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The responses of atmospheric variability to Tibetan Plateau (TP) snow cover (TPSC) at seasonal, interannual and decadal time scales have been extensively investigated. However, the atmospheric response to faster subseasonal variability of TPSC has been largely ignored. Here, we show that the subseasonal variability of TPSC, as revealed by daily data, is closely related to the subsequent East Asian atmospheric circulation at medium-range time scales (approximately 3–8 days later) during wintertime. TPSC acts as an elevated cooling source in the middle troposphere during wintertime and rapidly modulates the land surface thermal conditions over the TP. When TPSC is high, the upper-level geopotential height is lower, and the East Asia upper-level westerly jet stream is stronger. This finding improves our understanding of the influence of TPSC at multiple time scales. Furthermore, our work highlights the need to understand how atmospheric variability is rapidly modulated by fast snow cover changes., The atmospheric response to subseasonal variability of Tibetan Plateau snow cover has been largely ignored. Here the authors show that the fast subseasonal variability of Tibetan Plateau snow cover is closely related to the subsequent East Asian atmospheric circulation at medium-range time scales.

This study used a global hydrological model (GHM), PCR-GLOBWB, which simulates surface water storage changes, natural and human induced groundwater storage changes, and the interactions between surface water and subsurface water, to generate scaling factors by mimicking low-pass filtering of GRACE signals. Signal losses in GRACE data were subsequently restored by the scaling factors from PCR-GLOBWB. Results indicate greater spatial heterogeneity in scaling factor from PCR-GLOBWB and CLM4.0 than that from GLDAS-1 Noah due to comprehensive simulation of surface and subsurface water storage changes for PCR-GLOBWB and CLM4.0. Filtered GRACE total water storage (TWS) changes applied with PCR-GLOBWB scaling factors show closer agreement with water budget estimates of TWS changes than those with scaling factors from other land surface models (LSMs) in China's Yangtze River basin. Results of this study develop a further understanding of the behavior of scaling factors from different LSMs or GHMs over hydrologically complex basins, and could be valuable in providing more accurate TWS changes for hydrological applications (e.g., monitoring drought and groundwater storage depletion) over regions where human-induced interactions between surface water and subsurface water are intensive.

Interactions between soil moisture, evapotranspiration (ET), atmospheric moisture fluxes and precipitation are complex. It is difficult to attribute the variations of one variable to another. In this study, we investigate the influence of atmospheric moisture fluxes and land surface soil moisture on local precipitation, with a focus on the southern United States (U.S.), a region with a strong humidity gradient and intense moisture fluxes. Experiments with the Weather Research and Forecasting model show that the variation of moisture flux convergence (MFC) is more important than that of soil moisture for precipitation variation over the southern U.S. Further analyses decompose the precipitation change into several contributing factors and show that MFC affects precipitation both directly through changing moisture inflow (wet areas) and indirectly by changing the precipitation efficiency (transitional zones). Soil moisture affects precipitation mainly by changing the precipitation efficiency, and secondly through direct surface ET contribution. The greatest soil moisture effects are over transitional zones. MFC is more important for the probability of heavier rainfall; soil moisture has much weaker impact on rainfall probability and its roles are similar for the probability of intermediate-to-heavy rainfall (>10 mm day−1). Although MFC is more important than soil moisture for precipitation over most regions, the impact of soil moisture could be large over certain transitional regions. At the submonthly time scale, the African Sahel appears to be the only major region where soil moisture has a greater impact than MFC on precipitation. This study provides guidance to understanding and further investigation of the roles of local land surface processes and large-scale circulations on precipitation.

Using observational and reanalyses data, we investigated the impact of dust aerosols over the Middle East and the Arabian Sea (AS) on the Indian summer monsoon (ISM) rainfall. Satellite and aerosol reanalysis data show extremely heavy aerosol loading, mainly mineral dust, over the Middle East and AS during the ISM season. Multivariate empirical orthogonal function analyses suggest an aerosol-monsoon connection. This connection may be attributed to dust-induced atmospheric heating centered over the Iranian Plateau (IP), which enhances the meridional thermal contrast and strengthens the ISM circulation and rainfall. The enhanced circulation further transports more dust to the AS and IP, heating the atmosphere (positive feedback). The aerosols over the AS and the Arabian Peninsula have a significant correlation with rainfall over central and eastern India about 2 weeks later. This finding highlights the nonlocal radiative effect of dust on the ISM circulation and rainfall and may improve ISM rainfall forecasts.

