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Li Yan and Gen Li

Abstract

The southern subtropical dipole modes (SSDMs) and southern annular mode (SAM) are important climate modes, which are dominant in the southern middle and high latitudes, respectively, with considerable regional climatic impacts. However, the relationship between the two modes remains unclear. A close inspection reveals that the SAM was significantly correlated with the SSDMs during the austral summer before the mid-1980s. However, the correlations have degraded since then. This decadal shift in the relationship between these two southern dominant modes is due to a weakened connection between the SAM and the subtropical highs that control the SSDMs. This decadal change could be traced back to a poleward shift in the southern westerly belt. El Niño–Southern Oscillation (ENSO) typically plays a moderate role in influencing the precipitation in Australia and a minor role in influencing the precipitation in Africa and South America. Nevertheless, the two southern modes could still affect the austral summer rainfall in the midlatitudes, even though the ENSO signal is absent. All these links between the two southern modes and southern land precipitation may be attributable to the associated transport of moisture in the lower-level circulation.

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Yan Li, Lijuan Zhu, Xinyi Zhao, Shuangcheng Li, and Yan Yan

Abstract

The impact of urbanization on temperature trends in China was investigated with emphasis on two aspects of urbanization, land cover change, and human activity. A new station classification scheme was developed to incorporate these two aspects by utilizing land cover and energy consumption data. Observation temperature data of 274 stations and National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis temperature from 1979 to 2010 were used in conducting the observation minus reanalysis (OMR) method to detect urban influence. Results indicated that nearly half of the stations in the study area have been converted from nonurban to urban stations as a result of land cover change associated with urban expansion. It was determined that both land cover change and human activity play important roles in temperature change and contribute to the observed warming, particularly in urbanized stations, where the highest amount of warming was detected. Urbanized stations showed higher OMR temperature trends than those of unchanged stations. In addition, a statistically significant positive relationship was detected between human activity and temperature trends, which suggests that the observed warming is closely related to the intensity and spatial extent of human activity. In fact, the urbanization effect is strongly affected by specific characteristics of urbanization in local and regional scales.

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Yan Guo, Jianping Li, and Yun Li

Abstract

A time-scale decomposition (TSD) approach to statistically downscale summer rainfall over North China is described. It makes use of two distinct downscaling models respectively corresponding to the interannual and interdecadal rainfall variability. The two models were developed based on objective downscaling scheme that 1) identifies potential predictors based on correlation analysis between rainfall and considered climatic variables over the global scale and 2) selects the “optimal” predictors from the identified potential predictors via cross-validation-based stepwise regression. The downscaling model for the interannual rainfall variability is linked to El Niño–Southern Oscillation and the 850-hPa meridional wind over East China, while the one for the interdecadal rainfall variability is related to the sea level pressure over the southwest Indian Ocean. Taking the downscaled interannual and interdecadal components together the downscaled total rainfall was obtained. The results show that the TSD approach achieved a good skill to predict the observed rainfall with the correlation coefficient of 0.82 in the independent validation period. The authors further apply the model to obtain downscaled rainfall projections from three climate models under present climate and the A1B emission scenario in future. The resulting downscaled values provide a closer representation of the observation than the raw climate model simulations in the present climate; for the near future, climate models simulated a slight decrease in rainfall, while the downscaled values tend to be slightly higher than the present state.

