Future climate of Brussels and Paris for the 2050s under the A1B scenario

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Multi-temporal evaluation of soil moisture and land surface temperature dynamics using in situ and satellite observations

Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ˜ -0.6 to -0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ˜ - 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ˜ -0.5 to -0.7; satellite R ˜ -0.4 to -0.7) indicating SM–LST coupling, than in winter (in situ R ˜ +0.3; satellite R ˜ -0.3) indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ~0.12 m3/m3 , stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM–LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.Peer ReviewedPostprint (published version

Ventilated passive cooling : climatic cooling potential and cooling demand savings analysis at a large spatiotemporal scale

Tese de doutoramento, Sistemas Sustentáveis de Energia, Universidade de Lisboa, Faculdade de Ciências, 2017Buildings are the largest energy consuming sector, above industry and transportation, which places them as one of the major greenhouse gases emitters. Building thermal conditioning represents nearly 50% of their total energy use, mostly heating; however, trends show an increase in air-conditioning adoption for cooling, and, moreover, global warming will probably boost this tendency. The present thesis main goal is to contribute to the characterization of passive cooling systems, and their operating strategies, to reduce buildings energy demand for cooling. For that, first, one has developed a model able to estimate the cooling demand savings based on the climatic cooling potential (CCP) for different ventilated passive cooling systems for buildings. The CCP expresses the cooling load that a passive system can bring to a building as compared with standard ventilation from outdoor. The model was validated with extensive numerical simulations using the thermal simulation tool TRNSYS. For the 7,776 cases simulated, the model was able to reproduce the cooling demand savings with an error below 1% when compared to TRNSYS. In a second phase, using the same model and results from the Weather Research and Forecasting Model (WRF) forced by the global atmospheric reanalysis ERA-Interim, the CCP for direct ventilation and evaporative cooling was computed for the Iberian Peninsula. In a third phase, the WRF forced by the earth system model EC-EARTH for present and future climates allowed to assess the climate change impact on the CCP over Iberia, both for direct ventilation and evaporative cooling. The future simulations follow the Representative Concentration Pathway scenario RCP8.5 from IPCC. The results show that the CCP for both direct ventilation and evaporative cooling is expected to decrease by the end of the century due to increased air temperatures; nevertheless, the future CCP is at most 40% lower than the present CCP. Finally, a comparative analysis between present and future climates cooling degree hours for Iberia was carried out. The results show that, generally, future cooling degree hours increase 2.5 times comparing to the present, which is a higher increase than the corresponding decrease in the CCP. The model developed, as well as the spatiotemporal datasets derived from it, can be used to estimate the cooling demand savings associated to the use of direct ventilation and evaporative cooling in buildings for Iberia. The proposed methodology can be easily extended for other regions or climates.O sector dos edifícios é o maior consumidor energético, acima da indústria e do transporte. Este facto coloca o sector dos edifícios como um dos maiores responsáveis pela emissão de gases com efeito de estufa. O aquecimento e arrefecimento representa cerca de 50% da energia utilizada dentro dos edifícios, e, embora a maior fração desta energia se deva ao aquecimento do espaço, a tendência tem vindo a mostrar um aumento do número de aparelhos de ar-condicionado. Além disso, a problemática do aquecimento global contribuirá ainda mais para este aumento bem como para o aumento da quantidade de energia despendida no arrefecimento dos espaços dentro dos edifícios. Esta tese tem como principal objetivo contribuir para a caracterização dos sistemas de arrefecimento passivos com recurso a ventilação e das suas estratégias operacionais, possibilitando assim uma redução do consumo energético associado ao arrefecimento dos espaços nos edifícios. Para isso, numa primeira fase deste estudo, desenvolveu-se um modelo baseado no potencial climático de arrefecimento. Este modelo integra temporalmente o produto entre o caudal de ventilação de um dado sistema de arrefecimento passivo e as diferenças entre a temperatura de conforto do edifício durante a época de arrefecimento (aqui definida igual a 26ºC) e a temperatura do ar fornecido pelo sistema de arrefecimento passivo. Do modelo, designado de Potencial Climático de Arrefecimento, ou Climatic Cooling Potential (CCP), resulta o potencial de arrefecimento, ou, a quantidade máxima de energia térmica que um dado sistema de arrefecimento passivo sujeito a um determinado caudal de ventilação é capaz de extrair de um edifício num dado intervalo de tempo, em kWh. Os resultados provenientes do modelo CCP são comparados com as necessidades de arrefecimento do edifício num determinado intervalo de tempo, sendo que do valor mínimo entre as necessidades de arrefecimento e o CCP, nesse mesmo intervalo de tempo, resulta o potencial útil de arrefecimento ou Useful Cooling Potential (UCP). O UCP representa a quantidade de energia que se poderá poupar no arrefecimento de um edifício fazendo uso de um determinado sistema de arrefecimento passivo. Nesta tese