Turbulent Fluxes Of Sensible Heat Research Articles

Vertical profiles of temperature, wind, and turbulent fluxes across a deciduous forest over a slope observed with a UAV

To contribute to a better understanding of the dynamics of the atmosphere inside and above a forest, vertical profiles are flown with a remotely-controlled multicopter in the Steinkrug forest. This area is located over a slope in the Solling natural area in Lower Saxony (Germany), composed mostly of deciduous trees about 30 m tall. Fifteen vertical flights made near sunset between summer 2019 and spring 2020 were inspected from the surface to 100 m above ground level. These measurements provide information on the vertical structures of wind and temperature within and above the canopy, including the effects of shallow slope flows near the ground. Contrasting measurements downhill outside the forest were also made. The gathered data allow estimated profiles of the turbulent fluxes of sensible heat and momentum to be obtained by computing averages and fluctuations for layers of 5 m depth. A leaf area density profile in both leafy and leafless conditions could also be produced. The presence of a slope flow is inspected at both sites, and the applicability of existing theories is explored.

Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses

Abstract. A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses, ERA5, JRA-55, MERRA-2, and NCEP/CFSR (including both the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP Climate Forecast System Version 2 (CFSv2)), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15 but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-squares regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, using daily means of data, the average sensitivity is 400 W m−2 for the latent heat flux and over 800 W m−2 for the sensible heat flux per unit of SIC (change in SIC from 0 to 1), with differences between reanalyses that are as large as 300 W m−2 for the latent heat flux and 600 W m−2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened owing to the increasing surface temperature of sea ice and sea-ice decline. The results also indicate signs of a decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere–surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed (via surface roughness and atmospheric-boundary-layer stratification) partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitudes of both upward and downward fluxes.

Surface Turbulent Fluxes From the MOSAiC Campaign Predicted by Machine Learning

AbstractReliable boundary‐layer turbulence parametrizations for polar conditions are needed to reduce uncertainty in projections of Arctic sea ice melting rate and its potential global repercussions. Surface turbulent fluxes of sensible and latent heat are typically represented in weather/climate models using bulk formulae based on the Monin‐Obukhov Similarity Theory, sometimes finely tuned to high stability conditions and the potential presence of sea ice. In this study, we test the performance of new, machine‐learning (ML) flux parametrizations, using an advanced polar‐specific bulk algorithm as a baseline. Neural networks, trained on observations from previous Arctic campaigns, are used to predict surface turbulent fluxes measured over sea ice as part of the recent MOSAiC expedition. The ML parametrizations outperform the bulk at the MOSAiC sites, with RMSE reductions of up to 70 percent. We provide a plug‐in Fortran implementation of the neural networks for use in models.

Quality evaluation for measurements of wind field and turbulent fluxes from a UAV-based eddy covariance system

Abstract. Instrumentation packages for eddy covariance (EC) measurements have been developed for unoccupied aerial vehicles (UAVs) to measure the turbulent fluxes of latent heat (LE), sensible heat (H), and CO2 (Fc) in the atmospheric boundary layer. This study aims to evaluate the performance of this UAV-based EC system. First, the measurement precision (1σ) of georeferenced wind was estimated to be 0.07 m s−1. Then, the effect of the calibration parameter and aerodynamic characteristics of the UAV on wind measurement was examined by conducting a set of calibration flights. The results showed that the calibration improved the quality of the measured wind field, and the influence of upwash and the leverage effect can be ignored in wind measurement by the UAV. Third, for the measurements of turbulent fluxes, the error caused by instrumental noise was estimated to be 0.03 µmolm-2s-1 for Fc, 0.02 W m−2 for H, and 0.08 W m−2 for LE. Fourth, data from the standard operational flights were used to assess the influence of resonance on the measurements and to test the sensitivity of the measurement under the variation (±30 %) in the calibration parameters around their optimum value. The results showed that the effect of resonance mainly affected the measurement of CO2 (∼5 %). The pitch offset angle (εθ) significantly affected the measurement of vertical wind (∼30 %) and turbulent fluxes (∼15 %). The heading offset angle (εψ) mainly affected the measurement of horizontal wind (∼15 %), and other calibration parameters had no significant effect on the measurements. The results lend confidence to the use of the UAV-based EC system and suggest future improvements for the optimization of the next-generation system.

Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance

Abstract. The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottom-up approach to modeling carbon dioxide (CO2) emissions and uptake in urban areas. In this study, SUEWS is evaluated against the measured eddy covariance (EC) turbulent fluxes of sensible heat (QH), latent heat (QE), and CO2 (FC) in a densely built neighborhood in Beijing. The model sensitivity to maximum conductance (gmax) and leaf area index (LAI) is examined. Site-specific gmax is obtained from observations over local vegetation species, and LAI parameters are extracted by optimization with remotely sensed LAI obtained from a Landsat 7 data product. For the simulation of anthropogenic CO2 components, local traffic and population data are collected. In the model evaluation, the mismatch between the measurement source area and simulation domain is also considered. Using the optimized gmax and LAI, the modeling of heat fluxes is noticeably improved, showing higher correlation with observations, lower bias, and more realistic seasonal dynamics of QE and QH. The effect of the gmax adjustment is more significant than the LAI adjustment. Compared to heat fluxes, the FC module shows lower sensitivity to the choices of gmax and LAI. This can be explained by the low relative contribution of vegetation to the net FC in the modeled area. SUEWS successfully reproduces the average diurnal cycle of FC and annual cumulative sums. Depending on the size of the simulation domain, the modeled annual accumulated FC ranges from 7.4 to 8.7 kgCm-2yr-1, compared to 7.5 kgCm-2yr-1 observed by EC. Traffic is the dominant CO2 source, contributing 59 %–70 % to the annual total CO2 emissions, followed by human metabolism (14 %–18 %), buildings (11 %–14 %), and CO2 release by vegetation and soil respiration (6 %–10 %). Vegetation photosynthesis offsets only 5 %–10 % of the total CO2 emissions. We highlight the importance of choosing the optimal LAI parameters and gmax when SUEWS is used to model surface fluxes. The FC module of SUEWS is a promising tool in quantifying urban CO2 emissions at the local scale and therefore assisting in mitigating urban CO2 emissions.

Radiative controls by clouds and thermodynamics shape surface temperatures and turbulent fluxes over land

Land surface temperatures (LSTs) are strongly shaped by radiation but are modulated by turbulent fluxes and hydrologic cycling as the presence of water vapor in the atmosphere (clouds) and at the surface (evaporation) affects temperatures across regions. Here, we used a thermodynamic systems framework forced with independent observations to show that the climatological variations in LSTs across dry and humid regions are mainly mediated through radiative effects. We first show that the turbulent fluxes of sensible and latent heat are constrained by thermodynamics and the local radiative conditions. This constraint arises from the ability of radiative heating at the surface to perform work to maintain turbulent fluxes and sustain vertical mixing within the convective boundary layer. This implies that reduced evaporative cooling in dry regions is then compensated for by an increased sensible heat flux and buoyancy, which is consistent with observations. We show that the mean temperature variation across dry and humid regions is mainly controlled by clouds that reduce surface heating by solar radiation. Using satellite observations for cloudy and clear-sky conditions, we show that clouds cool the land surface over humid regions by up to 7 K, while in arid regions, this effect is absent due to the lack of clouds. We conclude that radiation and thermodynamic limits are the primary controls on LSTs and turbulent flux exchange which leads to an emergent simplicity in the observed climatological patterns within the complex climate system.

Turbulence regimes in the nocturnal roughness sublayer: Interaction with deep convection and tree mortality in the Amazon

We investigated the influence of seasonality and proximity to the forest canopy on nocturnal turbulence regimes in the roughness sublayer of a Central Amazon forest. Since convective systems of different scales are common in this region, we also analyzed the effect of extreme wind gusts (propagated from convective downdrafts) on the organization of the turbulence regimes, and their potential to cause the mortality of canopy trees. Our data include high-frequency winds, temperature and ozone concentration at different heights during the dry and wet seasons of 2014. In addition, we used critical wind-speed data derived from a tree-winching experiment and a modeling study conducted in the same study site. Two different turbulence regimes were identified at three heights above the canopy: a weakly stable (WS) and a very stable regime (VS). The threshold wind speeds that mark the transition between turbulence regimes were larger during the dry season and increased as a function of the height above the canopy. The turbulent fluxes of sensible heat and momentum during the WS accounted for 88% of the entire nighttime flux. Downdrafts occurred only in the WS and favored a fully coupled state of wind flow along the canopy profile. The destructive potential of winds was four times higher than on nights without downdrafts.

