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Rongwang Zhang
,
Weihao Guo
,
Xin Wang
, and
Chunzai Wang

Abstract

The tropical latent heat flux (LHF) has experienced a significant increase under the background of global warming in the past four decades. However, since the years around 1998, the long-term LHF variations in the tropics have been found to be quite different in various flux products. Three different trends in the LHF, climbing, near-zero, and declining, are suggested by five widely used flux products, which hinders our knowledge of the actual LHF variations. Although there are buoy observations in the tropics, these observations are hard to evaluate flux products as they have been assimilated and/or used as benchmarks in the flux data production. This study aims to identify a credible long-term LHF variations since 1998.

A linear model decomposing the LHF variations into contributions from sea surface wind (U) and air-sea humidity differences is firstly applied. The linear model results show that the LHF variations are more positively connected to U variations since 1998. Evidence from in situ and remote sensing observations is subsequently employed to identify how U varies recently. Both 82 Global Tropical Moored Buoy Array (GTMBA) buoy observations and a multi-sensor merged satellite product support a slightly downward trend in U in the last two decades. Such a the weakening of U is not conducive to oceanic evaporation and leads to a reduced LHF. Consequently, a declining LHF under a weakening U since the emergence of the global warming “hiatus” in approximately 1998 might be more convincing in the sense of data accuracy and physical consistency.

Restricted access
Hsiao-Chun Lin
,
Juanzhen Sun
,
Tammy M. Weckwerth
,
Everette Joseph
, and
Junkyung Kay

Abstract

The New York State Mesonet (NYSM) has provided continuous in situ and remote sensing observations near the surface and within the lower troposphere since 2017. The dense observing network can capture the evolution of mesoscale motions with high temporal and spatial resolution. The objective of this study was to investigate whether the assimilation of NYSM observations into numerical weather prediction models could be beneficial for improving model analysis and short-term weather prediction. The study was conducted using a convective event that occurred in New York on 21 June 2021. A line of severe thunderstorms developed, decayed, and then reintensified as it propagated eastward across the state. Several data assimilation (DA) experiments were conducted to investigate the impact of NYSM data using the operational DA system Gridpoint Statistical Interpolation with rapid update cycles. The assimilated datasets included National Centers for Environmental Prediction Automated Data Processing global upper-air and surface observations, NYSM surface observations, Doppler lidar wind retrievals, and microwave radiometer (MWR) thermodynamic retrievals at NYSM profiler sites. In comparison with the control experiment that assimilated only conventional data, the timing and location of the convection reintensification was significantly improved by assimilating NYSM data, especially the Doppler lidar wind data. Our analysis indicated that the improvement could be attributed to improved simulation of the Mohawk–Hudson Convergence. We also found that the MWR DA resulted in degraded forecasts, likely due to large errors in the MWR temperature retrievals. Overall, this case study suggested the positive impact of assimilating NYSM surface and profiler data on forecasting summertime severe weather.

Open access
Joshua D. Sandstrom
,
Jason M. Cordeira
,
Eric G. Hoffman
, and
Nicholas D. Metz

Abstract

Lake-effect precipitation is convective precipitation produced by relatively cold air passing over large and relatively warm bodies of water. This phenomenon most often occurs in North America over the southern and eastern shores of the Great Lakes, where high annual snowfalls and high-impact snowstorms frequently occur under prevailing west and northwest flow. Locally higher snow or rainfall amounts also occur due to lake-enhanced synoptic precipitation when conditionally unstable or neutrally stratified air is present in the lower troposphere. While likely less common, lake-effect and lake-enhanced precipitation can also occur with easterly winds, impacting the western shores of the Great Lakes. This study describes a 15-year climatology of easterly lake-effect (ELEfP) and lake-enhanced (ELEnP) precipitation (conjointly Easterly Lake Collective Precipitation: ELCP) events that developed in east-to-east-northeasterly flow over western Lake Superior from 2003 to 2018. ELCP occurs infrequently but often enough to have a notable climatological impact over western Lake Superior with an average of 14.6 events per year. The morphology favors both single shore-parallel ELEfP bands due to the convex western shoreline of Lake Superior and mixed-type banding due to ELEnP events occurring in association with “overrunning” synoptic-scale precipitation. ELEfP often occurs in association with a surface anticyclone to the north of Lake Superior. ELEnP typically features a similar northerly-displaced anticyclone and a surface cyclone located over the U.S. Upper Midwest that favor easterly boundary-layer winds over western Lake Superior.

