Estimation of surface energy fluxes under complex terrain of Mt. Qomolangma over the Tibetan Plateau

Surface solar radiation is an important parameter in surface energy balance models and in estimation of evapotranspiration. This study developed a DEM based radiation model to estimate instantaneous clear sky solar radiation for surface energy balance system to obtain accurate energy absorbed by the mountain surface. Efforts to improve spatial accuracy of satellite based surface energy budget in mountainous regions were made in this work. Based on eight scenes of Landsat TM/ETM+ (Thematic Mapper/Enhanced Thematic Mapper+) data and observations around the Qomolangma region of the Tibetan Plateau, the topographical enhanced surface energy balance system (TESEBS) was tested for deriving net radiation, ground heat flux, sensible heat flux and latent heat flux distributions over the heterogeneous land surface. The land surface energy fluxes over the study area showed a wide range in accordance with the surface features and their thermodynamic states. The model was validated by observations at QOMS/CAS site in the research area with a reasonable accuracy. The mean bias of net radiation, sensible heat flux, ground heat flux and latent heat flux is lower than 23.6 W m−2. The surface solar radiation estimated by the DEM based radiation model developed by this study has a mean bias as low as −9.6 W m−2. TESEBS has a decreased mean bias of about 5.9 W m−2 and 3.4 W m−2 for sensible heat and latent heat flux, respectively, compared to the Surface Energy Balance System (SEBS)

Algal growth and weathering crust state drive variability in western Greenland Ice Sheet ice albedo

One of the primary controls upon the melting of the Greenland Ice Sheet (GrIS) is albedo, a measure of how much solar radiation that hits a surface is reflected without being absorbed. Lower-albedo snow and ice surfaces therefore warm more quickly. There is a major difference in the albedo of snow-covered versus bare-ice surfaces, but observations also show that there is substantial spatio- temporal variability of up to ∼0.4 in bare-ice albedo. Variability in bare-ice albedo has been attributed to a number of processes including the accumulation of light-absorbing impurities (LAIs) and the changing physical properties of the near-surface ice. However, the combined impact of these processes upon albedo remains poorly constrained. Here we use field observations to show that pigmented glacier algae are ubiquitous and cause surface darkening both within and outside the south-west GrIS “dark zone” but that other factors including modification of the ice surface by algal bloom presence, surface topography and weathering crust state are also important in determining patterns of daily albedo variability. We further use observations from an unmanned aerial system (UAS) to examine the scale gap in albedo between ground versus remotely sensed measurements made by Sentinel-2 (S- 2) and MODIS. S-2 observations provide a highly conservative estimate of algal bloom presence because algal blooms occur in patches much smaller than the ground resolution of S-2 data. Nevertheless, the bare-ice albedo distribution at the scale of 20 m×20 m S-2 pixels is generally unimodal and unskewed. Conversely, bare-ice surfaces have a left-skewed albedo distribution at MODIS MOD10A1 scales. Thus, when MOD10A1 observations are used as input to energy balance modelling, meltwater production can be underestimated by ∼2  %. Our study highlights that (1) the impact of the weathering crust state is of similar importance to the direct darkening role of light-absorbing impurities upon ice albedo and (2) there is a spatial-scale dependency in albedo measurement which reduces detection of real changes at coarser resolutions

Advances in Process Understanding and Methods to Support River Temperature Modeling in Large Regulated Systems

River temperatures play a key role in determining the suitability of habitat for aquatic ecosystems. While thermal regimes are influenced by many factors, flow and temperature patterns in large rivers are often shaped by water development. As such, water management associated with large reservoirs and diversions have also altered aquatic ecosystems. As climate change introduces new climate and hydrologic patterns, the decisions water managers make to address changes in runoff may further impact aquatic ecosystems. This calls for robust modeling tools that can predict river and reservoir temperature responses to water management decisions over large regions. However, highly variable topography and data limitations that are inherent over large spatial scales complicate our understanding of river temperature controls. Further, differences among modeling frameworks need to be overcome in order to holistically understand ecosystem responses to water management decisions. This dissertation addresses these limitations by adapting mechanistic river temperature models to account for topographic shading and spatially varying weather information and describing methods for linking temperature responses to water management decisions. The Colorado River basin was used to evaluate these methods because it experiences significant flow regulation, remote river sections, and highly variable terrain. The findings here show that discharge and release temperatures from large reservoirs, particularly Lake Powell and Flaming Gorge, influence river temperatures over significant distances, while topographic shading increases the relative importance of heat fluxes, other than solar radiation, that require representative weather data for estimation. Spatially varying weather information from a climate reanalysis dataset, combined with elevation corrections, was tested in different modeling domains and found to significantly improve temperature predictions when compared to models using sparsely distributed ground-based weather stations. With the advances in modeling over topographically complex regions, water management models were linked to river temperature responses so that ecosystem indicators could be evaluated. Using an existing water management model for flow information and strategic resampling of weather and water temperature information, river temperatures were forecasted over more than 1000 km of river. The work presented here provides the foundational tools for evaluating climate and water management impacts on aquatic ecosystems in large managed basins

