scholarly journals The Study on Compound Drought and Heatwave Events in China Using Complex Networks

2021 ◽  
Vol 13 (22) ◽  
pp. 12774
Author(s):  
Kaiwen Li ◽  
Ming Wang ◽  
Kai Liu
Keyword(s):  
Extreme Events ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Maximum Temperature ◽  
Mean Frequency ◽  
Temperature And Precipitation ◽  
Annual Frequency ◽  
The Mean ◽  
Precipitation Changes ◽  
The Impact

Compound extreme events can severely impact water security, food security, and social and economic development. Compared with single-hazard events, compound extreme events cause greater losses. Therefore, understanding the spatial and temporal variations in compound extreme events is important to prevent the risks they cause. Only a few studies have analyzed the spatial and temporal relations of compound extreme events from the perspective of a complex network. In this study, we define compound drought and heatwave events (CDHEs) using the monthly scale standard precipitation index (SPI), and the definition of a heatwave is based on daily maximum temperature. We evaluate the spatial and temporal variations in CDHEs in China from 1961 to 2018 and discuss the impact of maximum temperature and precipitation changes on the annual frequency and annual magnitude trends of CDHEs. Furthermore, a synchronization strength network is established using the event synchronization method, and the proposed synchronization strength index (SSI) is used to divide the network into eight communities to identify the propagation extent of CDHEs, where each community represents a region with high synchronization strength. Finally, we explore the impact of summer Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) on CDHEs in different communities. The results show that, at a national scale, the mean frequency of CDHEs takes on a non-significant decreasing trend, and the mean magnitude of CDHEs takes on a non-significant increasing trend. The significant trends in the annual frequency and annual magnitude of CDHEs are attributed to maximum temperature and precipitation changes. AMO positively modulates the mean frequency and mean magnitude of CDHEs within community 1 and 2, and negatively modulates the mean magnitude of CDHEs within community 3. PDO negatively modulates the mean frequency and mean magnitude of CDHEs within community 4. AMO and PDO jointly modulate the mean magnitude of CDHEs within community 6 and 8. Overall, this study provides a new understanding of CDHEs to mitigate their severe effects.

scholarly journals Spatial and temporal variations of CO<sub>2</sub> mole fractions observed at Beijing, Xianghe and Xinglong in North China

2021 ◽  
Author(s):  
Yang Yang ◽  
Minqiang Zhou ◽  
Ting Wang ◽  
Bo Yao ◽  
Pengfei Han ◽  
...  
Keyword(s):  
Seasonal Variations ◽  
Human Activities ◽  
North China ◽  
Terrestrial Ecosystem ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Boundary Layer Height ◽  
The Mean ◽  
The Impact ◽  
First Time

Abstract. Atmospheric CO2 mole fractions are observed at Beijing (BJ), Xianghe (XH), and Xinglong (XL) in North China using the Picarro G2301 Cavity Ring-Down Spectroscopy instruments. The measurement system is described comprehensively for the first time. The geo-distances among these three sites are within 200 km, but they have very different surrounding environments: BJ is inside the megacity; XH is in the suburban area; XL is in the countryside on a mountain. The mean and standard deviation of CO2 mole fractions at BJ, XH, and XL between October 2018 and September 2019 are 448.4 ± 12.8 ppm, 436.0 ± 9.2 ppm and 420.6 ± 8.2 ppm, respectively. The seasonal variations of CO2 at these three sites are similar, with a maximum in winter and a minimum in summer, which is dominated by the terrestrial ecosystem. However, the seasonal variations of CO2 at BJ and XH are more affected by human activities as compared to XL. By using CO2 at XL as the background, CO2 enhancements are observed simultaneously at BJ and XH. The diurnal variations of CO2 are driven by the boundary layer height, photosynthesis and human activities at BJ, XH and XL. Moreover, we address the impact of the wind on the CO2 mole fractions at BJ and XL. This study provides an insight into the spatial and temporal variations of CO2 mole fractions in North China.

