Abstract

A well-known bias common to many bulk microphysics schemes currently being used in cloud-resolving models is the tendency to produce excessively large reflectivity values (e.g., 40 dBZ) in the middle and upper troposphere in simulated convective systems. The Rutledge and Hobbs–based bulk microphysics scheme in the Goddard Cumulus Ensemble model is modified to reduce this bias and improve realistic aspects. Modifications include lowering the efficiencies for snow/graupel riming and snow accreting cloud ice; converting less rimed snow to graupel; allowing snow/graupel sublimation; adding rime splintering, immersion freezing, and contact nucleation; replacing the Fletcher formulation for activated ice nuclei with that of Meyers et al.; allowing for ice supersaturation in the saturation adjustment; accounting for ambient RH in the growth of cloud ice to snow; and adding/accounting for cloud ice fall speeds. In addition, size-mapping schemes for snow/graupel were added as functions of temperature and mixing ratio, lowering particle sizes at colder temperatures but allowing larger particles near the melting level and at higher mixing ratios. The modifications were applied to a weakly organized continental case and an oceanic mesoscale convective system (MCS). Strong echoes in the middle and upper troposphere were reduced in both cases. Peak reflectivities agreed well with radar for the weaker land case but, despite improvement, remained too high for the MCS. Reflectivity distributions versus height were much improved versus radar for the less organized land case but not for the MCS despite fewer excessively strong echoes aloft due to a bias toward weaker echoes at storm top.

Abstract

Using the daily outgoing longwave radiation (OLR), the pentad Climate Prediction Center Merged Analysis of Precipitation (CMAP), and the 6-h Climate Forecast System Reanalysis (CFSR) dataset from 1979 to 2010, a composite analysis along with space–time wave filtering is performed to examine the linkage between the Madden–Julian oscillation (MJO) and the onset of the East Asian subtropical summer monsoon (EASSM) (over 20°–30°N, 110°–120°E). The onset of the EASSM is shown to be best characterized by the reversal of the mean meridional wind shear related to the rapid reestablishment of the South Asian high (SAH) over the southern Indochinese Peninsula in the upper troposphere. The mean date of EASMM onset is near the end of April, which is about a month earlier than the typical onset of the East Asian summer monsoon. Further analysis indicates that the onset of the EASSM and the reestablishment of SAH are often associated with the arrival of the wet phase of the tropical MJO over the central and eastern Indian Ocean.

Abstract

This study investigates the effects of meteorological conditions and aerosols on marine stratocumulus in the southeastern Pacific using the Weather Research and Forecasting (WRF) Model. Two regimes with different temperature and moisture conditions in the finest model domain are investigated. The western regime is around 87°–79°W, while the eastern regime is around 79°–71°W. In both regimes, cloud fraction, liquid water path (LWP), cloud thickness, and precipitation show significant diurnal cycles. Cloud fraction can be 0.83 during the night and down to 0.29 during the day in the western regime. The diurnal cycles in the eastern regime have smaller amplitudes but are still very strong. Stratocumulus properties also differ in the two regimes. Compared to the western regime, the eastern regime has lower temperature, higher relative humidity, and a more coupled boundary layer, leading to higher cloud fraction (by 0.11) and lower cloud-base height. The eastern regime also has lower inversion height that causes lower cloud-top height and thinner clouds and, hence, lower LWP and less precipitation.

Cloud microphysical properties are very sensitive to aerosols in both regimes. Increasing aerosols greatly increase cloud number concentration, decrease cloud effective radius, and suppress precipitation. Cloud macrophysical properties (cloud fraction, LWP) are not sensitive to aerosols in either regime, most notably in the eastern regime where precipitation amount is less. The changes in cloud fraction and LWP caused by changes in aerosol concentrations are smaller than the changes in the diurnal cycle and the spatial variability between the two regimes.

