Impact of weather data and climate change projections in the refurbishment design of residential buildings in cooling dominated climate

2021 ◽  
Vol 303 ◽  
pp. 117584
Author(s):  
Rosa Francesca De Masi ◽  
Antonio Gigante ◽  
Silvia Ruggiero ◽  
Giuseppe Peter Vanoli
Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 715
Author(s):  
Cristina Andrade ◽  
Sandra Mourato ◽  
João Ramos

Climate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating (HDD) and cooling (CDD) degree-days along with HDD + CDD were computed from an ensemble of seven high-resolution bias-corrected simulations attained from EURO-CORDEX under two Representative Concentration Pathways (RCP4.5 and RCP8.5). These three indicators were analyzed for 1971–2000 (from E-OBS) and 2011–2040, and 2041–2070, under both RCPs. Results predict a decrease in HDDs most significant under RCP8.5. Conversely, it is projected an increase of CDD values for both scenarios. The decrease in HDDs is projected to be higher than the increase in CDDs hinting to an increase in the energy demand to cool internal environments in Portugal. Statistically significant linear CDD trends were only found for 2041–2070 under RCP4.5. Towards 2070, higher(lower) CDD (HDD and HDD + CDD) anomaly amplitudes are depicted, mainly under RCP8.5. Within the five NUTS II


2011 ◽  
Vol 33 (4) ◽  
pp. 387-406 ◽  
Author(s):  
H Du ◽  
CP Underwood ◽  
JS Edge

In this study, test reference year (TRY) data for three UK cities are generated from the new UKCP09 climate change projections 1 for a variety of future time horizons and carbon emission scenario assumptions. The data are applied to the energy simulation of three commercial buildings and one house for the three city locations (London, Manchester and Edinburgh), three future time horizons in this century and three carbon emission scenarios. Results are compared with those generated using alternative TRYs from two other research groups who used UKCP09 1 as well as with the existing TRY data sets which form the CIBSE Future Weather Years 2 in order to produce robust results. Results of future simulations of peak summer operative temperatures, peak cooling demand, annual cooling energy, peak heating demand and annual heating energy are presented for the four building case studies benchmarked against control weather data for the period 1960–1989. The results show increasing internal operative temperatures (non-air-conditioned) and increasing air-conditioning demands (air-conditioned) throughout this century and though peak heating demands remain similar to control data, annual heating energy consumptions can be expected to fall sharply. Practical applications: Currently, practitioners can use Test Reference Years for use in building energy simulations. In 2009, the CIBSE released Future Weather Years, which go further by allowing practitioners to explore the thermal and comfort behaviour of buildings at future time horizons thus helping to ‘future proof’ a design. In 2009, the United Kingdom Climate Impacts Programme released a new generation of climate change scenario data (the UKCP09 climate change projections) using probabilistic methods. These are the most comprehensive data yet and provides a greater degree of detail than was available to generate the CIBSE Future Weather Years. It is therefore likely that the new data will gradually become the normal basis for investigating future building thermal and comfort response. In this study, a sample of TRY is generated from the UKCP09 data and applied to the simulation of a sample of ‘real’ buildings. The results are compared with both the existing CIBSE Future Weather Years as well as with Test Reference Years generated using UKCP09 by two other research groups. The results provide a robust way forward for simulating building thermal and comfort response using future weather data.


2020 ◽  
Vol 70 (1) ◽  
pp. 120
Author(s):  
Andrew J. Dowdy

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.


Author(s):  
Cristina Andrade ◽  
Sandra Mourato ◽  
João Ramos

Climate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating degree-day (HDD), the cooling degree-day (CDD) and the HDD+CDD were computed from an ensemble of 7 high-resolution bias-corrected simulations attained from EURO-CORDEX under RCP4.5 and RCP8.5. These three indicators were analyzed for 1971-2000 (from E-OBS) and 2011-2040 and 2041-2070, under both RCPs. Results show that the overall spatial distribution of HDD trends for the 3 time-periods points out an increase of energy demand to heat internal environments in Portugal's northern-eastern regions, most significant under RCP8.5. It is projected an increase of CDD values for both scenarios; however, statistically significant linear trends were only found for 2041-2070 under RCP4.5. The need for cooling is almost negligible for the remaining periods, though linear trend values are still considerably higher for 2041-2070 under RCP8.5. By the end of 2070, higher amplitudes for all indicators are depicted for southern Algarve and Alentejo regions, mainly under RCP8.5. For 2041-2070 the Centre and Alentejo (North and Centre) regions present major positive differences for HDD(CDD) under RCP4.5(RCP8.5), within the 5 NUTS II regions predicting higher heating(cooling) requirements for some locations.


2021 ◽  
pp. 1420326X2199391
Author(s):  
Naveen Kishore

This paper aims to investigate the implication of present and future bioclimatic potential of passive heating and cooling design strategies for climate change scenarios of five locations covering all climate zones of India. Weather data for future climate change were developed for A2 (medium-high) scenario of the Intergovernmental Panel on Climate Change (IPCC) for four time slices, namely TMY (Typical Meteorological Year), 2020, 2050 and 2080. A case study residential building was used for calibration and validation of the bioclimatic potential using EnergyPlus simulation. Results show a strong correlation between the annual bioclimatic summer and winter discomfort hours and the corresponding annual cooling and heating energy load for the changing climate scenarios. Results also show an overall increase in annual cooling energy load, over and above the base case, ranging from 18% to 89% among the five cities in 2020; 32% to 132% in 2050 and 58% to 184% in 2080 if residential buildings continue to be operated in the same manner as it is done today without passive strategies. The use of passive strategies may reduce the annual cooling load by about 50%– 60% in residential buildings in future.