Turbulent fluxes at kilometer scale determined by optical-microwave scintillometry in a heterogeneous oasis cropland of the Heihe River Basin

Observations of kilometer-scale turbulent fluxes of sensible (H) and latent heat (LE) are required for the validation of flux estimate algorithms from satellite remote-sensing data and the development of parameterization schemes in the hydro-meteorological models. Since 2019, two sets of Optical and Microwave scintillometer (OMS) systems have been operated in the Heihe River Basin of northwestern China, one on an alpine grassland of upper reaches, another on an oasis cropland of middle reaches, to measure both the areal H and LE. Combined with the observations of eddy-covariance (EC) and meteorological tower systems in both sites, an improved procedure for OMS data processing is proposed. The newly proposed procedure especially improves the preprocessing of raw scintillation data, properly uses the current probably better Lüdi et al. (2005) method in deriving meteorological structure parameters, and chooses the coefficients of similarity functions by Kooijmans and Hartogensis (2016) in calculating fluxes. Evaluated with the results of rather homogeneous grassland, the area-averaged H and LE over the heterogeneous oasis are then determined. Estimates of H and LE agree reasonably well with those obtained from EC in most cases. However, the most interesting is that LE over the oasis during the early crop growing stages is clearly larger than that of EC; while both agree well during the longer crop grown periods. Footprint analysis shows that, compared with EC, the OMS has clearly larger source area that contains a slight area of orchard and shelterbelts distributed near the light path, leading to larger LE during the early stages of crop growth. The area-averaged evapotranspiration (ET) over the oasis is then analyzed more acceptably, which varies from 3 to 5 mm day−1 depending on meteorological conditions during the 39 days of the crop growing period. These results are used to validate the Penman-Monteith-Leuning Version 2 (PML-V2) scheme.

Quantifying the Aerodynamic Roughness Length of Snow Surfaces With Time‐Lapse Structure‐From‐Motion

AbstractThe aerodynamic roughness length (z0m) of snow surfaces plays an essential role in estimating turbulent fluxes of latent and sensible heat (LE and H). Constant z0m has been widely used in previous studies due to the difficulty in monitoring dynamic z0m. This leads to large uncertainties in simulating LE and H fluxes. In the current study we used a time‐lapse structure‐from‐motion (SfM) photogrammetric method to study temporal variation in z0m of snow surfaces. The study site was located at the edge of the August‐one ice cap in the Qilian Mountains of China. The study ran from August 2019 to September 2020. We measured aerodynamic roughness length over time (z0m_t) using SfM‐based micro‐topographic methods and validated our results by comparing z0m_t with z0m_prof (profile‐based) and z0m_EC (EC‐based) data. The validated dynamic z0m_t was then used to estimate LE and H fluxes using a bulk aerodynamic method. These results were then compared with LE and H measured by eddy covariance (EC) and with manual sublimation/condensation measurements to indirectly validate z0m_t. We found that z0m_t overall agreed with profile‐based z0m_prof and EC‐based z0m_EC. Indeed, our estimates of bulk LE and H fluxes using dynamic z0m_t agreed slightly better with EC and manual sublimation/condensation measurements than did static EC‐based z0m_EC. Micro‐topographic z0m_t can be helpful in precisely estimating varying LE and H fluxes over the course of a year.

Inferring surface energy fluxes using drone data assimilation in large eddy simulations

Abstract. Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE). A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations. The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space. Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations. It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding well-calibrated posterior uncertainty with continuous ranked probability scores of 12 W m−2 for both H and LE, with standard deviations of 37 W m−2 (H) and 46 W m−2 (LE) for a 12 min vertical step profile by a single drone. Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions are viable for reducing the posterior spread. Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes. In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root mean square error values of 37 W m−2 (H), 52 W m−2 (LE), and 58 W m−2 (H+LE) and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H+LE). While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.

Flower trade‐wind clouds are shallow mesoscale convective systems

AbstractFlower clouds are trade‐wind shallow cumuli, with tops reaching 3 km altitude, organised into 100‐km wide clusters. They are widespread over the subtropics and associated with the strongest cloud radiative effect among trade‐wind cumuli mesoscale organisations. In the context of large uncertainty in climate projections due to the representation of shallow clouds, major knowledge gaps remain about the global impact of mesoscale organisations and the local processes driving them. Here, the processes governing the flower organisation are investigated based on the case study of February 2, 2020 from the Elucidate the Couplings Between Clouds, Convection, and Circulation (EURECA) campaign, east of Barbados. One flower cloud is simulated with a large‐eddy simulation (LES), using the Meso‐NH model at 100‐m horizontal grid spacing, and validated extensively with high‐resolution observations from the High Altitude and Long‐range Research Aircraft (HALO), dropsondes, and satellite measurements. The cloud‐top altitudes exhibit a trimodal distribution. The processes shaping flower clouds are wide cold pools and cloudy updrafts organised in one large arc at the western edge. These updrafts are responsible for the highest cloud tops and drive most of the vertical turbulent fluxes of sensible heat, humidity, and momentum. A mesoscale circulation takes place at the scale of the flower clouds and makes them very similar to deep mesoscale convective systems.

