Surface Energy Budget Components Research Articles

An Observational Case Study of a Radiation Fog Event

A micrometeorological fog experiment was carried out in Budapest, Hungary during the winter half year of 2020–2021. The field observation involved (i) standard meteorological and radiosonde measurements; (ii) surface radiation balance and energy budget components, and (iii) ceilometer measurements. 23 fog events occurred during the whole campaign. Foggy events were categorized based on two different methods suggested by Tardif and Rasmussen (2007) and Lin et al. (2022). Using the Present Weather Detector and Visibility sensor (PWD12), duration of foggy periods are approximately shorter (~ 9%) compared to ceilometer measurements. The categorization of fog based on two different methods suggests that duration of radiation fogs is lower compared to that of cloud base lowering (CBL) fogs. The results of analysis of observed data about the longest fog event suggest that (i) it was a radiation fog that developed from the surface upwards with condition of a near neutral temperature profile. Near the surface the turbulent kinetic energy and turbulent momentum fluxes remained smaller than 0.4 m2 s–2 and 0.06 kg m–1 s–2, respectively. In the surface layer the vertical profile of the sensible heat flux was near constant (it changes with height ~ 10%), and during the evolution of the fog, its maximum value was smaller than 25 W m–2, (ii) the dissipation of the fog occurred due to increase of turbulence, (iii) longwave energy budget was close to zero during fog, and a significant increase of virtual potential temperature with height was observed before fog onset. The complete dataset gives an opportunity to quantify local effects, such as tracking the effect of strengthening of wind for modification of stability, surface layer profiles and visibility. Fog formation, development and dissipation are quantified based on the micrometeorological observations performed in suburb area of Budapest, providing a processing algorithm for investigating various fog events for synoptic analysis and for optimization of numerical model parameterizations.

Estimation of Surface Sensible Heat Flux due to Precipitation over CONUS and Its Impact on Urban Extreme Precipitation Modeling

Abstract The surface sensible heat flux induced by precipitation (QP) is a consequence of the temperature difference between the surface and the rain droplets. Despite its seemingly negligible nature, QP is frequently omitted from both meteorological and climatological models. Nevertheless, it is important to acknowledge the numerous occasions in which the instantaneous values of QP can be significant, particularly during extreme precipitation events. This study undertakes a comprehensive assessment of QP across the contiguous United States (CONUS) utilizing high-resolution reanalysis, observational data, and numerical modeling to examine the influence of QP on precipitation and the surface energy budget. The findings indicate that the spatial distribution of QP climatology is analogous to that of precipitation, with magnitudes ranging from 2 to 3 W m−2 predominantly over the Midwest and Southeast regions. A seasonal analysis of QP reveals that the highest values occurring during the June–August (JJA) period, averaging 3.18 W m−2. Peak QP values of approximately 4 W m−2 are observed during JJA over the Great Plains region. We hypothesize that the QP during an extreme precipitation event would be nonnegligible and have a significant impact on the local weather. To test this conjecture, we perform high-resolution simulations with and without QP during an extreme precipitation event over the Chicago Metropolitan Area (CMA). The results show that the QP may be a dominant factor compared to other components of surface heat flux during the zenith of precipitation hours. Also, QP has the potential to not only diminish precipitation but also alter and reconfigure the remaining surface energy budget components.

The Influence of Meteorological Factors and the Time of Day on the Concentration of Ammonia in the Atmosphere Measured Using the Photoacoustic Method near a Cattle Farm—A Case Study

Influences of animals, time of day, air temperature and relative humidity, wind speed and direction on ammonia concentrations were investigated. A case study on a typical summer day from 7:00 to approximately 24:00 CEST (moderate wind speed, variable cloudiness and maximum global radiation higher than 950 W/m2) in west–central Poland is presented. Concentrations of this gas were measured at four heights (0.1–1.5 m), which were changed every 5 min, using a Nitrolux 1000 photoacoustic spectrometer. A micrometeorological station was established to also measure the surface energy budget components. The results presented are the average for each hour and for the entire day. The fine structure of concentration profiles, plume detection and uncertainty of ammonia flux calculation are also presented. The highest NH3 concentrations were at a 0.5 m height between 16:00 and 17:00 h when cows were grazing, but the lowest concentrations were between 23:00 and 24:00 h at the height of 1.5 m. The ammonia concentration increased with increasing air temperature and was the highest with a westerly wind direction and decreased with increasing air relative humidity. The greatest influence on the ammonia concentration was related to the presence of cows and the time of day, while a slightly smaller influence was noted in terms of air temperature and wind direction. A case study is suitable for presenting local effects, inhomogeneities and quantifying uncertainties in the bidirectional ammonia flux calculation.