The Southern Great Plains (SGP) has been shown as a region of significant soil moisture-precipitation (S-P) coupling. However, how strong evapotranspiration (ET) can affect regional precipitation remains largely unclear, impeding a full grasp of the S-P feedback in that area. The current study seeks to unravel, in a spring month (April), the potential role played by large-scale atmospheric conditions in shaping S (ET)-P feedback. Our regional climate modeling experiments demonstrate that the presence of anomalous low (high) pressure and cyclonic (anticyclonic) flows at the upper/middle troposphere over the relevant areas is associated with strongest (minimum) positive S-P feedback in the SGP. Their impacts are interpreted in terms of large-scale atmospheric dynamical disturbance, including the intensity and location of synoptic eddies. Further analyses of the vertical velocity fields corroborate these interpretations. In addition, the relationship between lower tropospheric moisture conditions (including winds) and feedback composites is evaluated.

A quasi-isentropic, back-trajectory scheme is applied to output from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and a land-only replay with corrected precipitation to estimate surface evaporative sources of moisture supplying precipitation over every ice-free land location for the period 1979–2005. The evaporative source patterns for any location and time period are effectively two-dimensional probability distributions. As such, the evaporative sources for extreme situations like droughts or wet intervals can be compared to the corresponding climatological distributions using the method of relative entropy. Significant differences are found to be common and widespread for droughts, but not wet periods, when monthly data are examined. At pentad temporal resolution, which is more able to isolate floods and situations of atmospheric rivers, values of relative entropy over North America are typically 50%–400% larger than at monthly time scales. Significant differences suggest that moisture transport may be a key factor in precipitation extremes. Where evaporative sources do not change significantly, it implies other local causes may underlie the extreme events.

Irrigation is an important human activity that may impact local and regional climate, but current climate model simulations and data assimilation systems generally do not explicitly include it. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) shows more irrigation signal in surface evapotranspiration (ET) than the Modern-Era Retrospective Analysis for Research and Applications (MERRA) because ERA-Interim adjusts soil moisture according to the observed surface temperature and humidity while MERRA has no explicit consideration of irrigation at the surface. But, when compared with the results from a hydrological model with detailed considerations of agriculture, the ET from both reanalyses show large deficiencies in capturing the impact of irrigation. Here, a back-trajectory method is used to estimate the contribution of irrigation to precipitation over local and surrounding regions, using MERRA with observation-based corrections and added irrigation-caused ET increase from the hydrological model. Results show substantial contributions of irrigation to precipitation over heavily irrigated regions in Asia, but the precipitation increase is much less than the ET increase over most areas, indicating that irrigation could lead to water deficits over these regions. For the same increase in ET, precipitation increases are larger over wetter areas where convection is more easily triggered, but the percentage increase in precipitation is similar for different areas. There are substantial regional differences in the patterns of irrigation impact, but, for all the studied regions, the highest percentage contribution to precipitation is over local land.

We present the first systematic study to quantify the impact of land initialization on seasonal temperature prediction in the Northern Hemisphere, emphasizing the role of land snow data assimilation (DA). Three suites of ensemble seasonal integrations are conducted for coupled land–atmosphere runs. The land component is initialized using datasets from (1) no DA, (2) assimilating MODIS snow cover fraction (SCF), and (3) assimilating both MODIS SCF and GRACE terrestrial water storage. Results show that snow DA improves temperature predictions especially in the Tibetan Plateau (by 5–20%) and high latitudes. Improvements at low latitudes are seen immediately and last up to 60 days, whereas improvements at high latitudes only appear later in transitional seasons. At high latitudes, assimilating GRACE data results in marked and prolonged improvements (by ~25%) due to large initial snow mass changes. This study has great implications for future land DA and seasonal climate prediction studies.

The absorptive properties of dust aerosols largely determine the magnitude of their radiative impacts on the climate system. Currently, climate models use globally constant values of dust imaginary refractive index (IRI), a parameter describing the dust absorption efficiency of solar radiation, although it is highly variable. Here we show with model experiments that the dust-induced Indian summer monsoon (ISM) rainfall differences (with dust minus without dust) change from −9% to 23% of long-term climatology as the dust IRI is changed from zero to the highest values used in the current literature. A comparison of the model results with surface observations, satellite retrievals, and reanalysis data sets indicates that the dust IRI values used in most current climate models are too low, tending to significantly underestimate dust radiative impacts on the ISM system. This study highlights the necessity for developing a parameterization of dust IRI for climate studies., King Abdullah University of Science and Technology, University of Texas at Austin. Jackson School of Geosciences

