Extreme Warm Year Research Articles

Renovating Post-First-Energy-Regulation Housing: Achieving Nearly Zero-Energy buildings under typical and extreme warm conditions in a temperate European city

Decarbonising the built environment is essential for achieving the 2050 European objectives. Multi-family buildings comprise nearly half of Europe’s building stock, yet there is no regulation mandating all housing renovations to be Nearly Zero-Energy Buildings (NZEBs). This paper highlights the role of NZEB renovation for residential buildings constructed between the first energy regulations, after the first oil crisis, and the EPBD 2002 implementation (post-first-energy-regulation housing), as a pivotal factor towards energy transition. In Spain, this period (1980–2006) encompasses approximately 45% of existing dwellings. A case study was conducted in a city with a Cfb temperate climate, focusing on the most representative residential typology of the target period: the linear block. The study evaluates its current state and defines potential measures to meet NZEB requirements, achieving zero consumption when renovation of thermal envelope is combined with heat recovery systems, heat pumps, and PV panels.This research is based on energy simulations for two climate scenarios: the typical meteorological year (TMY, official series 1970–2000) and the most extreme warm year (EWY, 2022) in Spain. Results indicate active cooling systems are necessary for NZEB renovations in Spanish Cfb climates. Furthermore, low-temperature radiators significantly reduce heating consumption, while splits are the most suitable cooling systems. This study shows Spain is not adequately preparing its housing stock to face climate change, as most typical renovation works are based on official weather files that need updating to reflect harsher summer conditions. Finally, the study aims to encourage new policies and enhance existing regulations to address rising temperatures and heatwaves.

Quantitative attribution of the temperature associated with winter extreme cold events in China

Extreme cold events exert enormous impacts on the ecosystems and human health. A quantitative attribution analysis of the surface temperature anomalies associated with extreme cold events in China is conducted using an updated version of the coupled atmosphere-surface climate feedback response analysis method. For both extreme warm and cold years, the effects of anomalous surface albedo, water vapor, atmospheric dynamics, and aerosols are the major positive contributors, and the processes associated with surface dynamics provide a major negative effect. Specifically, the effect of surface albedo (water vapor) is directly linked to the change in local snow cover (precipitable water) over China. More local snow cover (precipitable water) provides a cooling (warming) effect and vice versa. The process associated with atmospheric dynamics exhibits an asymmetric contribution for the extreme warm and cold years. It provides a warming effect over most of China in the extreme warm years, which is possibly related to the nation-wide warm air advection. On the other hand, the effect of atmospheric dynamics displays large regional and year-to-year variations for the cold cases. In addition, a considerable impact of anthropogenic aerosols is detected over central-southern China, which is possibly related to the emission reduction policies in China since 2013. Although clouds show only a moderate impact on the national-averaged temperature anomalies, they provide a considerable effect over southern China.

Climate Data to Support the Adaptation of Buildings to Climate Change in Canada

Climate change will continue to bring about unprecedented climate extremes in the future, and buildings and infrastructure will be exposed to such conditions. To ensure that new and existing buildings deliver satisfactory performance over their design lives, their performance under current and future projected climates needs to be assessed by undertaking building simulations. This study prepares climate data needed for building simulations for 564 locations by bias-correcting the Canadian Regional Climate Model version 4 (CanRCM4) large ensemble (LE) simulations with reference to observations. Technical validation results show that bias-correction effectively reduces the bias associated with CanRCM4-LE simulations in terms of their marginal distributions and the inter-relationship between climate variables. To ensure that the range of projected climate change impacts are encompassed within these data sets, and to furthermore provide building moisture and energy reference years, the reference year files were prepared from bias-corrected CanRCM4-LE simulations and are comprised of a typical meteorological year for building energy applications, a typical and extreme moisture reference year, a typical downscaled year, an extreme warm year, and an extreme cold year.

