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Pang-Chi Hsu
,
Yitian Qian
,
Yu Liu
,
Hiroyuki Murakami
, and
Yingxia Gao

Abstract

In the summer of 2018, Northeast Asia experienced a heatwave event that broke the existing high-temperature records in several locations in Japan, the Korean Peninsula, and northeastern China. At the same time, an unusually strong Madden–Julian oscillation (MJO) was observed to stay over the western Pacific warm pool. Based on reanalysis diagnosis, numerical experiments, and assessments of real-time forecast data from two subseasonal-to-seasonal (S2S) models, we discovered the importance of the western Pacific MJO in the generation of this heatwave event, as well as its predictability at the subseasonal time scale. During the prolonged extreme heat period (11 July–14 August), a high pressure anomaly with variability at the intraseasonal (30–90 days) time scale appeared over Northeast Asia, causing persistent adiabatic heating and clear skies in this region. As shown in the composites of MJO-related convection and circulation anomalies, the occurrence of this 30–90-day high anomaly over Northeast Asia was linked with an anomalous wave train induced by tropical heating associated with the western tropical Pacific MJO. The impact of the MJO on the heatwave was further confirmed by sensitivity experiments with a coupled GCM. As the western Pacific MJO-related components were removed by nudging prognostic variables over the tropics toward their annual cycle and longer time scales (>90 days) in the coupled GCM, the anomalous wave train along the East Asian coast disappeared and the surface air temperature in Northeast Asia lowered. The MJO over the western Pacific warm pool also influenced the predictability of the extratropical heatwave. Our assessments of two S2S models’ real-time forecasts suggest that the extremity of this Northeast Asian heatwave can be better predicted 1–4 weeks in advance if the enhancement of MJO convection over the western Pacific warm pool is predicted well.

Free access
Hiroyuki Murakami
,
Pang-Chi Hsu
,
Osamu Arakawa
, and
Tim Li

Abstract

The influence of model biases on projected future changes in the frequency of occurrence of tropical cyclones (FOCs) was investigated using a new empirical statistical method. Assessments were made of present-day (1979–2003) simulations and future (2075–99) projections, using atmospheric general circulation models under the Intergovernmental Panel on Climate Change (IPCC) A1B scenario and phase 5 of the Coupled Model Intercomparison Project (CMIP5) models under the representative concentration pathway (RCP) 4.5 and 8.5 scenarios. The models project significant decreases in global-total FOCs by approximately 6%–40%; however, model biases introduce an uncertainty of approximately 10% in the total future changes. The influence of biases depends on the model physics rather than model resolutions and emission scenarios. In general, the biases result in overestimates of projected future changes in basin-total FOCs in the north Indian Ocean (by +18%) and South Atlantic Ocean (+143%) and underestimates in the western North Pacific Ocean (−27%), eastern North Pacific Ocean (−29%), and North Atlantic Ocean (−53%). The calibration of model performance using the smaller bias influence appears crucial to deriving meaningful signals in future FOC projections. To obtain more reliable projections, ensemble averages were calculated using the models less influence by model biases. Results indicate marked decreases in projected FOCs in the basins of the Southern Hemisphere, Bay of Bengal, western North Pacific Ocean, eastern North Pacific, and Caribbean Sea and increases in the Arabian Sea and the subtropical central Pacific Ocean.

Full access
Yitian Qian
,
Hiroyuki Murakami
,
Pang-chi Hsu
, and
Sarah Kapnick
Free access
Hiroyuki Murakami
,
Gabriele Villarini
,
Gabriel A. Vecchi
,
Wei Zhang
, and
Richard Gudgel

Abstract

Retrospective seasonal forecasts of North Atlantic tropical cyclone (TC) activity over the period 1980–2014 are conducted using a GFDL high-resolution coupled climate model [Forecast-Oriented Low Ocean Resolution (FLOR)]. The focus is on basin-total TC and U.S. landfall frequency. The correlations between observed and model predicted basin-total TC counts range from 0.4 to 0.6 depending on the month of the initial forecast. The correlation values for U.S. landfalling activity based on individual TCs tracked from the model are smaller and between 0.1 and 0.4. Given the limited skill from the model, statistical methods are used to complement the dynamical seasonal TC prediction from the FLOR model. Observed and predicted TC tracks were classified into four groups using fuzzy c-mean clustering to evaluate the model’s predictability in observed classification of TC tracks. Analyses revealed that the FLOR model has the highest skill in predicting TC frequency for the cluster of TCs that tracks through the Caribbean and the Gulf of Mexico.

