ArticlePDF Available

Mesoscale Climatic Simulation of Surface Air Temperature Cooling by Highly Reflective Greenhouses in SE Spain


Abstract and Figures

A long-term local cooling trend in surface air temperature has been monitored at the biggest concentration of reflective greenhouses in the world, at the Province of Almeria, SE Spain, associated with a dramatic increase in surface albedo in the area. The availability of reliable long-term climatic field data at this site offers a unique opportunity to test the skill of meso-scale meteorological models describing and predicting the impacts of land use change on local climate. Using the Weather Research and Forecast (WRF) mesoscale model, we have run a sensitivity experiment to simulate the impact of the observed surface albedo change on monthly and annual surface air temperatures. The model output showed a mean annual cooling of 0.25 ºC associated with a 0.09 albedo increase, and a reduction of 22.8 W m-2 of net incoming solar radiation at surface. Mean reduction of summer daily maximum temperatures was 0.49 ºC, with the largest single-day decrease equal to 1.3 ºC. WRF output was evaluated and compared with observations. A mean annual warm bias (MBE) of 0.42 ºC was estimated. High correlation coefficients (R2>0.9) were found between modelled and observed values. This study has particular interest in the assessment of the potential for urban temperature cooling by cool roofs deployment projects, as well as in the evaluation of mesoscale climatic models performance.
Content may be subject to copyright.
Mesoscale Climatic Simulation of Surface Air Temperature Cooling
by Highly Reective Greenhouses in SE Spain
Pablo Campra*
and Dev Millstein
University of California Berkeley, Lawrence Berkeley National Lab, Energy Analyses & Environmental Impacts Department,
Berkeley, California 94720, United States
SSupporting Information
ABSTRACT: A long-term local cooling trend in surface air temperature
has been monitored at the largest concentration of reective greenhouses
in the world, at the Province of Almeria, SE Spain, associated with a
dramatic increase in surface albedo in the area. The availability of reliable
long-term climatic eld data at this site oers a unique opportunity to
test the skill of mesoscale meteorological models describing and
predicting the impacts of land use change on local climate. Using the
Weather Research and Forecast (WRF) mesoscale model, we have run a
sensitivity experiment to simulate the impact of the observed surface
albedo change on monthly and annual surface air temperatures. The
model output showed a mean annual cooling of 0.25 °C associated with a
0.09 albedo increase, and a reduction of 22.8 W m2of net incoming
solar radiation at surface. Mean reduction of summer daily maximum
temperatures was 0.49 °C, with the largest single-day decrease equal to 1.3 °C. WRF output was evaluated and compared with
observations. A mean annual warm bias (MBE) of 0.42 °C was estimated. High correlation coecients (R2> 0.9) were found
between modeled and observed values. This study has particular interest in the assessment of the potential for urban temperature
cooling by cool roofs deployment projects, as well as in the evaluation of mesoscale climatic models performance.
The largest concentration of reective greenhouses on the
planet is located in the coastal plains of the province of Almeria,
SE Spain. The greenhouses sustain high eciency horticul-
Greenhouse farming development from 1970 through
2000 dramatically transformed the semiarid pasture land, and
now, according to International Space Station personal, this
area is the only human settlement that can be seen from the
station with the naked eye. Since 2000, the total surface area of
greenhouses has remained roughly constant at 27 000 ha
(Supporting Information, SI, Figure S1).
The development of greenhouse agriculture has led to a local
long-term cooling trend in surface air temperatures at the area
of 0.3 °C decade1, despite the generalized warming in the
surrounding region (SE Spain) of +0.4 °C decade1.
analysis of the observational records suggested that the increase
in surface albedo associated with greenhouse development,
+0.09 averaged over all seasons, has been the most probable
cause of this cooling trend.
Urban heat islands, i.e., the dierence in temperatures
between urban and surrounding areas, can reach as much as 1°
to 6 °C in summer months.
Urban albedo modication to cool
local surface air temperature is a strategy already employed in
many cities. For example, reective roofs are required in certain
situations in California and mandates have been proposed in
the U.S. and Europe.
While the research community has used
meteorological modeling to estimate the impacts of cool roofs
requirements, no comparisons of modeled temperature changes
to real world temperature changes occurring with citywide
adoption of reective roofs has been pursued to date (as
citywide changes to roof characteristics are rare).
The albedo increase at the study area is employed here as an
ideal and unique pilot experiment, where mesoscale modeling
can be compared with eld observations, helping to better
determine the impact of albedo enhancement in future land
cover change projects. Another advantage of this experience is
that the net impact on global emissions of greenhouse farming
has been quantied by life cycle assessment (LCA).
Because of
all these interesting features, this particular farming model
appears as a promising option to avoid competition between
climate-change mitigation strategies based on land use, such as
forestry and biofuels, and new demands for land to produce
food for a growing population in the future.
In this work, we pursue two goals: rst, to demonstrate
through modeling the mechanistic link between historic surface
albedo increases and historic cooling trends observed in the
area; second, to evaluate dierences between observed and
modeled temperature perturbations in order to inform future
investigations of surface albedo enhancement strategies.
Received: May 10, 2013
Revised: September 16, 2013
Accepted: September 27, 2013
© XXXX American Chemical Society |Environ. Sci. Technol. XXXX, XXX, XXXXXX
Past modeling eorts and observational studies have
investigated climatic alterations driven by land use/land cover
changes at local, mesoscale, and regional scales.
particular, previous modeling simulations show that albedo
increases can cool surface air temperatures. For example, Betts
used a global circulation model (HadAM3) to show that
clearing of low albedo natural vegetation in Eurasian and
American agricultural regions may have reduced annual mean
temperatures in aected regions by 0.51°C.
Other studies
have used global models to simulate the eects of widespread
urban albedo brightening through global deployments of cool
In most cases, the low resolution of global models (0.5°-2.5°)
limits the comparisons of model output to local eld data. To
evaluate the impact of albedo enhancements on urban scales,
regional models with higher resolution and improved land
surface representation can be employed. For example,
simulations of 0.1 albedo increases during short episodes (a
few days) in several U.S. cities (Los Angeles, Atlanta, Detroit,
Philadelphia Baltimore, Washington, and New Orleans),
showed reductions in peak summertime temperatures ranging
from 0.14 to 1.5 °C.
Temperature decreases of as much as
3.75 °C at single grid cells where simulated by urban albedo
brightening in California using a slightly longer time frame of
57 days.
Other simulations from 1 to 6 days runs found up
to 12°C of peak temperature reductions could be achieved in
New York
and Athens (Greece)
given varying increases of
urban albedo.
In order to investigate feedbacks on the atmospheric system
that may develop over longer periods and larger domains,
simulations of urban albedo increases over 12 years with a
domain covering the continental U.S. with 25 by 25 km sized
grid cells showed year-round temperatures reductions in most
cities with the largest cooling found at Los Angeles (0.53 °C),
Detroit (0.39 °C), and New York (0.3 °C).
In this work, it
was assumed that albedo changes varied from 0.0 to +0.115,
depending on urban density, according to previous estima-
Over the long simulation period, some cities showed no
signicant temperature reductions, and a few regions downwind
of urban areas showed small but signicant temperature
increases. Temperature increases were correlated with
decreased cumulus precipitation, reduction in cloud cover,
and increased shortwave radiation reaching the surface.
Some empirical studies have analyzed direct eld observa-
tions to verify the impact of high albedo surfaces on air
temperatures registered by monitoring stations. For instance, it
was reported an average summer daytime cooling of 12°C,
for a +0.4 albedo dierence between high albedo sandy surfaces
and darker surrounding areas in New Mexico desert.
However, due to limited temporal and spatial coverage of
reliable eld data, few observational studies link long-term
albedo changes and cooling trends. Land cover changes usually
occur on decadal and longer time scales, such that the climatic
signal requires observations over this time period.
observational studies are scarce, as reliable temperature series of
at least 2530 years are needed in order to establish climatic
The impact on surface temperatures of land use
changes from grasslands to intensive agriculture in the U.S.
