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Pier Luigi Vidale, Kevin Hodges, Benoit Vannière, Paolo Davini, Malcolm J. Roberts, Kristian Strommen, Antje Weisheimer, Elina Plesca, and Susanna Corti

Abstract

The role of model resolution in simulating geophysical vortices with the characteristics of realistic tropical cyclones (TCs) is well established. The push for increasing resolution continues, with general circulation models (GCMs) starting to use sub-10-km grid spacing. In the same context it has been suggested that the use of stochastic physics (SP) may act as a surrogate for high resolution, providing some of the benefits at a fraction of the cost. Either technique can reduce model uncertainty, and enhance reliability, by providing a more dynamic environment for initial synoptic disturbances to be spawned and to grow into TCs. We present results from a systematic comparison of the role of model resolution and SP in the simulation of TCs, using EC-Earth simulations from project Climate-SPHINX, in large ensemble mode, spanning five different resolutions. All tropical cyclonic systems, including TCs, were tracked explicitly. As in previous studies, the number of simulated TCs increases with the use of higher resolution, but SP further enhances TC frequencies by ~30%, in a strikingly similar way. The use of SP is beneficial for removing systematic climate biases, albeit not consistently so for interannual variability; conversely, the use of SP improves the simulation of the seasonal cycle of TC frequency. An investigation of the mechanisms behind this response indicates that SP generates both higher TC (and TC seed) genesis rates, and more suitable environmental conditions, enabling a more efficient transition of TC seeds into TCs. These results were confirmed by the use of equivalent simulations with the HadGEM3-GC31 GCM.

Open access
Shu-Ya Chen, Cheng-Peng Shih, Ching-Yuang Huang, and Wen-Hsin Teng

Abstract

Conventional soundings are rather limited over the western North Pacific and can be largely compensated by GNSS radio occultation (RO) data. We utilize the GSI hybrid assimilation system to assimilate RO data and the multiresolution global model (MPAS) to investigate the RO data impact on prediction of Typhoon Nepartak that passed over southern Taiwan in 2016. In this study, the performances of assimilation with local RO refractivity and bending angle operators are compared for the assimilation analysis and typhoon forecast. Assimilations with both RO data have shown similar and comparable temperature and moisture increments after cycling assimilation and largely reduce the RMSEs of the forecast without RO data assimilation at later times. The forecast results at 60–15-km resolution show that RO data assimilation largely improves the typhoon track prediction compared to that without RO data assimilation, and assimilation with bending angle has better performances than assimilation with refractivity, in particular for wind forecast. The improvement in the forecasted track is mainly due to the improved simulation for the translation of the typhoon. Diagnostics of wavenumber-1 potential vorticity (PV) tendency budget indicates that the northwestward typhoon translation dominated by PV horizontal advection is slowed down by the southward tendency induced by the stronger differential diabatic heating south of the typhoon center for bending-angle assimilation. Simulations with the enhanced resolution of 3 km in the region of the storm track show further improvements in both typhoon track and intensity prediction with RO data assimilation. Positive RO impacts on track prediction are also illustrated for two other typhoons using the MPAS-GSI system.

Open access
Song Yang, Vincent Lao, Richard Bankert, Timothy R. Whitcomb, and Joshua Cossuth

Abstract

An accurate precipitation climatology is presented for tropical depression (TD), tropical storm (TS), and tropical cyclone (TC) occurrences over oceans using recently released, consistent, and high-quality precipitation datasets from all passive microwave sensors covering 1998–2012 along with the Automated Rotational Center Hurricane Eye Retrieval (ARCHER)-based TC center positions. Impacts with respect to the direction of both TC movement and the 200–850-hPa wind shear on the spatial distributions of TC precipitation are analyzed. The TC eyewall contraction process during its intensification is noted by a decrease in the radius of maximum rain rate with an increase in TC intensity. For global TCs, the maximum rain rate with respect to the direction of TC movement is located in the down-motion quadrants for TD, TS, and category-1–3 TCs, and in a concentric pattern for category-4/5 TCs. A consistent maximum TC precipitation with respect to the direction of the 200–850-hPa wind shear is shown in the downshear left quadrant (DSLQ). With respect to direction of TC movement, spatial patterns of TC precipitation vary with basins and show different features for weak and strong storms. The maximum rain rate is always located in DSLQ for all TC categories and basins, except the Southern Hemisphere basin where it is in the downshear right quadrant. This study not only confirms previously published results on TC precipitation distributions relative to vertical wind shear direction, but also provides a detailed distribution for each TC category and TS, while TD storms display an enhanced rainfall rate ahead of the downshear quadrants.

