakening phase and thus favorable for increased hurricane activity in the Main Development Region (MDR), a protective barrier of high VWS inhibits hurricane intensification along the U.S. East Coast. Here we show that this pattern is driven mostly by natural decadal variability, but that greenhouse gas (GHG) forcing erodes the pattern and degrades the natural barrier along the U.S. coast. Twenty-first century climate model projections show that the increased VWS along the U.S. East Coast during decadal periods of enhanced hurricane activity is substantially reduced by GHG forcing, which allows hurricanes approaching the U.S. coast to intensify more rapidly. The erosion of this natural intensification barrier is especially large following the Representative Concentration Pathway 8.5 (rcp8.5) emission scenario.