Large-Eddy Simulations of Marine Boundary Layer Clouds Associated with Cold-Air Outbreaks during the ACTIVATE Campaign. Part I: Case Setup and Sensitivities to Large-Scale Forcings

Abstract Large-eddy simulation (LES) is able to capture key boundary layer (BL) turbulence and cloud processes. Yet, large-scale forcing and surface turbulent fluxes of sensible and latent heat are often poorly prescribed for LESs. We derive these quantities from measurements and reanalysis obtained for two cold-air outbreak (CAO) events during Phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE) in February–March 2020. We study the two contrasting CAO cases by performing LES and test the sensitivity of BL structure and clouds to large-scale forcings and turbulent heat fluxes. Profiles of atmospheric state and large-scale divergence and surface turbulent heat fluxes obtained from ERA5 data agree reasonably well with those derived from ACTIVATE field measurements for both cases at the sampling time and location. Therefore, we adopt the time-evolving heat fluxes, wind, and advective tendencies profiles from ERA5 data to drive the LES. We find that large-scale thermodynamic advective tendencies and wind relaxations are important for the LES to capture the evolving observed BL meteorological states characterized by the hourly ERA5 data and validated by the observations. We show that the divergence (or vertical velocity) is important in regulating the BL growth driven by surface heat fluxes in LESs. The evolution of liquid water path is largely affected by the evolution of surface heat fluxes. The liquid water path simulated in LES agrees reasonably well with the ACTIVATE measurements. This study paves the path to investigate aerosol–cloud–meteorology interactions using LES informed and evaluated by ACTIVATE field measurements.

The impact of sea waves on turbulent heat fluxes in the Barents Sea according to numerical modeling

Abstract. This paper investigates the impact of sea waves on turbulent heat fluxes in the Barents Sea. The Coupled Ocean–Atmosphere Response Experiment (COARE) algorithm, meteorological data from reanalysis and wave data from the WAVEWATCH III wave model results were used. The turbulent heat fluxes were calculated using the modified Charnock parameterization for the roughness length and several parameterizations that explicitly account for the sea wave parameters. A catalog of storm wave events and a catalog of extreme cold-air outbreaks over the Barents Sea were created and used to calculate heat fluxes during extreme events. The important role of cold-air outbreaks in the energy exchange between the Barents Sea and the atmosphere is demonstrated. A high correlation was found between the number of cold-air outbreak days and turbulent fluxes of sensible and latent heat, as well as with the net flux of longwave radiation averaged over the ice-free surface of the Barents Sea during a cold season. The differences in the long-term mean values of heat fluxes calculated using different parameterizations for the roughness length are small and are on average 1 %–3 % of the flux magnitude. The parameterizations of Taylor and Yelland (2001) and Oost et al. (2002) lead to an increase in the magnitude of the fluxes on average, and the parameterization of Drennan et al. (2003) leads to a decrease in the magnitude of the fluxes over the entire sea compared with the Charnock parameterization. The magnitude of heat fluxes and their differences during the storm wave events exceed the mean values by a factor of 2. However, the effect of explicitly accounting for the wave parameters is, on average, small and multidirectional, depending on the parameterization used for the roughness length. With respect to the climatic aspect, it can be argued that explicitly accounting for sea waves in the calculations of heat fluxes can be neglected. However, during the simultaneously observed storm wave events and cold-air outbreaks, the sensitivity of the calculated values of fluxes to the parameterizations used increases along with the turbulent heat transfer increase. In some extreme cases, during storms and cold-air outbreaks, the difference exceeds 700 W m−2.