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Yan Guo, Jianping Li, and Yun Li

Abstract

A statistical downscaling model was developed with reanalysis data and applied to forecast northern China summer rainfall (NCSR) using the outputs of the real-time seasonal Climate Forecast System, version 2 (CFSv2). Large-scale climate signals in sea level pressure, 850-hPa meridional wind, and 500-hPa geopotential height as well as several well-known climate indices were considered as potential predictors. Through correlation analysis and stepwise screening, two “optimal” predictors (i.e., sea level pressure over the southwestern Indian Ocean and 850-hPa meridional wind over eastern China) were selected to fit the regression equation. Model reliability was validated with independent data during a test period (1991–2012), in which the simulated NCSR well represented the observed variability with a correlation coefficient of 0.59 and a root-mean-square error of 18.6%. The statistical downscaling model was applied to forecast NCSR for a 22-yr period (1991–2012) using forecast predictors from the CFSv2 with lead times from 1 to 6 months. The results showed much better forecast skills than that directly from the CFSv2 for all lead months, except the 3-month-lead example. The biggest improvement occurred in the 1-month-lead forecast, in which the hit rate increased to 77.3% from 45.5% in the CFSv2 forecast. In the forecast of rainfall at 15 stations, the statistical downscaling model also showed superior capability when compared with the CFSv2, with forecast skill being improved at 73% of stations. In particular, 13 of 15 stations obtained a hit rate exceeding 55%.

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Gen Li, Shang-Ping Xie, and Yan Du

Abstract

Long-standing biases of climate models limit the skills of climate prediction and projection. Overlooked are tropical Indian Ocean (IO) errors. Based on the phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble, the present study identifies a common error pattern in climate models that resembles the IO dipole (IOD) mode of interannual variability in nature, with a strong equatorial easterly wind bias during boreal autumn accompanied by physically consistent biases in precipitation, sea surface temperature (SST), and subsurface ocean temperature. The analyses show that such IOD-like biases can be traced back to errors in the South Asian summer monsoon. A southwest summer monsoon that is too weak over the Arabian Sea generates a warm SST bias over the western equatorial IO. In boreal autumn, Bjerknes feedback helps amplify the error into an IOD-like bias pattern in wind, precipitation, SST, and subsurface ocean temperature. Such mean state biases result in an interannual IOD variability that is too strong. Most models project an IOD-like future change for the boreal autumn mean state in the global warming scenario, which would result in more frequent occurrences of extreme positive IOD events in the future with important consequences to Indonesia and East Africa. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) characterizes this future IOD-like projection in the mean state as robust based on consistency among models, but the authors’ results cast doubts on this conclusion since models with larger IOD amplitude biases tend to produce stronger IOD-like projected changes in the future.

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Yan Guo, Jianping Li, and Jiangshan Zhu

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Because summer rainfall in the middle-lower reaches of the Yangtze River valley has remarkable interannual and decadal variability and because the precursors that modulate the interannual rainfall change with the decadal variation of the background state, a new model that employs a novel statistical idea is needed to yield an accurate prediction. In this study, the interannual rainfall model (IAM) and the decadal rainfall model (DM) were constructed. Moving updating of the IAM with the latest data within an optimal length of training period (20 yr) can partially offset the effect of decadal change of precursors in IAM. To predict the interannual rainfall of 2001–13 for validation, 13 regression models were fitted with precursors that change every 4–5 yr, from the preceding winter North Atlantic Ocean sea surface temperature anomaly (SSTA) dipole to the Mascarene high, followed by the East Asia sea level pressure anomaly (SLPA) dipole and the preceding autumn North Pacific SSTA dipole. The moving updated model demonstrated high skill in predicting interannual rainfall, with a correlation coefficient of 0.76 and a hit rate of 76.9%. The DM was linked to the April SLPA in the central tropical Pacific Ocean, and it maintained good performance in the testing period, with a correlation coefficient of 0.77 and a root-mean-square error (RMSE) of 7.7%. The statistical model exhibited superior capability even when compared with the best forecast by the Climate Forecast System, version 2 (CFSv2), initiated in early June, as indicated by increased correlation coefficient from 0.62 to 0.75 and reduced RMSE from 12.3% to 10.7%.