aplicaram-se os modelos suprarreferidos, a diferentes sistemas de arrefecimento passivos com recurso a ventilação, nomeadamente, ventilação direta, tubos enterrados, arrefecimento evaporativo, sistema de desfasamento térmico (phase shifter), e combinações entre estes. O modelo do CCP foi aplicado a cada um dos sistemas de arrefecimento passivo para uma base de dados climática horária da região de Genebra, Suíça, para os anos de 2003 e 2004. Além disso, paralelamente, conduziram-se uma série de simulações através do software de simulação térmica TRNSYS. As simulações via TRNSYS foram conduzidas para a mesma base de dados climática, utilizada no modelo do CCP. Estas simulações tiveram em conta a simulação integrada de cada um dos sistemas de arrefecimento passivo em foco, bem como as suas combinações para um edifício típico, onde se fez variar o tipo de isolamento, inércia térmica, ganhos internos, área de envidraçado e proteção solar exterior, resultando num total de 7,776 casos diferentes. Posteriormente, dos dados das necessidades de arrefecimento provenientes das simulações TRNSYS, para os casos em que não foram utilizados sistemas de arrefecimento passivos (casos de referência), e dos dados do modelo CCP, calculou-se o UCP. O UCP foi calculado tendo em conta dados horários, diários, semanais e mensais das necessidades de arrefecimento do edifício, e foi comparado com os resultados provenientes da simulação TRNSYS para cada um destes intervalos de tempo, para cada sistema de arrefecimento passivo e solução construtiva. Desta análise, verificou-se que que os dados das necessidades de arrefecimento para os quais o modelo do UCP produz os resultados da simulação via TRNSYS com menor erro são os dados diários. Nestes casos, o erro médio entre a poupança energética calculada através do TRNSYS e do modelo proposto é inferior a 1% (considerando a totalidade dos 7,776 casos). Nos casos em que se procedeu ao cálculo do UCP baseando-se em dados horários das necessidades de arrefecimento, o valor da poupança energética associada ao uso dos sistemas passivos é subestimado face aos resultados das simulações via TRNSYS. Para os casos em que se procedeu ao cálculo do UCP com base nos dados semanais e mensais das necessidades de arrefecimento, as poupanças via modelo UCP são sobrestimadas face aos resultados das simulações TRNSYS. No entanto, mesmo para o pior dos casos (dados horários das necessidades de arrefecimento), o coeficiente de correlação estatístico (R2) entre o modelo UCP e as simulações TRNSYS é superior a 0,95. Para o casos em que se utilizaram dados mensais das necessidades de arrefecimento, o valor de R2 é superior a 0,96. Assim, na primeira fase deste estudo, desenvolveu e validou-se um modelo capaz de estimar as poupanças energéticas associadas ao uso de diferentes sistemas de arrefecimento passivo por ventilação para uso em edifícios. O modelo não recorre a simulação térmica e é “independente das características do edifício”, sendo que para uso do mesmo, são apenas necessários os dados das necessidades de arrefecimento do edifício em causa, sejam estes dados horários, diários, semanais e/ou mensais. Numa segunda fase, face à boa correlação entre o modelo UCP e as simulações TRNSYS, o modelo foi adaptado por forma a que pudesse ser expresso em unidades de energia por volume (kWh/m3), e dessa forma possibilitar o mapeamento do CCP a uma larga escala espaciotemporal. Nesta fase do estudo, através da adaptação do modelo do CCP e fazendo uso de uma base de dados climática de alta resolução espácio-temporal, foi possível realizar um mapeamento ao nível da Península Ibérica do potencial de arrefecimento passivo possibilitado pelo uso dos sistemas de ventilação direta e arrefecimento evaporativo em edifícios. A base de dados climática utilizada foi criada através do modelo numérico de mesoescala atmosférico para a predição climática Weather Research and Forecasting (WRF), forçado pela reanálise ERA-Interim e comtempla um total de 21870 pontos inclusos na Península Ibérica, para os quais existem registos horários das variáveis meteorológicas. Numa terceira e última fase deste estudo, visando o impacto das alterações climáticas no comportamento dos sistemas de ventilação direta e arrefecimento evaporativo, o modelo WRF, forçado pelo modelo do sistema terra EC EARTH foi comparado face às simulações forçadas pela ERA-Interim, concluindo-se que as simulações forçadas pelo modelo EC-EARTH podem ser utilizadas para simulação do clima futuro. Assim, o modelo EC EARTH foi utilizado para os climas presente e futuro, permitindo-se avaliar o efeito da mudança climática no potencial de arrefecimento passivo na Península Ibérica para estes sistemas. As simulações de clima futuro do modelo EC-EARTH integram os dados das concentrações de CO2 do Representative Concentration Pathway scenario 8.5 (RCP8.5) do IPCC. Os resultados mostram que embora exista um decréscimo do potencial de arrefecimento passivo, devido ao aumento das temperaturas no final do século, este decréscimo é no máximo 40%. Por último, e de forma complementar, procedeu-se a uma análise comparativa entre os graus hora de arrefecimento para os climas presente e futuro, mostrando-se que os graus hora de arrefecimento aumentam em cerca de 2,5 vezes face ao presente, o que representa um aumento maior do que o decréscimo que se verifica no potencial de arrefecimento passivo para o clima futuro. O modelo aqui desenvolvido, bem como as bases de dados que dele derivam, podem ser facilmente utilizados para estimar as poupanças energéticas associadas ao uso dos sistemas de ventilação direta e arrefecimento evaporativo em edifícios na Península Ibérica. A metodologia aqui proposta pode ser utilizada noutras regiões e climas