Great Plains was investigated using 7-years MODIS (Moderate
Resolution Imaging Spectrometer) data, suggesting that
dierences in temperature might be due to irrigation but did
not investigate the role of albedo change.
We have selected a representative annual cycle (year 2005)
to run both control and albedo enhancement simulations, and
determine monthly and annual changes in surface air
temperatures, as well as in the surface energy budget. This
year was selected due to the availability of previous analyses
surface temperatures and MODIS surface albedo data to further
establish comparison between our results and these observa-
tional data. Two experiments were run for the same period and
domain, changing only the albedo values in the land surface
model to mimic either present greenhouses cover or previous
pastureland cover. The scope of our sensitivity experiments is
limited to assess the skill of WRF to simulate the impact of
albedo changes on surface air temperatures in the area. All
other biogeophysical and biogeochemical changes associated to
historic land cover change in the study area have intentionally
not been taken into account in order to focus on the potential
of albedo enhancement for local adaptation of any human
settlements to projected global warming.
Climatic and Land Surface Model. The Weather
Research and Forecasting Model (WRF) version 3.2.1 was
used for simulations.
WRF is a mesoscale model designed to
serve both operational forecasting and atmospheric research
needs. The basic computations are based on solving the
equations of motion, heat, and moisture and continuity. The
model uses higher-order numerics and the dynamics conserves
scalar variables.
The NOAH Land Surface Model (LSM)
has been coupled
to WRF
and was used to simulate surface soil moisture, soil
temperature, and canopy moisture. It provides surface uxes
and surface skin temperature as lower boundary conditions for
a coupled atmospheric model. WRF is suitable for a broad
spectrum of applications across scales ranging from meters to
thousands of kilometres.
WRF software architecture was built in CARVER IBM
iDataPlex supercomputer at the US Department of Energy
(DOE) National Energy Research Scientic Computing Center
(NERSC) in Berkeley, CA. A 3-nested grid conguration was
used, with grid sizes of 36, 12, and 4 km, respectively. The
center-point of the coarse domain is located at the greenhouse
farming area, 36.7 N and 2.7 W. The innermost domain (with
46 ×46 cells) covers the whole province of Almeria and part of
neighboring provinces of Granada and Jaen, SE of Spain. The
vertical dimension is divided onto 28 layers. Geographical data
sets were downloaded from NCEP/NCAR.
In our WRF sensitivity experiments, we have used a ceteris
paribusexperimental approach, i.e., holding all else constant,
intentionally assuming no changes in other biogeophysical land
cover properties and considering albedo as a single independent
variable, so that the eect on the dependent variable (2-m
temperature) can be isolated, thus focusing the model runs on
the impact on surface air temperatures of observed historic
albedo change in the area.
The 24-category U.S. Geological survey (USGS) land use
classication scheme was selected to provide land-cover data for
the model domains. Greenhouse farming is not specically
represented in available land use schemes. Instead, the study
area is still classied as USGS shrubland category (number 8),
along with the rest of semiarid lowlands in the province of
Almeria. This default category was selected to represent pre-
existing pasture land (PS) in the area now covered by
greenhouses, but albedo was modied and adjusted monthly
Environmental Science & Technology Article |Environ. Sci. Technol. XXXX, XXX, XXXXXXB
according to previous analysis of MODIS data.
To simulate
albedo change in the area, a new greenhouses category (GH)
was included in the scheme to represent present greenhouse
farming land cover, and inserted in the pixels where
greenhouses are located. Albedo was the only parameter that
was changed in this new category, keeping the rest of USGS
category shrublanddefault values unchanged. Time series of
surface reectance at 500 m resolution for the area of study
were acquired from the MODIS instrument on board of the
NASA Terra polar orbiting satellite for the year 2005. The
Surface Reectance product (MOD09A1) provides surface
spectral reectance estimates for bands 17 corresponding to 8
days composites removing atmospheric scattering and
absorption eects.
The entire area of greenhouses farming
located west of the city of Almeria (SI Figure S1) was selected
as representative of greenhouse surface for monthly albedo
determination. Currently, almost 70% of this coastal plain is
covered by greenhouses, although the albedo data used here
have been estimated for a parcel covering the whole area. This
way, although the nal albedo of an individual greenhouse can
reach as much as 0.4, monthly and annual values were
estimated averaging all greenhouses area.
Model Runs. Model initialization data and boundary
conditions were obtained from NCEP/NCAR Global Rean-
alysis 1 Data, GRIB1 format, 2.5°resolution, output frequency
6 h, 17 pressure levels (100010 hPa, excluding surface). Sea
surface temperatures (SST) where updated daily during model
runs, and were obtained from National Centers for Environ-
mental Prediction/Marine Modeling and Analysis Branch
(NCEP/MMAB) Real-Time SST archives (0.5°resolution
and daily output frequency).
Two separated monthly WRF runs were carried on for the
year 2005 over the 3 nested domains, with a spinup of 15 days
each. Pasture (PS) simulations were run with default USGS
land surface parameters, but albedo was adjusted monthly in
the shrubland category, according to MODIS eld data.
Average monthly albedo values from MODIS eld data were
inserted prior to every run at the two scenarios, high albedo
(GH) and low albedo (PS). Greenhouses (GH) simulations
were run inserting the eld albedo values from MODIS in the
pixels where greenhouses where located in the year of
simulation. ARW-WRF 3.2.1 physics options selected are
shown in SI Table S1.
Model Validation. Model performance was validated
comparing WRF-GH output 2m-temperatures (T2) at the
selected pixel against observations of near surface air temper-
atures registered at eld station PAL (Las PalmerillasCajamar
Foundation Research Station), located at 36°48N, 2°43W,
inside the 4 ×4 km pixel at x= 11, y= 22 of the innermost
domain. Another eld station, Mojonera (MOJ) (Institute for
Research and Training in Agriculture and Fisheries IFAPA,
Junta de Andalucia) is also located inside this pixel, and lays just
1.8 km from PAL. Results were analyzed at the Almeria
International airport station (AL), 36°50N, 2°21W, 30
km from the main greenhouses development area. PAL and
MOJ stations are included in the cooperative network of the
Spanish Agencia Estatal de Meteorologia (AEMET), and AL
station belongs to the Spanish ocial meteorological network.
Raw data from both stations have been subjected to dierent
quality controls, mostly gross error checks, internal consistency,
temporal and spatial coherence. The method for model
evaluation was adapted from previous ones.
Scatter plots
between both daily and monthly observed and modeled
temperatures were used as graphical displays to elucidate
model performance. Linear regression slopes and correlation
coecients were calculated. Monthly and annual estimations of
mean bias error (MBE), normalized bias error (MNBE), mean
absolute gross error (MAGE), and normalized mean absolute
gross error (MANGE) were calculated from modeled-observed
pairs of 24 h averages. All analyses included a Student ttest at
the 0.05 signicance level. Statgraphic Plus 4.1 was the software
used for statistical analyses.
Analysis of MODIS data indicated a 0.09 mean annual albedo
increase from PS to GH averaged over all greenhouses farming
Albedo increased most during the summer, with a
maximum monthly increase of +0.13 in August and September
(Table 1). The lowest albedo increase (+0.06) was observed in
the winter months of February and November.
At the greenhouses area, mean year-round surface temper-
ature was 0.25 °C cooler in the higher albedo simulation (GH)
than in the lower albedo scenario (PS), with average
temperature decreases found in all months (Figure 1), ranging
from 0.40 °C in June to 0.10 °C in December (Table 1). SI
Figure S2 shows maximum daily temperatures in summer
months (JunJulAug) for both scenarios. Mean reduction of
Table 1. Mean Monthly and Annual Changes in Albedo, Surface Air Temperature, Net Solar Radiation, and Heat Flux at
albedo change ΔT2(°C) ΔSWDOWN (W m2)
ΔHFX (W m2)
January +0.07 0.17 8.60 5.50 0.020
February +0.06 0.14 8.50 7.20 0.016
March +0.09 0.22 17.3 10.4 0.013
April +0.10 0.29 27.6 20.3 0.011
May +0.09 0.27 26.8 19.9 0.010
June +0.12 0.40 41.3 31.4 0.010
July +0.12 0.35 42.1 32.6 0.008
August +0.13 0.39 39.7 31.9 0.010
September +0.13 0.29 30.5 24.2 0.010
October +0.09 0.25 15.9 12.1 0.016
November +0.06 0.13 7.80 5.91 0.017
December +0.07 0.10 7.90 4.50 0.013
YEAR 2005 +0.09 0.25 22.8 17.1 0.011
Last column: ratio of temperature change per unit of net incoming radiation.
