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- Author or Editor: Qingyun Duan x
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Abstract
The Niangziguan Springs (NS) discharge is used as a proxy indicator of the variability of the karst groundwater system in relation to major climate indices such as El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Indian summer monsoon (ISM), and west North Pacific monsoon (WNPM). The relationships between spring discharge and these climate indices are determined using the multitaper method (MTM), continuous wavelet transform (CWT), and wavelet transform coherence (WTC). Significant periodic components of spring discharge in the 1-, 3.4-, and 26.8-yr periodicities are identified and reconstructed for further investigation of the correlation between spring discharge and large-scale climate patterns on these time scales. Correlation coefficients and WTC between spring discharge and the climate indices indicate that variability in spring discharge is significantly and positively correlated with monsoon indices in the 1-yr periodicity and negatively correlated with ENSO in the 3.4-yr periodicity and PDO in the 26.8-yr periodicity. This suggests that the oscillations of the spring discharge on annual, interannual, and interdecadal time scales are dominated by monsoon, ENSO, and PDO in the NS basin, respectively. Results show that monsoons modulate the spring discharge by affecting local meteorological parameters. ENSO and PDO impact the variability of the NS discharge by affecting the climate conditions in northern China.
Abstract
The Niangziguan Springs (NS) discharge is used as a proxy indicator of the variability of the karst groundwater system in relation to major climate indices such as El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Indian summer monsoon (ISM), and west North Pacific monsoon (WNPM). The relationships between spring discharge and these climate indices are determined using the multitaper method (MTM), continuous wavelet transform (CWT), and wavelet transform coherence (WTC). Significant periodic components of spring discharge in the 1-, 3.4-, and 26.8-yr periodicities are identified and reconstructed for further investigation of the correlation between spring discharge and large-scale climate patterns on these time scales. Correlation coefficients and WTC between spring discharge and the climate indices indicate that variability in spring discharge is significantly and positively correlated with monsoon indices in the 1-yr periodicity and negatively correlated with ENSO in the 3.4-yr periodicity and PDO in the 26.8-yr periodicity. This suggests that the oscillations of the spring discharge on annual, interannual, and interdecadal time scales are dominated by monsoon, ENSO, and PDO in the NS basin, respectively. Results show that monsoons modulate the spring discharge by affecting local meteorological parameters. ENSO and PDO impact the variability of the NS discharge by affecting the climate conditions in northern China.
Abstract
Predicting the changes in teleconnection patterns and related hydroclimate extremes can provide vital information necessary to adapt to the effects of the El Niño–Southern Oscillation (ENSO). This study uses the outputs of global climate models to assess the changes in ENSO-related dry/wet patterns and the frequency of severe dry/wet events. The results show anomalous precipitation responding asymmetrically to La Niña and El Niño, indicating the teleconnections may not simply be strengthened. A “dry to drier, wet to wetter” annual anomalous precipitation pattern was projected during La Niña phases in some regions, with drier conditions over southern North America, southern South America, and southern central Asia, and wetter conditions in Southeast Asia and Australia. These results are robust, with agreement from the 26 models and from a subset of 8 models selected for their good performance in capturing observed patterns. However, we did not observe a similar strengthening of anomalous precipitation during future El Niño phases, for which the uncertainties in the projected influences are large. Under the RCP4.5 emissions scenario, 45 river basins under El Niño conditions and 39 river basins under La Niña conditions were predicted to experience an increase in the frequency of severe dry events; similarly, 59 river basins under El Niño conditions and 61 river basins under La Niña conditions were predicted to have an increase in the frequency of severe wet events, suggesting a likely increase in the risk of floods. Our results highlight the implications of changes in ENSO patterns for natural hazards, disaster management, and engineering infrastructure.