Vegetation water use based on a thermal and optical remote sensing model in the mediterranean region of Doñana

Terrestrial evapotranspiration (ET) is a central process in the climate system, is a major component in the terrestrial water budget, and is responsible for the distribution of water and energy on land surfaces especially in arid and semiarid areas. In order to inform water management decisions especially in scarce water environments, it is important to assess ET vegetation use by differentiating irrigated socio-economic areas and natural ecosystems. The global remote sensing ET product MOD16 has proven to underestimate ET in semiarid regions where ET is very sensitive to soil moisture. The objective of this research was to test whether a modified version of the remote sensing ET model PT-JPL, proven to perform well in drylands at Eddy Covariance flux sites using the land surface temperature as a proxy to the surface moisture status (PT-JPL-thermal), could be up-scaled at regional levels introducing also a new formulation for net radiation from various MODIS products. We applied three methods to track the spatial and temporal characteristics of ET in the World Heritage UNESCO Doñana region: (i) a locally calibrated hydrological model (WATEN), (ii) the PT-JPL-thermal, and (iii) the global remote sensing ET product MOD16. The PT-JPL-thermal showed strong agreement with the WATEN ET in-situ calibrated estimates (ρ = 0.78, ρ1month-lag = 0.94) even though the MOD16 product did not (ρ = 0.48). The PT-JPL-thermal approach has proven to be a robust remote sensing model for detecting ET at a regional level in Mediterranean environments and it requires only air temperature and incoming solar radiation from climatic databases apart from freely available satellite products