Marginal Reefs Under Stress: Physiological Limits Render Galápagos Corals Susceptible to Ocean Acidification and Thermal Stress

AbstractOcean acidification (OA) and thermal stress may undermine corals' ability to calcify and support diverse reef communities, particularly in marginal environments. Coral calcification depends on aragonite supersaturation (Ω » 1) of the calcifying fluid (cf) from which the skeleton precipitates. Corals actively upregulate pHcfrelative to seawater to buffer against changes in temperature and dissolved inorganic carbon, which together control Ωcf. Here we assess the buffering capacity of modern and fossil corals from the Galápagos Islands that have been exposed to sub‐optimal conditions, extreme thermal stress, and OA. We demonstrate a significant decline in pHcfand Ωcfsince the pre‐industrial era, trends which are exacerbated during extreme warm years. These results suggest that there are likely physiological limits to corals' pH buffering capacity, and that these constraints render marginal reefs particularly susceptible to OA.

Empowering energy flexibility and climate resilience using collective intelligence based demand side management (CI-DSM)

This work investigates the effectiveness of Collective intelligence (CI) in demand side management (DSM) in urban areas to cope with extreme climate events. CI is a form of distributed intelligence that emerges in collaborative problem solving and decision making. It is used in a simulation platform to control the energy performance of buildings in an urban area in Stockholm, through developing CI-DSM and setting certain adaptation measures, including phase shifting in HVAC systems and building appliances. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measures. The performance of CI-DSM is simulated for three climate scenarios representing typical, extreme cold and extreme warm years in Stockholm. According to the results, CI-DSM increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI-DSM helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events.

Using collective intelligence to enhance demand flexibility and climate resilience in urban areas

Collective intelligence (CI) is a form of distributed intelligence that emerges in collaborative problem solving and decision making. This work investigates the potentials of CI in demand side management (DSM) in urban areas. CI is used to control the energy performance of representative groups of buildings in Stockholm, aiming to increase the demand flexibility and climate resilience in the urban scale. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measure, which is extending the indoor temperature range. A simple platform and algorithm are developed for modelling CI-DSM, considering two timescales of 15 min and 60 min. Three climate scenarios are used to represent typical, extreme cold and extreme warm years in Stockholm. Several indicators are used to assess the performance of CI-DSM, including Demand Flexibility Factor (DFF) and Agility Factor (AF), which are defined explicitly for this work. According to the results, CI increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events. Having a finer control timescale increases the flexibility and agility on the demand side, resulting in a faster adaptation to climate variations, shorter engagement of buildings, faster return to normal conditions and consequently a higher climate resilience.

Ground thermal and geomechanical conditions in a permafrost-affected high-latitude rock avalanche site (Polvartinden, northern Norway)

Abstract. On 26 June 2008, a rock avalanche detached in the northeast facing slope of Polvartinden, a high-alpine mountain in Signaldalen, northern Norway. Here, we report on the observed and modelled past and present near-surface temperature regime close to the failure zone, as well as on a subsequent simulation of the subsurface temperature regime, and on initial geomechanical mapping based on laser scanning. The volume of the rock avalanche was estimated to be approximately 500 000 m3. The depth to the actual failure surface was found to range from 40 m at the back of the failure zone to 0 m at its toe. Visible in situ ice was observed in the failure zone just after the rock avalanche. Between September 2009 and August 2013, ground surface temperatures were measured with miniature temperature data loggers at 14 different localities, close to the original failure zone along the northern ridge of Polvartinden and on the valley floor. The results from these measurements and from a basic three-dimensional heat conduction model suggest that the lower altitudinal limit of permafrost at present is at 600–650 m a.s.l., which corresponds to the upper limit of the failure zone. A coupling of our in situ data with regional climate data since 1958 suggests a general gradual warming and that the period with highest mean near surface temperatures on record ended four months before the Signaldalen rock avalanche detached. A comparison with a transient permafrost model run at 10 m depth, representative for areas where snow accumulates, strengthen these findings, which are also in congruence with measurements in nearby permafrost boreholes. It is likely that permafrost in and near the failure zone is presently subject to degradation. This degradation, in combination with the extreme warm year antecedent to the rock failure, is seen to have played an important role in the detaching of the Signaldalen rock avalanche.

Which Temperature and Precipitation Extremes Best Explain the Variation of Warm versus Cold Years and Wet versus Dry Years?

