Building heat transfer in a changing climate

The Antarctic Peninsula is warming rapidly, with more frequent extreme temperature and precipitation events, reduced sea ice, glacier retreat, ice shelf collapse, and ecological shifts. Here, we review its behaviour under present-day climate, and low (SSP 1-2.6), medium-high (SSP 3-7.0) and very high (SSP 5-8.5) future emissions scenarios, corresponding to global temperature increases of 1.8°C, 3.6°C and 4.4°C by 2100. Higher emissions will bring more days above 0°C, increased liquid precipitation, ocean warming, and more intense extreme weather events such as ocean heat waves and atmospheric rivers. Surface melt on ice shelves will increase, depleting firn air content and promoting meltwater ponding. Under the highest emission scenario, collapse of the Larsen C and Wilkins ice shelves is likely by 2100 CE, and loss of sea ice and ice shelves around the Peninsula will exacerbate the current trends of land-ice mass loss. Collapse of George VI Ice Shelf by 2300 under SSP 5-8.5 would substantially increase sea level contributions. Under this very high emissions scenario, sea level contributions from the Peninsula could reach 7.5 ± 14.1 mm by 2100 CE and 116.3 ± 66.9 mm by 2300 CE. Conversely, under the lower emissions scenarios, the Antarctic Peninsula’s sea ice remains similar to present, and land ice is predicted to undergo only minor grounding line recession and thinning. Changes in sea surface temperatures and the change from snow to rain will impact marine and terrestrial biota, altering species richness and enhancing colonisation by non-native species. Ranges of key species such as krill and salps are likely to contract to the south, impacting their marine vertebrate predators. These changing conditions will also influence Antarctic Peninsula research, fisheries, tourism, infrastructure and logistics. The future of the Peninsula depends on the choices made today. Limiting temperatures to below 2°C, and as close as possible to 1.5°C (by following the SSP 1-1.9 or 1-2.6 scenarios), combined with effective governance, will result in increased resilience and relatively modest changes. Any higher emissions scenarios will damage pristine systems, cause sustained, irreversible ice loss on human timescales, and spread to Antarctic regions beyond the Peninsula.

Climate change is expected to have a profound impact on species distributions, contracting suitable climate space. Biodiversity areas are important to mitigate these negative effects but are static by design and thus do not account for future projections of species distributions. The Harpy Eagle Harpia harpyja has a broad range across lowland Neotropical forests and thus its distribution could be negatively affected by climate change when combined with current rates of habitat loss. To test this hypothesis, we use spatial point process models fitted with climatic, topographical and landcover covariates to identify the current distribution. We then project to 24 future climate scenarios, using three General Circulation Models (GCMs) and two emission scenarios between the years 2021 and 2100 averaged over four, 20‐year periods. Our current model identified a core range across Amazonia and the Guiana Shield, with evergreen forest (71%), mean diurnal temperature range (13%) and elevation (6%) the most important predictors. Reclassifying the current model to a binary prediction estimated a range size of ~ 7.6 million km2, with the Important Bird and Biodiversity Area (IBA) network covering 18% of habitat (~ 1.4 million km2) within this range. By 2090, range size was predicted to decrease on average by 14.4% under a higher emissions scenario, and 7.3% under a lower emissions scenario. The IBA network would cover 14% less area under a higher emissions scenario, and 3.3% less distribution area under a lower emissions scenario by 2090. Southern Amazonia is predicted to have the greatest reduction in range size and subsequently highest loss of Harpy Eagle habitat within the IBA network. Our work demonstrates that the combination of climate change and subsequent habitat loss may result in substantial losses in distribution for this raptor across the southern edge of its range.