Assessment of Top of Atmosphere, Atmospheric and Surface Energy Budgets in CMIP6 Models on Regional Scales

AbstractWe examine top of atmosphere (TOA), atmospheric, and surface energy budget components of 53 CMIP6 models for the period 2000–2009 on regional scales with respect to two reference data sets: the NASA Energy and Water cycle Study (NEWS), from which we adopt the regional decomposition, and the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF). Focusing on regional scale, CMIP6 models tend to have more energy entering or less energy leaving the climate systems at TOA over the Northern Hemisphere land, Southern Hemisphere ocean and the polar regions compared to CERES EBAF, while the contrary applies in other regions. Atmospheric net shortwave and longwave fluxes both tend to be underestimated in CMIP6 models as compared to EBAF, with substantial regional differences. Regional surface radiative fluxes as reported by NEWS and EBAF can differ substantially. Nevertheless, robust regional biases exist in CMIP6. Surface upward shortwave radiation is overestimated by 43 (81%) models over Eurasia. For almost all surface radiative flux components over the North Atlantic and Indian Ocean, there are at least 21 (40%) models which fall outside the NEWS uncertainty range. Latent heat flux is overestimated over most of the land and ocean regions while firm conclusions for sensible heat flux remain elusive due to discrepancies between different reference data sets. Compared to CMIP5, there is an overall improvement in CMIP6 on regional scale. Still, substantial deficiencies and spreads on regional scale remain, which are potentially translated into an inadequate simulation of atmospheric dynamics or hydrological cycle.

Summertime surface mass balance and energy balance of Urumqi Glacier No. 1, Chinese Tien Shan, modeled by linking COSIMA and in-situ measured meteorological records

To get a better overview of atmosphere-driven mass changes at Urumqi Glacier No.1, Chinese Tien Shan, the surface energy budget and mass balance is modeled by linking the COupled Snowpack and Ice surface energy and MAss balance model (COSIMA) with in-situ measured meteorological records for the ablation period in 2018. The COSIMA is calibrated by manual optimization and the modeled results agree well with the in-situ surface temperature, snow height and seasonal mass balance. Our results reveal that Urumqi Glacier No.1 experienced a significant mass loss, with an average value of − 0.77 m w.e. over the ablation period 2018. The surface energy budget components can be classified into two categories: radiation (shortwave and longwave) and turbulent fluxes. Surface melt and solid precipitation were dominated components of mass balance. The COSIMA can reproduce the glaciological mass balance compared with other models. Sensitivity analysis showed that the mass balance was more sensitive to the temperature than precipitation, and mass loss caused by temperature increase of 1 K needed to be compensated by at least 40% precipitation increase. Air temperature during the ablation period was more important than annual precipitation in controlling mass balance changes on Urumqi Glacier No. 1. These findings will enhance our understanding of the mechanisms underlying mass balance processes of ablation period and their contribution to the acceleration of glacier retreat in Tien Shan.

The impact of climate oscillations on the surface energy budget over the Greenland Ice Sheet in a changing climate

