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- Author or Editor: Siyu Zhao x
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Abstract
The early season rainfall (ESR) over southern China, usually occurring from April to June, is a prominent meteorological phenomenon of the East Asian monsoon system. In this paper, output from the 45-day hindcast by the NCEP Climate Forecast System, version 2 (CFSv2), and various observational datasets are analyzed to assess the predictability of the ESR and associated atmospheric circulation. Results show that CFSv2 can successfully predict the ESR and associated circulation patterns over southern China. The lower-tropospheric convergence and upper-tropospheric divergence as well as the local upward motion over southern China lead to the formation of ESR. Analysis of bias shows small differences and close relationships between the predicted and observed ESR values when the forecast lead time is less than 2 weeks. The skill in the ESR predictions by CFSv2 decreases significantly when the lead time is longer than 2 weeks. Overall, CFSv2 has a higher level of skill when predicting the southern China ESR compared to the rainfall over other Asian regions during the same period of time.
Abstract
The early season rainfall (ESR) over southern China, usually occurring from April to June, is a prominent meteorological phenomenon of the East Asian monsoon system. In this paper, output from the 45-day hindcast by the NCEP Climate Forecast System, version 2 (CFSv2), and various observational datasets are analyzed to assess the predictability of the ESR and associated atmospheric circulation. Results show that CFSv2 can successfully predict the ESR and associated circulation patterns over southern China. The lower-tropospheric convergence and upper-tropospheric divergence as well as the local upward motion over southern China lead to the formation of ESR. Analysis of bias shows small differences and close relationships between the predicted and observed ESR values when the forecast lead time is less than 2 weeks. The skill in the ESR predictions by CFSv2 decreases significantly when the lead time is longer than 2 weeks. Overall, CFSv2 has a higher level of skill when predicting the southern China ESR compared to the rainfall over other Asian regions during the same period of time.
Abstract
The Pacific–North America–North Atlantic sector in general experienced a dryer and warmer climate in summer during the past 40 years. These changes are partly associated with declining midlatitude synoptic variability in boreal summer, especially over the two ocean basins. A nonmodal instability analysis of the boreal summer background flow is conducted for two periods, 1979–94 and 2000–15, to understand dynamical processes potentially responsible for the observed decline of synoptic variability. The synoptic variability associated with fast, nonmodal growth of atmospheric disturbances shows a decline over northern midlatitudes in the later period, in both a barotropic model and a two-level quasigeostrophic model. These results highlight the importance of the changing summer background flow in contributing to the observed changes in synoptic variability. Also discussed are factors likely associated with background flow changes including sea surface temperature and sea ice change.
Abstract
The Pacific–North America–North Atlantic sector in general experienced a dryer and warmer climate in summer during the past 40 years. These changes are partly associated with declining midlatitude synoptic variability in boreal summer, especially over the two ocean basins. A nonmodal instability analysis of the boreal summer background flow is conducted for two periods, 1979–94 and 2000–15, to understand dynamical processes potentially responsible for the observed decline of synoptic variability. The synoptic variability associated with fast, nonmodal growth of atmospheric disturbances shows a decline over northern midlatitudes in the later period, in both a barotropic model and a two-level quasigeostrophic model. These results highlight the importance of the changing summer background flow in contributing to the observed changes in synoptic variability. Also discussed are factors likely associated with background flow changes including sea surface temperature and sea ice change.
Abstract
Regional patterns of extreme precipitation events occurring over the continental United States are identified via hierarchical cluster analysis of observed daily precipitation for the period 1950–2005. Six canonical extreme precipitation patterns (EPPs) are isolated for the boreal warm season and five for the cool season. The large-scale meteorological pattern (LMP) inducing each EPP is identified and used to create a “base function” for evaluating a climate model’s potential for accurately representing the different patterns of precipitation extremes. A parallel analysis of the Community Climate System Model, version 4 (CCSM4), reveals that the CCSM4 successfully captures the main U.S. EPPs for both the warm and cool seasons, albeit with varying degrees of accuracy. The model’s skill in simulating each EPP tends to be positively correlated with its capability in representing the associated LMP. Model bias in the occurrence frequency of a governing LMP is directly related to the frequency bias in the corresponding EPP. In addition, however, discrepancies are found between the CCSM4’s representation of LMPs and EPPs over regions such as the western United States and Midwest, where topographic precipitation influences and organized convection are prominent, respectively. In these cases, the model representation of finer-scale physical processes appears to be at least equally important compared to the LMPs in driving the occurrence of extreme precipitation.
