scholarly journals Variable response of anuran calling activity to daily precipitation and temperature: implications for climate change

Ecosphere ◽  
2013 ◽  
Vol 4 (4) ◽  
pp. art47 ◽  
Author(s):  
Oscar E. Ospina ◽  
Luis J. Villanueva-Rivera ◽  
Carlos J. Corrada-Bravo ◽  
T. Mitchell Aide
Keyword(s):  
Climate Change ◽  
Daily Precipitation ◽  
Variable Response ◽  
Calling Activity ◽  
Precipitation And Temperature

Spatio-temporal variation of extreme indices derived from observed and reanalysis products for detection of climate change across India

2020 ◽  
Author(s):  
Sachidanand Kumar ◽  
Kironmala Chanda ◽  
Srinivas Pasupuleti
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Spatio Temporal ◽  
Precipitation And Temperature

<p><strong>Abstract</strong></p><p>This article reports the research findings in a recent study (Kumar et al., 2020) that utilizes eight indices of climate change recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) for analyzing spatio-temporal trends in extreme precipitation and temperature at the daily scale across India. Observed gridded precipitation (1971-2017) and temperature (1971-2013) datasets from India Meteorological Department (IMD) are used along with reanalysis products from Climate Prediction Centre (CPC). The trends are estimated using non-parametric Mann-Kendall (MK) test and regression analysis. The trends in ‘wet days’ (daily precipitation greater than 95<sup>th</sup> percentile) and ‘dry days’ (daily precipitation lower than 5<sup>th</sup> percentile) are examined considering the entire year (annual) as well as monsoon months only (seasonal). At the annual scale, about 13% of the grid locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme ‘wet days’) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme ‘dry days’). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The spatio-temporal trends in ‘warm days’ (daily maximum temperature greater than 95<sup>th</sup> percentile), ‘warm nights’ (daily minimum temperature greater than 95<sup>th</sup> percentile), ‘cold days’ (daily maximum temperature lower than 5<sup>th</sup> percentile) and ‘cold nights’ (daily minimum temperature lower than 5<sup>th</sup> percentile) are also investigated for the aforementioned period. The number of ‘warm days’ per year increased significantly at 14% of the locations, while the number of ‘cold days’, ‘warm nights’ and ‘cold nights’ per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices are also investigated for the future using CanESM2 projected data for RCP8.5 after suitable bias correction. Most of the locations (49% to 84%) indicate significant increasing (decreasing) trend in ‘warm days’ (‘cold days’) in the three epochs, 2006-2040, 2041-2070 and 2071-2100. Moreover, most locations (60% to 81%) show an increasing trend in ‘warm nights’ and a decreasing trend in ‘cold nights’ in all the epochs. A similar investigation for the historical and future periods using CPC data as the reference indicates that the trends, on comparison with IMD observations, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.</p><p><strong>Keywords: extreme precipitation and temperature, climate change indices, spatio-temporal variation, India</strong></p><p><strong>References:</strong></p><p>Kumar S., Chanda, K., Srinivas P., (2020), Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India, Theoretical and Applied Climatology, Springer, In press, DOI: 10.1007/s00704-020-03088-5.</p>

Beyond the mean: long-term variabilities in precipitation and temperature on the Qinghai-Tibetan Plateau

2021 ◽  
Author(s):  
Tong Guo ◽  
Yanhong Tang
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Interannual Variability ◽  
Daily Precipitation ◽  
Mean Annual Precipitation ◽  
High Altitudes ◽  
Annual Variability ◽  
Mean Temperature ◽  
Precipitation And Temperature

AbstractLong-term variabilities in daily precipitation and temperature are critical for assessing the impacts of climate change on ecosystems. We characterized intra- and interannual variabilities in daily precipitation and temperature obtained from 1960 to 2015 at 78 meteorological stations on the Qinghai-Tibetan Plateau. The results show that 1) The intra-annual variability of daily precipitation increases for 55 meteorological stations with a rate of 0.08 mm per decade. In contrast, the intra-annual variability markedly decreases for daily mean, daytime mean, and nighttime mean temperatures with a rate of 0.09, 0.07, and 0.12 °C per decade, respectively at 90% or more of stations. 2) Variabilities of daily precipitation and temperatures are quite sensitive to high altitudes (> 3500 m). The intra- and interannual variabilities of daily precipitation significantly decrease at 1.0 and 0.07 mm per 1000 m, respectively. However, variations of high altitudes increase the intra- and interannual variabilities of daily mean temperature at 1.0 °C and 0.2 °C per 1000 m. Moreover, the interannual variability of nighttime mean temperature varies at 0.3 °C per 1000 m, the fastest rate among three temperature indices. 3) A larger mean annual precipitation is accompanied by a higher intra- and interannual variability of daily precipitation on the Qinghai-Tibetan Plateau; however, a higher mean annual temperature leads to lower variabilities of daily temperatures. This study illustrates that long-term climatic variability is understudied in alpine ecosystems characterized by high climatic sensitivity. Precipitation and temperature variabilities should be characterized to improve predictions of vulnerable ecosystems responding to climate change.