scholarly journals Projected Changes in Climate Extremes Using CMIP6 Simulations Over SREX Regions

2021 ◽  
Vol 5 (3) ◽  
pp. 481-497
Author(s):  
Mansour Almazroui ◽  
Fahad Saeed ◽  
Sajjad Saeed ◽  
Muhammad Ismail ◽  
Muhammad Azhar Ehsan ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Extreme Events ◽  
First Century ◽  
Maximum Temperature ◽  
Annual Maximum ◽  
Temperature And Precipitation ◽  
Projected Change ◽  
Twenty First Century ◽  
Projected Changes ◽  
The Tropics

AbstractThis paper presents projected changes in extreme temperature and precipitation events by using Coupled Model Intercomparison Project phase 6 (CMIP6) data for mid-century (2036–2065) and end-century (2070–2099) periods with respect to the reference period (1985–2014). Four indices namely, Annual maximum of maximum temperature (TXx), Extreme heat wave days frequency (HWFI), Annual maximum consecutive 5-day precipitation (RX5day), and Consecutive Dry Days (CDD) were investigated under four socioeconomic scenarios (SSP1-2.6; SSP2-4.5; SSP3-7.0; SSP5-8.5) over the entire globe and its 26 Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) regions. The projections show an increase in intensity and frequency of hot temperature and precipitation extremes over land. The intensity of the hottest days (as measured by TXx) is projected to increase more in extratropical regions than in the tropics, while the frequency of extremely hot days (as measured by HWFI) is projected to increase more in the tropics. Drought frequency (as measured by CDD) is projected to increase more over Brazil, the Mediterranean, South Africa, and Australia. Meanwhile, the Asian monsoon regions (i.e., South Asia, East Asia, and Southeast Asia) become more prone to extreme flash flooding events later in the twenty-first century as shown by the higher RX5day index projections. The projected changes in extremes reveal large spatial variability within each SREX region. The spatial variability of the studied extreme events increases with increasing greenhouse gas concentration (GHG) and is higher at the end of the twenty-first century. The projected change in the extremes and the pattern of their spatial variability is minimum under the low-emission scenario SSP1-2.6. Our results indicate that an increased concentration of GHG leads to substantial increases in the extremes and their intensities. Hence, limiting CO2 emissions could substantially limit the risks associated with increases in extreme events in the twenty-first century.

scholarly journals Spatio-temporal variations of land surface temperature and precipitation due to climate change in the Jhelum river basin, India

MAUSAM ◽  
2021 ◽  
Vol 71 (4) ◽  
pp. 661-674
Author(s):  
HIMAYOUN DAR ◽  
ROSHNI THENDIYATH ◽  
MOHSIN FAROOQ
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
21St Century ◽  
Temporal Variations ◽  
Average Increase ◽  
Temperature And Precipitation ◽  
Rcp 8.5 ◽  
The Mean ◽  
Spatio Temporal ◽  
Precipitation And Temperature

The present study investigated the spatio-temporal variations of precipitation and temperature for the projected period (2011-2100) in the Jhelum basin, India. The precipitation and temperature variables are projected under RCP 8.5 scenario using statistical down scaling techniques such as Artificial Neural Network (ANN) and Wavelet Artificial Neural Network (WANN) models. Firstly, the screened predictors were downscaled to predictand using ANN and WANN models for all the study stations. On the basis of the performance criteria, the WANN model is selected as an efficient model for downscaling of precipitation and temperature. The future screened predictor data pertaining to RCP 8.5 of CanESM2 model were downscaled to monthly temperature and precipitation for future periods (2011-2100) using WANN models. The investigation of the future projections revealed an average increase of 17-25% in the mean annual precipitation and 20-25% average increase in the monthly mean precipitation for all the selected stations towards the end of 21st century. The monthly mean temperature also showed an increase of 2-3 °C for all the study stations towards the end of 21st century. The mean seasonal temperature of the projected period is found to be increasing for all the four seasons in most parts of the basin.

scholarly journals Spatial and temporal variations of rainy days and mean daily rainfall intensity in northern Nigeria.

