historical temperature
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Author(s):  
Casey terHorst ◽  
Mary-Alice Coffroth

In many cases, understanding species level responses to climate change requires understanding variation among individuals in response to such change. For species with strong symbiotic relationships, such as many coral reef species, genetic variation in symbiont responses to temperature may affect the response to increased ocean temperatures. To assess variation among symbiont genotypes, we examined the population dynamics and physiological responses of genotypes of Breviolum antillogorgium in response to increased temperature. We found broad temperature tolerance across genotypes, with all genotypes showing positive growth at 26, 30, and 32 C. Genotypes differed in the magnitude of the response of growth rate and carrying capacity to increasing temperature, suggesting that natural selection could favor different genotypes at different temperatures. However, the historical temperature at which genotypes were reared was not a good predictor of temperature response, suggesting a lack of adaptation to temperature over hundreds of generations. We found increased photosynthetic rates and decreased respiration rates with increasing temperature, and differences in physiology among genotypes, but found no significant differences in the response of different genotypes to temperature. In species with such broad thermal tolerance, selection experiments on symbionts outside of the host may not yield results sufficient for evolutionary rescue from climate change.


Author(s):  
Yuanfang Chai ◽  
Wouter R. Berghuijs ◽  
Kim Naudts ◽  
Thomas Albert Jacobus Janssen ◽  
Yao Yue ◽  
...  

Abstract Climate change affects the water cycle. Despite the improved accuracy of simulations of historical temperature, precipitation and runoff in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), the uncertainty of the future sensitivity of global runoff to temperature remains large. Here, we identify a statistical relationship at the global scale between the sensitivity of precipitation to temperature change (1979 – 2014) and the sensitivity of runoff to temperature change (2015 – 2100). We use this relation to constrain future runoff sensitivity estimates. Our statistical relationship only slightly reduces the uncertainty range of future runoff sensitivities (order 10% reduction). However, more importantly, it raises the expected global runoff sensitivity to background global warming by 36-104% compared to estimates taken directly from the CMIP6 model ensemble. The constrained sensitivities also indicate a shift towards globally more wet conditions and less dry conditions.


Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2337
Author(s):  
Sherien Fadhel ◽  
Mustafa Al Aukidy ◽  
May Samir Saleh

Most areas around the world lack fine rainfall records which are needed to derive Intensity-Duration-Frequency (IDF) curves, and those that are available are in the form of daily data. Thus, the disaggregation of rainfall data from coarse to fine temporal resolution may offer a solution to that problem. Most of the previous studies have adopted only historical rainfall data as the predictor to disaggregate daily rainfall data to hourly resolution, while only a few studies have adopted other historical climate variables besides rainfall for such a purpose. Therefore, this study adopts and assesses the performance of two methods of rainfall disaggregation one uses for historical temperature and rainfall variables while the other uses only historical rainfall data for disaggregation. The two methods are applied to disaggregate the current observed and projected modeled daily rainfall data to an hourly scale for a small urban area in the United Kingdom. Then, the IDF curves for the current and future climates are derived for each case of disaggregation and compared. After which, the uncertainties associated with the difference between the two cases are assessed. The constructed IDF curves (for the two cases of disaggregation) agree in the sense that they both show that there is a big difference between the current and future climates for all durations and frequencies. However, the uncertainty related to the difference between the results of the constructed IDF curves (for the two cases of disaggregation) for each climate is considerable, especially for short durations and long return periods. In addition, the projected and current rainfall values based on disaggregation case which adopts historical temperature and rainfall variables were higher than the corresponding projections and current values based on only rainfall data for the disaggregation.


2021 ◽  
pp. 002029402110130
Author(s):  
Xian Wang ◽  
Qian-cheng Zhao ◽  
Xue-bing Yang ◽  
Bing Zeng

The historical temperature data logged in the supervisory control and data acquisition (SCADA) system contains a wealth of information that can assist with the performance optimization of wind turbines (WTs). However, mining and using these long-term data is difficult and time-consuming due to their complexity, volume, etc. In this study, we tracked and analyzed the 5-year trends of major SCADA temperature rise variables in relation to the active power of four WTs in a real wind farm. To uncover useful information, an extended version of the bins method, which calculates the standard deviation (SD) as well as the average, is proposed and adopted. The implications of the analysis for engineering practice are discussed from multiple perspectives. The research results demonstrate a change in the patterns of the main temperature rise variables in a real wind farm, completeness of the monitoring of the WT internal temperature state, influence of wind turbine aging on temperature signals, a correlation between different measurement points, and a correlation between signals from different years. The knowledge gained from this research provides a reference for the development of more practical and comprehensive condition monitoring systems and methods, as well as better operation maintenance strategies.