North Atlantic Oscillation; a Climatic Indicator to Predict Hydropower Availability in Scandinavia

2002 ◽  
Vol 33 (5) ◽  
pp. 415-424 ◽  
Author(s):  
Cintia B. Uvo ◽  
Ronny Berndtsson
Keyword(s):  
Climate Change ◽  
Spatial Distribution ◽  
Climate Variability ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Winter Precipitation ◽  
Model Driven ◽  
The World ◽  
Hydropower Production ◽  
Precipitation And Temperature

Climate variability and climate change are of great concern to economists and energy producers as well as environmentalists as both affect the precipitation and temperature in many regions of the world. Among those affected by climate variability is the Scandinavian Peninsula. Particularly, its winter precipitation and temperature are affected by the variations of the so-called North Atlantic Oscillation (NAO). The objective of this paper is to analyze the spatial distribution of the influence of NAO over Scandinavia. This analysis is a first step to establishing a predictive model, driven by a climatic indicator such as NAO, for the available water resources of different regions in Scandinavia. Such a tool would be valuable for predicting potential of hydropower production one or more seasons in advance.

scholarly journals Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran)

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
T. Mesbahzadeh ◽  
M. M. Miglietta ◽  
M. Mirakbari ◽  
F. Soleimani Sardoo ◽  
M. Abdolhoseini
Keyword(s):  
Climate Change ◽  
Goodness Of Fit ◽  
Joint Modeling ◽  
Copula Function ◽  
Gaussian Copula ◽  
Climate Change Scenarios ◽  
Climatic Parameters ◽  
Goodness Of Fit Test ◽  
Copula Theory ◽  
Precipitation And Temperature

Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.

scholarly journals Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

2013 ◽  
Vol 17 (6) ◽  
pp. 2147-2159 ◽  
Author(s):  
E. P. Maurer ◽  
T. Das ◽  
D. R. Cayan
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Circulation Model ◽  
The United States ◽  
Historical Period ◽  
Late 20Th Century ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10 yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

scholarly journals The Fingerprint of Climate Change and Urbanization in South Korea

Atmosphere ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 273 ◽  
Author(s):  
Won-Ho Nam ◽  
Guillermo Baigorria ◽  
Eun-Mi Hong ◽  
Taegon Kim ◽  
Yong-Sang Choi ◽  
...  
Keyword(s):  
Climate Change ◽  
South Korea ◽  
Urban Areas ◽  
Temporal Trends ◽  
Weather Data ◽  
Maximum Temperature ◽  
Island Effect ◽  
Temperature Trends ◽  
Agricultural Areas ◽  
Precipitation And Temperature

Understanding long-term changes in precipitation and temperature patterns is important in the detection and characterization of climate change, as is understanding the implications of climate change when performing impact assessments. This study uses a statistically robust methodology to quantify long-, medium- and short-term changes for evaluating the degree to which climate change and urbanization have caused temporal changes in precipitation and temperature in South Korea. We sought to identify a fingerprint of changes in precipitation and temperature based on statistically significant differences at multiple-timescales. This study evaluates historical weather data during a 40-year period (1973–2012) and from 54 weather stations. Our results demonstrate that between 1993–2012, minimum and maximum temperature trends in the vicinity of urban and agricultural areas are significantly different from the two previous decades (1973–1992). The results for precipitation amounts show significant differences in urban areas. These results indicate that the climate in urbanized areas has been affected by both the heat island effect and global warming-caused climate change. The increase in the number of rainfall events in agricultural areas is highly significant, although the temporal trends for precipitation amounts showed no significant differences. Overall, the impacts of climate change and urbanization in South Korea have not been continuous over time and have been expressed locally and regionally in terms of precipitation and temperature changes.

Sign in / Sign up

Export Citation Format

Share Document