Change in net shortwave radiation.
Change in heat ux.
Environmental Science & Technology Article |Environ. Sci. Technol. XXXX, XXX, XXXXXXC
maximum daily temperatures in summer was 0.49 °C. The
largest decrease in daily maximum temperatures was 1.3 °Con
July 16th, one of the three hottest days of the year. Daytime
temperature dierences were roughly twice night-time dier-
ences (SI Figure S3). Additionally, results where analyzed at AL
station, 30 km from the main greenhouse development area.
As expected, the annual average temperature reduction between
GH and PS scenarios was lower than at the study site
surrounded by greenhouses (0.14 vs 0.25) (last column, SI
Table S3).
For both scenarios, mean WRF simulated annual solar
radiation (SWDOWN) reaching the surface was 223.9 W m2,
and ranged from 344 W m2in July to 106 W m2in
December. Changes to key energy budget components between
the PS and GH simulations are shown (Figure 2). As expected,
increased surface reectivity reduced net shortwave absorption
at the surface, with a mean annual decrease of 22.8 W m2
(Table 1). The largest reduction of net SWDOWN at the
surface occurred in June (42.1 W m2), and the minimum in
November (7.80 W m2). A similar pattern of seasonal change
was observed for sensible heat uxes (HFX), with a maximum
reduction in July of 32.6 W m2, and minimum in December of
4.50 W m2. Mean year-round HFX decrease was 17.1 W m2.
Latent heat (LH) changes were almost negligible, with a mean
annual change of 0.8 W m2. Changes in the long wave (LW)
radiation budget also had lower magnitudes than the shortwave
The changes in the diurnal cycles of net SWDOWN and
HFX for a hot summer day (July 16th) are shown in SI Figure
S4. Changes to HFX were found during daylight but were
almost negligible at night. On July 16th, the average 24 h net
SWDOWN (349 W m2) was reduced by 41.6 W m2(albedo
was increased in the GH simulation +0.12 for July). Maximum
values for net SWDOWN and HFX were seen at noon, 120.5
2, and 80 W m2, respectively.
To validate the model, simulated 2-m surface air temper-
atures (T2) were compared with eld data from the PAL
station. No statistically signicant dierence between the
annual means of the model and observation records was
found (95.0% condence level, Studentsttest). However,
signicant dierences in standard deviations and variances were
found, showing a larger degree of dispersion of model-
estimated temperatures than of observed values (at 95%
condence level based on F-test). The scatter-plot between
observed (PAL) and WRF-GH modeled daily averages of T2 is
shown in Figure 3 (and for monthly values at SI Figure S5).
The correlation coecient was high for the daily means series
(R2= 0.92), and even higher for monthly averages comparison
(R2= 0.99). Linear regression slopes (signicant at the 0.01
level based on a Studentsttest) ranged around 1.0, with 1.11
±0.02 and 1.12 ±0.02 for daily and monthly averages,
respectively, suggesting a good match between simulated and
observed values. Warm biases are suggested from the
scatterplot for daily values above 296 K, in the summer
temperatures range (Figure 3). Dierence statistics for monthly
values, MBE, MNBE, MAGE, and MANGE are shown in SI
Table S2. Warm bias was detected in summer and spring
months (March through September), while cold bias was
shown in winter (October through February). Annual MBE
was +0.42 °C, ranging from the coldest bias in December
(0.94 °C) to the warmest bias in July (+1.69 °C).
Figure 1. WRF-simulated mean monthly 2-m surface air temperature
series in pasture and greenhouses scenarios (PS, GH), and observed
eld data from stations La Mojonera (MOJ) and Las Palmerillas
(PAL). Year 2005.
Figure 2. Mean annual change in surface energy budget components
after albedo increase from pasture to greenhouses land use, year 2005.
(HFX = Heat ux; LH = Latent heat; GRDFLX = Ground ux;
SWDOWN = Incoming SW radiation; OSW = Outgoing SW
radiation; Net SW = Net SW radiation; GLW = Incoming LW
radiation; OLW = Outgoing LW radiation; and Net LW = net LW
Figure 3. Scatterplot and linear regression equation of mean daily
surface air temperature observations in PAL station and WRF-GH
output (high albedo scenario).
Environmental Science & Technology Article |Environ. Sci. Technol. XXXX, XXX, XXXXXXD
Additionally, model bias was calculated for AL station, showing
higher MBE (+0.64) (SI Table S3).
The results of our WRF simulations support our hypothesis
that the increase in albedo by greenhouses farming has been
one of the main drivers of the historical reduction in surface air
temperatures registered by eld stations. The modeled
reduction in net SWDOWN absorbed by the surface, 22.8 W
m2annual average, was similar to the observed change of
19.9 W m2found in our previous observational study,
derived from MODIS remote sensing data for the period
20012005. The temperature dierences between the WRF
simulations (PS and GH) varied along the annual cycle (Figure
4). Farmers whitewashgreenhouse roofs every June to
alleviate excess heat inside during summer months. The slaked
lime is later washed away at the end of September to allow
enough light to enter inside during winter growing season. A
sudden decrease of albedo in the site is shown by MODIS data
from September to October. In the simulations, the maximum
T2 dierence (June) was observed prior to the maximum
albedo dierence (August and September) when insolation is
decreasing but slaked lime still remains on top of the
However, the ratio of monthly temperature to net
SWDOWN changes is not constant along the annual cycle,
suggesting a higher sensitivity of the model output to changes
in radiation in winter, and lower values in summer (Table 1).
This observation might be related to the seasonal variation in
the partition between sensible to latent heat (Bowen ratio),
with higher air moisture content in summer months.
Field data show that 2-m temperature at AL was 0.53 K
warmer than PAL over the annual cycle (SI Table S3), raising
the question: can the relative cooling in PAL compared to AL
be attributed to greenhouses development? Simulated AL was
0.71 and 0.60 K warmer than PAL in the GH and PS annual
simulations, respectively. The 0.11 dierence between the two
deltas indicates that only some of the dierence in temperatures
between AL and PAL can be explained by greenhouses
development. Additionally, observed dierences between AL
and PAL were lowest in June when simulated dierences
peaked (SI Figure S6). The mismatch in timing of observed
and simulated peak dierences between AL and PAL may be
somewhat explained by missed timing in whitewashing
activities. However, the magnitude of the dierences between
simulated and observed monthly AL-PAL temperature
gradients is larger than the modeled sensitivity to greenhouse
albedo changes, indicating that xing albedo characterizations
would not be sucient to remove the discrepancies.
The potential inuence of irrigation on temperature
reductions was intentionally not addressed in our modeling
study. The values of latent heat (LH) change obtained in our
WRF-GH output data are not the result of new para-
metrizations of evapotranspiration (ET) by crops, or the
irrigation loaded onto the farming system, but only represents
the change in simulated LH driven by albedo increase. Mean
daily measured greenhouse reference potential ET ranges from
less than 1 mm day1during winter to values of approximately
4 mm day1during summer.
However, the majority of
irrigation in this area occurs during the winter and spring
growing season when greenhouse farming is most active, and
drip-irrigation is the major system applied, with reduced losses
by evaporation. There is little irrigation during July and August.
Thus, the role of increased evapotranspiration from green-
houses might be less important during summer months, when
the largest temperature changes were simulated.
However, including irrigation alone will not represent all the
factors that may alter the dynamics of moist enthalpy in this
unique type of land cover change.