Drought index downscaling using AI-based ensemble technique and satellite data

This study introduces and validates an artificial intelligence (AI)–based downscaling method for Standardized Precipitation Indices (SPI) in the northwest of Iran, utilizing PERSSIAN-CDR data and MODIS-derived drought-dependent variables. The correlation between SPI and two drought-dependent variables at a spatial resolution of 0.25° from 2000 to 2015 served as the basis for predicting SPI values at a finer spatial resolution of 0.05° for the period spanning 2016 to 2021. Shallow AI models (Support Vector Regression, Adaptive Neural Fuzzy Inference System, Feedforward Neural Network) and the Long Short-Term Memory (LSTM) deep learning method are employed for downscaling, followed by an ensemble post-processing technique for shallow AI models. Validation against rain gauge data indicates that all methods improve SPI simulation compared to PERSIANN-CDR products. The ensemble technique excels by 20% and 25% in the training and test phases, respectively, achieving the mean Determination Coefficient (DC) score of 0.67 in the validation phase. Results suggest that the deep learning LSTM method is less suitable for limited observed data compared to ensemble techniques. Additionally, the proposed methodology successfully detects approximately 80% of drought conditions. Notably, SPI-6 outperforms other temporal scales. This study advances the understanding of AI-driven downscaling for SPI, emphasizing the efficacy of ensemble approaches and providing valuable insights for regions with limited observational data.</p

Global and local contributors to the historical and projected regional climate change on the North Slope of Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2018This thesis includes four studies that explore and compare the impacts of four contributing factors resulting in regional climate change on the North Slope of Alaska based on a numerical simulation approach. These four contributing factors include global warming due to changes in radiative forcing, sea ice decline, earlier Arctic lake ice-off, and atmospheric circulation change over the Arctic. A set of dynamically downscaled regional climate products has been developed for the North Slope of Alaska over the period from 1950 up to 2100. A fine grid spacing (10 km) is employed to develop products that resolve detailed mesoscale features in the temperature and precipitation fields on the North Slope of Alaska. Processes resolved include the effects of topography on regional climate and extreme precipitation events. The Representative Concentration Pathway (RCP) 4.5 scenario projects lower rates of precipitation and temperature increase than RCP8.5 compared to the historical product. The increases of precipitation and temperature trends in the RCP8.5 projection are higher in fall and winter compared to the historical product and the RCP4.5 projection. The impacts of sea ice decline are addressed by conducting sensitivity experiments employing both an atmospheric model and a permafrost model. The sea ice decline impacts are most pronounced in late fall and early winter. The near surface atmospheric warming in late spring and early summer due to sea ice decline are projected to be stronger in the 21st century. Such a warming effect also reduces the total cloud cover on the North Slope of Alaska in summer by destabilizing the atmospheric boundary layer. The sea ice decline warms the atmosphere and the permafrost on the North Slope of Alaska less strongly than the global warming does, while it primarily results in higher seasonal variability of the positive temperature trend that is bigger in late fall and early winter than in other seasons. The ongoing and projected earlier melt of the Arctic lake ice also contributes to regional climate change on the Northern coast of Alaska, though only on a local and seasonal scale. Heat and moisture released from the opened lake surface primarily propagate downwind of the lakes. The impacts of the earlier lake ice-off on both the atmosphere and the permafrost underneath are comparable to those of the sea ice decline in late spring and early summer, while they are roughly six times weaker than those of sea ice decline in late fall and early winter. The permafrost warming resulted from the earlier lake ice-off is speculated to be stronger with more snowfall expected in the 21st century, while the overall atmospheric warming of global origin is speculated to continue growing. Two major Arctic summer-time climatic variability patterns, the Arctic Oscillation (AO) and the Arctic Dipole (AD), are evaluated in 12 global climate models in Coupled Model Intercomparison Program Phase 5 (CMIP5). A combined metric ranking approach ranks the models by the Pattern Correlation Coefficients (PCCs) and explained variances calculated from the model-produced summer AO and AD over the historical period. Higher-ranked models more consistently project a positive trend of the summer AO index and a negative trend of summer AD index in their RCP8.5 projections. Such long-term trends of large-scale climate patterns will inhibit the increase in air temperature while favoring the increase in precipitation on the North Slope of Alaska. In summary, this thesis bridges the gaps by quantifying the relative importance of multiple contributing factors to the regional climate change on the North Slope of Alaska. Global warming is the leading contributing factor, while other factors primarily contribute to the spatial and temporal asymmetries of the regional climate change. The results of this thesis lead to a better understanding of the physical mechanisms behind the climatic impacts to the hydrological and ecological changes of the North Slope of Alaska that have been become more severe and more frequent. They, together with the developed downscaling data products, serve as the climatic background information in such fields of study

A PCA-OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization