Neste trabalho analisamos a influência da variabilidade climática sobre um ecossistema representado principalmente por cerrado sensu stricto, na Gleba Pé de Gigante, em Santa Rita do Passa Quatro, SP, durante o período de 2001 a 2007. Os dados coletados para esta análise são provenientes da torre micrometeorológica localizada no Parque Estadual de Vassunuga, cuja instalação está associada ao desenvolvimento do projeto temático Interação Biosfera- Atmosfera Fase 2: Cerrados e Mudanças de Uso da Terra. As propriedades físicas do clima utilizadas para análise foram a precipitação, a temperatura do ar e a radiação solar. Um levantamento teórico da biomassa acima e abaixo do solo foi realizado para caracterizar a vegetação quanto ao potencial de estoque de carbono existente. A biomassa da área coberta por campo cerrado foi de 67,1 Mg.ha-1, da área coberta por cerrado sensu stricto, 185,6 Mg.ha-1 e da área coberta por cerrado denso, 242,7 Mg.ha-1. Uma relação entre estoques de carbono e fluxos de CO2 foi estabelecida, onde uma tonelada de carbono em cerrado sensu stricto é capaz de assimilar, em média, 0,27 KgC.ha-1.dia-1 da atmosfera. A combinação de diferentes intensidades das propriedades climáticas formam condições ambientais variadas que contribuem para o estado da vegetação e sua produtividade. O principal parâmetro usado para avaliar o estado da vegetação foi o albedo, tanto para a faixa espectral da radiação visível (albedo solar) como para a faixa da radiação fotossintéticamente ativa (albedo RFA). O comportamento sazonal do albedo permitiu verificar que a vegetação apresentou-se fortemente condicionada pela variabilidade climática, que ditou o ritmo da funcionalidade ecossistêmica. De maneira geral, a precipitação, a temperatura do ar e a oferta de energia solar oscilam de forma proporcional ao longo das estações, caracterizando dois períodos distintos: um período com condições favoráveis ao desenvolvimento vegetal, de outubro a março, e um período de estresse, de abril a setembro. Os valores mínimos e máximos de albedo solar sobre o cerrado sensu stricto, durante o período analisado, oscilou entre 15% (novembro/dezembro) e 9% (setembro/outubro) e, o albedo RFA oscilou entre 2% (fevereiro/março) e 6% (setembro/outubro). Na escala interanual, observou-se o aumento do albedo RFA em 2006 após um período de três anos de queda contínua da precipitação, entre 2003 e 2006, sendo 2006 o ano menos chuvoso de toda a série considerada. Em 2007, os valores de albedo RFA foram bem mais baixos do que os calculados para os demais anos, respondendo rapidamente ao alto índice de precipitação ocorrido na estação chuvosa entre 2006 e 2007. Embora tenha sido observado uma resposta relativamente rápida do albedo RFA à recuperação do estresse hídrico na escala sazonal, o padrão do albedo na escala interanual é distinto: entre 2003 e 2006, período em que se observou taxas negativas de precipitação consecutivas, o albedo RFA diminuiu ou ficou com valores aparentemente constantes, apresentando valores mais altos somente em 2006. Desta forma, conclui-se que o estado da vegetação é condicionado principalmente pelo índice de precipitação, uma vez que a temperatura do ar e a quantidade de radiação solar não apresentam variações bruscas na região considerada. Considerando-se a importância da estimativa de albedo RFA como um parâmetro para estimar a variação sazonal do estado da vegetação, sugeriu-se um ajuste linear simples para a estimativa de albedo RFA em cerrado sensu stricto com base nos valores de IVDN, cuja variância explicada foi igual a 0,68. / In this work we analyze the climatic variability influence over a woodland savannah ecosystem at Gleba Pe de Gigante, Santa Rita do Passa Quatro, SP, during the 2001-2007 period. The data collected for this analysis are from a micrometeorological tower located at Vassununga State Park, which was installed under the thematic project called Biosphere- Atmosphere Interaction Phase 2: Savannah and Land Use Change. The physical climate properties used for this analysis were precipitation, air temperature and solar radiation. A theoretical survey for above and below ground biomass was made to characterize the existing carbon stock potential related to the vegetation. The