Coral Reefs ◽  
2021 ◽  
Author(s):  
Jessie Pelosi ◽  
Katherine M. Eaton ◽  
Samantha Mychajliw ◽  
Casey P. terHorst ◽  
Mary Alice Coffroth

AbstractCoral reef ecosystems are under threat from the frequent and severe impacts of anthropogenic climate change, particularly rising sea surface temperatures. The effects of thermal stress may be ameliorated by adaptation and/or acclimation of the host, symbiont, or holobiont (host + symbiont) to increased temperatures. We examined the role of the symbiont in promoting thermal tolerance of the holobiont, using Antillogorgia bipinnata (octocoral host) and Breviolum antillogorgium (symbiont) as a model system. We identified five distinct genotypes of B. antillogorgium from symbiont populations isolated from Antillogorgia colonies in the Florida Keys. Three symbiont genotypes were cultured and maintained at 26 °C (ambient historical temperature), and two were cultured and maintained at 30 °C (elevated historical temperature) for 2 yrs. We analyzed the growth rate and carrying capacity of each symbiont genotype at both ambient and elevated temperatures in culture (in vitro). All genotypes grew well at both temperatures, indicating that thermal tolerance exists among these B. antillogorgium cultures. However, a history of long-term growth at 30 °C did not yield better performance for B. antillogorgium at 30 °C (as compared to 26 °C), suggesting that prior culturing at the elevated temperature did not result in increased thermal tolerance. We then inoculated juvenile A. bipinnata polyps with each of the five symbiont genotypes and reared these polyps at both ambient and elevated temperatures (in hospite experiment). All genotypes established symbioses with polyps in both temperature treatments. Survivorship of polyps at 30 °C was significantly lower than survivorship at 26 °C, but all treatments had surviving polyps at 56 d post-infection. Our results suggest broad thermal tolerance in B. antillogorgium, which may play a part in the increased resilience of Caribbean octocorals during heat stress events.


2021 ◽  
Vol 10 (3) ◽  
pp. e46210313616
Author(s):  
Yana Miranda Borges ◽  
Breno Gabriel da Silva ◽  
Brian Alvarez Ribeiro de Melo ◽  
Robério Rebouças da Silva

The relevance in studying climatological phenomena is based on the influence that variables of this nature exert on the world. Among the most observed variables, temperature stands out, whose effect of its variation may cause significant impacts, such as the proliferation of biological species, agricultural production, population health, etc. Probability distributions have been studied to verify the best fit to describe and/or predict the behavior of climate variables and, in this context, the present study evaluated, among six probability distributions, the best fit to describe a historical temperature series. minimum monthly mean. The series used in this study encompass a period of 38 years (1980 to 2018) separated by month from the weather station of the Manaus - AM station (OMM: 82331) obtained from INMET, totaling 459 observations. Difference-Sign and Turning Point tests were used to verify data independence and the maximum likelihood method to estimate the parameters. Kolmogorov-Smirnov, Anderson-Darling, Cramér-von Mises, Akaike Information Criterion and quantile-quantile plots were used to select the best fit distribution. Log-Normal, Gama, Weibull, Gumbel type II, Benini and Rice distributions were evaluated, with the best performing Rice, Log-Normal and Gumbel II distributions being highlighted.