Climatic impacts of vegetation dynamics in Eastern Africa

The climate system responds to changes in the structure and physiology of vegetation. These changes can be induced by seasonal growing cycles, anthropogenic land cover changes (LCCs), and precipitation extremes. The extent to which vegetation changes impact the climate depends on the type of ecosystem, the season, and the intensity of perturbations from LCCs and precipitation extremes. Under the growing impacts of climate change and human modification of natural vegetation cover, understanding and monitoring the underlying biogeophysical processes through which vegetation affects the climate are central to the development and implementation of effective land use plans and mitigation measures. In Eastern Africa (EA) the vegetation is characterized by multiple growing cycles and affected by agricultural expansion as well as recurrent and severe drought events. Nonetheless, the degrees to which vegetation changes affect the surface energy budget and land surface temperature (LST) remain uncertain. Moreover, the relative contributions of various biogeophysical mechanisms to land surface warming or cooling across biomes, seasons, and scales (regional to local) are unknown. The objective of this thesis was to analyze and quantify the climatic impacts of land changes induced by vegetation seasonal dynamics, agricultural expansion, and precipitation extremes in EA. In particular, this thesis investigated these impacts across biomes and spatio-temporal scales. To address this objective, satellite observation and meteorological data were utilized along with empirical models, observation-based metrics, and statistical methods. The results showed that rainfall–vegetation interaction had a strong impact on LST seasonality across ecoregions and rainfall modality patterns. Furthermore, seasonal LST dynamics were largely controlled by evapotranspiration (ET) changes that offset the albedo impact on the surface radiation balance. Forest loss disturbed the LST dynamics and increased local LST consistently and notably during dry seasons, whereas during the wet season its impact was limited because of strong rainfall–vegetation interaction. Moreover, drought events affected LST anomalies; however, the impact of droughts on temperature anomalies was highly regulated by vegetation greening. In addition, the conversion of forest to cropland generated the highest net warming (1.3 K) compared with other conversion types (savanna, shrubland, grassland, and cropland). Warming from the reduction of ET and surface roughness was up to ~10 times stronger than the cooling effect from albedo increases (−0.12 K). Furthermore, large scale analysis revealed a comparable warming magnitude during bushland-to-cropland conversion associated with the dominant impact of latent heat (LE) flux reduction, which outweighed the albedo effect by up to ~5 times. A similar mechanism dominated the surface feedback during precipitation extremes; where LE flux anomalies dominated the energy exchange causing the strongest LST anomaly in grassland, followed by savanna. By contrast, the impact was negligible in forest ecosystems. In conclusion, the results of this thesis clarify the mechanics and magnitude of the impacts of vegetation dynamics on LST across biomes and seasons. These results are crucial for guiding land use planning and climate change mitigation efforts in EA. The methods and results of this thesis can assist in the development of ecosystem-based mitigation strategies that are tailored to EA biomes. Moreover, they can be used for assessing the performance of climate models and observation-based global scale studies that focus on the biogeophysical impacts of LCCs. Keywords: LST seasonality; Land cover change; Bushland (Acacia-Commiphora); Biophysical effects; Precipitation extremes; Satellite observation.Ilmastojärjestelmä reagoi kasvillisuuden rakenteen ja fysiologian muutoksiin. Muutokset voivat johtua kasvukauden vaiheesta, ihmistoiminnan vaikutuksesta maanpeitteeseen ja sään ääri-ilmiöistä. Se missä määrin kasvillisuuden muutokset vaikuttavat ilmastoon riippuu ekosysteemistä ja vuodenajasta sekä maanpeitemuutosten ja sään ääri-ilmiöiden voimakkuudesta. Ilmastonmuutoksen ja maanpeitteen muokkaamisen vaikutusten voimistuessa on keskeistä ymmärtää ja seurata biogeofysikaalisia prosesseja, joiden kautta kasvillisuus vaikuttaa ilmastoon. Tällä tiedolla on keskeinen rooli tehokkaiden maankäyttösuunnitelmien kehittämisessä ja toteuttamisessa sekä ilmastonmuutoksen hillinnässä. Itä-Afrikassa kasvillisuudella on ominaisesti useita kasvukausia ja siihen vaikuttavat maatalouden laajentuminen sekä toistuvat ja vakavat kuivuusjaksot. Siitä huolimatta kasvillisuuden muutosten vaikutus energiataseeseen ja maanpinnan lämpötilaan on edelleen epävarmaa. Lisäksi eri biogeofysikaalisten mekanismien suhteellista vaikutusta maanpinnan lämpenemiseen tai jäähtymiseen eri biomien, vuodenaikojen ja mittakaavojen (alueellinen ja paikallinen) välillä ei tunneta. Tämän tutkielman tavoitteena oli analysoida ja kvantifioida kasvillisuuden vuodenaikaisvaihtelun, maatalouden laajentumisen ja sademäärän ääri-ilmiöiden aiheuttamien muutosten ilmastovaikutuksia Itä-Afrikassa. Erityisesti tutkielmassa tarkasteltiin vaikutuksia eri biomien ja mittakaavojen välillä. Tutkielmassa hyödynnettiin satelliittihavaintoja ja meteorologisia tietoja sekä empiirisiä malleja, havaintopohjaisia indeksejä ja tilastollisia menetelmiä. Tulokset osoittivat, että sademäärän ja kasvillisuuden vuorovaikutuksella oli voimakas vaikutus maanpinnan lämpötilan vuodenaikaisvaihteluun kasvillisuustyyppien ja sademoodien välillä. Maanpinnan lämpötilaa säätelivät suurelta osin evapotranspiraation muutokset, jotka kompensoivat albedon vaikutuksia pinnan säteilytasapainoon. Metsän häviäminen häiritsi maanpinnan lämpötilan dynamiikkaa ja lisäsi sitä paikallisesti, etenkin kuivina vuodenaikoina, kun taas sadekauden aikana sen vaikutus oli vähäinen sateen ja kasvillisuuden voimakkaan vuorovaikutuksen vuoksi. Lisäksi kuivuus vaikutti lämpötilan poikkeavuuksiin; kuivuuden vaikutusta sääteli kuitenkin voimakkaasti kasvillisuuden vihertyminen. Metsän muuntaminen viljelysmaaksi aiheutti suurimman nettolämmityksen (1.3 K) verrattuna muihin muutostyyppeihin (savanni, pensaikko, ruohostomaat ja viljelymaat). Evapotranspiraation vähenemisestä ja pinnan epätasaisuudesta aiheutuva lämpeneminen oli jopa noin 10 kertaa voimakkaampi kuin albedon jäähdytysvaikutus (−0.12 K). Lisäksi pensaikon muuntaminen viljelysmaaksi aiheutti vastaavan lämpenemisen. Lämpeneminen liittyi latentin lämpövuon merkityksen vähentymiseen, joka ylitti albedovaikutuksen jopa noin viisinkertaisesti. Samanlainen mekanismi hallitsi sademäärän ääripäiden aikana, jolloin latentin lämpövuon poikkeavuudet hallitsivat energianvaihtoa aiheuttaen voimakkaimman maanpinnan lämpötilan poikkeavuuden ruohostomailla ja savanneilla. Sitä vastoin metsissä vaikutus oli vähäinen. Yhteenvetona voidaan todeta, että tutkielman tulokset selventävät kasvillisuuden dynamiikan vaikutusten mekanismeja ja suuruutta maanpinnan lämpötilaan biomien ja vuodenaikojen välillä. Tulokset ovat tärkeitä Itä-Afrikan maankäytön suunnittelun ja ilmastonmuutoksen hillitsemistoimien ohjaamisessa. Tutkielman menetelmät ja tulokset voivat auttaa kehittämään Itä-Afrikan biomeille räätälöityjä ekosysteemipohjaisia lieventämisstrategioita. Lisäksi niitä voidaan käyttää arvioimaan ilmastomalleja ja havaintopohjaisia globaalin mittakaavan tutkimuksia, jotka keskittyvät maanpeitemuutosten biogeofysikaalisiin vaikutuksiin. Avainsanat: Maanpinnan lämpötilan vuodenaikaisvaihtelu; Maanpeitteen muutos; Pensaikko (AcaciaCommiphora); Biofysikaaliset vaikutukset; Sademäärä; Satelliittikaukokartoitus

mapping photosynthetically active radiation (PAR) using multiple remote sensing data

