Atmospheric controls on soil moisture-boundary layer interactions

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2001.Includes bibliographical references (leaves 163-168).This thesis addresses the question of how the early morning atmospheric thermodynamic structure affects the interaction between the soil moisture state and the growth and development of the boundary layer (BL), leading to the triggering of convection. It is concluded that in mid-latitudes, for matters of convective triggering and response to land surface conditions, the critical portion of the atmospher~approximately1 to 3 km above the ground surface is independent of geographic location and local synoptic setting. As long as the low levels of the troposphere are relatively humid but not extremely close to saturation, a negative feedback between soil moisture and rainfall is likely when the early morning temperature lapse rate in this region is dry adiabatic; a positive feedback is likely when it is moist adiabatic; and when there is a temperature inversion in this region, deep convection cannot occur, independent of the soil moisture. Additionally, when the low levels of the troposphere are extremely dry or very close to saturation, the occurrence of convection is determined solely by the atmospheric conditions. Essential characteristics of the temperature structure of the early-morning atmosphere are captured by a new thermodynamic measure, the Convective Triggering Potential (CTP), developed to distinguish between soundings favoring rainfall over dry soils from those favoring rainfall over wet soils. Many measures of atmospheric humidity are effective at separating atmospherically-controlled cases from cases where the land surface conditions can influence the likelihood for convection, but Hi low, a variation of a humidity index, proved most effective. A one-dimensional model of the planetary boundary layer (BL) and surface energy budget has been modified to allow the growing BL to entrain air from an observed atmospheric sounding. The model is used to analyze the impact of soil saturation on BL development and the triggering of convection in different atmospheric settings. Results from this 1D model and from the three-dimensional Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) show a small but significant positive soil moisture-rainfall feedback in Illinois. This is consistent with an analysis of the distribution of early morning sounding values of CTP and Hi low from Illinois, though wind effects important in the MM5 simulations are not captured by the CTP-HIhow framework. From the MM5 simulations, it is concluded that the land surface condition can impact the potential for convection only when the atmosphere is not already predisposed to convect or not to convect. This atmospheric predisposition can be determined by analyzing the CTP, the Hi low, and the vertical profile of the winds. Analyses of Hi low scatter plots from radiosonde stations across the contiguous 48 United States reveal that positive feedbacks are likely in much of the eastern half of the country. The only area showing a potential negative feedback is in the Dryline and Monsoon Region of the arid southwest. Land surface conditions are unlikely to impact convective triggering in the rest of the western half of the country. Use of the lD BL model at four additional stations confirms that HilowTP-Hi low framework used in this nationwide analysis is valid for regions far removed from Illinois, where it was originally developed.by Kirsten Lynn Findell.Ph.D

An analysis of the relationship between soil moisture, rainfall, and boundary layer conditions, based on direct observations from Illinois

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1997.Includes bibliographical references (p. 141-144).by Kirsten L. Findell.M.S

Scaling in Surface Hydrology: Progress and Challenges

This paper presents a review of the challenges in spatial and temporal scales in surface hydrology. Fundamental issues and gaps in our understanding of hydrologic scaling are highlighted and shown to limit predictive skill, with heterogeneities, nonlinearities, and non-local transport processes among the most significant difficulties faced in scaling. The discrepancy between the physical process scale and the measurement scale has played a major role in restricting the development of theories, for example, relating observational scales to scales of climate and weather models. Progress in our knowledge of scaling in hydrology requires systematic determination of critical scales and scale invariance of physical processes. In addition, viewing the surface hydrologic system as composed of interacting dynamical subsystems should facilitate the definition of scales observed in nature. Such an approach would inform the development of careful, resolution-dependent, physical law formulation based on mathematical techniques and physical laws

Precipitation Sensitivity to Surface Heat Fluxes over North America in Reanalysis and Model Data