Land surface
representations of local pasture and greenhouses agriculture
are still far from being adequate, and many uncertainties
remain. A full assessment of all of these changes would require
Figure 4. Monthly albedo dierences and absolute values of mean air
surface temperature change from high to low albedo scenarios (GH to
PS simulations).
Table 2. Modeling Studies of Albedo Enhancement and Impact on Local Summer Temperatures (°C)
albedo enhancement at pixel
scale peak T
reduction one single
summer day average summer daily max T
reduction grid size
and location
this study 0.12 1.3 0.49 4 km2,Almeria (Spain)
Taha (2008)
0.15 2 N/A
2Los Angeles
Sailor (2003)
0.1 0.140.58 N/A 2 km2U.S. cities
Synnefa et al. (2008)
0.4 1.5 N/A 0.67 km2Athens
Lynn et al. (2009)
0.35 12 N/A 1.3 km2New York
Millstein and
Menon (2011)
0.020.11 0.020.5 [+0.27, 0.64] Continental U.S.
Menon et al. (2010)
0.01 N/A 0.03 0.5°, U.S.
Oleson et al. (2010)
0.58 (roofs only) 0.5 1.9°×2.5°global
Akbari et al. (2012)
0.01 N/A 0.010.07
global urban
Innermost domain of the simulation.
N/A = no data given.
Global annual temperature change (K).
Grid size non available.
Environmental Science & Technology Article |Environ. Sci. Technol. XXXX, XXX, XXXXXXE
further parametrization of the land surface model, particularly
of greenhouses land cover properties. However, there is an
absence of greenhouse observational data required to properly
characterize key biogeophysical and biogeochemical properties
and their representation in the land use model used for
simulations, and thus adding a detailed parametrization of
greenhouse agriculture was beyond the scope of this work. The
focus on equivalent temperature or moist enthalpy as
independent variable of these experiments is an interesting
approach for the future assessment of overall changes in moist
and heat content in the near atmosphere over the study area.
To estimate the net forcing caused by overall biogeophysical
and biogeochemical changes that aect mesoscale climate,
particularly changes in moist enthalpy and the seasonal
partition between sensible and latent uxes in the surface air
over the greenhouses area, a number of factors must be
considered along with irrigation
and are discussed further in
the SI (Table S4).
The temperature reductions obtained in our sensitivity
experiments focused on albedo changes are comparable to
other existing modeling studies for a similar range of albedo
increase. A comparison between some of these simulations and
our results for summer months is shown in Table 2. Note that
most of these studies were run only during one or a few days of
summer, as opposed to the full year modeled here. There is not
a direct linear relationship between albedo increase and
temperature reduction across locations, as local variables can
inuence this relationship. Despite these variations, and
although some of these studies are not directly comparable,
all of them oer a common overview of the potential cooling
that can be achieved by the implementation of albedo
enhancement strategies.
Besides these considerations, the results of the WRF
simulations reported here, along with the conclusion of
previous empirical research at the study site
support the
hypothesis that the increased albedo from greenhouse develop-
ment has been one of the main drivers of historical temperature
cooling in the area. Although these results respond to a very
particular case of albedo enhancement by land cover change,
they support the use of mesoscale meteorological modeling as a
tool for predicting the eects of solar radiation management
strategies, such as urban cool roofs, as local adaptation
measures to warming and summer heat waves. Further
improvements of land model parametrization stated above
would help identify other factors associated to this particular
land use change that might also be responsible of the observed
cooling, in addition to albedo enhancement.
Feedbacks and other inuences to the global climatic system
were not addressed here. In this case, an approximation to the
indirect climatic impact of greenhouses development, including
an estimation of the net carbon footprint of greenhouse
horticulture and CO2osets equivalence of albedo increase, has
been reported elsewhere.
SSupporting Information
Location of the study site, WRF simulated maximum daily
surface temperatures in summer months, seven days averaged
dierence in hourly temperature from GH to PS simulations,
changes in diurnal cycles of net incoming radiation and heat
ux on one summer day, scatterplot of mean monthly observed
vs simulated temperatures, and annual change in the monthly
surface air temperature dierences between two stations (PAL
and AL) for observed temperatures, for high and low albedo
simulations. Tables: physics and dynamics options used in
WRF simulations, statistical measures for WRF output
validation, comparison of simulated and observed temperatures
at two locations, land surface properties to be considered in
future research in the site. This material is available free of
charge via the Internet at
Corresponding Author
Present Address
Ctra. Sacramento S/N Escuela Superior de Ingenieria, D.2.36.
University of Almeria, Almeria 04131, Spain.
Author Contributions
The manuscript was written through contributions of all
authors. All authors have given approval to the nal version of
the manuscript. The authors contributed equally.
The authors declare no competing nancial interest.
We would like to acknowledge Surabi Menon for providing
valuable advice and resources needed for the research, and Igor
Sednev for his valuable help in building WRF architecture. The
work at Lawrence Berkeley National Laboratory was supported
by the US Department of Energy under Contract No.DE-
AC0205CH1123. The Laboratory Directed Research and
Development Program at LBNL and the DOE Atmospheric
System Research Program supported this research. Other
expenses were covered by the Program Jose Castillejoof the
Ministerio de Educación, Cultura y Deporte, Spanish Govern-
(1) Castilla, N.; Hernandez, J. The plastic greenhouse industry in
Spain. Chronica Hort. 2005,45,1520.
(2) Pulido-Bosch, A.; Pulido-Leboeuf, P.; Molina-Sanchez, L.;
Vallejos, A.; Martin-Rosales, W. Intensive agriculture, wetlands,
quarries and water management. A case study (Campo de Dalias, SE
Spain). Environ. Geol. 2000,40, 163168.
(3) Fernandez, M. D.; Bonachela, S.; Orgaz, F.; Thompson, R.;
Lopez, J. C.; Granados, M. R.; Gallardo, M.; Fereres, E. Measurement
and estimation of plastic greenhouse reference evapotranspiration in a
Mediterranean climate. Irrigation Sci. 2010,28, 497509.
(4) Sanjuan, J. F. Deteccion De La Supercie Invernada En La Provincia
De Almeria a Traves De Imagenes Aster; Fundacion para la Investigacion
Agraria de la Provincia de Almeria: Almeria, Spain, 2007.
(5) Campra, P.; Garcia, M.; Canton, Y.; Palacios-Orueta, A. Surface
temperature cooling trends and negative radiative forcing due to land
use change toward greenhouse farming in southeastern Spain. J.
Geophys. Res. 2008,113, D18109 DOI: 10.1029/2008JD009912.
(6) Taha, H. Urban climates and heat islands: Albedo, evapotranspi-
ration, and anthropogenic heat. Energy Build. 1997,27,99103.
(7) Akbari, H.; Levinson, R. Evolution of cool roof standards in the
United States. Adv. Building Energy Res. 2008,132.
(8) Akbari, H.; Matthews, H. D. Global cooling updates: Reflective
roofs and pavements. Energy Build. 2012,55,26.
(9) Muñoz, I.; Campra, P.; Fernandez-Alba, A. Including CO2-
emission equivalence of changes in land surface albedo in life cycle
assessment. Methodology and case study on greenhouse agriculture. J.
Life Cycle Assess. 2010,15, 672681.
(10) Reilly, J.; Melillo, J.; Cai, Y.; Kicklighter, D.; Gurgel, A.; Paltsev,
S.; Cronin, T.; Sokolov, A.; Schlosser, A. Using land to mitigate
Environmental Science & Technology Article |Environ. Sci. Technol. XXXX, XXX, XXXXXXF
climate change: Hitting the target, recognizing the trade-offs. Environ.
Sci. Technol. 2012,46, 56725679.
(11) Mahmood, R.; Pielke, R. A., Sr.; Hubbard, K.; Niyogi, D.;
Dirmeyer, P.; McAlpine, C.; Carleton, A.; Hale, R.; Gameda, S.;
Beltrán-Przekurat, A.; Baker, B.; McNider, R.; Leegates, D.; Shepherd,
J.; Du, J.; Blanken, P.; Frauenfeld, O.; Nair, U.; Fall, S. Land cover
changes and their biogeophysical effects on climate. Int. J. Climatol.
