scholarly journals Evaluation of statistical downscaling methods for climate change projections over Spain: future conditions with pseudo reality (transferability experiment)

2021 ◽  
Author(s):  
Alfonso Hernanz ◽  
Juan Andrés García‐Valero ◽  
Marta Domínguez ◽  
Ernesto Rodríguez‐Camino
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Change Projections

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 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 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

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

Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi

2020 ◽  
Author(s):  
R. Manzanas ◽  
L. Fiwa ◽  
C. Vanya ◽  
H. Kanamaru ◽  
J. M. Gutiérrez
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Change Projections ◽  
Bias Adjustment

scholarly journals High resolution climate change projections for the Pyrenees region

2020 ◽  
Vol 17 ◽  
pp. 191-208
Author(s):  
María P. Amblar-Francés ◽  
Petra Ramos-Calzado ◽  
Jorge Sanchis-Lladó ◽  
Alfonso Hernanz-Lázaro ◽  
María C. Peral-García ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
21St Century ◽  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Climate Projections ◽  
Climate Change Projections ◽  
Climate Conditions ◽  
The Impact

Abstract. The Pyrenees, located in the transition zone of Atlantic and Mediterranean climates, constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions, with potential impact on the availability of water resources, mainly for downstream populations. High-resolution probabilistic climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this line, we have generated high–resolution climate projections for 21st century by applying two statistical downscaling methods (regression for max and min temperatures, and analogue for precipitation) over the Pyrenees region in the frame of the CLIMPY project over a new high-resolution (5 km × 5 km) observational grid using 24 climate models from CMIP5. The application of statistical downscaling to such a high resolution observational grid instead of station data partially circumvent the problems associated to the non-uniform distribution of observational in situ data. This new high resolution projections database based on statistical algorithms complements the widely used EUROCORDEX data based on dynamical downscaling and allows to identify features that are dependent on the particular downscaling method. In our analysis, we not only focus on maximum and minimum temperatures and precipitation changes but also on changes in some relevant extreme indexes, being 1986–2005 the reference period. Although climate models predict a general increase in temperature extremes for the end of the 21st century, the exact spatial distribution of changes in temperature and much more in precipitation remains uncertain as they are strongly model dependent. Besides, for precipitation, the uncertainty associated to models can mask – depending on the zones- the signal of change. However, the large number of downscaled models and the high resolution of the used grid allow us to provide differential information at least at massif level. The impact of the RCP becomes significant for the second half of the 21st century, with changes – differentiated by massifs – of extreme temperatures and analysed associated extreme indexes for RCP8.5 at the end of the century.

scholarly journals Statistical Downscaling in the Tropics Can Be Sensitive to Reanalysis Choice: A Case Study for Precipitation in the Philippines

2015 ◽  
Vol 28 (10) ◽  
pp. 4171-4184 ◽  
Author(s):  
R. Manzanas ◽  
S. Brands ◽  
D. San-Martín ◽  
A. Lucero ◽  
C. Limbo ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Model ◽  
Global Climate Model ◽  
The Philippines ◽  
Local Scale ◽  
Specific Humidity ◽  
Climate Change Signal ◽  
Climate Projections ◽  
Climate Change Projections

Abstract This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a generalized linear model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested separately with two distinct reanalyses (ERA-Interim and JRA-25) using a cross-validation scheme over the period 1981–2000. When the observed and downscaled time series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal–bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a global climate model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to “delta-change” estimates differing by up to 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is an important contributor to the uncertainty of local-scale climate change projections and, consequently, should be treated with as much care as other better-known sources of uncertainty (e.g., the choice of the GCM and/or downscaling method). Implications of the results for the entire tropics, as well as for the model output statistics downscaling approach are also briefly discussed.

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