Alpinia officinarum, commonly known as Galangal, is not only widely used as a medicinal plant but also holds significant ornamental value in horticulture and landscape design due to its unique plant structure and floral aesthetics in China. This study evaluates the impact of current and future climate change scenarios (ssp126, ssp245, ssp370, and ssp585) on the suitable habitats for A. officinarum in China. A total of 73 reliable distribution points for A. officinarum were collected, and 11 key environmental variables were selected. The ENMeval package was used to optimize the Maxent model, and the potential suitable areas for A. officinarum were predicted in combination with Biomod2. The results show that the optimized Maxent model accurately predicted the potential distribution of A. officinarum in China. Under low emission scenarios (ssp126 and ssp245), the suitable habitat area increased and expanded towards higher latitudes. However, under high emission scenarios (ssp370 and ssp585), the suitable habitat area significantly decreased, with the species distribution range shrinking by approximately 3.7% and 19.8%, respectively. Through Multivariate environmental similarity surface (MESS) and most dissimilar variable (MoD) analyses revealed that increased climate variability under high emission scenarios, especially in ssp585, led to large-scale habitat contraction due to rising temperatures and unstable precipitation patterns. Changes in the center of suitability location showed that the current center of A. officinarum's suitable habitat is located in Guangxi, China. Under low emission scenarios, the center of suitability gradually shifts northwest, while under high emission scenarios, this shift becomes more pronounced. These findings provide a scientific basis for the conservation of A. officinarum germplasm resources and the management strategies in response to climate change.

Modelling of heat and moisture transfer in buildings: I. Model program

Anthropogenic emissions contribute a significant percentage to surface ozone (O3) and aerosols globally. The rate of plant photosynthesis, which represents the capability of terrestrial ecosystems to sequester carbon dioxide (CO2), is significantly affected by anthropogenic air pollutants. While the changes in anthropogenic emissions perturb atmospheric components, their consequent impacts on ecosystem productivity in the future climate remain unclear. Here, we apply a fully coupled climate-vegetation-chemistry model, ModelE2-YIBs, to explore the effects of O3 and aerosols from anthropogenic emissions on global gross primary productivity (GPP) under both present-day and different future emission scenarios at 2060. At the present day, anthropogenic air pollutants induce a GPP loss of -1.67 Pg[C] (-4%) in boreal summer with the contributions of -2.18 Pg[C] by O3 and +0.52 Pg[C] by aerosols. At 2060, the detrimental effect of air pollutants on GPP is exacerbated to -1.85 Pg[C] under a high emissions scenario but alleviated to -0.59 Pg[C] under a low emission scenario. The mitigated GPP loss in the latter scenario is owing to the effective control of anthropogenic emissions that on average reduces surface O3 concentrations by 8.14 ppbv globally relative to 2010. Although the CO2 fertilization effect is weaker in the low emission scenario, the strong decline in air pollutants brings additional GPP gains compared to the high scenario. Regionally, such GPP amelioration is close to or even outweighs the CO2 fertilization effect in eastern China and U.S., suggesting that the deep cut of anthropogenic emissions can effectively promote future ecosystem productivity through the reduction of O3 and aerosols over the nowadays polluted regions.

Buildings are responsible for approximately 30% of CO2 emissions and about 40% of the world’s energy usage. This energy is mainly used in buildings for thermal comfort. Built structures are one of the major energy consumers that are eventually to blame for environmental degradation in these times of escalating environmental concerns. Dealing with the building’s energy demand will help us mitigate this issue. Heat transfer from the outside to the inside occurs within the building envelope, and the quantities are determined using some fundamental ideas. To better understand the building’s energy requirements, the thermal performance of the structure can be evaluated a number of methods. The goal of this research is to investigate the suitability of various methodologies for evaluating the thermal performance of buildings. To comprehend their applications and adaptability, the three methods of evaluating thermal performance—numerical, simulation, and physical data—have been investigated.