2013, DOI: 10.1002/joc.3736.
(12) Pielke, R. A., Sr.; Pitman, A.; Niyogi, D.; Mahmood, R.;
McAlpine, C.; Hossain, F.; Goldewijk, K.; Nair, U.; Betts, R.; Fall, S.;
Reichstein, M.; Kabat, P.; Noblet-Ducoudre, N. Land use/land cover
changes and climate: Modeling analysis and observational evidence.
WIREs Clim. Change 2011,2, 828850, DOI: 10.1002/wcc.144.
(13) Betts, R. A. Biogeophysical impacts of land use on present-day
climate: near-surface temperature change and radiative forcing. Atmos.
Sci. Lett. 2001,2,3951, DOI: 10.1006/asle.2001.0023.
(14) Menon, S.; Akbari, H.; Mahanama, S.; Sednev, I.; Levinson, R.
Radiative forcing and temperature response to changes in urban
albedos and associated CO2offsets. Environ. Res. Lett. 2010,5, 014005.
(15) Oleson, K. W.; Bonan, G. B.; Feddema, J. Effects of white roofs
on urban temperature in a global climate model. Geophys. Res. Lett.
2010,37, L03701.
(16) Akbari, H.; Matthews, H. D.; Seto, D. The long-term effect of
increasing the albedo of urban areas. Environ. Res. Lett. 2012,7
(024004), 10.
(17) Sailor, D. J. Streamlined mesoscale modeling of air temperature
impacts of heat island mitigation strategies; Final report, Portland State
University: Portland, 2003;
(18) Taha, H. Urban surface modification as a potential ozone air-
quality improvement strategy in California: A mesoscale modelling
study. Bound. Layer Meteorol. 2008,127, 21939.
(19) Lynn, B. H.; Carlson, T. N.; Rosenzweig, C.; Goldberg, R.;
Druyan, L.; Cox, J.; Gaffin, S.; Parshall, L.; Civerolo, K. A modification
to the NOAH LSM to simulate heat mitigation strategies in the New
York City metropolitan area. J. Appl. Meteorol. Climatol. 2009,48,
(20) Synnefa, A.; Dandou, A.; Santamouris, M.; Tombrou, M. On the
use of cool materials as a heat island mitigation strategy. J. Appl.
Meteorol. Climatol. 2008,47, 284656.
(21) Millstein, D.; Menon, S. Regional climate consequences of large-
scale cool roof and photovoltaic array deployment. Environ. Res. Lett.
2011,6, 034001.
(22) Akbari, H.; Menon, S.; Rosenfeld, A. Global cooling: increasing
world-wide urban albedos to offset CO2.Clim. Change 2009,94, 275
(23) Fishman, B.; Taha, H.; Akbari, H. Mesoscale Cooling Eects of
High-Albedo Surfaces: Analysis of Meteorological Data from White Sands
National Monument and White Sands Missile Range; Lawrence Berkeley
Laboratory Report No. 35056, Heat Island Group Reports, Lawrence
Berkeley National Laboratory: Berkeley, CA, 1994; http://www.osti.
(24) Arguez, A.; Russell, S. V. The definition of the standard WMO
climate normal: The key to deriving alternative climate normals. Bull.
Amer. Meteor. Soc. 2011,92, 699704, DOI: 10.1175/
(25) Ge, J. MODIS observed impacts of intensive agriculture on
surface temperature in the southern Great Plains. Int. J. Climatology
2010,30, 19942003.
(26) Skamarock, W. C.; Klemp J. B.; Dudhia J. Prototypes for the
WRF (Weather Research and Forecasting) model. Preprints. Ninth
conference on mesoscale processes, Fort Lauderdale, FL, Amer. Meteor.
Soc. 2001, CD-ROM, J1.5.
(27) Chen, F.; Dudhia, J. Coupling an advanced land surface-
hydrologymodel with the Penn State-NCAR MM5 modeling system.
Part I: model implementation and sensitivity. Mon. Weather Rev. 2001,
129, 569585.
(28) Tewari, M.; Chen, F.; Wang, W. Implementation and
verication of the unied NOAH land surface model in the WRF
model; 20th Conference on Weather Analysis and Forecasting/16th
Conference on Numerical Weather Prediction, 2004, 1115.
(29) Allen, M.; Pall, P.; Stone, D.; Stott, P.; Frame, D.; Min, S. K.;
Nozawak, T.; Yukimoto, S. Scientic Challenges in the Attribution of
Harm to Human Inuence on Climate; University of Pennsylvania Law
Rev., 2007, 155, 13531400.
(30) Vermote, E. F.; Vermeulen, A. Atmospheric correction
algorithm: Spectral reectances (MOD09), ATBD version 4.0, 1999.
(31) Zhang, Y. X.; Duliere, V.; Mote, P. W.; Salathe, E. P. Evaluation
of WRF and HadRM mesoscale climate simulations over the US
Pacific Northwest. J. Clim. 2009,22, 551126.
(32) Tesche, T. W.; McNally, D. E.; Tremback, C. Operational
Evaluation of The MM5 Meteorological Model over the continental United
States: Protocol for Annual and Episodic Evaluation Task Order 4TCG-
68027015 AG-TS-90/158.Oce of Air Quality Planning and
Standards U.S. Environmental Protection Agency: Boulder, CO,
2002, 803089195.
(33) Statistical Graphics Corporation. Users Guide Statgraphics Plus
version 4.1, USA 1999.
(34) Fall, S.; Diffenbaugh, N.; Niyogi, D.; Pielke, R. A., Sr.; Rochon,
G. Temperature and equivalent temperature over the United States
(19792005). Int. J. Climatol. 2010, DOI: 10.1002/joc.2094.
(35) Campra, P. Global and Local Eect of Increasing Land Surface
Albedo as a Geo-Engineering Adaptation/Mitigation Option: A Study
Case of Mediterranean Greenhouse Farming. In Climate Change -
Research and Technology for Adaptation and Mitigation; Blanco, J.,
Kheradmand, H., Eds.; InTech: Rijeka, 2011; pp 453. Available from:
Environmental Science & Technology Article |Environ. Sci. Technol. XXXX, XXX, XXXXXXG
... Overall, this intensive agricultural model inevitably modifies the patterns of land arrangements and landscape perception (González-Yebra et al., 2018). Specifically, the high-reflectivity and gas-tightness of plastic materials can alter energy balance and water cycles (Baille et al., 2006;Chang et al., 2013;Lu et al., 2014;Yang et al., 2017) and even lead to a longterm localized cooling trend in surface temperatures (Campra et al., 2008;Campra and Millstein, 2013). Moreover, plastic waste and chemical fertilizer have led to a number of issues, such as soil pollution, biodiversity degradation, and food safety (Hasituya et al., 2020;Hu et al., 2017;Levin et al., 2007;Perilla and Mas, 2019;Ramos-Miras et al., 2011). ...
As an efficient mode of modern agriculture, plastic greenhouse (PG) has significantly increased crop yields, but it is also criticized for changing the agriculture landscape and posing a threat to the environment. Accurate and timely information on PG distribution is essential for the strategic planning of modern agriculture as well as the projection of the environmental impacts. However, PG mapping over a large area has been a long-term challenge. Compared with classifier-based methods, index-based methods have the advantages of fast speed and convenience, which are very suitable for rapid large-scale mapping. The existing PG indices face the diversity of PG types and background environments, and the seasonal variation of PG spectra. To solve these problems, this study proposes a novel spectral index using Sentinel-2 images, namely the Advanced Plastic Greenhouse Index (APGI), to map PGs at a large scale. Four typical PG planting regions in the world, including Almería (Spain), Anamur (Turkey), Weifang (China), and Nantong (China), were selected as study areas. Based on the spectral analysis, some common spectral characteristics of PGs (i.e., high reflectance in NIR wavelengths and strong absorption in red and SWIR2 wavelengths) were observed and used in the APGI for highlighting PG areas. Besides, the coastal aerosol band and the red band were selected as optimal indicators to distinguish PG from other land covers which share similar spectral characteristics with PG. The experimental results indicate that the APGI has obvious advantages in enhancing PG information and suppressing non-PG backgrounds in various scenes compared with the existing indices. The APGI achieved the PG mapping accuracy with an OA of 90.63% ~ 97.50% and an F1 score of 80.56% ~ 96.20% in all study cases. Furthermore, the APGI also showed its robustness in seasonal variations and different datasets.
