Downscaled climate variables from MIROC5 GCM

2016 ◽  
Author(s):  
◽  
Carlos Gaitan
Keyword(s):  
Climate Variables

PRECIPITATION AND CLIMATE VARIABLES: A STUDY OF ISLAMABAD CITY

2019 ◽  
Vol 18 (11) ◽  
pp. 2377-2386
Author(s):  
Dalia Streimikiene ◽  
Rizwan Raheem Ahmed ◽  
Saghir Pervaiz Ghauri ◽  
Jolita Vveinhardt
Keyword(s):  
Climate Variables

scholarly journals Distribution and Attribution of Terrestrial Snow Cover Phenology Changes over the Northern Hemisphere during 2001–2020

2021 ◽  
Vol 13 (9) ◽  
pp. 1843
Author(s):  
Xiaona Chen ◽  
Yaping Yang ◽  
Yingzhao Ma ◽  
Huan Li
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Onset Date ◽  
Driving Factor ◽  
Climate Variables ◽  
Significance Level ◽  
The Past ◽  
Attribution Analysis ◽  
Altitudinal Distributions ◽  
Melting Season

Snow cover phenology has exhibited dramatic changes in the past decades. However, the distribution and attribution of the hemispheric scale snow cover phenology anomalies remain unclear. Using satellite-retrieved snow cover products, ground observations, and reanalysis climate variables, this study explored the distribution and attribution of snow onset date, snow end date, and snow duration days over the Northern Hemisphere from 2001 to 2020. The latitudinal and altitudinal distributions of the 20-year averaged snow onset date, snow end date, and snow duration days are well represented by satellite-retrieved snow cover phenology matrixes. The validation results by using 850 ground snow stations demonstrated that satellite-retrieved snow cover phenology matrixes capture the spatial variability of the snow onset date, snow end date, and snow duration days at the 95% significance level during the overlapping period of 2001–2017. Moreover, a delayed snow onset date and an earlier snow end date (1.12 days decade−1, p < 0.05) are detected over the Northern Hemisphere during 2001–2020 based on the satellite-retrieved snow cover phenology matrixes. In addition, the attribution analysis indicated that snow end date dominates snow cover phenology changes and that an increased melting season temperature is the key driving factor of snow end date anomalies over the NH during 2001–2020. These results are helpful in understanding recent snow cover change and can contribute to climate projection studies.

scholarly journals The spatiotemporal distribution of historical malaria cases in Sweden: a climatic perspective

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Tzu Tung Chen ◽  
Fredrik Charpentier Ljungqvist ◽  
Helene Castenbrandt ◽  
Franziska Hildebrandt ◽  
Mathias Mølbak Ingholt ◽  
...  
Keyword(s):  
Severe Malaria ◽  
Hot Spots ◽  
Climatic Variability ◽  
Warm Season ◽  
Temporal Variations ◽  
Malaria Vectors ◽  
Military Hospital ◽  
Climate Variables ◽  
Superposed Epoch Analysis ◽  
Historical Times

Abstract Background Understanding of the impacts of climatic variability on human health remains poor despite a possibly increasing burden of vector-borne diseases under global warming. Numerous socioeconomic variables make such studies challenging during the modern period while studies of climate–disease relationships in historical times are constrained by a lack of long datasets. Previous studies have identified the occurrence of malaria vectors, and their dependence on climate variables, during historical times in northern Europe. Yet, malaria in Sweden in relation to climate variables is understudied and relationships have never been rigorously statistically established. This study seeks to examine the relationship between malaria and climate fluctuations, and to characterise the spatio-temporal variations at parish level during severe malaria years in Sweden 1749–1859. Methods Symptom-based annual malaria case/death data were obtained from nationwide parish records and military hospital records in Stockholm. Pearson (rp) and Spearman’s rank (rs) correlation analyses were conducted to evaluate inter-annual relationship between malaria data and long meteorological series. The climate response to larger malaria events was further explored by Superposed Epoch Analysis, and through Geographic Information Systems analysis to map spatial variations of malaria deaths. Results The number of malaria deaths showed the most significant positive relationship with warm-season temperature of the preceding year. The strongest correlation was found between malaria deaths and the mean temperature of the preceding June–August (rs = 0.57, p < 0.01) during the 1756–1820 period. Only non-linear patterns can be found in response to precipitation variations. Most malaria hot-spots, during severe malaria years, concentrated in areas around big inland lakes and southern-most Sweden. Conclusions Unusually warm and/or dry summers appear to have contributed to malaria epidemics due to both indoor winter transmission and the evidenced long incubation and relapse time of P. vivax, but the results also highlight the difficulties in modelling climate–malaria associations. The inter-annual spatial variation of malaria hot-spots further shows that malaria outbreaks were more pronounced in the southern-most region of Sweden in the first half of the nineteenth century compared to the second half of the eighteenth century.

scholarly journals Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

2019 ◽  
Vol 11 (7) ◽  
pp. 866 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan A. Buehler
Keyword(s):  
Time Series ◽  
Radio Frequency ◽  
Time Dependent ◽  
Radio Frequency Interference ◽  
Climate Variables ◽  
Compelling Evidence ◽  
Climate Data ◽  
The Earth ◽  
Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China

2015 ◽  
Vol 26 (3) ◽  
pp. 545-555 ◽  
Author(s):  
Futao Guo ◽  
Guangyu Wang ◽  
John L. Innes ◽  
Xiangqing Ma ◽  
Long Sun ◽  
...  
Keyword(s):  
Linear Model ◽  
Forest Fire ◽  
Generalized Linear Model ◽  
Northeast China ◽  
Climate Variables ◽  
Area Burned
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