MAUSAM ◽  
2021 ◽  
Vol 44 (1) ◽  
pp. 85-92
Author(s):  
E. O. OLADIPO ◽  
S. SALAHU
Keyword(s):  
Rainfall Intensity ◽  
Daily Rainfall ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Northern Nigeria ◽  
Rainfall Trend ◽  
North Central ◽  
The North ◽  
The Mean ◽  
Rainy Days

The spatial and temporal variations of rainy Gays arid daily rainfall intensity for northern Nigeria for using 54 years data are analysed, The extent and nature of non-random changes, such as trend and fluctuations are Investigated. In general, both, the rainy day frequency and mean daily rainfall intensity decreases northwards except for localized orographic effect in the north central Part of the region. There is statistical evidence or decreasing trend in the, number of rainy days over the period of study, but the trend analysis showed no significance or the mean daily rainfall intensity. This suggests that the recent decreasing rainfall trend In the region particularly In the Sahellan zone, In the result of decrease In the frequency of rainy days and not due to any significant change In the rainfall intensity.  

Comparing independent climate-sensitive models of aboveground biomass and diameter growth with their compatible simultaneous model system for three larch species in China

2019 ◽  
Vol 12 (07) ◽  
pp. 1950053
Author(s):  
Zhigang Gao ◽  
Qiuyan Wang ◽  
Zongda Hu ◽  
Peng Luo ◽  
Guangshuang Duan ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Growth Models ◽  
Model System ◽  
Maximum Temperature ◽  
Tree Biomass ◽  
Climate Zones ◽  
Temperature And Precipitation ◽  
Larch Species ◽  
The Mean ◽  
Simultaneous Model

Accurate estimate of tree biomass is essential for forest management. In recent years, several climate-sensitive allometric biomass models with diameter at breast height [Formula: see text] as a predictor have been proposed for various tree species and climate zones to estimate tree aboveground biomass (AGB). But the allometric models only account for the potential effects of climate on tree biomass and do not simultaneously explain the influence of climate on [Formula: see text] growth. In this study, based on the AGB data from 256 destructively sampled trees of three larch species randomly distributed across the five secondary climate zones in northeastern and northern China, we first developed a climate-sensitive AGB base model and a climate-sensitive [Formula: see text] growth base model using a nonlinear least square regression separately. A compatible simultaneous model system was then developed with the climate-sensitive AGB and [Formula: see text] growth models using a nonlinear seemingly unrelated regression. The potential effects of several temperature and precipitation variables on AGB and [Formula: see text] growth were evaluated. The fitting results of climatic sensitive base models were compared against those of their compatible simultaneous model system. It was found that a decreased isothermality ([mean of monthly (maximum temperature-minimum temperature)]/(Maximum temperature of the warmest month-Minimum temperature of the coldest month)) and total growing season precipitation, and increased annual precipitation significantly increased the values of AGB; an increase of temperature seasonality (a standard deviation of the mean monthly temperature) and precipitation seasonality (a standard deviation of the mean monthly precipitation) could lead to the increase of [Formula: see text]. The differences of the model fitting results between the compatible simultaneous system with the consideration of climate effects on both AGB and [Formula: see text] growth and its corresponding climate-sensitive AGB and [Formula: see text] growth base models were very small and insignificant [Formula: see text]. Compared to the base models, the inherent correlation of AGB with [Formula: see text] was taken into account effectively by the proposed compatible model system developed with the climate-sensitive AGB and [Formula: see text] growth models. In addition, the compatible properties of the estimated AGB and [Formula: see text] were also addressed substantially in the proposed model system.