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David P. Rowell
and
Ségolène Berthou

Abstract

Convection-permitting (CP) models promise much in response to the demand for increased localization of future climate information: greater resolution of influential land surface characteristics, improved representation of convective storms, and unprecedented resolution of user-relevant data. In practice, however, it is contended that the benefits of enhanced resolution cannot be fully realized due to the gap between models’ computational and effective resolution. Nevertheless, where surface forcing is strongly heterogeneous, one can argue that usable information may persist close to the grid scale. Here we analyze a 4.5-km resolution CP projection for Africa, asking whether and where fine-scale projection detail is robust at sub-25-km scales, focusing on geolocated rainfall features (rather than Lagrangian motion). Statistically significant detail for seasonal means and daily extremes is most frequent in regions of high topographic variability, most prominently in East Africa throughout the annual cycle, West Africa in the monsoon season, and to a lesser extent over Southern Africa. Lake coastal features have smaller but significant impacts on projection detail, whereas ocean coastlines and urban conurbations have little or no detectable impact. The amplitude of this sub-25-km projection detail can be similar to that of the local climatology in mountainous regions (or around a third near East Africa’s lake shores), so potentially beneficial for improved localization of future climate information. In flatter regions distant from coasts (the majority of Africa), spatial heterogeneity can be explained by chaotic weather variability. Here, the robustness of local climate projection information can be substantially enhanced by spatial aggregation to approximately 25-km scales, especially for daily extremes and equatorial regions.

Significance Statement

Recent substantial increases in the horizontal resolution of climate models bring the potential for both more reliable and more local future climate information. However, the best spatial scale on which to analyze such data for impacts assessments remains unclear. We examine a 4.5-km resolution climate projection for Africa, focusing on seasonal and daily rainfall. Spatially fixed fine-scale projection detail is found to be statistically robust at sub-25-km scales in the most mountainous regions and to a lesser extent along lake coastlines. Elsewhere, the model data may be better aggregated to at least 25-km scales to reduce sampling uncertainties. Such evolving guidance on the circumstances and extent of high-resolution data aggregation will help users gain greater benefit from climate model projections.

Open access
Xinyue Hao
,
Yiquan Jiang
,
Xiu-Qun Yang
,
Xiaohong Liu
,
Yang Zhang
,
Minghuai Wang
,
Yuan Liang
, and
Yong Wang

Abstract

Both South Asia and East Asia are the most polluted regions of the world. Unlike East Asia, the aerosol optical depth (AOD) over South Asia keeps increasing for all recent years, which calls for more attentions. This study investigates the impacts of anthropogenic emissions over South Asia on downstream region climate during spring with Community Earth System Model 2 (CESM2). The model results suggest that South Asian pollutants have significant impacts on East Asian spring climate, and the impacts could be even larger than local emitted aerosols. Two possible dynamical pathways (i.e., the northern and the southern pathways) bridging South Asian aerosol forcing and East Asian climate are proposed, and both ways are associated with the black carbon (BC) induced climate feedbacks surrounding Tibetan Plateau (TP).

The northern pathway is mainly due to the TP warming induced by BC snow darkening effect (SDE), which significantly reduces the surface air temperature (SAT) over northern East Asia. BC induced TP warming increases meridional thermal gradient and accelerates middle latitude jet stream, which favors the cold air activities over northern East Asia. The southern pathway is associated with BC’s “Elevated Heat Pump” hypothesis, which mainly affects the precipitation in southern East Asia. BC from South Asia accumulates near the south slope of TP, induces an abnormal ascending motion near Bay of Bengal. A compensating anomalous sinking motion is then forced in South China, which suppresses the precipitation there. A primary observational analysis is also performed to verify both dynamical pathways.