total biomass estimated at grassland savannah was 67.1 Mg.ha-1, at woodland savannah was 185.6 Mg.ha-1 and at dense savannah was 242.7 Mg.ha-1. A relationship between carbon stocks and CO2 fluxes was established where one tone of carbon in woodland savannah absorbs an average of 0.27 KgC.ha-1.day-1 from the atmosphere. The combination of different climate properties and intensities generates different environmental conditions that lead to the vegetation state and its productivity. The main physical parameter considered to evaluate vegetation state was the albedo, which was shared in two spectral bands: visible spectrum (solar albedo) and photosynthetic active radiation (PAR albedo). The seasonal pattern of albedo allows checking that vegetation was strongly conditioned by climatic variability, which dictates the ecosystem functionality rhythm. Generally, precipitation, air temperature and solar radiation vary in a proportional way along the year, providing two different periods related to vegetation status: one period characterized by favorable conditions to vegetal development (October-March) and another by stressing conditions (April-September). Maximum and minimum values for solar albedo at woodland savannah varied, respectively, between 15% (November/December) and 9% (September/October); for PAR albedo, maximum and minimum values varied between 6% (September/October) and 2% (February/March). At annual scale, PAR albedo rose in 2006, after a four years period of falling precipitation rate, between 2003 and 2006. 2006 was the drier year among the others. In 2007, the PAR albedo values were much lower than those calculated for the remaining years, promptly responding to the high precipitation rate observed in the previous rainy season, 2006-2007. Even though a quick response in PAR albedo was noticed due to the recovered water stress in seasonal scale, the albedo pattern in annual scale held a different way: between 2003 and 2006, period characterized by consecutive and negative precipitation rates, vegetation was apparently associated to stable values of PAR albedo, presenting higher values only in 2006. Considering these results, we conclude that the vegetation state is mainly conditioned by precipitation rate, once the air temperature and solar radiation had not presented high variation in the study region. Based on the importance of PAR albedo as a parameter to estimate seasonal vegetation status, a simple linear adjustment according for woodland savannah PAR albedo based on NDVI values was suggested, which explained variance by NDVI was equal to 0.68.

Changes in large-scale vegetation structure triggered by processes such as deforestation, wildfires, and tree die-off alter surface structure, energy balance, and associated albedo-all critical for land surface models. Characterizing these properties usually requires long-term data, precluding characterization of rapid vegetation changes such as those increasingly occurring in the Anthropocene. Consequently, the characterization of rapid events is limited and only possible in a few specific areas. We use a campaign approach to characterize surface properties associated with vegetation structure. In our approach, a profiling LiDAR and hemispherical image analyses quantify vegetation structure and a portable mast instrumented with a net radiometer, wind-humidity-temperature stations in a vertical profile, and soil temperature-heat flux characterize surface properties. We illustrate the application of our approach in two forest types (boreal and semiarid) with disturbance-induced tree loss. Our prototype characterizes major structural changes associated with tree loss, changes in vertical wind profiles, surface roughness energy balance partitioning, a proxy for NDVI (Normalized Differential Vegetation Index), and albedo. Multi-day albedo estimates, which differed between control and disturbed areas, were similar to tower-based multiyear characterizations, highlighting the utility and potential of the campaign approach. Our prototype provides general characterization of surface and boundary-layer properties relevant for land surface models, strategically enabling preliminary characterization of rapid vegetation disturbance events.