Abstract

Coupled data assimilation uses a coupled model consisting of multiple time-scale media to extract information from observations that are available in one or more media. Because of the instantaneous exchanges of information among the coupled media, coupled data assimilation is expected to produce self-consistent and physically balanced coupled state estimates and optimal initialization for coupled model predictions. It is also expected that applying coupling error covariance between two media into observational adjustments in these media can provide direct observational impacts crossing the media and thereby improve the assimilation quality. However, because of the different time scales of variability in different media, accurately evaluating the error covariance between two variables residing in different media is usually very difficult. Using an ensemble filter together with a simple coupled model consisting of a Lorenz atmosphere and a pycnocline ocean model, which characterizes the interaction of multiple time-scale media in the climate system, the impact of the accuracy of coupling error covariance on the quality of coupled data assimilation is studied. Results show that it requires a large ensemble size to improve the assimilation quality by applying coupling error covariance in an ensemble coupled data assimilation system, and the poorly estimated coupling error covariance may otherwise degrade the assimilation quality. It is also found that a fast-varying medium has more difficulty being improved using observations in slow-varying media by applying coupling error covariance because the linear regression from the observational increment in slow-varying media has difficulty representing the high-frequency information of the fast-varying medium.

Abstract

The first rainy season (FRS), also known as the presummer rainy season, is the first standing stage of the East Asian summer monsoon when over 40% of the annual precipitation is received over South China. Based on the start and end dates of the FRS defined by the China Meteorological Administration, this study investigates the interannual variations of the FRS precipitation over South China and its mechanism with daily mean data. The length and start/end date of the FRS vary year to year, and the average length of the FRS is 90 days, spanning from 6 April to 4 July. Composite analyses reveal that the years with abundant FRS precipitation over South China feature weakened anticyclonic wind shear over the Indochina Peninsula in the upper troposphere, southwestward shift of the western Pacific subtropical high, and anticyclonic wind anomalies over the South China Sea in the lower troposphere. The lower-tropospheric southwesterly wind anomalies are especially important because they help to enhance warm advection and water vapor transport toward South China, increase the lower tropospheric convective instability, and shape the pattern of the anomalous ascent over South China. It is further proposed that a local positive feedback between circulation and precipitation exists in this process. The variability of the FRS precipitation can be well explained by a zonal sea surface temperature (SST) dipole in the tropical Pacific and the associated Matsuno–Gill-type Rossby wave response over the western North Pacific. The interannual variability of both the SST dipole and the FRS precipitation over South China is weakened after the year 2000.

Abstract

Variation of the Kuroshio path south of Japan has an important impact on weather, climate, and ecosystems due to its distinct features. Motivated by the ever-popular deep learning methods using neural network architectures in areas where more accurate reference data for oceanographic observations and reanalysis are available, we build four deep learning models based on the long short-term memory (LSTM) neural network, combined with the empirical orthogonal function (EOF) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), namely, the LSTM, EOF–LSTM, CEEMDAN–LSTM, and EOF–CEEMDAN–LSTM. Using these models, we conduct long-range predictions (120 days) of the Kuroshio path south of Japan based on 50-yr ocean reanalysis and nearly 15 years of satellite altimeter data. We show that the EOF–CEEMDAN–LSTM performs the best among the four models, by attaining approximately 0.739 anomaly correlation coefficient and 0.399° root-mean-square error for the 120-day prediction of the Kuroshio path south of Japan. The hindcasts of the EOF–CEEMDAN–LSTM are successful in reproducing the observed formation and decay of the Kuroshio large meander during 2004/05, and the formation of the latest large meander in 2017. Finally, we present predictions of the Kuroshio path south of Japan at 120-day lead time, which suggest that the Kuroshio will remain in the state of the large meander until November 2022.

Abstract

In recent years, the Earth System Research Laboratory (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) has developed a space and time mesoscale analysis system (STMAS), which is currently a sequential three-dimensional variational data assimilation (3DVAR) system and is developing into a sequential 4DVAR in the near future. It is implemented by using a multigrid method based on a variational approach to generate grid analyses. This study is to test how STMAS deals with 2D Doppler radar radial velocity and to what degree the 2D Doppler radar radial velocity can improve the conventional (in situ) observation analysis. Two idealized experiments and one experiment with real Doppler radar radial velocity data, handled by STMAS, demonstrated significant improvement of the conventional observation analysis. Because the radar radial wind data can provide additional wind information (even it is incomplete: e.g., missing tangential wind vector), the analyses by assimilating both radial wind data and conventional data showed better results than those by assimilating only conventional data. Especially in the case of sparse conventional data, radar radial wind data can provide significant information and improve the analyses considerably.