New hybrid models are developed to improve the prediction of observed basin-total TC and landfall TC frequencies. These models use large-scale climate predictors from the FLOR model as predictors for generalized linear models. The hybrid models show considerable improvements in the skill in predicting the basin-total TC frequencies relative to the dynamical model. The new hybrid model shows correlation coefficients as high as 0.75 for basinwide TC counts from the first two lead months and retains values around 0.50 even at the 6-month lead forecast. The hybrid model also shows comparable or higher skill in forecasting U.S. landfalling TCs relative to the dynamical predictions. The correlation coefficient is about 0.5 for the 2–5-month lead times.

Full access
Dasol Kim
,
Chang-Hoi Ho
,
Hiroyuki Murakami
, and
Doo-Sun R. Park

Abstract

Understanding the mechanisms related to the variations in the rainfall structure of tropical cyclones (TCs) is crucial in improving forecasting systems of TC rainfall and its impact. Using satellite precipitation and reanalysis data, we examined the influence of along-track large-scale environmental conditions on inner-core rainfall strength (RS) and total rainfall area (RA) for Atlantic TCs during the TC season (July–November) from 1998 to 2019. Factor analysis revealed three major factors associated with variations in RS and RA: large-scale low and high pressure systems [factor 1 (F1)]; environmental flows, sea surface temperature, and humidity [factor 2 (F2)]; and maximum wind speed of TCs [factor 3 (F3)]. Results from our study indicate that RS increases with an increase in the inherent primary circulation of TCs (i.e., F3) but is less affected by large-scale environmental conditions (i.e., F1 and F2), whereas RA is primarily influenced by large-scale low and high pressure systems (i.e., F1) over the entire North Atlantic and partially influenced by environmental flows, sea surface temperature, humidity, and maximum wind speed (i.e., F2 and F3). A multivariable regression model based on the three factors accounted for the variations of RS and RA across the entire basin. In addition, regional distributions of mean RS and RA from the model significantly resembled those from observations. Therefore, our study suggests that large-scale environmental conditions over the North Atlantic Ocean are important predictors for TC rainfall forecasts, particularly with regard to RA.

Full access
Gan Zhang
,
Hiroyuki Murakami
,
Xiaosong Yang
,
Kirsten L. Findell
,
Andrew T. Wittenberg
, and
Liwei Jia

Abstract

Climate models often show errors in simulating and predicting tropical cyclone (TC) activity, but the sources of these errors are not well understood. This study proposes an evaluation framework and analyzes three sets of experiments conducted using a seasonal prediction model developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These experiments apply the nudging technique to the model integration and/or initialization to estimate possible improvements from nearly perfect model conditions. The results suggest that reducing sea surface temperature (SST) errors remains important for better predicting TC activity at long forecast leads—even in a flux-adjusted model with reduced climatological biases. Other error sources also contribute to biases in simulated TC activity, with notable manifestations on regional scales. A novel finding is that the coupling and initialization of the land and atmosphere components can affect seasonal TC prediction skill. Simulated year-to-year variations in June land conditions over North America show a significant lead correlation with the North Atlantic large-scale environment and TC activity. Improved land–atmosphere initialization appears to improve the Atlantic TC predictions initialized in some summer months. For short-lead predictions initialized in June, the potential skill improvements attributable to land–atmosphere initialization might be comparable to those achievable with perfect SST predictions. Overall, this study delineates the SST and non-oceanic error sources in predicting TC activity and highlights avenues for improving predictions. The nudging-based evaluation framework can be applied to other models and help improve predictions of other weather extremes.

Full access
Yitian Qian
,
Pang-Chi Hsu
,
Hiroyuki Murakami
,
Gan Zhang
,
Huijun Wang
, and
Mingkeng Duan

