The influence of land surface heterogeneities on cloud size development

This study analyzes the effects of land surface heterogeneities at various horizontal scales on the transition from shallow to deep convection and on the cloud size distribution. An idealized case of mid-latitude summertime convection is simulated by means of large-eddy simulations coupled to an interactive land surface. The transition is accelerated over heterogeneous surfaces. The simulation with an intermediate patch size of 12.8 km exhibits the fastest transition with a transition time two thirds that over a homogeneous surface. A similar timing is observed for the precipitation onset whereas the total accumulated rainfall tends to increase with patch size. The cloud size distribution can be approximated by a power law with a scale break. The exponent of the power law is independent of the heterogeneity scale, implying a similar cloud cover between the simulations. In contrast, the scale break varies with patch size. The size of the largest clouds does not scale with the boundary layer height, although their maximum size scales with the patch size. Finally, the idea that larger clouds grow faster, known from homogeneous surface conditions, is not fully valid over heterogeneous surfaces. These various aspects can be understood from the complex interplay between the characteristics of the triggered mesoscale circulations and a cloud development acting in response to the diurnal cycle in surface heating. The results also call for adequate representation of such effects in convective parameterizations

Modeled contrast in the response of the surface energy balance to heat waves for forest and grassland

Observations have shown that differences in surface energy fluxes over grasslands and forests are amplified during heat waves. The role of land-atmosphere feedbacks in this process is still uncertain. In this study, a single-column model (SCM) is used to investigate the difference between forest and grassland in their energy response to heat waves. Three simulations for the period 2005-11 were carried out: a control run using vegetation characteristics for Cabauw (the Netherlands), a run where the vegetation is changed to 100% forest, and a run with 100% short grass as vegetation. A surface evaporation tendency equation is used to analyze the impact of the land-atmosphere feedbacks on evapotranspiration and sensible heat release under normal summer and heat wave conditions with excessive shortwave radiation. Land-atmosphere feedbacks modify the contrast in surface energy fluxes between forest and grass, particularly during heat wave conditions. The surface resistance feedback has the largest positive impact, while boundary layer feedbacks generally tend to reduce the contrast. Overall, forests give higher air temperatures and drier atmospheres during heat waves. In offline land surface model simulations, the difference between forest and grassland during heat waves cannot be diagnosed adequately owing to the absence of boundary layer feedbacks

Relation between convective rainfall properties and antecedent soil moisture heterogeneity conditions in North Africa

Recent observational studies have demonstrated the relevance of soil moisture heterogeneity and the associated thermally-induced circulation on deep convection and rainfall triggering. However, whether this dynamical mechanism further influences rainfall propertiessuch as rain volume or timinghas yet to be confirmed by observational data. Here, we analyze 10 years of satellite-based sub-daily soil moisture and precipitation records and explore the potential of strong spatial gradients in morning soil moisture to influence the properties of afternoon rainfall in the North African region, at the 100-km scale. We find that the convective rain systems that form over locally drier soils and anomalously strong soil moisture gradients have a tendency to initiate earlier in the afternoon; they also yield lower volumes of rain, weaker intensity and lower spatial variability. The strongest sensitivity to antecedent soil conditions is identified for the timing of the rain onset; it is found to be correlated with the magnitude of the soil moisture gradient. Further analysis shows that the early initiation of rainfall over dry soils and strong surface gradients yet requires the presence of a very moist boundary layer on that day. Our findings agree well with the expected effects of thermally-induced circulation on rainfall properties suggested by theoretical studies and point to the potential of locally drier and heterogeneous soils to influence convective rainfall development. The systematic nature of the identified effect of soil moisture state on the onset time of rainstorms in the region is of particular relevance and may help foster research on rainfall predictability

Ten years of 1 Hz solar irradiance observations at Cabauw, the Netherlands, with cloud observations, variability classifications, and statistics

Surface solar irradiance varies on scales down to seconds, and detailed long-term observational datasets of this variable are rare but in high demand. Here, we present an observational dataset of global, direct, and diffuse solar irradiance sampled at 1 Hz as well as fully resolved variability until at least 0.1 Hz over a period of 10 years from the Baseline Surface Radiation Network (BSRN) station at Cabauw, the Netherlands. The dataset is complemented with irradiance variability classifications, clear-sky irradiance and aerosol reanalysis, information about the solar position, observations of clouds and sky type, and wind measurements up to 200 m above ground level. Statistics of variability derived from all time series include approximately 185 000 detected events of both cloud enhancement and cloud shadows. Additional observations from the Cabauw measurement site are freely available from the open-data platform of the Royal Netherlands Meteorological Institute. This paper describes the observational site, quality control, classification algorithm with validation, and the processing method of complementary products. Additionally, we discuss and showcase (potential) applications, including limitations due to sensor response time. These observations and derived statistics provide detailed information to aid research into how clouds and atmospheric composition influence solar irradiance variability as well as information to help validate models that are starting to resolve variability at higher fidelity. The main datasets are available at https://doi.org/10.5281/zenodo.7093164 (Knap and Mol, 2022) and https://doi.org/10.5281/zenodo.7462362 (Mol et al., 2022); the reader is referred to the “Code and data availability” section of this paper for the complete list.</p

On the segregation of chemical species in a clear boundary layer over heterogeneous land surfaces