Abstract

An unmanned surface vehicle (USV) was designed and constructed to operate continuously for covering both flood and ebb and preferably a complete tidal cycle (e.g., ~24 h) to measure the vertical profiles of horizontal flow velocity. It was applied in a tidal channel at Port Fourchon, Louisiana. A bottom-mounted ADCP was deployed for 515 days. The first EOF mode of the velocity profiles showed a barotropic type of flow that explained more than 98.2% of the variability. The second mode showed a typical estuarine flow with two layers, which explained 0.47% of the variability. Using a linear regression of the total transport from the USV with the vertically averaged velocity from the bottom-mounted ADCP, with an R-squared value of 98%, the total along-channel transport throughout the deployment was calculated. A low-pass filtering of the transport allowed for examining the impact of 76 events with cold, warm, or combined cold–warm fronts passing the area. The top seven most severe events were discussed, as their associated transports obviously stood out in the time series, indicating the importance of weather. It is shown that large-scale weather systems with frontal lines of ~1500–3000-km horizontal length scale control the subtidal transport in the area. Cold (warm) fronts tend to generate outward (inward) transports, followed by a rebound. The maximum coherence between the atmospheric forcing and the ocean response reached ~71%–84%, which occurred at about a frequency f of ~0.29 cycle per day or T of ~3.4 days in the period, consistent with the atmospheric frontal return periods (~3–7 days).

Abstract

Detecting long-term changes in precipitation extremes over monsoon regions remains challenging due to large observational uncertainty, high internal variability at the regional scale, and climate models’ deficiency in simulating monsoon physics. This is particularly true for Eastern China, as illustrated by limited yet controversial detection results for daily scale precipitation extremes and the lack of detection analysis on hourly scale extremes there. Relying on high-quality gauge observations, two complementary techniques are used to detect the footprint of anthropogenic forcings in observed changes in both hourly and daily scale precipitation extremes across Eastern China. Results show that, scaled with global-mean surface temperature during 1970–2017, the regional-scale intensification nearly doubles the Clausius–Clapeyron rate (C-C; ~6.5% °C⁻¹) for the wettest 10 h in the period and almost triples the C-C rate for the top 10 heaviest daily precipitation extremes. The intensification at both time scales, as well as the resulting increase in frequency, is discernibly stronger and more widespread than expected due to random internal variability. This not only lends supports to the model-based detection of forced trends for daily scale precipitation extremes, but it also suggests that anthropogenic warming has already be intensifying hourly scale precipitation extremes in this monsoon region. The magnitude and detectability of observed changes arise primarily from systematic intensification of non-tropical-cyclone-related precipitation extremes in response to the past warming.

Abstract

Most El Niño events decay after a peak in boreal winter, but some persist and strengthen again in the following year. Several mechanisms for regulating its decay pace have been proposed; however, their relative contributions have not been thoroughly examined yet. By analyzing the fast-decaying and persistent types of the events in a 1200-yr coupled simulation, we quantify the key dynamic and thermodynamic processes in the decaying spring that are critical to determining the decay pace of El Niño. The zonal advection due to upwelling Kelvin wave accounts for twice as much the cooling difference as evaporation or meridional advection does. The upwelling Kelvin wave is much stronger in the fast-decaying events than the others, and its strength is equally attributed to the reflected equatorial Rossby wave and the equatorial easterly wind forcing over the western Pacific in the preceding 2–3 months. Relative to the fast-decaying events, the evaporative cooling is weaker but the meridional warm advection is stronger in the persistent events. The former is due to more meridionally asymmetric wind and sea surface temperature anomalies (SSTA) signaling positive Pacific meridional mode. The latter results from the advection of equatorial warm SSTA by climatological divergent flow, and the warmer SSTA persists from the mature stage subject to weaker cloud-radiative cooling in response to the central-Pacific-type SSTA distribution in the persistent events relative to the fast-decaying events. Our result consolidates the existing knowledge and provides a more comprehensive and physical pathway for the causality of El Niño’s diverse duration.