... Sparse ground air temperature monitoring network were extensively employed to investigate the effects of rapid urbanization on climatic change (Ren et al., 2007;Campra and Millstein, 2013;Zhao et al., 2014;Sun et al., 2016). There is an important issue in the evaluation and classification of weather station sites for the investigation of the regional UHI. ...
Full-text available
Many predictors are considered in estimating the spatial distribution of air temperature. However, a key problem remains the optimal selection of predictors. In this study, genetic algorithm (GA) was used to select optimal regression variables among 17 predictors, and GA-selected predictors were applied to random forest (RF) regression models for mapping air temperature in eastern and central China. By comparison with observed air temperature, results indicate that estimation errors of RF regression models constructed with GA-selected predictors first decrease (up to 5 predictors) and then remain stable. It is found that optimal combination of regression variables has the lowest estimation error (RMSE = 1.21°C) and the best fitting precision (R² = 0.9662), including 5 predictors that are latitude, relative humidity, elevation, distance to prefecture city and distance to third-order stream. Next, we compared the quantification of canopy layer heat island intensity (CLHII) based on the analysis of observed and estimated air temperature. We infer that quantification of CLHII using ground monitoring network results in obvious deviation due to the sparse weather stations only providing limited information over wide areas. Finally, we evaluated the robustness in quantifying the CLHII based on estimated air temperature derived from GA-1, -2, -3, -4, -5 and -17 models. Results show that the estimation of air temperature with low estimation error facilitates quantifying accurate CLHII, emphasizing the importance of selecting optimal regression variables. In summary, results reveal the effectiveness of GA in selecting optimal regression variables and provide insight into quantifying CLHII.
... As the cooling medium has a significant influence on the temperature field and microstructure of the FeCoCrNi HEAs coating [32], it is critical to define the correct convective heat transfer coefficient, which is determined by the cooling medium. The convective heat transfer coefficient is set to be 40 [J/(m 2 K)] since air is the cooling medium in this research [33], while the boundary convection condition is applied to all external surfaces of the finite element model. ...
The evolution of element distribution during laser cladding involves two dynamic behaviors, i.e., liquid molten pool flow and FeCoCrNi high-entropy alloy (HEA) coatings solidification. However, it is quite difficult to characterize element distribution during the flow of the liquid molten pool rigorously. The current investigation conducted the optical microscopy, scanning electron microscopy, X-ray diffraction analysis and energy dispersive spectrometer to study the dilution, phase composition, microstructure of the FeCoCrNi coatings. The flow field was simulated to uncover the dynamic change mechanism of the molten pool and explain the experimental results. The results indicated that the coating is substantially composed of FCC and BCC solid solution with a typical dendrite microstructure. Gray Laves phase-(Ni, Co)2Ti and a small number of white dot particles, Fe–Cr phase, are dispersed in the inter-dendritic region. The HEA atoms (Fe, Co, Cr, Ni) gradually aggregate from the center to the side at the coating boundary region, while the Ti atom is the opposite. The Marangoni flow inflection point at the molten pool boundary will cause HEA atoms to aggregate. On the contrary, Ti atom enters the molten pool from the bottom with the heat buoyance flow and then migrates to the boundary along with the Marangoni flow. Therefore, the content of Ti in the coating boundary decreases. The Marangoni flow, heat buoyance flow, and recoil pressure flow are interwoven in the middle region of the coating, resulting in a more uniform element distribution than the boundary region.
... Previous authors' studies (Campra et al., 2008;Campra and Millstein, 2013) have analysed the effects of changes in albedo over surface air temperatures and surface energy budget in a nearby location (Almeria province). In these papers the thermal effects of albedo enhancement were described, and changes in temperature and radiation were simulated by mesoscale numeric experiments. ...
The effects of increasing the surface reflectance by albedo modifications have been evaluated using an air quality modelling system. We have evaluated the influence over pollutant concentrations of increasing from 0.20 to 0.55 the roof surface albedo (scenario called Albedo1) and increasing from 0.15 to 0.30 the ground surface albedo and from 0.20 to 0.55 the roof surface albedo for all urban categories (scenario called Albedo2). To obtain a better representation of the local processes we have considered very high resolution (333.33 m) and up to 10 different urban categories. Changes in albedo cause changes in different meteorological parameters (planetary boundary layer height, radiation and temperature), modifying the pollutant concentration in every single scenario. Results show that this mitigation measure is effective during summer periods, providing not high NO 2 increments and O 3 reduction on the urban areas of the city of Madrid. Whilst during winter periods the measure induces NO 2 increments over polluted areas with high NO x emissions. In this way, the benefits of the measure, from the point of view of urban heat island effects, are greater than the detriments during summer periods, in comparison with air quality effects. Reference to this paper should be made as follows: González, M.Á., Arasa, R., Gámez, P., Picanyol, M. and Campra, P. (2019) 'Effects of increasing the surface reflectance over air quality levels using WRF-BEM/AEMM/CMAQ: application over the city of Madrid', Int. J. Environment and Pollution, Vol. 65, Nos. 1/2/3, pp.195-210. 196 M.Á. González et al. Biographical notes: M. Ángeles González works as a Project Manager at Meteosim with key responsibilities of managing emission inventories and air quality assessment. She previously worked on air quality modelling for five years in Centre for Energy, Environment and Technology (CIEMAT), where she researched air pollution and heavy metals with chemistry-transport models. She holds a PhD in Physics on Atmospheric sciences specialized on Air Quality, and a MSc in Geophysics and Meteorology (from the Complutense Univ. of Madrid). She has experience in meteorological and air quality modelling systems and has participated in emission inventories development, numerous air quality modelling studies, the model execution, and subsequent processing, and data analysis and graphics. Raúl Arasa is Chief Operations Officer of Meteosim, where he leads the technical department. He holds a PhD degree cum laude in Physics, a Master in Meteorology and a Master in Project Management. He has extensive experience in meteorology, air quality, climate and atmospheric modelling. During his career has worked in the development, implementation and execution of coupled air quality modelling systems, adapting different meteorological and dispersion/photochemical models, and implementing emissions inventories and emissions models. He has worked in more than 15 meteorological and air quality modelling systems in an operational mode and executed meteorological models in more than 100 different regions. He has participated in around 20 scientific peer-review contributions about atmospheric modelling and he has leaded more than 60 projects related to the atmosphere. Pedro Gámez holds an MSc in Meteorology and graduated in Environmental Sciences. He has been working in the fields of climate change and air quality modelling at the University of Barcelona for four years. His main expertise is the analysis of CMIP5 climate projections, photochemical (CMAQ) and meteorological (WRF) models, air quality studies, ozone forecast and development of emission inventories. His education and his experience in programming has enabled Meteosim to develop a new emissions model, to offer new services based on API solutions and to analyze climate models ensembles. Miquel Picanyol has 13 years of experience working in Meteosim. He has large experience with operational forecasting systems for different applications: air quality, weather, risk management, metocean) and with high computing infrastructure. He has been involved in more than 60 operational forecasting projects working with models like CMAQ, CALPUFF, AERMOD, HYSPLIT, MASS, MM5, WW3, SWAN, ROMS and WRF. He is the responsible of the R+D department and modelling activities, that allows to provide and improve the best solution for Meteosim consumers: design web platforms, system configuration, data assimilation, functionalities, etc. He has large experience working for public administration, private companies and research projects for the European Commission. Pablo Campra is a Professor at University of Almeria (Spain) since 2013. He Works in Department of Agronomy, High School of Engineering, with a large experience in assessment of the climatic impact of high albedo surfaces on human settlements. He has participated in many scientific publications related to Surface temperature cooling trends, radiative forcing and case studies on agriculture and greenhouse farming. He previously worked on air resources board, from Environmental Protection Agency, as an expert in potential effects of high albedo surfaces in urban cities. Effects of increasing the surface reflectance over air quality levels 197 This paper is a revised and expanded version of a paper entitled 'Effects of increasing the surface reflectance over air quality levels using WRF-BEM/AEMM/CMAQ. Application over the city of Madrid' presented at 18th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (HARMO18),
... A real experience of seasonal surface albedo change is applied over 20,000 reflective greenhouses in Almeria province, 500 km south of Madrid, where whitewash slaked/lime painting is applied over the roofs to limit excess heating inside the greenhouses in summer and is washed out in September to allow enough winter radiation inside them. The implementation of high albedo in the area has caused mean outdoor surface air temperature cooling, locally offsetting the impact of global warming [28,29]. The levels of albedo enhancement simulated here (round +0.1 at the pixel level) are similar to those implemented on the field over Almeria area, but with more than double intervention surface at Madrid urban area, well above the minimum critical intervention area for efficient cooling at similar comparable latitudes and insolation. ...
The worldwide use of agricultural plastic greenhouses (APGs) is crucial to provide sufficient food, including vegetables and fruits, for residents. However, the pollution problem created by plastic materials has also aroused widespread concern. Therefore, it is important to obtain the spatial distribution of APGs via different approaches, especially using remote sensing images. In this study, a deep learning method is adopted to map the distribution of APGs in Shouguang, Shandong Province, China, with high-resolution Google Earth images. The results suggest that the distribution of greenhouses can be accurately extracted with a mean intersection over union (mIOU) of 97.20%. The total area covered by APGs is 185.37 km2, and the total number of APGs is approximately 170,807. Both densely and sparsely distributed APGs can be extracted effectively. This research shows that the deep learning method can extract greenhouse information quickly and effectively from high-resolution images and can be used in agricultural pollution monitoring and agricultural development planning.
In the current research, the dual laser beam bilateral synchronous welding (DLBSW) of 2219 Al-Cu alloy T-joint is investigated by finite element (FE) simulation of temperature field and residual stress, which aims at the optimization of the welding process parameters. The FE mesh model of the T-joint is established by simplifying the equal boss of the skin. The combined Gaussian laser heat source is adopted to simulate the laser energy loaded on the Al-Cu alloy during welding process. Furthermore, a test table of process parameters with the laser power and welding speed is designed to investigate the parameters via the simulation results. The simulation with 16 groups of parameters are performed based on the heat source model which is checked by the experimental result. Two cases of parameters are filtered from the simulation results for the hoop tensile test experiment. It is employed to estimate the tensile and yield strength of the T-joints with the filtered parameters. Welding process parameters in case 6, which are the laser power of 4300 W and welding speed of 2.5 m/min, are the optimized parameters in the test table based on the tensile strength (337.28 MPa) and yield strength (243.51 MPa) obtained by the tensile test results.
Laser welding-brazing (LWB) technique is considered to offer advantages over fusion welding in terms of dissimilar jointing since it can avoid liquid dissimilar alloys mixing in the molten pool. In this paper, LWB technique is utilized to join 1050 aluminum alloy to Ti-6Al-4V. A 3D finite element model is developed to calculate the temperature field in the LWB process with a combined Gaussian heat source model. The simulated asymmetric temperature field is in good agreement with the experimental metallography, which confirms the validity of the present combined heat source model. The peak temperature of the center of the weld seam increases with time in the welding process. The experimental and simulation results both indicate that the heat-affected zone (HAZ) width of the upper is narrower than that of the bottom. Besides, the growth orientation of coarsened dendrite near the interface at root face is substantially perpendicular to interface. The grain morphology of the molten pool appears as dendrite, cellular dendrite and equiaxed crystal from the fusion line to the center of the weld seam.
Full-text available
p> Abstract: Two tractable New World west-coast Mediterranean climate zones, burdened with increasing urbanized populations, must each be transformed by humans because of inherent North-South variances of natural precipitation. In other words, each politically-defined landscapes must technically devise its own anthropogenic dimension for future prosperity induced by the prospect of a plentiful freshwater supply. Both the nation of Chile, as well as the State of California (USA), have investigated the hypothetical use of gigantic offshore buoyant freshwater pipelines to serve their coastal and inland population’s needs. This report is meant to inspire and engage the next generation of Macro-Imagineering experts. Key words: S ubmarine pipeline, freshwater transport, Chile, State of California, geographical similarities. =========================================================================== Duas zonas de clima Mediterrâneo na costa oeste do Novo Mundo, sobrecarregadas com o aumento das populações urbanizadas, devem ser transformadas por seres humanos devido às variações inerentes de precipitação natural entre o Norte e o Sul. Em outras palavras, cada cenário politicamente definido deve tecnicamente conceber sua própria dimensão antropogênica para a prosperidade futura, induzida pela perspectiva de um suprimento abundante de água doce. Tanto o Chile como o Estado da Califórnia (EUA) investigaram o uso hipotético de dutos de água doce flutuantes ( offshore ) gigantescos para atender às necessidades de suas populações costeiras e interiores. Este relatório tem como objetivo inspirar e engajar a próxima geração de especialistas em Macro-Imagineering . Palavras-chave: Aqueduto submarino, transporte de água doce, Chile, Estado da Califórnia, semelhanças geográficas.</p
Full-text available
p>The macroproject proposed, encompassing the arid Southwest of the USA and northern Mexico, has the potential to more than pay for itself. If a radical volumetric enlargement were competently completed by correctly educated advocates of Macro-Imagineering, supplemented by geothermal power-plants, it could make benign an over-polluted aquatic “monster” — the present-day stagnant and putrid Salton Sea — through induced importation of diluting Gulf of California saltwater resulting in rapid areal increase of the inanimate “creature”, converting it from its presently degraded smelly status to an amply beneficial condition as an anthropogenic extension of Mexico’s Gulf of California! Formation by Macro-Engineering means of a sustainable human development around and thereon can result in profitable voluminous desalinated seawater exportation from the State ofCaliforniato adjacentArizona,Nevadaand nearbyUtahas well asMexicobordering theUSA’s Southwest. The key infrastructure permitting these developments is a centralized multi-segment photovoltaic-powered desalination factory resting atop named Introduction floating artificial islands covering most of a deliberately enlarged and robotized Salton Sea. A particular macroproject proposed, the Southwest Water Alliance Project (SWAP), is fashioned somewhat after NEOM, an announced ecopolis, but still structurally unspecified robot megacity, scheduled to be built in northern Saudi Arabia connected by a yet-to-be-constructed fixed sea-strait crossing linking Tabuk, Saudi Arabia to Sharm el-Sheikh on the Sinai Peninsula of bordering Egypt. Key words: Seawater desalination, floating photovoltaic platforms, arid Southwest USA and Mexico development, Macro-Imagineering, Macro-Engineering. =========================================================================== O macroprojeto proposto, abrangendo o árido sudoeste dos EUA e norte do México, tem potencial para mais do que pagar por si mesmo. Se um aumento volumétrico radical fosse conduzido por defensores competentes da Macro-Imagineering, suplementado por usinas de energia geotérmica, seria possível tornar benigno um "monstro" aquático extremamente poluído — o atual e estagnado Mar de Salton — por meio de importações induzidas de água salgada diluente do Golfo da Califórnia, resultando em um rápido aumento de área da "criatura" inanimada, resgatando-a de seu estado atual degradado como uma extensão antropogênica do Golfo da Califórnia! Por meio da Macroengenharia é possível conduzir um desenvolvimento humano sustentável e lucrativo capaz de garantir uma expressiva exportação de água do mar dessalinizada do estado da Califórnia para os adjacentes Arizona, Nevada e Utah, além do México na fronteira com o sudoeste dos EUA. A principal infraestrutura que permite esse desenvolvimento é uma fábrica centralizada para dessalinização multissegmento baseada em energia fotovoltaica, formada por ilhas artificiais flutuantes que cobririam a maior parte do Mar de Salton em um sistema ampliado e robotizado. Em particular, um macroprojeto semelhante proposto é o Southwest Water Alliance Project (SWAP) — criado um pouco depois do NEOM —, uma ecópolis anunciada, megacidade robótica estruturalmente ainda não especificada, programada para ser construída no norte da Arábia Saudita, conectada por uma passagem estreita ligando Tabuk, na Arábia Saudita, a Sharm el-Sheikh, na Península do Sinai (fronteira com o Egito). Palavras-chave: Dessalinização da água do mar, plataformas fotovoltaicas flutuantes, desenvolvimento do sudoeste dos EUA e do México, Macroengenharia. </div
Full-text available
This work compares the Weather Research and Forecasting (WRF) and Hadley Centre Regional Model (HadRM) simulations with the observed daily maximum and minimum temperature (Tmax and Tmin) and precipitation at Historical Climatology Network (HCN) stations over the U.S. Pacific Northwest for 2003-07. The WRF and HadRM runs were driven by the NCEP/Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis (R-2) data. The simulated Tmax in WRF and HadRM as well as in R-2 compares well with the observations. Predominantly cold biases of Tmax are noted in WRF and HadRM in spring and summer, while in winter and fall more stations show warm biases, especially in HadRM. Large cold biases of Tmax are noted in R-2 at all times. The simulated Tmin compares reasonably well with the observations, although not as well as Tmax in both models and in the reanalysis R-2. Warm biases of Tmin prevail in both model simulations, while R-2 shows mainly cold biases. The R-2 data play a role in the model biases of Tmax, although there are also clear indications of resolution dependency. The model biases of Tmin originate mainly from the regional models. The temporal correlation between the simulated and observed daily precipitation is relatively low in both models and in the reanalysis; however, the correlation increases steadily for longer averaging times. The high-resolution models perform better than R-2, although the nested WRF domains do have the largest biases in precipitation during the winter and spring seasons.
