The energy consumption for heating and cooling of buildings in the cities located within the boundaries of the Hot Summer and Cold Winter (HSCW) zone in China is rapidly increasing due to the increased comfort expectations from well-resourced occupants. Guidance on how and to what extent it is possible to improve energy efficiency of buildings is thus required by policy makers as well as designers and building managers. The aim of this study is to demonstrate how the use of climate-sensitive passive design solutions can help the improvement of indoor thermal conditions while reducing the energy needs and ultimately carbon emissions. An extensive parametric analysis of several passive strategies such as building orientation, thermal insulation, glazing area, shading devices, air tightness and natural ventilation, is carried out for a typical apartment block located in the cities of Chongqing, Changsha and Shanghai, which lays respectively in the upper, middle and downstream of the Yangtze River. Detailed hourly dynamic simulations show how it is possible to extend the non-heating/cooling period and reduce the peak loads, highlighting the potentialities of each strategy according to different climate constraints. The recommended strategies provides quantitative guidance to either design of new or retrofitting of existing buildings. This research contributes to the building energy conservation knowledge for policy-makers, developers and building designers with insight on the feasibilities of the application of passive measures for the residential buildings located in the Yangtze River region with hot summer and cold winter climates.
Changes in the hydrological cycle have widespread consequences and remain uncertain under climate change. We analyse the changes in major water components of the hydrological cycle, that is, precipitation (P), runoff (Q), evapotranspiration (E), precipitation minus evapotranspiration (P − E), and terrestrial water storage (S), and quantify the uncertainties across the 21st century with Phase Six of the Coupled Model Intercomparison Project (CMIP6) simulations. The multimodel ensemble based on over 10 GCMs shows that P − E and Q share similar trends with P, with increases expected in northern high latitudes of Eurasia and North America, South Asia, and eastern Africa, and decreases expected in Central America, the Mediterranean, and the Amazon. The seasonal changes in S at mid and high latitudes are behind the large seasonal shifts in Q while changes in P − E are dominantly affected by P. The equatorial regions are expected to have the largest changes and intermodel variability. From low emission scenario SSP1‐2.6 to high emission scenario SSP5‐8.5, the spatial patterns for future changes remain consistent while more drastic and more widespread changes are expected globally over time with warming for all water components. Larger intermodel variability is also found under higher emission scenarios. The study provides a comprehensive perspective on the assessment of annual and seasonal changes in all water components within the hydrological cycle as well as the associated uncertainty with the latest CMIP6 simulations under three representative scenarios, providing the most updated climate information for formulating appropriate mitigation and adaptation.
Due to the considerable biases in general circulation models (GCMs) simulation, bias correction methods are required and widely applied to reduce the model biases for impact studies. This study evaluated the performance of two bias correction methods, quantile delta mapping (QDM) and scaled distribution mapping (SDM), for generating high-resolution daily maximum temperature (Tmax) and minimum temperature (Tmin) projections for Canada using the latest GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6). CMIP6 GCMs show overall consistency with observations before and after bias correction, with better performance on Tmax compared to Tmin. QDM shows better performance relative to observations while SDM shows superior skill in preserving the raw climate signals. QDM and SDM methods are effective in reducing the biases of Tmax and Tmin for all GCMs. Both methods show similar skills in reproducing monthly probability distribution and capturing seasonal spatial patterns over Canada. The study provides a comprehensive assessment of bias correction methods applications in individual CMIP6 GCMs for high-resolution daily temperature predictions for Canada, providing a reference significance for bias correction studies and technical support for further impact assessment and adaptation planning around the world.
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