Glaciers and ice caps (GIC) are central parts of the hydrological cycle, are key to understanding regional and global climate change, and are important contributors to global sea level rise, regional water resources and local biodiversity. Multispectral (visible and near-infrared) remote sensing has been used for studying GIC and their changing characteristics for several decades. Glacier surfaces can be classified into a range of facies, or zones, which can be used as proxies for annual mass balance and also play a significant role in understanding glacier energy balance. However, multispectral sensors were not designed explicitly for snow and ice observation, so it is not self-evident that they should be optimal for remote sensing of glaciers. There are no universal techniques for glacier surface classification which have been optimized with in situ reflectance spectra. Therefore, the roles that the various spectral, spatial, and radiometric properties of each sensor play in the success and output of resulting classifications remain largely unknown. Therefore, this study approaches the problem from an inverse perspective. Starting with in situ reflectance spectra from the full range of surfaces measured on two glaciers at the end of the melt season in order to capture the largest range of facies (Midtre Lovénbreen, Svalbard & Langjökull, Iceland), optimal wavelengths for glacier facies identification are investigated with principal component analysis. Two linear combinations are produced which capture the vast majority of variance in the data; the first highlights broadband albedo while the second emphasizes the difference in reflectance between blue and near-infrared wavelengths for glacier surface classification. The results confirm previous work which limited distinction to snow, slush, and ice facies. Based on these in situ data, a simple, and more importantly completely transferrable, classification scheme for glacier surfaces is presented for a range of satellite multispectral sensors. Again starting with in situ data, application of relative response functions, scaling factors, and calibration coefficients shows that almost all simulated multispectral sensors (at certain gain settings) are qualified to classify glacier accumulation and ablation areas but confuse classification of partly ash-covered glacier surfaces. In order to consider the spatial as well as the spectral properties of multispectral sensors, airborne data are spatially degraded to emulate satellite imagery; while medium-resolution sensors (~20-60 m) successfully reproduce high-resolution (2 m) observations, low-resolution sensors (i.e. 250 m+) are unable to do so. These results give confidence in results from current sensors such as ASTER and Landsat ETM+ as well as ESA’s upcoming Sentinel-2 and NASA’s recently launched LDCM. In addition, images from the Landsat data archive are used to classify glacier facies and calculate the albedo of glaciers on the Brøgger Peninsula, Svalbard. The time series is used to observe seasonal and interannual trends and investigate the role of melt-albedo feedback in thinning of Svalbard glaciers. The dissertation concludes with recommendations for glacier surface classification over a range of current and future multispectral sensors. Application of the classification schemes suggested should help to improve the understanding of recent and continuing change to GIC around the world.

The energy efficiency of a building is directly related to the heat transfer between the building and the outside environment. In order to limit the heat transfer to the building by solar radiation cool roofs have been developed which increase the solar reflectivity of roofs. This report investigates the potential application of high reflectivity coatings to roofs at Pomona College and the energy benefits that could result. Cool roofs are used to address two prevalent environmental concerns: high cooling loads and Urban Heat Islands. These two problems are linked and exhibit the potential micro and mesoscale benefits of reducing roof surface temperature. Cool roofs are part of a larger set of solutions to tackle these two issues and so must be considered in the context of the multitude of other mitigation measures. This report discusses the ways in which a cool roof affects a building envelope and Urban Heat Islands, and what this means in the context of Southern California and Pomona College. Due to the already energy efficient clay tile on most Pomona roofs, the gains from reflective coatings would be limited. However there are several flat roofs on campus that could benefit from the application of a reflective coating. These benefits would come in the form of cooling energy cost reduction to individual buildings. These benefits would not be so drastic as to necessitate immediately applying reflective coatings, but flat roofs should be updated with an energy efficient coating as part of regularly scheduled resurfacing

Remote sensing reflectance data collected with a remotely operated vehicle (ROV) were used to derive bottom albedo and optical properties for a shallow marine environment near Lee Stocking Island, Bahamas. Optical model inversion techniques were applied to hyperspectral measurements of remote-sensing reflectance to derive water absorption and backscatter coefficients. Using these derived water properties, path attenuation and radiance effects were removed from bottom observations to derive bottom albedos. Histograms from multispectral, hyperspatial video images were used to determine the albedo range of optical end members observed in scenes of sand and seagrass. Variations of spectral signatures for optical end members caused by path-adjacency effects are shown to influence the reflectance measurements. Low-altitude albedo histograms for heterogeneous scenes demonstrate higher contrast between sand and seagrass than is observed at higher altitudes, even after correction for path radiance and attenuation effects. For example, reflected light from bright sand scatters into the field of view of dark seagrass, while less light scatters out from the seagrass into the field of view of sand. This decreases the apparent sand albedo, and increases that for seagrass when viewed from higher altitudes, including aircraft. Evidence provided suggests that simple bottom classifications based upon expected albedo values for scene end members are in error unless the water depth is very shallow.