Abstract. Climate change is particularly strong in Greenland, primarily as a result of changes in the transport of heat and moisture from lower latitudes. The atmospheric structures involved influence the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS), and their patterns are largely explained by climate oscillations, which describe the internal climate variability. By using k-means clustering, we name the combination of the Greenland Blocking Index, the North Atlantic Oscillation index and the vertically integrated water vapor as NAG (North Atlantic influence on Greenland) with the optimal solution of three clusters (positive, neutral and negative phase). With the support of a polar-adapted regional climate model, typical climate features marked under certain NAG phases are inter-seasonally and regionally analyzed in order to assess the impact of large-scale systems from the North Atlantic on the surface energy budget (SEB) components over the GrIS. Given the pronounced summer mass loss in recent decades (1991–2020), we investigate spatio-temporal changes in SEB components within NAG phases in comparison to the reference period 1959–1990. We report significant atmospheric warming and moistening across all NAG phases. The pronounced atmospheric warming in conjunction with the increase in tropospheric water vapor enhance incoming longwave radiation and thus contribute to surface warming. Surface warming is most evident in winter, although its magnitude and spatial extent depend on the NAG phase. In summer, increases in net shortwave radiation are mainly connected to blocking systems (+ NAG), and their drivers are regionally different. In the southern part of Greenland, the atmosphere has become optically thinner due to the decrease in water vapor, thus allowing more incoming shortwave radiation to reach the surface. However, we find evidence that, in the southern regions, changes in net longwave radiation balance changes in net shortwave radiation, suggesting that the turbulent fluxes control the recent SEB changes. In contrast to South Greenland under + NAG, the moistening of North Greenland has contributed to decreases in surface albedo and has enhanced solar radiation absorption. Regardless of the NAG phase, increases in multiple atmospheric variables (e.g., integrated water vapor and net longwave radiation) are found across the northern parts of Greenland, suggesting that atmospheric drivers beyond heat and moisture originated from the North Atlantic. Especially in the northern ablation zone, sensible heat flux has significantly increased in summer due to larger vertical and horizontal temperature gradients combined with stronger near-surface winds. We attribute the near-surface wind intensification to the emerging open-water feedback, whereby surface pressure gradients between the ice/snow-covered surface and adjacent open seas are intensified.

A novel model to estimate sensible heat fluxes in urban areas using satellite-derived data

A novel model for estimating sensible heat flux in urban areas using satellite data is presented here. Sensible heat flux (QH) is a primary component of the urban surface energy budget and is critical to regulating air temperature in cities. The model employs data from the NASA & NOAA GOES-16 geostationary satellite, ground-based observations from NOAA Automated Surface Observation Stations (ASOS), and land cover data from the National Land Cover Database (NLCD) as inputs for an iterative algorithm that is based on surface-layer similarity theory for turbulence parameterization. The application of this model specifically to urban areas is enabled by the spatial extent of the GOES-16 satellite and an element roughness height estimation method based on NLCD land cover data. Model results were independently validated using three flux towers located in different areas of New York City. Statistics generated over a year-long validation period from June 2019 to May 2020 show a root-mean-square error (RMSE) of 47.32 W-m−2, a mean bias error (MBE) of 16.58 W-m−2, and an R2 correlation value of 0.70. Model results were also compared to results from the urbanized Weather Research and Forecasting (uWRF) model relative to flux tower observational data to allow for a comparison between numerical models. The dedicated QH model outperformed the uWRF model relative to observational data, with an RMSE reduction of 63.5 W-m−2, an MBE reduction of 17.5 W-m−2, and an R2 increase of 0.08. Validation results show good agreement between model and observed values and performance comparison results show an improvement over a current numerical method for estimation of QH, suggesting the use of satellite data as a cost-effective and accessible option for estimating QH in urban areas.

Single‐Column Validation of a Snow Subgrid Parameterization in the Rapid Update Cycle Land‐Surface Model (RUC LSM)

AbstractSubgrid variability of snow is important in studying surface‐atmosphere interactions as it affects grid‐scale processes. However, this dynamic variability is currently not well‐represented in most land‐surface models (LSMs). A stochastic snow model using the Fokker‐Planck Equation (FPE) has been developed specifically for representing subgrid variability of snow. In this study, the FPE snow model was coupled into the Rapid Update Cycle (RUC) LSM, which provides lower boundary conditions for the operational NOAA Rapid Refresh and High‐Resolution Rapid Refresh weather prediction systems. This coupled land‐surface model with subgrid snow processes is named the RUC‐Stochastic Snow (SS) LSM. The performance of RUC‐SS LSM and RUC LSM is analyzed in detail in initial offline single‐column testing over two 13‐km 13‐km grid cells. The results show that RUC‐SS LSM has a much better capability in estimating snow cover fraction than RUC LSM, especially during the late part of the snow‐melting season. The simulated duration of the partially snow‐covered period at the snow‐melting stage is extended from a few days with RUC LSM to about one month with the RUC‐SS LSM, thus improving prediction of surface energy budget components. For example, the latent and sensible heat fluxes and skin temperature increase gradually in the RUC‐SS LSM during the transition from full coverage to snow‐free conditions. In contrast, this transition happens too quickly in RUC LSM, and the energy‐budget components change too abruptly. The results of RUC‐SS demonstrate its capability to account for subgrid variability of snow cover and show that considering subgrid variability of snow in LSMs is important for simulating surface‐atmosphere interactions.