Drivers of Surface Downwelling Longwave Irradiance Changes from 1984 through 2017

2021 ◽  
Author(s):  
Joseph Clark
Keyword(s):  
Water Vapor ◽  
Greenhouse Gases ◽  
Atmospheric Temperature ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Atmospheric Gases ◽  
Background Temperature ◽  
The Impact

&lt;p&gt;Relatively few studies have taken observationally driven approaches toward understanding the impact that atmospheric gases and temperatures have on surface downwelling longwave irradiance (SDLI) changes. This is despite the fact that changes in SDLI contribute significantly to climate change. Using reanalysis, observations, and the Rapid Radiative Transfer Model Global (RRTMG; Mlawer et al. 1997; Iacono et al. 2008), we linearly separate the contributions to SDLI changes from 1984 through 2017 caused by the following variables: atmospheric temperature, H&lt;sub&gt;2&lt;/sub&gt;O, CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O, CFC-11, and CFC-12. The results show that spatial and temporal variations in SDLI are primarily caused by spatial and temporal variations in atmospheric temperatures and water vapor amounts. Specifically, we find that atmospheric temperatures and water vapor amounts contribute about 10 times more to SDLI variations from 1984 through 2017 than the remaining greenhouse gases. Climatologically, spatial variability in atmospheric temperature and water vapor also play a role in determining the impact on SDLI of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O, CFC-11, and CFC-12. SDLI trends directly attributable to CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O, CFC-11, and CFC-12 are strongest over regions with climatologically high temperatures and low water vapor amounts. In other words, the impact of the greenhouse gases varies in space, with its strength depending on the background temperature and moisture fields, even if the change in gas mixing ratio is spatially uniform. Finally, CO&lt;sub&gt;2 &lt;/sub&gt;contributed 10 times more to the SDLI trends of 0.05-0.30 W m&lt;sup&gt;-2&lt;/sup&gt; / decade (depending on location) from 1984 through 2017 than any other greenhouse gas.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;

scholarly journals Biases and Model Agreement in Projections of Climate Extremes over the Tropical Pacific

2011 ◽  
Vol 15 (24) ◽  
pp. 1-36 ◽  
Author(s):  
Sarah E. Perkins
Keyword(s):  
Minimum Temperature ◽  
General Circulation ◽  
Coupled Model ◽  
Climate Extremes ◽  
General Circulation Models ◽  
Maximum Temperature ◽  
Climate Indices ◽  
Temperature And Precipitation ◽  
Return Levels ◽  
The Mean

Abstract Using the Coupled Model Intercomparison Project phase 3 (CMIP3) general circulation models (GCMs), projections of a range of climate extremes are explored for the western Pacific. These projections include the 1-in-20-yr return levels and a selection of climate indices for minimum temperature, maximum temperature, and precipitation, and they are compared to corresponding mean projections for the Special Report on Emission Scenarios (SRES) A2 scenario during 2081–2100. Models are evaluated per variable based on their ability to simulate current extremes, as well as the overall daily distribution. Using the standardized evaluation scores for each variable, models are divided into four subsets where ensemble variability is calculated to measure model uncertainty and biases are calculated in respect to the multimodel ensemble (MME). Results show that higher uncertainty in projections of climate extremes exists when compared to the mean, even in those subsets consisting of higher-skilled models. Higher uncertainty exists for precipitation projections than for temperature, and biases and uncertainties in the 1-in-20-yr precipitation events are an order of magnitude higher than the corresponding mean. Poorer performing models exhibit a cooler bias in the mean and 1-in-20-yr return levels for maximum and minimum temperature, and ensemble variability is low among all subsets of mean minimum temperature, especially the lower-skilled subsets. Higher-skilled models project 1-in-20-yr precipitation return levels that are more intense than in the MME. The frequency of temperature extremes increase dramatically; however, this is explained by the underpinning small temperature range of the region. Although some systematic biases occur in the higher- and lower-skilled models and omitting the poorer performers is recommended, great care should be exercised when interpreting the reduction of uncertainty because the ensemble variability among the remaining models is comparable and in some cases greater than the MME. Such results should be treated on a case-by-case basis.

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