Sampling error in aircraft flux measurements based on a high-resolution large eddy simulation of the marine boundary layer

Abstract. A high-resolution (1.25 m) large eddy simulation (LES) of the nocturnal cloud-topped marine boundary layer is used to evaluate random error as a function of continuous track length L for virtual aircraft measurements of turbulent fluxes of sensible heat, latent heat, and horizontal momentum. Results are compared with the widely used formula of Lenschow and Stankov (1986). In support of these comparisons, the relevant integral length scales and correlations are evaluated and documented. It is shown that for heights up to approximately 100 m (z/zi=0.12), the length scales are accurately predicted by empirical expressions of the form If=Azb. The Lenschow and Stankov expression is found to be remarkably accurate at predicting the random error for shorter (7–10 km) flight tracks, but the empirically determined errors decay more rapidly with L than the L-1/2 relationship predicted from theory. Consistent with earlier findings, required track lengths to obtain useful precision increase sharply with altitude. In addition, an examination is undertaken of the role of uncertainties in empirically determined integral length scales and correlations in flux uncertainties as well as of the flux errors associated with crosswind and along-wind flight tracks. It is found that for 7.2 km flight tracks, flux errors are improved by factor of approximately 1.5 to 2 for most variables by making measurements in the crosswind direction.

Long-term surface energy balance of the western Greenland Ice Sheet and the role of large-scale circulation variability

Abstract. We present the surface energy balance (SEB) of the western Greenland Ice Sheet (GrIS) using an energy balance model forced with hourly observations from nine automatic weather stations (AWSs) along two transects: the Kangerlussuaq (K) transect with seven AWSs in the southwest and the Thule (T) transect with two AWSs in the northwest. Modeled and observed surface temperatures for non-melting conditions agree well with RMSEs of 1.1–1.6 K, while reasonable agreement is found between modeled and observed 10 d cumulative ice melt. Absorbed shortwave radiation (Snet) is the main energy source for melting (M), followed by the sensible heat flux (Qh). The multiyear average seasonal cycle of SEB components shows that Snet and M peak in July at all AWSs. The turbulent fluxes of sensible (Qh) and latent heat (Ql) decrease significantly with elevation, and the latter becomes negative at higher elevations, partly offsetting Qh. Average June, July and August (JJA) albedo values are <0.6 for stations below 1000 m a.s.l. and >0.7 for the higher stations. The near-surface climate variables and surface energy fluxes from reanalysis products ERA-Interim, ERA5 and the regional climate model RACMO2.3 were compared to the AWS values. The newer ERA5 product only significantly improves ERA-Interim for albedo. The regional model RACMO2.3, which has higher resolution (5.5 km) and a dedicated snow/ice module, unsurprisingly outperforms the reanalyses for (near-)surface climate variables, but the reanalyses are indispensable in detecting dependencies of west Greenland climate and melt on large-scale circulation variability. We correlate ERA5 with the AWS data to show a significant positive correlation of western GrIS summer surface temperature and melt with the Greenland Blocking Index (GBI) and weaker and opposite correlations with the North Atlantic Oscillation (NAO). This analysis may further help to explain melting patterns on the western GrIS from the perspective of circulation anomalies.

Flow over a snow-water-snow surface in the high Arctic, Svalbard: Turbulent fluxes and comparison of observation techniques

From observations in a High Arctic valley and ice-free fjord in Svalbard during March and April 2013 we show that, while some caution needs to be applied, ordinary slow-response instruments placed over a snow-water-snow surface can be effectively used as a proxy for more sophisticated measuring techniques at complex sites such as leads or a polynyas. The turbulent fluxes of momentum, sensible and latent heat were measured at three locations with a snow-water-snow fetch. At the snow site upwind of the water, the stability was generally stable, the momentum flux small, and the sensible heat flux positive. Over the water however, the internal boundary layer that was formed gave on average an increased vertical gradient in wind speed, temperature, and humidity and turbulent heat fluxes exceeding 400 W m−2. At the snow surface downwind of the water, the conditions were highly variable and all the fluxes were, on average, of very small magnitude. That the behaviour of the internal boundary layers can be highly variable is demonstrated through four case studies. This phenomenon is likely to increase in occurrence with a changing climate.

An Overview of the Integrated Meteorological Observations in Complex Terrain Region at Dali National Climate Observatory, China

Systematically observing components of the climate system as well as their processes and interactions are crucial to understand the weather, climate, climate change, etc. In order to launch long-term, continuous, stereoscopic, and integrated meteorological observations for key regions of the climate system in southwestern China where it is sensitive to interactions among multiple layers and exchanges of mass and energy, the Dali National Climate Observatory (DNCO) was established in May 2006. To date, the DNCO has gradually performed an integrated meteorological observation network in a complex terrain region over the southeastern Tibetan Plateau including the conventional observations of weather and climate, and the special observations of radiation, lightning, soil moisture, wind profile, water vapor, water quality, water level, water temperature profile, turbulent fluxes of momentum, sensible heat, latent heat, carbon dioxide, and methane, etc. Furthermore, the DNCO mainly focuses on the field observation experiments and scientific research activities for mountain meteorology. This paper presents an overview of the DNCO including its location, climatology, scientific objectives, research tasks, and existing observation projects. The progresses in observation and associated research including data quality controls and assessments, recent observation results, and regional numerical model tests are summarized. Future works are also discussed.