Using a Large-Eddy Simulation model, we have systematically studied the inability of boundary layer turbulence to efficiently mix reactive species. This creates regions where the species are accumulated in a correlated or anti-correlated way, thereby modifying the mean reactivity. We quantify this modification by the intensity of segregation, &lt;i&gt;I&lt;/i&gt;&lt;sub&gt;S&lt;/sub&gt;, and analyse the driving mechanisms: heterogeneity of the surface moisture and heat fluxes, various background wind patterns and non-uniform isoprene emissions. The heterogeneous surface conditions are characterized by cool and wet forested patches with high isoprene emissions, alternated with warm and dry patches that represents pasture with relatively low isoprene emissions. For typical conditions in the Amazon rain forest, applying homogeneous surface forcings and in the absence of free tropospheric NO&lt;sub&gt;x&lt;/sub&gt;, the isoprene-OH reaction rate is altered by less than 10%. This is substantially smaller than the previously assumed &lt;i&gt;I&lt;/i&gt;&lt;sub&gt;S&lt;/sub&gt; of 50% in recent large-scale model analyses of tropical rain forest chemistry. Spatial heterogeneous surface emissions enhance the segregation of species, leading to alterations of the chemical reaction rates up to 20%. The intensities of segregation are enhanced when the background wind direction is parallel to the borders between the patches and reduced in the case of a perpendicular wind direction. The effects of segregation on trace gas concentrations vary per species. For the highly reactive OH, the differences in concentration averaged over the boundary layer are less than 2% compared to homogeneous surface conditions, while the isoprene concentration is increased by as much as 12% due to the reduced chemical reaction rates. These processes take place at the sub-grid scale of chemistry transport models and therefore need to be parameterized

Predicting atmospheric optical properties for radiative transfer computations using neural networks

The radiative transfer equations are well-known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parameterization (RRTMGP). To minimize computational costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimised BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within \SI{0.5}{\flux} compared to RRTMGP. Our neural network-based gas optics parametrization is up to 4 times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations whilst retaining high accuracy.Comment: 13 pages,5 figures, submitted to Philosophical Transactions

Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier

Debris-covered glaciers account for almost onefifth of the total glacier ice volume in High Mountain Asia; however, their contribution to the total glacier melt remains uncertain, and the drivers controlling this melt are still largely unknown. Debris influences the properties (e.g. albedo, thermal conductivity, roughness) of the glacier surface and thus the surface energy balance and glacier melt. In this study we have used sensitivity tests to assess the effect of surface properties of debris on the spatial distribution of micrometeorological variables such as wind fields, moisture and temperature. Subsequently we investigated how those surface properties drive the turbulent fluxes and eventually the conductive heat flux of a debris-covered glacier. We simulated a debris-covered glacier (Lirung Glacier, Nepal) at a 1m resolution with the MicroHH model, with boundary conditions retrieved from an automatic weather station (temperature, wind and specific humidity) and unmanned aerial vehicle flights (digital elevation map and surface temperature). The model was validated using eddy covariance data. A sensitivity analysis was then performed to provide insight into how heterogeneous surface variables control the glacier microclimate. Additionally, we show that ice cliffs are local melt hot spots and that turbulent fluxes and local heat advection amplify spatial heterogeneity on the surface. The high spatial variability of small-scale meteorological variables suggests that point-based station observations cannot be simply extrapolated to an entire glacier. These outcomes should be considered in future studies for a better estimation of glacier melt in High Mountain Asia.</p

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

A conceptual framework to quantify the influence of convective boundary layer development on carbon dioxide mixing ratios

Interpretation of observed diurnal carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) mixing ratios near the surface requires knowledge of the local dynamics of the planetary boundary layer. In this paper, we study the relationship between the boundary layer dynamics and the CO&lt;sub&gt;2&lt;/sub&gt; budget in convective conditions through a newly derived set of analytical equations. From these equations, we are able to quantify how uncertainties in boundary layer dynamical variables or in the morning CO&lt;sub&gt;2&lt;/sub&gt; distribution in the mixed-layer or in the free atmosphere (FA) influence the bulk CO&lt;sub&gt;2&lt;/sub&gt; mixing ratio. &lt;br&gt;&lt;/br&gt; We find that the largest uncertainty incurred on the mid-day CO&lt;sub&gt;2&lt;/sub&gt; mixing ratio comes from the prescribed early morning CO&lt;sub&gt;2&lt;/sub&gt; mixing ratios in the stable boundary layer, and in the free atmosphere. Errors in these values influence CO&lt;sub&gt;2&lt;/sub&gt; mixing ratios inversely proportional to the boundary layer depth (&lt;i&gt;h&lt;/i&gt;), just like uncertainties in the assumed initial boundary layer depth and surface CO&lt;sub&gt;2&lt;/sub&gt; flux. The influence of uncertainties in the boundary layer depth itself is one order of magnitude smaller. If we "invert" the problem and calculate CO&lt;sub&gt;2&lt;/sub&gt; surface exchange from observed or simulated CO&lt;sub&gt;2&lt;/sub&gt; mixing ratios, the sensitivities to errors in boundary layer dynamics also invert: they become linearly proportional to the boundary layer depth. &lt;br&gt;&lt;/br&gt; We demonstrate these relations for a typical well characterized situation at the Cabauw site in The Netherlands, and conclude that knowledge of the temperature and carbon dioxide profiles of the atmosphere in the early morning are of vital importance to correctly interpret observed CO&lt;sub&gt;2&lt;/sub&gt; mixing ratios during midday