Abstract
Predicting the changes in teleconnection patterns and related hydroclimate extremes can provide vital information necessary to adapt to the effects of the El Niño–Southern Oscillation (ENSO). This study uses the outputs of global climate models to assess the changes in ENSO-related dry/wet patterns and the frequency of severe dry/wet events. The results show anomalous precipitation responding asymmetrically to La Niña and El Niño, indicating the teleconnections may not simply be strengthened. A “dry to drier, wet to wetter” annual anomalous precipitation pattern was projected during La Niña phases in some regions, with drier conditions over southern North America, southern South America, and southern central Asia, and wetter conditions in Southeast Asia and Australia. These results are robust, with agreement from the 26 models and from a subset of 8 models selected for their good performance in capturing observed patterns. However, we did not observe a similar strengthening of anomalous precipitation during future El Niño phases, for which the uncertainties in the projected influences are large. Under the RCP4.5 emissions scenario, 45 river basins under El Niño conditions and 39 river basins under La Niña conditions were predicted to experience an increase in the frequency of severe dry events; similarly, 59 river basins under El Niño conditions and 61 river basins under La Niña conditions were predicted to have an increase in the frequency of severe wet events, suggesting a likely increase in the risk of floods. Our results highlight the implications of changes in ENSO patterns for natural hazards, disaster management, and engineering infrastructure.
Abstract
The objective of this study is to develop a dataset of the soil hydraulic parameters associated with two empirical soil functions (i.e., a water retention curve and hydraulic conductivity) using multiple pedotransfer functions (PTFs). The dataset is designed specifically for regional land surface modeling for China. The authors selected 5 PTFs to derive the parameters in the Clapp and Hornberger functions and the van Genuchten and Mualem functions and 10 PTFs for soil water contents at capillary pressures of 33 and 1500 kPa. The inputs into the PTFs include soil particle size distribution, bulk density, and soil organic matter. The dataset provides 12 estimated parameters and their associated statistical values. The dataset is available at a 30 × 30 arc second geographical spatial resolution and with seven vertical layers to the depth of 1.38 m. The dataset has several distinct advantages even though the accuracy is unknown for lack of in situ and regional measurements. First, this dataset utilizes the best available soil characteristics dataset for China. The Chinese soil characteristics dataset was derived by using the 1:1 000 000 Soil Map of China and 8595 representative soil profiles. Second, this dataset represents the first attempt to estimate soil hydraulic parameters using PTFs directly for continental China at a high spatial resolution. Therefore, this dataset should capture spatial heterogeneity better than existing estimates based on lookup tables according to soil texture classes. Third, the authors derived soil hydraulic parameters using multiple PTFs to allow flexibility for data users to use the soil hydraulic parameters most preferable to or suitable for their applications.
Abstract
The objective of this study is to develop a dataset of the soil hydraulic parameters associated with two empirical soil functions (i.e., a water retention curve and hydraulic conductivity) using multiple pedotransfer functions (PTFs). The dataset is designed specifically for regional land surface modeling for China. The authors selected 5 PTFs to derive the parameters in the Clapp and Hornberger functions and the van Genuchten and Mualem functions and 10 PTFs for soil water contents at capillary pressures of 33 and 1500 kPa. The inputs into the PTFs include soil particle size distribution, bulk density, and soil organic matter. The dataset provides 12 estimated parameters and their associated statistical values. The dataset is available at a 30 × 30 arc second geographical spatial resolution and with seven vertical layers to the depth of 1.38 m. The dataset has several distinct advantages even though the accuracy is unknown for lack of in situ and regional measurements. First, this dataset utilizes the best available soil characteristics dataset for China. The Chinese soil characteristics dataset was derived by using the 1:1 000 000 Soil Map of China and 8595 representative soil profiles. Second, this dataset represents the first attempt to estimate soil hydraulic parameters using PTFs directly for continental China at a high spatial resolution. Therefore, this dataset should capture spatial heterogeneity better than existing estimates based on lookup tables according to soil texture classes. Third, the authors derived soil hydraulic parameters using multiple PTFs to allow flexibility for data users to use the soil hydraulic parameters most preferable to or suitable for their applications.
Abstract
Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June–August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.
Abstract
Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June–August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.
Abstract
The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.
Abstract
The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.
