Triggering Deep Convection with a Probabilistic Plume Model

A model unifying the representation of the planetary boundary layer and dry, shallow and deep convection, the Probabilistic Plume Model (PPM), is presented. Its capacity to reproduce the triggering of deep convection over land is analysed in detail. The model accurately reproduces the timing of shallow convection and of deep convection onset over land, which is a major issue in many current general climate models. The PPM is based on a distribution of plumes with varying thermodynamic states (potential temperature and specific humidity) induced by surface layer turbulence. Precipitation is computed by a simple ice microphysics, and with the onset of precipitation, downdrafts are initiated and lateral entrainment of environmental air into updrafts is reduced. The most buoyant updrafts are responsible for the triggering of moist convection, causing the rapid growth of clouds and precipitation. Organization of turbulence in the subcloud layer is induced by unsaturated downdrafts, and the effect of density currents is modeled through a reduction of the lateral entrainment. The reduction of entrainment induces further development from the precipitating congestus phase to full deep cumulonimbus. Model validation is performed by comparing cloud base, cloud top heights, timing of precipitation and environmental profiles against cloud resolving models and large-eddy simulations for two test cases. These comparisons demonstrate that PPM triggers deep convection at the proper time in the diurnal cycle, and produces reasonable precipitation. On the other hand, PPM underestimates cloud top height

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

Interdependence of climate, soil, and vegetation as constrained by the Budyko curve

The Budyko curve is an empirical relation among evapotranspiration, potential evapotranspiration and precipitation observed across a variety of landscapes and biomes around the world. Using data from more than three hundred catchments and a simple water balance model, the Budyko curve is inverted to explore the ecohydrological controls of the soil water balance. Comparing the results across catchments reveals that aboveground transpiration efficiency and belowground rooting structure have adapted to the dryness index and the phase lag between peak seasonal radiation and precipitation. The vertical and/or lateral extent of the rooting zone exhibits a maximum in semi-arid catchments or when peak radiation and precipitation are out of phase. This demonstrates plant strategies in Mediterranean climates in order to cope with water stress: the deeper rooting structure buffers the phase difference between precipitation and radiation. Results from this study can be used to constrain land-surface parameterizations in ungauged basins or general circulation models

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

Sensitivity of terrestrial precipitation trends to the structural evolution of sea surface temperatures

Pronounced intermodel differences in the projected response of land surface precipitation (LSP) to future anthropogenic forcing remain in the Coupled Model Intercomparison Project Phase 5 model integrations. A large fraction of the intermodel spread in projected LSP trends is demonstrated here to be associated with systematic differences in simulated sea surface temperature (SST) trends, especially the representation of changes in (i) the interhemispheric SST gradient and (ii) the tropical Pacific SSTs. By contrast, intermodel differences in global mean SST, representative of differing global climate sensitivities, exert limited systematic influence on LSP patterns. These results highlight the importance to regional terrestrial precipitation changes of properly simulating the spatial distribution of large-scale, remote changes as reflected in the SST response to increasing greenhouse gases. Moreover, they provide guidance regarding which region-specific precipitation projections may be potentially better constrained for use in climate change impact assessments

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

Amplification of wet and dry month occurrence over tropical land regions in response to global warming

Quantifying how global warming impacts the spatiotemporal distribution of precipitation represents a key scientific challenge with profound implications for human welfare. Utilizing monthly precipitation data from Coupled Model Intercomparison Project (CMIP3) climate change simulations, the results here show that the occurrence of very dry (10 mm/day) months comprises a straightforward, robust metric of anthropogenic warming on tropical land region rainfall. In particular, differencing tropics-wide precipitation frequency histograms for 25-year periods over the late 21st and 20th centuries shows increased late-21st-century occurrence of histogram extremes both in the model ensemble and across individual models. Mechanistically, such differences are consistent with the view of enhanced tropical precipitation spatial gradients. Similar diagnostics are calculated for two 15-year subperiods over 1979–2008 for the CMIP3 models and three observational precipitation products to assess whether the signature of late-21st-century warming has already emerged in response to recent warming. While both the observations and CMIP3 ensemble-mean hint at similar amplification in the warmer (1994–2008) subinterval, the changes are not robust, as substantial differences are evident among the observational products and the intraensemble spread is large. Comparing histograms computed from the warmest and coolest years of the observational period further demonstrates effects of internal variability, notably the El Niño/Southern Oscillation, which appear to oppose the impact of quasi-uniform anthropogenic warming on the wet tail of the monthly precipitation distribution. These results identify the increase of very dry and wet occurrences in monthly precipitation as a potential signature of anthropogenic global warming but also highlight the continuing dominance of internal climate variability on even bulk measures of tropical rainfall