Extended Range Arctic Sea Ice Forecast with Convolutional Long-Short Term Memory Networks

AbstractOperational Arctic sea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time-scales. Numerical models require near real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely Convolutional Long Short Term Memory Networks (ConvLSTM), to forecast sea ice in the Barents Sea at weather to sub-seasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is promising to enhance operational Arctic sea ice forecasting in the near future.

Case Study of Blowing Snow Impacts on the Antarctic Peninsula Lower Atmosphere and Surface Simulated With a Snow/Ice Enhanced WRF Model

AbstractTo better capture the air‐snow‐ice interaction, a snow/ice enhanced Weather Research and Forecasting (WRF‐ice) model has been developed. This study examines the performance of WRF‐ice and its blowing snow component during a strong cyclone event from October 23 to 27, 2017 over the Antarctic Peninsula, which is characterized by a synoptic cyclone crossing the northern part of the Peninsula and an embodied mesoscale cyclone over the Weddell Sea. Evolution of the cyclone is accurately reproduced in the 5‐km resolution WRF‐ice simulation, and the simulated near‐surface conditions agree well with station and satellite observations. Numerical simulations show that the process of blowing snow sublimation can be prominent within the lower atmosphere when the air is dry, and produces moistening and cooling effects. Over relatively warm and humid areas, cloud enhancement by blowing snow can lead to either colder or warmer surfaces because of competing effects of longwave and shortwave cloud radiative forcings. In particular, additional moisture from blowing snow sublimation can slightly intensify precipitation over the mountains. Surface energy budget analysis indicates that absorbed shortwave (Sa), incoming longwave (Ld), and outgoing longwave (Lu) are dominant components of surface energy budget. When increases in Ld, Lu, and sensible heat flux are combined with decreases in Sa and latent heat flux due to blowing snow effects, a negative surface net heat flux (∼0.5 W/m2) occurs during daytime. A positive domain‐total surface mass balance (∼0.43 Gt) is generated during the simulated cyclone event due to increases in precipitation, decreases in runoff, and decreases in sublimation.

Ground heat flux determination based on near-surface soil hydro-thermodynamics

Near-surface soil hydro-thermodynamics plays an important role in heat and water transfer between land and atmosphere. However, it is difficult to quantify these processes in field campaigns, such as movement of liquid water and water vapor. This has affected determination of ground heat flux, a key component of surface energy budget. This study aims to quantify soil heat and water processes utilizing in situ measurements, and to improve ground heat flux estimation and surface energy imbalance. A new model has been proposed based on three main physical processes, including thermal conduction, convection of heat by moving water, and convection of latent heat. The model was tested at a grassland site in Colorado, USA. Results show that the model can capture high values of soil water content during rain events, low values under intense solar radiation, and high-frequency fluctuations under intermittent cloudy conditions. Also, the model produces reasonable vertical velocity and mass flux of water vapor. For the estimation of ground heat flux, the method that considers vertical variation of soil thermal conductivity and the contribution of water vapor gives better energy balance ratio than methods that ignore these conditions. In spite of this, the energy balance ratio is still low. Footprint analysis further shows the seasonal variation of ground cover is a potential reason responsible for this.

The Summer Surface Energy Budget of the Ice-Free Area of Northern James Ross Island and Its Impact on the Ground Thermal Regime

Despite the key role of the surface energy budget in the global climate system, such investigations are rare in Antarctica. In this study, the surface energy budget measurements from the largest ice-free area on northern James Ross Island, in Antarctica, were obtained. The components of net radiation were measured by a net radiometer, while sensible heat flux was measured by a sonic anemometer and ground heat flux by heat flux plates. The surface energy budget was compared with the rest of the Antarctic Peninsula Region and selected places in the Arctic and the impact of surface energy budget components on the ground thermal regime was examined. Mean net radiation on James Ross Island during January–March 2018 reached 102.5 W m−2. The main surface energy budget component was the latent heat flux, while the sensible heat flux values were only 0.4 W m−2 lower. Mean ground heat flux was only 0.4 Wm-2, however, it was negative in 47% of January–March 2018, while it was positive in the rest of the time. The ground thermal regime was affected by surface energy budget components to a depth of 50 cm. The strongest relationship was found between ground heat flux and ground surface temperature. Further analysis confirmed that active layer refroze after a sequence of three days with negative ground heat flux even in summer months. Daily mean net radiation and ground heat flux were significantly reduced when cloud amount increased, while the influence of snow cover on ground surface temperature was negligible.

