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  • Author or Editor: Ernesto Rodríguez Camino x
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Alfonso Hernanz
,
Carlos Correa
,
Marta Domínguez
,
Esteban Rodríguez-Guisado
, and
Ernesto Rodríguez-Camino

Abstract

Statistical downscaling (SD) of climate change projections is a key piece for impact and adaptation studies due to its low computational expense compared to dynamical downscaling, which allows exploration of uncertainties through the generation of large ensembles. SD has been extensively evaluated and applied in the extratropics, but few examples exist in tropical regions. In this study, several state-of-the-art methods belonging to different families have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation (both from the ERA5 at 0.25°) in two regions with very different climates: Spain (midlatitudes) and Central America (tropics). Some key assumptions of SD have been tested: the strength of the predictor–predictand links, the skill of different approaches, and the extrapolation capability of each method. It has been found that relevant predictors are different in both regions, as is the behavior of statistical methods. For temperature, most methods perform significantly better in Spain than in Central America, where transfer function (TF) methods present important extrapolation problems, probably due to the low variability of the training sample (present climate). In both regions, model output statistics (MOS) methods have achieved the best results for temperature. In Central America, TF methods have achieved better results than MOS methods in the evaluation in the present climate, but they do not preserve trends in the future. For precipitation, MOS methods and the extreme gradient boost machine learning method have achieved the best results in both regions. In addition, it has been found that, although the use of humidity indices as predictors improves results for the downscaling of precipitation, future trends given by statistical methods are very sensitive to the use of one or another index. Three indices have been compared: relative humidity, specific humidity, and dewpoint depression. The use of the specific humidity has been found to lead to trends given by the downscaled projections that deviate seriously from those given by raw global climate models in both regions.

Open access
Anne M. Jochum
,
Ernesto Rodríguez Camino
,
Hendrik A. R. de Bruin
, and
Albert A. M. Holtslag

Abstract

Observations from the European Field Experiment in a Desertification-threatened Area (EFEDA) are used to evaluate the performance of the radiation, land surface, and boundary layer description of the numerical weather prediction (NWP) system High-Resolution Limited Area Model (HIRLAM) in semiarid conditions. Model analysis and 6-h forecast data of the fully coupled three-dimensional model are compared with the comprehensive dataset of a case study representing a sample of 22 days of anticyclonic conditions. Distributed micrometeorological surface stations, radiosondes, flux aircraft, and airborne lidar provide a unique validation dataset of the diurnal cycle of surface and boundary layer processes.

The model surface, soil, and boundary layer are found to be too moist and slightly too cold during most of the diurnal cycle. The model radiation and surface energy budgets are biased toward more humid conditions.

Model shortcomings are identified essentially in four areas. These are the moisture data assimilation, the land-use and soil classification with its associated physiographic database, the aerosol parameterization in the radiation code, and the boundary layer vertical resolution and entrainment description.

Practical steps for immediate improvement of the model performance are proposed. They focus on the use of a land-use and soil classification and physiographic database adapted to Mediterranean landscapes, in combination with the inclusion of aerosol parameters in the radiation scheme, that account for the typically higher aerosol load of arid and semiarid environments.

Full access
Tim Li
,
Abdallah Abida
,
Laura S. Aldeco
,
Eric J. Alfaro
,
Lincoln M. Alves
,
Jorge A. Amador
,
B. Andrade
,
Julian Baez
,
M. Yu. Bardin
,
Endalkachew Bekele
,
Eric Broedel
,
Brandon Bukunt
,
Blanca Calderón
,
Jayaka D. Campbell
,
Diego A. Campos Diaz
,
Gilma Carvajal
,
Elise Chandler
,
Vincent. Y. S. Cheng
,
Chulwoon Choi
,
Leonardo A. Clarke
,
Kris Correa
,
Felipe Costa
,
A. P. Cunha
,
Mesut Demircan
,
R. Dhurmea
,
Eliecer A. Díaz
,
M. ElKharrim
,
Bantwale D. Enyew
,
Jhan C. Espinoza
,
Amin Fazl-Kazem
,
Nava Fedaeff
,
Z. Feng
,
Chris Fenimore
,
S. D. Francis
,
Karin Gleason
,
Charles “Chip” P. Guard
,
Indra Gustari
,
S. Hagos
,
Richard R. Heim Jr.
,
Rafael Hernández
,
Hugo G. Hidalgo
,
J. A. Ijampy
,
Annie C. Joseph
,
Guillaume Jumaux
,
Khadija Kabidi
,
Johannes W. Kaiser
,
Pierre-Honore Kamsu-Tamo
,
John Kennedy
,
Valentina Khan
,
Mai Van Khiem
,
Khatuna Kokosadze
,
Natalia N. Korshunova
,
Andries C. Kruger
,
Nato Kutaladze
,
L. Labbé
,
Mónika Lakatos
,
Hoang Phuc Lam
,
Mark A. Lander
,
Waldo Lavado-Casimiro
,
T. C. Lee
,
Kinson H. Y. Leung
,
Andrew D. Magee
,
Jostein Mamen
,
José A. Marengo
,
Dora Marín
,
Charlotte McBride
,
Lia Megrelidze
,
Noelia Misevicius
,
Y. Mochizuki
,
Aurel Moise
,
Jorge Molina-Carpio
,
Natali Mora
,
Awatif E. Mostafa
,
uan José Nieto
,
Lamjav Oyunjargal
,
Reynaldo Pascual Ramírez
,
Maria Asuncion Pastor Saavedra
,
Uwe Pfeifroth
,
David Phillips
,
Madhavan Rajeevan
,
Andrea M. Ramos
,
Jayashree V. Revadekar
,
Miliaritiana Robjhon
,
Ernesto Rodriguez Camino
,
Esteban Rodriguez Guisado
,
Josyane Ronchail
,
Benjamin Rösner
,
Roberto Salinas
,
Amal Sayouri
,
Carl J. Schreck III
,
Serhat Sensoy
,
A. Shimpo
,
Fatou Sima
,
Adam Smith
,
Jacqueline Spence
,
Sandra Spillane
,
Arne Spitzer
,
A. K. Srivastava
,
José L. Stella
,
Kimberly A. Stephenson
,
Tannecia S. Stephenson
,
Michael A. Taylor
,
Wassila Thiaw
,
Skie Tobin
,
Dennis Todey
,
Katja Trachte
,
Adrian R. Trotman
,
Gerard van der Schrier
,
Cedric J. Van Meerbeeck
,
Ahad Vazifeh
,
José Vicencio Veloso
,
Wei Wang
,
Fei Xin
,
Peiqun Zhang
,
Zhiwei Zhu
, and
Jonas Zucule
Free access