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Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

In the past few decades, there have been more extreme climate events occurring worldwide, including Canada, which has also suffered from many extreme precipitation events. In this paper, trend analysis, probability distribution functions, principal component analysis, and wavelet analysis were used to investigate the spatial and temporal patterns of extreme precipitation events of Canada. Ten extreme precipitation indices were calculated using long-term daily precipitation data (1950–2012) from 164 Canadian gauging stations. Several large-scale climate patterns such as El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Pacific–North American (PNA), and North Atlantic Oscillation (NAO) were selected to analyze the relationships between extreme precipitation and climate indices. Convective available potential energy (CAPE), specific humidity, and surface temperature were employed to investigate potential causes of trends in extreme precipitation. The results reveal statistically significant positive trends for most extreme precipitation indices, which means that extreme precipitation of Canada has generally become more severe since the mid-twentieth century. The majority of indices display more increasing trends along the southern border of Canada while decreasing trends dominated the central Canadian Prairies. In addition, strong teleconnections are found between extreme precipitation and climate indices, but the effects of climate patterns differ from region to region. Furthermore, complex interactions of climate patterns with synoptic atmospheric circulations can also affect precipitation variability, and changes to the summer and winter extreme precipitation could be explained more by the thermodynamic impact and the combined thermodynamic and dynamic effects, respectively. The seasonal CAPE, specific humidity, and temperature are correlated to Canadian extreme precipitation, but the correlations are season dependent, which could be positive or negative. [ABSTRACT FROM AUTHOR]

One of the governing scientific objectives of the Boreal Ecosystem-Atmosphere Study (BOREAS) is the development of methods for applying process models over large spatial scales using remote sensing and other integrative modeling techniques. This paper presents the first step in a modeling strategy that focuses on scaling a point model up to the BOREAS regional scale. The objective of this paper is to compare the effect of differences in spatial resolution of land cover data to land-atmosphere model results relative to the effect of differences in land cover sensors and classification schemes. The analysis suggests that the uncertainty in model results arises mainly from the uncertainty in the land cover classification and that the lack of spatial resolution has a lower effect. Overall, an uncertainty of approximately 15percent in modeled energy and water balance fluxes and states has to be assigned because of the uncertainty in land cover classification. [ABSTRACT FROM AUTHOR]

This paper investigates how atmospheric circulation and orography affect the spatial variability of extreme precipitation in terms of depth-duration-frequency (DDF) curve parameters. To this aim, the Italian territory was considered because it is characterized by a complex orography and different precipitation dynamics and regimes. A database of 1494 time series with more than 20 years of maximum annual precipitation data was collected for the durations of 1, 3, 6, 12, and 24 h. For each data series, the parameters of DDF curves were estimated using a statistical simple scale invariance model. Hence, the combined effect of orography and atmospheric fields on parameter variability was investigated considering the spatial distribution of the parameters and their relation with elevation. The vertically integrated atmospheric moisture flux J was used as a measurement of the principal direction of the vapor transport at a given location. The analysis highlights the variability of DDF parameters and quantiles according to orography and precipitation climatology. This is confirmed by the evaluation of J modal direction over the study area. The variability of DDF parameters with mere elevation shows that maxima at high elevations seem to be upper bounded and more variable than those at lower elevations. Moreover, the mean of maximum annual precipitation of unit duration decreases with elevation. This last phenomenon is defined as 'reverse orographic effect' on extreme precipitation of short durations. [ABSTRACT FROM AUTHOR]

In this paper, preliminary results are presented showing that the two record-setting extreme events during 2010 summer (i.e., the Russian heat wave-wildfires and Pakistan flood) were physically connected. It is found that the Russian heat wave was associated with the development of an extraordinarily strong and prolonged extratropical atmospheric blocking event in association with the excitation of a large-scale atmospheric Rossby wave train spanning western Russia, Kazakhstan, and the northwestern China-Tibetan Plateau region. The southward penetration of upper-level vorticity perturbations in the leading trough of the Rossby wave was instrumental in triggering anomalously heavy rain events over northern Pakistan and vicinity in mid- to late July. Also shown are evidences that the Russian heat wave was amplified by a positive feedback through changes in surface energy fluxes between the atmospheric blocking pattern and an underlying extensive land region with below-normal soil moisture. The Pakistan heavy rain events were amplified and sustained by strong anomalous southeasterly flow along the Himalayan foothills and abundant moisture transport from the Bay of Bengal in connection with the northward propagation of the monsoonal intraseasonal oscillation. [ABSTRACT FROM AUTHOR]