The Greenland ice sheet (GIS) is a key component of the warming Arctic climate, having the potential to dramatically influence sea level through melting. Light-absorbing aerosols are thought to be significant contributors to warming in the Arctic, because of their effect on the radiation balance through both aerosol absorption in the atmosphere as well as absorption in surface snow after particulate deposition. At this time it is not possible to estimate the impact of aerosol absorption on the radiation balance over Greenland due to the lack of in-situ measurements. Here, we present time series and estimates of key aerosol optical properties in order to better understand the seasonality and sources of aerosols over central Greenland, and compare their values with other Arctic sites. In-situ measurements made at Summit, Greenland from May 8, 2011 to December 31, 2014 include aerosol light absorption coefficient (σap) and light scattering coefficient (σsp); calculated parameters include absorption Ångström exponent (AAE), and single scattering albedo (ωo). The light absorption and scattering coefficients were found to be low in the winter and highest in the spring and summer. Spring-summer means of σap and σsp were 0.15 ± 0.15 Mm-1 and 2.35 ± 2.80 Mm-1, respectively. Mean AAE was 0.97 ± 0.29 in the spring and summer, indicating that black carbon (BC), and not dust and/or organic brown carbon (BrC), is the main aerosol light absorber. Mean ωo was 0.93 ± 0.03, which is similar to values measured at Barrow, Alaska, USA (0.94 ± 0.05) and Ny-Ålesund, Svalbard, Norway (0.95 ± 0.06). Summit exhibits ωo as low as Barrow and Ny-Ålesund although it is an isolated high-altitude site indicating the importance of aerosol light absorption over the most remote Arctic locations.

Land surface albedo plays a key role in the surface-atmosphere internaction, because it greatly influences the shortwave radiation absorbed by the surface. Surface albedo depends on soil characteristics and vegetation types. Error in the specification of albedos of soil and vegetation may cause biases in the computation of ground temperature and surface fluxes, therefore accurate albedo estimates are essential for an accurate simulation of the Earth's climate. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterization in weather forecast and climate models as well as the remote sensing retrieval of surface solar fluxes through a series of three papers. First, the NCAR Community Climate System Model (CCSM2) albedo is evaluated using the MODIS BRDF and albedo data. The model and MODIS albedo differences are related to the deficiences in the model simulation of snow cover and soil moisture and in the model's specification of leaf and stem area indexes. They are also partially caused by the deficiency of the two-stream method. Second, motivated by these analyses, a new formulation for surface albedo is developed. Over desert, most land models assume that the bare soil albedo is a function of soil color and soil moisture but independent of solar zenith angle (SZA). However, analysis of MODIS BRDF/albedo data and in situ data indicates that bare soil albedo does vary with SZA. Furthermore this SZA dependence is found to affect the surface energy fluxes and temperature in the offline land surface model sensitivity tests. Finally, the MODIS BRDF algorithm is reformulated to derive a new two-parameter scheme for the computation of land surface albedo and its SZA dependence for use in weather and climate models as well as the remote sensing retrieval of surface solar fluxes. In this formulation, the season- and pixel-dependent black-sky albedo at 60 deg SZA can be directly prescribed using the MODIS BRDF data while the two parameters are taken as a function of vegetation type only. Comparison of this formulation with those used in weather, climate, and data assimilation models (at NCAR, NCEP, and NASA) as well as those used in remote sensing groups (University of Maryland, ISCCP-FD, and CERES/TRMM) reveals the deficiencies in the land surface albedo treatment in these models and remote sensing retrieval algorithm along with suggestions for improvement.