Incident Photosynthetically Active Radiation (PAR) is an important parameter for terrestrial ecosystem models. Presently, deriving PAR using remotely sensed data is the only practical approach to meet the needs for large scale ecosystem modeling. The usefulness of the currently available PAR products is constricted by their limited spatial and temporal resolution. In addition, the applicability of the existing algorithms for deriving PAR using remotely sensed data are limited by their requirements for external atmospheric information. This study develops new algorithms to estimate incident PAR using remotely sensed data from MODIS (Moderate Resolution Imaging Spectroradiometer), GOES (Geostationary Operational Environmental Satellite), and AVHRR (Advanced Very High Resolution Radiometer). The new PAR algorithms differ from existing algorithms in that the new algorithms derive surface properties and atmospheric optical properties using time-series of at-sensor radiance without external atmospheric information. First, a new PAR algorithm is developed for MODIS visible band data. The validity of the algorithm's underpinning theoretical basis is examined and associated errors are analyzed in light of their impact on PAR estimation accuracy. Second, the MODIS PAR algorithm is adapted to AVHRR in order to take advantage of the long data acquisition record of AVHRR. In addition, the scaling of remote sensing derived instantaneous PAR to daily PAR is addressed. Last, the new algorithm is extended to GOES visible band data. Two major improvements of GOES PAR algorithm over that of MODIS and AVHRR are the inclusion of the bi-directional reflectance distribution function for deriving surface reflectance, and the procedure for excluding cloud-shadowed pixels in searching for observations made under clear skies. Furthermore, the topographic impact on PAR is accessed and corrected. To assess the effectiveness of the newly developed PAR algorithms, validation efforts have been made using ground measurements made at FLUXNET sites. The validations indicate that the new PAR algorithms for MODIS, GOES, and AVHRR are capable of reaching reasonably high accuracy with no need for external atmospheric information. This work is the first attempt to develop a unified PAR estimation algorithm for both polar-orbiting and geostationary satellite data. The new algorithms developed in this study have been used to produce incident PAR over North America routinely to support the North America Carbon Program

Amélioration de la capabilité de modélisation et de mitigation du gel radiatif au milieu agricole