The authors of this Article review the current state of the science of attribution of anthropogenic climate change, with particular emphasis on the methodological challenges that are likely to confront any attempt to establish a direct causal link between greenhouse gas emissions and specific damaging weather events. Standard "detection and attribution" analyses, such as those cited by the Intergovernmental Panel on Climate Change (IPCC), are generally sufficient to establish the strength of human influence on large-scale, long-termaverage climate, but fall short of quantifying the role of greenhouse gas emissions in almost any conceivable case of actual harm, since nobody is directly exposed to a change in global average temperature alone. The authors argue that it should be possible to agree on a relatively objective approach to quantifying the role of human influence on climate in cases of actual harm. There are, however, a number of questions to be resolved, including: can we apply the concept of Fraction Attributable Risk, developed for population studies in epidemiology, to the analysis of an unprecedented change in a single system such as the world's climate? Can we rely on computer simulation to address counter-factual questions such as "what would the climate have been like in the absence of twentieth century greenhouse gas emissions, " given that we are working with imperfect simulation models? Due to multiple anthropogenic and natural contributions to changing weather risks, it will always be necessary to apply some kind of principle of ceteris paribus to quantify the role of any particular causal agent, such as greenhouse gas emissions. How is this principle to be applied? These questions are not, in themselves, scientific issues, although how they are to be resolved will have a direct bearing on how and whether climate science can inform specific causal attribution claims. In summary, we need the legal community to ask the scientific community the right questions. It is imperative that these issues be resolved as soon as possible, to avoid having them become entwined in the outcomes of specific cases. Thus, this Article serves as a kind of tutorial, going over some material that many will find familiar in order to place it in the context of attribution.
Land cover changes (LCCs) play an important role in the climate system. Research over recent decades highlights the impacts of these changes on atmospheric temperature, humidity, cloud cover, circulation, and precipitation. These impacts range from the local- and regional-scale to sub-continental and global-scale. It has been found that the impacts of regional-scale LCC in one area may also be manifested in other parts of the world as a climatic teleconnection. In light of these findings, this article provides an overview and synthesis of some of the most notable types of LCC and their impacts on climate. These LCC types include agriculture, deforestation and afforestation, desertification, and urbanization. In addition, this article provides a discussion on challenges to, and future research directions in, assessing the climatic impacts of LCC.
With increasing the solar reflectance of urban surfaces, the outflow of short-wave solar radiation increases, less solar heat energy is absorbed leading to lower surface temperatures and reduced outflow of thermal radiation into the atmosphere. This process of “negative radiative forcing” effectively counters global warming. Cool roofs also reduce cooling-energy use in air conditioned buildings and increase comfort in unconditioned buildings; and cool roofs and cool pavements mitigate summer urban heat islands, improving outdoor air quality and comfort. Installing cool roofs and cool pavements in cities worldwide is a compelling win–win–win activity that can be undertaken immediately, outside of international negotiations to cap CO2 emissions. We review the status of cool roof and cool pavements technologies, policies, and programs in the U.S., Europe, and Asia. We propose an international campaign to use solar reflective materials when roofs and pavements are built or resurfaced in temperate and tropical regions.
Roofs that have high solar reflectance and high thermal emittance stay cool in the sun. A roof with lower thermal emittance but exceptionally high solar reflectance can also stay cool in the sun. Substituting a cool roof for a noncool roof decreases cooling-electricity use, cooling-power demand, and cooling-equipment capacity requirements, while slightly increasing heating-energy consumption. Cool roofs can also lower citywide ambient air temperature in summer, slowing ozone formation and increasing human comfort. Provisions for cool roofs in energy-efficiency standards can promote the building- and climate-appropriate use of cool roofing technologies. Cool-roof requirements are designed to reduce building energy use, while energy-neutral cool-roof credits permit the use of less energy-efficient components (e.g., larger windows) in a building that has energy-saving cool roofs. Both types of measures can reduce the life-cycle cost of a building (initial cost plus lifetime energy cost). Since 1999, several widely used building energy-efficiency standards, including ASHRAE 90.1, ASHRAE 90.2, the International Energy Conservation Code, and California's Title 24 have adopted cool-roof credits or requirements. This paper reviews the technical development of cool-roof provisions in the ASHRAE 90.1, ASHRAE 90.2, and California Title 24 standards, and discusses the treatment of cool roofs in other standards and energy-efficiency programs. The techniques used to develop the ASHRAE and Title 24 cool-roof provisions can be used as models to address cool roofs in building energy-efficiency standards worldwide.
Changes in land cover affect climate through the surface energy and moisture budgets, but these biogeophysical impacts of land use have not yet been included in General Circulation Model (GCM) simulations of 20th century climate change. Here, the importance of these effects was assessed by comparing climate simulations performed with current and potential natural vegetation. The northern mid-latitude agricultural regions were simulated to be approximately 1–2 K cooler in winter and spring in comparison with their previously forested state, due to deforestation increasing the surface albedo by approximately 0.1 during periods of snow cover. Some other regions such as the Sahel and India experienced a small warming due to land use. Although the annual mean global temperature is only 0.02 K lower in the simulation with present-day land use, the more local temperature changes in some regions are of a similar magnitude to those observed since 1860. The global mean radiative forcing by anthropogenic surface albedo change relative to the natural state is simulated to be −0.2 Wm2, which is comparable with the estimated forcings relative to pre-industrial times by changes in stratospheric and tropospheric ozone, N2O, halocarbons, and the direct effect of anthropogenic aerosols. Since over half of global deforestation has occurred since 1860, simulations of climate since that date should include the biogeophysical effects of land use.
The World Meteorological Organization (WMO) defines the standard climate normal as a reasonable metric with respect to its primary utilizations. Climate scientists have urged for the development of alternative normal products that are better indicators of existing and future climate conditions. They state that the most straightforward approach for creating alternative climate normals is to change the generalized definition of the standard WMO climate normal. Every possible alternative climate normal that can be devised is the result of changing one or more of five fundamental attributes. These five attributes are described along with the formulation of a generalized equation for WMO-type climate normals and consideration of a few ways to change the standard WMO definition to devise alternative climate normals. Alternative normals products can be created by changing one or more of the five fundamental attributes.