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Hong Yin and Ying Sun

Abstract

Threshold indices of extreme temperature are defined based on temperature values that fall above or below fixed thresholds and thus have important implications for agriculture, engineering, and human health. Here, we focus on four extreme temperature fixed threshold indices and their detection and attribution at the global and continental scales, as well as within China. These indices include the number of days with daily minimum temperatures below 0°C [frost days (FD)] and above 20°C [tropical nights (TR)] and the number of days with daily maximum temperatures below 0°C [ice days (ID)] and above 25°C [summer days (SU)]. We employ an optimal fingerprinting method to compare the spatial and temporal changes in these fixed threshold indices assessed from observations and simulations performed with multiple models. We find that an anthropogenic signal can be robustly detected in these fixed threshold indices at scales of over the globe, most of the continents, and China. A natural signal cannot be identified in the changes in most of the indices, thus indicating the dominant role of anthropogenic forcing in producing these changes. In North and South America, the models show poor performance in reproducing the fixed threshold indices related to daily maximum temperature. The changes in summer days are not clearly related to their responses to external forcing over these two continents. This study provides a useful complement to other detection studies and sheds light on the importance of anthropogenic forcing in determining most of the fixed threshold indices at the global scale and over most of the continents, compared with internal variability.

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Juanzhen Sun and Ying Zhang

Abstract

This paper presents a case study on the assimilation of observations from multiple Doppler radars of the Next Generation Weather Radar (NEXRAD) network. A squall-line case documented during the International H2O Project (IHOP_2002) is used for the study. Radar radial velocity and reflectivity observations from four NEXRADs are assimilated into a convection-permitting model using a four-dimensional variational data assimilation (4DVAR) scheme. A mesoscale analysis using a supplementary sounding, velocity–azimuth display (VAD) profiles, and surface observations from Meteorological Aerodrome Reports (METAR) are produced and used to provide a background and boundary conditions for the 4DVAR radar data assimilation. Impact of the radar data assimilation is assessed by verifying the skill of the subsequent very short-term (5 h) forecasts.

Assimilation and forecasting experiments are conducted to examine the impact of radar data assimilation on the subsequent precipitation forecasts. It is found that the 4DVAR radar data assimilation significantly reduces the model spinup required in the experiments without radar data assimilation, resulting in significantly improved 5-h forecasts. Additional experiments are conducted to study the sensitivity of the precipitation forecasts with respect to 4DVAR cycling configurations. Results from these experiments suggest that the forecasts with three 4DVAR cycles are improved over those with cold start, but the cycling impact seems to diminish with more cycles. The impact of observations from each of the individual radars is also examined by conducting a set of experiments in which data from each radar are alternately excluded. It is found that the accurate analysis of the environmental wind surrounding the convective cells is important in successfully predicting the squall line.

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Shibo Gao, Juanzhen Sun, Jinzhong Min, Ying Zhang, and Zhuming Ying

Abstract

Radar reflectivity observations contain valuable information on precipitation and have been assimilated into numerical weather prediction models for improved microphysics initialization. However, low-reflectivity (or so-called no rain) echoes have often been ignored or not effectively used in radar data assimilation schemes. In this paper, a scheme to assimilate no-rain radar observations is described within the framework of the Weather Research and Forecasting Model’s three-dimensional variational data assimilation (3DVar) system, and its impact on precipitation forecasts is demonstrated. The key feature of the scheme is a neighborhood-based approach to adjusting water vapor when a grid point is deemed as no rain. The performance of the scheme is first examined using a severe convective case in the Front Range of the Colorado Rocky Mountains and then verified by running the 3DVar system in the same region, with and without the no-rain assimilation scheme for 68 days and 3-hourly rapid update cycles. It is shown that the no-rain data assimilation method reduces the bias and false alarm ratio of precipitation over its counterpart without that assimilation. The no-rain assimilation also improved humidity, temperature, and wind fields, with the largest error reduction in the water vapor field, both near the surface and at upper levels. It is also shown that the advantage of the scheme is in its ability to conserve total water content in cycled radar data assimilation, which cannot be achieved by assimilating only precipitation echoes.

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Siyan Dong, Ying Sun, and Chao Li

Abstract

This paper examines the possible influence of external forcings on observed changes in precipitation extremes in the mid-to-high latitudes of Asia during 1958–2012 and attempts to identify particular extreme precipitation indices on which there are better chances to detect the influence of external forcings. We compare a recently compiled dataset of observed extreme indices with those from phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations using an optimal fingerprinting method. We consider six indices that characterize different aspects of extreme precipitation, including annual maximum amount of precipitation falling in 1 day (Rx1day) or 5 days (Rx5day), the total amount of precipitation from the top 5% or top 1% daily amount on wet days, and the fraction of the annual total precipitation from these events. For single-signal analysis, the fingerprints of external forcings including anthropogenic agents are robustly detected in most studied extreme indices over all Asia and for midlatitude Asia but not for high-latitude Asia. For two-signal analysis, anthropogenic influence is detectable in these indices over Asia at 5% or slightly less than 5% significance level, whereas natural influence is not detectable. In high-latitude Asia, anthropogenic influence is detected only in a fractional index, representing a stark contrast to the midlatitude and full Asia results. We find relatively smaller internal variability and thus higher signal-to-noise ratio in the fractional indices when compared with the other ones. Our results point to the need for studying precipitation extreme indices that are less affected by internal variability while still representing the relevant nature of precipitation extremes to improve the possibility of detecting a forced signal if one is present in the data.