Le gel radiatif est une des conditions météorologiques sévère affect la production agricole dans de nombreuses région du monde. Les objectives de cette étude inclut deux innovations scientifiques liées aux dégâts causés par le gel radiatif : (1) l'amélioration de la capacité de prédiction du gel local (température nocturne minimale à une résolution de 30 mètres) grâce à un modèle d’échange énergétique entre la végétation et l’atmosphère, et (2) une nouvelle méthode de diminution des risques et de protection des cultures agricoles pendant les périodes de gel. La première innovation a été réalisée en suivant plusieurs objectifs spécifiques visant à améliorer les capacités d'un modèle de répartition spatiale météorologique (Micro-Met) via quatre sous-modèles : (i) estimation journalière du gradient thermique adiabatique de l'air, (ii) modification de l’équation de rayonnement des grandes longueurs d'onde en l’absence de nuage dans l’atmosphère, (iii) quantification des effets de l’écoulement de l’air froid sur la température de l’air, et (iv) quantifier l’effet de haies brise–vent sur la vitesse du vent. La deuxième innovation a été réalisée en mettant en œuvre et en testant une nouvelle méthode active basée sur le cycle thermodynamique. Le site d'étude se localise dans la région de Vallée de Coaticook de l’Estrie (Québec) subit les conséquences désastreuses du gel. Le premier sous-modèle utilise une combinaison de profils de température provenant du satellite AIRS et de stations météorologiques afin d’estimer quotidiennement et régionalement le gradient thermique de l’air. L'utilisation de valeurs journalières, au lieu de valeurs fixes, permet d’estimer plus précisément les conditions atmosphériques. Les résultats ont démontré l’utilité de l’utilisation de la température de l'air obtenue par AIRS (850 hPa et 700 hPa) pour l’estimation du gradient thermique. Le second sous-modèle utilise les données associées aux conditions synoptiques du gel radiatif pour obtenir une équation du rayonnement descendant localement ajustée. Alors que l’erreur aux moindres carrés (RMSE) de Micro-Met était de 176.95 Wm-2 avec une erreur absolue (MAE) moyenne de 176.40 Wm-2, la nouvelle équation génère une RMSE de 4.90 Wm-2 et une MAE de 4.00 Wm-2. Le troisième sous-modèle contient trois parties :la détection des vallées fermées, l’estimation de la rapidité de drainage de l’air, et l’intégration de la perte de chaleur sensible ainsi que le refroidissement radiatif en vallée durant la nuit. La comparaison entre les simulations Micro-Met et les mesures de la température de l’air montrent une MAE de 1.11 (°C) et une RMSE de 1.66 (°C). La comparaison avec le modèle amélioré indique un gain avec une MAE de 0.68 (°C) et une RMSE de 1.08 (°C). Le quatrième sous-modèle était construit sur des résultats expérimentaux de vitesse du vent générés en laboratoire par des simulations. Trois équations ont été proposées pour estimer la vitesse du vent. Les résultats indiquent un coefficient de corrélation (R2) de 71% pour une vitesse de vent en dessous de 6 ms-1. La version améliorée de Micro-Net fournit une nouvelle plateforme pour des modèles d’énergie végétation-atmosphère et permet de prévoir la température minimale nocturne. Les résultats des tests de prédiction de cette température minimum concordent avec les mesures in-situ. Ces mesures ont été prises dans 5 secteurs topographiques différents afin d’améliorer les modèles de prédiction et engendrent des erreurs pour des vallées fermées (RMSE = 1.34, MAE = 1.03), pour différentes pentes (RMAE = 0.93, MAE = 0.73), crêtes (RMSE = 1.02, MAE = 0.88), plaines (RMSE = 0.44, MAE = 0.40), et aux orées des forêts (RMSE = 0.58, MAE = 0.53). En plus des objectifs spécifiques précédents, cette étude a proposé une nouvelle méthode d'atténuation du gel basée sur la thermodynamique du transport de la vapeur d'eau d'une source humide à un puits sec. Nous avons ajouté au Selective Inverse System (SIS) déjà utilisé dans le milieu, un contenant d'eau chaude à sa base pour diffuser la vapeur d'eau dans l'air ambiant. Cette opération a augmenté l’humidité de l'air ambiant et augmenté l'entropie humide. Cet essai a été réalisé dans un verger. La méthode d'atténuation la plus courante se concentre sur la température de l'air. La méthode proposée repose plutôt sur les principes physiques de l'entropie humide, qui combinait à la fois la température et l'humidité de l'air et le contenu thermique représenté. Dans l'ensemble, pour ce projet de recherche, un modèle couplé a été conçu pour prévision la température minimale nocturne de l'air dans des terrains agricoles vallonnés. En particulier, en améliorant la précision des prévisions, nous avons élaboré et ajouté des sous-modèles pour estimer les baisses de température dues à la stagnation du drainage de l'air froid et à l'effet des brise-vent forestiers