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Gen Li, Shang-Ping Xie, and Yan Du

Abstract

Climate models consistently project reduced surface warming over the eastern equatorial Indian Ocean (IO) under increased greenhouse gas (GHG) forcing. This IO dipole (IOD)-like warming pattern, regarded as robust based on consistency among models by the new Intergovernmental Panel on Climate Change (IPCC) report, results in a large increase in the frequency of extreme positive IOD (pIOD) events, elevating the risk of climate and weather disasters in the future over IO rim countries. These projections, however, do not consider large model biases in both the mean state and interannual IOD variance. In particular, a “present–future relationship” is identified between the historical simulations and representative concentration pathway (RCP) 8.5 experiments from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble: models with an excessive IOD amplitude bias tend to project a strong IOD-like warming pattern in the mean and a large increase in extreme pIOD occurrences under increased GHG forcing. This relationship links the present simulation errors to future climate projections, and is also consistent with our understanding of Bjerknes ocean–atmosphere feedback. This study calibrates regional climate projections by using this present–future relationship and observed IOD amplitude. The results show that the projected IOD-like pattern of mean changes and frequency increase of extreme pIOD events are largely artifacts of model errors and unlikely to emerge in the future. These results illustrate that a robust projection may still be biased and it is important to consider the model bias effect.

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Gen Li, Shang-Ping Xie, and Yan Du

Abstract

An open-ocean thermocline dome south of the equator is a striking feature of the Indian Ocean (IO) as a result of equatorial westerly winds. Over the thermocline dome, the El Niño–forced Rossby waves help sustain the IO basin (IOB) mode and offer climate predictability for the IO and surrounding countries. This study shows that a common equatorial easterly wind bias, by forcing a westward-propagating downwelling Rossby wave in the southern IO, induces too deep a thermocline dome over the southwestern IO (SWIO) in state-of-the-art climate models. Such a deep SWIO thermocline weakens the influence of subsurface variability on sea surface temperature (SST), reducing the IOB amplitude and possibly limiting the models’ skill of regional climate prediction. To the extent that the equatorial easterly wind bias originates from errors of the South Asian summer monsoon, improving the monsoon simulation can lead to substantial improvements in simulating and predicting interannual variability in the IO.

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Zhengzheng Li, Yan Zhang, and Scott E. Giangrande

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This study develops a Gaussian mixture rainfall-rate estimator (GMRE) for polarimetric radar-based rainfall-rate estimation, following a general framework based on the Gaussian mixture model and Bayes least squares estimation for weather radar–based parameter estimations. The advantages of GMRE are 1) it is a minimum variance unbiased estimator; 2) it is a general estimator applicable to different rain regimes in different regions; and 3) it is flexible and may incorporate/exclude different polarimetric radar variables as inputs. This paper also discusses training the GMRE and the sensitivity of performance to mixture number. A large radar and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign is used to evaluate the GMRE approach. Results indicate that the GMRE approach can outperform existing polarimetric rainfall techniques optimized for this JPOLE dataset in terms of bias and root-mean-square error.

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Yuhan Yan, Riyu Lu, and Chaofan Li

Abstract

Confident model projections of regional climate, in particular precipitation, could be very useful for designing climate change adaptation, particularly for vulnerable regions such as the Sahel. However, there is an extremely large uncertainty in the future Sahel rainfall projections made by current climate models. In this study, we find a close relationship between the future Sahel rainfall projections and present rainfall simulation biases in South Asia and the western North Pacific in summer, using the historical simulations and future projections of phase 5 of the Coupled Model Intercomparison Project (CMIP5). This future–present relationship can be used to calibrate Sahel rainfall projections since historical simulation biases can be much more reliably estimated than future change. The accordingly calibrated results show a substantial increase in both precipitation and precipitation minus evaporation in the future Sahel, in comparison with the multimodel ensemble (MME) result. This relationship between the historical rainfall bias and future Sahel rainfall projection is suggested to lie with the different schemes of convective parameterization among models: some schemes tend to result in both overestimated (underestimated) historical rainfall in South Asia (the western North Pacific) and enhanced future Sahel rainfall projection, while other schemes result in the opposite.

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