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Snow and lake ice have very high albedos compared to other surfaces found in nature. Surface albedo is an important component of the surface energy budget especially when albedos are high since albedo governs how much shortwave radiation is absorbed or reflected at a surface. In particular, snow and lake ice albedos have been shown to affect the timing of lake ice break-up. Lakes are found throughout the Northern Hemisphere and lake ice has been shown to be sensitive to climatic variability. Therefore, the modelling of lake ice phenology, using lake ice models such as the Canadian Lake Ice Model (CLIMo), is important to the study of climatic variability in the Arctic and sub-Arctic regions and accurate snow and lake ice albedo measurements are required to ensure the accuracy of the simulations. However, snow and lake ice albedo can vary from day-to-day depending on factors such as air temperature, presence of impurities, age, and composition. Some factors are more difficult than others to model (e.g. presence of impurities). It would be more straight forward to just gather field measurements, but such measurements would be costly and lakes can be in remote locations and difficult to access. Instead, CLIMo contains an albedo parameterization scheme that models the evolution of snow and lake ice albedo in its simulations. However, parts of the albedo parameterization are based on sea-ice observations (which inherently have higher albedos due to brine inclusions) and the albedo parameterization does not take ice type (e.g. clear ice or snow ice) into account. Satellite remote sensing via the Moderate Resolution Imaging Spectroradiometer (MODIS) provides methods for retrieving albedo that may help enhance CLIMo’s albedo parameterization. CLIMo’s albedo parameterization as well the MODIS daily albedo products (MOD10A1 and MYD10A1) and 16-day product (MCD43A3) were evaluated against in situ albedo observations made over Malcolm Ramsay Lake near Churchill, Manitoba, during the winter of 2012. It was found that the snow albedo parameterization of CLIMo performs well when compared to average in situ observations, but the bare ice parameterization overestimated bare ice albedo observations. The MODIS albedo products compared well when evaluated against the in situ albedo observations and were able to capture changes in albedo throughout the study period. The MODIS albedo products were also compared against CLIMo’s melting ice parameterization, because the equipment had to be removed from the lake to prevent it from falling into the water during the melt season. Cloud cover interfered with the MODIS observations, but the comparison suggests that MODIS albedo products retrieved higher albedo values than the melting ice parameterization of CLIMo. The MODIS albedo products were then integrated directly into CLIMo in substitution of the albedo parameterization to see if they could enhance break-up date (ice off) simulations. MODIS albedo retrievals (MOD10A1, MYD10A1, and MCD43A3) were collected over Back Bay, Great Slave Lake (GSL) near Yellowknife, Northwest Territories, from 2000-2011. CLIMo was then run with and without the MODIS albedos integrated and compared against MODIS observed break-up dates. Simulations were also run under three difference snow cover scenarios (0%, 68%, and 100% snow cover). It was found that CLIMo without MODIS albedos performed better with the 0% snow cover scenario than with the MODIS albedos integrated in. Both simulations (with and without MODIS albedos) performed well with the snow cover scenarios. The MODIS albedo products slightly improved CLIMo break-up simulations when integrated up to a month in advance of actual lake ice break-up for Back Bay. With the MODIS albedo products integrated into CLIMo, break-up dates were simulated within 3-4 days of MODIS observed break-up. CLIMo without the MODIS albedos still performed very well simulating break-up within 4-5 days of MODIS observed break-up. It is uncertain whether this was a significant improvement or not with such a small study period and with the investigation being conducted at a single site (Back Bay). However, it has been found that CLIMo performs well with the original albedo parameterization and that MODIS albedos could potentially complement lake-wide break-up simulations in future studies.