sur la vitesse du vent. Pour réduire l'effet de gel, une nouvelle méthode de mitigation active respectueuse de l'environnement a été présentée. Cette étude a le potentiel d’aider les agriculteurs à réduire les dommages causés par le gel. De plus, elle peut être utile pour les services agricoles en termes de prise de décision, réduisant ainsi les dommages économiques.Abstract: The main objective of this study was related to radiation frost damage: (1) improving the forecasting capability of local frost, which was adapted to forecast nocturnal minimum temperature at a 30-meter resolution, using a vegetation atmosphere energy exchange framework, and (2) proposing a new mitigation approach to protect agricultural crops during frost periods. The first advance was achieved through several specific objectives to enhance the capabilities of a meteorological spatial distribution model (Micro-Met) on four sub-models: (i) estimating local air temperature lapse rate on a daily basis (ii) modifying downward longwave equation under clear sky condition, (iii) quantifying the effects of cold air drainage on air temperature, and (iv) quantifying the forest shelter effect on wind speed. The second advance advancement was accomplished by implementing and testing a new active method based on steam cycle thermodynamic. The first sub-model used AIRS (Atmosphere infrared sounder) air temperature profile and surface station data to estimate air temperature lapse rate on the daily and regional scale. The use of daily basis lapse rate, instead of the fixed value, allowed to present more accurate atmospheric condition. The results showed the potential of the AIRS air temperature profiles (850 hPa and 700 hPa) to estimate the temperature lapse rate. The second sub-model used observational data associated with synoptic conditions of radiation frost to present a locally adjusted downward longwave equation. The reported root means square error (RMSE) and mean absolute error (MAE) for the current version of Micro-Met were 176.95 (Wm-2) and 176.40 (Wm-2) respectively, while the results of the new equation led to an RMSE and MAE of 4.90 (Wm-2) and 4.00 (Wm-2) respectively. The third sub–model constituted three components: detected closed valley, estimated cold air drainage velocity, and integrated sensible heat loss and radiative cooling during the night on detected valleys. Comparison between the current Micro-Met simulation and the measured air temperature shows MAE of 1.11°C and RMSE of 1.66°C, while the comparison with the enhanced Micro-Met simulation indicated an improvement with MAE of 0.68 °C and RMSE of 1.08 °C. The fourth sub-model was based on experimental results of wind velocity produced in a laboratory with wind-tunnel models. Three separate equations were formulated for wind velocity estimation over the windward, through the shelterbelt, and leeward areas. The results indicated a coefficient of determination (R2) of 71% under the wind's velocity lower than 6ms-1. The Enhanced Micro-Met version provided a new platform to power vegetation-atmosphere energy model to forecast minimum nocturnal temperature. The performance test for forecasting minimum air temperatures indicated agreement with in-situ measurements. Measurements were taken on five topographic sectors in order to assess the improved modeled prediction and led to error assessment on closed valleys (RMSE=1.34, MAE = 1.03), different parts of slopes (RMAE = 0.93, MAE = 0.73), ridges (RMSE = 1.02, MAE = 0.88), flat areas (RMSE = 0.44, MAE = 0.40), and areas close to the forest (RMSE = 0.58, MAE = 0.53). In addition to previous specific objectives, this study proposed a new frost mitigation method based on the thermodynamics of water vapor transport from a moist source to dry sink. A vessel of warm water equipped with a Selective Inverted Sink (SIS) system was used to transport water vapor into the air, which ended up decreasing the air dryness and increasing moist entropy. This test was carried out in an orchard. The most common mitigation method focuses on air temperature. Instead, the proposed method was based on the physical principles of moist entropy, which combined both air temperature and humidity and depicted heat content. Overall, for this research project, a coupled model was designed to predict nocturnal minimum air temperature over hilly agricultural terrain. In particular, through improving prediction accuracy, we developed and added sub-models to estimate drops in temperature due to pooling and stagnation of cold air drainage and the effect of forest shelterbelt on wind velocity. To reduce frost effect, a new environmentally friendly active method was presented. This study served to help farmers reduce frost damages. Moreover, it can be useful for agricultural services in terms of decision-making, thereby, reducing economic damages