Integrated analysis of whole building heat, air and moisture transfer

<p>    Global warming-related SLR (sea level rise) constitutes a substantial threat to Macau, due to its low elevation, small size and ongoing land reclamation. This study was devised to determine the long-term variation of sea level change in Macau, as well as to develop future projections based on tide gauge and satellite data and GCM simulations, aiming to provide knowledge for SLR mitigation and adaptation.</p><p>    Based on local tide gauge records, sea level in Macau is now rising at an accelerated rate: 1.35 mm yr<sup>−1</sup> over 1925–2010 and jumping to 4.2 mm yr<sup>−1</sup> over 1970–2010, reflecting an apparent acceleration of SLR. Furthermore, the sea level near Macau rose 10% faster than the global mean during the period from 1993 to 2012. In addition, the rate of VLM (vertical land movement) at Macau is estimated at -0.153mm yr<sup>-1</sup>, contributing little to local sea level change.</p><p>    In the future, as projected by a suite of climate models, the rate of SLR in Macau will be about 20% higher than the global average. This is induced primarily by a greater-than-average rate of oceanic thermal expansion in Macau, together with enhanced southerly anomalies that lead to a piling up of sea water. Specifically, the sea level is projected to rise 8–12, 22–51 and 35–118 cm by 2020, 2060 and 2100 with respect to the 1986–2005 baseline climatology, respectively, depending on the emissions scenario and climate sensitivity. If we consider the medium emissions scenario RCP4.5 along with medium climate sensitivity, Macau can expect to experience an SLR of 10, 34 and 65 cm by 2020, 2060 and 2100. If the worst case happens (RCP8.5 plus high climate sensitivity), the SLR will be far higher than that in the medium case; namely, 12, 51 and 118 cm by 2020, 2060, and 2100, respectively. The SLR under the lower emissions scenario is expected to be less severe than that under the higher emissions scenarios: by 2100, an SLR of 65–118 cm in Macau under RCP8.5, almost twice as fast as that under RCP2.6. The key source of uncertainty stems from the emissions scenario and poor knowledge of climate sensitivity. By 2020, the uncertainty range is only 4 cm, yet by 2100 the range will be increased to 83 cm.</p>

Heat and moisture transfer in wall-to-floor thermal bridges and its influence on thermal performance

For future climate projections to be useful they must be actionable at the local level. In this study, we develop daily temperature and precipitation climate scenarios suitable for use in projections of drought, energy use, water use, and crop production. We investigate the magnitude of future changes to air temperature and precipitation in the Midwest United States in response to three future climate change scenarios. Results are used to assess changes to incidence of precipitation extremes and human comfort (using heat index) associated with the anticipated climate changes in the region. We use self‐organizing maps and random forest based techniques to generate daily realizations of temperature and precipitation for 279 weather stations in a region centred on Illinois. We determine that the random forest model performs best for maximum and minimum temperatures, while the self‐organizing map performs best for precipitation. Using nine models from the Coupled Model Inter‐Comparison Project Phase 5, downscaled daily temperature and precipitation values are generated for low, moderate, and high greenhouse gas emissions scenarios for historical and future periods. Based on recent trends, we focus our results on the high emissions scenario, and show an average increase of 4.3°C in maximum daily air temperature across the region for the 2071–2100 period. Precipitation decreases by up to 15% in the southern half of the study region, with a similar percentage increase in the northern half of the region. The regional environmental changes result in an increase of 5.8° in average summer heat index, and increase of 48% in the number of days likely to produce extreme heat, and a decrease in the average value of the standardized precipitation and evapotranspiration index of 1.9 (indicating increased drought) across the region by 2100.

We assessed the vulnerability of blanket peat to climate change in Great Britain using an ensemble of 8 bioclimatic envelope models. We used 4 published models that ranged from simple threshold models, based on total annual precipitation, to Generalised Linear Models (GLMs, based on mean annual temperature). In addition, 4 new models were developed which included measures of water deficit as threshold, classification tree, GLM and generalised additive models (GAM). Models that included measures of both hydrological conditions and maximum temperature provided a better fit to the mapped peat area than models based on hydrological variables alone. Under UKCIP02 projections for high (A1F1) and low (B1) greenhouse gas emission scenarios, 7 out of the 8 models showed a decline in the bioclimatic space associated with blanket peat. Eastern regions (Northumbria, North York Moors, Orkney) were shown to be more vulnerable than higher-altitude, western areas (Highlands, Western Isles and Argyle, Bute and The Trossachs). These results suggest a long-term decline in the distribution of actively growing blanket peat, especially under the high emissions scenario, although it is emphasised that existing peatlands may well persist for decades under a changing climate. Observational data from long-term monitoring and manipulation experiments in combination with process-based models are required to explore the nature and magnitude of climate change impacts on these vulnerable areas more fully.