Abstract
Regional patterns of extreme precipitation events occurring over the continental United States are identified via hierarchical cluster analysis of observed daily precipitation for the period 1950–2005. Six canonical extreme precipitation patterns (EPPs) are isolated for the boreal warm season and five for the cool season. The large-scale meteorological pattern (LMP) inducing each EPP is identified and used to create a “base function” for evaluating a climate model’s potential for accurately representing the different patterns of precipitation extremes. A parallel analysis of the Community Climate System Model, version 4 (CCSM4), reveals that the CCSM4 successfully captures the main U.S. EPPs for both the warm and cool seasons, albeit with varying degrees of accuracy. The model’s skill in simulating each EPP tends to be positively correlated with its capability in representing the associated LMP. Model bias in the occurrence frequency of a governing LMP is directly related to the frequency bias in the corresponding EPP. In addition, however, discrepancies are found between the CCSM4’s representation of LMPs and EPPs over regions such as the western United States and Midwest, where topographic precipitation influences and organized convection are prominent, respectively. In these cases, the model representation of finer-scale physical processes appears to be at least equally important compared to the LMPs in driving the occurrence of extreme precipitation.
Abstract
Warm season dry spells over the central and eastern United States are classified into three canonical types via a hierarchical cluster analysis for the period 1950–2005. Four CMIP5 models exhibit diverging skill in representing the observed behavior, ranging from southern Great Plains dry spells that are reasonably simulated by all four models to southeastern U.S. dry spells that are only accurately captured by one model. A model’s skill in representing a particular dry spell cluster is positively correlated with the model’s ability to simulate the large-scale meteorological patterns (LMPs) accompanying the dry spell. The interannual variability and overall observed decreasing trend in dry spell days are represented with varying degrees of accuracy by the four models. The results 1) highlight existing shortcomings in the climate model representation of regional dry spells and 2) illustrate the importance of properly simulating the observed spectrum of LMPs in minimizing these shortcomings.
Abstract
Warm season dry spells over the central and eastern United States are classified into three canonical types via a hierarchical cluster analysis for the period 1950–2005. Four CMIP5 models exhibit diverging skill in representing the observed behavior, ranging from southern Great Plains dry spells that are reasonably simulated by all four models to southeastern U.S. dry spells that are only accurately captured by one model. A model’s skill in representing a particular dry spell cluster is positively correlated with the model’s ability to simulate the large-scale meteorological patterns (LMPs) accompanying the dry spell. The interannual variability and overall observed decreasing trend in dry spell days are represented with varying degrees of accuracy by the four models. The results 1) highlight existing shortcomings in the climate model representation of regional dry spells and 2) illustrate the importance of properly simulating the observed spectrum of LMPs in minimizing these shortcomings.
Abstract
Intraseasonal modes of atmospheric variability over the Northern Hemisphere (NH) midlatitudes in boreal summer are identified via an empirical orthogonal function (EOF) analysis of the daily 10–90-day bandpass-filtered 250-hPa streamfunction for the period of 1950–2016. The first two EOF modes are characterized, respectively, by (i) a single-signed streamfunction anomaly that extends across the NH and (ii) a regional dipole structure with centers over the Aleutian Islands and northeastern Pacific. The third EOF mode (EOF-3) is a quasi-stationary wave train over the Pacific–North American sector with an equivalent barotropic structure in the vertical. EOF-3 is associated with a northwest–southeast oriented anomalous precipitation dipole over the United States. A nonmodal instability analysis of the boreal summer climatological flow in terms of the 250-hPa streamfunction reveals that one of the top “optimal mode” disturbances mimicking the EOF-3 structure grows from an initial precursor disturbance over East Asia through extracting kinetic energy from background flow and attains its maximum amplitude in around nine days. An additional lag regression analysis illustrates that anomalous latent heating associated with cloud and precipitation formation over East Asia is responsible for generating the precursor disturbance for the EOF-3-like optimal mode. This result suggests the existence of an important connection between the hydrological cycles of East Asia and North America, which is dynamically intrinsic to the boreal summer upper-tropospheric flow. Knowledge of such a connection will help us better understand and model hydroclimate variability over these two continents.