The operational coupled land–atmosphere forecast model from the National Centers for Environmental Prediction (NCEP) is evaluated for the strength and characteristics of its coupling in the water cycle between land and atmosphere. Following the protocols of the Global Land–Atmosphere Coupling Experiment (GLACE) it is found that the Global Forecast System (GFS) atmospheric model coupled to the Noah land surface model exhibits extraordinarily weak land–atmosphere coupling, much as its predecessor, the GFS–Oregon State University (OSU) coupled system. The coupling strength is evaluated by the ability of subsurface soil wetness to affect locally the time series of precipitation. The surface fluxes in Noah are also found to be rather insensitive to subsurface soil wetness. Comparison to another atmospheric model coupled to Noah as well as a different land surface model show that Noah is responsible for some of the lack of sensitivity, primarily because its thick (10 cm) surface layer dominates the variability in surface latent heat fluxes. Noah is found to be as responsive as other land surface models to surface soil wetness and temperature variations, suggesting the design of the GLACE sensitivity experiment (based only on subsurface soil wetness) handicapped the Noah model. Additional experiments, in which the parameterization of evapotranspiration is altered, as well as experiments where surface soil wetness is also constrained, isolate the GFS atmospheric model as the principal source of the weak sensitivity of precipitation to land surface states.

The Global Land–Atmosphere Coupling Experiment (GLACE) built a framework to estimate the strength of the land–atmosphere interaction across many weather and climate models. Within this framework, GLACE-type experiments are performed with a single atmospheric model coupled to three different land models. The precipitation time series is decomposed into three frequency bands to investigate the large-scale connection between external forcing, precipitation variability and predictability, and land–atmosphere coupling strength. It is found that coupling to different land models or prescribing subsurface soil moisture does not change the global pattern of precipitation predictability and variability too much. However, the regional impact of soil moisture can be highlighted by calculating the land–atmosphere coupling strength, which shows very different patterns for the three models. The estimated precipitation predictability and land–atmosphere coupling strength is mainly associated with the low-frequency component of precipitation (periods beyond 3 weeks). Based on these findings, the land–atmosphere coupling strength is conceptually decomposed into the impact of low-frequency external forcing and the impact of soil moisture. Because most models participating in GLACE have overestimated the low-frequency component of precipitation, a calibration to the GLACE-estimated land–atmosphere coupling strength is performed. The calibrated coupling strength is generally weaker, but the global pattern does not change much. This study provides an important clarification of land–atmosphere coupling strength and increases the understanding of the land–atmosphere interaction.

An atmospheric general circulation model (AGCM) is coupled to three different land surface schemes (LSSs), both individually and in combination (i.e., the LSSs receive the same AGCM forcing each time step and the averaged upward surface fluxes are passed back to the AGCM), to study the uncertainty of simulated climatologies and variabilities caused by different LSSs. This tiling of the LSSs is done to study the uncertainty of simulated mean climate and climate variability caused by variations between LSSs. The three LSSs produce significantly different surface fluxes over most of the land, no matter whether they are coupled individually or in combination. Although the three LSSs receive the same atmospheric forcing in the combined experiment, the inter-LSS spread of latent heat flux can be larger or smaller than the individually coupled experiment, depending mostly on the evaporation regime of the schemes in different regions. Differences in precipitation are the main reason for the different latent heat fluxes over semiarid regions, but for sensible heat flux, the atmospheric differences and LSS differences have comparable contributions. The influence of LSS uncertainties on the simulation of surface temperature is strongest in dry seasons, and its influence on daily maximum temperature is stronger than on minimum temperature. Land–atmosphere interaction can dampen the impact of LSS uncertainties on surface temperature in the tropics, but can strengthen their impact in middle to high latitudes. Variations in the persistence of surface heat fluxes exist among the LSSs, which, however, have little impact on the global pattern of precipitation persistence. The results provide guidance to future diagnosis of model uncertainties related to LSSs.