Abstract Intensifying climate extremes are one of the major concerns with climate change. Using 100-yr (1911–2010) daily temperature and precipitation records worldwide, 28 indices of extreme temperature and precipitation are calculated. A similarity percentage analysis is used to identify the key indices for distinguishing how extreme warm and cold years (annual temperature above the 90th and below the 10th percentile of the 100-yr distribution, respectively) differ from one another and from average years, and how extreme wet and dry years (annual precipitation above the 90th and below the 10th percentile of the 100-yr distribution, respectively) differ from each other and from average years. The analysis suggests that extreme warm years are primarily distinguished from average and extreme cold years by higher occurrence of warm nights (annual counts when night temperature >90th percentile), which occur about six more counts in extreme warm years compared with average years. Extreme wet years are mainly distinguished from average and extreme dry years by more occurrences of heavy precipitation events (events with ≥10 mm and ≥20 mm precipitation). Compared with average years, heavy events occur 60% more in extreme wet years and 50% less in extreme dry years. These indices consistently differ between extreme and average years across terrestrial ecoregions globally. These key indices need to be considered when analyzing climate model projections and designing climate change experiments that focus on ecosystem response to climate extremes.

Warmest extreme year in U.S. history alters thermal requirements for tree phenology

The frequency of extreme warm years is increasing across the majority of the planet. Shifts in plant phenology in response to extreme years can influence plant survival, productivity, and synchrony with pollinators/herbivores. Despite extensive work on plant phenological responses to climate change, little is known about responses to extreme warm years, particularly at the intraspecific level. Here we investigate 43 populations of white ash trees (Fraxinus americana) from throughout the species range that were all grown in a common garden. We compared the timing of leaf emergence during the warmest year in U.S. history (2012) with relatively non-extreme years. We show that (a) leaf emergence among white ash populations was accelerated by 21 days on average during the extreme warm year of 2012 relative to non-extreme years; (b) rank order for the timing of leaf emergence was maintained among populations across extreme and non-extreme years, with southern populations emerging earlier than northern populations; (c) greater amounts of warming units accumulated prior to leaf emergence during the extreme warm year relative to non-extreme years, and this constrained the potential for even earlier leaf emergence by an average of 9 days among populations; and (d) the extreme warm year reduced the reliability of a relevant phenological model for white ash by producing a consistent bias toward earlier predicted leaf emergence relative to observations. These results demonstrate a critical need to better understand how extreme warm years will impact tree phenology, particularly at the intraspecific level.

Projecting the impact of climatic warming on coldwater fish habitat in Lake Jingpo

Climate warming would have a great impact on the water temperature and dissolved oxygen (DO) distribution of lakes in cold regions. It can have an effect on fish populations. To evaluate the effect of climatic change on coldwater fish habitat in cold regions of China, a fish habitat simulation model was developed. The fish habitat simulation model was developed based on the output of A vertical year-round water temperature and dissolved oxygen model. Lake Jingpo in Northeast China was chosen as the representive lake. The coldwater stenothermal fish called Hucho taimen was selected as the indicator species. Constant Values Method and oxythermal habitat parameter method were applied to identify the refuge habitat. The simulation results show that under the future climate scenarios, the uninhabitable space in winter due to low dissolved oxygen in hypolimnion will decrease, the uninhabitable space in summer due to high temperature in epilimnion will expand. Total habitat depth in summer is projected to decrease up to 60%-90%, which means high probability of summer kill. It is projected that multi-year average value of ATDO3 vb (highest average daily TDO3 over variable benchmark period) will have an increase of about 2.5℃, which means Hucho taimen juvenile survival rate will decrease about 35%. In the extreme warm year, the Hucho taimen juvenile survival rate will decrease to 9.0%. The oxythermal habitat parameter method is generic and can be applied to other lakes and reservoirs in Northeast China to identify and compare the refuge habitat of coldwater fishes.

Attribution of the record high Central England temperature of 2014 to anthropogenic influences

In 2014, Central England experienced its warmest year in a record extending back to 1659. Using both state-of-the-art climate models and empirical techniques, our analysis shows a substantial and significant increase in the likelihood of record-breaking warm years, such as 2014, due to human influences on climate. With 90% confidence we find that anthropogenic forcings on the climate have increased the chances of record warm years in Central England by at least 13-fold. This study points to a large influence of human activities on extreme warm years despite the small region of study and the variable climate of Central England. Our analysis shows that climate change is clearly visible on the local-scale in this case.