Abstract

After the end of the 1970s, there has been a tendency for enhanced summer precipitation over south China and the Yangtze River valley and drought over north China and northeastern China. Coincidentally, Arctic ice concentration has decreased since the late 1970s, with a larger reduction in summer than spring. However, the Arctic warming is more significant in spring than summer, suggesting that spring Arctic conditions could be more important in their remote impacts. This study investigates the potential impacts of the Arctic on summer precipitation in China. The leading spatial patterns and time coefficients of the unfiltered, interannual, and interdecadal precipitation (1960–2008) modes were analyzed and compared using empirical orthogonal function (EOF) analysis, which shows that the first three EOFs can capture the principal precipitation patterns (northern, central, and southern patterns) over eastern China. Regression of the Arctic spring and summer temperature onto the time coefficients of the leading interannual and interdecadal precipitation modes shows that interdecadal summer precipitation in China is related to the Arctic spring warming but that the relationship with Arctic summer temperature is weak. Moreover, no notable relationships were found between the first three modes of interannual precipitation and Arctic spring or summer temperatures. Finally, correlations between summer precipitation and the Arctic Oscillation (AO) index from January to August were investigated, which indicate that summer precipitation in China correlates with AO only to some extent. Overall, this study suggests important relationships between the Arctic spring temperature and summer precipitation over China at the interdecadal time scale.

Abstract

This paper examines the sensitivity of regional climate simulations to increasing spatial resolution via nesting by means of a 20-yr simulation of the western United States at 40-km resolution and a 5-yr simulation at 13-km resolution for the Pacific Northwest and California. The regional simulation at 40-km resolution shows a lack of precipitation along coastal hills, good agreement with observations on the windward slopes of the Cascades and Sierra Nevada, but overprediction on the leeside and the basins beyond. Snowpack is grossly underpredicted throughout the western United States when compared against snowpack telemetry (snotel) observations. During winter, higher spatial resolution mainly improves the precipitation simulation in the coastal hills and basins. Along the Cascades and the Sierra Nevada range, precipitation is strongly amplified at the higher spatial resolution. Higher resolution generally improves the spatial distribution of precipitation to yield a higher spatial correlation between simulations and observations. During summer, higher resolution improves not only the spatial distribution but also the regional mean precipitation.

In the Olympic Mountains and along the Coastal Range, increased precipitation at higher resolution reflects mainly a shift from light to heavy precipitation events. In the Cascades and Sierra Nevada, increased precipitation is mainly associated with more frequent heavy precipitation at higher resolution. Changes in precipitation from 40- to 13-km resolution depend on synoptic conditions such as wind direction and moisture transport. The use of higher spatial resolution improves snowpack more than precipitation. However, results presented in this paper suggest that accuracy in the snow simulation is also limited by factors such as deficiencies in the land surface model or biases in other model variables.

Abstract

The moist thermodynamic processes that determine the time scale and energy of the Madden–Julian oscillation (MJO) are investigated using moisture and eddy available potential energy budget analyses on a cloud-resolving simulation. Two MJO episodes observed during the winter of 2007/08 are realistically simulated. During the inactive phase, moisture supplied by meridional moisture convergence and boundary layer diffusion generates shallow and congestus clouds that moisten the lower troposphere while horizontal mixing tends to dry it. As the lower troposphere is moistened, it becomes a source of moisture for the subsequent deep convection during the MJO active phase. As the active phase ends, the lower troposphere dries out primarily by condensation and horizontal divergence that dominates over the moisture supply by vertical transport. In the simulation, the characteristic time scales of convective vertical transport, mixing, and condensation of moisture in the midtroposphere are estimated to be about 2 days, 4 days, and 20 h respectively. The small differences among these time scales result in an effective time scale of MJO moistening of about 25 days, half the period of the simulated MJO. Furthermore, various cloud types have a destabilizing or damping effect on the amplitude of MJO temperature signals, depending on their characteristic latent heating profile and its temporal covariance with the temperature. The results are used to identify possible sources of the difficulties in simulating MJO in low-resolution models that rely on cumulus parameterizations.

