an assessment of long term temperature gumnet: variability in …€¦ · pc analysis 2nd mode...
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GuMNet:
Guadarrama Monitoring
Network
An Assessment of Long
Term Temperature
Variability in the Sierra
de Guadarrama Vegas & GuMNet Consortium
Departamento de Física de la Tierra y Astrofísica
UCM
Universidad Complutense de Madrid
UCM
Cartagena, AEC, 18.10.2018
Vegas-Cañas C., J. F. González-Rouco, J. Navarro-Montesinos, E. García-Bustamante, I. Álvarez-Arévalo
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Data
27 Km
� WRF: 1 km finest resolution
� no turbulent kinetic energy
(Gibbs et al., 2011).
� WRF: the closest grid points
to the observational stations
are selected �WRF*
� ERA Interim (ERAIT):
boundary conditions for WRF.
� ERAIT: 80km horizontal
resolution � just 2 grid points
2
9 Kmresolution � just 2 grid points
associated to the stations.
� Observations: 6 stations
located in the Sierra de
Guadarrama National Park
(SGNP).
https://www.ucm.es/gumnet
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Data
27 Km
� WRF: 1 km finest resolution
� no turbulent kinetic energy
(Gibbs et al., 2011).
� WRF: the closest grid points
to the observational stations
are selected �WRF*
� ERA Interim (ERAIT):
boundary conditions for WRF.
� ERAIT: 80km horizontal
resolution � just 2 grid points
1Km
3 Km
2
9 Kmresolution � just 2 grid points
associated to the stations.
� Observations: 6 stations
located in the Sierra de
Guadarrama National Park
(SGNP).
https://www.ucm.es/gumnet
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Evaluation of WRF. Mean temperature & vertical gradient
� Basic climatological description.
� Dominant orography.
� Local values in agreement with WRF.
� Regional averages � similar WRF & Obs.
Warmer bias in ERAIT
3
� WRF trend: -6.48 °C/km
� OBS trend: -4.64 °C/km
� OBS at high altitudes underestimated
by WRF
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Evaluation of WRF. Annual Cycles
� Annual cycle of Obs in agreement with
WRF.
� ERAIT in agreement with Obs & WRF
� ERAIT shows a warmer bias (+5°C).
� Same for the rest of the stations �
4
� Same for the rest of the stations �
associated to the same ERAIT grid
point
� WRF shows colder T than Obs at high
altitude stations, but warmer T at the
stations in the valley.
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Evaluation of WRF. Temperature anomalies
5
� WRF adds value at reproducing extreme events.
� Correlations >0.9 � 6 stations/WRF grid points are able to adequately
reproduce the variability in the Sierra de Guadarrama.
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Evaluation of WRF. Taylor Diagrams
� Winter: WRF shows a better
performance than ERAIT.
� Summer: WRF shows a better
performance than ERAIT, except at CTS
and ALM.
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PC Analysis
� 2nd mode explains
the 3% of the
variance, with 2
areas over the NW
and the SE.
� It explains some of
the variability in the
valleys for some
extreme situations
7
� 1st mode explains the
94% of the variance
� it explains to a
large extent the
overall variability in
the area.
� Very orographic
pattern
� 3rd mode explains only
the 1% of the variance
� barely significant
contribution to the
temperature anomalies.
� It shows a large
orographic influence �
highest altitude
locations
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ERAIT temperature anomalies reconstruction
8
� ERAIT reconstructed monthly anomalies are in agreement with both WRF and
the anomalies in Navacerrada
� High correlation over the Iberian Peninsula (about 0.9).
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Conclusions
� Evaluation of WRF: the model improves the bias of ERAIT and shows a more realistic
simulation, although it underestimates temperatures at high altitude stations
� Few sites, but representative of the temperatures over the Sierra de Guadarrama �
good estimate of the variability over the region .
� PCA. PC1: orografic. PC2: western flux. PC3: Radiative cooling?
9
� No long term trends since the 1980s
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Conclusions
� Evaluation of WRF: the model improves the bias of ERAIT and shows a more realistic
simulation, although it underestimates temperatures at high altitude stations
� Few sites, but representative of the temperatures over the Sierra de Guadarrama �
good estimate of the variability over the region .
� PCA. PC1: orografic. PC2: western flux. PC3: Radiative cooling?
9
THANK YOU!
¡Gracias!
� No long term trends since the 1980s
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GuMNet is a new infrastructure of atmosphere, surface and subsurface observation
It is composed of 10 sites distributed from 900 masl to 2200 masl
What is GuMNet?
a glimpse at the facility
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Natural resources Centinels of climate change
Resources
Seg
uri
dad
Res
ilien
ce
Watch
Risks Natural and cultural heritage
Education Research Health/Leisure Management
SustainabilityS
egu
rid
ad
Res
ilien
ce
Decision making
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Motivation
� Mountains offer many natural resources and a space for many activities.
� Mountains serve as home for many species, both animals and plants.
� Mountains have been greatly affected by climate change � extreme events (Kohler
et al., 2014) � observations in the mountains are very important.
� Obtaining meteorological observations represents a challenge � use of models as
an alternative.
13
� Complex terrains not easy to simulate � increase of the horizontal resolution.
� This study is focused on the Sierra de Guadarrama.
� Temperature variability will be analysed by the use of a high-resolution
Weather Research Forecast (WRF) model configuration, the ERA Interim
(ERAIT) reanalysis and observations. A Principal Component Analysis (PCA)
will be applied.
� WRF model will be evaluated.
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Evaluation of WRF. Mean temperature
Seasonal averages in
WRF and observations
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PC Analysis
15
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Extended PCA
� PC1: continental
pattern.
� PC2: NW - SE
contrast �
higher values
over the basins
of the main
rivers.
� PC3: influence of
16
� PC3: influence of
orography
� Regression
Pattern:
continental
pattern with the
highest
correlations far
from the ocean.
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Conclusions
� Two main targets: evaluation of the performance of the WRF model and the analysis
of the variability of temperature over the area of the Sierra de Guadarrama.
� The high resolution WRF model improves the bias of ERAIT and shows a more realistic
simulation, although it underestimates temperatures at high altitude stations.
� Few sites, but representative of the temperatures over the Sierra de Guadarrama �
good estimate of the variability over the region .
� PC1: orografic. PC2: western flux. PC3: Radiative ?
17
� PC1: orografic. PC2: western flux. PC3: Radiative ?
THANK YOU!
¡Gracias!
� No long term trends since the 1980s