A new methodology for assessing the impact of surface heat fluxes on precipitation is applied to data from the North American Regional Reanalysis (NARR) and to output from the Geophysical Fluid Dynamics Laboratory’s Atmospheric Model 2.1 (AM2.1). The method assesses the sensitivity of afternoon convective rainfall frequency and intensity to the late-morning partitioning of latent and sensible heating, quantified in terms of evaporative fraction (EF). Over North America, both NARR and AM2.1 indicate sensitivity of convective rainfall triggering to EF but no appreciable influence of EF on convective rainfall amounts. Functional relationships between the triggering feedback strength (TFS) metric and mean EF demonstrate the occurrence of stronger coupling for mean EF in the range of 0.6 to 0.8. To leading order, AM2.1 exhibits spatial distributions and seasonality of the EF impact on triggering resembling those seen in NARR: rainfall probability increases with higher EF over the eastern United States and Mexico and peaks in Northern Hemisphere summer. Over those regions, the impact of EF variability on afternoon rainfall triggering in summer can explain up to 50% of seasonal rainfall variability. However, the AM2.1 metrics also exhibit some features not present in NARR, for example, strong coupling extending northwestward from the central Great Plains into Canada. Sources of disagreement may include model hydroclimatic biases that affect the mean patterns and variability of surface flux partitioning, with EF variability typically much lower in NARR. Finally, the authors also discuss the consistency of their results with other assessments of land–precipitation coupling obtained from different methodologies

Neural Network–Based Sensitivity Analysis of Summertime Convection over the Continental United States

Although land–atmosphere coupling is thought to play a role in shaping the mean climate and its variability, it remains difficult to quantify precisely. The present study aims to isolate relationships between early morning surface turbulent fluxes partitioning [i.e., evaporative fraction (EF)] and subsequent afternoon convective precipitation frequency and intensity. A general approach involving statistical relationships among input and output variables, known as sensitivity analysis (SA), is used to develop a reduced complexity metamodel of the linkage between EF and convective precipitation. Two additional quantities characterizing the early morning convective environment, convective triggering potential (CTP) and low-level humidity (HIlow) deficit, are included. The SA approach is applied to the North American Regional Reanalysis (NARR) for June–August (JJA) conditions over the entire continental United States, Mexico, and Central America domain. Five land–atmosphere coupling regimes are objectively characterized based on CTP, HIlow, and EF. Two western regimes are largely atmospherically controlled, with a positive link to CTP and a negative link to HIlow. The other three regimes occupy Mexico and the eastern half of the domain and show positive links to EF and negative links to HIlow, suggesting that both surface fluxes and atmospheric humidity play a role in the triggering of rainfall in these regions. The regimes associated with high mean EF also tend to have high sensitivity of rainfall frequency to variations in EF. While these results may be sensitive to the choice of dataset, the approach can be applied across observational, reanalysis, and model datasets and thus represents a potentially powerful tool for intercomparison and validation as well as for characterizing land–atmosphere interaction regimes

Radiative convective equilibrium over a land surface

Radiative-convective equilibrium (RCE) describes an idealized state of the atmosphere in which the vertical temperature profile is determined by a balance between radiative and convective fluxes. While RCE has been applied extensively over oceans, its application over the land surface has been limited. The present study explores the properties of RCE over land using an atmospheric single column model (SCM) from the Laboratoire de Meteorologie Dynamique (LMD) General Circulation Model (LMDZ5B) coupled in temperature and moisture to a land surface model comprising a simplified bucket model with finite moisture capacity. Given the presence of a large-amplitude diurnal heat flux cycle, the resultant RCE exhibits multiple equilibria when conditions are neither strictly water- nor energy-limited. By varying top-of-the-atmosphere insolation (through changes in latitude), total system water content, and initial temperature conditions, the sensitivity of the land RCE states is assessed, with particular emphasis on the role of clouds. Based on this analysis, it appears that a necessary condition for the model to exhibit multiple equilibria is the presence of low-level clouds coupled to the diurnal cycle of radiation. In addition the simulated surface precipitation rate varies non-monotonically with latitude as a result of a tradeoff between in-cloud rain rate and subcloud rain re-evaporation, thus underscoring the importance of subcloud layer processes and unsaturated downdrafts. It is shown that clouds, especially at low levels, are key elements of the internal variability of the coupled land-atmosphere system through their feedback on radiation