2021 ◽  
Vol 13 (6) ◽  
pp. 3029
Author(s):  
Debashis Nath ◽  
Keerthi Sasikumar ◽  
Reshmita Nath ◽  
Wen Chen

The Coronavirus Disease 2019 (COVID-19) pandemic poses a serious threat to global health system and economy. It was first reported in Wuhan, China, and later appeared in Central Asia, Europe, North America, and South America. The spatial COVID-19 distribution pattern highly resembles the global population distribution and international travel routes. We select 48 cities in 16 countries across 4 continents having infection counts higher than 10,000 (by 25 April 2020) as the COVID-19 epicenters. At the initial stage, daily COVID-19 counts co-varies strongly with local temperature and humidity, which are clustered within 0–10 °C and 70–95%, respectively. Later, it spreads in colder (−15 °C) and warmer (25 °C) countries, due to faster adaptability in diverse environmental conditions. We introduce a combined temperature-humidity profile, which is essential for prediction of COVID-19 cases based on environmental conditions. The COVID-19 epicenters are collocated on global CO2 emission hotspots and its distribution maximizes at 7.49 °C, which is 1.35 °C/2.44 °C higher than current (2020)/historical (1961–1990) mean. Approximately 75% of the COVID-19 cases are clustered at severe-extreme end of historical temperature distribution spectrum, which establish its tighter and possible association with extreme climate change. A strong mitigation measure is essential to abate the GHG emissions, which may reduce the probability of such pandemics in the future.


2021 ◽  
Vol 14 (1) ◽  
pp. 327-339
Author(s):  
Kemuel III Quindala ◽  
Diane Carmeliza Cuaresma ◽  
Jonathan Mamplata

The behavior of temperature is one of the major factors in the study of climate change which has already invited a lot of researchers and policymakers. These studies help in deciding the best adaptation and mitigation strategy. However, there are little studies on the progression of climate change in a local setting, such as in a municipal or provincial level. This study explored to model, using regression, the daily temperature in the province of Laguna. The daily maximum and minimum temperature from 1960 to 2018 were modeled using the classical Ornstein-Uhlenbeck (OU) process with additive seasonality. The model showed that the province saw an increase of $1.16^\circ$C (resp. $0.55^\circ$C) in the mean daily minimum (resp. maximum) temperature from 1960 to 2018. It was also found that minimum temperature showed a steadier increase than maximum temperature, which poses threats to agricultural activities. Consistent with other international predictions, there was a $0.02^\circ$C annual increase in 1960 to a $0.05^\circ$C starting in 2010.  The proposed model can be used by authorities in designing and creating adaptive measures that would be more effective to the province of Laguna.


2020 ◽  
Author(s):  
Raphaël Hébert ◽  
Shaun Lovejoy ◽  
Bruno Tremblay

AbstractWe directly exploit the stochasticity of the internal variability, and the linearity of the forced response to make global temperature projections based on historical data and a Green’s function, or Climate Response Function (CRF). To make the problem tractable, we take advantage of the temporal scaling symmetry to define a scaling CRF characterized by the scaling exponent H, which controls the long-range memory of the climate, i.e. how fast the system tends toward a steady-state, and an inner scale $$\tau \approx 2$$ τ ≈ 2   years below which the higher-frequency response is smoothed out. An aerosol scaling factor and a non-linear volcanic damping exponent were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference which allows us to analytically calculate the transient climate response and the equilibrium climate sensitivity as: $$1.7^{+0.3} _{-0.2}$$ 1 . 7 - 0.2 + 0.3   K and $$2.4^{+1.3} _{-0.6}$$ 2 . 4 - 0.6 + 1.3   K respectively (likely range). Projections to 2100 according to the RCP 2.6, 4.5 and 8.5 scenarios yield warmings with respect to 1880–1910 of: $$1.5^{+0.4}_{-0.2}K$$ 1 . 5 - 0.2 + 0.4 K , $$2.3^{+0.7}_{-0.5}$$ 2 . 3 - 0.5 + 0.7   K and $$4.2^{+1.3}_{-0.9}$$ 4 . 2 - 0.9 + 1.3   K. These projection estimates are lower than the ones based on a Coupled Model Intercomparison Project phase 5 multi-model ensemble; more importantly, their uncertainties are smaller and only depend on historical temperature and forcing series. The key uncertainty is due to aerosol forcings; we find a modern (2005) forcing value of $$[-1.0, -0.3]\, \,\,\mathrm{Wm} ^{-2}$$ [ - 1.0 , - 0.3 ] Wm - 2 (90 % confidence interval) with median at $$-0.7 \,\,\mathrm{Wm} ^{-2}$$ - 0.7 Wm - 2 . Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to RCP 2.6 for which the probability to remain under 1.5 K is 48 %. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability.


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