An atmospheric general circulation model is coupled with three different land surface schemes (LSSs) to study the impact of LSS uncertainties on the simulation of surface warming caused by doubled CO2 and associated oceanic changes. It is found that the implemented LSSs can greatly impact the spatial distribution and amplitude of the simulated warming over land. In warm regions, the different descriptions of local land processes and their associated feedbacks are responsible for about half of the inter-model spread. In cold regions, almost all of the spread is caused by the circulation differences triggered by LSS differences, and the local land processes have little direct impact. On the other hand, as the annual global-average land surface warmings are very close no matter which LSS is coupled, this study suggests that the LSS has little impact on projections of annual global-average temperature change, despite strong regional impact in warm regions. Copyright © 2010 Royal Meteorological Society

An atmospheric general circulation model (AGCM) and an oceanic general circulation model (OGCM) are asynchronously coupled to simulate the climate of the mid-Holocene period. The role of the solar radiation and ocean in the mid-Holocene East Asian monsoon climate is analyzed and some mechanisms are revealed. At the forcing of changed solar radiation induced by the changed orbital parameters and the changed SST simulated by the OGCM, compared with when there is orbital forcing alone, there is more precipitation and the monsoon is stronger in the summer of East Asia, and the winter temperature increases over China. These agree better with the reconstructed data. It is revealed that the change of solar radiation can displace northward the ITCZ and the East Asia subtropical jet, which bring more precipitation over the south of Tibet and North and Northeast China. By analyzing the summer meridional latent heat transport, it is found that the influence of solar radiation change is mainly to increase the convergence of atmosphere toward the land, and the influence of SST change is mainly to transport more moisture to the sea surface atmosphere. Their synergistic effect on East Asian precipitation is much stronger than the sum of their respective effects.

[1] The ability of soil moisture to affect precipitation (SM-P) can be dissected into the ability of soil moisture to affect evapotranspiration (ET; SM-ET) and the ability of ET to affect precipitation (ET-P). SM-ET is a local process that is relatively easy to quantify, but ET-P includes nonlocal atmospheric processes and is more complex. Here, ET-P is quantified both locally and remotely with a back-trajectory method for water vapor transport, using corrected reanalysis data. It is found that, for SM-P and ET-P, local impact is greater than that from remote for most land areas with significant local impacts. By examining the responses of the three metrics (SM-ET, ET-P, and SM-P) to climate variations over different climate regimes, we show that SM-ET is the principal factor that determines the spatial pattern and variation of SM-P. For climatologically wet regions, SM-ET and SM-P are higher during dry periods, and vice versa for climatologically dry regions. All three metrics show highest values over the transitional zones.

To improve the automatic level of the large tank farm detection, the oil tank measurement and control system based on Lon works field bus were built, which can monitor the oil tank in remote monitoring center and display the measurement parameters by the LCD in the field. The system uses the advanced measurement approach to improve the measurement accuracy and real-time. The system consists of filed instruments on tanks, Data Processing Units, Lon Works network and monitoring computers. The Host-Based Data Processing Units can integrate a variety of instrument output signal or different communication protocols in one system, which ensured good compatibility and reliability. The designed system has been successfully applied to a tank farm. Thus another approach of remotely monitoring the large tank farm is founded. The system structure, the measurement principle, hardware and software design of Data Processing Units are introduced.

It is now well-established that the chaotic nature of the atmosphere severely limits the predictability of weather, while the slowly varying sea surface temperature (SST) and land surface states can enhance the predictability of atmospheric variations through surfaceatmosphere interactions or by providing a boundary condition (e.g., Shukla 1993, 1998; Shukla et al. 2000; Graham et al. 1994; Koster et al. 2000; Dirmeyer et al. 2003; Quan et al. 2004). Among them, the influence of ocean is more important on a global scale because it covers twice as much surface area as land and is a much larger heat and energy reservoir. But the impact of ocean may not be dominant over land, especially the mid-latitude land (Koster and Suarez 1995). The Global Land-Atmosphere Coupling Experiment (GLACE) (Koster et al., 2004, 2006) builds a framework to objectively estimate the potential contribution of land states to atmospheric predictability (called land-atmosphere coupling strength) in numerical weather and climate models. By averaging the estimated land-atmosphere coupling strength from 12 models participating in GLACE, an ensemble average coupling strength is obtained. However, the coupling strength varies widely among models. The discrepancy is certainly related to differences in the parameterization of processes and their complex interactions, from soil hydrology, vegetation physiology, to boundary layer, cloud and precipitation processes. It is difficult to determine what causes the relatively strong or weak coupling strengths seen in individual models. Some studies have identified the impact of soil moisture on evapotranspiration (ET) (denoted SM→ET) and the impact of ET on precipitation (denoted ET→P) as two key factors for land-atmosphere coupling (Guo et al. 2006 (hereafter GUO06); Dirmeyer et al. 2010). For soil moisture to have a strong impact on precipitation, both SM→ET and ET→P need to be strong. This usually happens in transitional zones between wet and dry climates (Dirmeyer 2006). In addition to the mean climate state, does the climate variability have some impact on land-atmosphere coupling? A theoretical study found that the strength of the external forcing can affect the coupling strength and the location of coupling hot spots (Wei et al. 2006). Even less is known about how the land-atmosphere coupling is related to the