Algal growth and weathering crust state drive variability in western Greenland Ice Sheet ice albedo

One of the primary controls upon the melting of the Greenland Ice Sheet (GrIS) is albedo, a measure of how much solar radiation that hits a surface is reflected without being absorbed. Lower-albedo snow and ice surfaces therefore warm more quickly. There is a major difference in the albedo of snow-covered versus bare-ice surfaces, but observations also show that there is substantial spatio- temporal variability of up to ∼0.4 in bare-ice albedo. Variability in bare-ice albedo has been attributed to a number of processes including the accumulation of light-absorbing impurities (LAIs) and the changing physical properties of the near-surface ice. However, the combined impact of these processes upon albedo remains poorly constrained. Here we use field observations to show that pigmented glacier algae are ubiquitous and cause surface darkening both within and outside the south-west GrIS “dark zone” but that other factors including modification of the ice surface by algal bloom presence, surface topography and weathering crust state are also important in determining patterns of daily albedo variability. We further use observations from an unmanned aerial system (UAS) to examine the scale gap in albedo between ground versus remotely sensed measurements made by Sentinel-2 (S- 2) and MODIS. S-2 observations provide a highly conservative estimate of algal bloom presence because algal blooms occur in patches much smaller than the ground resolution of S-2 data. Nevertheless, the bare-ice albedo distribution at the scale of 20 m×20 m S-2 pixels is generally unimodal and unskewed. Conversely, bare-ice surfaces have a left-skewed albedo distribution at MODIS MOD10A1 scales. Thus, when MOD10A1 observations are used as input to energy balance modelling, meltwater production can be underestimated by ∼2  %. Our study highlights that (1) the impact of the weathering crust state is of similar importance to the direct darkening role of light-absorbing impurities upon ice albedo and (2) there is a spatial-scale dependency in albedo measurement which reduces detection of real changes at coarser resolutions