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Adrian Rojas-Campos
,
Martin Wittenbrink
,
Pascal Nieters
,
Erik J Schaffernicht
,
Jan D Keller
, and
Gordon Pipa

Abstract

This study analyzes the potential of deep learning using probabilistic artificial neural networks (ANNs) for post-processing ensemble precipitation forecasts at four observation locations. We split the precipitation forecast problem into two tasks: estimating the probability of precipitation and predicting the hourly precipitation. We then compare the performance with classical statistical post-processing (logistical regression and GLM). ANNs show a higher performance at three of the four stations for estimating the probability of precipitation and at all stations for predicting the hourly precipitation. Further, two more general ANN models are trained using the merged data from all four stations. These general ANNs exhibit an increase in performance compared to the station-specific ANNs at most stations. However, they show a significant decay in performance at one of the stations at estimating the hourly precipitation. The general models seem capable of learning meaningful interactions in the data and generalizing these to improve the performance at other sites, which also causes the loss of local information at one station. Thus, this study indicates the potential of deep learning in weather forecasting workflows.

Restricted access
M. Andrés-Carcasona
,
M. Soria
,
E. García-Melendo
, and
A. Miró

Abstract

Robert’s rising thermal bubble (RRTB) is a benchmark case used to assess atmospheric models. In this paper, RRTB is further studied both using an analytical and a numerical approach, improving to a greater extent the qualitative description found in the literature. The theoretical framework used is that of buoyant thermals and scaling theory that together are able to predict part of the expected behavior of the bubble as it rises and, therefore, can be used to further validate the simulations. For the numerical experiments, we simulate both a two-dimensional and three-dimensional RRTB using a variety of convection schemes under the Boussinesq approximation and with a higher resolution. While the results are in agreement with those presented by previous authors on the same benchmark and also with the theoretical framework established, we add the quantitative measures to validate the underlying physics of the numerical model. Our results also show that, due to its completely chaotic nature when confined in a 2D plane, RRTB becomes a very challenging candidate to be used as a benchmark if only compared in a qualitative way, and when the 3D bubble is simulated, the shape changes significantly.

Restricted access
Chenyue Zhang
,
Ming Xue
,
Kefeng Zhu
, and
Xiaoding Yu

Abstract

A climatology of significant tornadoes [SIGTOR, tornadoes rated (E)F2+ on the (enhanced) Fujita scale] within China and in three subregions, including northern, central, and southern China, is first presented for the period 1980–2016. In total, 129 SIGTOR are recorded in China, with an average of 3.5 per year. The tornado inflow environments of the south-central and southeast regions of the United States (USC and USSE) are compared with those of China and its subregions based on sounding-derived parameters including shear, storm-relative helicity, convective available potential energy (CAPE), lifting condensation level (LCL), etc. Soundings are extracted from the ERA5 reanalysis dataset. The results confirm that the SIGTOR in USSE are characterized by high shear, low CAPE, and low LCL whereas those in USC are characterized by moderate-to-high shear, high CAPE, and high LCL. The thermodynamic conditions of tornadic cases are favorable for China, with moderate-to-high CAPE and low-to-moderate LCL, but their kinematic conditions are much less favorable than in the United States, a fact that is believed to be primarily responsible for the lower tornado frequency and intensity in China. The high CAPE in China is due mostly to high humidity. For three subregions in China, the central China cases account for 60% of total samples, and its environmental features are similar to those of China. The average shear with northern China cases is stronger than that with the other two subregions, and the midlayer is relatively dry. The southern China SIGTOR have the most conducive humidity conditions, but the CAPE and shear there are the lowest. The northern, central, and southern China environments can be considered as representative of midlatitude, subtropical, and tropical regions.