Confronting Arctic Troposphere, Clouds, and Surface Energy Budget Representations in Regional Climate Models With Observations

AbstractA coordinated regional climate model (RCM) evaluation and intercomparison project based on observations from a July–October 2014 trans‐Arctic Ocean field experiment (ACSE‐Arctic Clouds during Summer Experiment) is presented. Six state‐of‐the‐art RCMs were constrained with common reanalysis lateral boundary forcing and upper troposphere nudging techniques to explore how the RCMs represented the evolution of the surface energy budget (SEB) components and their relation to cloud properties. We find that the main reasons for the modeled differences in the SEB components are a direct consequence of the RCM treatment of cloud and cloud‐radiative interactions. The RCMs could be separated into groups by their overestimation or underestimation of cloud liquid. While radiative and turbulent heat flux errors were relatively large, they often invoke compensating errors. In addition, having the surface sea‐ice concentrations constrained by the reanalysis or satellite observations limited how errors in the modeled radiative fluxes could affect the SEB and ultimately the surface evolution and its coupling with lower tropospheric mixing and cloud properties. Many of these results are consistent with RCM biases reported in studies over a decade ago. One of the six models was a fully coupled ocean‐ice‐atmosphere model. Despite the biases in overestimating cloud liquid, and associated SEB errors due to too optically thick clouds, its simulations were useful in understanding how the fully coupled system is forced by, and responds to, the SEB evolution. Moving forward, we suggest that development of RCM studies need to consider the fully coupled climate system.

Impact of Surface Albedo Assimilation on Snow Estimation

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

Analog Site Experiment in the High Andes-Atacama Region: Surface Energy Budget Components on Ojos del Salado from Field Measurements and WRF Simulations.

Remote sensing data are abundant, whereas surface in situ verification of atmospheric conditions is rare on Mars. Earth-based analogs could help gain an understanding of soil and atmospheric processes on Mars and refine existing models. In this work, we evaluate the applicability of the Weather Research and Forecasting (WRF) model against measurements from the Mars analog High Andes-Atacama Desert. Validation focuses on the surface conditions and on the surface energy budget. Measurements show that the average daily net radiation, global radiation, and latent heat flux amount to 131, 273, and about 10 W/m2, respectively, indicating extremely dry atmospheric conditions. Dynamically, the effect of topography is also well simulated. One of the main modeling problems is the inaccurate initial soil and surface conditions in the area. Correction of soil moisture based on in situ and satellite soil moisture measurements, as well as the removal of snow coverage, reduced the surface skin temperature root mean square error from 9.8°C to 4.3°C. The model, however, has shortcomings when soil condition modeling is considered. Sensible heat flux estimations are on par with the measurements (daily maxima around 500 W/m2), but surface soil heat flux is greatly overestimated (by 150-500 W/m2). Soil temperature and soil moisture diurnal variations are inconsistent with the measurements, partially due to the lack of water vapor representation in soil calculations.

Conserving Land–Atmosphere Synthesis Suite (CLASS)

AbstractAccurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models’ ability to simulate future change. Various observational estimates are available for the individual budget terms; however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land–Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. Individual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source; 2) are observationally constrained with in situ measurements; 3) have uncertainty estimates that are consistent with their agreement with in situ observations; and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes. CLASS variable estimates are freely available viahttps://doi.org/10.25914/5c872258dc183.

