scholarly journals Climate change projections of temperature and precipitation in Chile based on statistical downscaling

2020 ◽  
Vol 54 (9-10) ◽  
pp. 4309-4330 ◽  
Author(s):  
Daniela Araya-Osses ◽  
Ana Casanueva ◽  
Celián Román-Figueroa ◽  
Juan Manuel Uribe ◽  
Manuel Paneque
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Change Projections ◽  
Temperature And Precipitation

scholarly journals Climate change projections of boreal summer precipitation over tropical America by using statistical downscaling from CMIP5 models

2017 ◽  
Vol 12 (12) ◽  
pp. 124011 ◽  
Author(s):  
Reiner Palomino-Lemus ◽  
Samir Córdoba-Machado ◽  
Sonia Raquel Gámiz-Fortis ◽  
Yolanda Castro-Díez ◽  
María Jesús Esteban-Parra
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Summer Precipitation ◽  
Cmip5 Models ◽  
Boreal Summer ◽  
Climate Change Projections ◽  
Tropical America

scholarly journals Statistical downscaling for climate change projections in the Mediterranean region: methods and results

2014 ◽  
Vol 14 (5) ◽  
pp. 1891-1906 ◽  
Author(s):  
Jucundus Jacobeit ◽  
Elke Hertig ◽  
Stefanie Seubert ◽  
Karin Lutz
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Mediterranean Region ◽  
Climate Change Projections ◽  
The Mediterranean

A High-Resolution Climate Model for the U.S. Pacific Northwest: Mesoscale Feedbacks and Local Responses to Climate Change*

2008 ◽  
Vol 21 (21) ◽  
pp. 5708-5726 ◽  
Author(s):  
Eric P. Salathé ◽  
Richard Steed ◽  
Clifford F. Mass ◽  
Patrick H. Zahn
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Snow Cover ◽  
Pacific Northwest ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Model ◽  
Mesoscale Model ◽  
Global Model ◽  
Temperature And Precipitation

Abstract Simulations of future climate scenarios produced with a high-resolution climate model show markedly different trends in temperature and precipitation over the Pacific Northwest than in the global model in which it is nested, apparently because of mesoscale processes not being resolved at coarse resolution. Present-day (1990–99) and future (2020–29, 2045–54, and 2090–99) conditions are simulated at high resolution (15-km grid spacing) using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) system and forced by ECHAM5 global simulations. Simulations use the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 emissions scenario, which assumes a rapid increase in greenhouse gas concentrations. The mesoscale simulations produce regional alterations in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land–water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. Warming is significantly amplified through snow–albedo feedback in regions where snow cover is lost. Increased onshore flow in the spring reduces the daytime warming along the coast. Precipitation increases in autumn are amplified over topography because of changes in the large-scale circulation and its interaction with the terrain. The robustness of the modeling results is established through comparisons with the observed and simulated seasonal variability and with statistical downscaling results.

scholarly journals Evaluation of statistical downscaling methods for climate change projections over Spain: present conditions with perfect predictors

2021 ◽  
Author(s):  
Alfonso Hernanz ◽  
Juan Andrés García‐Valero ◽  
Marta Domínguez ◽  
Petra Ramos‐Calzado ◽  
María A. Pastor‐Saavedra ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Change Projections

scholarly journals Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling

2019 ◽  
Author(s):  
Jorge Baño-Medina ◽  
Rodrigo Manzanas ◽  
José Manuel Gutiérrez
Keyword(s):  
Climate Change ◽  
Deep Learning ◽  
Statistical Downscaling ◽  
Linear Models ◽  
Added Value ◽  
Climate Conditions ◽  
Continental Scale ◽  
Temperature And Precipitation ◽  
Spatial Features ◽  
Learning Techniques

Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatio-temporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes difficult a proper assessment of the (possible) added value offered by these techniques. As a result, these models are usually seen as black-boxes generating distrust among the climate community, particularly in climate change problems. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied for downscaling temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their possible application in climate change studies. To do this, we use a warm test period as surrogate of possible future climate conditions. Our results show that, whilst the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones for the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g. continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as CORDEX.

scholarly journals Modeling Climate Change Projections for Ferozpur Sub-catchment of Jhelum Sub-basin of Kashmir Valley

2018 ◽  
pp. 39-52
Author(s):  
Syed Rouhullah Ali ◽  
Junaid N. Khan ◽  
Mehraj U. Din Dar ◽  
Shakeel Ahmad Bhat ◽  
Syed Midhat Fazil ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate Model ◽  
Climate Projections ◽  
Mean Annual Temperature ◽  
Kashmir Valley ◽  
Climate Change Projections ◽  
Intergovernmental Panel ◽  
Temperature And Precipitation

Aims: The study aimed at modeling the climate change projections for Ferozpur subcatchment of Jhelum sub-basin of Kashmir Valley using the SDSM model. Study Design: The study was carried out in three different time slices viz Baseline (1985-2015), Mid-century (2030-2059) and End-century (2070-2099). Place and Duration of Study: Division of Agricultural Engineering, SKUAST-K, Shalimar between August 2015 and July 2016. Methodology: Statistical downscaling model (SDSM) was applied in downscaling weather files (Tmax, Tminand precipitation). The study includes the calibration of the SDSM model by using Observed daily climate data (Tmax, Tmin and precipitation) of thirty one years and large scale atmospheric variables encompassing National Centers for Environmental Prediction (NCEP) reanalysis data, the validation of the model, and the outputs of downscaled scenario A2 of the Global Climate Model (GCM) data of Hadley Centre Coupled Model, Version 3 (HadCM3) model for the future. Daily Climate (Tmax, Tmin and precipitation) scenarios were generated from 1961 to 2099 under A2 defined by Intergovernmental Panel on Climate Change (IPCC). Results: The results showed that temperature and precipitation would increase by 0.29°C, 255.38 mm (30.97%) in MC (Mid-century) (2030-2059); and 0.67oC and 233.28 mm (28.29%) during EC (End-century) (2070-2099), respectively. Conclusion: The climate projections for 21st century under A2 scenario indicated that both mean annual temperature and precipitation are showing an increasing trend.

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