Significance Statement

We document the climatological characteristics of significant tornadoes (SIGTOR) within China and compare the inflow environments of SIGTOR in China and its subregions with those in the U.S. central and southeastern regions. The availability of hourly high-resolution ERA5 data makes the environments based on extracted proximity soundings much more accurate than possible with earlier reanalyses. The environmental characteristics show systematic differences in the tornado environments of different regions of China and the United States and suggest different roles played by thermodynamic and kinematic conditions for tornado formation. Overall, the environmental differences are consistent with the resulting frequency and intensities of SIGTOR. The findings are helpful toward improving tornado forecasting and warning or even understanding of potential impacts of climate change on SIGTOR, especially in China, where such studies are rarer.

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José C. Fernández-Alvarez
,
Marta Vázquez
,
Albenis Pérez-Alarcón
,
Raquel Nieto
, and
Luis Gimeno

Abstract

Moisture transport and changes in the source–sink relationship play a vital role in the atmospheric branch of the hydrological cycle. Lagrangian approaches have emerged as the dominant tool to account for estimations of moisture sources and sinks; those that use the FLEXPART model fed by ERA-Interim reanalysis are most commonly used. With the release of the higher spatial resolution ERA5, it is crucial to compare the representation of moisture sources and sinks using the FLEXPART Lagrangian model with different resolutions in the input data, as well as its version for WRF-ARW input data, the FLEXPART-WRF. In this study, we compare this model for 2014 and moisture sources for the Iberian Peninsula and moisture sinks of North Atlantic and Mediterranean. For comparison criteria, we considered FLEXPARTv9.0 outputs forced by ERA-Interim reanalysis as “control” values. It is concluded that FLEXPARTv10.3 forced with ERA5 data at various horizontal resolutions (0.5° and 1°) represents moisture source and sink zones as represented forced by ERA-Interim (1°). In addition, the version fed with the dynamic downscaling WRF-ARW outputs (∼20 km), previously forced with ERA5, also represents these patterns accurately, allowing this tool to be used in future investigations at higher resolutions and for regional domains.

Significance Statement

The FLEXPART dispersion model forced with ERA5 reanalysis data at various resolutions represents moisture source and sink zones compared to when it is forced by ERA-Interim. When the Weather Research and Forecasting Model is used to dynamically downscale ERA5, FLEXPART-WRF can also represent moisture sources and sinks, allowing this tool to be used in future investigations requiring higher resolution and regional domains and on regions with a predominance of complex orography due to its ability to represent local moisture transport.

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Samuel E. Muñoz
,
Brynnydd Hamilton
, and
B. Parazin

Abstract

The Mississippi River basin drains nearly one-half of the contiguous United States, and its rivers serve as economic corridors that facilitate trade and transportation. Flooding remains a perennial hazard on the major tributaries of the Mississippi River basin, and reducing the economic and humanitarian consequences of these events depends on improving their seasonal predictability. Here, we use climate reanalysis and river gauge data to document the evolution of floods on the Missouri and Ohio Rivers—the two largest tributaries of the Mississippi River—and how they are influenced by major modes of climate variability centered in the Pacific and Atlantic Oceans. We show that the largest floods on these tributaries are preceded by the advection and convergence of moisture from the Gulf of Mexico following distinct atmospheric mechanisms, where Missouri River floods are associated with heavy spring and summer precipitation events delivered by the Great Plains low-level jet, whereas Ohio River floods are associated with frontal precipitation events in winter when the North Atlantic subtropical high is anomalously strong. Further, we demonstrate that the El Niño–Southern Oscillation can serve as a precursor for floods on these rivers by mediating antecedent soil moisture, with Missouri River floods often preceded by a warm eastern tropical Pacific (El Niño) and Ohio River floods often preceded by a cool eastern tropical Pacific (La Niña) in the months leading up peak discharge. We also use recent floods in 2019 and 2021 to demonstrate how linking flood hazard to sea surface temperature anomalies holds potential to improve seasonal predictability of hydrologic extremes on these rivers.

Free access