Impact of Soil Moisture–Atmosphere Interactions on Surface Temperature Distribution

Understanding how different physical processes can shape the probability distribution function (PDF) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture–atmosphere interactions to surface temperature PDFs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back onto the near-surface climate—in particular, temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moisture dynamics enhance both temperature mean and variance over regional ‘‘hotspots’’ of land–atmosphere coupling.Moreover, higher-order distribution moments, such as skewness and kurtosis, are also significantly impacted, suggesting an asymmetric impact on the positive and negative extremes of the temperature PDF. Such changes are interpreted in the context of altered distributions of the surface turbulent and radiative fluxes. That the moments of the temperature distribution may respond differentially to soil moisture dynamics underscores the importance of analyzing moments beyond the mean and variance to characterize fully the interplay of soil moisture and near-surface temperature. In addition, it is shown that soil moisture dynamics impacts daily temperature variability at different time scales over different regions in the model

Impact of Soil Moisture–Atmosphere Interactions on Surface Temperature Distribution

Understanding how different physical processes can shape the probability distribution function (PDF) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture–atmosphere interactions to surface temperature PDFs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back onto the near-surface climate—in particular, temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moisture dynamics enhance both temperature mean and variance over regional ‘‘hotspots’’ of land–atmosphere coupling.Moreover, higher-order distribution moments, such as skewness and kurtosis, are also significantly impacted, suggesting an asymmetric impact on the positive and negative extremes of the temperature PDF. Such changes are interpreted in the context of altered distributions of the surface turbulent and radiative fluxes. That the moments of the temperature distribution may respond differentially to soil moisture dynamics underscores the importance of analyzing moments beyond the mean and variance to characterize fully the interplay of soil moisture and near-surface temperature. In addition, it is shown that soil moisture dynamics impacts daily temperature variability at different time scales over different regions in the model

A Probabilistic Bulk Model of Coupled Mixed Layer and Convection. Part I: Clear-Sky Case

A new bulk model of the convective boundary layer, the probabilistic bulk convection model (PBCM), is presented. Unlike prior bulk approaches that have modeled the mixed-layer-top buoyancy flux as a constant fraction of the surface buoyancy flux, PBCM implements a new mixed-layer-top entrainment closure based on the mass flux of updrafts overshooting the inversion. This mass flux is related to the variability of the surface state (potential temperature θ and specific humidity q) of an ensemble of updraft plumes. The authors evaluate the model against observed clear-sky weak and strong inversion cases and show that PBCM performs well. The height, state, and timing of the boundary layer growth are accurately reproduced. Sensitivity studies are performed highlighting the role of the main parameters (surface variances, lateral entrainment). The model is weakly sensitive to the exact specification of the variability at the surface and is most sensitive to the lateral entrainment of environmental air into the rising plumes. Apart from allowing time-dependent top-of-the-boundary-layer entrainment rates expressed in terms of surface properties, which can be observed in situ, PBCM naturally takes into account the transition to the shallow convection regime, as described in a companion paper. Thus, PBCM represents an important step toward a unified framework bridging parameterizations of mixed-layer entrainment velocity in both clear-sky and moist convective boundary layers

The Impact of Anthropogenic Land Use and Land Cover Change on Regional Climate Extremes

Recent research highlights the role of land surface processes in heat waves, droughts, and other extreme events. Here we use an earth system model (ESM) from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the regional impacts of historical anthropogenic land useland cover change (LULCC) on combined extremes of temperature and humidity. A bivariate assessment allows us to consider aridity and moist enthalpy extremes, quantities central to human experience of near-surface climate conditions. We show that according to this model, conversion of forests to cropland has contributed to much of the upper central US and central Europe experiencing extreme hot, dry summers every 2-3 years instead of every 10 years. In the tropics, historical patterns of wood harvesting, shifting cultivation and regrowth of secondary vegetation have enhanced near surface moist enthalpy, leading to extensive increases in the occurrence of humid conditions throughout the tropics year round. These critical land use processes and practices are not included in many current generation land models, yet these results identify them as critical factors in the energy and water cycles of the midlatitudes and tropics