This paper assesses the relative uncertainties from GCMs and from hydrological models in modeling climate change impact on runoff across southeast Australia. Five lumped conceptual daily rainfall-runoff models are used to model runoff using historical daily climate series and using future climate series obtained by empirically scaling the historical climate series informed by simulations from 15 GCMs. The majority of the GCMs project a drier future for this region, particularly in the southern parts, and this is amplified as a bigger reduction in the runoff. The results indicate that the uncertainty sourced from the GCMs is much larger than the uncertainty in the rainfall-runoff models. The variability in the climate change impact on runoff results for one rainfall-runoff model informed by 15 GCMs (an about 28%%-35%% difference between the minimum and maximum results for mean annual, mean seasonal, and high runoff) is considerably larger than the variability in the results between the five rainfall-runoff models informed by 1 GCM (a less than 7%% difference between the minimum and maximum results). The difference between the rainfall-runoff modeling results is larger in the drier regions for scenarios of big declines in future rainfall and in the low-flow characteristics. The rainfall-runoff modeling here considers only the runoff sensitivity to changes in the input climate data (primarily daily rainfall), and the difference between the hydrological modeling results is likely to be greater if potential changes in the climate-runoff relationship in a warmer and higher CO2 environment are modeled. [ABSTRACT FROM AUTHOR]

This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)--Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10%% of months during the cold half of the year (October--March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations. [ABSTRACT FROM AUTHOR]

This paper investigates the use of large-scale circulation patterns (El Niño–Southern Oscillation and the equatorial Indian Ocean Oscillation), local outgoing longwave radiation (OLR), and previous streamflow information for short-term (weekly) basin-scale streamflow forecasting. To model the complex relationship between these inputs and basin-scale streamflow, an artificial intelligence approach—genetic programming (GP)—has been employed. Research findings of this study indicate that the use of large-scale atmospheric circulation information and streamflow at previous time steps, along with OLR as a local meteorological input, potentially improves the performance of weekly basin-scale streamflow prediction. The genetic programming approach is found to capture the complex relationship between the weekly streamflow and various inputs. Different input variable combinations were explored to come up with the best one. The observed and predicted streamflows were found to correspond well with each other with a coefficient of determination of 0.653 (correlation coefficient r = 0.808), which may appear attractive for such a complex system. [ABSTRACT FROM AUTHOR]

This paper is Part I of a two-part study that uses high-resolution Regional Atmospheric Modeling System (RAMS) simulations to investigate mesoscale land–atmosphere interactions in the summertime U.S. Southern Great Plains. The focus is on the atmospheric dynamics associated with mesoscale heterogeneity in the underlying surface fluxes: how shifts in meteorological regimes modulate these diurnal, mesoscale processes, and their overall impact at larger scales and over multiple diurnal cycles. Part I examines individual case study time periods drawn from the simulations that illustrate general points about the key land–atmosphere interactions. The main findings are as follows: The mesoscale processes are embedded within a synoptic-scale organization that controls the background meteorological regime at a given location. During the clear, dry days in the simulated months, heterogeneity in the surface fluxes forces strong, lower-tropospheric, mesoscale circulations that exhibit a characteristic dynamical life cycle over diurnal time scales. In general, the background large-scale flow does not affect the overall intensity of these coherent roll structures, though strong large-scale subsidence can sometimes dampen them. In addition, depending on the thermodynamic profile, the strong vertical motions associated with these circulations are sufficient to trigger shallow or even deep convection, with associated clouds and precipitation. Furthermore, surface heterogeneity sufficient to force such circulations can arise even without heterogeneity in preexisting land cover characteristics such as vegetation, for example, solely as a result of spatial variability in rainfall and other atmospheric processes. In Part II the mesoscale land–atmosphere interactions in these case study periods are placed in the larger context of the full, monthlong simulations. [ABSTRACT FROM AUTHOR]

This is Part II of a two-part study of mesoscale land–atmosphere interactions in the summertime U.S. Southern Great Plains. Part I focused on case studies drawn from monthlong (July 1995–97), high-resolution Regional Atmospheric Modeling System (RAMS) simulations carried out to investigate these interactions. These case studies were chosen to highlight key features of the lower-tropospheric mesoscale circulations that frequently arise in this region and season due to mesoscale heterogeneity in the surface fluxes. In this paper, Part II, the RAMS-simulated mesoscale dynamical processes described in the Part I case studies are examined from a domain-averaged perspective to assess their importance in the overall regional hydrometeorology. The spatial statistics of key simulated mesoscale variables—for example, vertical velocity and the vertical flux of water vapor—are quantified here. Composite averages of the mesoscale and large-scale-mean variables over different meteorological or dynamical regimes are also calculated. The main finding is that, during dry periods, or similarly, during periods characterized by large-scale-mean subsidence, the characteristic signature of surface-heterogeneity-forced mesoscale circulations, including enhanced vertical motion variability and enhanced mesoscale fluxes in the lowest few kilometers of the atmosphere, consistently emerges. Furthermore, the impact of these mesoscale circulations is nonnegligible compared to the large-scale dynamics at domain-averaged (200 km × 200 km) spatial scales and weekly to monthly time scales. These findings support the hypothesis that the land– atmosphere interactions associated with mesoscale surface heterogeneity can provide pathways whereby diurnal, mesoscale atmospheric processes can scale up to have more general impacts at larger spatial scales and over longer time scales. [ABSTRACT FROM AUTHOR]