An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: future climate projections

2018 ◽  
Vol 52 (11) ◽  
pp. 6749-6771 ◽  
Author(s):  
Yi Yang ◽  
Jianping Tang ◽  
Zhe Xiong ◽  
Shuyu Wang ◽  
Jian Yuan
Keyword(s):  
Statistical Downscaling ◽  
Daily Precipitation ◽  
Future Climate ◽  
Climate Projections ◽  
Precipitation And Temperature ◽  
Future Climate Projections

scholarly journals Climate change projections of maximum temperature in the pre-monsoon season in Bangladesh using statistical downscaling of global climate models

2021 ◽  
Vol 18 ◽  
pp. 99-114
Author(s):  
M. Bazlur Rashid ◽  
Syed Shahadat Hossain ◽  
M. Abdul Mannan ◽  
Kajsa M. Parding ◽  
Hans Olav Hygen ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Models ◽  
Global Climate ◽  
Monsoon Season ◽  
Global Climate Models ◽  
Future Climate ◽  
Maximum Temperature ◽  
Climate Projections ◽  
Future Climate Projections

Abstract. The climate of Bangladesh is very likely to be influenced by global climate change. To quantify the influence on the climate of Bangladesh, Global Climate Models were downscaled statistically to produce future climate projections of maximum temperature during the pre-monsoon season (March–May) for the 21st century for Bangladesh. The future climate projections are generated based on three emission scenarios (RCP2.6, RCP4.5 and RCP8.5) provided by the fifth Coupled Model Intercomparison Project. The downscaling process is undertaken by relating the large-scale seasonal mean temperature, taken from the ERA5 reanalysis data set, to the leading principal components of the observed maximum temperature at stations under Bangladesh Meteorological Department in Bangladesh, and applying the relationship to the GCM ensemble. The in-situ temperature data has only recently been digitised, and this is the first time they have been used in statistical downscaling of local climate projections for Bangladesh. This analysis also provides an evaluation of the local data, and the local temperatures in Bangladesh show a close match with the ERA5 reanalysis. Compared to the reference period of 1981–2010, the projected maximum pre-monsoon temperature in Bangladesh indicate an increase by 0.7/0.7/0.7 ∘C in the near future (2021–2050) and 2.2/1.2/0.8 ∘C in the far future (2071–2100) assuming the RCP8.5/RCP4.5/RCP2.6 scenario, respectively.

Realised added value in dynamical downscaling of Australian climate change

2021 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Jason P. Evans ◽  
Alejandro Di Luca ◽  
Michael R. Grose ◽  
Vanessa Round ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Adaptation ◽  
Regional Climate ◽  
Future Climate ◽  
Added Value ◽  
Climate Projections ◽  
Eastern Australia ◽  
Australian Alps ◽  
Future Climate Projections

<p>Coarse resolution global climate models (GCM) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, ‘realised added value’, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.</p>

Historical trends and high-resolution future climate projections in northern Tuscany (Italy)

2017 ◽  
Vol 555 ◽  
pp. 708-723 ◽  
Author(s):  
Marco D'Oria ◽  
Massimo Ferraresi ◽  
Maria Giovanna Tanda
Keyword(s):  
High Resolution ◽  
Future Climate ◽  
Climate Projections ◽  
Historical Trends ◽  
Future Climate Projections

Future Climate Projections

2013 ◽  
pp. 53-118 ◽  
Author(s):  
Silvio Gualdi ◽  
Samuel Somot ◽  
Wilhelm May ◽  
Sergio Castellari ◽  
Michel Déqué ◽  
...  
Keyword(s):  
Future Climate ◽  
Climate Projections ◽  
Future Climate Projections

scholarly journals Technical note: Changes in cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers

2019 ◽  
Vol 23 (3) ◽  
pp. 1741-1749
Author(s):  
Jan Hnilica ◽  
Martin Hanel ◽  
Vladimír Puš
Keyword(s):  
Statistical Downscaling ◽  
Climate Model ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Dependence Structure ◽  
Climate Projections ◽  
Model Simulations ◽  
Correlation Structures ◽  
Climate Model Simulations ◽  
Dependence Structures

Abstract. Simulations of regional or global climate models are often used for climate change impact assessment. To eliminate systematic errors, which are inherent to all climate model simulations, a number of post-processing (statistical downscaling) methods have been proposed recently. In addition to basic statistical properties of simulated variables, some of these methods also consider a dependence structure between or within variables. In the present paper we assess the changes in cross- and auto-correlation structures of daily precipitation in six regional climate model simulations. In addition the effect of outliers is explored making a distinction between ordinary outliers (i.e. values exceptionally small or large) and dependence outliers (values deviating from dependence structures). It is demonstrated that correlation estimates can be strongly influenced by a few outliers even in large datasets. In turn, any statistical downscaling method relying on sample correlation can therefore provide misleading results. An exploratory procedure is proposed to detect the dependence outliers in multivariate data and to quantify their impact on correlation structures.

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