The large-scale shifts in weather patterns and an unprecedented change in climate have given rise to the interest in how climate change will affect the carbon emissions of supermarkets. This study investigates the implications of future climatic conditions on the operation of supermarkets in the UK. The investigation was conducted by performing a series of energy modelling simulations on a LIDL supermarket model in London, based on the UK Climate Projections (UKCP09) future weather years provided by the Chartered Institution of Building Services Engineers (CIBSE). Computational fluid dynamic (CFD) simulations were used to perform the experiment, and the baseline model was validated against the actual data. This investigation ascertains and quantifies the annual energy consumption, carbon emissions, and cooling and heating demand of the supermarket under different climatic projections, which further validate the scientific theory of annual temperature rise as a result of long-term climatic variation. The maximum percentage increase for the annual energy consumption for current and future weather data sets observed was 7.01 and 6.45 for the 2050s medium emissions scenario, (90th) percentile and high emissions scenario, (90th) percentile, respectively, and 11.05, 14.07, and 17.68 for the 2080s low emissions scenario, (90th) percentile, medium (90th) percentile and high emissions scenario (90th) percentile, respectively. A similar inclining trend in the case of annual CO2 emissions was observed where the peak increase percentage was 6.80 and 6.24 for the 2050s medium emissions scenario, (90th) percentile and high (90th) percentile, respectively and 10.84, 13.84, and 17.45 for the 2080s low emissions scenario, (90th) percentile, medium emissions scenario (90th) percentile and high emissions scenario (90th) percentile, respectively. The study also analyses the future heating and cooling demands of the three warmest months and three coldest months of the year, respectively, to determine future variance in their relative values.

Under the background of global climate change, the variation in the spatial distribution and ice volume of mountain glaciers have a profound influence on regional economic development and ecological security. The development of glaciers is like biological succession; when climate change approaches or exceeds the threshold of suitable conditions for glacier development, it will lead to changes in potential distribution pattern. Therefore, from the perspective of the "biological" characteristics of glaciers, it is a beneficial exploration and attempt in the field of glaciology to explore its potential distribution law with the help of the niche model. The maximum entropy model (MaxEnt) can explain the environmental conditions suitable for the survival of things by analyzing the mathematical characteristics and distribution laws of samples in space. According to glacier samples and the geographical environment data screened by correlation analysis and iterative calculation, the potential distribution pattern of Tianshan glaciers in China in reference years (1970–2000) was simulated by MaxEnt. This paper describes the contribution of geographical environmental factors to distribution of glaciers in Tianshan Mountains, quantifies the threshold range of factors affecting the suitable habitat of glaciers, and predicts the area variation and distribution pattern of glaciers under different climate scenarios (SSP1-2.6, SSP5-8.5) in the future (2040–2060, 2080–2100). The results show that the MaxEnt model has good adaptability to simulate the distribution of glaciers. The spatial heterogeneity of potential distribution of glaciers is caused by the spatio-temporal differences of hydrothermal combination and topographic conditions. Among the environmental variables, precipitation during the wettest month, altitude, annual mean temperature, and temperature seasonality have more significant effects on the potential distribution of glaciers. There is significant spatial heterogeneity in the potential distribution of glaciers in different watersheds, altitudes, and aspects. From the forecast results of glacier in various climatic scenarios in the future, about 18.16–27.62% of the total reference year glacier area are in an alternating change of melting and accumulation, among which few glaciers are increasing, but this has not changed the overall retreat trend of glaciers in the study area. Under the low emission scenario, the glacier area of the Tianshan Mountains in China decreased by 18.18% and 23.73% respectively in the middle and end of the 21st century compared with the reference years and decreased by 20.04% and 27.63%, respectively, under the high emission scenario, which showed that the extent of glacier retreat is more intense under the high emission scenario. Our study offers momentous theoretical value and practical significance for enriching and expanding the theories and analytical methods of the glacier change.

Thermal performance of the building envelope integrated with phase change material for thermal energy storage: an updated review.

The Projection of Temperature and Precipitation over China under RCP Scenarios using a CMIP5 Multi-Model Ensemble