Near- and medium-term hourly morphed mean and extreme future temperature datasets for Jyväskylä, Finland, for building thermal energy demand simulations

Abstract

Near- and medium-term hourly morphed outdoor temperature files were created for Jyväskylä, Finland, to be used in building energy simulation. These future outdoor temperature files were created according to a statistical down-scaling method, morphing, which utilizes both hourly baseline data, and monthly and daily future climate projections. The used baseline data included hourly test reference year and typical meteorological year data to represent a “typical” climate year, and were appended with weather files created based on the coldest and warmest near Januaries to represent extreme weather files. Climate change data included climate change projection data from 2 different data repositories for either results from Global Climate Model or Regional Climate Model simulations for RCP2.6, RCP4.5 and RCP8.5 climate change scenarios. Morphing all 5 different baseline scenarios with each of the available climate scenarios creates 25 future outdoor temperature files for 2030 (near-term) and 25 files for 2050 (medium-term). The created files were used in [1] to simulate future thermal energy demand in buildings.

Present day and future urban cooling enabled by integrated water management

“…The air temperature climate change signal obtained from CMIP6 data is superimposed onto the historic ERA5 time series via a "morphing" process. This technique refers to the application of a shift (in mean) and a stretch (in minimum and maximum) of the observed time series, to change both the mean and the variance (Belcher et al, 2005;Pulkkinen and Louis, 2021). For each month, a minimum, median, and maximum air temperature change is computed as the ensemble median over all CMIP6 models, per city, SSP scenario, and future period (see Figure 2 for Melbourne example).…”

Optimal Air Conditioner Placement Using a Simple Thermal Environment Analysis Method for Continuous Large Spaces with Predominant Advection

“…Both ES and CFD are currently used for thermal environment analysis in the design field, with CFD being easier to explain to clients. However, ES has the advantage of being able to analyze the thermal environment in a nonstationary manner, and thermal environment analysis methods, such as the heat distribution method [15] and the contribution ratio of indoor climate [16], have been developed. However, these analysis methods are not suitable for practical use because of concerns regarding high analysis loads.…”