[1] Two different Atmospheric General Circulation Models (AGCMs), each coupled to three different land surface schemes (LSSs) (six different model configurations in total), are used to study the roles of different model components and different action processes in land-atmosphere coupling. Experiments show that, for the six model configurations, the choice of AGCMs is the main reason for the substantially different precipitation variability, predictability, and land-atmosphere coupling strength among the configurations. The impact of different LSSs is secondary. Intraseasonal precipitation variability, which is mainly a property of the AGCM, can impact land-atmosphere coupling both directly in the atmosphere and indirectly through soil moisture response to precipitation. These results lead to a common conceptual decomposition of the land-atmosphere coupling strength and increases the understanding on large-scale land-atmosphere coupling.

[1] Precipitation analysis and soil moisture from the Global Land Data Assimilation System (GLDAS) are used to assess the land-atmosphere coupling in boreal summer. Correlations between antecedent soil moisture and precipitation suggest that regions of strong land-atmosphere coupling lie mainly in arid to semiarid transition zones or in semihumid forest to grassland transition zones. They consist of central Eurasia, the region from Mongolia to northern China, southwest China, the Sahel, the northern continental United States, and southern Europe. It is found that over these regions, positive soil moisture feedback accounts for typically 10–20% of the variance of monthly precipitation anomalies with the feedback efficiency of the order of 0.3–0.9 mm month−1 (0.1 standardized soil moisture)−1. While soil moisture feedback is dominated by the positive sign, negative feedback may exist in some areas, such as India and the western part and Quebec province of Canada. Generally, the land-atmosphere coupling strength estimated from the GLDAS data agrees well with those from the observational soil moisture in Illinois and the European Centre for Medium-Range Weather Forecasts 40-year reanalysis (ERA-40) soil moisture product. Physical mechanisms responsible for the findings are further discussed. This study provides a Northern Hemisphere distribution of the land-atmosphere coupling strength, which can be used to test the model simulations on monthly to seasonal time scales.

[1] By analyzing the data from the Second Global Soil Wetness Project (GSWP-2) sensitivity experiments, this study quantifies the sensitivities of soil wetness simulation to uncertainties in precipitation and radiation forcings and estimates the actual influence of these uncertainties. The sensitivity of soil moisture to uncertainties in radiation has a large seasonal cycle because of its temperature dependence. Sensitivity to uncertainties in precipitation is less temporally variable and is large in semiarid regions. Precipitation sensitivity is generally dominant over radiation sensitivity in cold climate, while in warm climate radiation sensitivity is dominant or both of them are high. However, actual uncertainties in precipitation are typically much larger than for net radiation in warm climate, so the practical impact is dominated by our lack of accurate precipitation estimates. Precipitation uncertainties account for about 2/3 of the total soil wetness uncertainties from all forcing data, while radiation uncertainties only account for about 1/6.

[1] A simple model is developed to describe the significant land-atmosphere interaction processes in the warm climate. It includes bulk soil hydrology, dynamic vegetation, and simple land-atmosphere interaction processes. The model can simulate the basic features of land surface control on evapotranspiration (ET) and exhibits a multiequilibrium behavior similar to that of some more complex models. In order to study the role of land surface processes in climate variability on monthly to seasonal timescales, a series of experiments are performed with the model over different land covers and at different external forcings. The major findings are: (1) The maximum soil wetness memory and precipitation predictability tend to occur at a sparser (denser) vegetation cover with the weakening (strengthening) of external forcing. (2) For vegetated region, the soil moisture memory and precipitation persistence will be underestimated if vegetation is not interactive, and the percentage of underestimation is larger over denser vegetation covers. (3) Interactive vegetation can enhance the low-frequency coherency between soil wetness and precipitation, but its influence on high-frequency coherency is small. (4) Large coherencies between soil wetness and precipitation in the time-frequency domain correspond to strong wavelet power of external forcing in the same domain. These findings provide guidance for the development of and study with more complex models.

The mechanical behavior of three-dimensional integral braided carbon-carbon composites (C/C) has been explored under uncoventional fatigue loading, and a series of special experimental phenomena were observed. In this present paper, one of these phenomena, the micro silk-like pleating of the pyrocarbon matrix, is discussed. With thermodynamic analysis, it is shown that small areas of high-temperature contribute to the micro-pleating formation. Moreover, slight friction between pyrocarbon laminates has been proven to produce the small high-temperature areas during cyclic tensile loading.