The assessment of the planetary boundary layer schemes in WRF over the central Tibetan Plateau

The planetary boundary layer (PBL) parameterization schemes play a critical role in the weather and climate models, while they describe physical processes associated with the momentum, heat and humidity exchange between land surface and atmosphere. The sensitivity of boundary layer variables to eight PBL parameterization schemes (three non-local and five local closure schemes), available in the Weather Research and Forecasting (WRF) model, is evaluated over the central Tibetan Plateau with field measurements of the Third Tibetan Plateau atmospheric scientific experiment (TIPEX III) in July. Model results showed acceptable behavior, but no particular scheme produced the best performances for all observation stations and meteorological parameters. All PBL schemes underestimated the surface temperature over the central Tibetan Plateau. The BouLac scheme showed the minimum cold bias of the surface temperature. For the surface energy budget components, it was found that the sensible heat flux and the downward longwave radiation were the main factors causing the lower surface temperature. The sub-grid scale gravity wave drag was added to reduce biases result from unresolved topography over the central Tibetan Plateau. It led to smaller cold bias, causing warmer lower-tropospheric temperature, smaller water vapor content and higher PBL height. The modified model results show more close to the observation.

Clear-Sky Longwave Downward Radiation Estimation by Integrating MODIS Data and Ground-Based Measurements

Surface energy budget has great influence on earth surface biogeophysical and biogeochemical processes. As one of the components of surface energy budget, the longwave downward radiation (LWDR) is essential to many land applications. In this research, a new LWDR estimation scheme under clear sky by integrating MODIS thermal bands radiance and in situ measurements through the multivariate adaptive regression splines (MARS) has been proposed. The MODIS products and corresponding multinetwork in situ LWDR observations under clear skies in 2004 were extracted for model training and cross-validation. The in situ LWDR observations from seven SURFRAD sites in 2008 were used to test the new method. In training phase, the cross-validation RMSE, bias, and R-square of the new method are, respectively, 24.66 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , -0.446 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and 0.88 in the daytime and 23.16 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , -0.272 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and 0.92 in the nighttime. The test results at the seven SURFRAD sites show that the proposed method has good performance, with RMSE, bias, and R-square of 17.15 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 0.028 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and 0.91 in the daytime and 14.08 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , -3.609 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and 0.94 in the nighttime, respectively. More importantly, the error analysis reveals that the predicted residual errors of the proposed method have a weak correlation with related meteorological parameters at global scale, which reflects the stability and robustness of the new method.

Topographic Effects on Radiation in the WRF Model with the Immersed Boundary Method: Implementation, Validation, and Application to Complex Terrain

Abstract Topographic effects on radiation, including both topographic shading and slope effects, are included in the Weather Research and Forecasting (WRF) Model, and here they are made compatible with the immersed boundary method (IBM). IBM is an alternative method for representing complex terrain that reduces numerical errors over sloped terrain, thus extending the range of slopes that can be represented in WRF simulations. The implementation of topographic effects on radiation is validated by comparing land surface fluxes, as well as temperature and velocity fields, between idealized WRF simulations both with and without IBM. Following validation, the topographic shading implementation is tested in a semirealistic simulation of flow over Granite Mountain, Utah, where topographic shading is known to affect downslope flow development in the evening. The horizontal grid spacing is 50 m and the vertical grid spacing is approximately 8–27 m near the surface. Such a case would fail to run in WRF with its native terrain-following coordinates because of large local slope values reaching up to 55°. Good agreement is found between modeled surface energy budget components and observations from the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) program at a location on the east slope of Granite Mountain. In addition, the model captures large spatiotemporal inhomogeneities in the surface sensible heat flux that are important for the development of thermally driven flows over complex terrain.

Projected Changes in Soil Temperature and Surface Energy Budget Components over the Alps and Northern Italy

This study investigates the potential changes in surface energy budget components under certain future climate conditions over the Alps and Northern Italy. The regional climate scenarios are obtained though the Regional Climate Model version 3 (RegCM3) runs, based on a reference climate (1961–1990) and the future climate (2071–2100) via the A2 and B2 scenarios. The energy budget components are calculated by employing the University of Torino model of land Processes Interaction with Atmosphere (UTOPIA), and using the RegCM3 outputs as input data. Our results depict a significant change in the energy budget components during springtime over high-mountain areas, whereas the most relevant difference over the plain areas is the increase in latent heat flux and hence, evapotranspiration during summertime. The precedence of snow-melting season over the Alps is evidenced by the earlier increase in sensible heat flux. The annual mean number of warm and cold days is evaluated by analyzing the top-layer soil temperature and shows a large increment (slight reduction) of warm (cold) days. These changes at the end of this century could influence the regional radiative properties and energy cycles and thus, exert significant impacts on human life and general infrastructures.