2010 ◽  
Vol 149 (1) ◽  
pp. 33-47 ◽  
Author(s):  
K. KRISTENSEN ◽  
K. SCHELDE ◽  
J. E. OLESEN

SUMMARYData on grain yield from field trials on winter wheat under conventional farming, harvested between 1992 and 2008, were combined with daily weather data available for 44 grids covering Denmark. Nine agroclimatic indices were calculated and used for describing the relation between weather data and grain yield. These indices were calculated as average temperature, radiation and precipitation during winter (1 October–31 March), spring (1 April–15 June) and summer (16 June–31 July), and they were included as linear and quadratic covariates in a mixed regression model. The model also included an effect of year to describe the change in yield caused by unrecorded variables such as management changes. The final model included all effects that were significant for at least one of the two soil types (sandy and loamy soils). Seven of the nine agroclimatic indices were included in the final model that was used to predict the wheat grain yield under five climate scenarios (a baseline for 1985 and two climate change projections for 2020 and 2040) for two soil types and two locations in Denmark.The agroclimatic index for summer temperature showed the strongest effect causing lower yields with increasing temperature, whereas yield increased with increasing radiation during summer and spring. Winter precipitation and spring temperature did not affect grain yield significantly. Grain yield responded non-linearly to mean winter temperature with the highest yield at 4·4°C and lower yields both below and above this inflection point.The application of the model predicted that the average yield would decrease under projected climate change. The average decrease varied between 0·1 and 0·8 t/ha (comparable to a relative reduction of 1·6–12.3%) depending on the climate projection, location and soil type. On average, the grain yield decreased by about 0·25 t/ha (c. 3.6%) from 1985 to 2020 and by about 0·55 t/ha (c. 8·0%) from 1985 to 2040. The predicted yield decrease depended on climate projection and was larger for wheat grown in West Zealand than in Central Jutland and in most cases also larger for loamy soils than for sandy soils.The inter-annual variation in grain yield varied greatly between climate projections. The coefficient of variation (CV) varied between 0·16 and 0·46 and was smallest for wheat grown on loamy soils in Central Jutland in the baseline climate and largest for winter wheat grown under one of the 2040 climate projections. The increase in CV is not so much an effect of increased climatic variability under the climate change projections, but more an effect of increased winter temperature, where more extreme winter temperatures (lower or higher than the inflection point at 4·4°C) increased the effect of winter temperatures.


2019 ◽  
Vol 282 ◽  
pp. 02066
Author(s):  
Fuad Mutasim Baba ◽  
Hua Ge

Buildings now produce more than a third of global greenhouse gases, making them more than any other sector contributing to climate change. This paper investigates the effect of climate change on the energy performance and thermal comfort of a high-rise residential building with different energy characteristic levels, i.e. bylaw to meet current National Energy Code of Canada for Buildings (NECB), and passive house (PH) under two climate zones in British Columbia, Canada. SRES A2, RCP-4.5 and RCP-8.5 emission scenarios are used to generate future horizon weather data for 2020, 2050, and 2080. The simulation results show that for both bylaw and PH cases, the heating energy consumption would be reduced while cooling energy consumption would be increased. As a result, for the bylaw case, the total energy consumption would be decreased for two climate zones, while for PH case, the total energy consumption would be increased for zone 4 and decreased for zone 7. In addition, the number of hours with overheating risks would be increased under future climates, e.g. doubled in 2080, compared to the historical weather data. Therefore, efforts should be made in building design to take into account the impact of climate change to ensure buildings built today would perform as intended under changing climate.


2011 ◽  
Vol 11 (9) ◽  
pp. 2541-2553 ◽  
Author(s):  
C. D. Børgesen ◽  
J. E. Olesen

Abstract. Climate change will impact agricultural production both directly and indirectly, but uncertainties related to likely impacts constrain current political decision making on adaptation. This analysis focuses on a methodology for applying probabilistic climate change projections to assess modelled wheat yields and nitrate leaching from arable land in Denmark. The probabilistic projections describe a range of possible changes in temperature and precipitation. Two methodologies to apply climate projections in impact models were tested. Method A was a straightforward correction of temperature and precipitation, where the same correction was applied to the baseline weather data for all days in the year, and method B used seasonal changes in precipitation and temperature to correct the baseline weather data. Based on climate change projections for the time span 2000 to 2100 and two soil types, the mean impact and the uncertainty of the climate change projections were analysed. Combining probability density functions of climate change projections with crop model simulations, the uncertainty and trends in nitrogen (N) leaching and grain yields with climate change were quantified. The uncertainty of climate change projections was the dominating source of uncertainty in the projections of yield and N leaching, whereas the methodology to seasonally apply climate change projections had a minor effect. For most conditions, the probability of large yield reductions and large N leaching losses tracked trends in mean yields and mean N leaching. The impacts of the uncertainty in climate change were higher for loamy sandy soil than for sandy soils due to generally higher yield levels for loamy sandy soils. There were large differences between soil types in response to climate change, illustrating the importance of including soil information for regional studies of climate change impacts on cropping systems.


2020 ◽  
Author(s):  
Sugata Narsey ◽  
Josephine R. Brown ◽  
Robert A. Colman ◽  
Francois Delage ◽  
Scott Brendan Power ◽  
...  

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