Abstract

A survey of tropical divergence from three GCMs, three global reanalyses, and four in situ soundings from field campaigns shows the existence of large uncertainties in the ubiquity of shallow divergent circulation as well as the depth and strength of the deep divergent circulation. More specifically, only two out of the three GCMs and three global reanalyses show significant shallow divergent circulation, which is present in all in situ soundings, and of the three GCMs and three global reanalyses, only two global reanalyses have deep divergence profiles that lie within the range of uncertainty of the soundings. The relationships of uncertainties in the shallow and deep divergent circulation to uncertainties in present-day and projected strength of the hydrological cycle from the GCMs are assessed. In the tropics and subtropics, deep divergent circulation is the largest contributor to moisture convergence that balances the net precipitation (precipitation minus evaporation), and intermodel differences in the present-day simulations carry over onto the future projections. In comparison to the soundings and reanalyses, the GCMs are found to have deeper and stronger divergent circulation. While these two characteristics of GCM divergence affect the strength of the hydrological cycle, they tend to compensate for each other so that their combined effect is relatively modest.

ABSTRACT

Warm-season rainfall associated with mesoscale convective systems (MCSs) in the central United States is characterized by higher intensity and nocturnal timing compared to rainfall from non-MCS systems, suggesting their potentially different footprints on the land surface. To differentiate the impacts of MCS and non-MCS rainfall on the surface water balance, a water tracer tool embedded in the Noah land surface model with multiparameterization options (WT-Noah-MP) is used to numerically “tag” water from MCS and non-MCS rainfall separately during April–August (1997–2018) and track their transit in the terrestrial system. From the water-tagging results, over 50% of warm-season rainfall leaves the surface–subsurface system through evapotranspiration by the end of August, but non-MCS rainfall contributes a larger fraction. However, MCS rainfall plays a more important role in generating surface runoff. These differences are mostly attributed to the rainfall intensity differences. The higher-intensity MCS rainfall tends to produce more surface runoff through infiltration excess flow and drives a deeper penetration of the rainwater into the soil. Over 70% of the top 10th percentile runoff is contributed by MCS rainfall, demonstrating its important contribution to local flooding. In contrast, lower-intensity non-MCS rainfall resides mostly in the top layer and contributes more to evapotranspiration through soil evaporation. Diurnal timing of rainfall has negligible effects on the flux partitioning for both MCS and non-MCS rainfall. Differences in soil moisture profiles for MCS and non-MCS rainfall and the resultant evapotranspiration suggest differences in their roles in soil moisture–precipitation feedbacks and ecohydrology.

Assessing future changes in air quality using downscaled climate scenarios is a relatively new application of the dynamical downscaling technique. This article compares and evaluates two downscaled simulations for the United States made using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model with the goal of understanding how errors in the downscaled climate simulations may introduce uncertainty in air quality assessment. The two downscaled simulations were driven by boundary conditions from the NCEP–NCAR global reanalysis and a global climate simulation generated by the Goddard Institute for Space Studies global circulation model, respectively. Comparisons of the model runs are made against the boundary layer and circulation characteristics of the North American Regional Reanalysis, and also against observed precipitation. The relative dependence of different simulated quantities on regional forcing, model parameterizations, and large-scale circulation provides a framework to understand similarities and differences between model simulations. Results show significant improvements in the downscaled diurnal wind patterns, in response to the complex orography, that are important for air quality assessment. Evaluation of downscaled boundary layer depth and winds, precipitation, and large-scale circulation shows larger biases related to model physics and biases in the GCM large-scale conditions. Based on the comparisons, recommendations are made to improve the utility of downscaled scenarios for air quality assessment.

Abstract

The regional climate of the western United States shows clear footprints of interaction between atmospheric circulation and orography. The unique features of this diverse climate regime challenges climate modeling. This paper provides detailed analyses of observations and regional climate simulations to improve our understanding and modeling of the climate of this region. The primary data used in this study are the 1/8° gridded temperature and precipitation based on station observations and the NCEP–NCAR global reanalyses. These data were used to evaluate a 20-yr regional climate simulation performed using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5) driven by large-scale conditions of the NCEP–NCAR reanalyses. Regional climate features examined include seasonal mean and extreme precipitation; distribution of precipitation rates; and precipitation intensity, frequency, and seasonality. The relationships between precipitation and surface temperature are also analyzed as a means to evaluate how well regional climate simulations can be used to simulate surface hydrology, and relationships between precipitation and elevation are analyzed as diagnostics of the impacts of surface topography and spatial resolution. The latter was performed at five east–west transects that cut across various topographic features in the western United States.

These analyses suggest that the regional simulation realistically captures many regional climate features. The simulated seasonal mean and extreme precipitation are comparable to observations. The regional simulation produces precipitation over a wide range of precipitation rates comparable to observations. Obvious biases in the simulation include the oversimulation of precipitation in the basins and intermountain West during the cold season, and the undersimulation in the Southwest in the warm season. There is a tendency of reduced precipitation frequency rather than intensity in the simulation during the summer in the Northwest and Southwest, which leads to the insufficient summer mean precipitation in those areas. Because of the general warm biases in the simulation, there is also a tendency for more precipitation events to be associated with warmer temperatures, which can affect the simulation of snowpack and runoff.

Abstract

A distinct feature of the atmospheric circulation response to increasing greenhouse gas forcing is the poleward shift of the zonal-mean westerly jet. The dynamical mechanisms of the zonal-mean poleward jet shift have been extensively studied in literature. At seasonal/regional scales, however, the westerly jets can shift equatorward, such as in the early-summer Asia–Pacific region, the late-winter America–Atlantic region, and the winter/spring east Pacific. These equatorward jet shifts imply climate impacts distinct from those of the poleward shifts, yet their causes are not well understood. Here, based on a hierarchy of coupled, prescribed-SST, and aquaplanet simulations, we attribute the seasonal/regional equatorward jet shifts to the enhanced tropical upper-level warming (ETUW), which arises from both the tropical moist adiabat and the enhanced equatorial surface warming. By steepening the meridional temperature gradient in the subtropical upper-to-middle level and assisted by positive eddy feedback, the ETUW increases the zonal wind equatorward of the climatological jet. When the regional/seasonal meridional temperature gradients (or equivalently the westerly jets) are weak and peak close to the tropics, the ETUW effect overcomes the poleward jet-shift mechanisms and leads to the equatorward jet shifts. This climatological-state dependency is consistently seen in the decomposed jet responses to uniform warming and surface warming pattern, and further demonstrated through idealized aquaplanet experiments with designed climatological states. For uniform warming, the ETUW arising from moist adiabat makes the general poleward jet shifts insignificant in the aforementioned favorable regions/seasons. For warming pattern, the ETUW from enhanced equatorial warming drives substantial equatorward jet shifts in these favorable seasons/regions.

Abstract

The distribution of latent heating released by mesoscale convective systems (MCSs) plays a crucial role in global energy and water cycles. To investigate the characteristics of MCS latent heating, five years (2014–19) of Global Precipitation Measurement (GPM) Ku-band Precipitation Radar observations and latent heating retrievals are combined with a newly developed global high-resolution (~10 km, hourly) MCS tracking dataset. The results suggest that midlatitude MCSs are shallower and have a lower maximum precipitation rate than tropical MCSs. However, MCSs occurring in the midlatitudes have larger precipitation areas and higher stratiform rain volume fraction, in agreement with previous studies. With substantial spatial and seasonal variability, MCS latent heating profiles are top-heavier in the middle and high latitudes than those in the tropics. Larger magnitudes of latent heating in the stratiform regions are found over the ocean than over land, which is the case for both the tropics and midlatitudes. The larger magnitude is related to a larger precipitating area/volume rather than a higher storm height or more intense convective core typically associated with land systems. A majority of midlatitude MCSs have a relatively high (>70%) stratiform fraction while this is not the case for tropical MCSs, suggesting that midlatitude MCSs tend to produce more stratiform rain while tropical MCSs are more convective. Importantly, the results of this study indicate that storm intensity, latent heating, and rainfall are different metrics of MCSs that can provide multiple constraints to inform development of convection parameterizations in global models.

Abstract

Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. Our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.