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Edward J. Walsh, C. W. Fairall, and Ivan PopStefanija

Abstract

The airborne NOAA Wide Swath Radar Altimeter (WSRA) is a 16-GHz digital beamforming radar altimeter that produces a topographic map of the waves as the aircraft advances. The wave topography is transformed by a two-dimensional FFT into directional wave spectra. The WSRA operates unattended on the aircraft and provides continuous real-time reporting of several data products: 1) significant wave height; 2) directional ocean wave spectra; 3) the wave height, wavelength, and direction of propagation of the primary and secondary wave fields; 4) rainfall rate; and 5) sea surface mean square slope (mss). During hurricane flights the data products are transmitted in real-time from the NOAA WP-3D aircraft through a satellite data link to a ground station and on to the National Hurricane Center (NHC) for use by the forecasters for intensity projections and incorporation in hurricane wave models. The WSRA is the only instrument that can quickly provide high-density measurements of the complex wave topography over a large area surrounding the eye of the storm.

Open access
Yue Sun, Haishan Chen, Siguang Zhu, Jie Zhang, and Jiangfeng Wei

Abstract

Under the background of global warming, the Eurasian warming features evident spatial heterogeneity, and Northeast Asia (NEA) is one of the regions with the most significant summer warming. Based on reanalysis data and the CESM1.2.2 model, we investigated the possible impacts of spring Eurasian snowmelt on recent NEA summer warming and the relevant mechanisms. Results show that increased (decreased) spring snowmelt over eastern Europe to western Siberia (EEWS) is closely linked to NEA summer warming (cooling). Increased spring snowmelt can wet the soil, weakening surface sensible heating to the atmosphere and cooling the atmosphere. The persistent anomalous soil moisture and surface sensible heat induce geopotential height decrease over EEWS and strengthen the eastward-propagating wave train. Furthermore, positive geopotential height anomalies appear in downstream NEA in summer via the adjustment of the atmospheric circulation. Controlled by the anomalous high pressure system, the west part of NEA is affected by the southerly warm advection, while the east is affected by adiabatic warming induced by the dominant descending motion. Meanwhile, decreased cloud and increased incident solar radiation over NEA favor summer land surface warming. Model results suggest that CESM1.2.2 can basically reproduce the positive correlation between NEA summer land surface temperature and EEWS spring snowmelt. With the positive spring snowmelt forcing, the simulated positive soil moisture and negative sensible heat anomalies persist from spring to summer over EEWS. Consequently, negative geopotential height anomalies appear over the snowmelt region while positive anomalies occur around Lake Baikal, resulting in evident NEA land surface warming.

Open access
John C. King, John Turner, Steve Colwell, Hua Lu, Andrew Orr, Tony Phillips, J. Scott Hosking, and Gareth J. Marshall

Abstract

Commencing in 1956, observations made at Halley Research Station in Antarctica provide one of the longest continuous series of near-surface temperature observations from the Antarctic continent. Since few other records of comparable length are available, the Halley record has been used extensively in studies of long-term Antarctic climate variability and change. The record does not, however, come from a single location but is a composite of observations from a sequence of seven stations, all situated on the Brunt Ice Shelf, that range from around 10 to 50 km in distance from the coast. Until now, it has generally been assumed that temperature data from all of these stations could be combined into a single composite record with no adjustment. Here, we examine this assumption of homogeneity. Application of a statistical changepoint algorithm to the composite record detects a sudden cooling associated with the move from Halley IV to Halley V station in 1992. We show that this temperature step is consistent with local temperature gradients measured by a network of automatic weather stations and with those simulated by a high-resolution atmospheric model. These temperature gradients are strongest in the coastal region and result from the onshore advection of maritime air. The detected inhomogeneity could account for the weak cooling trend seen in the uncorrected composite record. In future, studies that make use of the Halley record will need to account for its inhomogeneity.