Abstract
Intraseasonal modes of atmospheric variability over the Northern Hemisphere (NH) midlatitudes in boreal summer are identified via an empirical orthogonal function (EOF) analysis of the daily 10–90-day bandpass-filtered 250-hPa streamfunction for the period of 1950–2016. The first two EOF modes are characterized, respectively, by (i) a single-signed streamfunction anomaly that extends across the NH and (ii) a regional dipole structure with centers over the Aleutian Islands and northeastern Pacific. The third EOF mode (EOF-3) is a quasi-stationary wave train over the Pacific–North American sector with an equivalent barotropic structure in the vertical. EOF-3 is associated with a northwest–southeast oriented anomalous precipitation dipole over the United States. A nonmodal instability analysis of the boreal summer climatological flow in terms of the 250-hPa streamfunction reveals that one of the top “optimal mode” disturbances mimicking the EOF-3 structure grows from an initial precursor disturbance over East Asia through extracting kinetic energy from background flow and attains its maximum amplitude in around nine days. An additional lag regression analysis illustrates that anomalous latent heating associated with cloud and precipitation formation over East Asia is responsible for generating the precursor disturbance for the EOF-3-like optimal mode. This result suggests the existence of an important connection between the hydrological cycles of East Asia and North America, which is dynamically intrinsic to the boreal summer upper-tropospheric flow. Knowledge of such a connection will help us better understand and model hydroclimate variability over these two continents.
Abstract
This paper reports a comprehensive instability analysis of a 3D Charney-like model with an observationally compatible generic stratosphere. It is found that the values of a single nondimensional parameter
Abstract
This paper reports a comprehensive instability analysis of a 3D Charney-like model with an observationally compatible generic stratosphere. It is found that the values of a single nondimensional parameter
Abstract
We have developed two statistical models for extended seasonal predictions of the upper Colorado River basin (UCRB) natural streamflow during April–July: a stepwise linear regression (reduced to a simple regression with one predictor) and a neural network model. Monthly, basin-averaged soil moisture, snow water equivalent (SWE), precipitation, and the Pacific sea surface temperature (SST) are selected as potential predictors. Pacific SST predictors (PSPs) are derived from a dipole pattern over the Pacific (30°S–65°N) that is correlated with the lagging streamflow. For both models, the correlation between the hindcasted and observed streamflow exceeds 0.60 for lead times less than 4 months using soil moisture, SWE, and precipitation as predictors. This correlation is higher than that of an autoregression model (correlation ~ 0.50). Since these land surface and atmospheric variables have no statistically significant correlations with the streamflow, PSPs are then incorporated into the models. The two models have a correlation of ~0.50 using PSPs alone for lead times from 6 to 9 months, and such skills are probably associated with stronger correlation between SST and streamflow in recent decades. The similar prediction skills between the two models suggest a largely linear system between SST and streamflow. Four predictors together can further improve short-lead prediction skills (correlation ~ 0.80). Therefore, our results confirm the advantage of the Pacific SST information in predicting the UCRB streamflow with a long lead time and can provide useful climate information for water supply planning and decisions.
Abstract
We have developed two statistical models for extended seasonal predictions of the upper Colorado River basin (UCRB) natural streamflow during April–July: a stepwise linear regression (reduced to a simple regression with one predictor) and a neural network model. Monthly, basin-averaged soil moisture, snow water equivalent (SWE), precipitation, and the Pacific sea surface temperature (SST) are selected as potential predictors. Pacific SST predictors (PSPs) are derived from a dipole pattern over the Pacific (30°S–65°N) that is correlated with the lagging streamflow. For both models, the correlation between the hindcasted and observed streamflow exceeds 0.60 for lead times less than 4 months using soil moisture, SWE, and precipitation as predictors. This correlation is higher than that of an autoregression model (correlation ~ 0.50). Since these land surface and atmospheric variables have no statistically significant correlations with the streamflow, PSPs are then incorporated into the models. The two models have a correlation of ~0.50 using PSPs alone for lead times from 6 to 9 months, and such skills are probably associated with stronger correlation between SST and streamflow in recent decades. The similar prediction skills between the two models suggest a largely linear system between SST and streamflow. Four predictors together can further improve short-lead prediction skills (correlation ~ 0.80). Therefore, our results confirm the advantage of the Pacific SST information in predicting the UCRB streamflow with a long lead time and can provide useful climate information for water supply planning and decisions.