Open access
Guangxin He, Juanzhen Sun, and Zhuming Ying

Abstract

Accurate and automated dealiasing of radar data is important for data interpretation and downstream applications such as numerical weather prediction (NWP) models. In this paper an improved radial velocity dealiasing scheme is presented and evaluated using observations from several S-band radars under the severe weather conditions of typhoons and hurricanes. This scheme, named Automated Dealiasing for Typhoon and Hurricane (ADTH), is a further development of the China New Generation Doppler Weather Radar (CINRAD) improved dealiasing algorithm (CIDA). The upgraded algorithm ADTH includes three modules designed to select the first radial from which the dealiasing process starts, to conduct a two-way multipass dealiasing, and to perform an error check for a final local dealiasing. The dealiasing algorithm is applied to two typhoon hurricane cases and four typhoon cases observed with radars from CINRAD, NEXRAD of the United States, and the Taiwan radar network for a continuous period of 12 h for each of the selected cases. The results show that ADTH outperforms CIDA for all of the test cases.

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Ying Sun, Siyan Dong, Ting Hu, Xuebin Zhang, and Peter Stott
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Ying Sun, Siyan Dong, Ting Hu, Xuebin Zhang, and Peter Stott
Open access
Ying Sun, Susan Solomon, Aiguo Dai, and Robert W. Portmann

Abstract

Daily precipitation data from climate change simulations using the latest generation of coupled climate system models are analyzed for potential future changes in precipitation characteristics. For the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) B1 (a low projection), A1B (a medium projection), and A2 (a high projection) during the twenty-first century, all the models consistently show a shift toward more intense and extreme precipitation for the globe as a whole and over various regions. For both SRES B1 and A2, most models show decreased daily precipitation frequency and all the models show increased daily precipitation intensity. The multimodel averaged percentage increase in the precipitation intensity (2.0% K−1) is larger than the magnitude of the precipitation frequency decrease (−0.7% K−1). However, the shift in precipitation frequency distribution toward extremes results in large increases in very heavy precipitation events (>50 mm day−1), so that for very heavy precipitation, the percentage increase in frequency is much larger than the increase in intensity (31.2% versus 2.4%). The climate model projected increases in daily precipitation intensity are, however, smaller than that based on simple thermodynamics (∼7% K−1). Multimodel ensemble means show that precipitation amount increases during the twenty-first century over high latitudes, as well as over currently wet regions in low- and midlatitudes more than other regions. This increase mostly results from a combination of increased frequency and intensity. Over the dry regions in the subtropics, the precipitation amount generally declines because of decreases in both frequency and intensity. This indicates that wet regions may get wetter and dry regions may become drier mostly because of a simultaneous increase (decrease) of precipitation frequency and intensity.

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Claire Burke, Peter Stott, Andrew Ciavarella, and Ying Sun
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Jin-Yi Yu, Fengpeng Sun, and Hsun-Ying Kao

Abstract

The Community Climate System Model, version 3 (CCSM3), is known to produce many aspects of El Niño–Southern Oscillation (ENSO) realistically, but the simulated ENSO exhibits an overly strong biennial periodicity. Hypotheses on the cause of this excessive biennial tendency have thus far focused primarily on the model’s biases within the tropical Pacific. This study conducts CCSM3 experiments to show that the model’s biases in simulating the Indian Ocean mean sea surface temperatures (SSTs) and the Indian and Australian monsoon variability also contribute to the biennial ENSO tendency. Two CCSM3 simulations are contrasted: a control run that includes global ocean–atmosphere coupling and an experiment in which the air–sea coupling in the tropical Indian Ocean is turned off by replacing simulated SSTs with an observed monthly climatology. The decoupling experiment removes CCSM3’s warm bias in the tropical Indian Ocean and reduces the biennial variability in Indian and Australian monsoons by about 40% and 60%, respectively. The excessive biennial ENSO is found to reduce dramatically by about 75% in the decoupled experiment. It is shown that the biennial monsoon variability in CCSM3 excites an anomalous surface wind pattern in the western Pacific that projects well into the wind pattern associated with the onset phase of the simulated biennial ENSO. Therefore, the biennial monsoon variability is very effective in exciting biennial ENSO variability in CCSM3. The warm SST bias in the tropical Indian Ocean also increases ENSO variability by inducing stronger mean surface easterlies along the equatorial Pacific, which strengthen the Pacific ocean–atmosphere coupling and enhance the ENSO intensity.

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