A benchmark dataset of in situ Antarctic surface melt rates and energy balance

AbstractSurface melt on the coastal Antarctic ice sheet (AIS) determines the viability of its ice shelves and the stability of the grounded ice sheet, but very few in situ melt rate estimates exist to date. Here we present a benchmark dataset of in situ surface melt rates and energy balance from nine sites in the eastern Antarctic Peninsula (AP) and coastal Dronning Maud Land (DML), East Antarctica, seven of which are located on AIS ice shelves. Meteorological time series from eight automatic and one staffed weather station (Neumayer), ranging in length from 15 months to almost 24 years, serve as input for an energy-balance model to obtain consistent surface melt rates and energy-balance results. We find that surface melt rates exhibit large temporal, spatial and process variability. Intermittent summer melt in coastal DML is primarily driven by absorption of shortwave radiation, while non-summer melt events in the eastern AP occur during föhn events that force a large downward directed turbulent flux of sensible heat. We use the in situ surface melt rate dataset to evaluate melt rates from the regional atmospheric climate model RACMO2 and validate a melt product from the QuikSCAT satellite.

Water tracks intensify surface energy and mass exchange in the Antarctic McMurdo Dry Valleys

Abstract. The hydrologic cycle in the Antarctic McMurdo Dry Valleys (MDV) is mainly controlled by surface energy balance. Water tracks are channel-shaped high-moisture zones in the active layer of permafrost soils and are important solute and water pathways in the MDV. We evaluated the hypothesis that water tracks alter the surface energy balance in this dry, cold, and ice-sheet-free environment during summer warming and may therefore be an increasingly important hydrologic feature in the MDV in the face of landscape response to climate change. The surface energy balance was measured for one water track and two off-track reference locations in Taylor Valley over 26 d of the Antarctic summer of 2012–2013. Turbulent atmospheric fluxes of sensible heat and evaporation were observed using the eddy-covariance method in combination with flux footprint modeling, which was the first application of this technique in the MDV. Soil heat fluxes were analyzed by measuring the heat storage change in the thawed layer and approximating soil heat flux at ice table depth by surface energy balance residuals. For both water track and reference locations over 50 % of net radiation was transferred to sensible heat exchange, about 30 % to melting of the seasonally thawed layer, and the remainder to evaporation. The net energy flux in the thawed layer was zero. For the water track location, evaporation was increased by a factor of 3.0 relative to the reference locations, ground heat fluxes by 1.4, and net radiation by 1.1, while sensible heat fluxes were reduced down to 0.7. Expecting a positive snow and ground ice melt response to climate change in the MDV, we entertained a realistic climate change response scenario in which a doubling of the land cover fraction of water tracks increases the evaporation from soil surfaces in lower Taylor Valley in summer by 6 % to 0.36 mm d−1. Possible climate change pathways leading to this change in landscape are discussed. Considering our results, an expansion of water track area would make new soil habitats accessible, alter soil habitat suitability, and possibly increase biological activity in the MDV. In summary, we show that the surface energy balance of water tracks distinctly differs from that of the dominant dry soils in polar deserts. With an expected increase in area covered by water tracks, our findings have implications for hydrology and soil ecosystems across terrestrial Antarctica.

Effects of Tropical Deforestation on Surface Energy Balance Partitioning in Southeastern Amazonia Estimated From Maximum Convective Power

AbstractTo understand changes in land surface energy balance partitioning due to tropical deforestation, we use a physically based analytical formulation of the surface energy balance. Turbulent heat fluxes are constrained by the thermodynamic maximum power limit and a formulation for diurnal heat redistribution within the land‐atmosphere system. The derived turbulent fluxes of sensible and latent heat compare very well to in situ observations for sites with intact rainforest and soybean land cover in southeastern Amazonia. The equilibrium partitioning into sensible and latent heat flux compares well with observations for both sites, except for the soybean site during the dry season where water limitation needs to be explicitly accounted for. Our results show that tropical deforestation primarily affects the absorption of solar radiation and the water limitation of evapotranspiration, but not the overall magnitude of turbulent heat fluxes that is set by the thermodynamic maximum power limit.