Abstract

The intraseasonal variations in anticyclonic Rossby wave breaking (AWB) events, which are characterized by synoptic-scale irreversible meridional overturning of potential vorticity over the North Pacific, and their modulations on tropical cyclone (TC) activity over the western North Pacific (WNP), were investigated in this study. Spectral analysis of the AWB frequency shows significant variability within a period of 7–40 days, closely linked to the subseasonal variability of the jet stream intensity. When the jet stream weakens at its exit region over the North Pacific, the AWB occurs along with an equatorward Rossby wave flux. This AWB is preceded by an intensified Rossby wave train across Eurasia 12 days earlier. Simultaneously, a high potential vorticity intrusion is advected in the upper troposphere from the North Pacific toward the WNP, and suppressed TC activities are observed over the WNP open ocean where decreased moisture and temperature, subsidence, and increased vertical wind shear prevail. In contrast, anomalously enhanced convection, positive relative vorticity, and ascending motion are found in the southwestern quadrant of the AWB, facilitating enhanced TC activities over the South China Sea (SCS). Further analysis indicates that the impact of the AWB on TC activities over the WNP is robust and independent of the tropical intraseasonal convection over the tropical Indian Ocean and SCS, even though it accompanies the increased AWB frequency.

Restricted access
Wei Zhang
,
Gabriel A. Vecchi
,
Gabriele Villarini
,
Hiroyuki Murakami
,
Richard Gudgel
, and
Xiaosong Yang

Abstract

This study attempts to improve the prediction of western North Pacific (WNP) and East Asia (EA) landfalling tropical cyclones (TCs) using modes of large-scale climate variability [e.g., the Pacific meridional mode (PMM), the Atlantic meridional mode (AMM), and North Atlantic sea surface temperature anomalies (NASST)] as predictors in a hybrid statistical–dynamical scheme, based on dynamical model forecasts with the GFDL Forecast-Oriented Low Ocean Resolution version of CM2.5 with flux adjustments (FLOR-FA). Overall, the predictive skill of the hybrid model for the WNP TC frequency increases from lead month 5 (initialized in January) to lead month 0 (initialized in June) in terms of correlation coefficient and root-mean-square error (RMSE). The hybrid model outperforms FLOR-FA in predicting WNP TC frequency for all lead months. The predictive skill of the hybrid model improves as the forecast lead time decreases, with values of the correlation coefficient increasing from 0.56 for forecasts initialized in January to 0.69 in June. The hybrid models for landfalling TCs over the entire East Asian (EEA) coast and its three subregions [i.e., southern EA (SEA), middle EA (MEA), and northern EA (NEA)] dramatically outperform FLOR-FA. The correlation coefficient between predicted and observed TC landfall over SEA increases from 0.52 for forecasts initialized in January to 0.64 in June. The hybrid models substantially reduce the RMSE of landfalling TCs over SEA and EEA compared with FLOR-FA. This study suggests that the PMM and NASST/AMM can be used to improve statistical/hybrid forecast models for the frequencies of WNP or East Asia landfalling TCs.

Full access
Lakshmi Krishnamurthy
,
Gabriel A. Vecchi
,
Rym Msadek
,
Hiroyuki Murakami
,
Andrew Wittenberg
, and
Fanrong Zeng

Abstract

Tropical cyclone (TC) activity in the North Pacific and North Atlantic Oceans is known to be affected by the El Niño–Southern Oscillation (ENSO). This study uses the GFDL Forecast Oriented Low Ocean Resolution Model (FLOR), which has relatively high resolution in the atmosphere, as a tool to investigate the sensitivity of TC activity to the strength of ENSO events. This study shows that TCs exhibit a nonlinear response to the strength of ENSO in the tropical eastern North Pacific (ENP) but a quasi-linear response in the tropical western North Pacific (WNP) and tropical North Atlantic. Specifically, a stronger El Niño results in disproportionate inhibition of TCs in the ENP and North Atlantic, and leads to an eastward shift in the location of TCs in the southeast of the WNP. However, the character of the response of TCs in the Pacific is insensitive to the amplitude of La Niña events. The eastward shift of TCs in the southeast of the WNP in response to a strong El Niño is due to an eastward shift of the convection and of the associated environmental conditions favorable for TCs. The inhibition of TC activity in the ENP and Atlantic during El Niño is attributed to the increase in the number of days with strong vertical wind shear during stronger El Niño events. These results are further substantiated with coupled model experiments. Understanding of the impact of strong ENSO on TC activity is important for present and future climate as the frequency of occurrence of extreme ENSO events is projected to increase in the future.

Full access
Daehyun Kim
,
Yumin Moon
,
Suzana J. Camargo
,
Allison A. Wing
,
Adam H. Sobel
,
Hiroyuki Murakami
,
Gabriel A. Vecchi
,
Ming Zhao
, and
Eric Page

Abstract

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.

Open access