Identification of patterns in long-term observations of the cloudy boundary layer

Understanding atmospheric boundary layer (ABL) processes is a key aspect in improving parameterizations in weather forecast and climate prediction models, but also for renewable energy and air quality studies. The ABL, as the lowest part of the atmosphere, can be directly affected by heterogeneities in land surface properties like soil, vegetation and topography, creating patterns at different temporal and spatial scales. In this context, turbulent mixing plays an important role in connecting the atmosphere to the Earth's surface. The turbulent motions are responsible for the thermodynamic structure of the ABL by redistributing heat and moisture and the transport of constituents like aerosols and pollutants away from the surface. These processes are the main drivers for the development of ABL clouds, which in turn feed back to the ABL and surface through interaction with solar radiation, coupling to the large-scale circulation and precipitation formation. This links back to the aim of model improvement, since clouds are one of the largest source of uncertainty in global models. Therefore interdisciplinary research is required to capture the interplay between the different compartments of the Earth. The Transregional Collaborative Research Centre 32 (TR32) in its third phase is dedicated to find these patterns in the soil-vegetation-atmosphere system by a monitoring, modelling and data assimilation approach. Within the TR32 project D2 special emphasis is on measuring, modelling and understanding the spatio-temporal structures in land surface-atmosphere exchange at the Jülich ObservatorY for Cloud Evolution (JOYCE). For the typical ABL process scales of seconds to hours and meters to kilometers, ground-based remote sensing observations are well suited to continuously gather comprehensive information on the atmospheric state in a long-term perspective. With additional model simulations the conceptual process understanding can be improved. This study focuses on the long-term characterisation of the cloudy boundary layer to identify patterns that can be further linked to surface properties at JOYCE. For this purpose, a classification for characterizing ABL turbulence is developed (Publication I). The classification, based on Doppler wind lidar (DWL) data, identifies turbulence regions in the ABL and assigns a mixing source using multiple DWL quantities. In this way, convective, wind shear and cloud driven turbulence can be distinguished under most atmospheric conditions. The method is applied at two research sites, showing a distinct behavior for different climate regimes in terms of the diurnal and seasonal cycle of ABL development. In the analysis of the long-term data sets, nocturnal low-level jets (LLJ) are identified as an important source of shear generated mixing. Therefore, a long-term record of LLJ periods, compiled with DWL observations, is investigated in Publication II. The high frequency of occurrence and wind speeds, associated with significant turbulence close to the surface, reveal the relevance of LLJs for wind energy applications. In addition, a strong interaction of the wind field with the surrounding topography can be seen in the DWL measurements, as well as in the results of a high-resolution large-eddy simulation (LES). Also during the day, when the buoyancy production represents the main factor of convective ABL mixing, the interaction between the land surface and the atmosphere is strongly influenced by surface properties. In particular, the local transport of water vapor in moist thermals is a key mechanism for the coupling of clouds to the underlying land surface and a spatially heterogeneous distribution of land use types can lead to patterns in atmospheric water vapor fields (Publication III). Besides a scanning microwave radiometer (MWR), also satellite and LES data are taken into account, showing a good agreement in identifying the direction of water vapor sources. Convective clouds, that are frequently forming in the ABL due to this convective humidity transport, often contain small amounts of liquid water. These thin liquid water clouds, with a low liquid water path (LWP), are important in terms of their interaction with radiation. In the range of low LWP values, the radiative fluxes are very sensitive to small changes in the amount liquid water contained in the clouds. For a correct representation of the cloud microphysical and optical properties, statistical retrievals using a neural network approach are developed in Publication IV. The retrievals with low computational demand are derived from ground-based observations and make use of the distinct sensitivities in different spectral regimes. While the microwave regime suffers from high uncertainties in low LWP situations, the infrared regime reveals saturation effects for higher LWP. A combination of both spectral regimes yields the best results for the whole range of LWP values