Open access
Jingzhuo Wang, Jing Chen, Hanbin Zhang, Hua Tian, and Yining Shi

Abstract

Ensemble forecasting is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by the ensemble transform Kalman filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System–Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread–skill relationship, a faster ensemble spread growth rate, and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

Open access
Carlo Montes, Nachiketa Acharya, S. M. Quamrul Hassan, and Timothy J. Krupnik

Abstract

Extreme precipitation events are a serious threat to societal well-being over rainy areas such as Bangladesh. The reliability of studies of extreme events depends on data quality and their spatial and temporal distribution, although these subjects remain with knowledge gaps in many countries. This work focuses on the analysis of four satellite-based precipitation products for monitoring intense rainfall events: the Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), the PERSIANN–Climate Data Record (PERSIANN-CDR), the Integrated Multisatellite Retrievals (IMERG), and the CPC morphing technique (CMORPH). Five indices of intense rainfall were considered for the period 2000–19 and a set of 31 rain gauges for evaluation. The number and amount of precipitation associated with intense rainfall events are systematically underestimated or overestimated throughout the country. While random errors are higher over the wetter and higher-elevation northeastern and southeastern parts of Bangladesh, biases are more homogeneous. CHIRPS, PERSIANN-CDR, and IMERG perform similar for capturing total seasonal rainfall, but variability is better represented by CHIRPS and IMERG. Better results were obtained by IMERG, followed by PERSIANN-CDR and CHIRPS, in terms of climatological intensity indices based on percentiles, although the three products exhibited systematic errors. IMERG and CMORPH systematically overestimate the occurrence of intense precipitation events. IMERG showed the best performance representing events over a value of 20 mm day−1; CMORPH exhibited random and systematic errors strongly associated with a poor representation of interannual variability in seasonal total rainfall. The results suggest that the datasets have different potential uses and such differences should be considered in future applications regarding extreme rainfall events and risk assessment in Bangladesh.

Open access
Jun-Chao Yang, Yu Zhang, Ingo Richter, and Xiaopei Lin

Abstract

Moisture transport from the Atlantic to Pacific is important for the basin-scale freshwater budget and the formation of meridional ocean circulation. Although the climatological tropical Atlantic-to-Pacific moisture transport (TAPMORT) has been well investigated, few studies have focused on its variability. Here we investigate the interannual variability of TAPMORT based on the atmospheric reanalysis datasets. The TAPMORT interannual variability is dominated by the variations of transbasin winds across Central America, and peaks in late boreal summer and late boreal winter. 1) In late summer, a developing El Niño and a mature Atlantic Niña set up an interbasin sea surface temperature (SST) gradient that strengthens the low-level jet across Central America and therefore TAPMORT (with weakened TAPMORT for opposite signed events). This process typically occurs from July to September, with a peak in August. 2) In late winter, the strengthened southern North American center of the Pacific–North American (PNA)-like pattern intensifies the TAPMORT variations. Although atmospheric interannual variability dominates these variations, extreme El Niño events are also important for the teleconnections. This process shows a single peak in February, in contrast to the persistent peak in late summer. We further demonstrate that the persistent TAPMORT variability in late summer dominates the moisture divergence over the northwestern tropical Atlantic and modulates freshwater flux there. Thus, our study improves the understanding of how TAPMORT interannual variability and the related interbasin SST gradient regulate the northwestern tropical Atlantic freshwater budget and the related salinity variability.

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Lei Zhang, Weiqing Han, and Zeng-Zhen Hu

Abstract

An unprecedented extreme positive Indian Ocean dipole event (pIOD) occurred in 2019, which has caused widespread disastrous impacts on countries bordering the Indian Ocean, including the East African floods and vast bushfires in Australia. Here we investigate the causes for the 2019 pIOD by analyzing multiple observational datasets and performing numerical model experiments. We find that the 2019 pIOD was triggered in May by easterly wind bursts over the tropical Indian Ocean associated with the dry phase of the boreal summer intraseasonal oscillation, and it was sustained by the local atmosphere–ocean interaction thereafter. During September–November, warm sea surface temperature anomalies (SSTA) in the central-western tropical Pacific Ocean further enhanced the Indian Ocean’s easterly winds, bringing the pIOD to an extreme magnitude. The central-western tropical Pacific warm SSTA was strengthened by two consecutive Madden–Julian oscillation (MJO) events that originated from the tropical Indian Ocean. Our results highlight the important roles of cross-basin and cross-time-scale interactions in generating extreme IOD events. The lack of accurate representation of these interactions may be the root for a short lead time in predicting this extreme pIOD with a state-of-the-art climate forecast model.

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